An Atomically Thin Optoelectronic Machine Vision Processor

Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices, opening numerous opportunities across countless domains, including personalized healthcare and advanced robotics. Leveraging 3D integration, edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption. Here, we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications, including electroencephalogram (EEG)-based seizure prediction, electromyography (EMG)-based gesture recognition, and electrocardiogram (ECG)-based arrhythmia detection. With experiments on three biomedical datasets, we observe the classification accuracy improvement for the pretrained model with 2.93% on EEG, 4.90% on ECG, and 7.92% on EMG, respectively. The optical programming property of the device enables an ultra-low power (2.8 × 10-13 J) fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios. Moreover, the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions, making it promising for neuromorphic vision application. To display the benefits of these intricate synaptic properties, a 5 × 5 optoelectronic synapse array is developed, effectively simulating human visual perception and memory functions. The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.

The future of two-dimensional semiconductors beyond Moore's law

The primary challenge facing silicon-based electronics, crucial for modern technological progress, is difficulty in dimensional scaling. This stems from a severe deterioration of transistor performance due to carrier scattering when silicon thickness is reduced below a few nanometres. Atomically thin two-dimensional (2D) semiconductors still maintain their electrical characteristics even at sub-nanometre scales and offer the potential for monolithic three-dimensional (3D) integration. Here we explore a strategic shift aimed at addressing the scaling bottleneck of silicon by adopting 2D semiconductors as new channel materials. Examining both academic and industrial viewpoints, we delve into the latest trends in channel materials, the integration of metal contacts and gate dielectrics, and offer insights into the emerging landscape of industrializing 2D semiconductor-based transistors for monolithic 3D integration.

A Dual-Functional Integration of Photodetectors and Artificial Optoelectronic Synapses on a VO2/WO3 Heterojunction Device

Bionic visual systems require multimodal integration of eye-like photodetectors and brain-like image memory. However, the integration of photodetectors (PDs) and artificial optoelectronic synapses devices (OESDs) by one device remains a giant challenge due to their photoresponse discrepancy. Herein, a dual-functional integration of PDs and OESDs based on VO2/WO3 heterojunctions is presented. The device can be able to realize a dual-mode conversion between PDs and OESDs through tuning the bias voltage. Under zero bias voltage, the device exhibiting excellent photodetecting behaviors based on the photovoltaic effect, showing a high self-powered photoresponsivity of 18.5 mA W-1 and high detectivity of 7.5 × 1010 Jones with fast photoresponse. When the external bias voltages are applied, it can be acted as an OESD and exhibit versatile electrical and photonic synaptic characteristics based on the trapping and detrapping effects, including synaptic plasticity and learning-experience behaviors. More importantly, benefiting from the excellent photosensing ability and transporting properties, the device shows ultralow-power consumption of 39.0 pJ and a 4 × 4 OESDs array is developed to realize the visual perception and memory. This work not only supplies a novel route to realize complex functional integration just in one device, but also offers effective strategies for developing neuromorphic visual system.

Human somatosensory systems based on sensor-memory-integrated technology

As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.

VO2 /MoO3 Heterojunctions Artificial Optoelectronic Synapse Devices for Near-Infrared Optical Communication

Artificial optoelectronic synapses (OES) have attracted extensive attention in brain-inspired information processing and neuromorphic computing. However, OES at near-infrared wavelengths have rarely been reported, seriously limiting the application in modern optical communication. Herein, high-performance near-infrared OES devices based on VO2 /MoO3 heterojunctions are presented. The textured MoO3 films are deposited on the sputtered VO2 film by using the glancing-angle deposition technique to form a heterojunction device. Through tuning the oxygen defects in the VO2 film, the fabricated VO2 /MoO3 heterojunction exhibits versatile electrical synaptic functions. Benefiting from the highly efficient light harvesting and the unique interface effect, the photonic synaptic characteristics, mainly including the short/long-term plasticity and learning experience behavior are successfully realized at the O (1342 nm) and C (1550 nm) optical communication wavebands. Moreover, a single OES device can output messages accurately by converting light signals of the Morse code to distinct synaptic currents. More importantly, a 3 × 3 artificial OES array is constructed to demonstrate the impressive image perceiving and learning capabilities. This work not only indicates the feasibility of defect and interface engineering in modulating the synaptic plasticity of OES devices, but also provides effective strategies to develop advanced artificial neuromorphic visual systems for next-generation optical communication systems.

Technology and Integration Roadmap for Optoelectronic Memristor

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

Overcoming the Unfavorable Effects of "Boltzmann Tyranny:" Ultra-Low Subthreshold Swing in Organic Phototransistors via One-Transistor-One-Memristor Architecture

Organic phototransistors (OPTs), as photosensitive organic field-effect transistors (OFETs), have gained significant attention due to their pivotal roles in imaging, optical communication, and night vision. However, their performance is fundamentally limited by the Boltzmann distribution of charge carriers, which constrains the average subthreshold swing (SSave ) to a minimum of 60 mV/decade at room temperature. In this study, an innovative one-transistor-one-memristor (1T1R) architecture is proposed to overcome the Boltzmann limit in conventional OFETs. By replacing the source electrode in an OFET with a memristor, the 1T1R device exploits the memristor's sharp resistance state transitions to achieve an ultra-low SSave of 18 mV/decade. Consequently, the 1T1R devices demonstrate remarkable sensitivity to photo illumination, with a high specific detectivity of 3.9 × 109  cm W-1 Hz1/2 , outperforming conventional OPTs (4.9 × 104  cm W-1 Hz1/2 ) by more than four orders of magnitude. The 1T1R architecture presents a potentially universal solution for overcoming the detrimental effects of "Boltzmann tyranny," setting the stage for the development of ultra-low SSave devices in various optoelectronic applications.

Non-volatile rippled-assisted optoelectronic array for all-day motion detection and recognition

In-sensor processing has the potential to reduce the energy consumption and hardware complexity of motion detection and recognition. However, the state-of-the-art all-in-one array integration technologies with simultaneous broadband spectrum image capture (sensory), image memory (storage) and image processing (computation) functions are still insufficient. Here, macroscale (2 × 2 mm2) integration of a rippled-assisted optoelectronic array (18 × 18 pixels) for all-day motion detection and recognition. The rippled-assisted optoelectronic array exhibits remarkable uniformity in the memory window, optically stimulated non-volatile positive and negative photoconductance. Importantly, the array achieves an extensive optical storage dynamic range exceeding 106, and exceptionally high room-temperature mobility up to 406.7 cm2 V-1 s-1, four times higher than the International Roadmap for Device and Systems 2028 target. Additionally, the spectral range of each rippled-assisted optoelectronic processor covers visible to near-infrared (405 nm-940 nm), achieving function of motion detection and recognition.

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.

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.

Nanomaterial-Based Artificial Vision Systems: From Bioinspired Electronic Eyes to In-Sensor Processing Devices

High-performance robotic vision empowers mobile and humanoid robots to detect and identify their surrounding objects efficiently, which enables them to cooperate with humans and assist human activities. For error-free execution of these robots' tasks, efficient imaging and data processing capabilities are essential, even under diverse and complex environments. However, conventional technologies fall short of meeting the high-standard requirements of robotic vision under such circumstances. Here, we discuss recent progress in artificial vision systems with high-performance imaging and data processing capabilities enabled by distinctive electrical, optical, and mechanical characteristics of nanomaterials surpassing the limitations of traditional silicon technologies. In particular, we focus on nanomaterial-based electronic eyes and in-sensor processing devices inspired by biological eyes and animal visual recognition systems, respectively. We provide perspectives on key nanomaterials, device components, and their functionalities, as well as explain the remaining challenges and future prospects of the artificial vision systems.

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.

Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing

Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff's law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.

Retinomorphic Motion Detector Fabricated with Organic Infrared Semiconductors

Organic retinomorphic sensors offer the advantage of in-sensor processing to filter out redundant static backgrounds and are well suited for motion detection. To improve this promising structure, here, the key role of interfacial energetics in promoting charge accumulation to raise the inherent photoresponse of the light-sensitive capacitor is studied. Specifically, incorporating appropriate interfacial layers around the photoactive layer is crucial to extend the carrier lifetime, as confirmed by intensity-modulated photovoltage spectroscopy. Compared to its photodiode counterpart, the retinomorphic sensor shows better detectivity and response speed due to the additional insulating layer, which reduces the dark current and the RC time constant. Lastly, three retinomorphic sensors are integrated into a line array to demonstrate the detection of movement speed and direction, showing the potential of retinomorphic designs for efficient motion tracking.

Optical Enhancement of Indirect Bandgap 2D Transition Metal Dichalcogenides for Multi-Functional Optoelectronic Sensors

The unique electrical and optical properties of transition metal dichalcogenides (TMDs) make them attractive nanomaterials for optoelectronic applications, especially optical sensors. However, the optical characteristics of these materials are dependent on the number of layers. Monolayer TMDs have a direct bandgap that provides higher photoresponsivity compared to multilayer TMDs with an indirect bandgap. Nevertheless, multilayer TMDs are more appropriate for various photodetection applications due to their high carrier density, broad spectral response from UV to near-infrared, and ease of large-scale synthesis. Therefore, this review focuses on the modification of the optical properties of devices based on indirect bandgap TMDs and their emerging applications. Several successful developments in optical devices are examined, including band structure engineering, device structure optimization, and heterostructures. Furthermore, it introduces cutting-edge techniques and future directions for optoelectronic devices based on multilayer TMDs.

Parallel perception of visual motion using light-tunable memory matrix

Parallel perception of visual motion is of crucial significance to the development of an intelligent machine vision system. However, implementing in-sensor parallel visual motion perception using conventional complementary metal-oxide semiconductor technology is challenging, because the temporal and spatial information embedded in motion cannot be simultaneously encoded and perceived at the sensory level. Here, we demonstrate the parallel perception of diverse motion modes at the sensor level by exploiting light-tunable memory matrix in a van der Waals (vdW) heterostructure array. The optoelectronic characteristics of gate-tunable photoconductivity and light-tunable memory matrix enable devices in the array to realize simultaneous encoding and processing of incoming spatiotemporal light pattern. Furthermore, we implement a visual motion perceptron with the array capable of deciphering multiple motion parameters in parallel, including direction, velocity, acceleration, and angular velocity. Our work opens up a promising venue for the realization of an intelligent machine vision system based on in-sensor motion perception.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

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

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

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

An Aqueous Analog MAC Machine

Using ions in aqueous milieu for signal processing, like in biological circuits, may potentially lead to a bioinspired information processing platform. Studies, however, have focused on individual ionic diodes and transistors rather than circuits comprising many such devices. Here a 16 × 16 array of new ionic transistors is developed in an aqueous quinone solution. Each transistor features a concentric ring electrode pair with a disk electrode at the center. The electrochemistry of these electrodes in the solution provides the basis for the transistor operation. The ring pair electrochemically tunes the local electrolytic concentration to modulate the disk's Faradaic reaction rate. Thus, the disk current as a Faradaic reaction to the disk voltage is gated by the ring pair. The 16 × 16 array of these transistors performs analog multiply-accumulate (MAC) operations, a computing modality hotly pursued for low-power artificial neural networks. This exploits the transistor's operating regime where the disk current is a multiplication of the disk voltage and a weight parameter tuned by the ring pair gating. Such disk currents from multiple transistors are summated in a global reference electrode to complete a MAC task. This ionic circuit demonstrating analog computing is a step toward sophisticated aqueous ionics.

Chiroptical Synaptic Heterojunction Phototransistors Based on Self-Assembled Nanohelix of π-Conjugated Molecules for Direct Noise-Reduced Detection of Circularly Polarized Light

High-performance chiroptical synaptic phototransistors are successfully demonstrated using heterojunctions composed of a self-assembled nanohelix of a π-conjugated molecule and a metal oxide semiconductor. To impart strong chiroptical activity to the device, a diketopyrrolopyrrole-based π-conjugated molecule decorated with chiral glutamic acid is newly synthesized; this molecule is capable of supramolecular self-assembly through noncovalent intermolecular interactions. In particular, nanohelix formed by intertwinded fibers with strong and stable chiroptical activity in a solid-film state are obtained through hydrogen-bonding-driven, gelation-assisted self-assembly. Phototransistors based on interfacial charge transfer at the heterojunction from the chiroptical nanohelix to the metal oxide semiconductor show excellent chiroptical detection with a high photocurrent dissymmetry factor of 1.97 and a high photoresponsivity of 218 A W-1 . The chiroptical phototransistor demonstrates photonic synapse-like, time-dependent photocurrent generation, along with persistent photoconductivity, which is attributed to the interfacial charge trapping. Through the advantage of synaptic functionality, a trained convolutional neural network successfully recognizes noise-reduced circularly polarized images of handwritten alphabetic characters with better than 89.7% accuracy.

In-sensor computing using a MoS2 photodetector with programmable spectral responsivity

Optical spectroscopy is an indispensable technique in almost all areas of scientific research and industrial applications. After its acquisition, an optical spectrum is usually further processed using a mathematical algorithm to classify or quantify the measurement results. Here we present the design and realization of a smart photodetector that provides such information directly without the need to explicitly record a spectrum. This is achieved by tailoring the spectral responsivity of the device to a specific purpose. In-sensor computation is performed at the lowest possible level of the sensor system hierarchy - the physical level of photon detection - and does not require any external processing of the measurement data. The device can be programmed to cover different types of spectral regression or classification tasks. We present the analysis of spectral mixtures as an example, but the scheme can also be applied to any other algorithm that can be represented by a linear operator. Our prototype physical implementation utilizes an ensemble of optical cavity-enhanced MoS₂ photodetectors with different center wavelengths and individually adjustable peak responsivities. This spectroscopy method represents a significant advance in miniaturized and energy-efficient optical sensing.

Ultrabroadband Imaging Based on Wafer-Scale Tellurene

High-resolution imaging is at the heart of the revolutionary breakthroughs of intelligent technologies, and it is established as an important approach toward high-sensitivity information extraction/storage. However, due to the incompatibility between non-silicon optoelectronic materials and traditional integrated circuits as well as the lack of competent photosensitive semiconductors in the infrared region, the development of ultrabroadband imaging is severely impeded. Herein, the monolithic integration of wafer-scale tellurene photoelectric functional units by exploiting room-temperature pulsed-laser deposition is realized. Taking advantage of the surface plasmon polaritons of tellurene, which results in the thermal perturbation promoted exciton separation, in situ formation of out-of-plane homojunction and negative expansion promoted carrier transport, as well as the band bending promoted electron-hole pair separation enabled by the unique interconnected nanostrip morphology, the tellurene photodetectors demonstrate wide-spectrum photoresponse from 370.6 to 2240 nm and unprecedented photosensitivity with the optimized responsivity, external quantum efficiency and detectivity of 2.7 × 10⁷  A W^-1 , 8.2 × 10⁹ % and 4.5 × 10¹⁵  Jones. An ultrabroadband imager is demonstrated and high-resolution photoelectric imaging is realized. The proof-of-concept wafer-scale tellurene-based ultrabroadband photoelectric imaging system depicts a fascinating paradigm for the development of an advanced 2D imaging platform toward next-generation intelligent equipment.

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.

Geometric deep optical sensing

Geometry, an ancient yet vibrant branch of mathematics, has important and far-reaching impacts on various disciplines such as art, science, and engineering. Here, we introduce an emerging concept dubbed "geometric deep optical sensing" that is based on a number of recent demonstrations in advanced optical sensing and imaging, in which a reconfigurable sensor (or an array thereof) can directly decipher the rich information of an unknown incident light beam, including its intensity, spectrum, polarization, spatial features, and possibly angular momentum. We present the physical, mathematical, and engineering foundations of this concept, with particular emphases on the roles of classical and quantum geometry and deep neural networks. Furthermore, we discuss the new opportunities that this emerging scheme can enable and the challenges associated with future developments.

Nanomaterial-Based Synaptic Optoelectronic Devices for In-Sensor Preprocessing of Image Data

With the advance in information technologies involving machine vision applications, the demand for energy- and time-efficient acquisition, transfer, and processing of a large amount of image data has rapidly increased. However, current architectures of the machine vision system have inherent limitations in terms of power consumption and data latency owing to the physical isolation of image sensors and processors. Meanwhile, synaptic optoelectronic devices that exhibit photoresponse similar to the behaviors of the human synapse enable in-sensor preprocessing, which makes the front-end part of the image recognition process more efficient. Herein, we review recent progress in the development of synaptic optoelectronic devices using functional nanomaterials and their unique interfacial characteristics. First, we provide an overview of representative functional nanomaterials and device configurations for the synaptic optoelectronic devices. Then, we discuss the underlying physics of each nanomaterial in the synaptic optoelectronic device and explain related device characteristics that allow for the in-sensor preprocessing. We also discuss advantages achieved by the application of the synaptic optoelectronic devices to image preprocessing, such as contrast enhancement and image filtering. Finally, we conclude this review and present a short prospect.

Highly Stretchable Graphene Scrolls Transistors for Self-Powered Tribotronic Non-Mechanosensation Application

Owing to highly desired requirements in advanced disease diagnosis, therapy, and health monitoring, noncontact mechanosensation active matrix has drawn considerable attention. To satisfy the practical demands of high energy efficiency, in this report, combining the advantage of multiparameter monitoring, high sensitivity, and high resolution of active matrix field-effect transistor (FET) with triboelectric nanogenerators (TENG), we successfully developed the tribotronic mechanosensation active matrix based on tribotronic ion gel graphene scrolls field-effect transistors (GSFET). The tribopotential produced by TENG served as a gate voltage to modulate carrier transport along the semiconductor channel and realized self-powered ability with considerable decreased energy consumption. To achieve high spatial utilization and more pronounced responsivity of the dielectric of this transistor, ion gel was used to act as a triboelectric layer to conduct friction and contact electrification with external materials directly to produce triboelectric charges to power GFET. This tribopotential-driving device has excellent tactile sensing properties with high sensitivity (1.125 mm^-1), rapid response time (~16 ms), and a durability operation of thousands of cycles. Furthermore, the device was transparent and flexible with the capability of spatially mapping touch stimuli and monitoring real-time temperature. Due to all these unique characteristics, this novel noncontact mechanosensation GSFET active matrix provided a new method for self-powered E-skin with promising potential for self-powered wearable devices and intelligent robots.

Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning

In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.

Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting

Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency. Forgetting also plays an active role in learning. Mimicking the adaptive neurobiological mechanisms for seeing, learning, and forgetting can, therefore, accelerate the development of artificial intelligence (AI) and bridge the massive energy gap that exists between AI and biological intelligence. Here, we demonstrate a bioinspired machine vision system based on a 2D phototransistor array fabricated from large-area monolayer molybdenum disulfide (MoS₂) and integrated with an analog, nonvolatile, and programmable memory gate-stack; this architecture not only enables dynamic learning and relearning from visual stimuli but also offers learning adaptability under noisy illumination conditions at miniscule energy expenditure. In short, our demonstrated "all-in-one" hardware vision platform combines "sensing", "computing", and "storage" to not only overcome the von Neumann bottleneck of conventional complementary metal-oxide-semiconductor (CMOS) technology but also to eliminate the need for peripheral circuits and sensors.

Self-Powered Bidirectional Photoresponse in High-Detectivity WSe2 Phototransistor with Asymmetrical van der Waals Stacking for Retinal Neurons Emulation

An artificial retina system shows a promising potential to achieve fast response, low power consumption, and high integration density for vision sensing systems. Optoelectronic sensors, which can emulate the neurobiological functionalities of retinal neurons, are crucial in the artificial retina systems. Here, we propose a WSe₂ phototransistor with asymmetrical van der Waals (vdWs) stacking that can be used as an optoelectronic sensor in artificial retina systems. Through the utilization of the gate-tunable self-powered bidirectional photoresponse of this phototransistor, the neurobiological functionalities of both bipolar cells and cone cells, as well as the hierarchical connectivity between these two types of retinal neurons, are successfully mimicked by a single device. This self-powered bidirectional photoresponse is attributed to the asymmetrical vdWs stacking structure, which enables the transition from an n-p to p⁺-p homojunction in the WSe₂ channel under different polarities of gate bias. Moreover, the detectivity and ON/OFF ratio of this phototransistor reach as high as 1.8 × 10¹³ Jones and 5.3 × 10⁴, respectively, and a rise/fall time <80 μs is achieved, as well, which reveals good photodetection performance. The proof of this device provides a pathway for the future development of neuromorphic vision devices and systems.

Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices

Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moiré pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moiré engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field.

Nanopatterning Technologies of 2D Materials for Integrated Electronic and Optoelectronic Devices

With the reduction of feature size and increase of integration density, traditional 3D semiconductors are unable to meet the future requirements of chip integration. The current semiconductor fabrication technologies are approaching their physical limits based on Moore's law. 2D materials such as graphene, transitional metal dichalcogenides, etc., are of great promise for future memory, logic, and photonic devices due to their unique and excellent properties. To prompt 2D materials and devices from the laboratory research stage to the industrial integrated circuit-level, it is necessary to develop advanced nanopatterning methods to obtain high-quality, wafer-scale, and patterned 2D products. Herein, the recent development of nanopatterning technologies, particularly toward realizing large-scale practical application of 2D materials is reviewed. Based on the technological progress, the unique requirement and advances of the 2D integration process for logic, memory, and optoelectronic devices are further summarized. Finally, the opportunities and challenges of nanopatterning technologies of 2D materials for future integrated chip devices are prospected.

Active pixel sensor matrix based on monolayer MoS2 phototransistor array

In-sensor processing, which can reduce the energy and hardware burden for many machine vision applications, is currently lacking in state-of-the-art active pixel sensor (APS) technology. Photosensitive and semiconducting two-dimensional (2D) materials can bridge this technology gap by integrating image capture (sense) and image processing (compute) capabilities in a single device. Here, we introduce a 2D APS technology based on a monolayer MoS₂ phototransistor array, where each pixel uses a single programmable phototransistor, leading to a substantial reduction in footprint (900 pixels in ∼0.09 cm²) and energy consumption (100s of fJ per pixel). By exploiting gate-tunable persistent photoconductivity, we achieve a responsivity of ∼3.6 × 10⁷ A W^-1, specific detectivity of ∼5.6 × 10¹³ Jones, spectral uniformity, a high dynamic range of ∼80 dB and in-sensor de-noising capabilities. Further, we demonstrate near-ideal yield and uniformity in photoresponse across the 2D APS array.

A Bioinspired Retinomorphic Device for Spontaneous Chromatic Adaptation

Chromatic adaptation refers to the sensing and preprocessing of the spectral composition of incident light on the retina, and it is important for color-image recognition. It is challenging to apply sensing, memory, and processing functions to color images via the same physical process using the complementary metal-oxide-semiconductor technology because of redundant data detection, complicated signal conversion processes, and the requirement for additional memory modules. Inspired by the highly efficient chromatic adaptation of the human retina, a 2D oxygen-mediated platinum diselenide (PtSe₂ ) device is presented to simultaneously apply sensing, memory, and processing functions to color images. The device exhibits a wavelength-dependent bipolar photoresponse and the linear pulse-number dependence of photoconductivity, which is dominated by the photon-mediated physical adsorption and desorption of oxygen molecules on bilayer PtSe₂ . The proposed retinomorphic device shows superior image classification accuracy (over 90%) compared to an independent pseudocolor channel (less than 75%). Hence, it is promising for developing artificial vision perception systems with reduced architectural complexity.

A biomimetic ocular prosthesis system: emulating autonomic pupil and corneal reflections

The human light modulation response allows humans to perceive objects clearly by receiving the appropriate amount of light from the environment. This paper proposes a biomimetic ocular prosthesis system that mimics the human light modulation response capable of pupil and corneal reflections. First, photoinduced synaptic properties of the quantum dot embedded photonic synapse and its biosimilar signal transmission is confirmed. Subsequently, the pupillary light reflex is emulated by incorporating the quantum dot embedded photonic synapse, electrochromic device, and CMOS components. Moreover, a solenoid-based eyelid is connected to the pupillary light reflex system to emulate the corneal reflex. The proposed ocular prosthesis system represents a platform for biomimetic prosthesis that can accommodate an appropriate amount of stimulus by self-regulating the intensity of external stimuli.

Integration of synaptic phototransistors and quantum dot light-emitting diodes for visualization and recognition of UV patterns

Synaptic photodetectors exhibit photon-triggered synaptic plasticity, which thus can improve the image recognition rate by enhancing the image contrast. However, still, the visualization and recognition of invisible ultraviolet (UV) patterns are challenging, owing to intense background noise. Here, inspired by all-or-none potentiation of synapse, we develop an integrated device of synaptic phototransistors (SPTrs) and quantum dot light-emitting diodes (QLEDs), facilitating noise reduction and visualization of UV patterns through on-device preprocessing. The SPTrs convert noisy UV inputs into a weighted photocurrent, which is applied to the QLEDs as a voltage input through an external current-voltage-converting circuit. The threshold switching characteristics of the QLEDs result in amplified current and visible illumination by the suprathreshold input voltage or nearly zero current and no visible illumination by the input voltage below the threshold. The preprocessing of image data with the SPTr-QLED can amplify the image contrast, which is helpful for high-accuracy image recognition.

In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing

As machine vision technology generates large amounts of data from sensors, it requires efficient computational systems for visual cognitive processing. Recently, in-sensor computing systems have emerged as a potential solution for reducing unnecessary data transfer and realizing fast and energy-efficient visual cognitive processing. However, they still lack the capability to process stored images directly within the sensor. Here, we demonstrate a heterogeneously integrated 1-photodiode and 1 memristor (1P-1R) crossbar for in-sensor visual cognitive processing, emulating a mammalian image encoding process to extract features from the input images. Unlike other neuromorphic vision processes, the trained weight values are applied as an input voltage to the image-saved crossbar array instead of storing the weight value in the memristors, realizing the in-sensor computing paradigm. We believe the heterogeneously integrated in-sensor computing platform provides an advanced architecture for real-time and data-intensive machine-vision applications via bio-stimulus domain reduction.

Designing in-sensor computing systems remains a challenge. Here, the authors demonstrate artificial optical neurons based on the in-sensor computing architecture that fuses sensory and computing nodes into a single platform capable of reducing data transfer time and energy for encoding and classification.

A photosensor employing data-driven binning for ultrafast image recognition

Pixel binning is a technique, widely used in optical image acquisition and spectroscopy, in which adjacent detector elements of an image sensor are combined into larger pixels. This reduces the amount of data to be processed as well as the impact of noise, but comes at the cost of a loss of information. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single “superpixel” that extends over the whole face of the chip. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm. We demonstrate the classification of optically projected images from the MNIST dataset on a nanosecond timescale, with enhanced dynamic range and without loss of classification accuracy. Our concept is not limited to imaging alone but can also be applied in optical spectroscopy or other sensing applications.

Ocular Nanomedicine

Intrinsic shortcomings associated with conventional therapeutic strategies often compromise treatment efficacy in clinical ophthalmology, prompting the rapid development of versatile alternatives for satisfactory diagnostics and therapeutics. Given advances in material science, nanochemistry, and nanobiotechnology, a broad spectrum of functional nanosystems has been explored to satisfy the extensive requirements of ophthalmologic applications. In the present review, the recent progress in nanosystems, both conventional and emerging nanomaterials in ophthalmology from state-of-the-art studies, are comprehensively examined and the role of their fundamental physicochemical properties in bioavailability, tissue penetration, biodistribution, and elimination after interacting with the ophthalmologic microenvironment emphasized. Furthermore, along with the development of surface engineering of nanomaterials, emerging theranostic methodologies are promoted as potential alternatives for multipurpose ocular applications, such as emerging biomimetic ophthalmology (e.g., smart electrochemical eye), thus provoking a holistic review of "ocular nanomedicine." By affording insight into challenges encountered by ocular nanomedicine and further highlighting the direction of future studies, this review provides an incentive for enriching ocular nanomedicine-based fundamental research and future clinical translation.

A Fully Solution-Printed Photosynaptic Transistor Array with Ultralow Energy Consumption for Artificial-Vision Neural Networks

Photosynaptic organic field-effect transistors (OFETs) represent a viable pathway to develop bionic optoelectronics. However, the high operating voltage and current of traditional photosynaptic OFETs lead to huge energy consumption greater than that of the real biological synapses, hindering their further development in new-generation visual prosthetics and artificial perception systems. Here, a fully solution-printed photosynaptic OFET (FSP-OFET) with substantial energy consumption reduction is reported, where a source Schottky barrier is introduced to regulate charge-carrier injection, and which operates with a fundamentally different mechanism from traditional devices. The FSP-OFET not only significantly lowers the working voltage and current but also provides extraordinary neuromorphic light-perception capabilities. Consequently, the FSP-OFET successfully emulates visual nervous responses to external light stimuli with ultralow energy consumption of 0.07-34 fJ per spike in short-term plasticity and 0.41-19.87 fJ per spike in long-term plasticity, both approaching the energy efficiency of biological synapses (1-100 fJ). Moreover, an artificial optic-neural network made from an 8 × 8 FSP-OFET array on a flexible substrate shows excellent image recognition and reinforcement abilities at a low energy cost. The designed FSP-OFET offers an opportunity to realize photonic neuromorphic functionality with extremely low energy consumption dissipation.

Sparse pixel image sensor

As conventional frame-based cameras suffer from high energy consumption and latency, several new types of image sensors have been devised, with some of them exploiting the sparsity of natural images in some transform domain. Instead of sampling the full image, those devices capture only the coefficients of the most relevant spatial frequencies. The number of samples can be even sparser if a signal only needs to be classified rather than being fully reconstructed. Based on the corresponding mathematical framework, we developed an image sensor that can be trained to classify optically projected images by reading out the few most relevant pixels. The device is based on a two-dimensional array of metal–semiconductor–metal photodetectors with individually tunable photoresponsivity values. We demonstrate its use for the classification of handwritten digits with an accuracy comparable to that achieved by readout of the full image, but with lower delay and energy consumption.

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electronics requires increased energy efficiency of processors for specialized data-driven applications. Here, we show how an in-memory processor fabricated using a two-dimensional materials platform can potentially outperform its silicon counterparts in both standard and nontraditional Von Neumann architectures for artificial neural networks. We have fabricated a flash memory array with a two-dimensional channel using wafer-scale MoS₂. Simulations and experiments show that the device can be scaled down to sub-micrometer channel length without any significant impact on its memory performance and that in simulation a reasonable memory window still exists at sub-50 nm channel lengths. Each device conductance in our circuit can be tuned with a 4-bit precision by closed-loop programming. Using our physical circuit, we demonstrate seven-segment digit display classification with a 91.5% accuracy with training performed ex situ and transferred from a host. Further simulations project that at a system level, the large memory arrays can perform AlexNet classification with an upper limit of 50 000 TOpS/W, potentially outperforming neural network integrated circuits based on double-poly CMOS technology.

Programmable black phosphorus image sensor for broadband optoelectronic edge computing

Image sensors with internal computing capability enable in-sensor computing that can significantly reduce the communication latency and power consumption for machine vision in distributed systems and robotics. Two-dimensional semiconductors have many advantages in realizing such intelligent vision sensors because of their tunable electrical and optical properties and amenability for heterogeneous integration. Here, we report a multifunctional infrared image sensor based on an array of black phosphorous programmable phototransistors (bP-PPT). By controlling the stored charges in the gate dielectric layers electrically and optically, the bP-PPT’s electrical conductance and photoresponsivity can be locally or remotely programmed with 5-bit precision to implement an in-sensor convolutional neural network (CNN). The sensor array can receive optical images transmitted over a broad spectral range in the infrared and perform inference computation to process and recognize the images with 92% accuracy. The demonstrated bP image sensor array can be scaled up to build a more complex vision-sensory neural network, which will find many promising applications for distributed and remote multispectral sensing.

2D materials represent a promising platform for machine vision and edge computing applications, although usually limited to ultraviolet and visible wavelengths. Here, the authors report the realization of a programmable image sensor based on black phosphorus, implementing multispectral imaging and analog in-memory computing functionalities in the near- to mid-infrared range.

3D Hypersound Microscopy of van der Waals Heterostructures

The mechanical properties of the layered crystals in the few layer limit are largely unexplored. We employ a picosecond ultrasonic technique to access the corresponding mechanical parameters. Temporal variation of the reflection coefficient of the Al film that covers hBN/WSe₂/hBN (where hBN is hexagonal boron nitride) heterostructures on a sapphire substrate after the femtosecond laser pulse excitation is carefully measured using an interferometric technique with spatial resolution. The laser pulse generates a broadband sound wave packet propagating perpendicularly to the Al plane and partially reflecting from the heterostructural interfaces. The demonstrated technique allows one to resolve a WSe₂ monolayer embedded in hBN. We apply a multilayered model of the optoacoustical response to evaluate the mechanical parameters, in particular, the rigidity of the interfaces. Mapping of the Fourier spectra of the response visualizes different composition regions and may serve as an acoustic tomography tool. Almost zero phonon dissipation below 150 GHz demonstrates the van der Waals heterostructures' potential for nanoacoustical applications.

Retinomorphic optoelectronic devices for intelligent machine vision

Biological visual system can efficiently handle optical information within the retina and visual cortex of the brain, which suggests an alternative approach for the upgrading of the current low-intelligence, large energy consumption, and complex circuitry of the artificial vision system for high-performance edge computing applications. In recent years, retinomorphic machine vision based on the integration of optoelectronic image sensors and processors has been regarded as a promising candidate to improve this phenomenon. This novel intelligent machine vision technology can perform information preprocessing near or even within the sensor in the front end, thereby reducing the transmission of redundant raw data and improving the efficiency of the back-end processor for high-level computing tasks. In this contribution, we try to present a comprehensive review on the recent progress achieved in this emergent field.

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

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

An Annealing Accelerator for Ising Spin Systems Based on In-Memory Complementary 2D FETs

Metaheuristic algorithms such as simulated annealing (SA) are often implemented for optimization in combinatorial problems, especially for discreet problems. SA employs a stochastic search, where high-energy transitions ("hill-climbing") are allowed with a temperature-dependent probability to escape local optima. Ising spin glass systems have properties such as spin disorder and "frustration" and provide a discreet combinatorial problem with a high number of metastable states and ground-state degeneracy. In this work, subthreshold Boltzmann transport is exploited in complementary 2D field-effect transistors (p-type WSe₂ and n-type MoS₂ ) integrated with an analog, nonvolatile, and programmable floating-gate memory stack to develop in-memory computing primitives necessary for energy- and area-efficient hardware acceleration of SA for Ising spin systems. Search acceleration of >800× is demonstrated for 4 × 4 ferromagnetic, antiferromagnetic, and spin glass systems using SA compared to an exhaustive search using a brute force trial at miniscule total energy expenditure of ≈120 nJ. The hardware-realistic numerical simulations further highlight the astounding benefits of SA in accelerating the search for larger spin lattices.

In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor

State-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promising technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demonstrated a CMOS compatible silicon nitride (SiNx) MIS RS device with memristive properties. In this paper, a report on a new photodiode-based vision sensor architecture with in-memory computing capability, relying on memristive device, is disclosed. In this context, the resistance switching dynamics of our memristive device were measured and a data-fitted behavioral model was extracted. SPICE simulations were made highlighting the in-memory computing capabilities of the proposed photodiode-one memristor pixel vision sensor. Finally, an integration and manufacturing perspective was discussed.

Highly sensitive active pixel image sensor array driven by large-area bilayer MoS2 transistor circuitry

Various large-area growth methods for two-dimensional transition metal dichalcogenides have been developed recently for future electronic and photonic applications. However, they have not yet been employed for synthesizing active pixel image sensors. Here, we report on an active pixel image sensor array with a bilayer MoS2 film prepared via a two-step large-area growth method. The active pixel of image sensor is composed of 2D MoS2 switching transistors and 2D MoS2 phototransistors. The maximum photoresponsivity (Rph) of the bilayer MoS2 phototransistors in an 8 × 8 active pixel image sensor array is statistically measured as high as 119.16 A W-1. With the aid of computational modeling, we find that the main mechanism for the high Rph of the bilayer MoS2 phototransistor is a photo-gating effect by the holes trapped at subgap states. The image-sensing characteristics of the bilayer MoS2 active pixel image sensor array are successfully investigated using light stencil projection.

Highly Sensitive Artificial Visual Array Using Transistors Based on Porphyrins and Semiconductors

Artificial visual systems with image sensing and storage functions have considerable potential in the field of artificial intelligence. Light-stimulated synaptic devices can be applied for neuromorphic computing to build artificial visual systems. Here, optoelectronic synaptic transistors based on 5,15-(2-hydroxyphenyl)-10,20-(4-nitrophenyl)porphyrin (TPP) and dinaphtho[2,3-b:2',3'-f ]thieno[3,2-b]thiophene (DNTT) are demonstrated. By utilizing stable TPP with high light absorption, the number of photogenerated carriers in the transport layer can be increased significantly. The devices exhibit high photosensitivity and tunable synaptic plasticity. The synaptic weight can be effectively modulated by the intensity, width, and wavelength of the light signals. Due to the high light absorption of TPP, an ultrasensitive artificial visual array based on these devices is developed, which can detect weak light signals as low as 1 µW cm^-2 . Low-voltage operation is further demonstrated. Even with applied voltages as low as -70 µV, the devices can still show obvious responses, leading to an ultralow energy consumption of 1.4 fJ. The devices successfully demonstrate image sensing and storage functions, which can accurately identify visual information. In addition, the devices can preprocess information and achieve noise reduction. The excellent synaptic behavior of the TPP-based electronics suggests their good potential in the development of new intelligent visual systems.