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

Towards scalable memristive hardware for spiking neural networks

Spiking neural networks (SNNs) represent a promising frontier in artificial intelligence (AI), offering event-driven, energy-efficient computation that mimics rich neural dynamics in the brain. However, running large-scale SNNs on mainstream computing hardware faces significant challenges to efficiently emulate these dynamical processes using synchronized and logical chips. Memristor based systems have recently demonstrated great potential for AI acceleration, sparking speculations and explorations of using these emerging devices for SNN tasks. This paper reviews the promises and challenges of memristive devices in SNN implementations, and our discussions are focused on the scaling and integration of neuronal and synaptic devices. We survey recent progress in device and circuit development, discuss possible pathways for chip-level integration, and finally probe into hardware-oriented algorithm designs. This review offers a system-level perspective on implementing scalable memristor based SNN platforms.

Bio-Inspired P-type TeSeOx Synaptic Transistor Based on Multispectral Sensing for Neuromorphic Visual Multilevel Nociceptor

The development of neuromorphic color vision has significant research implications in the fields of machine vision and artificial intelligence. By mimicking the processing mechanisms of energy-efficient biological visual systems, it offers a unique potential for real-time color environment perception and dynamic adaptability. This paper reports on a multispectral color sensing synaptic device based on a novel p-type TeSeOx transistor, applied to a neuromorphic visual multilevel nociceptor. Due to the intrinsic properties of TeSeOx, its narrow bandgap allows for multi-wavelength (405, 532, 655 nm) response, and its oxide semiconductor-based persistent photoconductivity converts optical signals into stored electrical signals, successfully emulating key synaptic characteristics such as excitatory postsynaptic current (EPSC), multi-pulse facilitation, and the transition from short-term to long-term memory. Additionally, it simulates learning, forgetting, and relearning behaviors, as well as image memory under tricolor light. Finally, using optical signals as a pain stimulus, the fundamental functions of a nociceptor are realized, including "threshold," "non-adaptation," "relaxation," and "nociceptive sensitization". More importantly, by using tricolor light, multilevel pain perception is acheived. These results have the potential to advance fields such as autonomous driving, machine vision, and intelligent alert systems.

Tunable Bipolar Photothermoelectric Response from Mott Activation for In-Sensor Image Preprocessing

In-sensor image preprocessing, a subset of edge computing, offers a solution to mitigate frequent analog-digital conversions and the von Neumann bottleneck in conventional digital hardware. However, an efficient in-sensor device array with large-scale integration capability for high-density and low-power sensory processing is still lacking and highly desirable. This work introduces an adjustable broadband photothermoelectric detector based on a phase-change vanadium dioxide thin-film transistor. This transistor employs a vanadium dioxide/gallium nitride three-terminal structure with a gate-tunable phase transition at the gate-source junctions. This design allows for modulable photothermoelectric responsivities and alteration of the short-circuit photocurrent's polarities. The devices exhibit linear gate dependence for the broadband photoresponse and linear light-intensity dependence for both positive and negative photoresponsivities. The device's energy consumption is as low as 8 pJ per spike, which is one order of magnitude lower than that of previous Mott materials-based in-sensor preprocessing devices. A wafer-scale bipolar phototransistor array has also been fabricated by standard micro-/nano-fabrication techniques, exhibiting excellent stability and endurance (over 5000 cycles). More importantly, an integrated in-sensor convolutional network is successfully designed for simultaneous broadband image classification, medical image denoising, and retinal vessel segmentation, delivering exceptional performance and paving the way for future smart edge sensors.

Compact Artificial Synapse-Neuron Module with Chemically Mediated Spiking Behaviors

Neuromorphic electronic devices mimicking the structure and functionality of biological counterparts have shown promising applications in biorealistic computing and bioelectronic interfaces. However, current neuromorphic systems comprising synapses and neurons typically exhibit complex integrated structures and lack chemically mediated characteristics, hindering them from direct biointerfacing. Here, we report a compact artificial synapse-neuron module (ASNM) by seamlessly integrating an organic electrochemical synaptic transistor and a niobium dioxide Mott memristor, showing the chemically mediated synaptic plasticity and highly stable spiking characteristics (>1010 cycles). Sodium ions and dopamine neurotransmitter induce the short-term and long-term plasticity of synaptic transistors, respectively, thus enabling temporary and long-term modulation of the ASNM's firing frequency in a bioplausible range (0-100 Hz). Furthermore, we construct a chemically mediated artificial neuromuscular system based on the ASNM, which could replicate the learning processes of a shooting basketball. These results demonstrate that our ASNM could achieve multiple biorealistic functionalities including sensing, synaptic plasticity, and spiking in a compact structure, providing a promising way for direct biointerfacing.

Organic Photoelectrochemical Multisensory Integration

Neuromorphic perception capable of multisensory integration (MSI) in electrolytes is important but remains challenging. Here, the aqueous implementation of artificial MSI is reported based on the newly emerged organic photoelectrochemical transistor (OPECT) by representative visual (light)-gustatory (sour) perception. Under the co-modulation of light and H+/OH-, multisensory synaptic plasticity and several typical MSI characteristics are mimicked, including "super-additive response," "inverse effectiveness effect" and "temporal congruency." To demonstrate its potential usage, different types of multisensory associative learning and corresponding reflex activities are further emulated. The chemical MSI system is also utilized to control artificial salivation by a closed loop of real-time perception, processing, integration, and actuation to emulate the biological responses toward external stimuli. In contrast to previous solid-state operations, this work offers a new strategy for developing neuromorphic MSI in aqueous environments that are analogous to those in biology.

Nanofluidic Volatile Threshold Switching Ionic Memristor: A Perspective

The fast development of artificial intelligence and big data drives the exploration of low-power computing hardware. Neuromorphic devices represented by memristors may provide a possible computing paradigm beyond von Neumann's architecture because they enable the integration of processing and storage units by mimicking how the brain processes complex information in parallel. In the brain, information is processed via multilevel spiking coding and event-driven mechanisms, whose simplified neural circuit is represented by the leaky-integration-and-fire model combining volatile threshold switching memristors and capacitors. As a computing unit to emulate the working environment and explore the unique functions of ions and molecules of biological systems, nanofluidic volatile threshold switching ionic memristors become essential but are still missing. This Perspective will review the mechanism and role of threshold switching memristors as a building block for neuromorphic computing and list three possible routes for nanofluidic ones.

Achieving neuronal dynamics with spike encoding and spatial-temporal summation in vanadium-based threshold switching memristor for asynchronous signal integration

Artificial neuronal devices that emulate the dynamics of biological neurons are pivotal for advancing brain emulation and developing bio-inspired electronic systems. This paper presents the design and demonstration of an artificial neuron circuit based on a Pt/V/AlOx/Pt threshold switching memristor (TSM) integrated with an external resistor. By applying voltage pulses, we successfully exhibit the leaky integrate-and-fire (LIF) behavior, as well as both spatial and spatiotemporal summation capabilities, achieving the asynchronous signal integration. Notably, the Pt/V/AlOx/Pt TSM demonstrates ultrafast switching speeds (on/off times ∼165 ns/310 ns) and remarkable stability (endurance >102 cycles with cycle-to-cycle variations <2.5%). These attributes render the circuit highly suitable as a spike generator in neuromorphic computing applications. The Pt/V/AlOx/Pt TSM-based spike encoder can output current spikes at frequencies ranging from approximately 200 kHz to 800 kHz. The modulation of output spike frequency is achievable by adjusting the external resistor and capacitor within the spike encoder circuit, providing considerable operational flexibility. Additionally, the Pt/V/AlOx/Pt TSM boasts a lower threshold voltage (Vth ∼ 0.84 V) compared to previously reported VOx-based TSMs, leading to significantly reduced energy consumption for spike generation (∼2.75 nJ per spike).

Remote epitaxy and exfoliation of vanadium dioxide via sub-nanometer thick amorphous interlayer

The recently emerged remote epitaxy technique, utilizing 2D materials (mostly graphene) as interlayers between the epilayer and the substrate, enables the exfoliation of crystalline nanomembranes from the substrate, expanding the range of potential device applications. However, remote epitaxy has been so far applied to a limited range of material systems, owing to the need of stringent growth conditions to avoid graphene damaging, and has therefore remained challenging for the synthesis of oxide nanomembranes. Here, we demonstrate the remote epitaxial growth of an oxide nanomembrane (vanadium dioxide, VO2) with a sub-nanometer thick amorphous interlayer, which can withstand potential sputtering-induced damage and oxidation. By removing the amorphous interlayer, a 4-inch wafer-scale freestanding VO2 nanomembrane can be obtained, exhibiting intact crystalline structure and physical properties. In addition, multi-shaped freestanding infrared bolometers are fabricated based on the epitaxial VO2 nanomembranes, showing high detectivity and low current noise. Our strategy provides a promising way to explore various freestanding heteroepitaxial oxide materials for future large-scale integrated circuits and functional devices.

Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

Two-Terminal Neuromorphic Devices for Spiking Neural Networks: Neurons, Synapses, and Array Integration

The ever-increasing volume of complex data poses significant challenges to conventional sequential global processing methods, highlighting their inherent limitations. This computational burden has catalyzed interest in neuromorphic computing, particularly within artificial neural networks (ANNs). In pursuit of advancing neuromorphic hardware, researchers are focusing on developing computation strategies and constructing high-density crossbar arrays utilizing history-dependent, multistate nonvolatile memories tailored for multiply-accumulate (MAC) operations. However, the real-time collection and processing of massive, dynamic data sets require an innovative computational paradigm akin to that of the human brain. Spiking neural networks (SNNs), representing the third generation of ANNs, are emerging as a promising solution for real-time spatiotemporal information processing due to their event-based spatiotemporal capabilities. The ideal hardware supporting SNN operations comprises artificial neurons, artificial synapses, and their integrated arrays. Currently, the structural complexity of SNNs and spike-based methodologies requires hardware components with biomimetic behaviors that are distinct from those of conventional memristors used in deep neural networks. These distinctive characteristics required for neuron and synapses devices pose significant challenges. Developing effective building blocks for SNNs, therefore, necessitates leveraging the intrinsic properties of the materials constituting each unit and overcoming the integration barriers. This review focuses on the progress toward memristor-based spiking neural network neuromorphic hardware, emphasizing the role of individual components such as memristor-based neurons, synapses, and array integration along with relevant biological insights. We aim to provide valuable perspectives to researchers working on the next generation of brain-like computing systems based on these foundational elements.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges

Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.

A Multimodal Perception-Enabled Flexible Memristor with Combined Sensing-Storage-Memory Functions for Enhanced Artificial Injury Recognition

With the continuous advancement of wearable technology and advanced medical monitoring, there is an increasing demand for electronic devices that can adapt to complex environments and have high perceptual sensitivity. Here, a novel artificial injury perception device based on an Ag/HfOx/ITO/PET flexible memristor is designed to address the limitations of current technologies in multimodal perception and environmental adaptability. The memristor exhibits excellent resistive switching (RS) performance and mechanical flexibility under different bending angles (BAs), temperatures, humid environment, and repetitive folding conditions. Further, the device demonstrates the multimodal perception and conversion capabilities toward voltage, mechanical, and thermal stimuli through current response tests under different conditions, enabling not only the simulation of artificial injury perception but also holds promise for monitoring and controlling the movement of robotic arms. Moreover, the logical operation capability of the memristor-based reconfigurable logic (MRL) gates is also demonstrated, proving the device has great potential applications with sensing, storage, and memory functions. Overall, this study not only provides a direction for the development of the next-generation flexible multimodal sensors, but also has significant implications for technological advancements in many fields such as robotic arms, electronic skin (e-skin), and medical monitoring.

An evaluation of recent advancements in biological sensory organ-inspired neuromorphically tuned biomimetic devices

In the field of neuroscience, significant progress has been made regarding how the brain processes information. Unlike computer processors, the brain comprises neurons and synapses instead of memory blocks and transistors. Despite advancements in artificial neural networks, a complete understanding concerning brain functions remains elusive. For example, to achieve more accurate neuron replication, we must better understand signal transmission during synaptic processes, neural network tunability, and the creation of nanodevices featuring neurons and synapses. This study discusses the latest algorithms utilized in neuromorphic systems, the production of synaptic devices, differences between single and multisensory gadgets, recent advances in multisensory devices, and the promising research opportunities available in this field. We also explored the ability of an artificial synaptic device to mimic biological neural systems across diverse applications. Despite existing challenges, neuroscience-based computing technology holds promise for attracting scientists seeking to enhance solutions and augment the capabilities of neuromorphic devices, thereby fostering future breakthroughs in algorithms and the widespread application of cutting-edge technologies.

A Flexible Artificial Spiking Photoreceptor Enabled by a Single VO2 Mott Memristor for the Spike-Based Electronic Retina

The neuromorphic vision system that utilizes spikes as information carriers is crucial for the formation of spiking neural networks. Here, we present a bioinspired flexible artificial spiking photoreceptor (ASP), which is realized by using a single VO2 Mott memristor that can simultaneously sense and encode the stimulus light into spikes. The ASP has high spike-encoded photosensitivity and ultrawide photosensing range (405-808 nm) with good endurance (>7 × 107) and high flexibility (bending radius ∼5 mm). Then, we put forward an all-spike electronic retina architecture that comprises one layer of ASPs and one layer of artificial optical nerves (AONs) to process the spike information. Each AON consists of a single Mott memristor connected in series with a neuro-transistor that is a multiple-input floating-gate MOS transistor. Simulation results demonstrate that the all-spike electronic retina can successfully segment images with high Shannon entropy, thus laying the foundation for the development of a spike-based neuromorphic vision system.

Leveraging volatile memristors in neuromorphic computing: from materials to system implementation

Inspired by the functions of biological neural networks, volatile memristors are essential for implementing neuromorphic computing. These devices enable large-scale and energy-efficient data processing by emulating neural functionalities through dynamic resistance changes. The threshold switching characteristics of volatile memristors, which are driven by various mechanisms in materials ranging from oxides to chalcogenides, make them versatile and suitable for neuromorphic computing systems. Understanding these mechanisms and selecting appropriate devices for specific applications are crucial for optimizing the performance. However, the existing literature lacks a comprehensive review of switching mechanisms, their compatibility with different applications, and a deeper exploration of the spatiotemporal processing capabilities and inherent stochasticity of volatile memristors. This review begins with a detailed analysis of the operational principles and material characteristics of volatile memristors. Their diverse applications are then explored, emphasizing their role in crossbar arrays, artificial receptors, and neurons. Furthermore, the potential of volatile memristors in artificial inference systems and reservoir computing is discussed, due to their spatiotemporal processing capabilities. Hardware security applications and probabilistic computing are also examined, where the inherent stochasticity of the devices can improve the system robustness and adaptability. To conclude, the suitability of different switching mechanisms for various applications is evaluated, and future perspectives for the development and implementation of volatile memristors are presented. This review aims to fill the gaps in existing research and highlight the potential of volatile memristors to drive innovation in neuromorphic computing, paving the way for more efficient and powerful computational paradigms.

Tunable synaptic behaviors of solution-processed InGaO films for artificial visual systems

Due to their persistent photoconductivity, amorphous metal oxide thin films are promising for construction of artificial visual systems. In this work, large-scale, uniformly distributed amorphous InGaO thin films with an adjustable In/Ga ratio and thickness are prepared successfully by a low-cost environmentally friendly and easy-to-handle solution process for constructing artificial visual systems. With the increase of the In/Ga ratio and film thickness, the number of oxygen vacancies increases, along with the increase of post-synaptic current triggered by illumination, benefiting the transition of short-term plasticity to long-term plasticity. With an optimal In/Ga ratio and film thickness, the conductance response difference at a decay of 0 s between the 1st and the 10th views of a 5 × 5 array InGaO thin film transistor is up to 2.88 μA, along with an increase in the Idecay 30s/Idecay 0s ratio from 45.24% to 53.24%, resulting in a high image clarity and non-volatile artificial visual memory. Furthermore, a three-layer artificial vision network is constructed to evaluate the image recognition capability, exhibiting an accuracy of up to 91.32%. All results promise low-cost and easy-to-handle amorphous InGaO thin films for future visual information processing and image recognition.

Body-Integrated Ultrasensitive All-Textile Pressure Sensors for Skin-Inspired Artificial Sensory Systems

Tactile sensing plays a vital role in human somatosensory perception as it provides essential touch information necessary for interacting with the environment and accomplishing daily tasks. The progress in textile electronics has opened up opportunities for developing intelligent wearable devices that enable somatosensory perception and interaction. Herein, a skin-inspired all-textile pressure sensor (ATP) is presented that emulates the sensing and interaction functions of human skin, offering wearability, comfort, and breathability. The ATP demonstrates impressive features, including ultrahigh sensitivity (1.46 × 106 kPa-1), fast response time (1 ms), excellent stability and durability (over 2000 compression-release cycles), a low detection limit of 10 Pa, and remarkable breathability (93.2%). The multipixel array of ATPs has been proven to facilitate static and dynamic mapping of spatial pressure, as well as pressure trajectory monitoring functions. Moreover, by integrating ATP with oscillation circuits, external force stimuli can be directly encoded into digital frequency pulses that resemble human physiological signals. The frequency of output pulses increases with the applied pressure. Consequently, an ATP-based artificial sensory system is constructed for intelligent tactile perception. This work provides a simple and versatile strategy for practical applications of wearable electronics in the fields of robotics, sports science, and human-machine interfaces technologies.

Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel Machine Vision

In-sensor and near-sensor computing architectures enable multiply accumulate operations to be carried out directly at the point of sensing. In-sensor architectures offer dramatic power and speed improvements over traditional von Neumann architectures by eliminating multiple analog-to-digital conversions, data storage, and data movement operations. Current in-sensor processing approaches rely on tunable sensors or additional weighting elements to perform linear functions such as multiply accumulate operations as the sensor acquires data. This work implements in-sensor computing with an oscillatory retinal neuron device that converts incident optical signals into voltage oscillations. A computing scheme is introduced based on the frequency shift of coupled oscillators that enables parallel, frequency multiplexed, nonlinear operations on the inputs. An experimentally implemented 3 × 3 focal plane array of coupled neurons shows that functions approximating edge detection, thresholding, and segmentation occur in parallel. An example of inference on handwritten digits from the MNIST database is also experimentally demonstrated with a 3 × 3 array of coupled neurons feeding into a single hidden layer neural network, approximating a liquid-state machine. Finally, the equivalent energy consumption to carry out image processing operations, including peripherals such as the Fourier transform circuits, is projected to be <20 fJ/OP, possibly reaching as low as 15 aJ/OP.

Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system

Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO2 memristor including endurance (>1012), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces.

Collective dynamics and long-range order in thermal neuristor networks

In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed "thermal neuristors." These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Highly Promising 2D/1D BP-C/CNT Bionic Opto-Olfactory Co-Sensory Artificial Synapses for Multisensory Integration

The development of high-performance artificial synaptic neuromorphic devices poses a significant challenge in the creation of biomimetic sensing neural systems that seamlessly integrate both sensory and computational functionalities. In pursuit of this objective, promising bionic opto-olfactory co-sensory artificial synapse devices are constructed utilizing the BP-C/CNT (2D/1D) hybrid filter membrane as the resistive layer. Experimental results demonstrated that the devices seamlessly integrated the light modulation, gas detection, and biological synaptic functions into a single device while addressing the challenge with separating artificial synaptic devices from sensors. These devices offered the following advantages: 1) Simulating visual synapses, they can effectively replicate fundamental synaptic functions under both electrical and optical stimulation. 2) By emulating olfactory synapse responses to specific gases, they can achieve ultra-low detection limits and rapid identification of ethanol and acetone gases. 3) They enable photo-olfactory co-sensing simulations that mimic synaptic function under light-modulated pulse conditions in distinct gas environments, facilitating the study of synaptic learning rules and Pavlovian responses. This work provides a pioneering approach for exploring highly stable 2D BP-based optoelectronics and advancing the development of biomimetic neural systems.

Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors

Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.

Dimension-Controlled VO2 Film for Optoelectronic Logic Gates and Information Encryption

As the fields of photonics and information technology develop, a lot of novel applications based on VO2 material, such as optoelectronic computing and information encryption, have been developed. While the performance of these devices was not only closely associated with the VO2 phase transition properties but also depended on their dimensional characteristics. In the current study, we conducted the dimension-controlled vanadium dioxide (VO2) film growth, resulting in the epitaxial 2-dimensional (2D) VO2 film and well-distributed 3-dimensional (3D) VO2 crystal film deposition, respectively. It was revealed that, unlike the 2D film, the pronounced localized surface plasmon resonance dominated the near-infrared spectrum across the phase transition for the 3D VO2 film due to the naturally formed meta-surface structure, which showed a transmittance valley in the infrared spectrum after metallization. Based on this distinct infrared spectrum feature in the 3D VO2 film, we proposed an optoelectronic logic gate controlled by the input voltage and the probing Vis/IR light. By detecting the transmittance states of the probing light with different wavelengths, we achieved multistate encoding functions and demonstrated the information encryption application. This new conception device also showed great potential for some other applications such as optoelectronic coupled computing, information encryption, and optical near-field sensing computing.

Optoelectronic Devices for In-Sensor Computing

The demand for accurate perception of the physical world leads to a dramatic increase in sensory nodes. However, the transmission of massive and unstructured sensory data from sensors to computing units poses great challenges in terms of power-efficiency, transmission bandwidth, data storage, time latency, and security. To efficiently process massive sensory data, it is crucial to achieve data compression and structuring at the sensory terminals. In-sensor computing integrates perception, memory, and processing functions within sensors, enabling sensory terminals to perform data compression and data structuring. Here, vision sensors are adopted as an example and discuss the functions of electronic, optical, and optoelectronic hardware for visual processing. Particularly, hardware implementations of optoelectronic devices for in-sensor visual processing that can compress and structure multidimensional vision information are examined. The underlying resistive switching mechanisms of volatile/nonvolatile optoelectronic devices and their processing operations are explored. Finally, a perspective on the future development of optoelectronic devices for in-sensor computing is provided.

Computational elements based on coupled VO2 oscillators via tunable thermal triggering

Computational technologies based on coupled oscillators are of great interest for energy efficient computing. A key to developing such technologies is the tunable control of the interaction among oscillators which today is accomplished by additional electronic components. Here we show that the synchronization of closely spaced vanadium dioxide (VO2) oscillators can be controlled via a simple thermal triggering element that itself is formed from VO2. The net energy consumed by the oscillators is lower during thermal coupling compared with the situation where they are oscillating independently. As the size of the oscillator shrinks from 6 μm to 200 nm both the energy efficiency and the oscillator frequency increases. Based on such oscillators with active tuning, we demonstrate AND, NAND, and NOR logic gates and various firing patterns that mimic the behavior of spiking neurons. Our findings demonstrate an innovative approach towards computational techniques based on networks of thermally coupled oscillators.

Biorealistic Neuronal Temperature-Sensitive Dynamics within Threshold Switching Memristors: Toward Neuromorphic Thermosensation

Neuromorphic nanoelectronic devices that can emulate the temperature-sensitive dynamics of biological neurons are of great interest for bioinspired robotics and advanced applications such as in silico neuroscience. In this work, we demonstrate the biomimetic thermosensitive properties of two-terminal V3O5 memristive devices and showcase their similarity to the firing characteristics of thermosensitive biological neurons. The temperature-dependent electrical characteristics of V3O5-based memristors are used to understand the spiking response of a simple relaxation oscillator. The temperature-dependent dynamics of these oscillators are then compared with those of biological neurons through numerical simulations of a conductance-based neuron model, the Morris-Lecar neuron model. Finally, we demonstrate a robust neuromorphic thermosensation system inspired by biological thermoreceptors for bioinspired thermal perception and representation. These results not only demonstrate the biorealistic emulative potential of threshold-switching memristors but also establish V3O5 as a functional material for realizing solid-state neurons for neuromorphic computing and sensing applications.

Tuning the Intrinsic Stochasticity of Resistive Switching in VO2 Microresistors

Vanadium dioxide (VO2) microresistors exhibit resistive switching above a certain threshold voltage, allowing them to emulate neurons in neuromorphic systems. However, such devices present intrinsic cycle-to-cycle variations in their resistances and threshold voltages, which can be detrimental or beneficial, depending on their use. Here, we study this stochasticity in VO2 microresistors with various grain sizes and dimensions, through high-resolution electrical and optical measurements across numerous cycles. Our results highlight that the cycle-to-cycle variations in threshold voltage increase as the grain size becomes comparable to the device dimensions. We also present observations of multimodal threshold voltage distributions in the smaller-length resistors. To understand the underlying phenomenon, we investigate the relationship between the device insulating resistance and threshold voltage distributions, showing that these modes could correspond to distinct percolation paths and filaments. Our findings provide the first experimentally verified guidelines for designing VO2 devices with minimized/maximized stochasticity, depending on the targeted application.

Atomically Resolved Phase Coexistence in VO2 Thin Films

Concurrent structural and electronic transformations in VO2 thin films are of 2-fold importance: enabling fine-tuning of the emergent electrical properties in functional devices, yet creating an intricate interfacial domain structure of transitional phases. Despite the importance of understanding the structure of VO2 thin films, a detailed real-space atomic structure analysis in which the oxygen atomic columns are also resolved is lacking. Moreover, intermediate atomic structures have remained elusive due to the lack of robust atomically resolved quantitative analysis. Here, we directly resolve both V and O atomic columns and discover the presence of intermediate monoclinic (M2) phase nanolayers (less than 2 nm thick) in epitaxially grown VO2 films on a TiO2 (001) substrate, where the dominant part of VO2 undergoes a transition from the tetragonal (rutile) phase to the monoclinic M1 phase. Strain analysis suggests that the presence of the M2 phase is related to local strain gradients near the TiO2/VO2 interface. We unfold the crucial role of imaging the spatial configurations of the oxygen anions (in addition to V cations) by utilizing atomic-resolution electron microscopy. Our approach can be used to unravel the structural transitions in a wide range of correlated oxides, offering substantial implications for, e.g., optoelectronics and ferroelectrics.

Ovonic threshold switching-based artificial afferent neurons for thermal in-sensor computing

Artificial afferent neurons in the sensory nervous system inspired by biology have enormous potential for efficiently perceiving and processing environmental information. However, the previously reported artificial afferent neurons suffer from two prominent challenges: considerable power consumption and limited scalability efficiency. Herein, addressing these challenges, a bioinspired artificial thermal afferent neuron based on a N-doped SiTe ovonic threshold switching (OTS) device is presented for the first time. The engineered OTS device shows remarkable uniformity and robust endurance, ensuring the reliability and efficacy of the artificial afferent neurons. A substantially decreased leakage current of the SiTe OTS device by nitrogen doping results in ultra-low power consumption less than 0.3 nJ per spike for artificial afferent neurons. The inherent temperature response exhibited by N-doped SiTe OTS materials allows us to construct a highly compact artificial thermal afferent neuron over a wide temperature range. An edge detection task is performed to further verify its thermal perceptual computing function. Our work provides an insight into OTS-based artificial afferent neurons for electronic skin and sensory neurorobotics.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Inhomogeneous Photosusceptibility of VO_{2} Films at the Nanoscale

Pump-probe nano-optical experiments were used to study the light-induced insulator to metal transition (IMT) in thin films of vanadium dioxide (VO_{2}), a prototypical correlated electron system. We show that inhomogeneous optical contrast is prompted by spatially uniform photoexcitation, indicating an inhomogeneous photosusceptibility of VO_{2}. We locally characterize temperature and time dependent variations of the photoexcitation threshold necessary to induce the IMT on picosecond timescales with hundred nanometer spatial resolution. We separately measure the critical temperature T_{L}, where the IMT onsets and the local transient electronic nano-optical contrast at the nanoscale. Our data reveal variations in the photosusceptibility of VO_{2} within nanoscopic regions characterized by the same critical temperature T_{L} where metallic domains can first nucleate.

Reconfigurable Ag/HfO2/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System

Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO2/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO2/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO2/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.

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.

Neuromorphic one-shot learning utilizing a phase-transition material

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.

Magnetic Flux Sensor Based on Spiking Neurons with Josephson Junctions

Josephson junctions (JJs) are superconductor-based devices used to build highly sensitive magnetic flux sensors called superconducting quantum interference devices (SQUIDs). These sensors may vary in design, being the radio frequency (RF) SQUID, direct current (DC) SQUID, and hybrid, such as D-SQUID. In addition, recently many of JJ's applications were found in spiking models of neurons exhibiting nearly biological behavior. In this study, we propose and investigate a new circuit model of a sensory neuron based on DC SQUID as part of the circuit. The dependence of the dynamics of the designed model on the external magnetic flux is demonstrated. The design of the circuit and derivation of the corresponding differential equations that describe the dynamics of the system are given. Numerical simulation is used for experimental evaluation. The experimental results confirm the applicability and good performance of the proposed magnetic-flux-sensitive neuron concept: the considered device can encode the magnetic flux in the form of neuronal dynamics with the linear section. Furthermore, some complex behavior was discovered in the model, namely the intermittent chaotic spiking and plateau bursting. The proposed design can be efficiently applied to developing the interfaces between circuitry and spiking neural networks. However, it should be noted that the proposed neuron design shares the main limitation of all the superconductor-based technologies, i.e., the need for a cryogenic and shielding system.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.

Recent progress in emerging two-dimensional organic-inorganic van der Waals heterojunctions

Two-dimensional (2D) materials have attracted significant attention in recent decades due to their exceptional optoelectronic properties. Among them, to meet the growing demand for multifunctional applications, 2D organic-inorganic van der Waals (vdW) heterojunctions have become increasingly popular in the development of optoelectronic devices. These heterojunctions demonstrate impressive capability to synergistically combine the favourable characteristics of organic and inorganic materials, thereby offering a wide range of advantages. Also, they enable the creation of innovative device structures and introduce novel functionalities in existing 2D materials, avoiding the need for lattice matching in different material systems. Presently, researchers are actively working on improving the performance of devices based on 2D organic-inorganic vdW heterojunctions by focusing on enhancing the quality of 2D materials, precise stacking methods, energy band regulation, and material selection. Therefore, this review presents a thorough examination of the emerging 2D organic-inorganic vdW heterojunctions, including their classification, fabrication, and corresponding devices. Additionally, this review offers profound and comprehensive insight into the challenges in this field to inspire future research directions. It is expected to propel researchers to harness the extraordinary capabilities of 2D organic-inorganic vdW heterojunctions for a wider range of applications by further advancing the understanding of their fundamental properties, expanding the range of available materials, and exploring novel device architectures. The ongoing research and development in this field hold potential to unlock captivating advancements and foster practical applications across diverse industries.

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.

VO2 memristor-based frequency converter with in-situ synthesize and mix for wireless internet-of-things

Wireless internet-of-things (WIoT) with data acquisition sensors are evolving rapidly and the demand for transmission efficiency is growing rapidly. Frequency converter that synthesizes signals at different frequencies and mixes them with sensor datastreams is a key component for efficient wireless transmission. However, existing frequency converters employ separate synthesize and mix circuits with complex digital and analog circuits using complementary metal-oxide semiconductor (CMOS) devices, naturally incurring excessive latency and energy consumption. Here we report a highly uniform and calibratable VO2 memristor oscillator, based on which we build memristor-based frequency converter using 8[Formula: see text]8 VO2 array that can realize in-situ frequency synthesize and mix with help of compact periphery circuits. We investigate the self-oscillation based on negative differential resistance of VO2 memristors and the programmability with different driving currents and calibration resistances, demonstrating capabilities of such frequency converter for in-situ frequency synthesize and mix for 2 ~ 8 channels with frequencies up to 48 kHz for low frequency transmission link. When transmitting classical sensor data (acoustic, vision and spatial) in an end-to-end WIoT experimental setup, our VO2-based memristive frequency converter presents up to 1.45× ~ 1.94× power enhancement with only 0.02 ~ 0.21 dB performance degradations compared with conventional CMOS-based frequency converter. This work highlights the potential in solving frequency converter's speed and energy efficiency problems in WIoT using high crystalline quality epitaxially grown VO2 and calibratable VO2-based oscillator array, revealing a promising direction for next-generation WIoT system design.

Efficient polarization-insensitive quasi-BIC modulation by VO2 thin films

Bound states in the continuum (BIC) offer great design freedom for realizing high-quality factor metasurfaces. By deliberately disrupting the inherent symmetries, BIC can degenerate into quasi-BIC exhibiting sharp spectra with strong light confinement. This transformation has been exploited to develop cutting-edge sensors and modulators. However, most proposed quasi-BICs in metasurfaces are composed of unit cells with Cs symmetry that may experience performance degradation due to polarization deviation, posing challenges in practical applications. Addressing this critical issue, our research introduces an innovative approach by incorporating metasurfaces with C4v unit cell symmetry to eliminate polarization response sensitivity. Vanadium Dioxide (VO2) is a phase-change material with a relatively low transition temperature and reversibility. Here, we theoretically investigate the polarization-insensitive quasi-BIC modulation in Si-VO2 hybrid metasurfaces. By introducing defects into metasurfaces with Cs, C4, and C4v symmetries, we enable the emergence of quasi-BICs characterized by strong Fano resonance in their transmission spectra. Via numerically calculating the multipole decomposition, distinct dominant multipoles for different quasi-BICs are identified. A comprehensive investigation into the polarization responses of these structures under varying directions of linearly polarized light reveals the superior polarization-independent characteristics of metasurfaces with C4 and C4v symmetries, a feature that ensures the maintenance of maximum resonance peaks irrespective of polarization direction. Utilizing the polarization-insensitive quasi-BIC, we thus designed two different Si-VO2 hybrid metasurfaces with C4v symmetry. Each configuration presents complementary benefits, leveraging the VO2 phase transition's loss change to facilitate efficient modulation. Our quantitative calculation indicates notable achievements in modulation depth, with a maximum relative modulation depth reaching up to 342%. For the first time, our research demonstrates efficient modulation using polarization-insensitive quasi-BICs in designed Si-VO2 hybrid metasurfaces, achieving identical polarization responses for quasi-BIC-based applications. Our work paves the way for designing polarization-independent quasi-BICs in metasurfaces and marks a notable advancement in the field of tunable integrated devices.

Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-Olfactory Crossmodal Perception

The crossmodal interaction of different senses, which is an important basis for learning and memory in the human brain, is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception, but related researches are scarce. Here, we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus (VP) van der Waals heterojunctions. Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene, the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude, reaching up to 7.7 A W-1. Excited by ultraviolet light, multiple synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, short/long-term plasticity and "learning-experience" behavior, were demonstrated with a low power consumption. Furthermore, the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments, enabling it to simulate the interaction of visual and olfactory information for crossmodal perception. This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.

Nanoelectronics Using Metal-Insulator Transition

Metal-insulator transition (MIT) coupled with an ultrafast, significant, and reversible resistive change in Mott insulators has attracted tremendous interest for investigation into next-generation electronic and optoelectronic devices, as well as a fundamental understanding of condensed matter systems. Although the mechanism of MIT in Mott insulators is still controversial, great efforts have been made to understand and modulate MIT behavior for various electronic and optoelectronic applications. In this review, recent progress in the field of nanoelectronics utilizing MIT is highlighted. A brief introduction to the physics of MIT and its underlying mechanisms is begun. After discussing the MIT behaviors of various Mott insulators, recent advances in the design and fabrication of nanoelectronics devices based on MIT, including memories, gas sensors, photodetectors, logic circuits, and artificial neural networks are described. Finally, an outlook on the development and future applications of nanoelectronics utilizing MIT is provided. This review can serve as an overview and a comprehensive understanding of the design of MIT-based nanoelectronics for future electronic and optoelectronic devices.

Artificial sensory system based on memristive devices

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.

Artificial Tactile Perception System Based on Spiking Tactile Neurons and Spiking Neural Networks

The artificial tactile perception system of this work utilizes a fully connected spiking neural network (SNN) comprising two layers. Its architecture is streamlined and energy-efficient as it directly integrates spiking tactile neurons with piezoresistive sensors and Pt/NbOx/TiN memristors as input neurons. These spiking tactile neurons possess the ability to perceive and integrate pressure stimuli from multiple sensors and encode the information into rate-coded electrical spikes, closely resembling the behavior of a biological tactile neuron. The system's real-time information processing capability is demonstrated through an artificial perceptual learning system that successfully encodes and decodes the Morse code; the artificial perceptual learning system accurately recognizes and displays 26 English letters. Furthermore, the artificial tactile perception system is evaluated for the recognition of the MNIST data set, achieving a classification accuracy of 85.7% with the supervised spiking-rate-dependent plasticity learning rule. The key advantages of this artificial tactile perception system are its simple structure and high efficiency, which contributes to its practicality for various real-world applications.

Self-Rectifying Memristors for Three-Dimensional In-Memory Computing

Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor is developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large-scale and high-density crossbar arrays, particularly in three-dimensional (3D) integration. As a perfect solution to the sneak-path issue, a self-rectifying memristor (SRM) is proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (aJ level) and scalability (>102  Mbit). Moreover, SRM-configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.

A Butterfly-Inspired Multisensory Neuromorphic Platform for Integration of Visual and Chemical Cues

Unisensory cues are often insufficient for animals to effectively engage in foraging, mating, and predatory activities. In contrast, integration of cues collected from multiple sensory organs enhances the overall perceptual experience and thereby facilitates better decision-making. Despite the importance of multisensory integration in animals, the field of artificial intelligence (AI) and neuromorphic computing has primarily focused on processing unisensory information. This lack of emphasis on multisensory integration can be attributed to the absence of a miniaturized hardware platform capable of co-locating multiple sensing modalities and enabling in-sensor and near-sensor processing. In this study, this limitation is addressed by utilizing the chemo-sensing properties of graphene and the photo-sensing capability of monolayer molybdenum disulfide (MoS2 ) to create a multisensory platform for visuochemical integration. Additionally, the in-memory-compute capability of MoS2 memtransistors is leveraged to develop neural circuits that facilitate multisensory decision-making. The visuochemical integration platform is inspired by intricate courtship of Heliconius butterflies, where female species rely on the integration of visual cues (such as wing color) and chemical cues (such as pheromones) generated by the male butterflies for mate selection. The butterfly-inspired visuochemical integration platform has significant implications in both robotics and the advancement of neuromorphic computing, going beyond unisensory intelligence and information processing.

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.

Generative complex networks within a dynamic memristor with intrinsic variability

Artificial neural networks (ANNs) have gained considerable momentum in the past decade. Although at first the main task of the ANN paradigm was to tune the connection weights in fixed-architecture networks, there has recently been growing interest in evolving network architectures toward the goal of creating artificial general intelligence. Lagging behind this trend, current ANN hardware struggles for a balance between flexibility and efficiency but cannot achieve both. Here, we report on a novel approach for the on-demand generation of complex networks within a single memristor where multiple virtual nodes are created by time multiplexing and the non-trivial topological features, such as small-worldness, are generated by exploiting device dynamics with intrinsic cycle-to-cycle variability. When used for reservoir computing, memristive complex networks can achieve a noticeable increase in memory capacity a and respectable performance boost compared to conventional reservoirs trivially implemented as fully connected networks. This work expands the functionality of memristors for ANN computing.

A bio-inspired visuotactile neuron for multisensory integration

Multisensory integration is a salient feature of the brain which enables better and faster responses in comparison to unisensory integration, especially when the unisensory cues are weak. Specialized neurons that receive convergent input from two or more sensory modalities are responsible for such multisensory integration. Solid-state devices that can emulate the response of these multisensory neurons can advance neuromorphic computing and bridge the gap between artificial and natural intelligence. Here, we introduce an artificial visuotactile neuron based on the integration of a photosensitive monolayer MoS2 memtransistor and a triboelectric tactile sensor which minutely captures the three essential features of multisensory integration, namely, super-additive response, inverse effectiveness effect, and temporal congruency. We have also realized a circuit which can encode visuotactile information into digital spiking events, with probability of spiking determined by the strength of the visual and tactile cues. We believe that our comprehensive demonstration of bio-inspired and multisensory visuotactile neuron and spike encoding circuitry will advance the field of neuromorphic computing, which has thus far primarily focused on unisensory intelligence and information processing.