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.

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.

An in-sensor humidity computing system for contactless human-computer interaction.

Being capable of processing large amounts of redundant data and decreasing power consumption, in-sensor computing approaches play significant roles in neuromorphic computing and are attracting increasing interest in perceptual information processing. Herein, we proposed a high performance humidity-sensitive memristor based on a Ti/graphene oxide (GO)/HfOx/Pt structure and verified its potential for application in remote health management and contactless human-machine interfaces. Since GO possesses abundant hydrophilic groups (carbonyl, epoxide, and hydroxyl), the memristor shows a high humidity sensitivity, fast response, and wide response range. By utilizing the proton-modulated redox reaction, humidity exposure to the memristor induces a dynamic change in the switching between high and low resistance states, ensuring essential synaptic learning functions, such as paired-pulse facilitation, spike number-dependent plasticity, and spike amplitude-dependent plasticity. More importantly, based on the humidity-induced salient features originating from the abundant hydrophilic functional groups in GO, we have implemented a noncontact human-machine interface utilizing the respiratory mode in humans, demonstrating the potential of promoting health monitoring applications and effectively blocking virus transmission. In addition, the high recognition accuracy of contactless handwriting in a 5 × 5 array artificial neural network was successfully achieved, which is attributed to the excellent emulated synaptic behaviors. This study provides a feasible method to develop an excellent humidity-sensitive memristor for constructing efficient in-sensor computing for application in health management and contactless human-computer interaction.

Ni Single-Atoms Based Memristors with Ultrafast Speed and Ultralong Data Retention.

Memristor with low-power, high density, and scalability fulfills the requirements of the applications of the new computing system beyond Moore's law. However, there are still nonideal device characteristics observed in the memristor to be solved. The important observation is that retention and speed are correlated parameters of memristor with trade off against each other. The delicately modulating distribution and trapping level of defects in electron migration-based memristor is expected to provide a compromise method to address the contradictory issue of improving both switching speed and retention capability. Here, high-performance memristor based on the structure of ITO/Ni single-atoms (NiSAs/N-C)/Polyvinyl pyrrolidone (PVP)/Au is reported. By utilizing well-distributed trapping sites , small tunneling barriers/distance and high charging energy, the memristor with an ultrafast switching speed of 100 ns, ultralong retention capability of 106  s, a low set voltage (Vset ) of ≈0.7 V, a substantial ON/OFF ration of 103 , and low spatial variation in cycle-to-cycle (500 cycles) and device-to-device characteristics (128 devices) is demonstrated. On the premise of preserving the strengths of a fast switching speed, this memristor exhibits ultralong retention capability comparable to the commercialized flash memory. Finally, a memristor ratioed logic-based combinational memristor array to realize the one-bit full adder is further implemented.

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.

Covalent Organic Frameworks for Neuromorphic Devices.

Neuromorphic computing could enable the potential to break the inherent limitations of conventional von Neumann architectures, which has led to widespread research interest in developing novel neuromorphic memory devices, such as memristors and bioinspired artificial synaptic devices. Covalent organic frameworks (COFs), as crystalline porous polymers, have tailorable skeletons and pores, providing unique platforms for the interplay with photons, excitons, electrons, holes, ions, spins, and molecules. Such features encourage the rising research interest in COF materials in neuromorphic electronics. To develop high-performance COF-based neuromorphic memory devices, it is necessary to comprehensively understand materials, devices, and applications. Therefore, this Perspective focuses on discussing the use of COF materials for neuromorphic memory devices in terms of molecular design, thin-film processing, and neuromorphic applications. Finally, we provide an outlook for future directions and potential applications of COF-based neuromorphic electronics.

Biocompatible Material-Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems.

Human beings have a greater need to pursue life and manage personal or family health in the context of the rapid growth of artificial intelligence, big data, the Internet of Things, and 5G/6G technologies. The application of micro biosensing devices is crucial in connecting technology and personalized medicine. Here, the progress and current status from biocompatible inorganic materials to organic materials and composites are reviewed and the material-to-device processing is described. Next, the operating principles of pressure, chemical, optical, and temperature sensors are dissected and the application of these flexible biosensors in wearable/implantable devices is discussed. Different biosensing systems acting in vivo and in vitro, including signal communication and energy supply are then illustrated. The potential of in-sensor computing for applications in sensing systems is also discussed. Finally, some essential needs for commercial translation are highlighted and future opportunities for flexible biosensors are considered.

Recent Progress on Wavelength-Selective Perovskite Photodetectors for Image Sensing.

Spectral sensing plays a crucial part in imaging technologies, optical communication, and other fields. However, complicated optical elements, such as prisms, interferometric filters, and diffraction grating, are required for commercial multispectral detectors, which hampers their advance toward miniaturization and integration. In recent years, metal halide perovskites have been emerging for optical-component-free wavelength-selective photodetectors (PDs) because of their continuously tunable bandgap, fascinating optoelectronic properties, and simple preparation processes. In this review, recent advances in wavelength-selective perovskite PDs, including narrowband PDs, dual-band PDs, multispectral-recognizable PDs, and X-ray PDs, are highlighted, with an emphasis on device structure designs, working mechanisms, and optoelectronic performances. Meanwhile, the applications of wavelength-selective PDs in image sensing for single-/dual-color imaging, full-color imaging, and X-ray imaging are introduced. Finally, the remaining challenges and perspectives in this emerging field are presented.

Electrical Contacts With 2D Materials: Current Developments and Future Prospects.

Current electrical contact models are occasionally insufficient at the nanoscale owing to the wide variations in outcomes between 2D mono and multi-layered and bulk materials that result from their distinctive electrostatics and geometries. Contrarily, devices based on 2D semiconductors present a significant challenge due to the requirement for electrical contact with resistances close to the quantum limit. The next generation of low-power devices is already hindered by the lack of high-quality and low-contact-resistance contacts on 2D materials. The physics and materials science of electrical contact resistance in 2D materials-based nanoelectronics, interface configurations, charge injection mechanisms, and numerical modeling of electrical contacts, as well as the most pressing issues that need to be resolved in the field of research and development, will all be covered in this review.

Emerging 2D Metal Oxides: From Synthesis to Device Integration.

2D metal oxides have aroused increasing attention in the field of electronics and optoelectronics due to their intriguing physical properties. In this review, an overview of recent advances on synthesis of 2D metal oxides and their electronic applications is presented. First, the tunable physical properties of 2D metal oxides that relate to the structure (various oxidation-state forms, polymorphism, etc.), crystallinity and defects (anisotropy, point defects, and grain boundary), and thickness (quantum confinement effect, interfacial effect, etc.) are discussed. Then, advanced synthesis methods for 2D metal oxides besides mechanical exfoliation are introduced and classified into solution process, vapor-phase deposition, and native oxidation on a metal source. Later, the various roles of 2D metal oxides in widespread applications, i.e., transistors, inverters, photodetectors, piezotronics, memristors, and potential applications (solar cell, spintronics, and superconducting devices) are discussed. Finally, an outlook of existing challenges and future opportunities in 2D metal oxides is proposed.

A Perovskite Memristor with Large Dynamic Space for Analog-Encoded Image Recognition.

Reservoir computing (RC) is a computational architecture capable of efficiently processing temporal information, which allows low-cost hardware implementation. However, the previously reported memristor-based RC mostly utilized binarized data sets to reduce the difficulty of signal processing of the memristor, which inevitably induces data distortion to a certain extent, leading to poor network computing performance. Here, we report on a RC system in a fully memristive architecture based on solution-processed perovskite memristors. The perovskite memristor exhibits 10000 conductance states with a modulation range of more than 4 orders of magnitude. The obtained tens of thousands of finely spaced conductance states with a near-ideal analog property provide a sufficiently large dynamic range and enough intermediate states, which were further applied as a reservoir to map the feature information on different sequential inputs in an analog way. The computing capability of the image classification task of a Fashion-MNIST data set with a high recognition accuracy of up to 90.1% shows that the excellent analog and short-term properties of our perovskite memristor allow the hardware implementation of neuromorphic computing with a reduced training cost.

Flexible Floating-Gate Electric-Double-Layer Organic Transistor for Neuromorphic Computing.

The key to the study of flexible neuromorphic computing is the excellent weight update characteristic of neuromorphic devices. Electric-double-layer transistors (EDLTs) include high transconductance, excellent stability of threshold voltage, linear weight updates, and repetitive ion-concentration-dependent switching properties. However, up to now, there is no report on a flexible EDLT that provides all the aforementioned performance characteristics. Here, a planar flexible floating-gate EDLT including an excellent linear/symmetric weight update, a large number (>800) of conductance states, repetitive switching endurance (>100 cycles), and low variation in weight update is reported. After 800 signal stimulations, it is found that the nonlinearity values of LTP are between 0.20 and 0.85, those of LTD fall between 0.66 and 1.55, the symmetricity values are between 120.7 and 639.8, and the dynamic range is between 150 and 352 nS. The study of 8 × 8 flexible floating-gate EDLT arrays shows that the average deviation and standard deviation between the experimental and theoretical values are 1.36 and 1.93, respectively, indicating that the conductance regulation in the array has a relatively small deviation. The different bending angles and the mechanical stability of the floating-gate EDLT are also studied, which exhibit the excellent bending properties. Furthermore, we studied the recognition of MNIST handwritten digit images by a three-layer perceptron artificial neural network with the experimental weight update, and the maximal recognition accuracy is up to 87.8%.

2D Layers of Group VA Semiconductors: Fundamental Properties and Potential Applications.

Members of the 2D group VA semiconductors (phosphorene, arsenene, antimonene, and bismuthine) present a new class of 2D materials, which are recently gaining a lot of research interest. These materials possess layered morphology, tunable direct bandgap, high charge carrier mobility, high stability, unique in-plane anisotropy, and negative Poisson's ratio. They prepare the ground for novel and multifunctional applications in electronics, optoelectronics, and batteries. The most recent analytical and empirical developments in the fundamental characteristics, fabrication techniques, and potential implementation of 2D group VA materials in this review, along with presenting insights and concerns for the field's future are analyzed.

Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity.

Get in-depth understanding of each part of visual pathway yields insights to conquer the challenges that classic computer vision is facing. Here, we first report the bioinspired striate cortex with binocular and orientation selective receptive field based on the crossbar array of self-powered memristors which is solution-processed monolithic all-perovskite system with each cross-point containing one CsFAPbI3 solar cell directly stacking on the CsPbBr2I memristor. The plasticity of self-powered memristor can be modulated by optical stimuli following triplet-STDP rules. Furthermore, plasticity of 3 × 3 flexible crossbar array of self-powered memristors has been successfully modulated based on generalized BCM learning rule for optical-encoded pattern recognition. Finally, we implemented artificial striate cortex with binocularity and orientation selectivity based on two simulated 9 × 9 self-powered memristors networks. The emulation of striate cortex with binocular and orientation selectivity will facilitate the brisk edge and corner detection for machine vision in the future applications.

The floating body effect of a WSe2 transistor with volatile memory performance.

The floating body effect in Meta-Stable-Dip RAM (MSDRAM) has been broadly employed in implementing single-transistor capacitor-less (1T0C) dynamic random access memory (DRAM) cells to break through the limitation of finite size reduction of peripheral capacitors. However, the majority of them were broadly demonstrated in conventional CMOS technology, while emerging semiconductor systems are rarely explored. Here, we creatively explore exfoliated multilayer tungsten diselenide (WSe2) for the application of 1T0C DRAM, breaking the limitation of channel thickness in the traditional architecture. Through the comparison of the electrical characteristics among three dual-gate transistors with different lengths of top-gate, we demonstrated the essential role of the floating body effect in achieving the function of 1T0C DRAM displaying two distinct states that are differentiated by hole population within the floating body. Moreover, according to the analysis of in situ electrostatic force microscopy (EFM) measurements and theoretical calculation via density functional theory (DFT), the injection of holes through band-to-band (B2B) tunneling can be ascribed to the effectively electrostatic modulation. These consequences prove our innovative concept to achieve the function of 1T0C DRAM through employing the ML WSe2, which is a vital step toward the breakthrough of the inherent limitations of DRAM cells.

Novel charm of 2D materials engineering in memristor: when electronics encounter layered morphology.

The family of two-dimensional (2D) materials composed of atomically thin layers connected via van der Waals interactions has attracted much curiosity due to a variety of intriguing physical, optical, and electrical characteristics. The significance of analyzing statistics on electrical devices and circuits based on 2D materials is seldom underestimated. Certain requirements must be met to deliver scientific knowledge that is beneficial in the field of 2D electronics: synthesis and fabrication must occur at the wafer level, variations in morphology and lattice alterations must be visible and statistically verified, and device dimensions must be appropriate. The authors discussed the most recent significant concerns of 2D materials in the provided prose and attempted to highlight the prerequisites for synthesis, yield, and mechanism behind device-to-device variability, reliability, and durability benchmarking under memristors characteristics; they also indexed some useful approaches that have already been reported to be advantageous in large-scale production. Commercial applications, on the other hand, will necessitate further effort.

Filament Engineering of Two-Dimensional h-BN for a Self-Power Mechano-Nociceptor System.

The switching variability caused by intrinsic stochasticity of the ionic/atomic motions during the conductive filaments (CFs) formation process largely limits the applications of diffusive memristors (DMs), including artificial neurons, neuromorphic computing and artificial sensory systems. In this study, a DM device with improved device uniformity based on well-crystallized two-dimensional (2D) h-BN, which can restrict the CFs formation from three to two dimensions due to the high migration barrier of Ag+ between h-BN interlayer, is developed. The BN-DM has potential arrayable feature with high device yield of 88%, which can be applied for building a reservoir computing system for digital pattern recognition with high accuracy rate of 96%, and used as an artificial nociceptor to sense the external noxious stimuli and mimic the important biological nociceptor properties. By connecting the BN-DM to a self-made triboelectric nanogenerator (TENG), a self-power mechano-nociceptor system, which can successfully mimic the important nociceptor features of "threshold", "relaxation" and "allodynia" is designed.

A van der Waals Integrated Damage-Free Memristor Based on Layered 2D Hexagonal Boron Nitride.

2D materials with intriguing properties have been widely used in optoelectronics. However, electronic devices suffered from structural damage due to the ultrathin materials and uncontrolled defects at interfaces upon metallization, which hindered the development of reliable devices. Here, a damage-free Au/h-BN/Au memristor is reported using a clean, water-assisted metal transfer approach by physically assembling Au electrodes onto the layered h-BN which minimized the structural damage and undesired interfacial defects. The memristors demonstrate significantly improved performance with the coexistence of nonpolar and threshold switching as well as tunable current levels by controlling the compliance current, compared with devices with evaporated contacts. The devices integrated into an array show suppressed sneak path current and can work as both logic gates and latches to implement logic operations allowing in-memory computing. Cross-sectional scanning transmission electron microscopy analysis validates the feasibility of this nondestructive metal integration approach, the crucial role of high-quality atomically sharp interface in resistive switching, and a direct observation of percolation path. The underlying mechanism of boron vacancies-assisted transport is further supported experimentally by conductive atomic force microscopy free from process-induced damage, and theoretically by ab initio simulations.

2D oriented covalent organic frameworks for alcohol-sensory synapses.

Resistive random access memories (RRAMs) based on the electrochemical metallization mechanism (ECM) have potential applications in high-density data storage and efficient neuromorphic computing. However, the high variability of ECM devices still hinders their application in artificial intelligence owing to the random formation of conductive filaments (CFs). Here, we demonstrate 2D covalent organic framework (COF) RRAM with electroforming-free resistive switching behavior, low spatial/temporal variations, and excellent retention capability up to 105 s. The one-dimensional channels of the oriented COF-5 film can not only confine the shape of filaments but also modulate the transition direction of Ag ions. Moreover, alcohol vapors could activate the device to achieve gas-mediated multilevel resistive switching since COF materials can absorb small molecules through host guest interactions to vary the conductivity. An alcohol gas recognition system constructed by integrating the COF RRAM as a sensor and filter part with the k-nearest neighbors (KNN) algorithm as a classifier was demonstrated with a recognition accuracy of 87.2%. Furthermore, the effect of alcohol inhibition stimulation in the human nervous system is successfully emulated by the COF RRAM.