Brain‐Inspired Photonic Neuromorphic Devices using Photodynamic Amorphous Oxide Semiconductors and their Persistent Photoconductivity

The combination of a neuromorphic architecture and photonic computing may open up a new era for computational systems owing to the possibility of attaining high bandwidths and the low‐computation‐power requirements. Here, the demonstration of photonic neuromorphic devices based on amorphous oxide semiconductors (AOSs) that mimic major synaptic functions, such as short‐term memory/long‐term memory, spike‐timing‐dependent plasticity, and neural facilitation, is reported. The synaptic functions are successfully emulated using the inherent persistent photoconductivity (PPC) characteristic of AOSs. Systematic analysis of the dynamics of photogenerated carriers for various AOSs is carried out to understand the fundamental mechanisms underlying the photoinduced carrier‐generation and relaxation behaviors, and to search for a proper channel material for photonic neuromorphic devices. It is found that the activation energy for the neutralization of ionized oxygen vacancies has a significant influence on the photocarrier‐generation and time‐variant recovery behaviors of AOSs, affecting the PPC behavior.     

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

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

2D MoS2 Neuromorphic Devices for Brain‐Like Computational Systems

Hardware implementation of artificial synapses/neurons with 2D solid‐state devices is of great significance for nanoscale brain‐like computational systems. Here, 2D MoS₂ synaptic/neuronal transistors are fabricated by using poly(vinyl alcohol) as the laterally coupled, proton‐conducting electrolytes. Fundamental synaptic functions, such as an excitatory postsynaptic current, paired‐pulse facilitation, and a dynamic filter for information transmission of biological synapse, are successfully emulated. Most importantly, with multiple input gates and one modulatory gate, spiking‐dependent logic operation/modulation, multiplicative neural coding, and neuronal gain modulation are also experimentally demonstrated. The results indicate that the intriguing 2D MoS₂ transistors are also very promising for the next‐generation of nanoscale neuromorphic device applications.     

Truly Concomitant and Independently Expressed Short‐ and Long‐Term Plasticity in a Bi2O2Se‐Based Three‐Terminal Memristor

Concomitance of diverse synaptic plasticity across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive neuromorphic systems, devices that are capable of emulating short‐term (STP) and long‐term plasticity (LTP) concomitantly are essential. In existing memristors, however, STP and LTP can only be induced selectively because of the inability to be decoupled using different loci and mechanisms. In this work, the first demonstration of truly concomitant STP and LTP is reported in a three‐terminal memristor that uses independent physical phenomena to represent each form of plasticity. The emerging layered material Bi₂O₂Se is used for memristors for the first time, opening up the prospects for ultrathin, high‐speed, and low‐power neuromorphic devices. The concerted action of STP and LTP allows full‐range modulation of the transient synaptic efficacy, from depression to facilitation, by stimulus frequency or intensity, providing a versatile device platform for neuromorphic function implementation. A heuristic recurrent neural circuitry model is developed to simulate the intricate “sleep–wake cycle autoregulation” process, in which the concomitance of STP and LTP is posited as a key factor in enabling this neural homeostasis. This work sheds new light on the development of generic memristor platforms for highly dynamic neuromorphic computing.     

Two‐Dimensional Materials for Halide Perovskite‐Based Optoelectronic Devices

Halide perovskites have high light absorption coefficients, long charge carrier diffusion lengths, intense photoluminescence, and slow rates of non‐radiative charge recombination. Thus, they are attractive photoactive materials for developing high‐performance optoelectronic devices. These devices are also cheap and easy to be fabricated. To realize the optimal performances of halide perovskite‐based optoelectronic devices (HPODs), perovskite photoactive layers should work effectively with other functional materials such as electrodes, interfacial layers and encapsulating films. Conventional two‐dimensional (2D) materials are promising candidates for this purpose because of their unique structures and/or interesting optoelectronic properties. Here, we comprehensively summarize the recent advancements in the applications of conventional 2D materials for halide perovskite‐based photodetectors, solar cells and light‐emitting diodes. The examples of these 2D materials are graphene and its derivatives, mono‐ and few‐layer transition metal dichalcogenides (TMDs), graphdiyne and metal nanosheets, etc. The research related to 2D nanostructured perovskites and 2D Ruddlesden–Popper perovskites as efficient and stable photoactive layers is also outlined. The syntheses, functions and working mechanisms of relevant 2D materials are introduced, and the challenges to achieving practical applications of HPODs using 2D materials are also discussed.     

Synergistic Gating of Electro‐Iono‐Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity

Emulation of brain‐like signal processing with thin‐film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di‐chalcogenide (MoS₂) neuristors are designed to mimic intracellular ion endocytosis–exocytosis dynamics/neurotransmitter‐release in chemical synapses using three approaches: (i) electronic‐mode: a defect modulation approach where the traps at the semiconductor–dielectric interface are perturbed; (ii) ionotronic‐mode: where electronic responses are modulated via ionic gating; and (iii) photoactive‐mode: harnessing persistent photoconductivity or trap‐assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge‐trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve “plasticity of plasticity–metaplasticity” via dynamic control of Hebbian spike‐time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures.     

Two‐Terminal Multibit Optical Memory via van der Waals Heterostructure

2D van der Waals (vdWs) heterostructures exhibit intriguing optoelectronic properties in photodetectors, solar cells, and light‐emitting diodes. In addition, these materials have the potential to be further extended to optical memories with promising broadband applications for image sensing, logic gates, and synaptic devices for neuromorphic computing. In particular, high programming voltage, high off‐power consumption, and circuital complexity in integration are primary concerns in the development of three‐terminal optical memory devices. This study describes a multilevel nonvolatile optical memory device with a two‐terminal floating‐gate field‐effect transistor with a MoS₂/hexagonal boron nitride/graphene heterostructure. The device exhibits an extremely low off‐current of ≈10^?14 A and high optical switching on/off current ratio of over ≈10⁶, allowing 18 distinct current levels corresponding to more than four‐bit information storage. Furthermore, it demonstrates an extended endurance of over ≈10⁴ program–erase cycles and a long retention time exceeding 3.6 × 10⁴ s with a low programming voltage of ?10 V. This device paves the way for miniaturization and high‐density integration of future optical memories with vdWs heterostructures.     

Environment‐Adaptable Artificial Visual Perception Behaviors Using a Light‐Adjustable Optoelectronic Neuromorphic Device Array

Emulating the biological visual perception system typically requires a complex architecture including the integration of an artificial retina and optic nerves with various synaptic behaviors. However, self‐adaptive synaptic behaviors, which are frequently translated into visual nerves to adjust environmental light intensities, have been one of the serious challenges for the artificial visual perception system. Here, an artificial optoelectronic neuromorphic device array to emulate the light‐adaptable synaptic functions (photopic and scotopic adaptation) of the biological visual perception system is presented. By employing an artificial visual perception circuit including a metal chalcogenide photoreceptor transistor and a metal oxide synaptic transistor, the optoelectronic neuromorphic device successfully demonstrates diverse visual synaptic functions such as phototriggered short‐term plasticity, long‐term potentiation, and neural facilitation. More importantly, the environment‐adaptable perception behaviors at various levels of the light illumination are well reproduced by adjusting load transistor in the circuit, exhibiting the acts of variable dynamic ranges of biological system. This development paves a new way to fabricate an environmental‐adaptable artificial visual perception system with profound implications for the field of future neuromorphic electronics.     

Chemically Integrated Inorganic‐Graphene Two‐Dimensional Hybrid Materials for Flexible Energy Storage Devices

State‐of‐the‐art energy storage devices are capable of delivering reasonably high energy density (lithium ion batteries) or high power density (supercapacitors). There is an increasing need for these power sources with not only superior electrochemical performance, but also exceptional flexibility. Graphene has come on to the scene and advancements are being made in integration of various electrochemically active compounds onto graphene or its derivatives so as to utilize their flexibility. Many innovative synthesis techniques have led to novel graphene‐based hybrid two‐dimensional nanostructures. Here, the chemically integrated inorganic‐graphene hybrid two‐dimensional materials and their applications for energy storage devices are examined. First, the synthesis and characterization of different kinds of inorganic‐graphene hybrid nanostructures are summarized, and then the most relevant applications of inorganic‐graphene hybrid materials in flexible energy storage devices are reviewed. The general design rules of using graphene‐based hybrid 2D materials for energy storage devices and their current limitations and future potential to advance energy storage technologies are also discussed.     

The Design of 3D‐Interface Architecture in an Ultralow‐Power,Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

Synaptic electronics is a new technology for developing functional electronic devices that can mimic the structure and functions of biological counterparts. It has broad application prospects in wearable computing chips, human–machine interfaces, and neuron prostheses. These types of applications require synaptic devices with ultralow energy consumption as the effective energy supply for wearable electronics, which is still very difficult. Here, artificial synapse emulation is demonstrated by solid‐ion gated organic field‐effect transistors (OFETs) with a 3D‐interface conducting channel for ultralow‐power synaptic simulation. The basic features of the artificial synapse, excitatory postsynaptic current (EPSC), paired‐pulse facilitation (PPF), and high‐pass filtering, are successfully realized. Furthermore, the single‐fiber based artificial synapse can be operated by an ultralow presynaptic spike down to ?0.5 mV with an ultralow reading voltage at ?0.1 mV due to the large contact surface between the ionic electrolyte and fiber‐like semiconducting channel. Therefore, the ultralow energy consumption at one spike of the artificial synapse can be realized as low as ≈3.9 fJ, which provides great potential in a low‐power integrated synaptic circuit.     

Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small‐scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High‐precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single‐layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible.     

A Dual‐Organic‐Transistor‐Based Tactile‐Perception System with Signal‐Processing Functionality

Organic‐device‐based tactile‐perception systems can open up new opportunities for the next generation of intelligent products. To meet the critical requirements of artificial perception systems, the efficient construction of organic smart elements with integrated sensing and signal processing functionalities is highly desired, but remains a challenge. This study presents a dual‐organic‐transistor‐based tactile‐perception element (DOT‐TPE) with biomimetic functionality by the construction of organic synaptic transistors with integrated sensing transistors. The unique geometry of the DOT‐TPE permits instantaneous sensing of pressure stimuli and synapse‐like processing of an electric signal in a single element. More importantly, these organic‐transistor‐based tactile‐perception elements can be built into arrays to serve as bionic tactile‐perception systems. The combined biomimetic functionality of tactile‐perception systems, together with their promising features of flexibility and large‐area fabrication, makes this work represent a step forward toward novel e‐skin devices for artificial intelligence.