SAW-based passive sensors.
\r\n\tHydroxyapatite (HA) is an important member of the calcium phosphate chemical family. It has been used in several medical applications for the past decades, due to its chemical similarity to the mineral phase of bone and high biocompatibility. Several studies demonstrated that bone mineral presents several ion substitutions, so in order to prepare a synthetic material with an even closer composition to bone mineral, HA has been prepared with the incorporation of several ions like, silicon or fluoride. These ions induced not only structural changes on HA lattice, but also on its biocompatibility.
\r\n\tSignificant advances in nanotechnologies resulted in the preparation of HA in different forms, with a wider range of applications, from support to drug and gene delivery.
\r\n\tThis book aims to collect the most relevant information regarding HA properties, modifications and its application in the biomedical field.
Since the radar system was invented in 1922, the development of devices communicating by means of reflected power has experienceda continuously growing interest. In 1948, Harry Stockman published a paper [1] in which he laid the basis for the idea of radio frequency identification(RFID), and the first patent had been filed in 1973 by Charles Walton. After decades of research and commercialization, RFID products became a part of everyday life (e.g. logistics, access control, security). With the growing interest in remote and battery-free devices, researchers are pushing the boundaries of RFID technology to find solutions in new fields like sensing applications.
For many sensors such as those operated in remote or harsh environments, the sensitivity is not the only evaluation criteria. The lifetime, especially of the power source, and the complexity added by wiring often demand wireless and passive operation.Batteries have limited lifetime and also add to the size and mass of the sensors. Alternatively, energy harvesting oran RF-based wireless power supply can be employed [2, 3]. The former method depends on environmental conditions such as solar radiation, temperature change, chemical reagents, vibration etc., which are often not constantly or not sufficiently available.RF power sources, on the other hand, transmit power wirelesslyand with full control over amount and timing.
Passive and remote sensors utilizing SAW transpondersaredevices, which are powered by an RF source. These systems require an interrogation device that requests the sensor signal, a SAW transponder plus a sensing element and two antennas. The basic idea is that an RF signal of certain frequencies generated by the interrogator is received by the SAW transponder, which reflects back a signal modified by the sensing element. This signalcontains the environmental information in an amplitude and phase change, which is converted into the physical parameters by the interrogator. In most cases, SAW sensors are coded by having different reflector designs in order to have multiple measurement capabilities from sensors located in the same interrogation area. A great amount of research has been carried out in the past decades in this field, and, as a result, different wireless SAW sensors have been developed to measure a variety of physical and chemical parameters including temperature, stress, torque, pressure, humidity, magnetic field, chemical vapor etc. [4-9]. Several devices are already commercialized [10-12].
SAW-based magnetic sensors have, so far, not been studied in detail. Magnetic sensors are one of the most pervasive kinds of sensors for a large number of applications and are employed in different fields like automotive, biomedical or consumer electronics. Integrating magnetic sensors with SAW transponders enables remote and passive operation, thereby, opens a door for further applications. Early in 1975, a magnetically tuned SAW phase shifter was proposed by Ganguly et al [13]. A thin film of magnetostrictive material was fabricated on the delay line of a SAW device. A phase shift was observed due to the dependence of the wave propagation velocity on the external magnetic field.Recently, a new concept of amagnetic sensor based on a SAW resonator has been published [8]. A magnetostrictive material was used to fabricate the interdigital transducers (IDT) of the SAW device. The resonant frequency of the device changes with an external magnetic field. A different idea was put forth by Hauser and Steindl[14-16] combining a SAW transducer with a giant magnetoimpedance (GMI) microwire sensor. The GMI sensor has a magnetic sensitivity, at least, one order higher than contemporary giant magnetoresistance (GMR) sensors and can be used to measure very low magnetic fields such as those generated by the human heart or muscles. A GMI sensor is operated by an accurrent and the impedance changes upon changes of a magnetic field. This makes it a suitable load for a SAW transponder, which converts this impedance change into a magnitude and phase change of the reflected acoustic waves. In order to reduce the size and improve the level of integration of the senor, a new design of an integrated SAW transponder and thin film GMI sensor has been proposed and developed recently by the authors [17, 18]. The SAW transponder and GMI thin film were integrated on the same chip using standard micro-fabrication technology suitable for mass fabrication.
The ideal SAW-based magnetic sensor is small and highly integrated, inexpensive, passive, remotely controlled and have a high magnetic sensitivity together with a large linear range. With regard to these criterions, an SAW-GMI sensor is avery promising candidate.
SAW-based magnetic sensors have been studied for several years.However, this topic has yet not been comprehensively summarized, and the aim of this chapter is to provide a systematic review of the past research as well as the latest results. The performance of the devices crucially depends on different design parameters in a complex fashion. This will be shown by a detailed description and analysis for a device consisting of a SAW transponder and GMI thin film sensor.
A basic SAW device consists of an input interdigital transducer (IDT) and an output (or reflector) IDT, which are fabricated on a piezoelectric substrate. The area between the input IDT and output IDT is called the delay line. The IDT is made of two metallic, comb-like structuresarranged in an interdigital fashion, whereby the distance between two fingers of a comb defines the periodicity (p) (Fig. 1). Upon application of a voltage, charges accumulate at the fingers of the IDT depending on the capacitance of the structure.The resulting electric field generates stress in the substrate due to the piezoelectric effect.If an ac input voltage is applied, the continuously changing polarity of the charges will excite an SAW (Rayleigh wave) traveling through the substrate. At the operating (resonant) frequency of the SAW device,the value ofp equals the wavelength of the SAW, and the SAW amplitude showsa maximum value due to constructive superposition.
Schematic of a SAW device.
The value of vmainly depends on the substrate’s material. A typical SAW velocity for piezoelectric materials is several thousand meters per second. Due to the intrinsic anisotropy of piezoelectric materials, v is dependent on the direction of propagation. Since different acoustic modes have different wave velocities, a device can resonate at different frequencies. The SAWs are Rayleigh waves, which have a longitudinal and a vertical shear component that can couple with any media in contact with the surface. This coupling strongly affects the amplitude and velocity of the wave allowing SAW sensors to directly sense, e.g., mass loads.
Electro-Mechanical Coupling Coefficient: The electro-mechanical coupling coefficient (κ) defines the conversion efficiency of the piezoelectric material between the electrical and mechanical energies, determined by
A high coupling coefficient reduces the insertion loss caused by the energy conversion, which results in smaller energy consumption as well as larger effective readout distance of a SAW-based wireless sensor.
SAW Delay Line: The SAW delay line refers to the area between the input IDT and output IDT on the substrate (Fig. 1). It creates a time delay between the input signal and the output signal depending on the SAW velocity and the length of the delay line. Due to this feature, SAW devices are widely used in RF electronics. It is also used in sensing applications, where the measurand causes, e.g., a change in the SAW.
Temperature Coefficient of Delay (TCD): The TCD reflects the temperature dependence of the time delay and is connectedwith the thermal expansion coefficient (α) and the temperature coefficient of the phase velocity (TCV) by
The temperature dependence of the time delay is the basis of SAW temperature sensors, where higher TCD values yield higher sensitivity. However, for other SAW devices, the influence of the TCD on the time delay is undesirableand has to be minimized or eliminated. For this purpose, temperature compensated cuts of the crystalline substrates are employed, where the TCD is minimized over certain temperature ranges [19-20]. Piezoelectric bi-layers are another concept that has been utilized in order to compensate the TCD in sensing applications [21, 22].
Passive SAW sensors typically operate asresonators, delay lines or loaded transponders. In case of resonators, the reflection of the interrogation signal from the SAW device is a function of the SAW device’s resonant frequency, which depends on the measurand. In case of delay lines, the request signal is separated from the response signal by a time difference, whereby this time difference depends on the measurand. Similarly, the request signal and response signal are separated by a time difference in case of a loaded transponder. However, the time difference is constant and the measurand affects the signal amplitude. Intrinsic SAW sensors utilize a change of the substrate’s properties. For example, intrinsic temperature sensors were realized by detecting the resonant frequency or phase change of the SAW in materials with large TCD [23]. Intrinsic stress sensors utilize the length change of the delay line caused by mechanical strain applied to the substrate. The stress can be evaluated by measuring the SAW phase shift [24]. Extrinsic SAW sensors can be realized by integrating a SAW device and an additional sensing element. A common extrinsic sensor conceptutilizes selective thin films on top of the delay line leading to a change in mass by the measurand [13, 25]. This can be, for example,a thin film with high CO2 solubility and selectivity [26]. As CO2 dissolves into the film, the additionalmass loadcausesa detectable phase shift in the SAW. Another extrinsic concept utilizes a sensitive IDT. For example, in case of a magnetostrictive IDT, a magnetic field applied to the sensor causes a change of the resonant frequency [8]. A loaded transponder is another extrinsic design, where the output IDT is connected to a sensor, which changes the IDT’s electrical characteristics as a function of the measurand.An example for a load sensor is a pair of conducting rods placed in the earth with a certain distance from each other. As the water level changes, the resistance between the rods changes, which can be detected as a magnitude and phase change of the signal reflected from the load IDT [27]. Another example for a load is a giant magnetoimpedance sensor [16, 17]. Achange in the magnetic field yields a change in the sensor’s impedance. Consequently, changes the reflectivity of the output IDT.
Some SAW sensors, their classification and method of detection are presented in Table 1.
Sensor Type | Commercialization | Year | Intrinsic/ Extrinsic | Design | Detection Method | Access | Paper |
Temperature | Yes | 1990 | Intrinsic | Resonator | Frequency | None | [23] |
2003 | Intrinsic | Delay line | Phase velocity | None | [28] | ||
Pressure | Yes | 2001 | Extrinsic | Loaded Transponder | Phase | Capacitive pressure sensor | [25] |
2007 | Intrinsic | Delay line | Phase | None | [24] | ||
Bio/Chem | No/Yes | 2006 | Extrinsic | Resonator | Frequency | Thin film | [29] |
2011 | Extrinsic | Delay line | Phase | Thin film | [26] | ||
2001 | Extrinsic | Loaded Transponder | Amplitude/Phase | Conducting rods | [30] | ||
Magnetic | No | 1975 | Extrinsic | Delay line | Phase | Thin film | [13] |
2011 | Extrinsic | Resonator | Frequency | Magnetostrictive IDTs | [8] | ||
2006/11 | Extrinsic | Loaded Transponder | Amplitude | GMI wire/ thin film | [16,17] | ||
Sound | No | 2005 | Extrinsic | Loaded Transponder | Phase | Capacitive pressure sensor | [31] |
Torque | Yes | 1996 | Intrinsic | Delay line | Phase | None | [32] |
SAW-based passive sensors.
Magnetic sensors are one of the most versatile sensors employed not only for the task of measuring magnetic fields but for a large number of different applications, thereby detecting the measurand indirectly, e.g., via a change of material parameters in construction monitoring or a change of distance in position monitoring. A passive and remote operation of magnetic sensors can be advantageous in many cases and considerably increase their applicability.
A SAW-based passive magnetic sensor can be realized either by adding an additional material layer, which is sensitive to magnetic fields, or by loading the output IDT with a magnetic sensor. In the first case, the magnetic layer changes the delay line or the resonant frequency of the SAW device. While in the second case, the sensor changes the reflection signal of the output IDT. Since SAW devices are operated by RF power, the sensor element has to work at the operation frequency of the SAW device. Among the available magnetic sensors, GMI sensors are the most suitable candidates as they have a high magnetic sensitivity as well as a high operating frequency.
Magnetostriction defines the relationship between the strain and the magnetization states of a material. It is an important property of ferromagnetic materials and was first observed by James Joule in 1842 in nickel samples. For a positive/negative magnetostrictive material, an applied magnetic field causes the material to expand/shrink in the field direction. Inversely, when a stress is applied to the magnetostrictive material, its magnetic anisotropy will change accordingly.
A magnetostrictive-piezoelectric resonator consists of amorphous magnetostrictive material layers as the electrodes sandwiching a piezoelectric core (Fig. 2). An ac signal applied to the electrodes causes the quartz layer to oscillate. The resonant frequency of this oscillation depends on the thickness of the piezoelectric material, the crystal orientation, temperatureand mechanical stress, etc.
Structure of a compositemagnetostrictive/piezoelectric resonator.The magnetic anisotropy is perpendicular to the external magnetic field Hdc.
When a magnetic fieldHdc is applied, the length change induced in the magnetostrictive film exerts stress to the piezoelectric material and, consequently, shifts the resonant frequency of the device. Utilizing this concept,a magnetic sensitivity high enough to detect the terrestrialfield has been achieved [33].In a similar work, a magnetostrictive-piezoelectric tri-layer structure has been embedded in a coil. The dc magnetic field sensitivity was as high as 10-8 T [34].
A SAW-based, passive resonator for magnetic field detection was developed recently by Kadota et al [8]. Nickel, which is a negative magnetostrictive material, was used to fabricate the sensing IDT on a quartz substrate (Fig. 3). Upon the application of a magnetic field, stress will be induced to the substrate by the IDT change causing a change in the resonant frequency. This sensor showed a frequency change of 200 ppm for a magnetic field of 100 mTapplied perpendicularly to the direction of SAW propagation.
Schematic of amagnetic sensor device using a magnetostrictive IDT on a SAW substrate.
A magnetically tuned SAW phase shifter is a one-port SAW structure with a magnetic sensing functionality achieved through a delay line sensitive to magnetic fields. This idea was first introduced by Ganguly et al in 1975 [13]. In their device, the acoustic velocity is varied by an external magnetic field. This functionality is facilitated by a magnetostrictive thin film deposited on top of the delay line (Fig. 4). The propagation velocity of the SAW in the film region depends on the magnetic field. Hence, there is a correlation between the time shift of the reflected signal and the magnetic field.
Schematic of a magnetically tuned SAW wave phase shifter.
Later, research efforts focused on different magnetostrictive materials and measurement methods [9, 35, 36], and a magnetic sensitivity of 10-4%/kOe was achieved.
A SAW-based, magnetic and passive sensorscomprises a two-port SAW transponder and a magnetic sensor acting as a load at the output IDT. Among the available magnetic field sensors, giant magnetoimpedance (GMI) sensors offer favorable characteristics like high sensitivity to magnetic fields and high operation frequency (compatible with SAW transponders) making them a very suitable load. SAW-GMI sensors have been fabricated by combining SAW transponders with GMI wire sensors as well as thin film GMI sensors. Both of these methods have shown a higher magnetic sensitivity than direct designs.
The GMI effect was first observed in Co-based amorphous wires by Panina and Mohri in 1994[37] and has since attracted strong interest due to its sensitivity enabling magnetic field measurement with a nT resolution. The GMI effect isthe impedance change of an ac-powered ferromagnetic conductor upon the change of a magnetic field. The relative impedance change, also called GMI ratio, is expressedas
whereZ(H0) is the impedance at zero magnetic field andZ(Hmax) is the impedance atsaturation field. Both definitions have particular aspects that should be considered. In case of the first expression,Z(H0) depends on the remanent state of the magnetic material while,in the second case, Z(Hmax) is not always achievable and equipment dependent.
The GMI effect is explained by classical electromagnetism.The change of the complex impedance mainly originates from the skin effect in conjunction with a change of the complex permeability.Analytically, the complex impedance (Z) of a conductor is defined by
whereUacis the applied ac voltage, Iacis the current, L is the length and σ the conductivity.Sand qrefer to the surface and the cross section of the conductor, respectively.Jzis the current density in the longitudinal direction obtained by solving Maxwell’s equations.In ferromagnetic materials, by neglecting displacement currents (
J is the current density,H is the applied magnetic field, M is the magnetization of the ferromagnetic material, ρfis the free charge density and μ0is the permeability of vacuum.From Equ.(6) to (8), the expression
can be derived. Equ. (9) can be solved using the Landau-Lifshitz equation, whichrelates MandH
whereγ is the gyromagnetic ratio,Msis the saturation magnetization,α is the damping parameterandHeffis the effective magnetic field expressed as [30]
whereH is the internal magnetic field that includes the applied field and demagnetizing field, Hais the anisotropy field andA is the exchange stiffness constant..
By combining Equ.(5) to (11), a theoretical impedance model can be evaluated for GMI sensors with different geometries [37-40].
Although the experimentally obtained GMI effect shows a large sensitivity compared to other effects exploited for magnetic sensors, the theoretically estimatedvalues have not been achieved yet. Therefore, a lot of effort has been putinto improving the magnetic properties of GMI materials [41-44]. At the same time, GMI sensors of different structures have been developed such as glass-coated wires, thin films, multi layer thin films, meander structures, ribbons, etc. [45-47]
As the first discovered GMI sensor structures, GMI wire sensors have been extensively studied. Based on the classical electromagnetism, the theoretical model of the GMI wire is (Panina et al, 1994) [37]
where
and
Rdc is the dc resistance of the wire, ζ 0, ζ 1 are the Bessel functions, r is the radius of the wire, j is the imaginary unit, δm is the penetration depth, c is the speed of light, f is the frequency of the ac current, μø is the circumferential magnetic anisotropy. The origin of the GMI effect lies in the dependence of μø on an axial magnetic field resulting in a change of δm. In order to obtain a high GMI ratio, the value of δmhas to be close to the thickness of the conductor.Hence,the thinner a ferromagnetic conductor and the lower its permeability,the higher the operation frequency required.A well-defined circumferential magnetic anisotropy in combination with a soft magnetic behavior is desirable, since it will provide a large permeability change for small magnetic fields.
Different amorphous and ferromagnetic materials were used to fabricate GMI wires [48], and various fabrication methods were developed such as melt spinning, in-rotating water spinning, glass-coated melt spinning etc. [45, 49, 50].Glass-coated micro-wires (Fig. 5)present outstanding properties in terms of the magnetic anisotropy distribution, which is reinforced by the strong mechanical stress induced by the coating. (CoxFe1-x)72.5Si12.5B15is one of the most typical materials. By adjusting x from 0 to 1, the magnetostriction of the material changes from positive at high Fe content to negative at high Co content. Negative magnetostrictive compositions in combination with the compressive, radial stress induced by quenching and the glass coating provide the best results, since it supports a strong circumferential anisotropy.
SEM image of a glass-coated amorphous micro-wire (Courtesy of M. Vazquez, Inst. Materials Science of Madrid, CSIC).
GMI ratio of Co67Fe3.85Ni1.45B11.5Si14.5Mo1.7 glass-coated wires with different geometric ratio ρ(themetallic nucleus diameter to the total microwire diameter)at 10 MHz.
Wire-type GMI sensorsprovide the best performance in terms of the GMI ratio with values as high as 615% (Fig. 6)achieved with optimized glass coated microwires (Zhukova et al, 2002) [43].The value of the magnetic field at which the maximum GMI ratio is obtained increasesas the diameter of the magnetic nucleus decreases compared to the diameter of the glass coating. This is attributed to the different anisotropies induced by the stress from the coating. Due to the high sensitivity provided by GMI wiresthey have been commercialized despite the facts that fabrication is not silicon based, does not use standard microfabrication methods and, as a consequence, integration with electronics is complex.
a) Layout of the commercialized GMI sensor from Aichi Steel Co. (b) Noise output of the GMI sensor.
Fig. 7showsa GMI sensor developed by Aichi Steel Co., which has a very high sensitivity of 1V/μT and a noise level of 1 nT[51].
Magnetic ribbons discussed in this section are planar structuresof rectangular shape with a thickness of a few tens of micrometers and a length and width from several millimeters to centimeters.Similar to the micro-wires, magnetostriction is utilized in order to create certain anisotropies during the ribbon’s fabrication. Magnetic ribbons that exhibit a strong GMI effect have a high permeability as well as a transversal magnetic anisotropy.
For a planar film of infinite width, the impedance is given by
whereRdc is the dc resistance, a is the thickness of the ribbon, kand δm can be obtained fromEqu. (14) with the only difference that μørepresents the transversalpermeability instead of the circumferential one [52].
Again, Fe-and Co-based amorphous alloys are preferably used as the magnetic material. The standard fabrication method for the ribbons is melt spinning, where a rotating copper wheel is used to rapidly solidifythe liquid alloy.This method produces magnetic ribbons with a thickness of about 25μm and a width of several mm. With this thickness, ribbon GMI sensorsoperate at comparably low frequencies of hundred kHz up to a few MHz.A GMI ratio of, e.g., 640% has been obtained with a GMI ribbon made of Fe71Al2Si14B8.5Cu1Nb3.5at 5 MHz [53].
In theory, a single layer magnetic thin film is similar to a magnetic ribbon,and the same analytical expressions are applied for modeling the GMI effect. Practically, the main difference is the fabrication method. Thin film fabrication is a standard micro-fabricationtechnology producing a film thickness of somenanometersup to a few micrometers. Thin film GMI sensorsare of great interest due to the advantages arising from the fabrication in terms of the flexibility in design and integration. They can easily be fabricated on the same substrate as the electronic circuit and other devices. In the context of passive and remote sensors, this is particularly relevant, since the GMI element can be easily integrated with an SAW device. For this reason, GMI thin film sensors will be discussed in more detail and our recent results will be presented.
Compared to wires and ribbons, the results obtained with thin film sensors have not been as good, and the highest GMI ratios reported are around250%[42]. This may be due to the differencesin the magnetic softness as well as the magnetic anisotropy, which is very well established in circumferential and transversal direction in wires and ribbons, respectively, and is difficult to control in thin films. Inthin films transverse anisotropy is mainly realized through magnetic field deposition or field annealing,Fig. 8 (a) and (b) show the magnetization curve and domain structure of aNi80Fe20thin film (100nm thick) fabricated under a magnetic field of 200 Oe during deposition. A magnetic easy axis and domain structures in transverse direction are observed. Due to the small thickness, thin film GMI sensors normally operate at a higher frequency from hundred MHz to several GHz where the penetration depth is in the range of the film thickness.
a) Magnetization curves obtained by vibrating sample magnetometry of a magneticthin film (100nm of Ni80Fe20) in transversal and longitudinal directions. (b) Domain pattern of the magnetic layer. (c) Schematic of a typical multi layer GMI structure. The arrows in the ferromagnetic material indicate the magnetization of individual domains (simplified). Upon application of an external magnetic field Hext, the magnetization rotates into the direction of Hext (dotted arrows).
In general, a GMI sensor with high sensitivity consists of a stack of several material layers. In case of a tri-layer element, one conducting layer is sandwiched between two magnetic layers as shown in Fig. 8 (c). The conducting layer ensures a high conductivity and, in combination with the highly permeable magnetic layers, a large skin effect is obtained [51, 54].An alternating current Iac mainly flowing through the conductor generates a transversal flux Btran, which magnetizes the magnetic layers. Upon the application of an external field Hextin longitudinal direction, the magnetization caused by Iacwill be changed. This is equivalent to a change of the transversal permeability of the magnetic layers and is reflected by an impedance change.
The analytical model of the impedance for a magnetic/conducting/magnetic tri-layer structureis given by
whereRdc is the dc resistance of the inner conductor, 2d1is the thickness of the conductor,d2is the thickness of the magnetic layers as shown in Fig. 8 (c) andδc is the penetration depth of the conducting layer[39].
Analytical solutions for the impedance of thin film GMI sensors can only be found for rather simple structures. In order to calculate the impedance of more complicated geometries, for example,a sandwich structure with isolation layersbetween the conductor and the magnetic layers [41], a meander structure multilayer [46] or to take into account edge effects, the finite element method (FEM) provides a viable solution [55].
Fig. 9 shows the comparison of the GMI ratios simulated for a single magnetic layer, a tri-layerstructure made of a magnetic/conducting/magnetic stack and a five-layer structure with isolation layers between the conducting and magnetic layers using the FEM. The simulated GMI sensors have a width of w = 50 µm and length of l= 200µm. The magnetic layers have a thickness of tmag = 1 µm andthe conducting layer has a thickness oftmet = 4 µm. The material of the isolation layer is SiO2 with a thickness of 1µm. The conductivity of the ferromagnetic and conducting layers are 7.69×105S/m ((CoFe)80B20) and 4.56×107S/m (Gold), respectively. All parameters including Ms = 5.6×105A/m,
Simulated GMI ratios of single layer, sandwiched multilayer and isolated sandwiched multilayer structures.
The resultsclearly show the performance increase achieved with the multilayer structures. Specifically, the isolated sandwich structure has a superior performance, which is due to preventing the current from flowing in the magnetic layer.
For the fabrication of thin film GMI sensors, Co-based and Fe-based amorphous magnetic alloys were used in earlierstudies. Recently, permalloy, which is a NiFe compound, became popular as it provides very high permeability, zero magnetostriction and simple fabrication. Meander shaped multilayers and different stacks of magnetic and conductive layers using permalloy were developed. Some results are summarized in Table 2.
Year | Material | Frequency | GMI Ratio (%) | Sensitivity (%/Oe) | Reference |
1999 | FeNiCrSiB/Cu/FeNiCrSiB | 13MHz | 77 | 2.8 | [57] |
2000 | FeSiBCuNb/Cu/FeSiBCuNb | 13MHz | 80 | 2.8 | [58] |
2004 | Ni81Fe19/Au/Ni81Fe19 | 300MHz | 150 | 30 | [59] |
2004 | (Ni81Fe19/Ag)n | 1.8GHz | 250 | 9.3 | [42] |
2005 | FeCuNbSiB/SiO2/Cu/SiO2/FeCuNbSiB | 5.45MHz | 33 | 1.5 | [60] |
2011 | NiFe/Ag/NiFe | 1.8GHz | 55 | 1.2 | [61] |
2011 | NiFe/Cu/NiFe | 20MHz | 166 | 8.3 | [62] |
Recent results on thin film GMI sensors.
GMI thin film sensors not only offer the advantages of standard microfabrication and straight-forward integration with SAW devices, but, as can be seen from Table 2, the operation frequency of GMI thin film sensors is also compatible with the one of SAW devices (usually from hundred MHz to several GHz and can be adjusted within a wide range.
In the first studies, SAW transponders and GMI wire sensors werecombined to form remote devices [14-16]. GMI wires were selected for their high sensitivity, and they were bonded totheoutput IDT of the SAW device, which operated as a reflector, in order to act as load impedance. The strong dependence of the impedance on magnetic fields causes a considerable amplitude dependence of the reflected signal on magnetic fields. Even though these studies provided good results for passive and remote magnetic field sensors, the fabrication method for the GMI wires, which is not compatible with standard microfabrication, is a considerable problem with respect to reproducibility and costs, hence, hindering commercial success of such devices. In order to conquer this problem, a fully integrated SAW-GMI design utilizing standard microfabrication processes is required. The most viable option is a thin film GMI sensorsfor the following reasons:
Thin film GMI sensors can be produced by the same metallization processes as the SAW transponders and on the same substrate.
Standard photolithography technique guarantees an accurate and reproducible alignment of the two devices.
Thin film GMI sensors provide a wide range of working frequencies up to GHz, which matches the high frequency requirement of the SAW transponders.
Thin film GMI sensor can have a minimized and flexible design as well as large magnetic field sensitivity.
In this section, a detailed description of our recent work on the design, fabrication and testing of an integrated SAW-GMI sensor is presented.
DesignFig. 10 shows a schematic of a GMI thin film sensor integrated with a SAW transponder. A wireless signal applied to the source IDT (IDT1) is converted to anSAWand propagates towards the other end of the substrate, where it is reflected from the reference IDT (IDT2) and the load IDT (IDT3). The reflected waves containing the reference and load information are received by IDT1 at different time instants and reconverted to a wireless electrical signal sent out via the antenna.
Schematic of an integrated passive and remote magnetic field sensor consisting of a SAW transponder and thin film GMI sensor.
In order to obtain high magnetic field sensitivity, the GMI sensor is matched to the output port (IDT3) at the working frequency of the SAW device. As the impedance of the GMI sensor changes with an applied magnetic field, the matching deteriorates, which causes the amplitude of the signal reflected from IDT3 to change. Since the piezoelectric material is sensitive to environmental changes, e.g. temperature, a reference IDT is used to provide a signal that enables the suppression of such noise by means of signal processing. Two metallic pads next to the input and output IDTs act as mechanical absorbers and suppress reflections from other structures on the substrate or the edge of the substrate.
Matching the sensor load to the optimal working point of IDT3 is a crucial aspect in the device design.Therefore, the influence of the load on the signal reflected from IDT3 issimulated. The interaction of a SAW with an IDT can be described by the P-matrix model introduced by Tobolka [63]. As shown in Fig. 11, P11 is the acoustic wave reflection at the output IDT [24]. Specifically, the dependence of P11 on the load impedance Z = Z(Hext)+ Zm, where Z(Hext) is the impedance of the GMI element and Zm is the matching impedance, is expressed as
whereP11,sc is the short circuit reflection coefficient, P13 is the electro-acoustic transfer coefficient andP33 is the input admittance of the transducer. In order to have a large change of P11, which is equivalent to the sensitivity of the SAW device loaded by an impedance sensor, the influence of Z in equation (17) needs to be large. Therefore, a SAW transducer with a small P11,scand largeP13 will provide a large sensitivity. P11,sc can be minimized by using a double electrode IDT design as shown in Fig. 10, which provides cancelation of the internal mechanical reflections of the IDT.
Electric and acoustic ports of the SAW sensor
The electro-acoustic transfer coefficient P13 and input admittance P33 can be obtained by,
Where rm is the ratio of the electrical to acoustical transformer, CIDT is the capacitance of the IDT,Za is the acoustic impedance and
Since the GMI sensor is an inductive element, matching is accomplished by a series capacitance resulting a load impedance
Z = 1/jωCm + R +jωL(Hext),(20)
whereCm is the matching capacitance, R is the average resistance (over the considered magnetic field range) of the GMI sensor and L(Hext) the inductance of the GMI sensor.
Fig. 12 (a) shows the simulation result of the IDT’s reflectivity as a function of the load. The slope of this plot corresponds to the magnetic field sensitivity. Therefore, the optimum matching capacitance can be determined. Fig. 12 (b) presents the rate of change of P11for 1nH inductance changes (corresponding to a field change of approximately 50A/m). The result shows that with the optimum matching capacitance a maximum reflectivity change rate of 0.3dB/nH can be achieved. As the fabricated GMI sensor has an inductance change from 5nH to 15nH, a reflectivity change of 3dB can be expected.
a) Magnitude P11 as a function of the matching capacitance and sensor inductance. (b) Rate of change (absolute value) ofP11 for 1nH load inductance change.
The piezoelectric substrate chosen for this application is LiNbO3 as it provides a strong electromechanical coupling corresponding to a high value ofP13. The detailed design parameters of the SAW transponder are shown in Tab. 3. The working frequency of the device is 80MHz, resulting in a periodicityp of 50μm (Equ. (1). The value ofp determines the electrode width and gap. The distances between the IDTs yielda1.25μs delay between IDT1 and IDT2 and a 0.625μs delay between IDT2 and IDT3.
Design parameter | Design parameter | |||||
Substrate material | LiNbO3 (128 deg. Y-X cut) | Electrode material | Gold | |||
Center frequency | 80MHz | Aperture | 30λ | |||
Periodicity | 50μm | Electrode/gap width | 6.25μm | |||
Electrodes per segment | 4 | IDT segment number | 30 |
Design parameters for the SAW device.
The GMI sensor consists of a tri-layer structure with two ferromagnetic layers of 100nm in thickness made of Ni80Fe20 and a conducting copper layer with a thickness of 200nm. The sensor has a rectangular geometry of 100 μm× 4000 μm. The conducting layer is connected to the IDT3 [18].
Fabrication
The fabrication of the combined device is accomplished in several steps as shown in Fig. 13. On a LiNbO3 wafer, a 40 nm Ti adhesion layer and 200 nm gold layerare sputter deposited and patterned by ion milling into individual SAW devices. The leads and SMD footprints are designed together with the SAW device to facilitate an on-chip impedance matching circuit, which was accomplished by a 150pF capacitor connected in series with the GMI element.The GMI element comprises a tri-layer structure (Ni80Fe20(100nm)/Cu(200nm)/Ni80Fe20(100nm)) deposited at room temperature with a uniaxial magnetic field of 200 Oe applied in the transversal direction.
Fabrication flow chart of the integrated SAW-GMI device.
Results
A network analyzer (Agilent E8363C) is used to applyan RF signal to IDT1 and measure the electric reflection coefficient (S11) of the input IDT, which is related to the admittance matrix of the whole device and P11.The time domain signal of S11is converted from the frequency domain using fast Fourier transform. As shown in Fig. 14 (a),two reflection peaks at 2.45μs and 3.55μsare observed indicating the reflections from the reference IDT and the load IDT accordingly. The magnetic response of the integrated device is determined by applying a variable magnetic field in longitudinal direction to the device. A 2.4dB amplitude change of the reflection signal can be observed. A comparison of the simulated and experimental results together with the measured GMI ratio curve is shown in Fig. 14 (b).
a) Time domain measurement of the SAW-GMI device. Inset: Frequency domain measurement. (b) Comparison of the simulated and experimental device response together with the measured GMI ratio curve.
Magnetic field sensors are one of the most widely used sensors and employed for many different applications. Current commercial magnetic sensors are wire connected to a circuit providing power and readout. These wire connections prevent the sensors from being used for certain applications. In addition, as the complexity and the number of devices, increases in modern systems such as automobiles, wire connections are becoming an increasing problem due to limited space. For those and other reasons, wireless solutions are being much sought after.
As pointed out in the previous sections, SAW-based sensors have been developed for different applications, and this technology also provides a platform for realizing wireless and passive magnetic sensors. They can provide solutions for various applications, for example, where the sensors have towithstand harsh environmental conditions or are required to have a long lifetime without maintenance.
Out of the countless applications for SAW-based passive and remote magnetic field sensors, a few will be highlighted in the following.
Nanotechnology and miniaturized systems are becoming increasingly popular in the biomedical field. Technologies based on magnetic effects are of particular interest since they can be controlled remotely via magnetic fields. For example, NVE Corporation recentlydeveloped a battery operated magnetic sensor to be used as a magnetic switch for implantable devices. When a magnetic field is applied, the sensor turns on triggering a specified action. It turns off when the field is removed. The sensor works at a stable operating point of 15 Oe [64]. Magnetic beads have been extensively used in many biomedical applications. These magnetic beads are made of ferromagnetic material ranging in size between 5 nm to 500 um. A new application of such particles promises benefits in cancer therapy by employing the particles either as drug carriers or heat sources (hyperthermia) [65]. In order to have better control of the treatment, magnetic sensors are considered to measure and detect the concentration of these magnetic particles.
The automotive industry extensively uses magnetic sensors for different purposes, for example, to measure current in electric vehicles [66] or the rotation speed of gears [67]. Another application employs magnetic sensors to detect passing vehicles using lane markers [68]. Such a system could also be used to detect vehicle speed by measuring the time between two markers of a fixed distance. In yet another application, developed by Stendl et al [69], the wear of a vehicle’s tire is detected by measuring the field of magnetic beads embedded in the rubber of tire treads. As the tread size decreases, the magnetic field alsodecreases. A wireless magnetic sensor is placed just below the threads.
Construction monitoring is an upcoming application for wireless sensor. Long-term monitoring of metallic reinforcements in, e.g., bridges or buildings requires passive and remote sensors, which are capable of detecting changes of the metal. Similarly, the detection of internal defects or corrosion of pipelines is of great interest. Gloria et al [70] developed an Internal Corrosion Sensor (ICS) consisting of a magnet and a Hall sensor. A disturbance in the magnetic field caused by changes of the metal changes the sensor readout. This information is used for both to detect and size the defects.
In this chapter we discussed different types of SAW-based, magnetic sensors including resonators, phase shifters and loaded transponders. Sensitivity to magnetic fields can be achieved byeither changing the properties of the IDT or delay line utilizing magnetostrictive materials or loading the output IDT with a magnetic field sensor. GMI sensors feature a very high sensitivity and wide range of operating frequencies and, therefore, constitute an especially suitable load. The principle of GMI sensors is described in detail and different GMI structures are discussed. While the highest sensitivity has been obtained with GMI microwires, thin film GMI sensor are advantageous because they can be produced using standard microfabricationmethods, and they can be easily integrated with a SAW transponder on the same substrate. These features are crucial with respect to production complexity and costs.
A SAW transponder combined with a GMI element connected to the output IDT is a passive and remote magnetic field sensor, which responds to an interrogation signal with a delayed response signal. The design of such a device needs to take into account different aspects like operation frequency, dimensions of IDTs and delay line or matching the load with the output IDT. In order to obtain a high sensitivity, an impedance change of the GMI element caused by a magnetic field, has to yield a large change in the SAW reflected from the output IDT. A model is presented to simulate the electro-acoustic interaction of the output IDT with the GMI sensor’s impedance and the impedance matching capacitance. The simulation results provide information regarding the matching parameters and are invaluable for obtaining anoptimized performance. A detailed description of the fabrication of an integrated SAW-GMI sensor is providedusingstandard microfabricationtechnologies.The GMI ratio of the fabricated sensor is 45 % The SAW-GMI sensor provides a sensitivity of 3 dB/mT, and its output corresponds well with the simulation results.
Magnetic field sensors have countless applications and are widely used in many different fields. The trend towards wireless operation, which is generally observed nowadays, drives the development of passive and remote magnetic field sensors. Several concepts of such sensors employing SAW devices have been presented in this chapter. The most promising one is a SAW-GMI sensor, which has been discussed in detail and which features wireless and battery less operation as well as durability and the ability to withstand harsh environments. This kind of sensor is considerable not only for providing existing applications with a wireless mode; it also largely expands the potential applications of magnetic field sensors.
Consciousness is a complex term to tackle objectively due to its broad epistemological spectrum. From a clinical view, consciousness has been neurophysiologically and behaviorally parameterized for its assessment [1, 2]. It is a central nervous process (reduccionism) that multiple neural long-range connections control (conexionism) and that is teleonomically goal directed. This neurofunctional point of view converges with theories about the emergence of new features in complex systems [3]. Various authors propose that high brain connectivity between distinct and distant neural groups is an elemental characteristic for the emergence of consciousness [3, 4, 5]. In this respect, consciousness is a neurophysiological phenomenon regulated by different brain networks that create qualia, the subjective experience of consciousness [6, 7, 8, 9, 10, 11].
\nConsciousness should be interpreted as a physiological state of the central nervous system that changes over time and space. This functional mutability allows high-order cognitive functions to take place [6, 12, 13] to produce an overt and/or covert behavior that can be measured via direct observation or neuroimage [14, 15, 16]. All of these intermingled processes are supported via various brain networks that integrate endogenous and exogenous information with the intention of responding effectively to organic and psychological demands [6, 8, 11, 17, 18]. In this regard, acquired brain damage can impair the regular activity of brain networks, disorganizing cognition and behavior (mild, moderate, or severe brain damage), or even inhibiting the experience of consciousness (disorder of consciousness) [14, 19, 20, 21]. Therefore, from a clinical view, the structural and neurophysiological integrity of the neural substrate that underlies consciousness will determine the functional behavior of individuals [6, 22, 23]. Thus, consciousness can be described as a basal, dynamic, and transitive brain state that supports the high-order cognitive processing of information to produce suitable behaviors for environmental demands [24].
\nA huge number of theories seem to agree on many assumptions about consciousness, although they diverge regarding the descriptive approach. Some of them, such as the Global Neural Workspace Theory, focus on its neurophysiological components [11]. Meanwhile, others, such as the Global Workspace Theory, focus on its cognitive components [25]. In addition, the Integrated Information Theory focuses on its computational components [8, 26, 27]; the Temporo-Spatial Theory of Consciousness focuses on its inner space and time characteristics [6]; and the PFC-feedback System [28] focuses on its feedforward and feedback components. Crick and Koch introduced one of the first approaches to the study of consciousness [9]. Their approach posits that the experience of consciousness will be determined based on the long-range connectivity between the front and back parts of the brain. All of these authors and theories have shed light on the phenomena of consciousness and have probably contributed to the very first theoretical foundations for the study of consciousness objectively:
Consciousness depends on bioelectrical and biochemical brain activity.
Some neurophysiological processes are required to experience consciousness as awareness (i.e., the object or event has to trigger a P300 wave on the cortex).
These neurophysiological processes are regulated via various neural groups that process information in a rapid, automatic, and stereotypical manner (back brain), as well as via other neural groups that process information in a slow and voluntary manner (front brain).
Consciousness needs long-range connectivity between distinct and distant brain areas.
These long-range connections (probably in beta bands) assemble distinct and distant neural groups into extended neural networks that regulate various physiological and phenomenological dimensions that are necessary for the experience of consciousness.
One of the main neural models that are emerging currently about neural processing is the “predictive coding model” [29, 30]. This model posits that neural processing occurs within feedforward and feedback loops between upper and lower brain structures and slices. Lower structures/slices send predictions to upper structures and these structures send back error predictions to adjust neural processes to make the ongoing behavior efficient [29, 30, 31, 32, 33]. Llinás has already suggested that consciousness could be more related to a close-loop neural network than to the emergent consequence of a sensory input [34]. In this sense, a functional and preserved consciousness could depend on the predictive codification between inferior (brainstem and thalamus) and superior brain structures (cortex), where the prefrontal cortex (PFC) receives “end-of-the-line” bottom-up predictions and sends top-down error predictions to the thalamus to adjust new top-down projections [24, 35, 36, 37, 38, 39, 40].
\nDespite all of the theories and experimental evidence about the neural networks involved in consciousness, no global theoretical framework exists to describe how these neural networks operate to produce and maintain consciousness. The present chapter will introduce a neurofunctional model that organizes the interaction and functioning of the neural networks into three neurofunctional loops: (1) the Brainstem-Thalamic neural loop (B-T neural loop), (2) the Thalamo-Cortical neural loop (T-C neural loop), and (3) the Cortico-Cortical neural loop (C-C Neural Loop). Each of these loops are formed via differentiated and semi-independent neural structures that are involved in specific aspects of the phenomenological consciousness.
\nThe brainstem plays a key role in the regulation of consciousness due to the control that it exerts to the Ascending Reticular Activating System (ARAS) and therefore to wakefulness (wakefulness and awareness are the two clinical dimensions typically related to consciousness) [41, 42]. The ARAS is composed of myriad brainstem nuclei (dorsal raphe locus coeruleus, median raphe, pedunculopontine, and parabrachial nuclei), with connections to the thalamus, hypothalamus, and basal forebrain [42, 43, 44, 45, 46, 47, 48], and even with the prefrontal areas [49] and the precuneus (Pcu) [50]. The lower dorsal ARAS connects the pontine reticular formation to the intralaminar thalamic nuclei (ILN), the lower ventral ARAS connects the pontine reticular formation to the hypothalamus, and the upper ARAS connects the intralaminar thalamic nuclei to the cerebral cortex [51, 52, 53, 54]. Whereas hypothalamic-basal forebrain pathways regulate sleep-wakefulness cycles [48, 55, 56], the ILN, as part of the non-specific thalamic nuclei, can block thalamocortical rhythms and therefore the emergence of arousal and awareness [22, 57, 58, 59, 60]. Baars [18] called this circuit the Extended Reticular-Thalamic Activating System, which he considered to be the principal neural assembly in the experience of consciousness.
\nA significant amount of evidence points out that reciprocal interactions between the thalamus and cortex are a fundamental component of the proper functioning of the thalamo-cortical system [61], which is related to consciousness [62]. This thalamo-cortico-thalamic connectivity starts to develop in the late prenatal and early postnatal stages [61, 63, 64], and the efficient deployment of these developmental processes will determine the functional state of the thalamo-cortical system in the adult stage [65]. The thalamus has been proposed as the main neural structure of the thalamo-cortical system, as it operates as a regulator of cortical functional connectivity, whereby it is involved in the ongoing cognitive processes [66, 67, 68, 69, 70]. The thalamus can be divided into three nuclear groups: first-order thalamic relay nuclei, higher-order thalamic relay nuclei, or non-specific thalamic nuclei. First-order thalamic nuclei send afferent projections to the primary sensory cortical areas, whereas higher-order nuclei receive projections from the primary sensory cortical areas and send these projections back to the higher visual cortical areas forming the cortico-thalamo-cortico circuits. Finally, nonspecific thalamic nuclei are those that receive projections from the ARAS and send diffuse projections throughout the brain [71, 72, 73]. The nonspecific thalamic nuclei are composed of three main nuclear groups: the thalamic reticular nucleus (TRN), the ILN, and the midline thalamic nuclei (MTN). The TRN-ILN-MTN thalamic axis has been related to consciousness [22, 62, 74] with strong implications in the distribution of neural information throughout the brain [24].
\nThe functional extent of each nonspecific thalamic nuclei is related to the control and regulation of a specific cognitive domain [24] . The TRN is one of the main neural nodes that regulates the activity of the thalamus and therefore the activity of the entire thalamo-cortical system [75, 76, 77]. The TRN receives afferent glutamatergic projections from the entire brain, and in turn, it sends only efferent GABAergic projections to the thalamus, thus regulating thalamo-cortical and cortico-cortical activity [28, 78, 79]. On a morphological level, the TRN is divided into sensory and motor regions [80]. Whereas the sensory region modulates attentional processes via connections with the prefrontal cortex [38], the motor region is involved in limbic and motor processes due to high connectivity with the ILN-NMT, the ventrolateral, and the anterior thalamic nuclei [81, 82, 83, 84, 85]. Various authors have referred to the involvement of the TRN in the attentional processes as the “attention spotlight” and “attentional door” that regulate the flow of information between the thalamus and the cortex [35, 86, 87]. The capacity to control neural information throughout the brain is due to the inhibition that it exerts to the thalamic nuclei [37, 76, 86]. This inhibition mechanism underlying the “attention spotlight” selects the information needed to face psychological and physiological demands while suppressing those that are not relevant. Some authors suggest that the TRN is involved in the content of consciousness by controlling selective attentional processes and the thalamus activity [28, 86]. According to Crick [35], the short-term synaptic plasticity of the TRN could influence first-order thalamic relay nuclei in the formation of temporal connections between brain areas related to the content of consciousness [35]. Hence, this capacity to modulate the content of consciousness could be mediated by the control of attentional processes [88, 89, 90].
\nOn the other hand, the functions of the ILN and the MTN are functionally differentiated, but their activity are highly dependent [91, 92, 93, 94, 95]. Regarding consciousness, both nuclei (due to its multiple connections with the ARAS) activate the excitability of the cerebral cortex to maintain vigilance and arousal [42, 58, 59, 60, 76, 91]. For instance, the ILN send and receive projections from the prefrontal, motor, and parietal cortices. Meanwhile, the MTN is connected to the medial prefrontal cortex (mPFC) and the hippocampus (HPC). These diffuse connections spread to the cortex, thus allowing the synchronization of brain activity through the adjustment of the brain waves’ phases. Thus, distinct and distant neural groups assemble into cortico-cortical networks to facilitate the flow of neural information [91]. In addition, The ILN and MTN are also involved in the regulation of the striatal-thalamocortical circuits [96] due to the multiple efferent inhibitory connections that receive from the TRN, the basal ganglia, and the reticular formation of the ARAS [97, 98, 99]. These connections with the striatum, the brainstem, and the cortex highlight the relevance of the ILN and the MTN in the motor, somatic, and visceral functions, which are essential for controlling arousal, perception, and even emotion expression [100].
\nSpecifically, the ILN have been associated with the regulation of cortical activity and the restoration of consciousness [22, 68, 101, 102]. The anterior region of the ILN react to motor inputs [103, 104], whereas the posterior region organizes motor, limbic, and associative information [60, 97, 105, 106]. Projections to limbic structures and sensori-motor areas suggest the relevance of the integration of the affective and motor functions that underly propositional behaviors [107]. In addition, they are involved in tasks that require the focalization of attention and the selection of actions for unexpected events [108, 109]. Kinomura and colleagues pointed out that arousal and attention require the simultaneous activation of the reticular formation of the midbrain and the ILN [110]. This evidence places the ILN as the basic neural nodes for the integration of brain functions, such as arousal, attention, and motor control, to trigger high-level cognitive performance [86, 104, 110, 111, 112, 113]. This functional characteristic of the ILN in the regulation of the arousal has been employed for deep brain stimulation in cases of minimally conscious state. Schiff [22, 114] showed that stimulating the ILN in minimally conscious state patients could improve their motor behavior, but without showing any sign of “real” consciousness [22, 114, 115]. Therefore, although the ILN seems to be involved in consciousness, it cannot produce a constant and fluent stream of consciousness by itself.
\nFinally, the MTN have been reported as the main “gateway” of information to the HPC and the limbic system, with a high dependence on the individual’s arousal levels [116, 117, 118, 119]. Concretely, the nucleus reuniens and rhomboid of the MTN jointly with the mPFC and the HPC form a specialized neural circuit that contribute to learning and to the cognitive flexibility [120], probably due to its relationship with the working memory [116, 117]. This circuit constituted by the MTN-HPC-mPFC could be modified via the functional state of the TRN [121] and also affect the content of consciousness [122]. Other authors propose that the circuit formed via the orbital and mPFC, the amygdala, the hypothalamus, and the MTN could also be involved in the visceral and emotional control of human behavior [123, 124, 125, 126, 127, 128]. The MTN directly influences the arousal and attentional processes through its involvement in emotional regulation [129]. Thence, it is implicated in the emotional adjustment of behavior in a continuously changing environment [130]. According to these authors, the MTN could mediate the selection of the most suitable behavior depending on the emotional tone inputs received in a specific moment [118, 130]. This evidence places the MTN as a remarkable interface between the diverse structures of the limbic system to integrate memory, emotion, and cognition [100, 119, 129, 131].
\nAll of this evidence points out that the TRN-ILN-MTX thalamic axis and its connections throughout the brain are essential components for being conscious and aware of our surroundings due to the axis’s capacity to place the T-C neural loop in an optimal functional state [24, 35]. In this sense, it is important to distinguish between “be aware” and the “formation of consciousness.” Being aware of something means that our cognitive systems are prepared to receive and manipulate the content of consciousness, but the formation of the content of consciousness depends on other neural processes. The content of consciousness is formed mainly in the posterior cortex [132, 133] through cortico-thalamo-cortico circuits, which facilitate connections among various sensory cortical areas in the “content-specific Neural Correlates of Consciousness (NCC)” [70, 133, 134, 135, 136]. Regardless of the content-specific NCC, when it comes to accessing consciousness, some neurophysiological requirements, such as a late P300 wave, are needed to ignite a global brain activation that will trigger awareness [137]. The conscious perception of the content of consciousness is the end of the concatenation of neurophysiological events that propagate from the back to the front cortex [6, 138]. It would be like a competition among various neural coalitions to access consciousness, and once a winning coalition exists (the first to break neurophysiological requirements), a specific representation or the content of consciousness can be perceived as generating a genuine experience of consciousness [137]. Afterward, this content of consciousness is controlled by high-order cognitive functions and is incorporated into plans, desires, and/or thoughts [6, 139].
\nOnce the content of consciousness is created in the back brain [132, 133], various cortico-cortical networks consciously manipulate the information [140]. One of the main cortico-cortical networks, which is broadly documented, is the Default Mode Network (DMN) [141, 142, 143, 144]. This network is formed by the anterior and posterior cingulate cortex, the mPFC, the orbital PFC, the medial temporal lobe (parahippocampal cortex and HPC), the retrosplenial cortex, and the inferior parietal lobe [145] . The DMN is a rest neural network, whose activity is maximum when the subject is awake and the cognitive demand is low (low-level processing of exogenous information) [146]. Moreover, the DMN is characterized by a high metabolism during rest states [147, 148, 149, 150], a progressive deactivation when more cognitive resources are needed to process information [147], and a high connectivity with other cortico-cortical networks to exchange information [140, 143, 151]. Traditionally, the DMN has been related to internal processes, such as self-reference thoughts and mind-wandering [152, 153, 154], although some studies currently link its activity to extrinsic processes, such as certain attentional processes [155] and the recall of memories [156, 157, 158, 159]. Recently, it has been posed that the DMN could also be involved in the integration of spatial, self-reference, and temporal information, thus generating episodic memories [160]. These authors suggest that, henceforth, the DMN is mostly activated in all of the cognitive processes [160].
\nOne of the key points for understanding the role of the DMN in consciousness is to conceive it as a cognitive system that modulate cortico-cortical activity through its mediation in the transfer of information from resting states or task-negative networks to cognitively active states or task-positive networks [140, 147, 156, 161, 162, 163, 164]. When a subject is resting (with the low-level processing of exogenous information), the DMN controls cortical activity with the posterior cingulate cortex (PCC) and the precuneus (Pcu) as their main neural nodes. However, as long as elaborated processing is required and the load of the working memory increases, the physiological burden of the DMN decreases in favor of task-positive networks: the fronto-parietal central executive network (FPN), the dorsal attention network (DAN), and the salience network (SN). The FPN includes the dorsolateral PFC, the mPFC, the anterior insula (aINS), the Pcu, and the interior parietal lobe [140, 165, 166, 167]. On the other hand, the DAN is formed by the frontal eye field and the intraparietal sulcus [168], and the SN by the aINS, the dorsal anterior cingulate cortex, the amygdala, the ventral striatum, and the ventral tegmental area of the mesencephalon [169]. All of these networks share overlapping regions whereby they can exchange neural information depending on the ongoing cognitive activity [147, 149, 150, 170, 171, 172, 173] . The outcome of the continuous interactions among the cortico-cortical networks will define the functional conscious state of the individual [163].
\nThe FPN, DAN, and SN play a key role in conscious behavior due to its capacity to operate jointly and synchronically in a highly coordinated and temporally accurate manner [140, 165, 174]. For instance, the DAN has been related to focalized attention and working memory, whereas the SN has been related to social communication, social behavior, and self-consciousness [171, 175, 176, 177, 178] . When all of these task-positive networks are operating, the DMN needs to deactivate [179, 180, 181] to facilitate the transition from low-energy cognitive states to high-energy cognitive states [147]. In these high-energy cognitive states, the mPFC takes control of the global brain activity at the expense of the PCC and the Pcu [170, 182]. Therefore, the alteration of structural and functional connectivity “within and between cortico-cortical networks” could cause the individual to experience a broad spectrum of neuropsychiatric and neurocognitive disorders [162, 163, 180, 183, 184].
\nThe FPN and SN, especially in the prefrontal regions, regulate the cognitive processes involved in the achievement of conscious goals through the regulation of the physiological equilibrium between the DMN and the rest of the cortico-cortical networks (cognitive control) [140, 165, 166, 167, 185, 186]. Some studies point out that the mPFC and aINS regulate physiological equilibrium among brain networks [178, 187]. For instance, Crone and colleagues compared the activation/deactivation of the DMN in vegetative states (currently known as “unresponsive wakefulness state”), minimally conscious states, and individuals with preserved and functional consciousness (control subjects) [182]. They suggested that although the deactivation of the DMN was normal in control subjects, the same deactivation was significantly diminished in overlapped areas between the DMN and the FPN in a minimally conscious state, and it was absent in unresponsive wakefulness state patients. In other words, the cohesive and functional integrity between the DMN and the task-positive networks is a crucial factor in the transition between rest states (those with a low cognitive burden) to high-demand cognitive states (those with a high cognitive burden) [147]. Our team conducted an investigation whereby we compared cortical connectivity between minimally conscious states and severe neurocognitive disorders [4]. Our results revealed how the degree of connectivity between the anterior and the posterior cortex in the beta band was essential for maintaining a preserved consciousness. In this investigation, patients with minimally conscious states showed a low connectivity between the posterior and the anterior cortex, which could explain why their consciousness fluctuates over time [4]. In contrast, subjects with preserved consciousness showed a high connectivity between the anterior and the posterior cortex, whereby they can operate continuously without the absence of consciousness [4]. In this sense, in a case study, an unresponsive conscious patient emerged to a minimally conscious state when connectivity between the anterior and the posterior cortex increased [188]. Thus, the integration of the posterior and the anterior cortex into long-distance cortico-cortical networks is one of the principal prerequisites for maintaining functional consciousness [9, 182, 189, 190].
\n\n
The nFMC is a theoretical and referential framework from which the study of consciousness can be tackled in all of its operative dimensions: neurophysiological, clinical, neuropharmacological, and phenomenological.
Consciousness is a global neural process that keeps the individual in an optimal and continuous functional state, thus allowing qualia and high-order processes to take place to drive behavior.
The nFMC divides global neural activity into three large systems, or functional loops, that are morphologically differentiated (although they share overlapped areas) and have semi-independent neurophysiological processes: the B-T neural loop, T-C neural loop, and C-C neural loop (see Figure 1).
Cognitive, behavioral, and emotional expression due to brain damage will depend on the location and extension of the lesion within the neural loop, thus leading to clinical outcomes that they may vary from a mild cognitive impairment to a disorder of consciousness, such as a coma, minimally conscious state, or unresponsive wakefulness state.
Each neural loop is activated hierarchically and sequentially by its preceding level, thus extending a representation of the neural processes that took place in the lower level, as well as integrating and transforming this neural representation into new information.
The nFMC is in accordance with predictive coding models that present brain activity as a system in which lower brain structures project predictions/signals via bottom–up processing, and where higher cortical areas send prediction errors back via top-down processes.
Neural processes (both automatic and controlled) related to consciousness (such as P300, brain rhythms, and neurotransmitter discharges) can be localized within either of the neural loops or in their reciprocal interactions.
The nFMC is complementary and comprises several assumptions considered in previous theories and investigations of consciousness:
Consciousness can be deemed a Global Neural Workspace in which distinct neural networks compete to access consciousness [11, 25, 192].
Consciousness is the result of functional units or complexes that integrate information and that are activated or deactivated depending on the ongoing sensorial/visceral necessities [8, 26, 27].
Consciousness is a neurophysiological continuum commanded by inner spatio-temporal brain laws [6].
Regarding the neural mechanisms or processes involved in the formation of the content of consciousness, the nFMC aligns with models and evidence that posit that the contents of consciousness are formed in the back brain via cortico-thalamo-cortical connections [70, 132, 133, 134, 135, 136]. In addition, the nFMC recognizes that PFC top-down connections could modulate the selection and even the formation of the content of consciousness [28].
Consciousness is the phenomenological quality of human existence that arises from a hierarchical, parallel, and serial activation of long-distance brain networks [7], which operate as neural loops that “inform” upper and lower levels about their own operations [29, 30]. These loops receive input from lower levels (which contains new information/predictions) and input from upper levels (error predictions). The loop will integrate all of this new information, updating its own functional state and, consequently, also the functional state of the rest of the loops and the brain [29, 30, 31, 32, 191]. ARAS: Ascending reticular activating system; TNN: Task-negative networks; TPN: Task-positive networks.
Human behavior has to be understood as a global brain activity dominated by complex and hierarchical neural processes that cannot be divided and explained by isolated functional units. Consciousness is the “operating system” running underneath the “interface” of overt and covert human behavior, and it is dominated by the interactions of various neural levels composed of differentiated and semi-independent neural networks. Thence, the nFMC gathers reliable knowledge generated in the study on neural correlates of consciousness, providing a novel theoretical and referential framework that will help clinicians, researchers, and even students to localize the neural processes of interest within a global brain activity model. A further proposal should extend the structures and connectivity involved within and between each neural loop introduced in the nFMC.
\nThe authors have no conflict of interest to declare.
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