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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.
Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, India
Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar, India
Jeny Gosai
Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar, India
Advanced Hybrid Nanomaterial Laboratory, Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India
Nitin Chaudhari
Advanced Hybrid Nanomaterial Laboratory, Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India
Ankur Solanki*
Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, India
Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar, India
*Address all correspondence to: ankur.solanki@sot.pdpu.ac.in
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.
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].
Figure 1.
Illustrations of the (a) MIM structure of memristor (b) I–V characteristics of the fundamental circuit elements.
Based on the switching mechanism, memristors have been classified into three different types: resistive switching, ferroelectric, and phase change (as shown in Figure 2).
Figure 2.
Illustration of types of switching mechanisms in various memristors.
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].
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).
Illustrations of types of active materials used in memristors.
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.
Figure 4.
Illustration of basic structure of (a) perovskite, (b) Ruddlesden-Propper phase, and (c) Dion-Jacobson phase.
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].
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). CNN (convolutional neural network): Memristor arrays allow for the concurrent and as well as on computation of vector–matrix multiplication operations, which significantly speeds up inference and training for convolutional neural networks (CNNs) and related deep neural networks (DNNs). Through Kirchhoff’s current law and Ohm’s law, the memristor crossbar arrays are employed to store the weights and carry out simultaneous multiply-accumulate operations [75]. In this system, the outputs are represented as the accumulated currents on the columns, while the inputs are represented as voltage pulses applied to the rows. ADCs or a sense amplifier may then read out the output activations after being quantized. First single layer perceptron is used to demonstrate simple pattern recognition, which serve as the foundation for memristor-based artificial neural network development [76, 77]. Later these work on multi-layer and CNN based architecture used for image recognition from well-known data sets as MNIST and CIFAR 10 [78]. SNN (spiking neural network): Memristors’ internal dynamics and processes bear strong resemblances to biological processes, opening the door to the development of bio-faithful neuromorphic systems without the need for intricate circuitry and sophisticated algorithms. Information processing can instead be carried out locally by device dynamics. SNN is one of the applications of the internal dynamics based on memristor devices, this system maximizes the efficiency of complex functions. SNNs have the potential to mimic the biological brain closely due to the owing spike-driven communication. This system fires a signal when the potential from input reach to threshold and the fired signal transfer to the neighbor neuron. The STDP learning rule for SNN training comes from neuroscience. For train to SNN, two types of learning are used: supervised learning and unsupervised learning. The unsupervised STDP learning is very energy efficient because of the local learning nature and ability to learn in only few spikes. Where accuracy is considered the most important factor, supervised learning is generally utilized for training neural networks. The inability to differentiate spiking events is the main obstacle to backpropagation in SNN [79].
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].
Figure 5.
Illustrations of the memristor-based bionic systems (a) memristor as a touch sensor, (b) artificial retina, and (c) nociceptor.
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].
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.
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.
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Written By
Mansi Patel, Jeny Gosai, Nitin Chaudhari and Ankur Solanki
Submitted: 24 November 2022Reviewed: 29 November 2022Published: 03 January 2023
Open access peer-reviewed chapter
