\r\n\tThis book intends to provide the reader information about various dynamic analysis methods and evaluated structures with this methods. This book also presents an overview and a state-of-the art compilation of time integration methods for solving problems of dynamics, with a particular focus on developments that have occurred during the past years.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"6282bc749f4a87573e21c325adaced45",bookSignature:"Dr. Hakan Yalciner, Dr. Atila Kumbasaroglu and Mr. Alper Celik",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9296.jpg",keywords:"Numerical Methods, Finite Element Methods, Algorithms, Time History Analysis, Earthquake Engineering, Structural Behavior, Performance Based Design, Crane Loads, RC Design, Retrofitting, Fiber Reinforced Polymer",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 29th 2019",dateEndSecondStepPublish:"June 19th 2019",dateEndThirdStepPublish:"August 18th 2019",dateEndFourthStepPublish:"November 6th 2019",dateEndFifthStepPublish:"January 5th 2020",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"72283",title:"Associate Prof.",name:"Dr. Hakan",middleName:null,surname:"Yalçıner",slug:"dr.-hakan-yalciner",fullName:"Dr. Hakan Yalçıner",profilePictureURL:"https://mts.intechopen.com/storage/users/72283/images/system/72283.jpeg",biography:"Associate Professor Dr. Hakan Yalçıner is an earthquake and structure engineer in Erzincan Binali Yıldırım University and chair in the Department of Civil Engineering. 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1. Introduction
The development of scanning probe microscopy was driven by the challenge articulated by Richard Feynman [1], namely that it should be possible to write (and read) the Encyclopedia Britannica on the head of a pin. By using a focused electron beam to pattern advanced resist materials and later transfer them into solid matter by various processes, this challenge has been overcome. By improving the electron beam, it also became possible to image sub-10-nm-scaled structures in the scanning electron microscope (SEM, relying on the secondary electron generation) or the transmission electron microscope (TEM). The latter technique makes it possible to image individual atoms; however, it is restricted to thin samples. Encouraged by the nanometer scale resolution of the SEM, the replacement of electrons with ions was researched. Since energetic ions generate secondary electrons when interacting with matter, the focused ion beam (FIB) can be used for imaging as well. Due to the lack of other available ion sources, the first one to gain widespread adaptation was the gallium liquid metal ion source (LMIS), which relies on the formation of the so-called Taylor cone. In addition, since ions are significantly slower than electrons for a given energy and their charge-to-mass ratio is smaller, the magnetic lenses in the SEM are replaced by electrostatic lenses. While a sub-5-nm beam diameter for gallium is possible today, the imaging resolution is limited to a few nm due to the immediate surface sputtering by the energetic ion impacts and the requirement to collect sufficient number of secondary electrons [2].
Energetic ions are used in other technological fields as well, such as ion implantation and surface etching (ion milling). These methods can be locally restricted by integrating with a mask; however, this requires several additional process steps, including a lithographic mask, and has limited resolution. Furthermore, this approach has a large initial cost. Instead, FIB can be used to locally mill structures at the nanometer scale with great flexibility. Important parameters are the etch rate and selectivity (difference of etch rate between two different materials for the same milling conditions), which can be improved by the so-called gas-assisted etching. For this purpose, thin needles are introduced close to the beam-sample interaction region, and minute amounts of gas are introduced. While the gas concentration is high at the point of injection, it is quickly evacuated by the pump system of the low-pressure sample chamber. By introducing special volatile precursor gases that are disassociated by the ion beam, the deposition of conducting as well as insulating materials is possible.
Gallium FIB is now widely used for rapid prototyping, circuit editing, lithographic mask repair, and TEM lamella preparation. However, several limitations have become more pressing in recent years:
The low milling resolution of the gallium ion (due to the large interaction volume of the heavy ions) is not sufficient for sub-10-nm fabrication, and the repair of next-generation optical masks becomes challenging. Furthermore, reflective UV masks have a higher requirement for defect-free repair.
The implanted gallium atoms can degrade the properties of the specimen. This is particularly critical for the preparation of TEM lamellae of certain samples and circuit editing where the semiconducting properties of transistors can change.
To improve the resolution of the gallium FIB, very low beam currents in the single-digit pA range are used. At these currents, the low secondary electron yield makes it difficult to do end-point detection, that is, notice when a given layer is fully removed.
In consequence, through meticulous ingenuity, the gas field ion source (GFIS) was commercialized. Initially, work focused on the ionization of helium ions as its ionization field strength clearly distinguishes it from other gases, and the light ions promise a superior imaging resolution while limiting the sample damage. However, today, there is a wide range of gases used, including helium, neon, hydrogen, and nitrogen. We discuss the GFIS in general in Section 2, and focus more on the uniqueness of the nitrogen field ionization in Section 3. In Section 4, we show how the nitrogen ion microscopy (N2IM)1 can be used for imaging and patterning. Due to the limited space, not all aspects can be discussed in full detail. As there is much overlap with the helium ion microscope, the excellent book edited by Hlawacek and Gölzhäuser [3] should provide answers to outstanding questions.
2. The gas field ion source
The GFIS, as the name suggests, is a type of ion source that works by ionizing gas atoms or molecules in a strong electric field. In this section, first, a short history of the GFIS is given for the interested mind, followed by the fundamental principle of the field ionization and field evaporation in strong electric fields. This also includes the discussion of the fundamental limits. In Section 2.3, the more technical aspects of exploiting the field evaporation and gas etching in order to make a good ion source are discussed. We also summarize some recent ion source developments beyond the GFIS that have been fuelled by the commercial success of the GFIS.
2.1. History of the gas field ion source
The foundation of the GFIS was laid by Müller [4], when he developed the field ion microscope (FIM). In a FIM, individual surface atoms of a metal tip can be imaged with relatively simple methods by applying a strong electrical field in a gas atmosphere. The gas atoms (typically helium) are ionized and are then accelerated away from the metal surface (Figure 1a). By allowing these accelerated ions to pass through a hole in the so-called extraction electrode, they can be visualized on a detector screen, as shown in Figure 1b. As the electric field at protruding atoms is stronger, ionization from these locations is more likely. This setup has to be located in a vacuum chamber to avoid high-voltage breakdown (arcing), and the emitting metal is typically cooled to increase the ionization rate. It quickly became clear that the emission from a single atom could, in principle, facilitate an ion source [5, 6] with unprecedented resolution (as will be discussed in Section 2.3); however, current stability turned out to be a serious issue [7]. In the meantime, the gallium LMIS was developed and offered sufficient performance for the time: a wide range of beam currents from the pA to the nA range, and sufficient resolution to allow prototyping and circuit editing in the semiconductor industry.
Figure 1.
(a) Operation principle of the field ion microscope. (b) FIM image of pt tip (derivative of platinum (https://www.flickr.com/photos/dxdt22/315745462) by Tatsuo Iwata licensed under CC BY 2.0).
The development of methods to controllably shape noble metal tips through electrochemical etching, field-assisted evaporation, and etching remained active due to the demand for electron emitters.2 The effort up until 2005 to develop high brightness, monochromatic noble gas ion sources was reviewed by Tondare [8]. It is now known that sometime in the 1990s, the active development of a GFIS commenced at the Micrion Corporation. The goal was to create a next-generation ion source to allow repair of photolithographic masks in the semiconductor industry, and the work was encouraged by the technological advances and the better understanding of the field emission [9]. However, after the first performance values were obtained and the theory had been reexamined, it became clear that in spite of being a source with unprecedented brightness, the overall emission current would not be on par with existing ion sources. The merit of this challenging technology became questionable, and furthermore, the development team was haunted by stability issues of the novel source. Nevertheless, the development team succeeded in forming a GFIS emitter from Tungsten that was terminated by three atoms aligned in a triangle, the so-called trimer. The trimer can improve the stability compared to a single atom tip for certain tip materials and crystal orientations; however, only one-third of the total brightness is usable. First results demonstrated that the GFIS would be an ideal candidate for an ion microscope that could excel the SEM with a sub-1-nm beam diameter and favorable beam/sample interaction. However, commercialization was not yet viable.
With this result, Bill Ward continued the improvement of the source stability—the last remaining obstacle before commercialization—after parting with Micrion upon their acquisition by the field electron and ion company (FEI). After 2 more years of ups and downs, and just as the team had moved to a new location, a demonstration was to be given to potential investors. To the surprise of Ward himself, the stability and performance was significantly better than just before the move. As it later turned out, due to time constraints, a special custom-made gas purifier was not installed to the gas supply in the new location. At some unknown point in the past, a piece of wooden cork had slipped into the tube of the purifier and got stuck. Thus, the gas supplied to the ion source had been passing through the cork during the development time, and this was the root cause for the low stability. However, as Ward recalled during his speech at the 2016 AVS International Symposium & Exhibition in Nashville, Tennessee, commemorating the 10th anniversary of the commercialization of the GFIS [10], during the struggle with the stability of the GFIS, the team gained experience and knowledge that turned out to be indispensable to the commercialization. Would it not have been for the long development time and investment to overcome the instability before realizing the current limitation, the effort to commercialize the GFIS would have likely been dropped earlier in the development.
With investors on board, the Atomic Level Ion Source (ALIS) company went public with its results in 2006 [11, 12]. Shortly after, ALIS was acquired by Carl Zeiss’ Nano Technology Systems Division (NTS), and the Zeiss Orion helium ion microscope was the first commercial GFIS tool released in the same year. Development in the time since then progressed; however, the fundamental GFIS technology remains the same, and new tools are now fitted with helium and neon gas. The events surrounding the development of the HIM at Micrion and ALIS were also recollected by Economou et al. [13] and Ward himself ([3], Appendix A).
The impressive results achieved by the Zeiss Orion certainly renewed the interest in GFIS, and the Japanese gallium FIB specialist SII Nanotechnology presented results on their hydrogen and nitrogen GFIS-FIB nanofabrication tool for the first time in 2011. Unprecedented mask repair performance, particularly of reflective UV masks, was reported [14]. Shortly after, at the beginning of 2013, SII Nanotechnology was acquired by Hitachi High-Technologies and continues operating under the name Hitachi High-Tech Science. Zeiss and Hitachi appear to remain the only commercial manufacturer of GFIS tools. Nevertheless, it is likely that sooner or later other players will enter the field of light gas ion FIB technology. As we show in Section 2.4, this does not have to be necessarily a GFIS.
2.2. Field ionization and field evaporation
To understand the field ionization (gas atoms losing an electron in a strong electric field) and field evaporation (surface atoms detaching due to high electric field), some general knowledge about solid state physics is required. The GFIS is used by scientists and engineers from a broad range of fields, and for the daily use the details that will be explained in this section are not necessary. Nevertheless, it is helpful to understand the potential and limitation of this technology. Therefore, we give an explanation in a way that can be followed by readers from various fields, and details can be found in the excellent books that have been written over the years [15, 16, 17, 18].
In solid state physics, there are a few concepts which are prerequisites in this discussion:
Energy versus position is a common representation of the inner properties of solids as seen by electrons. The position is typically a one-dimensional line through the solid as it contains the essence of the solid and can still be easily interpreted. Two-dimensional (2D) or even three-dimensional (3D) representations exist, but are less common. The energy (sometimes called potential, y-scale) is given in units of electron volt (eV) and defines relative energies, that is, there is no zero point as it is irrelevant.
Fermi energy (EF) is the highest energy an electron in a given material can have at absolute zero Kelvin. At elevated temperature, some electrons from below EF are excited and gain some additional energy.
Vacuum energy (Evacuum) is defined as the energy outside of the solid in vacuum.
Work function is an energy difference that is unique to each individual electron in a solid. It is the energy that has to be added to the electron’s energy to make it reach Evacuum, thus separating it from the solid.
Quantum tunneling is a phenomenon where electrons can cross a potential barrier although they do not have enough energy to reach the top of the barrier (given that the space behind the barrier is unoccupied). Electron energy is conserved. The tunneling probability increases rapidly with the decrease of the barrier width, and the Pauli principle forbids tunneling into energies below EF. This phenomenon does not occur in the visible world. A possible image of quantum tunneling could be as follows: Let us imagine a golf ball lying in the grass next to a solid concrete wall. Tunneling would mean that there is a chance that the ball moves to the other side of the wall without being touched.
Figure 2a shows a 2D cut through a large metal, where the atoms are assembled in a regular structure. If we plot the energies along the red line, we obtain Figure 2b. Here, + represents the positively charged cores of the individual atoms. They create energy wells for electrons through their Coulomb potential, which are filled with some electrons of specific (quantized) energy. Since the energy of these electrons is considerably smaller than the top of the barrier between the atoms, they are assumed to be localized and do not contribute to electrical conduction. However, since EF is higher than the barriers (this is only true for metals), there are some electrons that can move freely from left to right. They are part of the so-called electron gas which gives metal its high conductivity. Since the metal is periodic, this energy profile is periodic as well.
Figure 2.
(a) Model of metal atoms inside a bulk metal. The potential along the red arrow is shown in (b). (b) Potential inside bulk metal. Electrons are either localized near the atoms or can move freely if they have sufficient energy. These electrons are referred to as electron gas and give metal the high electrical conductivity. (c) Model of metal surface with an extractor electrode at distance x and a gas atom. (d) Potential along red arrow in (c). (e) Narrow potential barrier formed by negative ΔU resulting in electron emission. (f) Positive ΔU causes electron tunneling of outer electron of nearby gas atoms. The ion is immediately accelerated toward the extractor. (g) Further increase of ΔU causes field evaporation of surface atoms.
As pointed out at the beginning of this section, field ionization occurs only at the surface. Therefore, let us see what happens when we cut our metal to create a surface as illustrated in Figure 2c. We ignore the extractor for now (ΔU=0), and observe the energy profile along the red arrow as shown in Figure 2d. To the left of the surface, the potentials are unchanged; however, at the surface, a potential barrier reaching up to Evacuum is formed. Thus, the electrons in the electron gas are reflected at the surface and remain inside the metal. Furthermore, a gas atom that is far away from the surface creates its own potential well, and all its electrons are localized. Note that the electron with the highest energy is referred to as the outer electron. If we were to observe the gas atom and surface at this condition, we would see that the gas atom moves randomly in respect to the metal surface, but no ionization occurs. Also, since the energy of the outer electron of the gas atom is below EF of the metal, the electron cannot tunnel to the metal, even if the barrier width was greatly reduced due to collision with the metal surface (Pauli principle). The system is stable.
To actually cause anything to happen, an electric field has to be applied between the metal surface and the extractor electrode. Again, let us first ignore the gas molecule and discuss the electron emission. If we apply a large positive voltage to the extractor (at a distance x) while the tip is grounded,3 the potential barrier at the surface is deformed as shown in Figure 2e and electrons can tunnel from the metal into the vacuum. The slope equals eΔUx, where e is the electron charge. As the density of electrons in the metal is high and a virtually infinite supply is available, very high emission currents can occur, and Joule heating of the metal poses the limit.
However, since we are trying to ionize gas, let us go back to the configuration shown in Figure 2d and apply a positive ΔU instead (Figure 2f). Two things are now happening: first, the vacuum energy increases away from the surface, and second, the energy of the outer electron of the gas atom becomes higher than EF of the metal. Gas atoms have fluctuating dipole moments as the outer electrons are orbiting around the core, and this causes the gas molecule to be attracted to the metal surface in the strong field. In consequence, all prerequisites for tunneling of the outer electron of the gas atom are fulfilled: the barrier is narrow, and there is space for the electron in the metal at the energy of the outer electron. After tunneling, the now positively charged gas ion is immediately accelerated in the strong electric field away from the tip and toward the extractor. The ion has an energy corresponding to ΔU. For the ionization to happen, the field has to be in the range of 1010 V/m. For a flat surface, this would correspond to 100 MV at x=1 mm. However, by using a sharp needle instead of a flat surface, the electric field is concentrated at the tip apex, and only 5 kV is required for a tip diameter of 100 nm. Some individual surface atoms that stick out due to the crystal structure cause a localized concentration of the field, and ionization occurs predominantly there (see Figure 1b).
By further increasing the electric field, the attractive force of the gas atom is stronger, resulting in a higher tunneling probability (which can be estimated by the Wentzel-Kramers-Brillouin approximation). This is directly reflected in a higher ion current. However, an exceedingly large field will cause field evaporation (Figure 2g). The potential of the surface metal atom is slightly increased, and one of the localized electrons can tunnel into the bulk. The temporary positive charge of the surface atom can be enough to break it away in the strong electric field. For field ionization, we can therefore conclude that we require enough electric field to attract the gas atom and allow tunneling; however, the evaporation field strength should not be reached. The actual voltage that has to be applied to the extractor depends strongly on the distance x and the shape of the tip. Furthermore, different gases require different field strengths for ionization, with helium being the highest at 4.4×1010 V/m [19]. This poses a technological challenge for the control of the ion beam energy: by grounding the extractor electrode and varying the tip potential, the kinetic energy of the ions after passing through the hole in the extractor can be vastly different. This is solved similar to the LMIS and FEG with the introduction of an accelerator electrode that allows extraction control independent of the final beam energy (see Figure 3a). The ion column of the GFIS-FIB nanofabrication tool located at the Japan Advanced Institute of Science and Technology is illustrated in Figure 3b. The microchannel plate that can be inserted into the ion beam is used to image the emission tip in the FIM mode.
Figure 3.
(a) Configuration of emission tip, extractor, and accelerator electrode in the GFIS. (b) Schematic illustration of the ion column of the GFIS-FIB nanofabrication tool. The field ionization process is illustrated in the inset. Reproduced with permission [20].
2.3. The emitter tip
As discussed in the previous section, to reduce the requirement on the voltage that has to be applied to the extractor, a sharp tip is used. However, this is not the only reason. In the GFIS, the limit of current (i.e., the rate of ionization events) is mostly limited by the supply of the gas atoms. In fact, the main route of gas supply is through the attraction of gas atoms to the shaft of the tip where the electric field is weaker, followed by migration or hopping toward the apex. Thermal vibration of the tip can repel some of the adsorbed gas atoms and constitutes source vibration, thus the cryogenic cooling mentioned earlier. The ionization rate can be directly controlled by the pressure of source gas around the tip; however, exceeding a certain limit might cause arcing between highly charged parts, which has to be avoided. Also, atom-atom collisions reduce the mean free path. It should thus be clear that if a tip has several ionization sites close to each other, they compete with each other for the available gas.
The achievable diameter of a focused ion beam, D, is given by [21].
D=2R=2MRS2+14Csiαi32+12CciαiΔEE2,E1
where R is the beam radius, M the magnification of the optics between the source and the sample (between 0.3 and 2), RS the source size, αi the beam cone angle, Csi and Cci the spherical and chromatic aberration coefficients, respectively, and E the acceleration voltage. ΔE is the energy spread, which is the variation of energy of individual ions. To minimize D and thus have the highest resolution, the atomically small RS and the low ΔE of less than 1 eV [11] are unique to the GFIS and surpass other ion sources. The beam diameter estimation in Eq. (1) assumes that only the emission from a single emission site is aligned with the microscope column, and the emission from the remaining ionization sites is either lost in the ion optics or filtered by a narrow aperture. There had been considerations to focus emission from multiple sites; however, this has proven to be technically not feasible. For the HIM with the tungsten tip, the trimer is a compromise between stability (i.e., the three atoms of the pyramid stabilize each other) and brightness of ∼4×109 A/cm2sr which can be achieved [12]. In other cases, a single atom tip (SAT) can be formed [22, 23], which can improve source brightness especially for heavier gas atoms that are not as mobile as helium, and the total supply of gas atoms is the limiting factor.
The formation of good emitter tips requires a high level of specialization. Starting from a thin wire of pure metal with as few crystal defects as possible, electrochemical etching [24] is used to form the nanoscopic shape. Then, while observing the emission in an FIM, field evaporation, gas-assisted etching (surface atoms at the shaft of the tip are removed in oxygen or nitrogen gas atmosphere at fields above the individual ionization field strengths [25]), and emitter heating (causing migration of surface atoms) are used to form the atomistic tip structure. The latter processes can be performed in situ once the emitter is fitted in a microscope, as well. As the ion emission originates from the tip apex and follows the electric field lines, the positioning requirement of the emitter tip in respect to the ion column is unproblematic.
Recently, it has been demonstrated that a tungsten tip can be coated by a thin layer of iridium [26]. Due to the softness of tungsten, it can be shaped more easily, while the extremely sturdy iridium can improve emission stability.
2.4. Other novel ion sources
The renaissance of the FIB with the significantly improved performance of the GFIS compared to the LMIS, as well as the prospect of a large ion portfolio, has led to intensive research into other ion sources as well. For example, gas atoms can be trapped and cooled in the magneto-optical trap (MOT). After condensing them, another light source is used for photoionization, and the ions are extracted in an electrical field [27]. The gas trapping can also be achieved by laser cooling alone [28]. Although the range of possible gases to be ionized by this method is unlimited, the wavelength of the cooling and ionization lasers has to be adjusted for each species which adds to the cost of the source.
3. The nitrogen ion beam
Nitrogen is unique compared to other source gases used in GFIS, as it forms one of the strongest bonds known in nature (binding energy: 9.79 eV; bond length: 0.11 nm) and naturally occurs only as N2. Nitrogen can be ionized by electron impact, and the effect of nitrogen ion impact on various materials has been investigated [29]. By annealing exposed silicon samples, the formation of Si3N4 was reported [30, 31]. The measurement of electron impact ionized nitrogen mass spectra revealed that while N2+ ions dominated, some N+ ions are generated as well [32]. Since the resolution of ion beams in accelerators is in general no issue, a mass filter is typically added to the beam line that removes unwanted ions and leaves a pure beam of required ions. For the GFIS, where we strive for highest resolution, a mass filter would degrade the beam and the ion beam should be pure “as is,” Otherwise, two beams are scanned across the sample with a separation depending on the mass difference and the magnetic field occurring along the ion’s path. In this regard, N2 has the advantage of naturally high isotopic purity.
In Section 3.1, we explain what happens during field ionization of nitrogen and interaction with a silicon sample. Then, the nitrogen GFIS-FIB is compared with other ion species used in the GFIS in Section 3.2.
3.1. Field ionization of molecular nitrogen gas and solid-ion interaction
The possible ionization mechanisms of N2 gas and how they behave after impact on a solid silicon sample are shown in Figure 4. In addition, Monte Carlo (MC) simulation results for these two possibilities with an acceleration voltage of 25 kV are shown. Collision cascades (i.e., the way atoms proceed through a solid after collision) are very difficult to simulate.
Figure 4.
Possible ionization mechanisms of N2 gas ionized in a GFIS [32, 33] together with MC simulation results for 25-kV acceleration voltage: (a) The molecule splits during ionization and interacts with the sample as an atomic ion with full energy and (b) the molecule stays together during ionization but splits upon impact (modeled as N with 1/2 energy). The interaction volume and nuclear-stopping power depth profiles for the two cases show the different sizes of the expected damage region. In addition, the interaction volume for 16-kV acceleration voltage is shown (modeled as N with 8 keV). Reprinted with permission [39]. Copyright 2017, American Vacuum Society.
The most accurate method is the so-called molecular dynamics (MD) approach, where each individual atom is modeled, and the forces acting onto each other are evaluated accurately. With each additional atom in the simulation model, the complexity increases exponentially, and it is not realistic to simulate a system with several thousand atoms—the number of atoms in the range of the ion—due to the computational cost. Furthermore, the result would be vastly different depending on whether the ion directly collides with a surface atom (possibly resulting in backscattering) or channels along crystallographic directions. Thus, to obtain a complete understanding of the possible effect of the ion beam, a large number of MD simulations would be required where the position and angle in respect to the target crystal are varied. This approach is only viable in special cases, as demonstrated in a study of ion interaction with an atomically thin membrane [35]. MC takes a different approach, which uses probability to simplify complex processes. If an ion is traveling through a medium, it is easy to understand that it has a chance to collide with atom cores or pass between atoms. Instead of explicitly calculating the outcome, a probability of collision is considered that depends on the projectile and the density and composition of the target sample. Furthermore, the possible outcome of collisions (i.e., the amount of energy lost and the change of direction) can also be assigned to probabilities. Now, to actually perform the MC simulation, a particle with a given direction and velocity is assumed. Based on these parameters, the position of the particle after a certain time τ is calculated. Furthermore, the occurrence and type of interaction is calculated for this time step with the help of the previously mentioned probabilities and a random number generator. With this, we can define a new direction and velocity (which can be unchanged or point to the side) and calculate the position after the next time step. This process is repeated until the particle either left the medium or its velocity dropped below a threshold. Since this result might be far away from the reality as it relies on the random number generator, the outcome of particles with the same initial conditions is repeated for several thousand times, each time with a different outcome. By plotting all the trajectories and evaluating the recorded collision details, the principle shape of the interaction region as well as the mean range can be estimated with small computational cost. For example, we can see that for a N+ ion at 25 keV (Figure 4), damage deeper than 100 nm from the surface will occur. Factors limiting the accuracy of the MC simulation method is the fact that each “shot” is performed onto a pristine target (i.e., no accumulation of damage), and it requires experimental data to model the particle-medium interaction as precisely as possible. SRIM is one software package for MC simulation that has proven useful for many cases [36], but other MC codes exist [37, 38].
Figure 5.
High-resolution HAADF-STEM micrographs of N2+ bombarded bulk crystalline Si for 25-keV beam energy at doses of (a) 9.5 to (d) 0.24×103 ions/nm, and (e) 16 keV at 0.8×103 ions/nm. Scale bar is the same for all images. (f) High-resolution ABF-STEM micrograph of the transition region from crystalline to amorphous Si for the implantation shown in (a). Although some amorphization is observed up to 10 nm away from the amorphous region, the well-defined transition is visible. (g) Atomic resolution HAADF-STEM image of the area indicated by the rectangle in (a), which shows the dumbbell structure of Si(110). Reprinted with permission [39]. Copyright 2017, American Vacuum Society.
To determine which of the two possible ionization mechanisms shown in Figure 4 is the one that occurs in the GFIS, a crystalline silicon sample is exposed, and the cross section can be observed by TEM, as shown in Figure 5. Here, several exposures with different doses were performed with 25 keV and one additional one at 16 keV. As silicon changes from crystalline to amorphous above a well-defined disorder threshold, the observed shape in the TEM is a good indicator of the range of ions. By comparing the shape of the bell-like amorphous region with the MC simulation, it was thus possible to confirm that the N2 molecules are ionized to N2+. As energetic ions are instantaneously neutralized upon impact onto a solid through the pickup of an electron, the impact of a N2 molecule onto silicon was calculated by atomistic simulation as well [39]. It confirmed that the N-N bond is broken within a few atomic layer, and the N atoms continue their path through the sample independently. Therefore, the ion beam with an acceleration voltage of 25 kV in the N2 GFIS has the same effect on a sample as a N+ beam with 12.5 keV would have.
3.2. Comparison with other ion species
Previously, various atomic ion species used or potentially usable for FIBs had been compared, based on MC simulation at 30 keV [40]. It impressively shows the advantage of light ions compared to heavier ions, namely the small interaction volume close to the surface, resulting in high milling and imaging resolution. To add nitrogen ions to this comparison, however, some care has to be taken. The MC simulations are based on atomic projectiles. Thus, when simulating the effect of the N2+ GFIS-FIB with an acceleration voltage of 25 kV, we have to perform a simulation of N with an acceleration voltage of 12.5 kV instead. If the N2+ GFIS-FIB was to be added to the comparison by Tan [40], the acceleration voltage of 60 kV would have to be assumed. This is outside the operation range of FIB microscopes. Therefore, in Figure 6, a comparison of different ions for a fixed energy (12.5 keV) is shown instead [39]. As expected from the different masses of the atoms, the penetration depth of nitrogen falls between beryllium and neon, with a sputter yield of 0.73 (i.e., the number of silicon atoms sputtered per impinging ion). It should be noted that this sputter yield considers a single N atom with 12.5 keV; however, the N2+ beam will actually yield two of such projectiles. To characterize ion exposure, the number of primary ions is typically reported. Thus, if we compare a certain dose of helium ions at 12.5 keV with N+ at 12.5 keV (actually generated from a 25 keV N2+ beam), the nitrogen dose has to be doubled to predict the number of secondary electrons.
Figure 6.
Comparison of interaction volume and sputter yield for Ga, Ar, Ne, N, Be, He, and H for corresponding ion energies of 12.5 keV. Reprinted with permission [34]. Copyright 2017, American Vacuum Society.
4. Nitrogen ion microscopy
Focused ion beams can be used for different types of microscopy. For example, back-scattered ions can reveal details about the atomic mass of target atoms (the imaged target atoms have to be heavier than the ion species). In the secondary-ion mass spectrometry (SIMS), target atoms are sputtered and characterized in a mass spectrometer. Furthermore, ionoluminescence and transmission of ions through a thin probe can be used for analysis and imaging. However, we focus our discussion on the predominantly used secondary electron imaging due to the simplicity and the good perceptive sense of surface topography in images.
Light ions generate more secondary electrons compared to gallium ions [41]. Therefore, lower doses are sufficient to image a sample compared to gallium FIB, or for low doses visibility is better with light ions. The recent down-scaling of integrated circuits has increased the demand for milling at the single-nm scale, which can only be achieved by gallium FIB with extremely low beam currents. This results in the difficulty to do end-point detection and drives the interest in SE imaging by light ions. A large amount of HIM SE images can be found throughout the literature. The shorter wavelength of helium ions compared to electrons allows a smaller spot, and the small interaction volume at the surface means that the secondary electron generation is restricted to a smaller area on the sample. Regarding nitrogen, Schmidt et al. reported secondary electron images of samples imaged by helium and by nitrogen ions [20].
Secondary imaging is, even with an electron beam in the SEM, almost always a destructive method. Sample chambers in scanning probe microscopes have typical base pressures of >1×10−5 Pa due to the fact that rubber o-rings are used to seal some of the large openings that are required to install the sample stage. The ultra-high vacuum alternative is the usage of copper gaskets that, through compression between knife-edge flanges, create extremely good vacuum seals. This increases the cost of the chamber and machine maintenance. Alternatively, it is possible to achieve better vacuum by installing two rubber o-rings, where the space between the rings is evacuated by a separate pump. Such measures would further require ultra-high cleanliness of the images specimen and reduce the machine throughput. The contamination in the chamber is typically hydrocarbons that are emitted from the pump system, and as the beam (again, this applies to electron as well as ion beams) hits the sample, some of the adsorbed hydrocarbons are disassociated, and amorphous carbon is deposited on the sample. This carbon modifies the sample surface and can influence secondary electron emission. In addition, the charging of the sample occurs immediately. Other effects that influence SE emission are as follows:
work function,
charging (note that ion beams will always cause positive charge due to the implantation of positive ions and emission of electrons, while negative charging can occur in SEM),
escape depth of SE,
energy spectrum of SE.
Due to all these effects and the fact that imaging is “destructive,” the prediction of SE yield remains impossible even for electrons. Therefore, while secondary electron yield (number of SEs emitted per incident particle) observed in a typical microscope might not be universal, they represent values “appropriate to the actual operational environment” [42]. Comparison of images has to be carefully discussed.
4.1. Unique contrast in carbon-based samples
By imaging samples with carbon-based materials by focused nitrogen and helium ion beams, it was reported that an improved SE contrast is observed by nitrogen [20]. The sample that was imaged is shown in Figure 7, which comprises electrically contacted four-layer graphene and graphite on SiO2 with gold electrodes. Particularly, it was noticed that the graphene, which is pulled-in onto the SiO2 substrate, is only visible by N2IM. In addition, the authors confirmed that the observed contrast difference was not caused by equipment differences, and re-imaged the structures in the Zeiss Orion Plus [42].
Figure 7.
Secondary electron images acquired on identical location on nano-patterned sample. (a+b) N2IM (GFIS-FIB) images with 14 and 144 ions/px imaging dose at 25 keV. Graphite and four-layer graphene have good contrast against SiO2 and Au electrodes. (c) HIM (GFIS-FIB) image with 82 ions/px at 25 keV. (d) HIM (Zeiss Orion Plus) image with 206 ions/px at 30 keV. The dark area in the graphite (arrow) is only visible in this image and probably formed after the previous imaging. Marek E. Schmidt, Shinichi Ogawa, Hiroshi Mizuta, “Contrast Differences Between Nitrogen and Helium Ion Induced Secondary Electron Images Beyond Instrument Effects,” MRS Advances, DOI: 10.1557/adv.2018.33, reproduced with permission [42].
Interestingly, the thick graphite and the SiO2 substrate had been exposed to a relatively strong oxygen reactive ion etching process, and the contrast difference was not observed for nanocrystalline graphene (NCG), a polycrystalline carbon film that can be deposited via a metal-free plasma-enhanced chemical vapor deposition at the wafer scale [43]. By imaging another sample comprising suspended graphene and electrically contacted graphite that was not exposed to the RIE (Figure 8), the contrast difference could be gradually induced by an accumulated nitrogen ion dose of more than 4×1014 ions/cm2 [20]. Although the final cause for the contrast difference is not yet fully understood, it has thus become clear that the surface modification through the RIE process together with the nitrogen beam results in the observed effect. Strangely, however, the contrast difference appears to be temporary while observing with nitrogen. It should be interesting to see if adsorption of the reactive nitrogen atoms to the surface (nitrogen can be ejected from the sample surface, compare Figure 6) can cause a temporary change of work function and is removed shortly after imaging is stopped. Nevertheless, any added contrast can enable the imaging of samples that would otherwise remain unobservable. The effect of charging on the contrast is clearly visible in Figure 8a. Isolated gold and graphite gradually become darker as it is scanned by the nitrogen beam from top to bottom, while the contacted graphite retains its initial brightness.
Figure 8.
(a) Low-resolution N2+ GFIS-FIB image (∼6×1010 ion/cm2) of sample C6 showing large Au electrodes with a contacted graphite flake, as well as isolated graphene and Au pattern. The isolated pattern shows decreased brightness from top to bottom caused by charging during the scan (scan direction illustrated in inset). (b–e) Series of images with a gradually increasing dwell time of identical location on sample C6 comprising suspended bilayer graphene and a larger graphite flake. The dose is increased from 6 (∼3.8×1012 ion/cm2) to 24, 120, and 600 ion/cm2. (f) A higher-resolution scan of area indicated by black square in (e) with 120 ion/px (∼8.5×1014 ion/cm2). (g) Pixel brightness profile along the arrow shown in (b). (h) Relative graphite brightness as a function of accumulated dose extracted from (g) showing a decrease at high dose. Reproduced with permission [20].
4.2. Nanomachining
Nanomachining of nanoscopic structures is one of the main advantages of the FIB over the SEM. At the beginning of this chapter, we discussed the motivation of the light gas ion source development, namely a higher milling resolution than the heavy gallium.
Helium has been demonstrated to offer unprecedented milling resolution of suspended layers [44] with a ∼5-nm wide graphene nanoribbon (GNR) realized in suspended graphene. As the number of experimental reports with the HIM increased, a serious limitation of the helium beam milling in bulk samples has been observed. Since the helium ion interacts with the surface, a small number of surface atoms are sputtered in a very well-controlled area; however, the ions continue to penetrate into the specimen and are implanted at a depth of 100–500 nm (depending on the energy) and remain there. Helium as a noble gas cannot be absorbed into the target specimen, but can be “squeezed” into the space between atoms. At doses above ∼1016 ions/cm2, however, the pressure of the helium in the target substrate becomes sufficiently large to break the inter-atomic bonds and form helium nanobubbles [45]. It has been reported that a high-power laser pulse can be used in situ during milling to locally heat the target area and alleviate the damage, but crystal defects and surface deformation remain above 1×1018 ions/cm2, which is the required dose to pattern graphene [46]. For reflection-type UV masks, any defects in the atomically precise adsorber stack will lead to a degradation; therefore, hydrogen (which is small and reactive and thus does not cause significant corruption to the target material [14]) or heavier ion species are preferred.
Figure 9a shows a MoSi film on quartz substrate with a single-line etching with a nitrogen-focused ion beam [47]. The cut width of ∼9 nm is smaller than what can be achieved by gallium, and vertical side walls are achieved for the whole depth. Figure 9b shows how the focused nitrogen beam can be used to correct protrusive defects in the MoSi-adsorbing layer. By careful alignment and control of the milling conditions, the repaired edge is not distinguishable from the non-defective edge—an important requirement for mask repair.
Figure 9.
(left) Scanning electron microscopy image of a MoSi film after a one-line etching with the focused nitrogen beam. The width of the deep cut is ∼9-nm wide. (right) N2IM images showing the removal of protrusive defect in MoSi film on quartz substrate. After removal, the location of the modification is not visible. Reproduced with permission [47].
N2 GFIS-FIB was also used for the successful formation of quantum point contacts in high-in-content InGaAs [48], as shown in Figure 10. Here, ∼30-nm wide trenches were carefully aligned to previously wet-etched areas with an inner separation of down to ∼30 nm, smaller than the size available by electron beam lithography. Half-integer quantized conductance behaviors were observed under magnetic fields, which demonstrates that N2 GFIS-FIB milling is a promising method to realize quantum devices.
Figure 10.
Scanning electron microscopy image of a quantum point contact (QPC) fabricated using N2 GFIS-FIB. Two horizontal cuts with a width of ∼30 nm are placed with a separation of ∼100 nm. Copyright 2014 the Japan Society of Applied Physics [48].
4.3. Other applications
We have shown so far that the N2IM can be used for imaging and milling applications. These can also be performed by helium or neon beams, albeit sacrificing the material contrast discussed in Section 4.1. Nitrogen, however, has a unique effect when implanted into diamond. When a C atom of the diamond lattice is replaced with a nitrogen nearby a vacancy, the so-called nitrogen-vacancy (N-V) center is formed. It has shown to be a scientifically valuable phenomenon as the photoluminescence from a single N-V center can be detected by confocal microscopy [49, 50]. As it turns out, the photoluminescence is affected by magnetic fields [51], electric fields [52], temperature [53], and mechanical stress [54]. Furthermore, N-V centers can be viewed as a basic unit of a quantum computer [55]. To fully exploit these effects, it is necessary to produce N-V centers with nm precision.
To generate N-V centers, either nitrogen implantation followed by annealing is used, or a small amount of nitrogen is added during the chemical vapor deposition of diamond. N-V centers can also be generated by the focused nitrogen ion beam from the GFIS. In this regard, the sub-2-nm beam diameter and variable energy make it a powerful tool. However, some points should be noted about the N-V center generation by ion implantation that needs to be technologically controlled. First, the so-called “conversion efficiency,” which signifies the number of N-V centers per implanted nitrogen, is in the single-digit percentage. Therefore, it is not possible to deterministically control the number of N-V centers. Another (smaller) uncertainty is the minute current fluctuation of the GFIS, which means that repeatedly dwelling the beam for a given time will result in different numbers of nitrogen ions and potentially N-V centers; however, this effect is small compared to the conversion efficiency uncertainty. Second, the trajectory of an ion through a solid is subject to a certain randomness. To implant deep N-V centers (which do not disappear through migration to the sample surface during the required postimplantation annealing), a sufficiently large acceleration voltage is required, and the control over the position is lost. As we saw in Figure 6, the horizontal distance the ion can travel from the landing point is significant. For nitrogen in a diamond with 12.5 kV, the range is ∼50 nm. A possible solution would be the use of a very low acceleration voltage for the GFIS-FIB nitrogen implantation (thus with an accurate spatial control), followed by an in situ deposition of a diamond capping layer without breaking vacuum. This is certainly an interesting technical challenge.
5. Summary and conclusion
We have given an introduction to the nitrogen ion microscopy (N2IM). Starting with the exciting history, development, and principle of the GFIS, which is at the heart of the recent technological advancement in FIB technology, we have discussed how the molecular nitrogen source gas is different from the atomic source gases. We have reviewed the interaction of nitrogen ions with a silicon sample which shows that nitrogen (N2) is ionized to N2+ and splits within few atomic layers after collision with a sample. The unusual contrast of carbon-based films exposed to a high dose of ion damage in SE images was shown. In terms of machining, nitrogen is a good compromise between helium (high-resolution and low sputter yield) that unfortunately leads to sample swelling at higher doses, and the established gallium (low-resolution and high sputter yield). It was shown how quantum point contacts and photolithographic mask repair is enabled by the nitrogen FIB.
Acknowledgments
This work was supported by the Center of Innovation (COI) program of the Japan Science and Technology Agency and the Grant- in-Aid for Scientific Research No. 25220904, 16K13650, and 16K18090 from the Japan Society for the Promotion of Science (JSPS).
\n',keywords:"gas field ion source, N2IM, GFIS-FIB, secondary electron",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/61027.pdf",chapterXML:"https://mts.intechopen.com/source/xml/61027.xml",downloadPdfUrl:"/chapter/pdf-download/61027",previewPdfUrl:"/chapter/pdf-preview/61027",totalDownloads:574,totalViews:272,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"November 1st 2017",dateReviewed:"March 12th 2018",datePrePublished:"November 5th 2018",datePublished:"July 18th 2018",dateFinished:null,readingETA:"0",abstract:"The gas field ion source (GFIS) can be used to generate beams of helium, neon, hydrogen, and nitrogen ions, among others. Due to the low energy spread and the atomically small virtual source size, highly focused ion beams (FIB) can be obtained. We discuss the history of the GFIS and explain the field ionization and field evaporation process in general. Then, the unique properties of the nitrogen ionization, originating from the molecular nature, are explained. We show how the nitrogen ion microscopy (N2IM) can be used to image and pattern samples. The unique contrast observed in samples with graphene or carbon is reported. Finally, we conclude with an outlook of the technology and possible key applications such as spatially localized nitrogen-vacancy center implantation.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/61027",risUrl:"/chapter/ris/61027",book:{slug:"ion-beam-applications"},signatures:"Marek E. Schmidt, Masashi Akabori and Hiroshi Mizuta",authors:[{id:"231719",title:"Dr.",name:"Marek",middleName:null,surname:"Schmidt",fullName:"Marek Schmidt",slug:"marek-schmidt",email:"schmidtm@jaist.ac.jp",position:null,institution:null},{id:"231725",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Mizuta",fullName:"Hiroshi Mizuta",slug:"hiroshi-mizuta",email:"mizuta@jaist.ac.jp",position:null,institution:null},{id:"231870",title:"Prof.",name:"Masashi",middleName:null,surname:"Akabori",fullName:"Masashi Akabori",slug:"masashi-akabori",email:"akabori@jaist.ac.jp",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. The gas field ion source",level:"1"},{id:"sec_2_2",title:"2.1. History of the gas field ion source",level:"2"},{id:"sec_3_2",title:"2.2. Field ionization and field evaporation",level:"2"},{id:"sec_4_2",title:"2.3. The emitter tip",level:"2"},{id:"sec_5_2",title:"2.4. Other novel ion sources",level:"2"},{id:"sec_7",title:"3. The nitrogen ion beam",level:"1"},{id:"sec_7_2",title:"3.1. Field ionization of molecular nitrogen gas and solid-ion interaction",level:"2"},{id:"sec_8_2",title:"3.2. Comparison with other ion species",level:"2"},{id:"sec_10",title:"4. Nitrogen ion microscopy",level:"1"},{id:"sec_10_2",title:"4.1. Unique contrast in carbon-based samples",level:"2"},{id:"sec_11_2",title:"4.2. Nanomachining",level:"2"},{id:"sec_12_2",title:"4.3. Other applications",level:"2"},{id:"sec_14",title:"5. Summary and conclusion",level:"1"},{id:"sec_15",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Feynman RP. There’s plenty of room at the bottom. Engineering and Science. 1960;23(5):22-36'},{id:"B2",body:'Orloff J, Swanson LW, Utlaut M. Fundamental limits to imaging resolution for focused ion beams. 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Ionization of nitrogen, oxygen, water, and carbon dioxide molecules by near-threshold Electron impact. Technical Physics. 2005;50(4):402-407'},{id:"B34",body:'Åhlgren EH, Kotakoski J, Krasheninnikov AV. Atomistic simulations of the implantation of low-energy boron and nitrogen ions into graphene. Physical Review B. 2011;83(11):115424'},{id:"B35",body:'Ziegler JF, Ziegler MD, Biersack JP. SRIM – The stopping and range of ions in matter (2010). Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 2010;268(11–12):1818-1823. 19th International Conference on Ion Beam Analysis'},{id:"B36",body:'Mahady K et al. Monte Carlo simulations of secondary Electron emission due to ion beam milling. Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena. 2017;35(4):041805'},{id:"B37",body:'Ohya K, Ishitani T. 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Springer US, 1992'},{id:"B42",body:'Schmidt ME, Ogawa S, Mizuta H. Contrast differences between nitrogen and helium ion induced secondary electron images beyond instrument effects. MRS Advances. 2018:3(10):505-510'},{id:"B43",body:'Schmidt ME, Cigang X, et al. Metal-free plasma-enhanced chemical vapor deposition of large area nanocrystalline graphene. Materials Research Express. 2014;1(2):025031'},{id:"B44",body:'Pickard D, Scipioni L. Graphene Nano-Ribbon Patterning in the ORION® PLUS. In: Zeiss application note. 2009'},{id:"B45",body:'Livengood R et al. Subsurface damage from helium ions as a function of dose, beam energy, and dose rate. Journal of Vacuum Science & Technology B. 2009;27(6):3244-3249'},{id:"B46",body:'Stanford MG et al. In situ mitigation of subsurface and peripheral focused ion beam damage via simultaneous pulsed laser heating. Small. 2016;12(13):1779-1787'},{id:"B47",body:'Aramaki F, Kozakai T, Matsuda O, Yasaka A, et al. Performance of GFIS mask repair system for various mask material. In: Proceedings of SPIE. Photomask Technology 2014. Vol. 9235. Monterey; 2014. pp. 92350F-92350F-8'},{id:"B48",body:'Akabori M et al. High-in-content InGaAs quantum point contacts fabricated using focused ion beam system equipped with N2 gas field ion source. Japanese Journal of Applied Physics. 2014;53(11):118002'},{id:"B49",body:'Gruber A et al. Scanning confocal optical microscopy and magnetic resonance on single defect centers. Science. 1997;276(5321):2012-2014'},{id:"B50",body:'Iwasaki T et al. Direct nanoscale sensing of the internal electric field in operating semiconductor devices using single Electron spins. ACS Nano. 2017;11(2):1238-1245'},{id:"B51",body:'Balasubramanian G et al. Nanoscale imaging magnetometry with diamond spins under ambient conditions. Nature. 2008;455(7213):648-651. pmid: 18833276'},{id:"B52",body:'Dolde F et al. Electric-field sensing using single diamond spins. Nature Physics. 2011;7(6):459-463'},{id:"B53",body:'Doherty MW et al. Electronic properties and metrology applications of the diamond NV− center under pressure. Physical Review Letters. 2014;112(4):047601'},{id:"B54",body:'MacQuarrie ER et al. Mechanical spin control of nitrogen-vacancy centers in diamond. Physical Review Letters. 2013;111(22):227602'},{id:"B55",body:'Wrachtrup J, Jelezko F. Processing quantum information in diamond. Journal of Physics: Condensed Matter. 2006;18(21):S807'}],footnotes:[{id:"fn1",explanation:"The acronym NIM is now widely used to refer to the neon ion microscopy."},{id:"fn2",explanation:"Technically, the difference between a GFIS and a field emitter gun for electrons is the polarity of the tip with respect to the extractor electrode. Since electrons are supplied through the tip, cooling is not required for electron emission."},{id:"fn3",explanation:"We can also apply a negative voltage to the tip and ground the extractor. Important is the potential difference ΔU. Note that lower potential corresponds to a higher energy. This counter-intuitive definition is of historic origin."}],contributors:[{corresp:"yes",contributorFullName:"Marek E. Schmidt",address:"schmidtm@jaist.ac.jp",affiliation:'
Japan Advanced Institute of Science and Technology, Nomi, Japan
Japan Advanced Institute of Science and Technology, Nomi, Japan
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1. Introduction
Biometric identification has been widely applied in modern society, such as electronic payment, entrance control, and forensic identification. As a reliable solution for identity authentication, biological characteristics refer to the inherent physiological or behavioral characteristics of the human body, including the iris, pattern, retina, palmprint, fingerprint, face and also voiceprint, gait, signature, key strength, etc. In the last decade, we have witnessed the successful employment of recognition systems using fingerprint, iris, and face. With the development of image capture devices and recognition algorithms, palmprint recognition receives more and more attention recently. Palmprint image contains principal lines, wrinkles, ridges, and texture that are regarded as useful features for palmprint representation and can be captured with a low-resolution image [1]. Palmprint recognition has several advantages compared with other biometrics: (1) the line features and texture features in a palmprint are discriminative and robust, which can be easily fused with other hand features (dorsal hand vein, fingerprint, finger knuckle); (2) the pattern of palmprint is mainly controlled by genetic genes, when combined with palm vein information it can achieve high antispoof capability; (3) palmprint image acquisition is convenient and low-cost, and a relative low-resolution camera and a light source are sufficient to acquire the images; (4) the palmprint acquisition is hygienic and user friendly in the real applications. Based on the custom acquisition devices, more information can be retrieved in a multispectral image or 3D palmprint image. A 2D gray scale palmprint example with feature definitions is shown in Figure 1. The purpose of this chapter is to review recent research on palmprint acquisition systems to trace the development of palmprint recognition-based biometric systems. In this chapter, we coarsely divide the devices into three types by acquisition mode: touch-based devices, touchless devices, and portable devices. Touch-based devices usually have pegs to constrain the hand pose and position, which can capture the details of palmprint to the most extent. The illuminating environment is also stable during capturing process. These constrains ensure the captured palmprint images to be high quality. For touchless devices, users can freely place their palms in front of the camera while the hand pose is generally required to spread out the fingers. The environment during the capturing process becomes more complicated, especially the illumination. There are also datasets composed of palmprint images captured in a relatively free fashion. These images may be collected on the Internet which we will not discuss here. Otherwise, collectors use digital cameras or phone cameras to capture palmprint image, and usually, there are no strict conditions forced on the user. In the rest of this chapter, first, we will introduce the representative palmprint acquisition devices, and then study the relationship between the palm distance, image sharpness, hardware parameters, and the final recognition performance. Table 1 summarizes the palmprint acquisition devices.
Capture palmprint and palm vein images in the device; established the current biggest publicly available database
Table 1.
The palmprint recognition systems.
2. The current palmprint recognition devices
2.1 Touch-based devices
Reference [1] is a pioneer work for palmprint acquisition and recognition that builds the first large-scale public palmprint dataset. The captured palmprint images are low-resolution with 75 pixels per inch (PPI), so that the whole process can be completed in 1 s, which achieves real-time palmprint identification. The palmprint capture device includes a ring light source, charge-coupled device (CCD) camera, a frame grabber, and an analog-to-digital (AD) converter. Six pegs are serving as control points that constrain the user’s hands. To guarantee the image quality, during palmprint image capturing, the device environment is semiclosed, and the ring source provides uniform lighting conditions. After capturing the palmprint, the AD converter directly transmits the captured images by the CCD camera to a computer. The well-designed acquisition system can capture high-quality images, which boosts the performance of the identification algorithm. The experiment result also demonstrates that low-resolution palmprint can achieve efficient person identification. Our palms are not pure planes, and many personal characteristics lie on the palm surface. From this view, 2D palmprint recognition has some inherent drawbacks. On one hand, much 3D depth information is neglected in 2D imaging. The main features in 2D palmprint are line features including principal lines and wrinkles, which is not robust to the illumination variations and contamination influence. On the other hand, the 2D palmprint image is easy to be counterfeited so that the anti-forgery ability of 2D palmprint needs improvement. For capturing depth information in palmprint, [4, 14] explores a 3D palmprint acquisition system that leverages the structured light imaging technique. Compared to 2D palmprint images, several unique features, including mean curvature image, Gaussian curvature image, and surface type, are extracted in 3D images. Many studies have proposed different algorithms that encode the line features on the palm surface; however, the discriminative and antispoof capability of palm code needs to be further improved for large-scale identification. To obtain more biometric information in the palm, in [5] a multispectral palmprint acquisition system is designed, which can capture both red, green, and blue (RGB) images and near-infrared (NIR) images of one palm. It consists of a CCD camera, lens, an A/D converter, a multispectral light source, and a light controller. The monochromatic CCD is placed at the bottom of the device to capture palmprint images, and the light controller is used to control the multispectral light. In the visible spectrum, a three-mono-color LED array is used with red peaking at 660 nm, green peaking at 525 nm, and blue peaking at 470 nm. In the NIR spectrum, a NIR LED array peaking at 880 nm is used. It has been shown that light in the 700 to 1000 nm range can penetrate the human skin, whereas 880–930 nm provides a good contrast of subcutaneous veins. The system is low-cost, and the acquired palmprint images are high-quality. By fusing the information provided by multispectral palmprint images, the identification algorithm achieves higher performance on recognition accuracy and antispoof capacity.
2.2 Touchless devices
Touch-based devices can easily capture high-quality palmprint images which contribute to high performance in person identification, while their drawbacks also lie in this acquisition mode. Firstly, users may have hygienic concerns since the device cannot be cleaned immediately. Secondly, some users may feel uncomfortable with the control pegs and constrained capture environment. Thirdly, the volume of the device is usually larger than palm, which causes problems of portability and usability. As the first attempt to solve the above issues, [2] presents a real-time touchless palmprint recognition system, and the capture processes are conducted under unconstrained scenes. Two complementary metal-oxide semiconductor (CMOS) web cameras are placed in parallel, one is a near-infrared (NIR) camera, and the other is a traditional red green blue (RGB) camera. A band pass filter is fixed on the camera lens to eliminate the influence of NIR light on the palm. The two cameras work simultaneously, and the resolution of both cameras is 640 × 480. For further hand detection process, during the image capture, users need to open their hands and place palm regions in front of the cameras. Also, the palm plane needs to be approximately flat and orthogonal to the optical axis of cameras. Minor in-plane rotation is allowed. The distance between the hand and device should be in a fixed range (35–50 cm) to ensure the clarity of the palmprint images. In [3], a novel touchless device with a single camera is proposed. The principle of device design is similar to [2]. During the input process, the user places his/her hand in front of the camera without touching the device, and there are no strict constraints on its pose and location. The main difference is that the paddles are placed around the camera to reduce the effect of illumination changes. By these measures, the acquisition process becomes flexible and efficient. [6] presents a touchless palmprint and palm vein recognition system. The structure of the device is similar to that in [3], which mainly contains two parallel mounted cameras with visible light and IR light. The flexibility of this touchless device is further improved. Users are allowed to position their hands freely above the sensor, and they can move their hands during the acquisition process. The acquisition program will give feedback to the user that he/she is placing his/her hand correctly inside the working volume. In this way, the device can capture high-quality palmprint and palm vein images at the same time. In [7], the palmprint, palm vein, and dorsal vein images are simultaneously captured with a touchless acquisition device. In the capturing process, the users are asked to put their hands into the device with five fingers separated. The time cost is less than 1 s. The multimodal images can be fused in the algorithm to boost the identification performance.
2.3 Portable devices
With the widespread application of digital cameras and smartphones, more and more portable biometric devices appear to us. To investigate the problem of palmprint recognition across different portable devices and build the available dataset, [8] uses one digital camera and two smartphones to acquire palmprints in a free manner.
2.4 Key problems in device design
As is discussed above, the main parts of palmprint acquisition devices are cameras and light sources. So, the problems we need to consider when designing new devices are as follows:
The resolution of the imaging sensor
The focal length of the lens
The distance range of the palm
The sharpness range of the final palmprint image
The light source intensity
The signal-to-noise ratio of the palmprint image
Many previous works have studied the light sources [15, 16, 17]. Generally, the basic goal is avoiding overexposure and underexposure. Image noise increases under low illumination conditions. Although many new deep learning-based denoising techniques are proposed [18], the most effective solution for palmprint imaging is developing active light sources to provide suitable illumination conditions. In this work, we only focus on the first four problems. We developed three palm image capture devices to test the performance of different hardware frameworks (as is shown in Figure 2). We denote them as devicea, deviceb, and devicec. Among them, devicea and deviceb are touch-based devices. devicea is designed to generate high-quality palmprint images. The device contains an ultra-high-definition imaging sensor (about 500 M pixels) and a distortion-free lens. The long working distance is designed to further guarantee the image quality. During the capture process, the user’s palm is put on the device to avoid motion blur. deviceb is designed to generate high-distortion palmprint images. It contains a high-definition imaging sensor (about 120 M pixels) and an ultrawide lens. The working distance is very short (about 2 cm). devicec is a touchless device; it is designed to capture high- and low-definition images in touchless scenarios. It has two cameras, one is high-definition (120 M pixels), and the other one is low-definition (30 M pixels); both of them are equipped with distortion-free lenses. We use different devices to collect palm images from the same palm; the captured images are shown in Figure 2(d)–(e). We can see that the 500 M pixel camera can capture clear ridges and valleys of the palmprint, the 120 M pixel camera can capture most of the ridges and valleys, and the 30 M pixel camera only can capture the principal lines and coarse-grained skin textures. For touchless applications, the distance between the palm and the camera is not stable. Distance variations may decrease the palm image PPI and cause defocus-blur. In practice, it is very hard to guarantee the quality of the captured images. Hence, what we want to know is which level of image sharpness is sufficient for palmprint identification.
Figure 2.
Different palmprint acquisition devices and the palm images generated by them. (a) The touch-based device with a 500 M pixel imaging sensor and a long imaging distance. (b) The touch-based device with a 120 M pixel imaging sensor and a very short imaging distance. (c) The multicamera touchless device with 120 M and 30 M pixel imaging sensors and a long imaging distance. (d) The palm image captured by (a) and the corresponding enlarged local regions. (e) The palm image captured by (b) and the corresponding enlarged local regions. (f) The palm images captured by (c) and the corresponding enlarged local regions.
3. System design based on palm image sharpness
3.1 Palm distance and recognition performance
The imaging model is shown in Figure 3. Let lp and wp stand for the statistical information of the length and width of the palm, respectively. Let Zmin and Zmax stand for the minimum and maximum distance the palm can reach in the field of view (FOV). If the hand want to be captured completely, we need l≥lp and w≥wp, where l and w are the corresponding sizes of the field of view (FOV) of the camera (as is shown in Figure 3). Then Zmin could be estimated by
Figure 3.
Imaging model and related notations.
Zmin=maxlp/2tanθuwp/2tanθvE1
where θu and θv are half angles of the FOV along directions of u and v, respectively. As is shown in Figure 3, in the generated image, pw (in units of pixel) is the palm width. rw (in units of pixel) is the length of the tangent line formed by two finger valley key points. We introduce it here, because most of the region of interest (ROI) localization methods utilize those two key points [1]. The PPI is calculated by
ppi=pw/wpE2
in which wp is the fixed real palm size. Based on the triangle geometry constraints defined in the pin-hole imaging model [19], we have
pw/f=wp/zE3
where f is the focal length (in units of pixel), which is related with the pixel size of the imaging sensor and the focal length of the lens; z is the distance between the palm and the camera’s optical center. So pw changes according to different palm distances. Eq. (3) shows the constraints of the image palm width pw, equivalent focal length f, palm distance z, and the palm width wp. According to Eqs. (2) and (3), we have
z=f/ppiE4
Hence,
Zmax=f/ppiminE5
where ppimin is the minimum PPI for palmprint recognition. So, what we need to know is the relation between image PPI and system equal error rate (EER). Here, EER is an index of the system’s recognition performance; lower is better. In data collection process, it is very difficult to let the users to put and hold their hands at the designed target distances, so we plan to utilize the public database to conduct simulation experiments to study the relationship between EER and PPI. In this section, database COEP [20] is selected to use, due to it is collected in a highly constrained environment. The images in it are captured by single-lens reflex camera (SLR), so they have a high signal-to-noise ratio (SNR) and very low distortions. During capturing, the user’s palm is put stably on the backboard. The image resolution is sufficient to record the palmprint ridges and valleys. So we take images in COEP as the ground truth; it means they are captured with proper focus and sufficient PPI. Then the images are resized to generate palm images with different PPI. The mean PPI of a database is defined as
ppi¯=1N∑i=1NppiiE6
where N is the image number of the dataset and ppii is the ppi value of the i-th palm image. However, in practice the captured image may contain radial and tangential distortions. The distortion parameters of the imaging model could be estimated by camera calibration [19]. Based on the imaging model, the captured image could be undistorted. Image undistortion also introduces image blur to the undistorted image. Taking this into consideration, we select four different kinds of lenses for testing, they are long-focus, standard, wide-angle, and ultrawide-angle lenses (as is shown in Figure 4). We use them to capture checkboard images from different views. After camera calibration, we got the corresponding intrinsic parameters. They are listed in Table 2. fu and fv are focal length along u and v directions, respectively. θu and θv are half angle of the FOV along u and v directions, respectively. k1, k2, and k3 are radial distortion coefficients. p1 and p2 are tangential distortion factors. As is shown in Figure 5, the images in COEP first are distorted by the four distortion parameter sets and then undistorted by coordinates mapping and pixel interpolation based on the distortion model. The obtained images are further resized to generate different PPI palm images. According to [21], the average palm width is 84 mm for male and 74 mm for female. In [22], the average palm width is 84.18±6.81 mm for German and 82.38±11.82 mm for Chinese, and most of their subjects are male. Since palm width varies with gender, age, and race, it depends on the specific application scenarios. For simplicity, we set wp¯≈80mm (3.15 inches) and lp≈110 mm (4.33 inches) in our work. The original image size of COEP is 1600 × 1200. In order to delete the background area, they are cropped to size of 1280 × 960. In this experiment, we totally generate 10 datasets by image resizing; detail statistical information is listed in Table 3. For each palm image, using the ROI localization method proposed in [1], we can detect the tangent line of the two finger valleys, and then rw is obtained. pw also could be detected based on the relative coordinate system of the palm. Given a dataset, the mean pw and mean rw are defined as.
pw¯=1N∑i=1NpwiE7
rw¯=1N∑i=1NrwiE8
where N is the image number of the dataset and pwi and rwi are the pw and rw values of the i-th palm image. Here, pw¯ is selected as the index to measure the resolution of the palm image. The sample images and corresponding enlarged local patches of the generated datasets are shown in Figure 5. Table 4 describes the EERs and thresholds obtained by CompCode on different datasets. Here, eav¯ is an index for sharpness assessment [23]. It should be noted that the sharpness level (eav¯) obtained here has not taken the defocus-blur into consideration. It will be further studied in the next subsection. The distribution curves of pw¯ and corresponding EER and eav¯ are shown in Figure 6. From it, we can see that the affection on image sharpness caused by undistortion is not quite obvious. Among the four cameras (as is shown in Figure 4), the long-focus lens obtains the highest sharpness, and wide-angle lens reaches the lowest sharpness. As to the ultrawide-angle lens, many newly designed lenses have improved their optical models to generate big distortions just in the boundary regions and small distortions in the center region. In this experiment, the wide-angle lens gains more distortions than the ultrawide-angle lens; it depends on the specific optical model the manufacturer used. Generally, the palm is put at the center of the image, so the differences between the four lenses are not large. Although the long-focus lens can provide high sharpness palm images, in real-world scenarios, the wide-angle lens is more recommended because its wide FOV provides better user experience for image capturing. As is shown in Figure 6, the EERs increase drastically when pw¯ is less than 130 pixels. So when we were selecting the imaging sensor and determining the working distance, at least we should guarantee, in the final palm image, the palm width should be large than 130 pixels; 300 pixels is recommended according to Figure 6.
Figure 4.
Images captured by different lenses. (a) The imaging device and different kinds of lenses. (b) An image captured by long-focus lens. (c) An image captured by standard lens. (d) An image captured by ultrawide-angle lens.
Lens
fu
fv
θu
θv
k1
k2
k3
p1
p2
Long-focus
3507.05
3497.24
10.4°
7.9°
−0.37
−1.36
—
−0.0018
−0.0000
Standard
706.96
707.29
48.7°
37.5°
0.13
−0.51
—
0.0055
−0.0001
Wide-angle
435.57
436.10
72.6°
57.7°
−0.41
0.14
—
0.0014
0.0006
Ultrawide
217.19
217.99
111.7°
95.5°
0.05
−0.07
0.0105
−0.0002
−0.0018
Table 2.
The calibrated parameters of different camera lenses.
Figure 5.
Images obtained at different distances (PPI) using different distortion models.
Palm region size
1280 ×960
1120 ×840
960 ×720
800 ×600
640 ×480
480 ×360
320 ×240
240 ×180
160 ×120
80 ×60
rw¯
304.8
266.7
228.6
190.5
152.4
114.3
76.2
57.2
38.1
19.1
pw¯
524.8
459.2
393.6
328.0
262.4
196.8
131.2
98.4
65.6
32.8
ppi¯
166.6
145.8
125.0
104.1
83.3
62.5
41.7
31.2
20.8
10.4
Table 3.
Palm region size, palm width, and corresponding ppi¯.
pw¯
Long-focus
Standard
Wide-angle
Ultrawide
EER (%)
eav¯
EER (%)
eav¯
EER (%)
eav¯
EER (%)
eav¯
524.8
1.445
29.0
1.539
28.6
1.508
28.1
1.634
28.4
459.2
1.477
26.5
1.634
26.3
1.571
25.9
1.602
26.1
393.6
1.445
26.1
1.619
25.9
1.553
25.5
1.634
25.8
328.0
1.414
25.4
1.571
25.3
1.550
25.1
1.631
25.3
262.4
1.414
23.7
1.602
23.6
1.508
23.4
1.539
23.6
196.8
1.477
23.9
1.571
23.5
1.539
23.1
1.602
23.2
131.2
1.508
20.2
1.783
20.0
1.634
19.7
1.627
19.8
98.4
1.571
18.4
1.759
18.3
1.728
18.1
1.728
18.2
65.6
2.177
14.8
2.136
14.7
2.325
14.6
2.262
14.7
32.8
6.346
9.9
6.313
9.8
6.274
9.8
6.535
9.8
Table 4.
The EERs obtained from different palm width using different lens models.
Figure 6.
The relationship between recognition performance, image sharpness, and palm width (in units of pixel).
3.2 Image sharpness range and recognition performance
In the above subsection, based on the imaging model and the capture device, we studied the relationship between palm distance, PPI, and EER. However, the hardware and the parameters of the imaging model are not always available in practice. Besides FOV, depth of focus (DOF) should be considered, since defocus-blur also will affect the final accuracy. DOF is highly related to specific applications. Our previous work [23] shows that the accuracy of palmprint recognition has a relationship with the image sharpness. Here, what we want to know is in which sharpness range the palmprint recognition accuracy is acceptable.
In this section, we try to analyze the palmprint image sharpness based on the Gaussian scale space [24]. The transform function is defined as
Lxy=Ixy∗GσE9
where x,y is the specific coordinates of the pixel and σ is the scale-coordinate. Gσ is the Gaussian smooth filter used for smooth the input image, and σ is its standard deviation. I is the initial image, and L is the smoothed image. So images in the scale space have different sharpness levels. As is shown in Figure 7, scale space function tries to generate all the potential palmprint images that may be captured in practice. In order to achieve the scale-invariant capacity, SIFT [24] tries to utilize all the information of the scale space. The method proposed in [25] is utilized here to conduct SIFT-based palmprint verifications, in which each palmprint ROI image will match against all the other images in the database. After SIFT feature extraction and matching, the random sample consensus (RANSAC) algorithm will be used to further delete the outliers. The matching between two images captured from the same palm is genuine matching, and the matching between two images captured from different palms is impostor matching. The matching number is selected as the matching score. A Gaussian image pyramid is a sampling subset of the Gaussian scale space. We wonder whether all the image layers in the Gaussian image pyramid has the same contribution to the final matches. In this experiment, once two key points from the two intra-class images are matched, the points’ scales are recorded. At last, the statistical information of σ is shown in Figure 8. From it, we can see that the contributions of different scales are not the same; most of the distinctive local patterns only exist in some specific scales. The other layers are not discriminative. So the captured palm ROI image should not fall into those useless scale ranges. In fact, the palmprint shows different patterns at different scales. When the image is captured clearly, the palmprint consists of principal lines, wrinkles, ridges, valleys, and some minutiae points. When σ is increasing, the palmprint ROI image tends to show the spot patterns; the fine-grained ridges and valleys are smoothed and reduced to be large-scale textures. It could be seen in Figure 1. Different patterns have different discriminative capacities; as a result, the recognition performance changes with the image sharpness. In practice, the scale index σ corresponds to palm distance. Once the palm is moved away from the DOF of the system, the generated image suffers from defocus-blur, and the recognition performance changes.
Figure 7.
The palmprint Gaussian scale space.
Figure 8.
Scale contributions for key point matching: (a) obtained from COEP, (b) obtained from IITD, (c) obtained from KTU, (d) obtained from GPDS.
In order to analyze the recognition performance variations, we utilize the Gaussian image pyramid to generate palmprint images at different scales. For a given dataset, all the ROI images in it are filtered with Gaussian filter banks, and then 20 scaled datasets are generated. The σ used in this experiment is defined as
σ=σ0⋅2o+s/SE10
k=21/SE11
id=o−omin⋅S+sE12
where σ0 is the base standard deviation; k is the step factor for increasing and decreasing σ; S is the number of intervals in each octave; o and s are octave and interval induces, respectively; and id is the image layer ID in the Gaussian scale space. omin is the minimum octave index. If omin<0, it can generate a σ smaller than σ0. Here, σ0=1.6∗k which is the default setting in VLfeat [26]. In this experiment, omin=−2, smin=0, and S=4, so the range of σ is from 0.476 to 5.709, which covers the range used in [27]. So, given one dataset, we can generate 20 datasets according to different scales. The mean EAV (eav¯) is utilized to quantify the sharpness level of each generated dataset. Figure 9 shows the distributions of eav¯ and scale index σ on different publicly available palmprint databases. It shows that the sharpness level decreases almost linearly with id in the Gaussian scale space when id is smaller than 10 (σ=2.3). Of course, the specific parameters of the curves are not the same on different databases; they are related to the database’s initial sharpness level eav¯.
Figure 9.
The curves of eav¯ and corresponding scale induces on different databases.
The work reported in [27] shows that there exist a relationship between the recognition performance and the image sharpness. In their work, a sharpness adjustment technique is developed to improve the system EER. Different sharpness induces are tested, and EAV performs better. But only one touch-based palmprint database is tested in their study. In order to ensure the idea is applicable on different databases, devices, and algorithms, we utilize CompCode [28], OLOF [29], and RLOC [30] to further test the recognition accuracy variations on those generated datasets. In this experiment, different databases are used including GPDS [31], IITD [32], KTU [33], and TJU [34]. Figure 10 shows the curves of EER and corresponding eav¯. From it we can see that the trend of GPDS is not the same with the other databases. It is because GPDS is a difficult database, which contains big illumination variations and localization errors. Hence, the recognition accuracy of this database is affected more by other factors. According to Figure 10, in order to guarantee the system’s discriminative capacity, eav¯ should be large than 10.
Figure 10.
The curves of EER and eav¯ on different databases obtained by different recognition algorithms. (a) The EER is obtained by Competitive Code. (b) The EER is obtained by OLOF. (c) The EER is obtained by RLOC.
4. Conclusions
When designing a touchless palmprint recognition system, FOV and DOF are two key problems of palmprint imaging. FOV is related to image PPI, and DOF is related to image blur. Figure 11 shows the main idea and framework of our system. In this chapter, we first studied the required image PPI for palmprint identification. Based on it, the minimum and maximum palm distances are determined in the FOV. It also provides a reference for image sensor resolution selection. Then, image blur is taken into consideration; different datasets are generated by Gaussian scale space function. The EER variation curves are obtained by different features on different databases. During the image collection process, when the palm moves out of the DOF, the sharpness of the captured image changes, so eav can be an index to show whether the palm is put correctly in the DOF.
Figure 11.
The framework of this chapter.
Based on the findings of this research, when designing new systems, the palm width in the captured image should be larger than 300 pixels; it at least should not smaller than 130 pixels. After the system is deployed, when the user is putting his/her hand, the eav of the ROI image should be larger than 10. A more precise eav threshold should be obtained from the training dataset of the real system, because some other factors may affect the final EER distributions, such as the auto-exposure-control and auto-white-balance-control functions of the imaging sensor. But the major trends are similar. The main contribution of this work is providing some key references for system design based on image sharpness.
Acknowledgments
This work is supported in part by the NSFC under grant 61332011, in part by the Shenzhen Fundamental Research under grants JCYJ20180306172023949 and JCYJ20170412170438636, in part by the Shenzhen Institute of Artificial Intelligence and Robotics for Society.
\n',keywords:"palmprint recognition, system design, image sharpness assessment, scale space, field of view, depth of focus",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72616.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72616.xml",downloadPdfUrl:"/chapter/pdf-download/72616",previewPdfUrl:"/chapter/pdf-preview/72616",totalDownloads:208,totalViews:0,totalCrossrefCites:0,dateSubmitted:"March 9th 2020",dateReviewed:"May 14th 2020",datePrePublished:"July 22nd 2020",datePublished:"February 10th 2021",dateFinished:"June 25th 2020",readingETA:"0",abstract:"Currently, many palmprint acquisition devices have been proposed, but how to design the systems are seldom studied, such as how to choose the imaging sensor, the lens, and the working distance. This chapter aims to find the relationship between image sharpness and recognition performance and then utilize this information to direct the system design. In this chapter, firstly, we introduce the development of recent palmprint acquisition systems and abstract their basic frameworks to propose the key problems needed to be solved when designing new systems. Secondly, the relationship between the palm distance in the field of view (FOV) and image pixels per inch (PPI) is studied based on the imaging model. Suggestions about how to select the imaging sensor and camera lens are provided. Thirdly, image blur and depth of focus (DOF) are taken into consideration; the recognition performances of the image layers in the Gaussian scale space are analyzed. Based on this, an image sharpness range is determined for optimal imaging. The experiment results are obtained using different algorithms on various touchless palmprint databases collected using different kinds of devices. They could be references for new system design.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72616",risUrl:"/chapter/ris/72616",signatures:"Xu Liang, Zhaoqun Li, Jinyang Yang and David Zhang",book:{id:"9905",title:"Biometric Systems",subtitle:null,fullTitle:"Biometric Systems",slug:"biometric-systems",publishedDate:"February 10th 2021",bookSignature:"Muhammad Sarfraz",coverURL:"https://cdn.intechopen.com/books/images_new/9905.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"319722",title:"Prof.",name:"David",middleName:null,surname:"Zhang",fullName:"David Zhang",slug:"david-zhang",email:"csdzhang@comp.polyu.edu.hk",position:null,institution:null},{id:"319723",title:"Ph.D. Student",name:"Xu",middleName:null,surname:"Liang",fullName:"Xu Liang",slug:"xu-liang",email:"sunniflyer@163.com",position:null,institution:null},{id:"321174",title:"Dr.",name:"Zhaoqun",middleName:null,surname:"Li",fullName:"Zhaoqun Li",slug:"zhaoqun-li",email:"zhaoqunli@link.cuhk.edu.cn",position:null,institution:{name:"Chinese University of Hong Kong",institutionURL:null,country:{name:"China"}}},{id:"321175",title:"BSc.",name:"Jinyang",middleName:null,surname:"Yang",fullName:"Jinyang Yang",slug:"jinyang-yang",email:"1224624112@qq.com",position:null,institution:{name:"Harbin Institute of Technology Shenzhen Graduate School",institutionURL:null,country:{name:"China"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. The current palmprint recognition devices",level:"1"},{id:"sec_2_2",title:"2.1 Touch-based devices",level:"2"},{id:"sec_3_2",title:"2.2 Touchless devices",level:"2"},{id:"sec_4_2",title:"2.3 Portable devices",level:"2"},{id:"sec_5_2",title:"2.4 Key problems in device design",level:"2"},{id:"sec_7",title:"3. System design based on palm image sharpness",level:"1"},{id:"sec_7_2",title:"3.1 Palm distance and recognition performance",level:"2"},{id:"sec_8_2",title:"3.2 Image sharpness range and recognition performance",level:"2"},{id:"sec_10",title:"4. Conclusions",level:"1"},{id:"sec_11",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Zhang D, Kong WK, You J, Wong M. Online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003;25(9):1041-1050'},{id:"B2",body:'Han Y, Sun Z, Wang F, Tan T. Palmprint recognition under unconstrained scenes. In: Proceedings of the 8th Asian Conference on Computer Vision (ACCV’07); 18-22 November 2007; Tokyo, Japan. Vol. 4844. Switzerland: Springer; LNCS(PART2). 2007. pp. 1-11'},{id:"B3",body:'Michael GKO, Connie T, Jin ATB. Touch-less palm print biometrics: Novel design and implementation. Image and Vision Computing. 2008;26(12):1551-1560'},{id:"B4",body:'Zhang D, Lu G, Li W, Zhang L, Luo N. Palmprint recognition using 3-D information. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2009;39(5):505-519'},{id:"B5",body:'Zhang D, Guo Z, Lu G, Zhang L, Zuo W. An online system of multispectral palmprint verification. IEEE Transactions on Instrumentation and Measurement. 2010;59(2):480-490'},{id:"B6",body:'Michael GKO, Connie T, Jin ATB. Design and implementation of a contactless palm print and palm vein sensor. In: Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision, (ICARCV’10). 7–10 December 2010. Singapore; NewYork: IEEE; 2010. pp. 1268-1273'},{id:"B7",body:'Bu W, Zhao Q, Wu X, Tang Y, Wang K. A novel contactless multimodal biometric system based on multiple hand features. In: Proceedings of International Conference on Hand-Based Biometrics, (ICHB’11); Hong Kong, China. New York: IEEE; 2011. pp. 289-294'},{id:"B8",body:'Jia W, Hu RX, Gui J, Zhao Y, Ren XM. Palmprint recognition across different devices. Sensors. 2012;12(6):7938-7964'},{id:"B9",body:'Zhao Q, Bu W, Wu X, Zhang D. Design and implementation of a contactless multiple hand feature acquisition system. In: Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring II; and Biometric Technology for Human Identification IX. Vol. 8371. 2012. pp. 83711Q. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8371/1/Design-and-implementation-of-a-contactless-multiple-hand-feature-acquisition/10.1117/12.919100.full [Accessed: 03 May 2020]'},{id:"B10",body:'Nikisins O, Eglitis T, Pudzs M, Greitans M. Algorithms for a novel touchless bimodal palm biometric system. In: Proceedings of 2015 International Conference on Biometrics, (ICB’15); 19-22 May 2015; Phuket, Thailand. New York: IEEE; pp. 436-443'},{id:"B11",body:'Qu X, Zhang D, Lu G. A novel line-scan palmprint acquisition system. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2016;46(11):1481-1491'},{id:"B12",body:'Qu X, Zhang D, Lu G, Guo Z. Door knob hand recognition system. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017;47(11):2870-2881'},{id:"B13",body:'Zhang L, Cheng Z, Shen Y, Wang D. Palmprint and palmvein recognition based on DCNN and a new large-scale contactless palmvein dataset. Symmetry. 2018;10(4):1-15'},{id:"B14",body:'Li W, Zhang D, Lu G, Luo N. A novel 3-D palmprint acquisition system. IEEE Transactions on Systems, Man, and Cybernetics - Part A. 2012;42(2):443-452'},{id:"B15",body:'Guo Z, Zhang D, Zhang L, Zuo W, Lu G. Empirical study of light source selection for palmprint recognition. Pattern Recognition Letters. 2011;32(2):120-126'},{id:"B16",body:'Guo Z, Zhang D, Zhang L. Is white light the best illumination for palmprint recognition? In: Proceedings of International the 13th Conference on Computer Analysis of Images and Patterns (CARP’09); 2–4 September 2009; Münster, Germany. Switzerland: Springer; 2009. pp. 50-57'},{id:"B17",body:'Liang X, Zhang D, Lu G, Guo Z, Luo N. A novel multicamera system for high-speed touchless palm recognition. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2019. DOI: 10.1109/TSMC.2019.2898684. Available from: https://ieeexplore.ieee.org/abstract/document/8666082'},{id:"B18",body:'Tian C, Xu Y, Zuo W. Image denoising using deep CNN with batch renormalization. Neural Networks. 2020;121:461-473'},{id:"B19",body:'Zhang Z. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000;22(11):1330-1334'},{id:"B20",body:'COEP database. Available from: https://www.coep.org.in/resources/coeppalmprintdatabase [Accessed: 03 May 2020]'},{id:"B21",body:'Average Hand Size For Men, Women, And Children. [Internet] Available from: https://www.theaveragebody.com/average-hand-size/ [Accessed: 09 May 2020]'},{id:"B22",body:'Rau PP, Zhang Y, Biaggi L, Engels R, Qian L, Ribjerg H. How large is your phone? A cross-cultural study of smartphone comfort perception and preference between Germans and Chinese. Procedia Manufacturing. 2015;3:2149-2154'},{id:"B23",body:'Zhang K, Huang D, Zhang B, Zhang D. Improving texture analysis performance in biometrics by adjusting image sharpness. Pattern Recognition. 2017;66:16-25'},{id:"B24",body:'Lowe D. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision. 2004;60(2):91-110'},{id:"B25",body:'Wu X, Zhao Q, Bu W. A SIFT-based contactless palmprint verification approach using iterative RANSAC and local palmprint descriptors. Pattern Recognition. 2014;47(10):3314-3326'},{id:"B26",body:'Vedaldi A, Fulkerson B. VLFeat: An Open and Portable Library of Computer Vision Algorithms. Available from: http://www.vlfeat.org [Accessed: May 03, 2020]'},{id:"B27",body:'Zhang K, Huang D, Zhang D. An optimized palmprint recognition approach based on image sharpness. Pattern Recognition Letters. 2017;85:65-71'},{id:"B28",body:'Kong A, Zhang D. Competitive coding scheme for palmprint verification. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04); 23–26 August 2004. Cambridge, UK. New York: IEEE; 2004. pp. 520-523'},{id:"B29",body:'Sun Z, Tan T, Wang Y, Li SZ. Ordinal palmprint representation for personal identification [represention read representation]. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05); 20–26 June 2005. San Diego, CA. New York: IEEE; 2005. pp. 279-284'},{id:"B30",body:'Jia W, Huang D, Zhang D. Palmprint verification based on robust line orientation code. Pattern Recognition. 2008;41(5):1504-1513'},{id:"B31",body:'GPDS database. Available from: www.gpds.ulpgc.es/downloadnew/download'},{id:"B32",body:'IITD database. Available from: https://www4.comp.polyu.edu.hk/∼csajaykr/IITD/Database_Palm.htm [Accessed: 03 May 2020]'},{id:"B33",body:'KTU database. Available from: https://ceng2.ktu.edu.tr/∼cvpr/contactlessPalmDB.htm [Accessed: 03 May 2020]'},{id:"B34",body:'TJU database. Available from: https://sse.tongji.edu.cn/linzhang/contactlesspalm/index.htm [Accessed: 03 May 2020]'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Xu Liang",address:null,affiliation:'
Harbin Institute of Technology, China
Shenzhen Institute of Artificial Intelligence and Robotics for Society, China
Shenzhen Institute of Artificial Intelligence and Robotics for Society, China
School of Science and Engineering, The Chinese University of Hong Kong, China
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