Summary of reported materials for memristors.
Abstract
Modern electronic devices are being developed for cutting-edge applications, as a result of recent developments in artificial intelligence (AI) and machine learning (ML). The demand for “universal memory” devices with exceptional qualities, such as high data transmission speed, storage capacity, non-volatility, and low operation voltage has increased as a result of the industry’s ability to sustain such a high growth rate. In this chapter, we elaborate on the history of the evaluation of novel memristor structures, various switching mechanisms, and materials for developing memristor devices. The current state of the art of the memristor for various applications, such as data storage, artificial synapse, light-induced resistive switching, logic gates, and mimicking human behavior is also systematically summarized.
Keywords
- hybrid perovskite
- data storage
- neuromorphic computation
- resistive switching
- memristor devices
1. Introduction
Since the discovery of the first programmable computer, the dependency associated with paper and canvas-based mediums to store and process information has significantly declined. According to Moore’s law and Dennard’s law, the technology based on the traditional complementary metal-oxide semiconductor (CMOS) has great strides in the last few decades and led to a sharp rise in digital capabilities [1, 2, 3]. In traditional computing technology known as Von Neumann architecture, the system comprises two separate units, namely central processing unit (CPU) and data storage unit, connected through the bridge known as data busses. This traditional architecture causes the delay in processing and consumption of more power in the process, introduced as bottleneck of Von Neumann architecture. The memristor has emerged as a novel device to improve or develop new technology based on the fusion of memory and processor. In the last few years, memristor has captured the significant attention of researchers due to its excellent properties, such as simple structure, high-density data storage, low power consumption, fast switching speed, long endurance and retention, multistage and high scalability. Due to these properties, memristors can be used for artificial intelligence (AI), the Internet of Things (IoT), wearable electronics, smart medical applications, logic circuits, neuromorphic computing, etc. [4, 5].
In this chapter, we discuss the history and various switching mechanisms in memristors. Different including organics, inorganics, and hybrid materials have been discussed to use as active layers for memristive applications. Metal halide organic–inorganic perovskites are well taken as an example of hybrid materials due to outstanding electrical, optical, and structural properties. Various applications of memristors, such as data storage, logic gates, and photonic devices, including many bionic electronic systems as artificial synapses, neural networking, nociceptors, artificial retina, etc., have been summarized.
2. History of memristor
Before the discovery of the memristor, resistor (1827), capacitor (1745), and inductor (1831) were considered only three fundamental passive circuit elements. In the year 1971, Leon Chua theoretically proposed the fourth fundamental element named memristor (memory resistor), which shows the relation between charge and flux. Few decades later, in 2008, a strong connection between Chua’s theory and the experimental model was observed in Hewlett Packard lab and the first physical memristor model based on TiO2 material was realized [6]. This prototype memristor showed the data storage capabilities, processing logical operation with long retention time and low operating voltage, as a result of the change in their resistance states [6, 7]. Due to its potential scalability and low power consumption for memory applications, memristor continues to stimulate a steady expansion in the research industry on a global scale [8].
Memristor is a non-volatile two-terminal electrical component with a sandwich structure called metal–insulator–metal (MIM), as shown in Figure 1(a). Memristor is basically dependent on charge and magnetic flux also called memristance, which varies as a function of the electric charge (q) and magnetic flux (φ) (Figure 1(b)). This property cannot be obtained by any relation of the other fundamental elements, such as resistor, capacitor, and inductor [9, 10]. A most curious feature of the memristor is its memory function, which originates from its resistance states [10]. Memristors are devices that switch between low resistance state (LRS) and high resistance states (HRS), according to applying voltage bias, by applying positive bias memristor resistance changed from HRS to LRS at a particular voltage (set voltage or VSET) and opposite applying negative bias resistance state changed from LRS to HRS (reset voltage or VRESET). This feature may be used to store data in a resistant state with high adoption properties [11].
3. Classification of Memristors
Based on the switching mechanism, memristors have been classified into three different types: resistive switching, ferroelectric, and phase change (as shown in Figure 2).
3.1 Resistive switching (RS)
Resistive switching is the one of most prevalent types of memristor devices that was first observed in TiO2-based devices. The RS mechanism is one of the most complicated and also conflicts between number of parameters, such as electrodes/active layer interfaces, grain size, active area of the device, types of defects, and many more [12]. The basic characteristics of resistive switching are based on the movement of ions, such as oxygen vacancies [13, 14, 15], active metal cations [3, 16, 17], and anions, like halides [18, 19] and sulfurs [20]. The two most common type of resistive switching mechanism is the electrochemical mechanism (ECM) and valence charge mechanism (VCM) as discussed below:
3.1.1 Electrochemical mechanism (ECM)
Here, an electrochemical redox reaction, at the active electrode under an external voltage applied, produces the RS characteristics. Initially, a positive voltage was applied to the active electrode (top electrode Ag), which caused the metal atoms (Ag) to oxidize and transform into the corresponding ions (Ag+), which can subsequently diffuse through the active layer to the bottom inert electrode and reduced to Ag atoms at the interface. There are two resistance states in memristor: low resistance state (LRS) and high resistance state (HRS), which refers to the formation and rapture of the conduction filaments in the active layer between the top (Ag) and bottom electrode, respectively. This reaction represents a common electrochemical oxidation–reduction process. Some other materials used as active electrodes are Cu, Au, Pt, and Al [21, 22].
3.1.2 Valence charge mechanism (VCM)
In terms of integration and scaling, filamentary VCMs are the most sophisticated mechanism. The two electrodes are shorted by the formation and bursting of conductive filaments (CF), which are caused by a concentrated localized area of defects. There may be two or more stable resistance states, depending on how the CF diameter and/or dissolution are modulated or controlled. The conductance of interfacial VCM devices is assumed to scale with the device junction area through a homogenous oxygen ion flow across the oxides, either at the electrode/oxide or oxide/oxide interface. Bilayer stacks of difficult oxides, such as TiO2/TaO2 [23] and a-Si/TiO2 or complex oxides as bismuth ferrite [24] and praseodymium calcium manganite, form the foundation of reference material systems [25].
3.2 Ferroelectric
Despite the fact that ferroelectric memory has been around for a while, it has not captured the attention due to various scale-up issues. In 2006, the first device as ferroelectric tunnel junctions (FTJs) [26] was realized and led as novel concept for data storage and neuromorphic computing [27, 28]. An FTJ comprises two metal electrodes separated by a thin ferroelectric insulator. Quantum electron tunneling, in which electrons pass through a potential barrier of the ultrathin insulator, is the dominating process in this ferroic nanostructure, which is made up of the ultrathin ferroelectric barrier. The alignment of ferroelectric polarization in the insulator can change the stream of electrons, producing the enormous tunnel electro-resistance (TER) effect. Depending on the polarity of the ferroelectric layer, the tunneling electrons are either attracted or repulsive. An energy band profile becomes asymmetric when two distinct metals are placed across the ultrathin ferroelectric layer. When voltages are applied across the device, the electric potential at the interface increases or decreases depending on the polarization direction. As a result, the modified energy potential control the electrons transport through these contacts [29]. This screening phenomena causes the FTJ to exhibit significant resistance changes, allowing for the storage and processing of data [30].
3.3 Phase change
A device can be referred to as a hysteretic memristor when the joule heating led the phase changes between two states: amorphous and crystalline. Among all new memristor technologies, the phase-change-based memristor is the most developed and commercialized in the storage class memory (SCM) sector. One could say that phase-change memory technology has made a significant contribution to the growth of new electronic technologies. The first time, phase-change technique originally described was nearly 50 years ago [31]. However, recently, the phase-change technology has only gained popularity as a result of research on chalcogenide materials such as Ge2Sb2Te5 [32] or Ag- and In-doped Sb2Te [33]. Figure 2 illustrates a phase-change memory instance [34]. The narrow metal heater’s current saturation promotes the joule heating process when an electrical field is applied between the top electrode and bottom metal heater. An internal temperature change gradually heats the phase-change material [35]. The resistance contrast results from the phasechange between an HRS that is amorphous and an LRS that is crystalline. The distribution of internal voids determines the variation in structural disorder between two states. The band structure is redesigned as a result of ordered vacancies, and the conductivity rises as a result of the localization of charge carriers in a crystalline state [36].
4. Active materials for Memristor
A plethora of materials used as active layers for memristors can be categorized as biomaterials, organic, inorganic, and hybrid materials as discussed below:
4.1 Inorganic materials
Inorganic materials consist of metal oxides, halides, chalcogenides, 2D materials, etc. The main advantage of inorganic materials is their strong environmental stability in varied conditions. The first ever memristor was prepared by Hewlett-Packard (HP), composed of two inorganic layers: stoichiometric titanium oxide and oxygen-deficient non-stoichiometric TiO2-x layers [6]. The conductivity was achieved by the migration of the oxygen vacancies when the external electric field is applied. Later, many other inorganic oxides, such as HfO2, CuO [37], NiO [38], and TiO2 [39]. attracted the attention of researchers. Many 2D materials were also used as the active layer, such as nitrides, transition-metal dichalcogenides (MoS2 and WS2) [40], InSe [41], black phosphorus (BP) [42], MXenes [43], bismuthene [44], and tellurene [45]. Few nanorod structures explored consist of ZnO [46], TiO2 [47], HfO2 [48], etc. Table 1 summarizes some of the advancements in the materials used till date (Figure 3).
Material type | Material Name | ON/OFF ratio | Vset/Vreset (Voltage) | Endurance (Cycle) | Retention (Seconds) | Reference |
---|---|---|---|---|---|---|
Inorganic | Cuo | 10 | -1 V/3.2 V | 100 | 2 x 104S | [37] |
Nitride | 100 | -1.5 V.2 V | — | — | [49] | |
Black phosphorus (BP) | 2 x 107 | -2 V/1 V | 100 | 104 | [42] | |
Organic | P3HT | 10 | — | 1000 | [50] | |
PEDOT:PSS | 10 | 4 V/-3 V | 1000 | [51] | ||
PCBM | 400 | 0.5V/-1V | 47 | — | [52] | |
PFT-PI | 104 | -2 V/2 V | 180 | 104 | [53] | |
Cu-TCNQ | 103 | 3.5 V/0.4 V | 103 | — | [54] | |
PVK | 104 | -1 V/3.3 V | — | 104 | [55] | |
Hybrid | MAPbI3 | 102 | 0.7 V/−0.61 V | 600 | 104 | [56] |
CsPbI3 | 105 | −0.95 V/−0.71 V | 100 | 104 | [57] | |
(PEA)2MA4Pb5I16 (quasi-2D) | 104 | 0.15 V/-1 V | 500 | 300 | [58] | |
(IFA)3PbI5 (1D) | 103 | 0.2 V/−2.1 V | 200 | 104 | [59] | |
Cs3Sb2I9 (0D) | 102 | 1 V/-1 V | 500 | 5000 | [60] |
4.2 Organics materials
Organic active layer materials are generally small organic molecules, polymers (synthetic and natural), etc. These materials have gained a great deal of interest from researchers for resistive switching due to their attractive properties compared to traditional metal oxides [61]. The properties include easy solution processing and good chemical, mechanical and morphological properties; also, it possesses high intrinsic flexibility. Although some of the organic molecules lack in stability portion; they overcome it by having a low operating voltage as well as a good memory window. These materials can serve as a good candidate for low-power operating memory systems. Some materials, such as poly(3-hexylthiophene) (P3HT) [50], poly(,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) [51], phenyl-C61-butyric acid methyl ester (PCBM) [62], phenanthrol [9,10-d] imidazole (PFT− PI) [53, 63], cobalt(III)-containing conjugated and nonconjugated polymers [63], polyaniline (PANI) [64], copper-tetracyanoquinodimethane (Cu-TCNQ) [54], etc., have shown good performance not only as the active layer but also as electron transfer layer in transistors, light-emitting diodes, and photovoltaic devices. These materials usually have shown the dual filament formation as an RS mechanism including the phase change, redox reactions, conformation change, and charge transfer mechanism. Due to the advantages of small organic molecules, such as easy modification in electronic properties and easy processing, these molecules can be used as an active medium in synaptic as well as memory devices.
4.3 Hybrid materials
Hybrid materials are combinations of organic and inorganic components, for instance, graphene oxides and composites, polymer-oxide composites, hybrid perovskites, etc. The organic and inorganic components, when merged, can create some desirable property material with enhanced quality and diminished defects. These materials are quite popular for non-volatile memory applications having low power consumption and high speed. The first hybrid perovskite (MA)PbX3 (MA = methylammonium and X = Cl, Br, I) was first reported by Dieter Weber in 1978 [65]. Perovskites have a general chemical formula ABX3, where A occupies eight corners of the cubic unit cell, it is a large monovalent cation, such as methylammonium (MA+) and formamidium (FA+). B is represented by a divalent metal cation, occupying body central position in the cubic unit, for example, Pb2+, Sn2+, Eu2+, Cu2+, etc. X is the halide anion, such as I−, Br−, and Cl−, six of which surround the B cation in the octahedral geometry. Making [BX6]4− octahedron [66]. The structure of perovskite is depicted in Figure 4.
Hybrid organic–inorganic perovskites (HOIPs) are considered a recent family in the perovskites class. Due to the number of possible combinations of the A, B, and X, there are many perovskite family members reported, also they can form 3D, 2D, 1D, and 0D structures as well as having the same unit cell. The structural and chemical diversity offered by HOIPs give rise to tuning to achieve desirable properties and opening doors to many potential applications [67]. HOIPs have certain exclusive properties, such as tunable band gap, wide range of light absorption, ambipolar charge transport, long electron–hole diffusion length, and optical absorption, making them apt for devices such as solar cells, light emitting diodes (LEDs), transistors, and memristors. In 2016, the first time reported the resistive switching phenomenon and synaptic properties in three-dimensional (3D) HOIP (MAPbX3, X = Br and I) devices, due to the presence of ion migration. HOIPs containing organic-based cations are hydrophilic in nature, thermally unstable, and immediately degrade in ambient air. Furthermore, inorganic materials are mixed with organic materials to reduce operating voltage. By decreasing dimensionality from three dimension (3D) to two dimension (2D), increasing quantity of organic insulating cations, which increase the activation energy of halide ions in perovskite layer. As a result, 2D has a high amount of insulating cations with the ability of lowest magnitude of energy consumption. Mainly, memristors operated due to the ion migration has reported, but several studies also claim that metallic filament growth plays an important role in resistive switching properties. For synthesizing stable materials, low-dimensional perovskite phases, namely defined as Ruddlesden-Propper (RP) and Dion-Jacobson (DJ) perovskites, which distinguished by the change in their interlayer spacer cation alignment (Figure 4b and c) [4, 68, 69].
5. Applications
Perovskite materials have the potential to change properties with light; because of this property, this material is used in the application of optoelectronic as well as in data storage devices. By combining both to prepare optoelectronic based logic gate devices, and this electrical signal is used as write, and the optical signal is used as erase in resistive switching devices and by changing the light intensity increase/decrease set and reset voltage [70, 71]. Wang and colleagues programmed the Au/MAPbI3-xClx structure devices to set/reset by photo/electrical bias [72]. For multilayer storage RRAMs, the set voltage falls as light intensity rises. The gadget can carry out logical operations and coincidental event detection tasks by using optical and electrical pulses. In their experiment, Chai and colleagues discovered that light might lower the device’s set voltage, and this finding might be used to develop logic circuits [73].
Memristors can potentially be useful in ultrahigh storage density computing technologies. An immediate application for these devices is the resistive random-access memory (ReRAM). To meet the growing demands of next-generation data storage devices, ReRAMs must exhibit characteristics such as small write voltage (few hundred millivolts), short write time (<10 ns), small read voltage so that there is no change of internal resistance, high OFF-to-ON resistance ratio (>10), high endurance (∼103), high retention (∼10 years), and small device size (<10 nm), in addition to low-cost fabrication and flexibility [74].
Memristor can be used as a programmable logic gate with the building of crossbar architecture. When comparing CMOS-based devices with memristor devices are far more variable. The major problem in many logic gate architectures using memristor is the endurance and device-to-device variation. Also, memristor-based gates become less reliable but the capacity to accept changes in weight values has a high level of device variability tolerance.
Memristors are further used in artificial neural networks. Here, two of them are discussed, CNN (convolution neural network) and SNN (spiking neural network).
Memristor can also be useful as artificial vision sensor, touch sensor, and pain sensor in the arena of bionic electronics. Vision is the crucial sense system through which most of the information are collected by humans. To replicate artificial vision sensor, memristor is used as visual neuromorphic device. This involves two types of impulse signals, optical and electrical, as presynaptic and postsynaptic impulses as input and output signals, respectively. This kind of device is also called photonic memristor and that used as photodetector with photo stimuli as retain of human eyes that collect process visual picture and transport [80].
Another application is pressure or touch sensor, where a small electrical signal obtains as a result of pressure, which is promptly transferred to a sensory receptor as an input response for further processing. This system is only initiated when the pressure touch is converted into electrical signal and therefore postsynaptic current. As shown in Figure 5(a), changing pressure on device by finger folding fire signal in form of electrical current. It can measure the quantity, frequency and speed, and time duration of that touch pressure [81].
In reaction to harmful stimuli, the body feels the unpleasant emotion of pain. When external stimuli activate pain receptors in the stomach or body, the central nervous system receives, interprets, and sends the pain information. The ability of AI systems to perceive pain and become sensitized to it is essential for significantly increasing the efficacy of hardware devices since it enables them to have different sensitivities to external stimuli for various purposes [82].
6. Conclusions
Memristor-based computing platforms have been proposed to give great processing efficiency for data-centric applications, in order to address the drawbacks of conventional computing systems based on the von Neumann architecture. Despite recent major advancements in this area, there are still a lot of obstacles to overcome. To address the stochasticity and CMOS compatibility difficulties, which have an impact on the underlying electrical performance, material system engineering is essential. The integration and reliability problems might have a potential answer to tackle. To solve the concerns with variability and device non-ideality, more material and device development is still necessary. However, novel materials and architecture, or circuits have not yet reached unraveling full potential of memristor for advanced technologies.
Acknowledgments
A.S. and M.S. gratefully acknowledge the financial support from Science and Engineering Research Board (SERB) core research grant CRG/2020/000869. A.S. would also like to gratefully acknowledge grant GUJCOST/STI/2021-2022/3873 from the Government of Gujarat, India, and ORSP/R&D/PDPU/2019/AS00/ROO47 from Pandit Deendayal Energy University (PDEU) to perform this study. J.D. and N.C. would like to acknowledge PDEU Start-up grant ORSP/R&D/PDPU/2021/NC00/R0069.
References
- 1.
Moore GE. Cramming more components onto integrated circuits, Reprinted from Electronics, volume 38, number 8, April 19, 1965, pp.114 ff. IEEE Solid-State Circuits Society Newsletter. 2006; 11 :33 - 2.
Kawahara A, Azuma R, Ikeda Y, Kawai K, Katoh Y, Hayakawa Y, et al. An 8 Mb multi-layered cross-point ReRAM macro with 443 MB/s write throughput. IEEE Journal of Solid-State Circuits. 2013; 48 :178 - 3.
Choi J, Le QV, Hong K, Moon CW, Han JS, Kwon KC, et al. Enhanced endurance organolead halide perovskite resistive switching memories operable under an extremely low bending radius. ACS Applied Material Interfaces. 2017; 9 :30764 - 4.
Gogoi HJ, Bajpai K, Mallajosyula AT, Solanki A. Advances in flexible memristors with hybrid perovskites. Journal of Physical Chemistry Letters. 2021; 12 :8798 - 5.
Perrissin N, Gregoire G, Lequeux S, Tillie L, Strelkov N, Auffret S, et al. Perpendicular shape anisotropy spin transfer torque magnetic random-access memory: towards sub-10 nm devices. Journal of Physics D. 2019; 52 :234001 - 6.
Strukov DB, Snider GS, Stewart DR, Williams RS. The missing memristor found. Nature. 2008; 453 :80 - 7.
Yang JJ, Strukov DB, Stewart DR. Memristive devices for computing. Nature Nanotechnology. 2013; 8 :13 - 8.
Waser R, Aono M. Nanoionics-based resistive switching memories. Nature Materials. 2007; 6 :833 - 9.
Chua L. Resistance switching memories are memristors. Applied Physics A: Materials Science & Processing. 2011; 102 :765 - 10.
Chua LO, Sung Mo K. Memristive devices and systems. Proceedings of the IEEE. 1976; 64 :209 - 11.
Kumbhar D, Tripathi B, Patel M, Dongale T, Solanki A. Mathematical investigation of the hysteresis mechanism in hybrid perovskite-based heterojunction devices for the resistive switching applications. In: Presented at 2022 2nd Asian Conference on Innovation in Technology (ASIANCON). 26-28 Aug 2022 - 12.
Liao K, Lei P, Tu M, Luo S, Jiang T, Jie W, et al. Memristor based on inorganic and organic two-dimensional materials: mechanisms, performance, and synaptic applications. ACS Applied Material Interfaces. 2021; 13 :32606 - 13.
Yoon JH, Song SJ, Yoo I-H, Seok JY, Yoon KJ, Kwon DE, et al. Highly uniform, electroforming-free, and self-rectifying resistive memory in the Pt/Ta2O5/HfO2-x/TiN structure. Advanced Functional Materials. 2014; 24 :5086 - 14.
Yoon JH, Kim KM, Song SJ, Seok JY, Yoon KJ, Kwon DE, et al. Pt/ta2o5/hfo2−x/ti resistive switching memory competing with multilevel NAND flash. Advanced Materials. 2015; 27 :3811 - 15.
Jiang H, Han L, Lin P, Wang Z, Jang MH, Wu Q, et al. Sub-10 nm ta channel responsible for superior performance of a HfO2 memristor. Scientific Reports. 2016; 6 :28525 - 16.
Han JS, Le QV, Choi J, Hong K, Moon CW, Kim TL, et al. Air-stable cesium lead iodide perovskite for ultra-low operating voltage resistive switching. Advanced Functional Materials. 2018; 28 :1705783 - 17.
Seo J-Y, Choi J, Kim H-S, Kim J, Yang J-M, Cuhadar C, et al. Wafer-scale reliable switching memory based on 2-dimensional layered organic–inorganic halide perovskite. Nanoscale. 2017; 9 :15278 - 18.
Han JS, Le QV, Choi J, Kim H, Kim SG, Hong K, et al. Lead-free all-inorganic cesium tin iodide perovskite for filamentary and interface-type resistive switching toward environment-friendly and temperature-tolerant nonvolatile memories. ACS Applied Material Interfaces. 2019; 11 :8155 - 19.
Kumbhar D, Jain M, Solanki A. Forming free non-volatile resistive switching mechanism in ruddlesden popper perovskite memristors. In: Presented at 2022 International Conference for Advancement in Technology (ICONAT). 21-22 Jan 2022 - 20.
Zhang P, Gao C, Xu B, Qi L, Jiang C, Gao M, et al. Structural phase transition effect on resistive switching behavior of mos2-polyvinylpyrrolidone nanocomposites films for flexible memory devices. Small. 2016; 12 :2077 - 21.
Saylan S, Aldosari HM, Humood K, Abi Jaoude M, Ravaux F, Mohammad B. Effects of top electrode material in hafnium-oxide-based memristive systems on highly-doped Si. Scientific Reports. 2020; 10 :19541 - 22.
Praveen P, Rose TP, Saji KJ. Top electrode dependent resistive switching in M/ZnO/ITO memristors, M = Al, ITO, Cu, and Au. Microelectronics Journal. 2022; 121 :105388 - 23.
Gao L, Wang IT, Chen PY, Vrudhula S, Seo JS, Cao Y, et al. Fully parallel write/read in resistive synaptic array for accelerating on-chip learning. Nanotechnology. 2015; 26 :455204 - 24.
You T, Ou X, Niu G, Bärwolf F, Li G, Du N, et al. Engineering interface-type resistive switching in BiFeO3 thin film switches by Ti implantation of bottom electrodes. Scientific Reports. 2015; 5 :18623 - 25.
Alessandro F, Severin S, Junwoo J, Pritish N, Robert MS, Geoffrey WB, et al. Bidirectional non-filamentary RRAM as an analog neuromorphic synapse, part I: Al/Mo/Pr0.7Ca0.3MnO3 material improvements and device measurements. IEEE Journal of the Electron Devices Society. 2018; 6 :146 - 26.
Tsymbal EY, Kohlstedt H. Tunneling across a ferroelectric. Applied Physics. 2006; 313 :181 - 27.
Garcia V, Bibes M. Ferroelectric tunnel junctions for information storage and processing. Nature Communications. 2014; 5 :4289 - 28.
Jeong DS, Hwang CS. Nonvolatile memory materials for neuromorphic intelligent machines. Advanced Materials. 2018; 30 :1704729 - 29.
Wen Z, Wu D. Ferroelectric tunnel junctions: Modulations on the potential barrier. Advanced Materials. 2020; 32 :1904123 - 30.
Zhang Q, Solanki A, Parida K, Giovanni D, Li M, Jansen TLC, et al. Tunable ferroelectricity in ruddlesden–popper halide perovskites. ACS Applied Material Interfaces. 2019; 11 :13523 - 31.
Ovshinsky SR. Reversible electrical switching phenomena in disordered structures. Physical Review Letters. 1968; 21 :1450 - 32.
Yamada N, Ohno E, Nishiuchi K, Akahira N, Takao M. Rapid-phase transitions of GeTe-Sb2Te3 pseudobinary amorphous thin films for an optical disk memory. Journal of Applied Physics. 1991; 69 :2849 - 33.
Tominaga J, Kikukawa T, Takahashi M, Phillips RT. Structure of the optical phase change memory alloy, Ag–V–In–Sb–Te, determined by optical spectroscopy and electron diffraction. Journal of Applied Physics. 1997; 82 :3214 - 34.
Cassinerio M, Ciocchini N, Ielmini D. Logic computation in phase change materials by threshold and memory switching. Advanced Materials. 2013; 25 :5975 - 35.
Burr GW, Breitwisch MJ, Franceschini M, Garetto D, Gopalakrishnan K, Jackson B, et al. Phase change memory technology. Journal of Vacuum Science & Technology B. 2010; 28 :223 - 36.
Siegrist T, Jost P, Volker H, Woda M, Merkelbach P, Schlockermann C, et al. Disorder-induced localization in crystalline phase-change materials. Nature Materials. 2011; 10 :202 - 37.
Shariffar A, Salman H, Siddique TA, Gebril W, Manasreh MO. Resistive switching in FTO/CuO–Cu2O/Au memory devices. Micro & Nano Letters. 2020; 15 :853 - 38.
Cui H-P, Li J-C, Yuan H-L. Bending effect on the resistive switching behavior of a NiO/TiO2 p–n heterojunction. RSC Advances. 2018; 8 :19861 - 39.
Gergel-Hackett N, Hamadani B, Dunlap B, Suehle J, Richter C, Hacker C, et al. A flexible solution-processed memristor. IEEE Electron Device Letters. 2009; 30 :706 - 40.
Wang M, Cai S, Pan C, Wang C, Lian X, Zhuo Y, et al. Robust memristors based on layered two-dimensional materials. Nature Electronics. 2018; 1 :130 - 41.
Li Z, Qiao H, Guo Z, Ren X, Huang Z, Qi X, et al. High-Performance Photo-Electrochemical Photodetector Based on Liquid-Exfoliated Few-Layered InSe Nanosheets with Enhanced Stability. Advanced Functional Materials. 2018; 28 :1705237 - 42.
Wang Y, Wu F, Liu X, Lin J, Chen J-Y, Wu W-W, et al. High on/off ratio black phosphorus based memristor with ultra-thin phosphorus oxide layer. Applied Physics Letters. 2019; 115 :193503 - 43.
Patel M, Hemanth NR, Gosai J, Mohili R, Solanki A, Roy M, et al. MXenes: promising 2D memristor materials for neuromorphic computing components. Trends in Chemistry. 2022; 4 :835 - 44.
Xing C, Huang W, Xie Z, Zhao J, Ma D, Fan T, et al. Ultrasmall bismuth quantum dots: Facile liquid-phase exfoliation, characterization, and application in high-performance UV–Vis photodetector. ACS Photonics. 2018; 5 :621 - 45.
Shi Z, Cao R, Khan K, Tareen AK, Liu X, Liang W, et al. Two-dimensional tellurium: Progress, challenges, and prospects. Nano-Micro Letters. 2020; 12 :99 - 46.
Huang C-H, Huang J-S, Lin S-M, Chang W-Y, He J-H, Chueh Y-L. ZnO1–x nanorod arrays/ZnO thin film bilayer structure: From homojunction diode and high-performance memristor to complementary 1D1R application. ACS Nano. 2012; 6 :8407 - 47.
Yu Y, Wang C, Jiang C, Abrahams I, Du Z, Zhang Q, et al. Resistive switching behavior in memristors with TiO2 nanorod arrays of different dimensions. Applied Surface Science. 2019; 485 :222 - 48.
Kwon JU, Song YG, Kim JE, Chun SY, Kim GH, Noh G, et al. Surface-dominated HfO2 nanorod-based memristor exhibiting highly linear and symmetrical conductance modulation for high-precision neuromorphic computing. ACS Applied Material Interfaces. 2022; 14 :44550 - 49.
Choi BJ, Yang JJ, Zhang MX, Norris KJ, Ohlberg DAA, Kobayashi NP, et al. Nitride memristors. Applied Physics A: Materials Science & Processing. 2012; 109 :1 - 50.
Khalid Rahmani M, Ali Khan S, Farooq Khan M, Hee Kang M. Fully solution-processed organic RRAM device with highly stable butterfly-shaped hysteresis. Materials Science and Engineering: B. 2022; 282 :115784 - 51.
Nguyen VC, Lee PS. Coexistence of write once read many memory and memristor in blend of poly(3,4-ethylenedioxythiophene): Polystyrene sulfonate and polyvinyl alcohol. Scientific Reports. 2016; 6 :38816 - 52.
Park JH, Khan SA, Rahmani MK, Cho J, Kang MH. Enhancement of resistive switching behavior of organic resistive random access memory devices through UV-Ozone treatment. Materials Research Express. 2022; 9 :085903 - 53.
Wu H-C, Yu A-D, Lee W-Y, Liu C-L, Chen W-C. A poly(fluorene-thiophene) donor with a tethered phenanthro[9,10-d]imidazole acceptor for flexible nonvolatile flash resistive memory devices. Chemical Communications. 2012; 48 :9135 - 54.
Basori R, Kumar M, Raychaudhuri AK. Sustained resistive switching in a single Cu:7,7,8,8-tetracyanoquinodimethane nanowire: A promising material for resistive random access memory. Scientific Reports. 2016; 6 :26764 - 55.
Li L, Sun Y-M, Wen D-Z. The storage cell circuit with memristor characteristics of poly(N-vinylcarbazole) films. Chinese Physics Letters. 2015; 32 :078501 - 56.
Heo JH, Shin DH, Moon SH, Lee MH, Kim DH, Oh SH, et al. Memory effect behavior with respect to the crystal grain size in the organic-inorganic hybrid perovskite nonvolatile resistive random access memory. Scientific Reports. 2017; 7 :16586 - 57.
Wu Y, Wei Y, Huang Y, Cao F, Yu D, Li X, et al. Capping CsPbBr3 with ZnO to improve performance and stability of perovskite memristors. Nano Research. 2017; 10 :1584 - 58.
Solanki A, Guerrero A, Zhang Q, Bisquert J, Sum TC. Interfacial mechanism for efficient resistive switching in ruddlesden–popper perovskites for non-volatile memories. Journal of Physical Chemistry Letters. 2020; 11 :463 - 59.
Song X, Yin H, Chang Q, Qian Y, Lyu C, Min H, et al. One-dimensional (perovskite for ultralow power consumption resistive memory). Research. 2021; 2021 :9760729 - 60.
Park Y, Kim SH, Lee D, Lee J-S. Designing zero-dimensional dimer-type all-inorganic perovskites for ultra-fast switching memory. Nature Communications. 2021; 12 :3527 - 61.
Mu B, Hsu H-H, Kuo C-C, Han S-T, Zhou Y. Organic small molecule-based RRAM for data storage and neuromorphic computing. Journal of Materials Chemistry C. 2020; 8 :12714 - 62.
Solanki A, Wu B, Salim T, Lam YM, Sum TC. Correlation between blend morphology and recombination dynamics in additive-added P3HT:PCBM solar cells. Physical Chemistry Chemical Physics. 2015; 17 :26111 - 63.
Hung C-C, Chiu Y-C, Wu H-C, Lu C, Bouilhac C, Otsuka I, et al. Conception of stretchable resistive memory devices based on nanostructure-controlled carbohydrate-block-polyisoprene block copolymers. Advanced Functional Materials. 2017; 27 :1606161 - 64.
Chiolerio A, Bocchini S, Crepaldi M, Bejtka K, Pirri CF. Bridging electrochemical and electron devices: fast resistive switching based on polyaniline from one pot synthesis using FeCl3 as oxidant and co-doping agent. Synthetic Metals. 2017; 229 :72 - 65.
Weber D. CH3NH3PbX3, ein Pb(II)-system mit kubischer perowskitstruktur / CH3NH3PbX3, a Pb(II)-system with cubic perovskite structure. Zeitschrift für Naturforschung B. 1978; 33 :1443 - 66.
Sum TC, Mathews N. Advancements in perovskite solar cells: Photophysics behind the photovoltaics. Energy & Environmental Science. 2014; 7 :2518 - 67.
Berry J, Buonassisi T, Egger DA, Hodes G, Kronik L, Loo Y-L, et al. Hybrid organic–inorganic perovskites (HOIPs): Opportunities and challenges. Advanced Materials. 2015; 27 :5102 - 68.
Stoumpos CC, Cao DH, Clark DJ, Young J, Rondinelli JM, Jang JI, et al. Ruddlesden–popper hybrid lead iodide perovskite 2D homologous semiconductors. Chemistry of Materials. 2016; 28 :2852 - 69.
Mao L, Ke W, Pedesseau L, Wu Y, Katan C, Even J, et al. Hybrid Dion–Jacobson 2D lead iodide perovskites. Journal of the American Chemical Society. 2018; 140 :3775 - 70.
Zhu X, Lee J, Lu WD. Iodine vacancy redistribution in organic–inorganic halide perovskite films and resistive switching effects. Advanced Materials. 2017; 29 :1700527 - 71.
Zhu X, Lu WD. Optogenetics-Inspired tunable synaptic functions in memristors. ACS Nano. 2018; 12 :1242 - 72.
Ham S, Choi S, Cho H, Na S-I, Wang G. Photonic organolead halide perovskite artificial synapse capable of accelerated learning at low power inspired by dopamine-facilitated synaptic activity. Advanced Functional Materials. 2019; 29 :1806646 - 73.
Zhou F, Liu Y, Shen X, Wang M, Yuan F, Chai Y. Low-voltage, optoelectronic CH3NH3PbI3−xClx memory with integrated sensing and logic operations. Advanced Functional Materials. 2018; 28 :1800080 - 74.
Kim D, Kim I-J, Lee J-S. Memory devices for flexible and neuromorphic device applications. Advanced Intelligent Systems. 2021; 3 :2000206 - 75.
Zidan M, Jeong Y, Lee J, Chen B, Huang S, Kushner M, et al. A general memristor-based partial differential equation solver. Nature Electronics. 2018; 1 :411 - 76.
Prezioso M, Merrikh-Bayat F, Hoskins BD, Adam GC, Likharev KK, Strukov DB. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature. 2015; 521 :61 - 77.
Alibart F, Zamanidoost E, Strukov DB. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nature Communications. 2013; 4 :2072 - 78.
Yao P, Wu H, Gao B, Tang J, Zhang Q, Zhang W, et al. Fully hardware-implemented memristor convolutional neural network. Nature. 2020; 577 :641 - 79.
Wu Y, Wang X, Lu WD. Dynamic resistive switching devices for neuromorphic computing. Semiconductor Science and Technology. 2022; 37 :024003 - 80.
Haupt C, Huber AB. How axons see their way--axonal guidance in the visual system. Frontiers in Bioscience. 2008; 13 :3136 - 81.
Saal HP, Bensmaia SJ. Biomimetic approaches to bionic touch through a peripheral nerve interface. Neuropsychologia. 2015; 79 :344 - 82.
Baron R, Maier C, Attal N, Binder A, Bouhassira D, Cruccu G, et al. Peripheral neuropathic pain: a mechanism-related organizing principle based on sensory profiles. Pain. 2017:158