Nociceptive Memristor

A Suspended, 3D Morphing Sensory System for Robots to Feel and Protect

Artificial sensory systems with synergistic touch and pain perception hold substantial promise for environment interaction and human-robot communication. However, the realization of biological skin-like functional integration of sensors with sensitive touch and pain perception still remains a challenge. Here, a concept is proposed of suspended electronic skins enabling 3D deformation-mechanical contact interactions for achieving synergetic ultrasensitive touch and adjustable pain perception. The suspended sensory system can sensitively capture tiny touch stimuli as low as 0.02 Pa and actively perceive pain response with reliable 5200 cycles via 3D deformation and mechanical contact mechanism, respectively. Based on the touch-pain effect, a visualized feedback demo with miniaturized sensor arrays on artificial fingers is rationally designed to give a pain perception mapping on sharp surfaces. Furthermore, the capability is shown of the suspended electronic skin serving as a safe human-robot communication interface from active and passive view through a feedback control system, demonstrating potential in bionic electronics and intelligent robotics.

Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence

Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.

Tunable stochastic memristors for energy-efficient encryption and computing

Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirements - security (encryption) requires a source of unpredictability, while computing generally requires predictability. Each of these contrasting requirements presently necessitates distinct conventional Si-based hardware units with power-hungry overheads. This work demonstrates Cu0.3Te0.7/HfO2 ('CuTeHO') ion-migration-driven memristors that satisfy the contrasting requirements. Under specific operating biases, CuTeHO memristors generate truly random and physically unclonable functions, while under other biases, they perform universal Boolean logic. Using these computing primitives, this work experimentally demonstrates a single system that performs cryptographic key generation, universal Boolean logic operations, and encryption/decryption. Circuit-based calculations reveal the energy and latency advantages of the CuTeHO memristors in these operations. This work illustrates the functional flexibility of memristors in implementing operations with varying component-level requirements.

Cobalt-doped zinc oxide based memristors with nociceptor characteristics for bio-inspired technology

Neuromorphic computing is a new field of information technology, which is inspired by the biomimetic properties of the memristor as an electronic synapse and neuron. If there are electronic receptors that can transmit exterior impulses to the internal nervous system, then the use of memristors can be expanded to artificial nerves. In this study, a layer type memristor is used to build an artificial nociceptor in a very feasible and straightforward manner. An artificial nociceptor is demonstrated here through the fabrication and characterization of a cobalt-doped zinc oxide (CZO)/Au based memristor. In order to increase threshold switching performance, the surface effects of the CZO layer are eliminated by adding cobalt cobalt-doped zinc oxide (CZO) layer between the P++-Si and Au electrodes. Allodynia, hyperalgesia, threshold, and relaxation are the four distinct nociceptive behaviours that the device displays based on the strength, rate of relapse, and duration of the external stimuli. The electrons that are trapped in or released from the CZO layer's traps are responsible for these nociceptive behaviours. A multipurpose nociceptor performance is produced by this type of CZO-based device, which is crucial for artificial intelligence system applications such as neural integrated devices with nanometer-sized characteristics.

High-order sensory processing nanocircuit based on coupled VO2 oscillators

Conventional circuit elements are constrained by limitations in area and power efficiency at processing physical signals. Recently, researchers have delved into high-order dynamics and coupled oscillation dynamics utilizing Mott devices, revealing potent nonlinear computing capabilities. However, the intricate yet manageable population dynamics of multiple artificial sensory neurons with spatiotemporal coupling remain unexplored. Here, we present an experimental hardware demonstration featuring a capacitance-coupled VO2 phase-change oscillatory network. This network serves as a continuous-time dynamic system for sensory pre-processing and encodes information in phase differences. Besides, a decision-making module for special post-processing through software simulation is designed to complete a bio-inspired dynamic sensory system. Our experiments provide compelling evidence that this transistor-free coupling network excels in sensory processing tasks such as touch recognition and gesture recognition, achieving significant advantages of fewer devices and lower energy-delay-product compared to conventional methods. This work paves the way towards an efficient and compact neuromorphic sensory system based on nano-scale nonlinear dynamics.

Stretchable and neuromorphic transistors for pain perception and sensitization emulation

Pain perception nociceptors (PPN), an important type of sensory neuron, are capable of sending out alarm signals when the human body is exposed to destructive stimuli. Simulating the human ability to perceive the external environment and spontaneously avoid injury is a critical function of neural sensing of artificial intelligence devices. The demand for developing artificial PPN has subsequently increased. However, due to the application scenarios of bionic electronic devices such as human skin, electronic prostheses, and robot bodies, where a certain degree of surface deformation constantly occurs, the ideal artificial PPN should have the stretchability to adapt to real scenarios. Here, an organic semiconductor nanofiber artificial pain perception nociceptor (NAPPN) based on a pre-stretching strategy is demonstrated to achieve key pain aspects such as threshold, sensitization, and desensitization. Remarkably, while stretching up to 50%, the synaptic behaviors and injury warning ability of NAPPN can be retained. To verify the wearability of the device, NAPPN was attached to a curved human finger joint, on which PPN behaviors were successfully mimicked. This provides a promising strategy for realizing neural sensing function on either deformed or mobile electronic devices.

A Biomimetic Nociceptor Using Centrosymmetric Crystals for Machine Intelligence

Pain sensation is a crucial aspect of perception in the body. Force-activated nociceptors encode electrochemical signals and yield multilevel information of pain, thus enabling smart feedback. Inspired by the natural template, multi-dimensional mechano-sensing materials provide promising approaches for biomimetic nociceptors in intelligent terminals. However, the reliance on non-centrosymmetric crystals has narrowed the range of these materials. Here centrosymmetric crystal Cr3+ -doped zinc gallogermanate (ZGGO:Cr) with multi-dimensional mechano-sensing is reported, eliminating the limitation of crystal structure. Under forces, ZGGO:Cr generates electrical signals imitating those of neuronal systems, and produces luminescence for spatial mapping of mechanical stimuli, suggesting a path toward bionic pain perception. On that basis, a wireless biomimetic nociceptor system is developed and a smart pain reflex in a robotic hand and robot-assisted biopsy surgery of rat and dog is achieved.

Spatiotemporal Data Processing with Memristor Crossbar-Array-Based Graph Reservoir

Memristor-based physical reservoir computing (RC) is a robust framework for processing complex spatiotemporal data parallelly. However, conventional memristor-based reservoirs cannot capture the spatial relationship between the time-varying inputs due to the specific mapping scheme assigning one input signal to one memristor conductance. Here, a physical "graph reservoir" is introduced using a metal cell at the diagonal-crossbar array (mCBA) with dynamic self-rectifying memristors. Input and inverted input signals are applied to the word and bit lines of the mCBA, respectively, storing the correlation information between input signals in the memristors. In this way, the mCBA graph reservoirs can map the spatiotemporal correlation of the input data in a high-dimensional feature space. The high-dimensional mapping characteristics of the graph reservoir achieve notable results, including a normalized root-mean-square error of 0.09 in Mackey-Glass time series prediction, a 97.21% accuracy in MNIST recognition, and an 80.0% diagnostic accuracy in human connectome classification.

2Memristor-1Capacitor Integrated Temporal Kernel for High-Dimensional Data Mapping

Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality. This study proposes an integrated temporal kernel composed of a 2-memristor and 1-capacitor (2M1C) using a W/HfO2 /TiN memristor and TiN/ZrO2 /Al2 O3 /ZrO2 /TiN capacitor to achieve higher dimensionality and tunable dynamics. The kernel elements are carefully designed and fabricated into an integrated array, of which performances are evaluated under diverse conditions. By optimizing the time dynamics of the 2M1C kernel, each memristor simultaneously extracts complementary information from input signals. The MNIST benchmark digit classification task achieves a high accuracy of 94.3% with a (196×10) single-layer network. Analog input mapping ability is tested with a Mackey-Glass time series prediction, and the system records a normalized root mean square error of 0.04 with a 20×1 readout network, the smallest readout network ever used for Mackey-Glass prediction in RC. These performances demonstrate its high potential for efficient temporal data analysis.

Cross-Wired Memristive Crossbar Array for Effective Graph Data Analysis

Graphs adequately represent the enormous interconnections among numerous entities in big data, incurring high computational costs in analyzing them with conventional hardware. Physical graph representation (PGR) is an approach that replicates the graph within a physical system, allowing for efficient analysis. This study introduces a cross-wired crossbar array (cwCBA), uniquely connecting diagonal and non-diagonal components in a CBA by a cross-wiring process. The cross-wired diagonal cells enable cwCBA to achieve precise PGR and dynamic node state control. For this purpose, a cwCBA is fabricated using Pt/Ta2 O5 /HfO2 /TiN (PTHT) memristor with high on/off and self-rectifying characteristics. The structural and device benefits of PTHT cwCBA for enhanced PGR precision are highlighted, and the practical efficacy is demonstrated for two applications. First, it executes a dynamic path-finding algorithm, identifying the shortest paths in a dynamic graph. PTHT cwCBA shows a more accurate inferred distance and ≈1/3800 lower processing complexity than the conventional method. Second, it analyzes the protein-protein interaction (PPI) networks containing self-interacting proteins, which possess intricate characteristics compared to typical graphs. The PPI prediction results exhibit an average of 30.5% and 21.3% improvement in area under the curve and F1-score, respectively, compared to existing algorithms.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications

As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.

Threshold Modulative Artificial GABAergic Nociceptor

Gamma-aminobutyric acid (GABA) is a crucial inhibitory neurotransmitter of the central nervous system. It modifies the signal threshold of the nociceptor, allowing it to react to external stimuli in various circumstances. Thus, GABAergic behaviors are critical characteristics of adaptive behavior in life. Here, a threshold-modulative artificial GABAergic nociceptor is reported for the first time at a Pt/Ti/Nb2 O5- x /Al2 O3- y /Pt/Ti (top to bottom) of the double charge trapping structure. The Al2 O3- y layer contains deep defect states that function similarly to the GABA neurotransmitter in modulating the signal threshold. Meanwhile, the Nb2 O5- x layer traps volatile charges and produces nociceptive behaviors. The combined dynamics of the two layers readily offer threshold-modulative GABAergic nociceptive behaviors. Based on these GABAergic behaviors, a method of implementing hot- and cold-sensitive thermoreceptors is demonstrated and shows its potential applications in advanced sensory devices.

Neuromorphic applications in medicine

In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.

Flexible Organic-Inorganic Halide Perovskite-Based Diffusive Memristor for Artificial Nociceptors

With the current evolution in the artificial intelligence technology, more biomimetic functions are essential to execute increasingly complicated tasks and respond to challenging work environments. Therefore, an artificial nociceptor plays a significant role in the advancement of humanoid robots. Organic-inorganic halide perovskites (OHPs) have the potential to mimic the biological neurons due to their inherent ion migration. Herein, a versatile and reliable diffusive memristor built on an OHP is reported as an artificial nociceptor. This OHP diffusive memristor showed threshold switching properties with excellent uniformity, forming-free behavior, a high I_ON/I_OFF ratio (10⁴), and bending endurance over >10² cycles. To emulate the biological nociceptor functionalities, four significant characteristics of the artificial nociceptor, such as threshold, no adaptation, relaxation, and sensitization, are demonstrated. Further, the feasibility of OHP nociceptors in artificial intelligence is being investigated by fabricating a thermoreceptor system. These findings suggest a prospective application of an OHP-based diffusive memristor in the future neuromorphic intelligence platform.

Biological function simulation in neuromorphic devices: from synapse and neuron to behavior

As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.

Threshold switching in nickel-doped zinc oxide based memristor for artificial sensory applications

Electronic devices featuring biomimetic behaviour as electronic synapses and neurons have motivated the emergence of a new era in information and humanoid robotics technologies. In the human body, a nociceptor is a unique sensory neuron receptor that is capable of detecting harmful signals, leading to the central nervous system initiating a motor response. Herein, a nickel-doped zinc oxide (NZO)/Au based memristor is fabricated for the first time and characterized for artificial nociceptor application. For this, the introduction of a nickel-doped zinc oxide (NZO) layer between P⁺⁺-Si and Au electrodes is used to eliminate the surface effects of the NZO layer, resulting in improved volatile threshold switching performance. Depending on the intensity, duration, and repetition rate of the external stimuli, this newly created memristor exhibits various critical nociceptive functions, including threshold, relaxation, allodynia, and hyperalgesia. The electron trapping/detrapping to/from the traps in the NZO layer is responsible for these nociceptive properties. This kind of NZO-based device produces a multifunctional nociceptor performance that is essential for applications in artificial intelligence systems, such as neural integrated devices with nanometer-sized features.

Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware

A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta₂ O₅ /Nb₂ O_{5- x} /Al₂ O_{3- y} /Ti CTM stack exhibiting high retention and array-level uniformity is proposed, allowing a highly reliable selector-less MCA. It shows high self-rectifying and nonlinear current-voltage characteristics below 1 µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 10⁵ s at 150 °C, suggesting the device is highly stable for non-volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self-rectifying and nonlinear characteristics allow reducing the on-chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme.

Emerging memristive neurons for neuromorphic computing and sensing

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.

Memristors with Nociceptor Characteristics Using Threshold Switching of Pt/HfO2/TaOx/TaN Devices

As artificial intelligence technology advances, it is necessary to imitate various biological functions to complete more complex tasks. Among them, studies have been reported on the nociceptor, a critical receptor of sensory neurons that can detect harmful stimuli. Although a complex CMOS circuit is required to electrically realize a nociceptor, a memristor with threshold switching characteristics can implement the nociceptor as a single device. Here, we suggest a memristor with a Pt/HfO₂/TaO_x/TaN bilayer structure. This device can mimic the characteristics of a nociceptor including the threshold, relaxation, allodynia, and hyperalgesia. Additionally, we contrast different electrical properties according to the thickness of the HfO₂ layer. Moreover, Pt/HfO₂/TaO_x/TaN with a 3 nm thick HfO₂ layer has a stable endurance of 1000 cycles and controllable threshold switching characteristics. Finally, this study emphasizes the importance of the material selection and fabrication method in the memristor by comparing Pt/HfO₂/TaO_x/TaN with Pt/TaO_x/TaN, which has insufficient performance to be used as a nociceptor.

Progress of Materials and Devices for Neuromorphic Vision Sensors

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.

Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu_0.1Te_0.9/HfO₂/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be ‘0’ and ‘1’, of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization.

Designing a computing scheme to solve complex tasks as the big data field proliferates remains a challenge. Here, the authors present a probabilistic bit generation hardware built using the random nature of Cu_xTe_1−x/HfO₂/Pt memristors capable of performing logic gates with invertible mode, showing the expandability to complex logic circuits.

Recent Advances in Transistor-Based Bionic Perceptual Devices for Artificial Sensory Systems

The sensory nervous system serves as the window for human beings to perceive the outside world by converting external stimuli into distinctive spiking trains. The sensory neurons in this system can process multimodal sensory signals with extremely low power consumption. Therefore, new-concept devices inspired by the sensory neuron are promising candidates to address energy issues in nowadays’ robotics, prosthetics and even computing systems. Recent years have witnessed rapid development in transistor-based bionic perceptual devices, and it is urgent to summarize the research and development of these devices. In this review, the latest progress of transistor-based bionic perceptual devices for artificial sense is reviewed and summarized in five aspects, i.e., vision, touch, hearing, smell, and pain. Finally, the opportunities and challenges related to these areas are also discussed. It would have bright prospects in the fields of artificial intelligence, prosthetics, brain-computer interface, robotics, and medical testing.

A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system

Neuromorphic perception systems inspired by biology have tremendous potential in efficiently processing multi-sensory signals from the physical world, but a highly efficient hardware element capable of sensing and encoding multiple physical signals is still lacking. Here, we report a spike-based neuromorphic perception system consisting of calibratable artificial sensory neurons based on epitaxial VO₂, where the high crystalline quality of VO₂ leads to significantly improved cycle-to-cycle uniformity. A calibration resistor is introduced to optimize device-to-device consistency, and to adapt the VO₂ neuron to different sensors with varied resistance level, a scaling resistor is further incorporated, demonstrating cross-sensory neuromorphic perception component that can encode illuminance, temperature, pressure and curvature signals into spikes. These components are utilized to monitor the curvatures of fingers, thereby achieving hand gesture classification. This study addresses the fundamental cycle-to-cycle and device-to-device variation issues of sensory neurons, therefore promoting the construction of neuromorphic perception systems for e-skin and neurorobotics.

A Bioinspired Artificial Injury Response System Based on a Robust Polymer Memristor to Mimic a Sense of Pain, Sign of Injury, and Healing

Flexible electronic skin with features that include sensing, processing, and responding to stimuli have transformed human-robot interactions. However, more advanced capabilities, such as human-like self-protection modalities with a sense of pain, sign of injury, and healing, are more challenging. Herein, a novel, flexible, and robust diffusive memristor based on a copolymer of chlorotrifluoroethylene and vinylidene fluoride (FK-800) as an artificial nociceptor (pain sensor) is reported. Devices composed of Ag/FK-800/Pt have outstanding switching endurance >10⁶  cycles, orders of magnitude higher than any other two-terminal polymer/organic memristors in literature (typically 10² -10³ cycles). In situ conductive atomic force microscopy is employed to dynamically switch individual filaments, which demonstrates that conductive filaments correlate with polymer grain boundaries and FK-800 has superior morphological stability under repeated switching cycles. It is hypothesized that the high thermal stability and high elasticity of FK-800 contribute to the stability under local Joule heating associated with electrical switching. To mimic biological nociceptors, four signature nociceptive characteristics are demonstrated: threshold triggering, no adaptation, relaxation, and sensitization. Lastly, by integrating a triboelectric generator (artificial mechanoreceptor), memristor (artificial nociceptor), and light emitting diode (artificial bruise), the first bioinspired injury response system capable of sensing pain, showing signs of injury, and healing, is demonstrated.

Nociceptors: Their Role in Body’s Defenses, Tissue Specific Variations and Anatomical Update

The human body is constantly under the influence of numerous pathological factors: both external and internal. These factors can be potentially harmful and are perceived as such with a specialized nervous system subunit: the nociceptive system. The functional unit of the nociceptive system is the nociceptor. Recent studies have shown that nociceptors play a crucial role in maintaining of defensive homeostasis (responsive, immune, behavioral). Nociceptors respond to potentially harmful stimuli within viscera, bones, muscles, skin and specialized sensory organs. They function as complex predictors of harm through formation of pain stimulus. Their function and structures vary within different tissues. This variability reflects the anatomical and pathological peculiarities of varying tissues. Nociceptors play a significant role in adaptive, protective and behavioral reactions. Their functional capabilities and vast spread throughout the body make them the main units of the body’s defense system, allowing us to interact with the inner and outer environments.

Artificial Adaptive and Maladaptive Sensory Receptors Based on a Surface-Dominated Diffusive Memristor

A biological receptor serves as sensory transduction from an external stimulus to an electrical signal. It allows humans to better match the environment by filtering out repetitive innocuous information and recognize potentially damaging stimuli through key features, including adaptive and maladaptive behaviors. Herein, for the first time, the authors develop substantial artificial receptors involving both adaptive and maladaptive behaviors using diffusive memristor. Metal-oxide nanorods (NR) as a switching matrix enable the electromigration of an active metal along the surface of the NRs under electrical stimulation, resulting in unique surface-dominated switching dynamics with the advantage of fast Ag migration and fine controllability of the conductive filament. To experimentally demonstrate its potential application, a thermoreceptor system is constructed using memristive artificial receptors. The proposed surface-dominated diffusive memristor allows the direct emulation of the biological receptors, which represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.

Site-Specific Regulated Memristors via Electron-Beam-Induced Functionalization of HfO2

Emerging nonvolatile resistive switching, also known as the memristor, works with a distinct concept that relies mainly on the change in the composition of the active materials, rather than to store the charge. Particularly for oxide-based memristors, the switching is often governed by the random and unpredicted temporal/spatial migration of oxygen defects, resulting in possessing limitations in terms of control over conduction channel formation and inability to regulate hysteresis loop opening. Therefore, site specific dynamic control of defect concentration in the active materials can offer a unique opportunity to realize on-demand regulation of memory storage and artificial intelligence capabilities. Here, high-performance, site-specific spatially scalable memristor devices are fabricated by stabilizing the conduction channel via manipulation of oxygen defects using electron-beam irradiation. Specifically, the memristors exhibit highly stable and electron-beam dose-regulated multilevel analog hysteresis loop opening with adjustable switching ratios even higher than 10⁴ . Additionally, broad modulation of neural activities, including short- and long-term plasticity, paired-pulse facilitation, spike-timing-dependent plasticity, and dynamic multipattern memory processing, are demonstrated. The work opens a new possibility to regulate the resistive switching behavior and control mimicking of neural activities, providing a hitherto unseen tunability in two-terminal oxide-based memristors.

A flexible capacitive photoreceptor for the biomimetic retina

Neuromorphic vision sensors have been extremely beneficial in developing energy-efficient intelligent systems for robotics and privacy-preserving security applications. There is a dire need for devices to mimic the retina's photoreceptors that encode the light illumination into a sequence of spikes to develop such sensors. Herein, we develop a hybrid perovskite-based flexible photoreceptor whose capacitance changes proportionally to the light intensity mimicking the retina's rod cells, paving the way for developing an efficient artificial retina network. The proposed device constitutes a hybrid nanocomposite of perovskites (methyl-ammonium lead bromide) and the ferroelectric terpolymer (polyvinylidene fluoride trifluoroethylene-chlorofluoroethylene). A metal-insulator-metal type capacitor with the prepared composite exhibits the unique and photosensitive capacitive behavior at various light intensities in the visible light spectrum. The proposed photoreceptor mimics the spectral sensitivity curve of human photopic vision. The hybrid nanocomposite is stable in ambient air for 129 weeks, with no observable degradation of the composite due to the encapsulation of hybrid perovskites in the hydrophobic polymer. The functionality of the proposed photoreceptor to recognize handwritten digits (MNIST) dataset using an unsupervised trained spiking neural network with 72.05% recognition accuracy is demonstrated. This demonstration proves the potential of the proposed sensor for neuromorphic vision applications.

Bio‑inspired Tactile Nociceptor Constructed by Integrating Wearable Sensing Paper and VO2 Threshold Switching Memristor

The sensations of touch and pain are fundamental components of our daily life, which can transport vital information about the surroundings and provide protection to our bodies. In this study,...

Analytically and empirically consistent characterization of the resistive switching mechanism in a Ag conducting-bridge random-access memory device through a pseudo-liquid interpretation approach

A new physical analysis of the filament formation in a Ag conducting-bridge random-access memory (CBRAM) device in consideration of the existence of inter-atomic attractions caused by metal bonding is suggested. The movement of Ag atoms inside the switching layer is characterized hydrodynamically using the Young-Laplace equation during set and reset operations. Both meridional and azimuthal curvatures of the Ag filament protruding from the Ag electrode are accurately calculated to track down the exact shape of the Ag filament with change in the applied voltage. The second-order partial differential equation for the Ag filament geometry is derived from the equation of equilibrium between the electrostatic pressure and the Laplace one. The solution to the equation is numerically obtained, and furthermore, the abrupt set operation in the forming process, bipolar resistive-switching, and the threshold switching operation in the reset operations are successfully simulated in accordance with the numerical solutions. Also, it is demonstrated that the currents extracted from the suggested model show good agreement with the I-V characteristics measured from the fabricated Ag CBRAM device.

High performance and low power consumption resistive random access memory with Ag/Fe2O3/Pt structure

Due to magnetic field tunability and the abundance of iron in the Earth's crust, iron oxide-based resistive random access memory (RRAM) is considered to be low cost and potential for multi-level storage. However, the relatively high operation voltage (>1 V) and small storage window (<100) limit its application. In this work, the devices with simple Ag/Fe₂O₃/Pt structure exhibit typical bipolar resistive switching with ultralow set voltage (V_set) of 0.16 V, ultralow reset voltage (V_reset) of -0.04 V, high OFF/ON resistance ratio of 10³, excellent cycling endurance more than 10⁴and good retention time longer than 10⁴s. Each major parameter has about an order of magnitude improvement compared to the previous data. The devices demonstrate outstanding stable low power consumption quality. Based on the analysis of the experimental results, a percolation model of silver ion migration was established and confirmed that low operation voltage is attributed to the amorphous oxide layer with large porosity. During electrical testing, the compliance current (I_c) and maximum reset voltage (V_max) can also affect the device performance. This discovery suggests Fe₂O₃memristor has significant potential for application and provides a new idea for the realization of high-performance low-power RRAM.

An Artificial Mechano-Nociceptor with Mott Transition

Intelligent touch sensing is now becoming an essential part of various human-machine interactions and communication, including in touchpads, autonomous vehicles, and smart robotics. Usually, sensing of physical objects is enabled by applied force/pressure sensors; however, reported conventional tactile devices are not able to differentiate sharp and blunt objects, although sharp objects can cause unavoidable damage. Therefore, it is central issue to implement electronic devices that can classify sense of touch and simultaneously generate pain signals to avoid further potential damage from sharp objects. Here, concept of force-enabled nociceptive behavior is proposed and demonstrated using vanadium oxide-based artificial receptors. Specifically, versatile criteria of bio-nociceptor like threshold, relaxation, no adaptation, allodynia, and hyperalgesia behaviors are triggered by pointed force, but the device does not mimic any of these by the force applied by blunt objects; thus, the proposed device classifies the intent of touch. Further, supported by finite element simulation, the nanoscale dynamic is unambiguously revealed by conductive atomic force microscopy and results are attributed to the point force-triggered Mott transition, as also confirmed by temperature-dependent measurements. The reported features open a new avenue for developing mechano-nociceptors, which enable a high-level of artificial intelligence within the device to classify physical touch.

Artificial Skin Perception

Skin is the largest organ, with the functionalities of protection, regulation, and sensation. The emulation of human skin via flexible and stretchable electronics gives rise to electronic skin (e-skin), which has realized artificial sensation and other functions that cannot be achieved by conventional electronics. To date, tremendous progress has been made in data acquisition and transmission for e-skin systems, while the implementation of perception within systems, that is, sensory data processing, is still in its infancy. Integrating the perception functionality into a flexible and stretchable sensing system, namely artificial skin perception, is critical to endow current e-skin systems with higher intelligence. Here, recent progress in the design and fabrication of artificial skin perception devices and systems is summarized, and challenges and prospects are discussed. The strategies for implementing artificial skin perception utilize either conventional silicon-based circuits or novel flexible computing devices such as memristive devices and synaptic transistors, which enable artificial skin to surpass human skin, with a distributed, low-latency, and energy-efficient information-processing ability. In future, artificial skin perception would be a new enabling technology to construct next-generation intelligent electronic devices and systems for advanced applications, such as robotic surgery, rehabilitation, and prosthetics.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Transparent photovoltaic memory for neuromorphic device

Bio-inspired electronic devices have significant potential for use in memory devices of the future, including in the context of neuromorphic computing and architecture. This study proposes a transparent heterojunction device for the artificial human visual cortex. Owing to their high transparency, such devices directly react to incoming light to mimic neurological and biological processes in the nervous system. Metal-oxide materials are applied to form a transparent heterojunction (n-type ZnO/p-type NiO) in the proposed device that also provides the photovoltaic function to realize the optic nerve system. The device also exhibits nociceptive features. Its transparent photovoltaic feature endows it with self-powered operation that ensures long-term reliability without needing to replace the power system. This self-powered and highly transparent visual electronic device can provide a route for sustainable applications of neuromorphic computing, including artificial eyes.

A visible light-triggered artificial photonic nociceptor with adaptive tunability of threshold

An artificial photonic nociceptor that can accurately emulate the activation of a human visual nociceptive pathway is highly desired for the development of advanced intelligent optoelectronic information processing systems. However, the realization of such an artificial device needs sophisticated materials design and is pending to date. Herein, we demonstrate a visible light-triggered artificial nociceptor, with a simple ITO/CeO_2-x/Pt sandwich structure, that can well reproduce the pain-perceptual characteristics of the human visual system. The abundant oxygen vacancies in the CeO_2-x layer account for visible light activation, and the notable built-in electric field due to work function difference of the two electrodes enables the device to work even in a self-powered mode. Key nociceptive characteristics, including threshold, no adaptation, relaxation, and sensitization, are realized in the device and are attributed to the oxygen vacancy-associated electron trapping and detrapping processes within the CeO_2-x layer. More importantly, the threshold light intensity to activate the device can be readily manipulated using a sub-1 V external voltage, resembling the ambient luminance-dependent tunability of threshold of the human visual system. This work opens up a new avenue towards the development of next-generation intelligent and low-power perceptual systems, such as visual prostheses, artificial eyes, and humanoid robots.

Nano-engineering and functionalization of hybrid Au-MexOy-TiO2 (Me = W, Ga) hetero-interfaces for optoelectronic receptors and nociceptors

Bio-inspired nano-electronic devices are key instruments for the development of advanced artificial intelligence systems, which will shape the future of humanoid nano-robotics. An emerging demand is realized for an accurate reception of environmental stimuli via visual perception, processing and realization of optical signals. The present study demonstrates the capability of functionalized all-oxide heterostructured two-dimensional (2D) plasmonic devices for the self-adaptive recognition of visual optical pulses. Specifically, the nano-engineering of the metal/semiconductor interface and co-modulation of heterostructured 2D semiconductor hetero-interfaces of Au/WO3 : TiO2 and Au/Ga2O3 : TiO2 facilitated the receptive and nociceptive detection of visible light pulses. A decrease in the dark current of the Au/WO3 : TiO2 unit resulted in the development of sensitive visible light photoreceptors. Furthermore, the modulation of charge transfers at the Au/Ga2O3 : TiO2 hetero-interfaces were the key parameter to determine the optical reception characteristics and nociceptive performance of all-oxide optoelectronic devices. Specifically, the rapid thermal annealing (RTA) of 2D Ga2O3 in N2 atmosphere ensured the modulation of charge transfer at Au/Ga2O3 : TiO2 hetero-interfaces in plasmonic devices. Thus, hetero-interface engineering enabled the effective control of charge transfer at 2D hetero-interfaces for an adaptive perception of visible optical pulses. Consequently, the fabricated sensitive Au/Ga2O3 (N2) : TiO2 bio-inspired unit emulated the optical functionalities of corneal nociceptors.

Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and -memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness.

NeuroMem: Analog Graphene-Based Resistive Memory for Artificial Neural Networks

Artificial Intelligence (AI) at the edge has become a hot subject of the recent technology-minded publications. The challenges related to IoT nodes gave rise to research on efficient hardware-based accelerators. In this context, analog memristor devices are crucial elements to efficiently perform the multiply-and-add (MAD) operations found in many AI algorithms. This is due to the ability of memristor devices to perform in-memory-computing (IMC) in a way that mimics the synapses in human brain. Here, we present a novel planar analog memristor, namely NeuroMem, that includes a partially reduced Graphene Oxide (prGO) thin film. The analog and non-volatile resistance switching of NeuroMem enable tuning it to any value within the R_ON and R_OFF range. These two features make NeuroMem a potential candidate for emerging IMC applications such as inference engine for AI systems. Moreover, the prGO thin film of the memristor is patterned on a flexible substrate of Cyclic Olefin Copolymer (COC) using standard microfabrication techniques. This provides new opportunities for simple, flexible, and cost-effective fabrication of solution-based Graphene-based memristors. In addition to providing detailed electrical characterization of the device, a crossbar of the technology has been fabricated to demonstrate its ability to implement IMC for MAD operations targeting fully connected layer of Artificial Neural Network. This work is the first to report on the great potential of this technology for AI inference application especially for edge devices.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

The Design of Memristive Circuit for Affective Multi-Associative Learning

In this work, a memristive circuit with affective multi-associative learning function is proposed, which mimics the process of human affective formation. It mainly contains three modules: affective associative learning, affective formation, affective expression. The first module is composed of several affective single-associative learning circuits consisting of memristive neurons and synapses. Memristive neuron will be activated and output pulses if its input exceeds the threshold. After it is activated, memristive neuron can automatically return to the inactive state. Memristive synapse can realize learning and forgetting functions based on the signals from pre- and post-neurons. The learning rule is pre-neuron activated lags behind post-neuron for a short time; the forgetting rule is to repeatedly activate pre-neuron after the emotion is learned. The process of learning or forgetting corresponds to facilitating or inhibiting synaptic weight, that is, decreasing or increasing memristance continuously. Different voltage signals applied to memristors and different parameters of memristors would lead to different synaptic weights which indicate different affective association. The second module can convert affective signals to corresponding emotions. The formed emotions can be shown in a face by the third module. The simulation results in PSPICE show that the proposed circuit system can learn, forget and form emotions like human. If the proposed circuit is further used on a humanoid robot platform through further research, the robot will have the ability of affective interaction with human so that it can be effectively used in affective company and other aspects.

Nanoscale All-Oxide-Heterostructured Bio-inspired Optoresponsive Nociceptor

Retina nociceptor, as a key sensory receptor, not only enables the transport of warning signals to the human central nervous system upon its exposure to noxious stimuli, but also triggers the motor response that minimizes potential sensitization. In this study, the capability of two-dimensional all-oxide-heterostructured artificial nociceptor as a single device with tunable properties was confirmed. Newly designed nociceptors utilize ultra-thin sub-stoichiometric TiO₂-Ga₂O₃ heterostructures, where the thermally annealed Ga₂O₃ films play the role of charge transfer controlling component. It is discovered that the phase transformation in Ga₂O₃ is accompanied by substantial jump in conductivity, induced by thermally assisted internal redox reaction of Ga₂O₃ nanostructure during annealing. It is also experimentally confirmed that the charge transfer in all-oxide heterostructures can be tuned and controlled by the heterointerfaces manipulation. Results demonstrate that the engineering of heterointerfaces of two-dimensional (2D) films enables the fabrication of either high-sensitive TiO₂-Ga₂O₃ (Ar) or high-threshold TiO₂-Ga₂O₃ (N₂) nociceptors. The hypersensitive nociceptor mimics the functionalities of corneal nociceptors of human eye, whereas the delayed reaction of nociceptor is similar to high-threshold nociceptive characteristics of human sensory system. The long-term stability of 2D nociceptors demonstrates the capability of heterointerfaces engineering for effective control of charge transfer at 2D heterostructured devices.

A Sub-10 nm Vertical Organic/Inorganic Hybrid Transistor for Pain-Perceptual and Sensitization-Regulated Nociceptor Emulation

Pain-perceptual nociceptors (PPN) are essential sensory neurons that recognize harmful stimuli and can empower the human body to react appropriately and perceive precisely unusual or dangerous conditions in the real world. Furthermore, the sensitization-regulated nociceptors (SRN) can greatly assist pain-sensitive human to reduce pain sensation by normalizing hyperexcitable central neural activity. Therefore, the implementation of PPNs and SRNs in hardware using emerging nanoscale devices can greatly improve the efficiency of bionic medical machines by giving them different sensitivities to external stimuli according to different purposes. However, current most-normal organic/oxide transistors face a great challenge due to channel scaling, especially in the sub-10 nm channel technology. Here, a sub-10 nm indium-tin-oxide transistor with an ultrashort vertical channel as low as ≈3 nm, using sodium alginate bio-polymer electrolyte as gate dielectric, is demonstrated. This device can emulate important characteristics of PPN such as pain threshold, memory of prior injury, and pain sensitization/desensitization. Furthermore, the most intriguing character of SRN can be achieved by tuning the channel thickness. The proposed device can open new avenues for the fascinating applications of next-generation neuromorphic brain-like systems, such as bio-inspired electronic skins and humanoid robots.

A UV damage-sensing nociceptive device for bionic applications

Artificial limbs have been widely investigated in the past several decades, and multifuncional bionic limbs have already been constructed. However, due the lack of nociceptive systems, amputees still cannot feel ubiquitous noxious stimuli through bionic limbs. The construction of artificial nociceptors can bring bionic limbs closer to real flesh and bone. In daily life, UV irradiation is an invisible potential noxious stimulus to human skin and eyes. Furthermore, it is well known that the synthetic polymers widely used in bionic limbs can be degraded by UV radiation, accelerating their aging. Based on the above, UV damage-sensing nociceptors could be a feasible strategy to solve these existing problems. Here, azobenzene-functionalized gold nanoparticles (Azo-Au NPs) are embedded in insulating poly(methyl methacrylate) (PMMA) to construct a two-terminal memristor. With UV irradiation as a light damage medium, major nociceptive behaviors such as "threshold", "relaxation" and "sensitization" are successfully emulated, demonstrating its potential application as a nociceptive system.

An artificial spiking afferent nerve based on Mott memristors for neurorobotics

Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbO_x Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future.

Though artificial sensory systems based on electronic devices have been realized, further transformation of data into spikes is required for neural network optimization. Here, based on NbO_x Mott memristors, the authors report artificial spiking afferent nerves for accessing spiking systems and demonstrate spiking mechanoreceptor systems.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

As the research on artificial intelligence booms, there is broad interest in brain-inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re-visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

A Transparent Photonic Artificial Visual Cortex

Mimicking brain-like functionality with an electronic device is an essential step toward the design of future technologies including artificial visual and memory applications. Here, a proof-of-concept all-oxide-based (NiO/TiO₂ ) highly transparent (54%) heterostructure is proposed and demonstrated, which mimics the primitive functions of the visual cortex. Specifically, orientation selectivity and spatiotemporal processing similar to that of the visual cortex are demonstrated using direct optical stimuli under the self-biased condition due to photovoltaic effect, illustrating an energy-efficient approach for neuromorphic computing. The photocurrent of the device can be modulated from zero to 80 µA by simply rotating the slit by 90°. The device shows fast rise and fall times of 3 and 6 ms, respectively. Based on Kelvin probe force measurements, the observed results are attributed to a lateral photovoltaic effect. This highly transparent, self-biased, photonic triggered device paves the way for the advancement of energy-efficient neuromorphic computation.

Memristive devices based on emerging two-dimensional materials beyond graphene

With the explosion of data in the information universe and the approaching of fundamental limits in silicon-based flash memories, the exploration of new device architectures and alternative materials is necessary for next-generation memory technology. Accordingly, emerging two-dimensional (2D) material-based memristive devices have attracted increasing attention due to their unique properties and great potential in flexible and wearable devices, and even neuromorphic computing systems. Herein, we provide an overview of the recent progress on memristive devices based on 2D materials beyond graphene. The device structures and choice of active materials and electrodes materials are summarized for various types of 2D material-based memristive devices. Following the discussion and classification on the device performances and mechanisms, the challenges and perspectives on future research based on 2D materials are also presented.

On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits

Memristive devices have found application in both random access memory and neuromorphic circuits. In particular, it is known that their behavior resembles that of neuronal synapses. However, it is not simple to come by samples of memristors and adjusting their parameters to change their response requires a laborious fabrication process. Moreover, sample to sample variability makes experimentation with memristor-based synapses even harder. The usual alternatives are to either simulate or emulate the memristive systems under study. Both methodologies require the use of accurate modeling equations. In this paper, we present a diffusive compact model of memristive behavior that has already been experimentally validated. Furthermore, we implement an emulation architecture that enables us to freely explore the synapse-like characteristics of memristors. The main advantage of emulation over simulation is that the former allows us to work with real-world circuits. Our results can give some insight into the desirable characteristics of the memristors for neuromorphic applications.