Retina-Inspired Color-Cognitive Learning via Chromatically Controllable Mixed Quantum Dot Synaptic Transistor Arrays

Optoelectronic Devices for In-Sensor Computing

The demand for accurate perception of the physical world leads to a dramatic increase in sensory nodes. However, the transmission of massive and unstructured sensory data from sensors to computing units poses great challenges in terms of power-efficiency, transmission bandwidth, data storage, time latency, and security. To efficiently process massive sensory data, it is crucial to achieve data compression and structuring at the sensory terminals. In-sensor computing integrates perception, memory, and processing functions within sensors, enabling sensory terminals to perform data compression and data structuring. Here, vision sensors are adopted as an example and discuss the functions of electronic, optical, and optoelectronic hardware for visual processing. Particularly, hardware implementations of optoelectronic devices for in-sensor visual processing that can compress and structure multidimensional vision information are examined. The underlying resistive switching mechanisms of volatile/nonvolatile optoelectronic devices and their processing operations are explored. Finally, a perspective on the future development of optoelectronic devices for in-sensor computing is provided.

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.

A Reconfigurable All-Optical-Controlled Synaptic Device for Neuromorphic Computing Applications

Retina-inspired visual sensors play a crucial role in the realization of neuromorphic visual systems. Nevertheless, significant obstacles persist in the pursuit of achieving bidirectional synaptic behavior and attaining high performance in the context of photostimulation. In this study, we propose a reconfigurable all-optical controlled synaptic device based on the IGZO/SnO/SnS heterostructure, which integrates sensing, storage and processing functions. Relying on the simple heterojunction stack structure and the role of energy band engineering, synaptic excitatory and inhibitory behaviors can be observed under the light stimulation of ultraviolet (266 nm) and visible light (405, 520 and 658 nm) without additional voltage modulation. In particular, junction field-effect transistors based on the IGZO/SnO/SnS heterostructure were fabricated to elucidate the underlying bidirectional photoresponse mechanism. In addition to optical signal processing, an artificial neural network simulator based on the optoelectrical synapse was trained and recognized handwritten numerals with a recognition rate of 91%. Furthermore, we prepared an 8 × 8 optoelectrical synaptic array and successfully demonstrated the process of perception and memory for image recognition in the human brain, as well as simulated the situation of damage to the retina by ultraviolet light. This work provides an effective strategy for the development of high-performance all-optical controlled optoelectronic synapses and a practical approach to the design of multifunctional artificial neural vision systems.

Enhancement-Mode Carbon Nanotube Optoelectronic Synaptic Transistors with Large and Controllable Threshold Voltage Modulation Window for Broadband Flexible Vision Systems

The development of large-scale integration of optoelectronic neuromorphic devices with ultralow power consumption and broadband responses is essential for high-performance bionics vision systems. In this work, we developed a strategy to construct large-scale (40 × 30) enhancement-mode carbon nanotube optoelectronic synaptic transistors with ultralow power consumption (33.9 aJ per pulse) and broadband responses (from 365 to 620 nm) using low-work function yttrium (Y)-gate electrodes and the mixture of eco-friendly photosensitive Ag2S quantum dots (QDs) and ionic liquids (ILs)-cross-linking-poly(4-vinylphenol) (PVP) (ILs-c-PVP) as the dielectric layers. Solution-processable carbon nanotube thin-film transistors (TFTs) showed enhancement-mode characteristics with the wide and controllable threshold voltage window (-1 V∼0 V) owing to use of the low-work-function Y-gate electrodes. It is noted that carbon nanotube optoelectronic synaptic transistors exhibited high on/off ratios (>106), small hysteresis and low operating voltage (≤2 V), and enhancement mode even under the illumination of ultraviolet (UV, 365 nm), blue (450 nm), and green (550 nm) to red (620 nm) pulse lights when introducing eco-friendly Ag2S QDs in dielectric layers, demonstrating that they have the strong fault-tolerant ability for the threshold voltage drifts caused by various manufacturing scenarios. Furthermore, some important bionic functions including a high paired pulse facilitation index (PPF index, up to 290%), learning and memory function with the long duration (200 s), and rapid recovery (2 s). Pavlov's dog experiment (retention time up to 20 min) and visual memory forgetting experiments (the duration of high current for 180 s) are also demonstrated. Significantly, the optoelectronic synaptic transistors can be used to simulate the adaptive process of vision in varying light conditions, and we demonstrated the dynamic transition of light adaptation to dark adaptation based on light-induced conditional behavior. This work undoubtedly provides valuable insights for the future development of artificial vision systems.

PBDB-T/Pentacene-Based Organic Optoelectronic Synaptic Transistor with Adjustable Critical Flicker Fusion Frequency for Dynamic Vision

In the era of the Internet of Things and the rapid progress of artificial intelligence, there is a growing demand for advanced dynamic vision systems. Vision systems are no longer confined to static object detection and recognition, as the detection and recognition of moving objects are becoming increasingly important. To meet the requirements for more precise and efficient dynamic vision, the development of adaptive multimodal motion detection devices becomes imperative. Inspired by the varied response rates in biological vision, we introduce the concept of critical flicker fusion frequency (cFFF) and develop an organic optoelectronic synaptic transistor with adjustable cFFF. In situ Kelvin probe force microscopy analysis reveals that light signal recognition in this device originates from charge transfer in the poly[(2,6-(4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b:4,5-b']dithiophene)-co-(1,3-di(5-thiophene-2-yl)-5,7-bis(2-ethylhexyl)-benzo[1,2-c:4,5-c']dithiophene-4,8-dione)] (PBDB-T)/pentacene heterojunction, which can be effectively modulated by gate voltage. Building upon this, we implement different cFFF within a single device to facilitate the detection and recognition of objects moving at different speeds. This approach allows for resource allocation during dynamic detection, resulting in a reduction in power consumption. Our research holds great potential for enhancing the capabilities of dynamic visual 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.

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.

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.

Humidity/Oxygen-Insensitive Organic Synaptic Transistors Based on Optical Radical Effect

For most organic synaptic transistors based on the charge trapping effect, different atmosphere conditions lead to significantly different device performance. Some devices even lose the synaptic responses under vacuum or inert atmosphere. The stable device performance of these organic synaptic transistors under varied working environments with different humidity and oxygen levels can be a challenge. Herein, a moisture- and oxygen-insensitive organic synaptic device based on the organic semiconductor and photoinitiator molecules is reported. Unlike the widely reported charge trapping effect, the photoinduced free radical is utilized to realize the photosynaptic performance. The resulting synaptic transistor displays typical excitatory postsynaptic current, paired-pulse facilitation, learning, and forgetting behaviors. Furthermore, the device exhibits decent and stable photosynaptic performances under high humidity and vacuum conditions. This type of organic synaptic device also demonstrates high potential in ultraviolet B perception based on its environmental stability and broad ultraviolet detection capability. Finally, the contrast-enhanced capability of the device is successfully validated by the single-layer-perceptron/double-layer network based Modified National Institute of Standards and Technology pattern recognition. This work could have important implications for the development of next-generation environment-stable organic synaptic devices and systems.

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.

High-Density Artificial Synapse Array Consisting of Homogeneous Electrolyte-Gated Transistors

The artificial synapse array with an electrolyte-gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo-lithography. This study presents a scheme based on a lateral-gate structure to realize high-density integration of EGTs and proposes the integration of 100 × 100 EGTs into a 2.5 × 2.5 cm2 glass, with a unit density of up to 1600 devices cm-2 . Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 × 10-3  S cm-1 owing to the porosity of zeolitic imidazolate frameworks-67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.

Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications

As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.

Tailoring Wavelength-Adaptive Visual Neuroplasticity Transitions of Synaptic Transistors Comprising Rod-Coil Block Copolymers for Dual-Mode Photoswitchable Learning/Forgetting Neural Functions

The vision-inspired artificial neural network based on optical synapses has drawn a tremendous amount of attention for emulating biological senses. Although photoexcitation-induced synaptic functionalities have been widely studied, optical habituation via the photoinhibitory pathway is yet to be demonstrated for sophisticated biomimetic visual adaptive systems. Here, the first optical neuromorphic block copolymer (BCP) phototransistor is demonstrated as an all-optical operation responding to various wavelengths, fulfilling photoassisted dynamic learning/forgetting cycles via optical potentiation without gate bias. The polyfluorene BCPs were precisely designed to enable wavelength-adaptive responses, benefiting from interfacial semiconductor/electret morphology and the crystallinity/electron affinity of the BCPs. Notably, this is the first work to simultaneously exhibit fully light-controlled short- and long-term memory based on organic material systems. The device presents a high current contrast above 100-fold and long-term retention over 104 s. As a proof-of-concept for neural networks, a 6 × 6 array of photosynapses performed outstanding visual pattern learning/forgetting with high accuracy. This study exploits the design strategy of a conjugated BCP electret to unleash the full potential of wavelength-adaptive visual neuroplasticity transitions. It provides an effective architecture for designing high-performance and high-storage capacity required applications in next-generation neuromorphic systems.

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

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

Molecular template growth of organic heterojunctions to tailor visual neuroplasticity for high performance phototransistors with ultralow energy consumption

The optical and charge transport properties of organic semiconductors are strongly influenced by their morphology and molecular structures. Here we report the influence of a molecular template strategy on anisotropic control via weak epitaxial growth of a semiconducting channel for a dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DNTT)/para-sexiphenyl (p-6P) heterojunction. The aim is to improve charge transport and trapping, to enable tailoring of visual neuroplasticity. The proposed phototransistor devices, comprising a molecular heterojunction with optimized molecular template thickness, exhibited an excellent memory ratio (I_ON/I_OFF) and retention characteristics in response to light stimulation, owing to the enhanced orientation/packing of DNTT molecules and a favorable match between the LUMO/HOMO levels of p-6P and DNTT. The best performing heterojunction exhibits visual synaptic functionalities, including an extremely high pair-pulse facilitation index of ∼206%, ultralow energy consumption of 0.54 fJ, and zero-gate operation, under ultrashort pulse light stimulation to mimic human-like sensing, computing, and memory functions. An array of heterojunction photosynapses possess a high degree of visual pattern recognition and learning, to mimic the neuroplasticity of human brain activities through a rehearsal learning process. This study provides a guide to the design of molecular heterojunctions for tailoring high-performance photonic memory and synapses for neuromorphic computing and artificial intelligence systems.

Photoactivated organic phosphorescence by stereo-hindrance engineering for mimicking synaptic plasticity

Purely organic phosphorescent materials with dynamically tunable optical properties and persistent luminescent characteristics enable more novel applications in intelligent optoelectronics. Herein, we reported a concise and universal strategy to achieve photoactivated ultralong phosphorescence at room temperature through stereo-hindrance engineering. Such dynamically photoactivated phosphorescence behavior was ascribed to the suppression of non-radiative transitions and improvement of spin-orbit coupling (SOC) as the variation of the distorted molecular conformation by the synergistic effect of electrostatic repulsion and steric hindrance. This "trainable" phosphorescent behavior was first proposed to mimic biological synaptic plasticity, especially for unique experience-dependent plasticity, by the manipulation of pulse intensity and numbers. This study not only outlines a principle to design newly dynamic phosphorescent materials, but also broadens their utility in intelligent sensors and robotics.

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.

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

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

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.

One-Step Preparation of Semiconductor/Dielectric Bilayer Structures for the Simulation of Flexible Bionic Photonic Synapses

Flexible synaptic devices with information sensing, processing, and storage functions are indispensable in the development of wearable artificial intelligence electronic systems. Here, a semiconductor/dielectric bilayer structure was prepared by a one-step deposition method and used for the first time in a flexible biomimetic photonic synaptic transistor device. Specifically, poly(3-hexylthiophene)-block-poly(phenyl isocyanide) with pentafluorophenyl ester (P3HT-b-PPI(5F)) was prepared as the device active layer, where the P3HT segment served as a carrier transport channel and optical gate and the PPI(5F) segment was used for charge trapping. Various biomimetic synaptic behaviors, such as excitatory postsynaptic currents, paired-pulse facilitation, and short-term/long-term memory, were successfully simulated under green light stimulation. An ultra-low energy consumption of 1.82 fJ was achieved with a greatly reduced operating voltage. Further, the "Morse-code" optical decoding was simulated using the excellent synaptic plasticity of the device. In addition, flexible synaptic devices were prepared by a one-step deposition method and can be well-affixed to arbitrary substrates. This has promising applications in the field of wearable bionic electronics.

Retina-Inspired Organic Photonic Synapses for Selective Detection of SWIR Light

Photonic synapses with the dual function of optical signal detection and information processing can simulate human visual system. However, photonic synapses with selective detection of short-wavelength infrared (SWIR) light have never been reported, which can not only broaden the human vision region but also integrate neuromorphic computation and infrared optical communication. Here, organic photonic synapses based on a new donor-acceptor copolymer P1 are fabricated, which exhibit excellent synaptic characteristics with selective detection for SWIR and extremely low energy consumption (2.85 fJ). The working mechanism is rooted in energy level barriers and unbalanced charge transportation. Moreover, these photonic synapses demonstrate excellent performance in multi-signal logic editing, letter imaging and memory with noise reduction function. This contribution provides ideas of constructing selective-response synapses for artificial visual system and neuromorphic computing.

Progress of Materials and Devices for Neuromorphic Vision Sensors

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.