Intercorrelated Ferroelectricity and Bulk Photovoltaic Effect in Two-Dimensional Sn2P2S6 Semiconductor for Polarization-Sensitive Photodetection.

A two-dimensional (2D) ferroelectric semiconductor, which is coupled with photosensitivity and room-temperature ferroelectricity, provides the possibility of coordinated conductance modulation by both electric field and light illumination and is promising for triggering the revolution of optoelectronics for monolithic multifunctional integration. Here, we report that semiconducting Sn2P2S6 crystals can be achieved in a 2D morphology using a chemical vapor transport approach with the assistant of space confinement and experimentally demonstrate the robust ferroelectricity in atomic-thin Sn2P2S6 nanosheet at room temperature. The intercorrelated programming of ferroelectric order along out-of-plane (OOP) and in-plane (IP) directions enables a tunable bulk photovoltaic (BPV) effect through multidirectional electrical control. By combining the capability of anisotropic in-plane optical absorption, a highly integrated Sn2P2S6 optoelectronic device vertically sandwiched with graphene electrodes yields the polarization-dependent open-circuit photovoltage with a dichroic ratio of 2.0 under 405 nm light illumination. The reintroduction of ferroelectric Sn2P2S6 to the 2D asymmetric semiconductor family provides possibilities to hardware implement of the self-powered polarization-sensitive photodetection and spotlights the promising applications for next-generation photovoltaic devices.

Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor.

The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104  s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.

Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing.

In-sensor and near-sensor computing are becoming the next-generation computing paradigm for high-density and low-power sensory processing. To fulfil a high-density and efficient neuromorphic visual system with fully hierarchical emulation of the retina and visual cortex, emerging multimodal neuromorphic devices for multi-stage processing and a fully hardware-implemented system with versatile image processing functions are still lacking and highly desirable. Here we demonstrate an emerging multimodal-multifunctional resistive random-access memory (RRAM) device array based on modified silk fibroin protein (MSFP), exhibiting both optoelectronic RRAM (ORRAM) mode featured by unique negative and positive photoconductance memory and electrical RRAM (ERRAM) mode featured by analogue resistive switching. A full hardware implementation of the artificial visual system with versatile image processing functions is realised for the first time, including ORRAM mode array for the in-sensor image pre-processing (contrast enhancement, background denoising, feature extraction) and ERRAM mode array for near-sensor high-level image recognition, which hugely improves the integration density, and simply the circuit design and the fabrication and integration complexity.

Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction.

Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS2/MoS2 hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS2/MoS2 phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.

CMOS backend-of-line compatible memory array and logic circuitries enabled by high performance atomic layer deposited ZnO thin-film transistor.

The development of high-performance oxide-based transistors is critical to enable very large-scale integration (VLSI) of monolithic 3-D integrated circuit (IC) in complementary metal oxide semiconductor (CMOS) backend-of-line (BEOL). Atomic layer deposition (ALD) deposited ZnO is an attractive candidate due to its excellent electrical properties, low processing temperature below copper interconnect thermal budget, and conformal sidewall deposition for novel 3D architecture. An optimized ALD deposited ZnO thin-film transistor achieving a record field-effect and intrinsic mobility (µFE /µo) of 85/140 cm2/V·s is presented here. The ZnO TFT was integrated with HfO2 RRAM in a 1 kbit (32 × 32) 1T1R array, demonstrating functionalities in RRAM switching. In order to co-design for future technology requiring high performance BEOL circuitries implementation, a spice-compatible model of the ZnO TFTs was developed. We then present designs of various ZnO TFT-based inverters, and 5-stage ring oscillators through simulations and experiments with working frequency exceeding 10's of MHz.

A Self-Rectifying Synaptic Memristor Array with Ultrahigh Weight Potentiation Linearity for a Self-Organizing-Map Neural Network.

Two-terminal self-rectifying (SR)-synaptic memristors are preeminent candidates for high-density and efficient neuromorphic computing, especially for future three-dimensional integrated systems, which can self-suppress the sneak path current in crossbar arrays. However, SR-synaptic memristors face the critical challenges of nonlinear weight potentiation and steep depression, hindering their application in conventional artificial neural networks (ANNs). Here, a SR-synaptic memristor (Pt/NiOx/WO3-x:Ti/W) and cross-point array with sneak path current suppression features and ultrahigh-weight potentiation linearity up to 0.9997 are introduced. The image contrast enhancement and background filtering are demonstrated on the basis of the device array. Moreover, an unsupervised self-organizing map (SOM) neural network is first developed for orientation recognition with high recognition accuracy (0.98) and training efficiency and high resilience toward both noises and steep synaptic depression. These results solve the challenges of SR memristors in the conventional ANN, extending the possibilities of large-scale oxide SR-synaptic arrays for high-density, efficient, and accurate neuromorphic computing.

All Solution-Processed Inorganic, Multilevel Memristors Utilizing Liquid Metals Electrodes Suitable for Analog Computing.

Herein, we report a solution-processable memristive device based on bismuth vanadate (BiVO4) and titanium dioxide (TiO2) with gallium-based eutectic gallium-indium (EGaIn) and gallium-indium-tin alloy (GaInSn) liquid metal as the top electrode. Scanning electron microscopy (SEM) shows the formation of a nonporous structure of BiVO4 and TiO2 for efficient resistive switching. Additionally, the gallium-based liquid metal (GLM)-contacted memristors exhibit stable memristor behavior over a wide temperature range from -10 to +90 °C. Gallium atoms in the liquid metal play an important role in the conductive filament formation as well as the device's operation stability as elucidated by I-V characteristics. The synaptic behavior of the GLM-memristors was characterized, with excellent long-term potentiation (LTP) and long-term depression (LTD) linearity. Using the performance of our device in a multilayer perceptron (MLP) network, a ∼90% accuracy in the handwriting recognition of modified national institute of standards and technology database (MNIST) was achieved. Our findings pave a path for solution-processed/GLM-based memristors which can be used in neuromorphic applications on flexible substrates in a harsh environment.

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 FeO x -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 FeO x 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.

Anisotropic Signal Processing with Trigonal Selenium Nanosheet Synaptic Transistors.

Hardware implementation of an artificial neural network requires neuromorphic devices to process information with low energy consumption and high heterogeneity. Here we demonstrate an electrolyte-gated synaptic transistor (EGT) based on a trigonal selenium (t-Se) nanosheet. Due to the intrinsic low conductivity of the Se channel, the t-Se synaptic transistor exhibits ultralow energy consumption, less than 0.1 pJ per spike. More importantly, the intrinsic low symmetry of t-Se offers a strong anisotropy along its c- and a-axis in electrical conductance with a ratio of up to 8.6. The multiterminal EGT device exhibits an anisotropic response of filtering behavior to the same external stimulus, which enables it to mimic the heterogeneous signal transmission process of the axon-multisynapse biostructure in the human brain. The proof-of-concept device in this work represents an important step to develop neuromorphic electronics for processing complex signals.

A dual mode electronic synapse based on layered SnSe films fabricated by pulsed laser deposition.

An artificial synapse, such as a memristive electronic synapse, has caught world-wide attention due to its potential in neuromorphic computing, which may tremendously reduce computer volume and energy consumption. The introduction of layered two-dimensional materials has been reported to enhance the performance of the memristive electronic synapse. However, it is still a challenge to fabricate large-area layered two-dimensional films by scalable methods, which has greatly limited the industrial application potential of two-dimensional materials. In this work, a scalable pulsed laser deposition (PLD) method has been utilized to fabricate large-area layered SnSe films, which are used as the functional layers of the memristive electronic synapse with dual modes. Both long-term memristive behaviour with gradually changed resistance (Mode 1) and short-term memristive behavior with abruptly reduced resistance (Mode 2) have been achieved in this SnSe-based memristive electronic synapse. The switching between Mode 1 and Mode 2 can be realized by a series of voltage sweeping and programmed pulses. The formation and recovery of Sn vacancies were believed to induce the short-term memristive behaviour, and the joint action of Ag filament formation/rupture and Schottky barrier modulation can be the origin of long-term memristive behaviour. DFT calculations were performed to further illustrate how Ag atoms and Sn vacancies diffuse through the SnSe layer and form filaments. The successful emulation of synaptic functions by the layered chalcogenide memristor fabricated by the PLD method suggests the application potential in future neuromorphic computers.

2D Materials Based Optoelectronic Memory: Convergence of Electronic Memory and Optical Sensor.

The continuous development of electron devices towards the trend of "More than Moore" requires functional diversification that can collect data (sensors) and store (memories) and process (computing units) information. Considering the large occupation proportion of image data in both data center and edge devices, a device integration with optical sensing and data storage and processing is highly demanded for future energy-efficient and miniaturized electronic system. Two-dimensional (2D) materials and their heterostructures have exhibited broadband photoresponse and high photoresponsivity in the configuration of optical sensors and showed fast switching speed, multi-bit data storage, and large ON/OFF ratio in memory devices. In addition, its ultrathin body thickness and transfer process at low temperature allow 2D materials to be heterogeneously integrated with other existing materials system. In this paper, we overview the state-of-the-art optoelectronic random-access memories (ORAMs) based on 2D materials, as well as ORAM synaptic devices and their applications in neural network and image processing. The ORAM devices potentially enable direct storage/processing of sensory data from external environment. We also provide perspectives on possible directions of other neuromorphic sensor design (e.g., auditory and olfactory) based on 2D materials towards the future smart electronic systems for artificial intelligence.

Optoelectronic resistive random access memory for neuromorphic vision sensors.

Neuromorphic visual systems have considerable potential to emulate basic functions of the human visual system even beyond the visible light region. However, the complex circuitry of artificial visual systems based on conventional image sensors, memory and processing units presents serious challenges in terms of device integration and power consumption. Here we show simple two-terminal optoelectronic resistive random access memory (ORRAM) synaptic devices for an efficient neuromorphic visual system that exhibit non-volatile optical resistive switching and light-tunable synaptic behaviours. The ORRAM arrays enable image sensing and memory functions as well as neuromorphic visual pre-processing with an improved processing efficiency and image recognition rate in the subsequent processing tasks. The proof-of-concept device provides the potential to simplify the circuitry of a neuromorphic visual system and contribute to the development of applications in edge computing and the internet of things.

Real-Time Observation of the Electrode-Size-Dependent Evolution Dynamics of the Conducting Filaments in a SiO2 Layer.

Conducting bridge random access memory (CBRAM) is one of the most promising candidates for future nonvolatile memories. It is important to understand the scalability and retention of CBRAM cells to realize better memory performance. Here, we directly observe the switching dynamics of Cu tip/SiO2/W cells with various active electrode sizes using in situ transmission electron microscopy. Conducting filaments (CFs) grow from the active electrode (Cu tip) to inert electrode (W) during the SET operations. The size of the Cu tip affects the electric-field distribution, the amount of the cation injection into electrolyte, and the dimension of the CF. This study provides helpful understanding on the relationship between power consumption and retention of CBRAM cells. We also construct a theoretical model to explain the electrode-size-dependent CF growth in SET operations, showing good agreement with our experimental results.

Graphene-Draped Semiconductors for Enhanced Photocorrosion Resistance and Photocatalytic Properties.

Semiconductor photocatalysts have been widely used for photochemical water splitting, purification of organic contaminants, and bacterial detoxification. However, most photocatalysts suffer greatly from photocorrosion under visible-light irradiation. Here we report a viable strategy to markedly improve photocorrosion resistance of photocatalysts by draping ultrathin yet highly impermeable graphene layers over a semiconductor CdS electrode. Remarkably, the average lifetime of three-layer-graphene-draped CdS photocatalyst is prolonged by 8 times compared to the as-prepared CdS counterpart without graphene draping. The introduction of graphene layers largely suppresses the charge carrier recombination of the CdS film and decreases the carrier transfer resistance at the graphene-draped CdS electrode/electrolyte interface, as revealed by the photoluminescence (PL) and electrochemical impedance spectroscopy studies, respectively, thereby leading to increased photocurrent and enhanced photocatalytic performance (i.e., a 2.5-fold increase in comparison to that in as-prepared CdS case). Our density functional theory calculations also show that electrons are readily transferred from CdS to graphene, correlating well with the PL measurement. The photocorrosion is mainly caused by oxidation reaction between CdS and O2 and H2O assisted with photogenerated holes, evidenced by X-ray photoelectron spectroscopy characterization. The draped graphene effectively prevents the direct contact between the CdS film and O2 and H2O, thus considerably retarding the photocorrosion of CdS upon visible-light exposure. This simple yet robust graphene-draping strategy for antiphotocorrosion of semiconductor photocatalysts is environmentally friendly as it prevents them from entering into the surrounding environment, thus eliminating the possible secondary pollution.

Perovskite Photovoltachromic Supercapacitor with All-Transparent Electrodes.

Photovoltachromic cells (PVCCs) are of great interest for the self-powered smart windows of architectures and vehicles, which require widely tunable transmittance and automatic color change under photostimuli. Organolead halide perovskite possesses high light absorption coefficient and enables thin and semitransparent photovoltaic device. In this work, we demonstrate co-anode and co-cathode photovoltachromic supercapacitors (PVCSs) by vertically integrating a perovskite solar cell (PSC) with MoO3/Au/MoO3 transparent electrode and electrochromic supercapacitor. The PVCSs provide a seamless integration of energy harvesting/storage device, automatic and wide color tunability, and enhanced photostability of PSCs. Compared with conventional PVCC, the counter electrodes of our PVCSs provide sufficient balancing charge, eliminate the necessity of reverse bias voltage for bleaching the device, and realize reasonable in situ energy storage. The color states of PVCSs not only indicate the amount of energy stored and energy consumed in real time, but also enhance the photostability of photovoltaic component by preventing its long-time photoexposure under fully charged state of PVCSs. This work designs PVCS devices for multifunctional smart window applications commonly made of glass.