Artificial Sensory Memory

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.

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.

Autonomous Artificial Olfactory Sensor Systems with Homeostasis Recovery via a Seamless Neuromorphic Architecture

Neuromorphic olfactory systems have been actively studied in recent years owing to their considerable potential in electronic noses, robotics, and neuromorphic data processing systems. However, conventional gas sensors typically have the ability to detect hazardous gas levels but lack synaptic functions such as memory and recognition of gas accumulation, which are essential for realizing human-like neuromorphic sensory system. In this study, a seamless architecture for a neuromorphic olfactory system capable of detecting and memorizing the present level and accumulation status of nitrogen dioxide (NO2) during continuous gas exposure, regulating a self-alarm implementation triggered after 147 and 85 s at a continuous gas exposure of 20 and 40 ppm, respectively. Thin-film-transistor type gas sensors utilizing carbon nanotube semiconductors detect NO2 gas molecules through carrier trapping and exhibit long-term retention properties, which are compatible with neuromorphic excitatory applications. Additionally, the neuromorphic inhibitory performance is also characterized via gas desorption with programmable ultraviolet light exposure, demonstrating homeostasis recovery. These results provide a promising strategy for developing a facile artificial olfactory system that demonstrates complicated biological synaptic functions with a seamless and simplified system architecture.

Flexible Organic Transistors for Biosensing: Devices and Applications

Flexible and stretchable biosensors can offer seamless and conformable biological-electronic interfaces for continuously acquiring high-fidelity signals, permitting numerous emerging applications. Organic thin film transistors (OTFTs) are ideal transducers for flexible and stretchable biosensing due to their soft nature, inherent amplification function, biocompatibility, ease of functionalization, low cost, and device diversity. In consideration of the rapid advances in flexible-OTFT-based biosensors and their broad applications, herein, a timely and comprehensive review is provided. It starts with a detailed introduction to the features of various OTFTs including organic field-effect transistors and organic electrochemical transistors, and the functionalization strategies for biosensing, with a highlight on the seminal work and up-to-date achievements. Then, the applications of flexible-OTFT-based biosensors in wearable, implantable, and portable electronics, as well as neuromorphic biointerfaces are detailed. Subsequently, special attention is paid to emerging stretchable organic transistors including planar and fibrous devices. The routes to impart stretchability, including structural engineering and material engineering, are discussed, and the implementations of stretchable organic transistors in e-skin and smart textiles are included. Finally, the remaining challenges and the future opportunities in this field are summarized.

Paraffin-Enabled Superlattice Customization for a Photostimulated Gradient-Responsive Artificial Reflex Arc

The development of photostimulated-motion artificial reflex arcs - a neural circuit inspired by light-driven motion reflexes - holds significant promises for advancements in robotic perception, navigation, and motion control. However, the fabrication of such systems, especially those that accommodate multiple actions and exhibit gradient responses, remains challenging. Here, a gradient-responsive photostimulated-motion artificial reflex arc is developed by integrating a programmable and tunable photoreceptor based on folded MoS2 at different twist angles. The twisted folded bilayer MoS2 used as photoreceptors can be customized via the transfer technique using patternable paraffin, where the twist angle and fold-line could be controlled. The photoluminescence (PL) intensity is 3.7 times higher at a twist angle of 29° compared to that at 0°, showing a monotonically decreasing indirect bandgap. Through tunable interlayer carrier transport, photoreceptors fabricated using folded bilayer MoS2 at different twist angles demonstrate gradient response time, enabling the photostimulated-motion artificial reflex arc for multiaction responses. They are transformed to digital command flow and studied via machine learning to control the gestures of a robotic hand, showing a prototype of photostimulated gradient-responsive artificial reflex arcs for the first time. This work provides a unique idea for developing intelligent soft robots and next-generation human-computer interfaces.

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.

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models. However, hardware neural network reports still mainly focus on shallow networks (2 to 5 layers). Implementing deep neural networks in hardware is challenging due to the layer-by-layer structure, resulting in long training times, signal interference, and low accuracy due to gradient explosion/vanishing. Here, we utilize negative ultraviolet photoconductive light-emitting memristors with intrinsic parallelism and hardware-software co-design to achieve electrical information's optical cross-layer transmission. We propose a hybrid ultra-deep photoelectric neural network and an ultra-deep super-resolution reconstruction neural network using light-emitting memristors and cross-layer block, expanding the networks to 54 and 135 layers, respectively. Further, two networks enable transfer learning, approaching or surpassing software-designed networks in multi-dataset recognition and high-resolution restoration tasks. These proposed strategies show great potential for high-precision multifunctional hardware neural networks and edge artificial intelligence.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.

Toward a Brain-Neuromorphics Interface

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.

An Infrared Near-Sensor Reservoir Computing System Based on Large-Dynamic-Space Memristor with Tens of Thousands of States for Dynamic Gesture Perception

To efficiently process the massive amount of sensor data, it is demanding to develop a new paradigm. Inspired by neurobiological systems, an infrared near-senor reservoir computing (RC) system, consisting of infrared sensors and memristors based on single-crystalline LiTaO3 and LiNbO3 (LN) thin film respectively, is demonstrated. The analog memristor is used as a reservoir in the RC system to process sensor signals with spatiotemporal characteristics. LN crystal structure stacked with oxygen octahedra provides favorable conditions for reliable Mott variable-range hopping conduction, which provides the memristor with tens of thousands of reservoir states within a large dynamic range. With the characteristics, the analog sensor signals with high data fidelity can be directly fed to the memristive reservoir, and the spatiotemporal features can be separated and mapped. The system demonstrated a dynamic gesture perception task, achieving an accuracy of 99.6%, which highlights the great application potential of the memristor in signal sensor processing and will advance the application of artificial intelligence in sensor systems. Crystal ion slicing techniques are used to fabricate a single-crystalline thin film for both the memristor and sensor, which opens up the possibility of realizing monolithic integration of a memristor-based near-sensor computing system.

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.

Optoelectronic Synapse Based on 2D Electron Gas in Stoichiometry-Controlled Oxide Heterostructures

Emulating synaptic functionalities in optoelectronic devices is significant in developing artificial visual-perception systems and neuromorphic photonic computing. Persistent photoconductivity (PPC) in metal oxides provides a facile way to realize the optoelectronic synaptic devices, but the PPC performance is often limited due to the oxygen vacancy defects that release excess conduction electrons without external stimuli. Herein, a high-performance optoelectronic synapse based on the stoichiometry-controlled LaAlO3 /SrTiO3 (LAO/STO) heterostructure is developed. By increasing La/Al ratio up to 1.057:1, the PPC is effectively enhanced but suppressed the background conductivity at the LAO/STO interface, achieving strong synaptic behaviors. The spectral noise analyses reveal that the synaptic behaviors are attributed to the cation-related point defects and their charge compensation mechanism near the LAO/STO interface. The short-term and long-term plasticity is demonstrated, including the paired-pulse facilitation, in the La-rich LAO/STO device upon exposure to UV light pulses. As proof of concepts, two essential synaptic functionalities, the pulse-number-dependent plasticity and the self-noise cancellation, are emulated using the 5 × 5 array of La-rich LAO/STO synapses. Beyond the typical oxygen deficiency control, the results show how harnessing the cation stoichiometry can be used to design oxide heterostructures for advanced optoelectronic synapses and neuromorphic applications.

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.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

An Artificial Olfactory System Based on a Memristor Can Simulate Organ Injury and Functions in Air Purification

Artificial olfactory systems are receiving increasing attention because of their potential applications in humanoid robots, artificial noses, and the next generation of human-computer interactions. However, simulating the human olfactory system, which recognizes, remembers, and automatically takes protective measures against gases, remains a challenge. In this paper, a WO3-TiO2@Ag NPs (silver nanoparticle) gas sensor was prepared by the sol-gel method, and an Al/pectin:AgNP/ITO memristor was prepared by spin coating and vacuum evaporation. The gas sensor has been combined with the memristor to simulate physical damage to humans in a dangerous gas environment for a long time, and an artificial olfactory system is constructed by field-programmable gate array external control. The WO3-TiO2@Ag NPs gas sensor can sense and identify ethanol vapor through changes in resistance, and the signal transmitted to the pectin-based memristor can switch the resistance state of the memristor to store gas information. Furthermore, the activation of the memristor can also trigger rotation of the fan to purify the gas and reduce damage caused by excessive exposure to dangerous gases. This artificial olfactory system provides a promising strategy for the development of artificial intelligence and human-computer interaction systems.

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.

Artificial Visual Systems Fabricated with Ferroelectric van der Waals Heterostructure for In-Memory Computing Applications

Rapid developments in artificial neural network techniques and retina-inspired artificial visual systems are required to realize the sensing, processing, and memorization of an optical signal in a single device. Herein, a ferroelectric field-effect transistor fabricated with CuInP2S6 and α-In2Se3 van der Waals heterostructures is proposed and demonstrated for the development of an artificial visual system. The dipole polarizations are coupled and bidirectionally locked inside the ferroelectric α-In2Se3 along the in-plane and out-of-plane directions and are controlled by the gate voltages. Furthermore, light-induced polarization can change the order of polarization of the dipoles inside α-In2Se3. We demonstrate that using the combined control of these electrical and optical signals, the device may function like a retina-inspired vision system. The device can operate across a wide wavelength range (405-850 nm) and detect very low incident light (0.03 mW/cm2). Color recognition, high paired-pulse facilitation (∼170%), and short- to long-term memory transitions through quick learning are observed using this device. Additionally, this device demonstrates different complex processing abilities, including pattern recognition, light adaptation, optical logic operation, and event learning. The proposed ferroelectric heterostructure-based artificial visual system can serve as an essential bridge for fulfilling the future requirements of all-in-one sensing and memory-processing devices.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

Tailoring Wavelength-Adaptive Visual Neuroplasticity Transitions of Synaptic Transistors Comprising Rod-Coil Block Copolymers for Dual-Mode Photoswitchable Learning/Forgetting Neural Functions

The vision-inspired artificial neural network based on optical synapses has drawn a tremendous amount of attention for emulating biological senses. Although photoexcitation-induced synaptic functionalities have been widely studied, optical habituation via the photoinhibitory pathway is yet to be demonstrated for sophisticated biomimetic visual adaptive systems. Here, the first optical neuromorphic block copolymer (BCP) phototransistor is demonstrated as an all-optical operation responding to various wavelengths, fulfilling photoassisted dynamic learning/forgetting cycles via optical potentiation without gate bias. The polyfluorene BCPs were precisely designed to enable wavelength-adaptive responses, benefiting from interfacial semiconductor/electret morphology and the crystallinity/electron affinity of the BCPs. Notably, this is the first work to simultaneously exhibit fully light-controlled short- and long-term memory based on organic material systems. The device presents a high current contrast above 100-fold and long-term retention over 104 s. As a proof-of-concept for neural networks, a 6 × 6 array of photosynapses performed outstanding visual pattern learning/forgetting with high accuracy. This study exploits the design strategy of a conjugated BCP electret to unleash the full potential of wavelength-adaptive visual neuroplasticity transitions. It provides an effective architecture for designing high-performance and high-storage capacity required applications in next-generation neuromorphic systems.

Energy Efficient Artificial Olfactory System with Integrated Sensing and Computing Capabilities for Food Spoilage Detection

Artificial olfactory systems (AOSs) that mimic biological olfactory systems are of great interest. However, most existing AOSs suffer from high energy consumption levels and latency issues due to data conversion and transmission. In this work, an energy- and area-efficient AOS based on near-sensor computing is proposed. The AOS efficiently integrates an array of sensing units (merged field effect transistor (FET)-type gas sensors and amplifier circuits) and an AND-type nonvolatile memory (NVM) array. The signals of the sensing units are directly connected to the NVM array and are computed in memory, and the meaningful linear combinations of signals are output as bit line currents. The AOS is designed to detect food spoilage by employing thin zinc oxide films as gas-sensing materials, and it exhibits low detection limits for H2 S and NH3 gases (0.01 ppm), which are high-protein food spoilage markers. As a proof of concept, monitoring the entire spoilage process of chicken tenderloin is demonstrated. The system can continuously track freshness scores and food conditions throughout the spoilage process. The proposed AOS platform is applicable to various applications due to its ability to change the sensing temperature and programmable NVM cells.

One-Phototransistor-One-Memristor Array with High-Linearity Light-Tunable Weight for Optic Neuromorphic Computing

The recent advances in optic neuromorphic devices have led to a subsequent rise in use for construction of energy-efficient artificial-vision systems. The widespread use can be attributed to their ability to capture, store, and process visual information from the environment. The primary limitations of existing optic neuromorphic devices include nonlinear weight updates, cross-talk issues, and silicon process incompatibility. In this study, a highly linear, light-tunable, cross-talk-free, and silicon-compatible one-phototransistor-one-memristor (1PT1R) optic memristor is experimentally demonstrated for the implementation of an optic artificial neural network (OANN). For optic image recognition in the experiment, an OANN is constructed using a 16 × 3 1PT1R memristor array, and it is trained on an online platform. The model yields an accuracy of 99.3% after only ten training epochs. The 1PT1R memristor, which shows good performance, demonstrates its ability as an excellent hardware solution for highly efficient optic neuromorphic and edge computing.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

An Artificial Olfactory System Based on a Chemi-Memristive Device

Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Adaptive Memory of a Neuromorphic Transistor with Multi-Sensory Signal Fusion

One of the ultimate goals of artificial intelligence is to achieve the capability of memory evolution and adaptability to changing environments, which is termed adaptive memory. To realize adaptive memory in artificial neuromorphic devices, artificial synapses with multi-sensing capability are required to collect and analyze various sensory cues from the external changing environments. However, due to the lack of platforms for mediating multiple sensory signals, most artificial synapses have been mainly limited to unimodal or bimodal sensory devices. Herein, we present a multi-modal artificial sensory synapse (MASS) based on an organic synapse to realize sensory fusion and adaptive memory. The MASS receives optical, electrical, and pressure information and in turn generates typical synaptic behaviors, mimicking the multi-sensory neurons in the brain. Sophisticated synaptic behaviors, such as Pavlovian dogs, writing/erasing, signal accumulation, and offset, were emulated to demonstrate the joint efforts of bimodal sensory cues. Moreover, associative memory can be formed in the device and be subsequently reshaped by signals from a third perception, mimicking modification of the memory and cognition when encountering a new environment. Our MASS provides a step toward next-generation artificial neural networks with an adaptive memory capability.

Vertically integrated spiking cone photoreceptor arrays for color perception

The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a metal oxide-based vertically integrated spiking cone photoreceptor array, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. Such spiking cone photoreceptors have an ultralow power consumption of less than 400 picowatts per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo-three-primary colors to form 'colorful' images for recognition tasks, and the device with the ability to discriminate mixed colors shows better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Intelligent matter endows reconfigurable temperature and humidity sensations for in-sensor computing

Data-centric tactics with in-sensor computing go beyond the conventional computing-centric tactic that is suffering from processing latency and excessive energy consumption. The multifunctional intelligent matter with dynamic smart responses to environmental variations paves the way to implement data-centric tactics with high computing efficiency. However, intelligent matter with humidity and temperature sensitivity has not been reported. In this work, a design is demonstrated based on a single memristive device to achieve reconfigurable temperature and humidity sensations. Opposite temperature sensations at the low resistance state (LRS) and high resistance state (HRS) were observed for low-level sensory data processing. Integrated devices mimicking intelligent electronic skin (e-skin) can work in three modes to adapt to different scenarios. Additionally, the device acts as a humidity-sensory artificial synapse that can implement high-level cognitive in-sensor computing. The intelligent matter with reconfigurable temperature and humidity sensations is promising for energy-efficient artificial intelligence (AI) systems.

A low-power and flexible bioinspired artificial sensory neuron capable of tactile perceptual and associative learning

Biomimetic haptic neuron systems have received a lot of attention from the booming artificial intelligence industry for their wide applications in personal health monitoring, electronic skin, and human-machine interfaces. In this work, inspired by the human tactile afferent nerve, we developed a flexible and low energy consumption artificial tactile neuron, which was constructed by combining a dual network (DN) hydrogel-based sensor and a low power memristor. The tactile sensor (ITO/PAM:CS-Fe³⁺ hydrogel/ITO) serves as E-skin, with mechanical properties including pressure and stretching. The memristor (Ti:ITO/BiFeO₃/ITO) serving as an artificial synapse has low power (∼3.96 × 10^-7 W), remarkable uniformity, a large memory window of 500 and excellent plasticity. Remarkably, the pattern recognition simulation based on a neuromorphic network is conducted with a high recognition accuracy of ∼89.81%. In the constructed system, the artificial synapse could be activated by the electrical information from the E-skin induced by an external pressure, to generate excitatory postsynaptic currents. The system shows functions of perception and memory functions, and it also enables tactile associative learning. The present work is important for the development of empowering robots and prostheses with the capability of perceptual learning, and it provides a paradigm for next-generation artificial sensory systems with low-power, wearable and low-cost features.

In-Memory Tactile Sensor with Tunable Steep-Slope Region for Low-Artifact and Real-Time Perception of Mechanical Signals

A tactile sensor needs to perceive static pressures and dynamic forces in real-time with high accuracy for early diagnosis of diseases and development of intelligent medical prosthetics. However, biomechanical and external mechanical signals are always aliased (including variable physiological and pathological events and motion artifacts), bringing great challenges to precise identification of the signals of interest (SOI). Although the existing signal segmentation methods can extract SOI and remove artifacts by blind source separation and/or additional filters, they may restrict the recognizable patterns of the device, and even cause signal distortion. Herein, an in-memory tactile sensor (IMT) with a dynamically adjustable steep-slope region (SSR) and nanocavity-induced nonvolatility (retention time >1000 s) is proposed on the basis of a machano-gated transistor, which directly transduces the tactile stimuli to various dope states of the channel. The programmable SSR endows the sensor with a critical window of responsiveness, realizing the perception of signals on demand. Owing to the nonvolatility of the sensor, the mapping of mechanical cues with high spatiotemporal accuracy and associative learning between two physical inputs are realized, contributing to the accurate assessment of the tissue health status and ultralow-power (about 25.1 μW) identification of an occasionally occurring tremor.

Neuromorphic Gustatory System with Salt-Taste Perception, Information Processing, and Excessive-Intake Warning Capabilities

Emulation of the process of a biological gustatory system could benefit the reconstruction of sense of taste. Here we demonstrate the first neuromorphic gustatory system that emulates the ability of taste perception, information processing, and excessive-intake warning functions. The system integrates a chitosan-derived ion-gel sensor, SnO₂ nanowire artificial synapses, and an effect-executive unit. The system accomplish perception and encoding behaviors for taste stimulation without using complex circuits and multivariate analysis, showing short response delay (<1 s), long taste memory duration (>2 h), and a wide perceptive concentration range (0.02-6 wt % salt solution). Especially, SnO₂ NW artificial synapses have extremely small response voltage (1 mV), exceeding the biological level by orders of magnitude, representing so-far the highest sensitivity record. This work provides a promising strategy to develop bioinspired and biointegrated electronics with the intention of mimicking and restoring the functions of biological sensory systems.

Emerging electrolyte-gated transistors for neuromorphic perception

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.

A luminescent Eu@SOF film fabricated by electrophoretic deposition as ultrasensitive platform for styrene gas quantitative monitoring through fluorescence sensing and ANNs model

Styrene is a harmful gas widely existing in the air, which can damage human organs. Therefore, it is very crucial to develop a sensitive, portable and simple sensor for monitoring styrene. Herein, we design and fabricate a luminescent Eu@TMA-ME/FTO film (F) through EPD method. F emits bright red light of Eu(III) ions and shows superior fluorescence response to styrene gas as a sensor, which enable real-time and quantitative monitoring for styrene gas. More importantly, F exhibits a linear response to styrene gas in a wide concentration range of 10^-7 to 10^-2 M and a low DL with 0.20 ppm. The efficient PET process to styrene induced by ME and the competitive absorption between styrene and F are responsible for the sensing mechanism. Besides, the detection of styrene solution is also investigated in deionized water, tap water and river water. For the further application, an intelligent ANNs model has been constructed to process the fluorescence sensing results, which can convert fluorescence sensing images to the concentration of styrene gas. The data demonstrates that ANNs model can accurately monitor the concentration of styrene gas via deep ML without tedious data processing.

A Flexible Tribotronic Artificial Synapse with Bioinspired Neurosensory Behavior

As key components of artificial afferent nervous systems, synaptic devices can mimic the physiological synaptic behaviors, which have attracted extensive attentions. Here, a flexible tribotronic artificial synapse (TAS) with bioinspired neurosensory behavior is developed. The triboelectric potential generated by the external contact electrification is used as the ion-gel-gate voltage of the organic thin film transistor, which can tune the carriers transport through the migration/accumulation of ions. The TAS successfully demonstrates a series of synaptic behaviors by external stimuli, such as excitatory postsynaptic current, paired-pulse facilitation, and the hierarchical memory process from sensory memory to short-term memory and long-term memory. Moreover, the synaptic behaviors remained stable under the strain condition with a bending radius of 20 mm, and the TAS still exhibits excellent durability after 1000 bending cycles. Finally, Pavlovian conditioning has been successfully mimicked by applying force and vibration as food and bell, respectively. This work demonstrates a bioinspired flexible artificial synapse that will help to facilitate the development of artificial afferent nervous systems, which is great significance to the practical application of artificial limbs, robotics, and bionics in future.

Bioinspired Artificial Motion Sensory System for Rotation Recognition and Rapid Self-Protection

As one of the most common synergies between the exteroceptors and proprioceptors, the synergy between visual and vestibule enables the human brain to judge the state of human motion, which is essential for motion recognition and human self-protection. Hence, in this work, an artificial motion sensory system (AMSS) based on artificial vestibule and visual is developed, which consists of a tribo-nanogenerator (TENG) as a vestibule that can sense rotation and synaptic transistor array as retina. The principle of temporal congruency has been successfully realized by multisensory input. In addition, pattern recognition results show that the accuracy of multisensory integration is more than 15% higher than that of single sensory. Moreover, due to the rotation recognition and visual recognition functions of AMSS, we realized multimodal information recognition including angles and numbers in the spiking correlated neural network (SCNN), and the accuracy rate reached 89.82%. Besides, the rapid self-protection of a human was successfully realized by AMSS in the case of simulated amusement rides, and the reaction time of multiple motion sensory integration is only one-third of that of a single vestibule. The development of AMSS based on the synergy of simulated vision and vestibule will show great potential in neural robot, artificial limbs, and soft electronics.

Engineering Polymeric Nanofluidic Membranes for Efficient Ionic Transport: Biomimetic Design, Material Construction, and Advanced Functionalities

Design elements extracted from biological ion channels guide the engineering of artificial nanofluidic membranes for efficient ionic transport and spawn biomimetic devices with great potential in many cutting-edge areas. In this context, polymeric nanofluidic membranes can be especially attractive because of their inherent flexibility and benign processability, which facilitate massive fabrication and facile device integration for large-scale applications. Herein, the state-of-the-art achievements of polymeric nanofluidic membranes are systematically summarized. Theoretical fundamentals underlying both biological and synthetic ion channels are introduced. The advances of engineering polymeric nanofluidic membranes are then detailed from aspects of structural design, material construction, and chemical functionalization, emphasizing their broad chemical and reticular/topological variety as well as considerable property tunability. After that, this Review expands on examples of evolving these polymeric membranes into macroscopic devices and their potentials in addressing compelling issues in energy conversion and storage systems where efficient ion transport is highly desirable. Finally, a brief outlook on possible future developments in this field is provided.

Second-order associative memory circuit hardware implemented by the evolution from battery-like capacitance to resistive switching memory

Memristor-based Pavlov associative memory circuit presented today only realizes the simple condition reflex process. The secondary condition reflex endows the simple condition reflex process with more bionic, but it is only demonstrated in design and involves the large number of redundant circuits. A FeOx-based memristor exhibits an evolution process from battery-like capacitance (BLC) state to resistive switching (RS) memory as the I-V sweeping increase. The BLC is triggered by the active metal ion and hydroxide ion originated from water molecule splitting at different interfaces, while the RS memory behavior is dominated by the diffusion and migration of ion in the FeOx switching function layer. The evolution processes share the nearly same biophysical mechanism with the second-order conditioning. It enables a hardware-implemented second-order associative memory circuit to be feasible and simple. This work provides a novel path to realize the associative memory circuit with the second-order conditioning at hardware level.

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.

Artificial Vision Adaption Mimicked by an Optoelectrical In2O3 Transistor Array

Simulation of biological visual perception has gained considerable attention. In this paper, an optoelectrical In₂O₃ transistor array with a negative photoconductivity behavior is designed using a side-gate structure and a screen-printed ion-gel as the gate insulator. This paper is the first to observe a negative photoconductivity in electrolyte-gated oxide devices. Furthermore, an artificial visual perception system capable of self-adapting to environmental lightness is mimicked using the proposed device array. The transistor device array shows a self-adaptive behavior of light under different levels of light intensity, successfully demonstrating the visual adaption with an adjustable threshold range to the external environment. This study provides a new way to create an environmentally adaptive artificial visual perception system and has far-reaching significance for the future of neuromorphic electronics.

Flexible Electronics and Devices as Human-Machine Interfaces for Medical Robotics

Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human-machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human-machine interfaces on soft robotics will make a promising alternative to conventional rigid devices, which can potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible human-machine interfaces are summarized by recent and renowned applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human-machine interface in medical robotics.

Retina-Inspired Self-Powered Artificial Optoelectronic Synapses with Selective Detection in Organic Asymmetric Heterojunctions

The retina, the most crucial unit of the human visual perception system, combines sensing with wavelength selectivity and signal preprocessing. Incorporating energy conversion into these superior neurobiological features to generate core visual signals directly from incoming light under various conditions is essential for artificial optoelectronic synapses to emulate biological processing in the real retina. Herein, self-powered optoelectronic synapses that can selectively detect and preprocess the ultraviolet (UV) light are presented, which benefit from high-quality organic asymmetric heterojunctions with ultrathin molecular semiconducting crystalline films, intrinsic heterogeneous interfaces, and typical photovoltaic properties. These devices exhibit diverse synaptic behaviors, such as excitatory postsynaptic current, paired-pulse facilitation, and high-pass filtering characteristics, which successfully reproduce the unique connectivity among sensory neurons. These zero-power optical-sensing synaptic operations further facilitate a demonstration of image sharpening. Additionally, the charge transfer at the heterojunction interface can be modulated by tuning the gate voltage to achieve multispectral sensing ranging from the UV to near-infrared region. Therefore, this work sheds new light on more advanced retinomorphic visual systems in the post-Moore era.

From Biological Synapses to “Intelligent” Robots

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems.

Stretchable Neuromorphic Transistor That Combines Multisensing and Information Processing for Epidermal Gesture Recognition

We fabricated a nanowire-channel intrinsically stretchable neuromorphic transistor (NISNT) that perceives both tactile and visual information and emulates neuromorphic processing capabilities. The device demonstrated excellent stretching endurance of 1000 stretch cycles while retaining stable electrical properties. The device was then applied as a multisensitive afferent nerve that processes information in parallel. Compatible with skin deformation, the devices are attached to fingers to serve as conformal strain sensors and neuromorphic information-processing units for gesture recognition. The excitatory postsynaptic current in each device represents shape changes and is then analyzed using softmax activation processing of the neural network to recognize gestures. A multistage neural network that uses NISNT was used to further confirm the gestures. This work demonstrated an idea toward multisensory artificial nerves and neuromorphic systems.

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.

Bio-inspired flexible electronics for smart E-skin

"Learning from nature" provides endless inspiration for scientists to invent new materials and devices. Here, we review state-of-the-art technologies in flexible electronics, with a focus on bio-inspired smart skins. This review focuses on the development of E-skin for sensing a variety of parameters such as mechanical loads, temperature, light, and biochemical cues, with a trend of increased integration of multiple functions. It highlights the most recent advances in flexible electronics inspired by animals such as chameleons, squids, and octopi whose bodies have remarkable camouflage, mimicry, or self-healing attributes. Implantable devices, being overlapped with smart E-skin in a broad sense, are included in this review. This review outlines the remaining challenges in flexible electronics and the prospects for future development for biomedical applications. STATEMENT OF SIGNIFICANCE: This article reviews the state-of-the-art technologies of bio-inspired smart electronic skin (E-skin) developed in a "learning-mimicking-creating" (LMC) cycle. We emphasize the most recent innovations in the development of E-skin for sensing physical changes and biochemical cues, and for integrating multiple sensing modalities. We discuss the achievements in implantable materials, wireless communication, and device design pertaining to implantable flexible electronics. This review will provide prospective insights integrating material, electronics, and mechanical engineering viewpoints to foster new ideas for next-generation smart E-skin.

Integrated In-Sensor Computing Optoelectronic Device for Environment-Adaptable Artificial Retina Perception Application

With the development and application of artificial intelligence, there is an appeal to the exploitation of various sensors and memories. As the most important perception of human beings, vision occupies more than 80% of all the received information. Inspired by biological eyes, an artificial retina based on 2D Janus MoSSe was fabricated, which could simulate functions of visual perception with electronic/ion and optical comodulation. Furthermore, inspired by human brain, sensing, memory, and neuromorphic computing functions were integrated on one device for multifunctional intelligent electronics, which was beneficial for scalability and high efficiency. Through the formation of faradic electric double layer (EDL) at the metal-oxide/electrolyte interfaces could realize synaptic weight changes. On the basis of the optoelectronic performances, light adaptation of biological eyes, preprocessing, and recognition of handwritten digits were implemented successfully. This work may provide a strategy for the future integrated sensing-memory-processing device for optoelectronic artificial retina perception application.