An Optogenetics-Inspired Flexible van der Waals Optoelectronic Synapse and its Application to a Convolutional Neural Network

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

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

Ultra-low power carbon nanotube/porphyrin synaptic arrays for persistent photoconductivity and neuromorphic computing

Developing devices with a wide-temperature range persistent photoconductivity (PPC) and ultra-low power consumption remains a significant challenge for optical synaptic devices used in neuromorphic computing. By harnessing the PPC properties in materials, it can achieve optical storage and neuromorphic computing, surpassing the von Neuman architecture-based systems. However, previous research implemented PPC required additional gate voltages and low temperatures, which need additional energy consumption and PPC cannot be achieved across a wide temperature range. Here, we fabricated a simple heterojunctions using zinc(II)-meso-tetraphenyl porphyrin (ZnTPP) and single-walled carbon nanotubes (SWCNTs). By leveraging the strong binding energy at the heterojunction interface and the unique band structure, the heterojunction achieved PPC over an exceptionally wide temperature range (77 K-400 K). Remarkably, it demonstrated nonvolatile storage for up to 2×104 s, without additional gate voltage. The minimum energy consumption for each synaptic event is as low as 6.5 aJ. Furthermore, we successfully demonstrate the feasibility to manufacture a flexible wafer-scale array utilizing this heterojunction. We applied it to autonomous driving under extreme temperatures and achieved as a high impressive accuracy rate as 94.5%. This tunable and stable wide-temperature PPC capability holds promise for ultra-low-power neuromorphic computing.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

Artificial Intelligence-Guided Gut-Microenvironment-Triggered Imaging Sensor Reveals Potential Indicators of Parkinson's Disease

The gut-brain axis has recently emerged as a crucial link in the development and progression of Parkinson's disease (PD). Dysregulation of the gut microbiota has been implicated in the pathogenesis of this disease, sparking growing interest in the quest for non-invasive biomarkers derived from the gut for early PD diagnosis. Herein, an artificial intelligence-guided gut-microenvironment-triggered imaging sensor (Eu-MOF@Au-Aptmer) to achieve non-invasive, accurate screening for various stages of PD is presented. The sensor works by analyzing α-Syn in the gut using deep learning algorithms. By monitoring changes in α-Syn, the sensor can predict the onset of PD with high accuracy. This work has the potential to revolutionize the diagnosis and treatment of PD by allowing for early intervention and personalized treatment plans. Moreover, it exemplifies the promising prospects of integrating artificial intelligence (AI) and advanced sensors in the monitoring and prediction of a broad spectrum of diseases and health conditions.

Junctionless Negative-Differential-Resistance Device Using 2D Van-Der-Waals Layered Materials for Ternary Parallel Computing

Negative-differential-resistance (NDR) devices offer a promising pathway for developing future computing technologies characterized by exceptionally low energy consumption, especially multivalued logic computing. Nevertheless, conventional approaches aimed at attaining the NDR phenomenon involve intricate junction configurations and/or external doping processes in the channel region, impeding the progress of NDR devices to the circuit and system levels. Here, an NDR device is presented that incorporates a channel without junctions. The NDR phenomenon is achieved by introducing a metal-insulator-semiconductor capacitor to a portion of the channel area. This approach establishes partial potential barrier and well that effectively restrict the movement of hole and electron carriers within specific voltage ranges. Consequently, this facilitates the implementation of both a ternary inverter and a ternary static-random-access-memory, which are essential components in the development of multivalued logic computing technology.

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.

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.

Intrinsic Defect-Driven Synergistic Synaptic Heterostructures for Gate-Free Neuromorphic Phototransistors

The optoelectronic synaptic devices based on two-dimensional (2D) materials offer great advances for future neuromorphic visual systems with dramatically improved integration density and power efficiency. The effective charge capture and retention are considered as one vital prerequisite to realizing the synaptic memory function. However, the current 2D synaptic devices are predominantly relied on materials with artificially-engineered defects or intricate gate-controlled architectures to realize the charge trapping process. These approaches, unfortunately, suffer from the degradation of pristine materials, rapid device failure, and unnecessary complication of device structures. To address these challenges, an innovative gate-free heterostructure paradigm is introduced herein. The heterostructure presents a distinctive dome-like morphology wherein a defect-rich Fe7S8 core is enveloped snugly by a curved MoS2 dome shell (Fe7S8@MoS2), allowing the realization of effective photocarrier trapping through the intrinsic defects in the adjacent Fe7S8 core. The resultant neuromorphic devices exhibit remarkable light-tunable synaptic behaviors with memory time up to ≈800 s under single optical pulse, thus demonstrating great advances in simulating visual recognition system with significantly improved image recognition efficiency. The emergence of such heterostructures foreshadows a promising trajectory for underpinning future synaptic devices, catalyzing the realization of high-efficiency and intricate visual processing applications.

Inorganic Halide Perovskite Nanowires/Conjugated Polymer Heterojunction-Based Optoelectronic Synaptic Transistors for Dynamic Machine Vision

Inspired by the retina, artificial optoelectronic synapses have groundbreaking potential for machine vision. The field-effect transistor is a crucial platform for optoelectronic synapses that is highly sensitive to external stimuli and can modulate conductivity. On the basis of the decent optical absorption, perovskite materials have been widely employed for constructing optoelectronic synaptic transistors. However, the reported optoelectronic synaptic transistors focus on the static processing of independent stimuli at different moments, while the natural visual information consists of temporal signals. Here, we report CsPbBrI2 nanowire-based optoelectronic synaptic transistors to study the dynamic responses of artificial synaptic transistors to time-varying visual information for the first time. Moreover, on the basis of the dynamic synaptic behavior, a hardware system with an accuracy of 85% is built to the trajectory of moving objects. This work offers a new way to develop artificial optoelectronic synapses for the construction of dynamic machine vision systems.

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.

Recent Advances in Low-Dimensional Nanomaterials for Photodetectors

New emerging low-dimensional such as 0D, 1D, and 2D nanomaterials have attracted tremendous research interests in various fields of state-of-the-art electronics, optoelectronics, and photonic applications due to their unique structural features and associated electronic, mechanical, and optical properties as well as high-throughput fabrication for large-area and low-cost production and integration. Particularly, photodetectors which transform light to electrical signals are one of the key components in modern optical communication and developed imaging technologies for whole application spectrum in the daily lives, including X-rays and ultraviolet biomedical imaging, visible light camera, and infrared night vision and spectroscopy. Today, diverse photodetector technologies are growing in terms of functionality and performance beyond the conventional silicon semiconductor, and low-dimensional nanomaterials have been demonstrated as promising potential platforms. In this review, the current states of progress on the development of these nanomaterials and their applications in the field of photodetectors are summarized. From the elemental combination for material design and lattice structure to the essential investigations of hybrid device architectures, various devices and recent developments including wearable photodetectors and neuromorphic applications are fully introduced. Finally, the future perspectives and challenges of the low-dimensional nanomaterials based photodetectors are also discussed.

Miniaturized spectrometer with intrinsic long-term image memory

Miniaturized spectrometers have great potential for use in portable optoelectronics and wearable sensors. However, current strategies for miniaturization rely on von Neumann architectures, which separate the spectral sensing, storage, and processing modules spatially, resulting in high energy consumption and limited processing speeds due to the storage-wall problem. Here, we present a miniaturized spectrometer that utilizes a single SnS2/ReSe2 van der Waals heterostructure, providing photodetection, spectrum reconstruction, spectral imaging, long-term image memory, and signal processing capabilities. Interface trap states are found to induce a gate-tunable and wavelength-dependent photogating effect and a non-volatile optoelectronic memory effect. Our approach achieves a footprint of 19 μm, a bandwidth from 400 to 800 nm, a spectral resolution of 5 nm, and a > 104 s long-term image memory. Our single-detector computational spectrometer represents a path beyond von Neumann architectures.

Harnessing 2D Ruddlesden-Popper Perovskite with Polar Organic Cation for Ultrasensitive Multibit Nonvolatile Transistor-Type Photomemristors

Photomemristors have been regarded as one of the most promising candidates for next-generation hardware-based neuromorphic computing due to their potentials of fast data transmission and low power consumption. However, intriguingly, so far, photomemristors seldom display truly nonvolatile memory characteristics with high light sensitivity. Herein, we demonstrate ultrasensitive photomemristors utilizing two-dimensional (2D) Ruddlesden-Popper (RP) perovskites with a highly polar donor-acceptor-type push-pull organic cation, 4-(5-(2-aminoethyl)thiophen-2-yl)benzonitrile+ (EATPCN+), as charge-trapping layers. High linearity and almost zero-decay retention are observed in (EATPCN)2PbI4 devices, which are very distinct from that of the traditional 2D RP perovskite devices consisting of nonpolar organic cations, such as phenethylamine+ (PEA+) and octylamine+ (OA+), and traditional 3D perovskite devices consisting of methylamine+ (MA+). The 2-fold advantages, including desirable spatial crystal arrangement and engineered energetic band alignment, clarify the mechanism of superior performance in (EATPCN)2PbI4 devices. The optimized (EATPCN)2PbI4 photomemristor also shows a memory window of 87.9 V and an on/off ratio of 106 with a retention time of at least 2.4 × 105 s and remains unchanged after >105 writing-reading-erasing-reading endurance cycles. Very low energy consumptions of 1.12 and 6 fJ for both light stimulation and the reading process of each status update are also demonstrated. The extremely low power consumption and high photoresponsivity were simultaneously achieved. The high photosensitivity surpasses that of a state-of-the-art commercial pulse energy meter by several orders of magnitude. With their outstanding linearity and retention, rabbit images have been rebuilt by (EATPCN)2PbI4 photomemristors, which truthfully render the image without fading over time. Finally, by utilizing the powerful ∼8 bits of nonvolatile potentiation and depression levels of (EATPCN)2PbI4 photomemristors, the accuracies of the recognition tasks of CIFAR-10 image classification and MNIST handwritten digit classification have reached 89% and 94.8%, respectively. This study represents the first report of utilizing a functional donor-acceptor type of organic cation in 2D RP perovskites for high-performance photomemristors with characteristics that are not found in current halide perovskites.

Energy-Efficient ReS2-Based Optoelectronic Synapse for 3D Object Reconstruction and Recognition

The neuromorphic vision system (NVS) equipped with optoelectronic synapses integrates perception, storage, and processing and is expected to address the issues of traditional machine vision. However, owing to their lack of stereo vision, existing NVSs focus on 2D image processing, which makes it difficult to solve problems such as spatial cognition errors and low-precision interpretation. Consequently, inspired by the human visual system, an NVS with stereo vision is developed to achieve 3D object recognition, depending on the prepared ReS2 optoelectronic synapse with 12.12 fJ ultralow power consumption. This device exhibits excellent optical synaptic plasticity derived from the persistent photoconductivity effect. As the cornerstone for 3D vision, color planar information is successfully discriminated and stored in situ at the sensor end, benefiting from its wavelength-dependent plasticity in the visible region. Importantly, the dependence of the channel conductance on the target distance is experimentally revealed, implying that the structure information on the object can be directly captured and stored by the synapse. The 3D image of the object is successfully reconstructed via fusion of its planar and depth images. Therefore, the proposed 3D-NVS based on ReS2 synapses for 3D objects achieves a recognition accuracy of 97.0%, which is much higher than that for 2D objects (32.6%), demonstrating its strong ability to prevent 2D-photo spoofing in applications such as face payment, entrance guard systems, and others.

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.

Programmable Physical Unclonable Functions Using Randomly Anisotropic Two-Dimensional Flakes

Physical unclonable functions (PUFs) have been developed as promising strategies for secure authentication. Conventional strategies of PUFs have a limitation in the aspect of security for their static single channel. The introduction of polarization will endow a static PUF with many dynamic transformations based on polarized properties. Herein, high-security PUFs based on the polarized luminescence of chaotic luminescent patterns are fabricated by anisotropic rare earth (RE) material Er3O4Cl flakes. These derivatives under different polarizations show strong randomness (with similarity of the same PUF as high as 97%, while that for different PUFs is as low as 44%), which further widens the security and capacity of PUFs. Polarized luminescence control of Er3O4Cl patterns gives freedom to the PUFs and ensures a high encoding capacity of 2380000. Furthermore, we build a convolutional neural network (CNN) to realize fast intelligent authentication by extracting image features for convolution operation with a very high accuracy of 99.8% and fast classification speed in only 5 epochs.

Illuminating the Brain: Advances and Perspectives in Optoelectronics for Neural Activity Monitoring and Modulation

Optogenetic modulation of brain neural activity that combines optical and electrical modes in a unitary neural system has recently gained robust momentum. Controlling illumination spatial coverage, designing light-activated modulators, and developing wireless light delivery and data transmission are crucial for maximizing the use of optical neuromodulation. To this end, biocompatible electrodes with enhanced optoelectrical performance, device integration for multiplexed addressing, wireless transmission, and multimodal operation in soft systems have been developed. This review provides an outlook for uniformly illuminating large brain areas while spatiotemporally imaging the neural responses upon optoelectrical stimulation with little artifacts. Representative concepts and important breakthroughs, such as head-mounted illumination, multiple implanted optical fibers, and micro-light-delivery devices, are discussed. Examples of techniques that incorporate electrophysiological monitoring and optoelectrical stimulation are presented. Challenges and perspectives are posed for further research efforts toward high-density optoelectrical neural interface modulation, with the potential for nonpharmacological neurological disease treatments and wireless optoelectrical stimulation.

Light-enhanced molecular polarity enabling multispectral color-cognitive memristor for neuromorphic visual system

An optoelectronic synapse having a multispectral color-discriminating ability is an essential prerequisite to emulate the human retina for realizing a neuromorphic visual system. Several studies based on the three-terminal transistor architecture have shown its feasibility; however, its implementation with a two-terminal memristor architecture, advantageous to achieving high integration density as a simple crossbar array for an ultra-high-resolution vision chip, remains a challenge. Furthermore, regardless of the architecture, it requires specific material combinations to exhibit the photo-synaptic functionalities, and thus its integration into various systems is limited. Here, we suggest an approach that can universally introduce a color-discriminating synaptic functionality into a two-terminal memristor irrespective of the kinds of switching medium. This is possible by simply introducing the molecular interlayer with long-lasting photo-enhanced dipoles that can adjust the resistance of the memristor at the light-irradiation. We also propose the molecular design principle that can afford this feature. The optoelectronic synapse array having a color-discriminating functionality is confirmed to improve the inference accuracy of the convolutional neural network for the colorful image recognition tasks through a visual pre-processing. Additionally, the wavelength-dependent optoelectronic synapse can also be leveraged in the design of a light-programmable reservoir computing system.

Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse

Sensory neuromorphic systems are a promising technology, because they can replicate the way the human peripheral nervous system processes signals from the five sensory organs. Despite this potential, there are limited studies on how to implement these systems on a hardware neural network platform. In our research, we propose a tactile neuromorphic system that uses a poly(dimethylsiloxane) (PDMS)-based triboelectric sensor and a molybdenum disulfide (MoS2)/poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)) heterostructure-based ferroelectric synapse. The triboelectric sensor mimics a human tactile organ by converting tactile stimuli into electrical signals in real time. The ferroelectric synapse we developed demonstrates exceptional long-term potentiation/depression characteristics with a maximum dynamic range of 78 and a symmetrical value of 4.7. To assess the practicality of our proposed system, we conducted training and recognition simulations using Morse code alphabets and MNIST handwritten digits. The maximum recognition rate that we achieved was 96.17%.

Probing Optical Multi-Level Memory Effects in Single Core-Shell Quantum Dots and Application Through 2D-0D Hybrid Inverters

Challenges in the development of a multi-level memory (MM) device for multinary arithmetic computers have posed an obstacle to low-power, ultra-high-speed operation. For the effective transfer of a huge amount of data between arithmetic and storage devices, optical communication technology represents a compelling solution. Here, by replicating a floating gate architecture with CdSe/ZnS type-I core/shell quantum dots (QDs), a 2D-0D hybrid optical multi-level memory (OMM) device operated is demonstrated by laser pulses. In the device, laser pulses create linear optically trapped currents with MM characteristics, while conversely, voltage pulses reset all the trapped currents at once. Assuming electron transfer via the energy band alignment between MoS2 and CdSe, the study also establishes the mechanism of the OMM effect. Analysis of the designed device led to a new hypothesis that charge transfer is difficult for laterally adjacent QDs facing a double ZnS shell, which is tested by separately stimulating different positions on the 2D-0D hybrid structure with finely focused laser pulses. Results indicate that each laser pulse induced independent MM characteristics in the 2D-0D hybrid architecture. Based on this phenomenon, we propose a MM inverter to produce MM effects, such as programming and erasing, solely through the use of laser pulses. Finally, the feasibility of a fully optically-controlled intelligent system based on the proposed OMM inverters is evaluated through a CIFAR-10 pattern recognition task using a convolutional neural network.

Localized coherent phonon generation in monolayer MoSe2 from ultrafast exciton trapping at shallow traps

We report spectroscopic evidence for the ultrafast trapping of band edge excitons at defects and the subsequent generation of defect-localized coherent phonons (CPs) in monolayer MoSe2. While the photoluminescence measurement provides signals of exciton recombination at both shallow and deep traps, our time-resolved pump-probe spectroscopy on the sub-picosecond time scale detects localized CPs only from the ultrafast exciton trapping at shallow traps. Based on occupation-constrained density functional calculations, we identify the Se vacancy and the oxygen molecule adsorbed on a Se vacancy as the atomistic origins of deep and shallow traps, respectively. Establishing the correlations between the defect-induced ultrafast exciton trapping and the generation of defect-localized CPs, our work could open up new avenues to engineer photoexcited carriers through lattice defects in two-dimensional materials.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

An Optoelectronic Synapse Based on Two-Dimensional Violet Phosphorus Heterostructure

Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light-matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light-to-dark ratio of 10⁶ , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60 dB, 128 (7-bit) distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. Thanks to the excellent DR and multi-states, high-precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high-precision neuromorphic computing.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

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

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

Structure modulation of two-dimensional transition metal chalcogenides: recent advances in methodology, mechanism and applications

Together with the development of two-dimensional (2D) materials, transition metal dichalcogenides (TMDs) have become one of the most popular series of model materials for fundamental sciences and practical applications. Due to the ever-growing requirements of customization and multi-function, dozens of modulated structures have been introduced in TMDs. In this review, we present a systematic and comprehensive overview of the structure modulation of TMDs, including point, linear and out-of-plane structures, following and updating the conventional classification for silicon and related bulk semiconductors. In particular, we focus on the structural characteristics of modulated TMD structures and analyse the corresponding root causes. We also summarize the recent progress in modulating methods, mechanisms, properties and applications based on modulated TMD structures. Finally, we demonstrate challenges and prospects in the structure modulation of TMDs and forecast potential directions about what and how breakthroughs can be achieved.

Optoelectronic Memristive Synapse Behavior for the Architecture of Cu2ZnSnS4@BiOBr Embedded in Poly(methyl methacrylate)

The great potential of artificial optoelectronic devices that are capable of mimicking biosynapse functions in brain-like neuromorphic computing applications has aroused extensive interest, and the architecture design is decisive yet challenging. Herein, a new architecture of p-type Cu₂ZnSnS₄@BiOBr nanosheets embedded in poly(methyl methacrylate) (PMMA) films (CZTS@BOB-PMMA) is presented acting as a switching layer, which not only shows the bipolar resistive switching features (SET/RESET voltages, ∼ -0.93/+1.35 V; retention, >10⁴ s) and electrical- and near-infrared light-induced synapse plasticity but also demonstrates electrical-driven excitatory postsynaptic current, spiking-time-dependent plasticity, paired pulse facilitation, long-term plasticity, long- and short-term memory, and "learning-forgetting-learning" behaviors. The approach is a rewarding attempt to broaden the research of optoelectric controllable memristive devices for building neuromorphic architectures mimicking human brain functionalities.

Optical synaptic devices with ultra-low power consumption for neuromorphic computing

Brain-inspired neuromorphic computing, featured by parallel computing, is considered as one of the most energy-efficient and time-saving architectures for massive data computing. However, photonic synapse, one of the key components, is still suffering high power consumption, potentially limiting its applications in artificial neural system. In this study, we present a BP/CdS heterostructure-based artificial photonic synapse with ultra-low power consumption. The device shows remarkable negative light response with maximum responsivity up to 4.1 × 10⁸ A W^-1 at V_D = 0.5 V and light power intensity of 0.16 μW cm^-2 (1.78 × 10⁸ A W^-1 on average), which further enables artificial synaptic applications with average power consumption as low as 4.78 fJ for each training process, representing the lowest among the reported results. Finally, a fully-connected optoelectronic neural network (FONN) is simulated with maximum image recognition accuracy up to 94.1%. This study provides new concept towards the designing of energy-efficient artificial photonic synapse and shows great potential in high-performance neuromorphic vision systems.

Van der Waals-Interface-Dominated All-2D Electronics

The interface is the device. As the feature size rapidly shrinks, silicon-based electronic devices are facing multiple challenges of material performance decrease and interface quality degradation. Ultrathin 2D materials are considered as potential candidates in future electronics by their atomically flat surfaces and excellent immunity to short-channel effects. Moreover, due to naturally terminated surfaces and weak van der Waals (vdW) interactions between layers, 2D materials can be freely stacked without the lattice matching limit to form high-quality heterostructure interfaces with arbitrary components and twist angles. Controlled interlayer band alignment and optimized interfacial carrier behavior allow all-2D electronics based on 2D vdW interfaces to exhibit more comprehensive functionality and better performance. Especially, achieving the same computing capacity of multiple conventional devices with small footprint all-2D devices is considered to be the key development direction of future electronics. Herein, the unique properties of all-2D vdW interfaces and their construction methods are systematically reviewed and the main performance contributions of different vdW interfaces in 2D electronics are summarized, respectively. Finally, the recent progress and challenges for all-2D vdW electronics are discussed, and how to improve the compatibility of 2D material devices with silicon-based industrial technology is pointed out as a critical challenge.

A Van Der Waals Reconfigurable Multi-Valued Logic Device and Circuit Based on Tunable Negative-Differential-Resistance Phenomenon

Multi-valued logic (MVL) technology that utilizes more than two logic states has recently been reconsidered because of the demand for greater power saving in current binary logic systems. Extensive efforts have been invested in developing MVL devices with multiple threshold voltages by adopting negative differential transconductance and resistance. In this study, a reconfigurable, multiple negative-differential-resistance (m-NDR) device with an electric-field-induced tunability of multiple threshold voltages is reported, which comprises a BP/ReS₂ heterojunction and a ReS₂ /h-BN/metal capacitor. Tunability for the m-NDR phenomenon is achieved via the resistance modulation of the ReS₂ layer by electrical pulses applied to the capacitor region. Reconfigurability is verified in terms of the function of an MVL circuit composed of a reconfigurable m-NDR device and a load transistor, wherein staggered-type and broken-type double peak-NDR device operations are adopted for ternary inverter and latch circuits, respectively.

2D Heterostructures for Ubiquitous Electronics and Optoelectronics: Principles, Opportunities, and Challenges

A grand family of two-dimensional (2D) materials and their heterostructures have been discovered through the extensive experimental and theoretical efforts of chemists, material scientists, physicists, and technologists. These pioneering works contribute to realizing the fundamental platforms to explore and analyze new physical/chemical properties and technological phenomena at the micro-nano-pico scales. Engineering 2D van der Waals (vdW) materials and their heterostructures via chemical and physical methods with a suitable choice of stacking order, thickness, and interlayer interactions enable exotic carrier dynamics, showing potential in high-frequency electronics, broadband optoelectronics, low-power neuromorphic computing, and ubiquitous electronics. This comprehensive review addresses recent advances in terms of representative 2D materials, the general fabrication methods, and characterization techniques and the vital role of the physical parameters affecting the quality of 2D heterostructures. The main emphasis is on 2D heterostructures and 3D-bulk (3D) hybrid systems exhibiting intrinsic quantum mechanical responses in the optical, valley, and topological states. Finally, we discuss the universality of 2D heterostructures with representative applications and trends for future electronics and optoelectronics (FEO) under the challenges and opportunities from physical, nanotechnological, and material synthesis perspectives.

Electrolyte-Gated Vertical Synapse Array based on Van Der Waals Heterostructure for Parallel Computing

Recently, three-terminal synaptic devices, which separate read and write terminals, have attracted significant attention because they enable nondestructive read-out and parallel-access for updating synaptic weights. However, owing to their structural features, it is difficult to address the relatively high device density compared with two-terminal synaptic devices. In this study, a vertical synaptic device featuring remotely controllable weight updates via e-field-dependent movement of mobile ions in the ion-gel layer is developed. This synaptic device successfully demonstrates all essential synaptic characteristics, such as excitatory/inhibitory postsynaptic current (E/IPSC), paired-pulse facilitation (PPF), and long-term potentiation/depression (LTP/D) by electrical measurements, and exhibits competitive LTP/D characteristics with a dynamic range (G_max /G_min ) of 31.3, and asymmetry (AS) of 8.56. The stability of the LTP/D characteristics is also verified through repeated measurements over 50 cycles; the relative standard deviations (RSDs) of G_max /G_min and AS are calculated as 1.65% and 0.25%, respectively. These excellent synaptic properties enable a recognition rate of ≈99% in the training and inference tasks for acoustic and emotional information patterns. This study is expected to be an important foundation for the realization of future parallel computing networks for energy-efficient and high-speed data processing.