Ultralow Power Optical Synapses Based on MoS2 Layers by Indium-Induced Surface Charge Doping for Biomimetic Eyes

Frontier applications of retinal nanomedicine: progress, challenges and perspectives

The human retina is a fragile and sophisticated light-sensitive tissue in the central nervous system. Unhealthy retinas can cause irreversible visual deterioration and permanent vision loss. Effective therapeutic strategies are restricted to the treatment or reversal of these conditions. In recent years, nanoscience and nanotechnology have revolutionized targeted management of retinal diseases. Pharmaceuticals, theranostics, regenerative medicine, gene therapy, and retinal prostheses are indispensable for retinal interventions and have been significantly advanced by nanomedical innovations. Hence, this review presents novel insights into the use of versatile nanomaterial-based nanocomposites for frontier retinal applications, including non-invasive drug delivery, theranostic contrast agents, therapeutic nanoagents, gene therapy, stem cell-based therapy, retinal optogenetics and retinal prostheses, which have mainly been reported within the last 5 years. Furthermore, recent progress, potential challenges, and future perspectives in this field are highlighted and discussed in detail, which may shed light on future clinical translations and ultimately, benefit patients with retinal disorders.

Multifunctional Artificial Electric Synapse of MoSe2-Based Memristor toward Neuromorphic Application

Research on memristive devices to seamlessly integrate and replicate the dynamic behaviors of biological synapses will illuminate the mechanisms underlying parallel processing and information storage in the human brain, thereby affording novel insights for the advancement of artificial intelligence. Here, an artificial electric synapse is demonstrated on a one-step Mo-selenized MoSe2 memristor, having not only long-term stable resistive switching characteristics (reset 0.51 ± 0.01 V, on/off ratio > 30, retention > 103 s) but also diverse electrically adjustable synaptic behaviors, including multilevel conductance (synaptic weight), excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/D), spike-timing-dependent plasticity (STDP), and especially activity-dependent synaptic plasticity (ADSP). More significantly, neuromorphic functions of both image edge extraction and biological perception imitation have been successfully achieved. These results present a promising design toward synaptic devices for advancing neuromorphic systems with integrated brain-like neural sensing, memory, and recognition.

Efficient Carbon-Based Optoelectronic Synapses for Dynamic Visual Recognition

The human visual nervous system excels at recognizing and processing external stimuli, essential for various physiological functions. Biomimetic visual systems leverage biological synapse properties to improve memory encoding and perception. Optoelectronic devices mimicking these synapses can enhance wearable electronics, with layered heterojunction materials being ideal materials for optoelectronic synapses due to their tunable properties and biocompatibility. However, conventional synthesis methods are complex and environmentally harmful, leading to issues such as poor stability and low charge transfer efficiency. Therefore, it is imperative to develop a more efficient, convenient, and eco-friendly method for preparing layered heterojunction materials. Here, a one-step ultrasonic method is employed to mix fullerene (C60) with graphene oxide (GO), yielding a homogeneous layered heterojunction composite film via self-assembly. The biomimetic optoelectronic synapse based on this film achieves 97.3% accuracy in dynamic visual recognition tasks and exhibits capabilities such as synaptic plasticity. Experiments utilizing X-ray photoelectron spectroscopy (XPS), X-ray diffraction spectroscopy (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-vis), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) confirms stable π-π interactions between GO and C60, facilitating electron transfer and prolonging carrier recombination times. The novel approach leveraging high-density π electron materials advances artificial intelligence and neuromorphic systems.

Two-Dimensional Materials for Brain-Inspired Computing Hardware

Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed. As a result of their diverse unique properties, atomically thin two-dimensional (2D) materials are promising building blocks for next-generation electronics including nonvolatile memory, in-memory and neuromorphic computing, and flexible edge-computing systems. Furthermore, 2D materials achieve biorealistic synaptic and neuronal responses that extend beyond conventional logic and memory systems. Here, we provide a comprehensive review of the growth, fabrication, and integration of 2D materials and van der Waals heterojunctions for neuromorphic electronic and optoelectronic devices, circuits, and systems. For each case, the relationship between physical properties and device responses is emphasized followed by a critical comparison of technologies for different applications. We conclude with a forward-looking perspective on the key remaining challenges and opportunities for neuromorphic applications that leverage the fundamental properties of 2D materials and heterojunctions.

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.

Harnessing Defects in SnSe Film via Photo-Induced Doping for Fully Light-Controlled Artificial Synapse

2D-layered materials are recognized as up-and-coming candidates to overcome the intrinsic physical limitation of silicon-based devices. Herein, the coexistence of positive persistent photoconductivity (PPPC) and negative persistent photoconductivity (NPPC) in SnSe thin films prepared by pulsed laser deposition provides an excellent avenue for engineering novel devices. It is determined that surface oxygen is co-regulated by physisorption and chemisorption, and the NPPC is attributed to the photo-controllable oxygen desorption behavior. The dominant behavior of chemisorption induces high stability, while physisorption provides room for adjusting NPPC. A simple fully light-modulated artificial synaptic device based on SnSe film is constructed to operate various synaptic plasticity and reversible modulation of conductance by applying 430 and 255 nm illuminations. A three-layer artificial neural network structure with a high accuracy of 95.33% to recognize handwritten digital images is implemented based on the device. Furthermore, the pressure-related cognition response of humans while climbing and the foraging and recognition behaviors of anemonefish are mimicked. This work demonstrates the potential of 2D-layered materials for developing neuromorphic computing and simulating biological behaviors without additional treatment. Furthermore, the one-step method for preparation is highly adaptable and expected to realize large-area growth and integration of SnSe-based devices.

Ultra-Low Power Consumption Artificial Photoelectric Synapses Based on Lewis Acid Doped WSe2 for Neuromorphic Computing

Capitalizing on the extensive spectral capacity and minimal crosstalk properties inherent in optical signals, photoelectric synapses are poised to assume a pivotal stance in the realm of neuromorphic computation. Herein, a photoelectric synapse based on Lewis acid-doped semiconducting tungsten diselenide (WSe2) is introduced, exhibiting tunable short-term and long-term plasticity. The device consumes a mere 0.1 fJ per synaptic operation, which is lower than the energy required by a single synaptic event observed in the human brain. Furthermore, these devices demonstrate high-pass filtering capabilities, highlighting their potential in image-sharpening applications. In particular, by synergistically modulating the photoconductivity and electrical gate bias, versatile logic capabilities are demonstrated within a single device, enabling it to flexibly perform both Boolean AND and OR gate operations. This work demonstrates a viable approach for Lewis acid-treated TMDs to realize multifunctional photoelectric synapses for neuromorphic computing.

Wrinkled Rhenium Disulfide for Anisotropic Nonvolatile Memory and Multiple Artificial Neuromorphic Synapses

Two-dimensional materials are emerging as potential solutions for high-density nonvolatile memory and efficient neuromorphic computing. However, integrating multidimensional memory and an ideal linear weight updating synapse in a simple device configuration to achieve versatile biomimetic neuromorphic systems remains challenging. Here, we introduce a wrinkled rhenium disulfide (ReS2) transistor, where the wrinkled structure facilitates the carrier trapping/detrapping at the dielectric interface, thus enabling the fusion of nonvolatile memory and both electronic and optoelectronic synaptic functionalities. As a nonvolatile memory, anisotropic wrinkled ReS2 can yield three distinct sets of data across three crystal orientations under identical programming operations. Each set demonstrates exceptional retention and endurance properties. As a neuromorphic synapse, it realizes the linear and symmetric updates of conductance states up to 9 bits and 8 bits, the ultra-low-energy consumption of 75 fJ and 2.5 pJ under the electrical and optical stimuli, respectively. The artificial neural network (ANN) based on electronic synapses gives a superior recognition accuracy of 92.9% for the original handwritten digits. The anisotropic synaptic responses and multiwavelength sensitivities of optoelectronic synapses enable them to execute advanced memory and recognition functions for complex images that encompass a variety of pattern features or color information. This underscores its substantial potential for integration into efficient biomimetic visual systems.

Inspiration from Visual Ecology for Advancing Multifunctional Robotic Vision Systems: Bio-inspired Electronic Eyes and Neuromorphic Image Sensors

In robotics, particularly for autonomous navigation and human-robot collaboration, the significance of unconventional imaging techniques and efficient data processing capabilities is paramount. The unstructured environments encountered by robots, coupled with complex missions assigned to them, present numerous challenges necessitating diverse visual functionalities, and consequently, the development of multifunctional robotic vision systems has become indispensable. Meanwhile, rich diversity inherent in animal vision systems, honed over evolutionary epochs to meet their survival demands across varied habitats, serves as a profound source of inspirations. Here, recent advancements in multifunctional robotic vision systems drawing inspiration from natural ocular structures and their visual perception mechanisms are delineated. First, unique imaging functionalities of natural eyes across terrestrial, aerial, and aquatic habitats and visual signal processing mechanism of humans are explored. Then, designs and functionalities of bio-inspired electronic eyes are explored, engineered to mimic key components and underlying optical principles of natural eyes. Furthermore, neuromorphic image sensors are discussed, emulating functional properties of synapses, neurons, and retinas and thereby enhancing accuracy and efficiency of robotic vision tasks. Next, integration examples of electronic eyes with mobile robotic/biological systems are introduced. Finally, a forward-looking outlook on the development of bio-inspired electronic eyes and neuromorphic image sensors is provided.

Centimeter-Scale Assembly of Fractal Organic Crystals with Crisscross Structures for Flexible Photosynapses

Fractal assembly technology enables scalable construction of organic crystal patterns for emerging nanoelectronics and optoelectronics. Here, a polymer-templating assembly strategy is presented for centimeter-scale patterned growth of fractal organic crystals (FOCs). These structures are formed by drop-coating perylene solution directly onto a gelatin-modified surface, resulting in the formation of crisscross fractal patterns. By adjusting the tilt angle of the template, the morphology of FOCs can be effectively controlled, with the diameter distribution of each level branch ranging from hundreds to ten micrometers. The planar FOC device exhibits flexible photoreception and photosynaptic capabilities, with a high specific detectivity of 1.35 × 109 Jones and paired-pulse facilitation (PPF) index of 104%, withstanding a 0.5 cm bending radius during bending test. These findings present a reliable route for large-scale assembly of flexible organic crystalline materials toward neuromorphic electronics.

Tailored Environment-Friendly Reverse Type-I Colloidal Quantum Dots for a Near-Infrared Optical Synapse and Artificial Vision System

Colloidal quantum dots (QDs) are emerging as potential candidates for constructing near-infrared (NIR) photodetectors (PDs) and artificial optoelectronic synapses due to solution processability and a tunable bandgap. However, most of the current NIR QDs-optoelectronic devices are still fabricated using QDs with incorporated harmful heavy metals of lead (Pb) and mercury (Hg), showing potential health and environment risks. In this work, we tailored eco-friendly reverse type-I ZnSe/InP QDs by copper (Cu) doping and extended the photoresponse from the visible to NIR region. Transient absorption spectroscopy analysis revealed the presence of Cu dopant states in ZnSe/InP:Cu QDs that facilitated the extraction of photogenerated charge carriers, leading to an enhanced photodetection performance. Specifically, under 400 nm illumination, the Cu-doped ZnSe/InP QDs-based PDs presented a broadband photodetection ranging from ultraviolet (UV) to NIR, with a responsivity of 70.5 A W-1 and detectivity of 2.8 × 1011 Jones, surpassing those of the undoped ZnSe/InP QDs-based PDs (49.4 A W-1 and 1.9 × 1011 Jones, respectively). More importantly, the ZnSe/InP:Cu QDs-PDs demonstrated various synapse-like characteristics of short-term plasticity (STP), long-term plasticity (LTP), and learning-forging-relearning under NIR light illumination, which were further used to construct PD array devices for simulating the artificial visual system that is available in prospective optical neuromorphic applications.

Comprehensively Modulated Sub-Attojoule Operated Optoelectronic Synapses for Image Encryption and Inpainting

High-performance optoelectronic synaptic transistors play a crucial role in developing and emulating artificial visual systems. However, due to the predominant use of single-structure material modulation in optimizing optoelectronic synapses, their energy consumption significantly trails behind that of electronic synapses by several orders of magnitude. Herein, polymer dielectric layers and optimized contact strategies are adopted to realize the ultralow consumption optoelectronic synapses. Integration of polyimide dielectric significantly enhances photogenerated charge carrier dissociation, leading to substantial improvements in photoresponsivity (1.5 × 106 A·W-1), photodetectivity (6.9 × 1012 Jones), and external quantum efficiency (4.0 × 108%). Additionally, optimized contact properties augment their appeal for ultralow energy consumption in optoelectronic synapse applications. Excitatory postsynaptic current is triggered at an incredibly low voltage of 5 μV and boosts an impressively low energy consumption of 0.05 aJ, ranking among the best-reported results in this field. Next, we demonstrate an integrated system combining the MoS2 optoelectronic synapses with a recurrent neural network enabling 100% accurate recognition of optical signals, particularly in scenarios with aJ-leveled energy consumption. Finally, an image encryption system has been developed, in which images are encrypted by photoelectronic conversion of synapse arrays with random voltage settings and decrypted according to the recurrent neural network-based accuracy. More importantly, once partially damaged images are encrypted, through the decryption image inpainting can be realized due to the high accuracy. The proposed innovative approach holds promise for advancing artificial intelligence applications with improved energy efficiency, information security, and computational capabilities.

Multicolor Fully Light-Modulated Artificial Synapse Based on P-MoSe2/PxOy Heterostructured Memristor

Developing brain-inspired neuromorphic paradigms is imperative to breaking through the von Neumann bottleneck. The emulation of synaptic functionality has motivated the exploration of optoelectronic memristive devices as high-performance artificial synapses, yet the realization of such a modulatory terminal capable of full light-modulation, especially near-infrared stimuli, remains a challenge. Here, a fully light-modulated synaptic memristor is reported on a P-MoSe2/PxOy heterostructure formed by a facile one-step selenization process. The results demonstrate successful achievement of multiwavelength (visible 470 nm to near-infrared 808 nm) modulated switching operations (reset in 0.21-0.97 V) and diverse synaptic behaviors, including postsynaptic current, paired-pulse facilitation, short- and long-term memory (STM and LTM), and learning-forgetting. Notably, the device can exhibit a 3.42 μA PSC increase under six identical 655 nm stimuli, a 11.90-46.24 μA PSC modulation by changing 808 nm light intensity from 6 to 14 mW/cm2, and a transition from STM to LTM lasting between 2.47 and 4.27 s by a prolonged 808 nm pulse from 1 to 30 s. A novel possible light-induced switching mechanism in such a heterostructure is proposed. Furthermore, brain-like light-stimulated memory behavior and Pavlov's classical conditioning demonstrate the device's capacity for processing complex inputs. The study presents a design toward a multiwavelength modulated artificial visual system for color recognition.

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

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

Anti-distortion bioinspired camera with an inhomogeneous photo-pixel array

The bioinspired camera, comprising a single lens and a curved image sensor-a photodiode array on a curved surface-, was born of flexible electronics. Its economical build lends itself well to space-constrained machine vision applications. The curved sensor, much akin to the retina, helps image focusing, but the curvature also creates a problem of image distortion, which can undermine machine vision tasks such as object recognition. Here we report an anti-distortion single-lens camera, where 4096 silicon photodiodes arrayed on a curved surface in a nonuniform pattern assimilated to the distorting optics are the key to anti-distortion engineering. That is, the photo-pixel distribution pattern itself is warped in the same manner as images are warped, which correctively reverses distortion. Acquired images feature no appreciable distortion across a 120° horizontal view, as confirmed by their neural-network recognition accuracies. This distortion correction via photo-pixel array reconfiguration is a form of in-sensor computing.

Two-Terminal Perovskite Optoelectronic Synapse for Rapid Trained Neuromorphic Computation with High Accuracy

Emerging neural morphological vision sensors inspired by biological systems that integrate image perception, memory, and information computing are expected to transform the landscape of machine vision and artificial intelligence. However, stable and reconfigurable light-induced synaptic behavior always relies on independent gateport modulation. Despite its potential, the limitations of uncontrollable defects and ionic characteristics have led to simpler, smaller, and more integration-friendly two-terminal devices being used as sidelines. In this work, the synergy between ion migration barriers and readout voltage is proven to be the key to realizing stable, reconfigurable, and precisely controllable postsynaptic current in two-terminal devices. Following the same mechanism, optical and electrical signal synchronous triggering is proposed to serve as a preprocessing method to achieve a recognition accuracy of 96.5%. Impressively, the gradual ion accumulation during the training process induces photocurrent evolution, serving as a reference for the dynamic learning rate and boosting accuracy to 97.8% in just 10 epochs. The PSC modulation potential under short optical pulse of 20 ns is also revealed. This optoelectronic device with perception, memory, and computation capabilities can promote the development of new devices for future photonic neural morphological circuits and artificial vision.

Electrically Tunable Multiple-Effects Synergistic and Boosted Photoelectric Performance in Te/WSe2 Mixed-Dimensional Heterojunction Phototransistors

Mix-dimensional heterojunctions (MDHJs) photodetectors (PDs) built from bulk and 2D materials are the research focus to develop hetero-integrated and multifunctional optoelectronic sensor systems. However, it is still an open issue for achieving multiple effects synergistic characteristics to boost sensitivity and enrich the prospect in artificial bionic systems. Herein, electrically tunable Te/WSe2 MDHJs phototransistors are constructed, and an ultralow dark current below 0.1 pA and a large on/off rectification ratio of 106 is achieved. Photoconductive, photovoltaic, and photo-thermoelectric conversions are simultaneously demonstrated by tuning the gate and bias. By these synergistic effects, responsivity and detectivity respectively reach 13.9 A W-1 and 1.37 × 1012 Jones with 400 times increment. The Te/WSe2 MDHJs PDs can function as artificial bionic visual systems due to the comparable response time to those of the human visual system and the presence of transient positive and negative response signals. This work offers an available strategy for intelligent optoelectronic devices with hetero-integration and multifunctions.

A bionic self-driven retinomorphic eye with ionogel photosynaptic retina

Bioinspired bionic eyes should be self-driving, repairable and conformal to arbitrary geometries. Such eye would enable wide-field detection and efficient visual signal processing without requiring external energy, along with retinal transplantation by replacing dysfunctional photoreceptors with healthy ones for vision restoration. A variety of artificial eyes have been constructed with hemispherical silicon, perovskite and heterostructure photoreceptors, but creating zero-powered retinomorphic system with transplantable conformal features remains elusive. By combining neuromorphic principle with retinal and ionoelastomer engineering, we demonstrate a self-driven hemispherical retinomorphic eye with elastomeric retina made of ionogel heterojunction as photoreceptors. The receptor driven by photothermoelectric effect shows photoperception with broadband light detection (365 to 970 nm), wide field-of-view (180°) and photosynaptic (paired-pulse facilitation index, 153%) behaviors for biosimilar visual learning. The retinal photoreceptors are transplantable and conformal to any complex surface, enabling visual restoration for dynamic optical imaging and motion tracking.

Green Alga-Inspired Underwater Vision Based on Light-Driven Active Ion Transport across Janus Dual-Field Heterostructures

Natural organisms have evolved various biological ion channels to make timely responses toward different physical and/or chemical stimuli, giving guidance to construct artificial counterparts and expand the corresponding applications. They have also shown promising potential to overcome disadvantages of traditional electronic devices (e.g., energy-consuming operation and adverse humidity interference). Herein, we constructed a green alga-inspired nanofluidic system based on a Janus dual-field heterogeneous membrane (i.e., J-HM), which can function underwater as an artificial visual platform for light perception through enhanced active ion transport. The J-HM was obtained through sequentially assembled MXene and Cu-HHTP (i.e., a metal-organic framework based on the reaction between 2,3,6,7,10,11-hexahydroxytriphenylene hydrate (HHTP) and Cu2+) building units. Due to the formed temperature gradient and intramembrane electric field caused by the localized thermal excitation and efficient charge separation of J-HM under illumination, thermo-osmotic and photo-driven forces are generated for preferential cation transport from Cu-HHTP to MXene. Furthermore, unidirectional active transport can be enhanced by self-diffusion under a concentration gradient. Then, the corresponding underwater light perceptions at various light illumination conditions are explored, showing nearly a linear correlation with the light intensity. Finally, it is demonstrated that the visual platform can achieve object shape, definition, and distance recognition using a defined pixelated matrix, giving impetus to develop ionic signal transmission based sensing 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.

Solid-Liquid Interfacial Engineered Large-Area Two-Dimensional Covalent Organic Framework Films

Constructing uniform covalent organic framework (COF) film on substrates for electronic devices is highly desirable. Here, a simple and mild strategy is developed to prepare them by polymerization on a solid-liquid interface. The universality of the method is confirmed by the successful preparation of five COF films with different microstructures. These films have large lateral size, controllable thickness, and high crystalline quality. And COF patterns can also be directly achieved on substrates via hydrophilic and hydrophobic interface engineering, which is in favor of preparing device array. For application studies, the PyTTA-TPA (PyTTA: 4,4',4'',4'''-(1,3,6,8-Tetrakis(4-aminophenyl)pyrene and TPA: terephthalaldehyde) COF film has a high photoresponsivity of 59.79 μA W-1 at 420 nm for photoelectrochemical (PEC) detection. When employed as an active material for optoelectronic synaptic devices for the first attempt, it shows excellent light-stimulated synaptic plasticity properties such as short-term plasticity (STP), long-term plasticity (LTP), and the conversion of STP to LTP, which can be used to simulate biological synaptic functions.

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.

2D Materials in Flexible Electronics: Recent Advances and Future Prospectives

Flexible electronics have recently gained considerable attention due to their potential to provide new and innovative solutions to a wide range of challenges in various electronic fields. These electronics require specific material properties and performance because they need to be integrated into a variety of surfaces or folded and rolled for newly formatted electronics. Two-dimensional (2D) materials have emerged as promising candidates for flexible electronics due to their unique mechanical, electrical, and optical properties, as well as their compatibility with other materials, enabling the creation of various flexible electronic devices. This article provides a comprehensive review of the progress made in developing flexible electronic devices using 2D materials. In addition, it highlights the key aspects of materials, scalable material production, and device fabrication processes for flexible applications, along with important examples of demonstrations that achieved breakthroughs in various flexible and wearable electronic applications. Finally, we discuss the opportunities, current challenges, potential solutions, and future investigative directions about this field.

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.

In Situ Defect Engineering of Controllable Carrier Types in WSe2 for Homomaterial Inverters and Self-Powered Photodetectors

WSe2 has a high mobility of electrons and holes, which is an ideal choice as active channels of electronics in extensive fields. However, carrier-type tunability of WSe2 still has enormous challenges, which are essential to overcome for practical applications. In this work, the direct growth of n-doped few-layer WSe2 is realized via in situ defect engineering. The n-doping of WSe2 is attributed to Se vacancies induced by the H2 flow purged in the cooling process. The electrical measurements based on field effect transistors demonstrate that the carrier type of WSe2 synthesized is successfully transferred from the conventional p-type to the rarely reported n-type. The electron carrier concentration is efficiently modulated by the concentration of H2 during the cooling process. Furthermore, homomaterial inverters and self-powered photodetectors are fabricated based on the doping-type-tunable WSe2. This work reveals a significant way to realize the controllable carrier type of two-dimensional (2D) materials, exhibiting great potential in future 2D electronics engineering.

Bio-inspired synaptic behavior simulation in thin-film transistors based on molybdenum disulfide

Synaptic behavior simulation in transistors based on MoS2 has been reported. MoS2 was utilized as the active layer to prepare ambipolar thin-film transistors. The excitatory postsynaptic current phenomenon was simulated, observing a gradual voltage decay following the removal of applied pulses, ultimately resulting in a response current slightly higher than the initial current. Subsequently, ±5 V voltages were separately applied for ten consecutive pulse voltage tests, revealing short-term potentiation and short-term depression behaviors. After 92 consecutive positive pulses, the device current transitioned from an initial value of 0.14 to 28.3 mA. Similarly, following 88 consecutive negative pulses, the device current changed, indicating long-term potentiation and long-term depression behaviors. We also employed a pair of continuous triangular wave pulses to evaluate paired-pulse facilitation behavior, observing that the response current of the second stimulus pulse was ∼1.2× greater than that of the first stimulus pulse. The advantages and prospects of using MoS2 as a material for thin-film transistors were thoroughly displayed.

Wavelength-Controlled Photoconductance Polarity Switching via Harnessing Defects in Doped PdSe2 for Artificial Synaptic Features

Optoelectronic synapses are currently drawing significant attention as fundamental building blocks of neuromorphic computing to mimic brain functions. In this study, a two-terminal synaptic device based on a doped PdSe2 flake is proposed to imitate the key neural functions in an optical pathway. Due to the wavelength-dependent desorption of oxygen clusters near the intrinsic selenide vacancy defects, the doped PdSe2 photodetector achieves a high negative photoresponsivity of -7.8 × 103 A W-1 at 473 nm and a positive photoresponsivity of 181 A W-1 at 1064 nm. This wavelength-selective bi-direction photoresponse endows an all-optical pathway to imitate the fundamental functions of artificial synapses on a device level, such as psychological learning and forgetting capability, as well as dynamic logic functions. The underpinning photoresponse is further demonstrated on a flexible platform, providing a viable technology for neuromorphic computing in wearable electronics. Furthermore, the p-type doping results in an effective increase of the channel's electrical conductivity and a significant reduction in power consumption. Such low-power-consuming optical synapses with simple device architecture and low-dimensional features demonstrate tremendous promise for building multifunctional artificial neuromorphic systems in the future.

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.

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

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

Nearly Panoramic Neuromorphic Vision with Transparent Photosynapses

Neuromorphic vision based on photonic synapses has the ability to mimic sensitivity, adaptivity, and sophistication of bio-visual systems. Significant advances in artificial photosynapses are achieved recently. However, conventional photosyanptic devices normally employ opaque metal conductors and vertical device configuration, performing a limited hemispherical field of view. Here, a transparent planar photonic synapse (TPPS) is presented that offers dual-side photosensitive capability for nearly panoramic neuromorphic vision. The TPPS consisting of all two dimensional (2D) carbon-based derivatives exhibits ultra-broadband photodetecting (365-970 nm) and ≈360° omnidirectional viewing angle. With its intrinsic persistent photoconductivity effect, the detector possesses bio-synaptic behaviors such as short/long-term memory, experience learning, light adaptation, and a 171% pair-pulse-facilitation index, enabling the synapse array to achieve image recognition enhancement (92%) and moving object detection.

Biomimetic Compound Eyes with Gradient Ommatidium Arrays

Compound eyes are high-performing natural optical perception systems with compact configurations, generating extensive research interest. Existing compound eye systems are often combinations of simple uniform microlens arrays; there are still challenges in making more ommatidia on the compound eye surface to focus to the same plane. Here, a biomimetic gradient compound eye is presented by artificially mimicking dragonflies. The multiple replication process efficiently endows compound eyes with the gradient characteristics of dragonfly compound eyes. Experimental results show that the manufactured compound eye allows multifocus imaging by virtue of the gradient ommatidium array arranged closely in a honeycomb pattern while ensuring excellent optical properties and compact configurations. Thousands of ommatidia showing a gradient trend at the millimeter scale while remaining relatively uniform at the micron scale have gradient focal lengths ranging from 260 to 450 μm. This gradient compound eye allows more ommatidia to focus on the same plane than traditional uniform compound eyes, which have experimentally been shown to capture more than 1100 in-plane clear images simultaneously, promising potential applications in micro-optical devices, optical imaging, and biochemical sensing.

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.

Bio-inspired artificial synaptic transistors: evolution from innovative basic units to system integration

The investigation of transistor-based artificial synapses in bioinspired information processing is undergoing booming exploration, and is the stable building block for brain-like computing. Given that the storage and computing separation architecture of von Neumann construction is not conducive to the current explosive information processing, it is critical to accelerate the connection between hardware systems and software simulations of intelligent synapses. So far, various works based on a transistor-based synaptic system successfully simulated functions similar to biological nerves in the human brain. However, the influence of the semiconductor and the device structural design on synaptic properties is still poorly linked. This review concretely emphasizes the recent advances in the novel structure design of semiconductor materials and devices used in synaptic transistors, not only from a single multifunction synaptic device but also to system application with various connected routes and related working mechanisms. Finally, crises and opportunities in transistor-based synaptic interconnection are discussed and predicted.

Optoelectronic Synaptic Memtransistor Based on 2D SnSe/MoS2 van der Waals Heterostructure under UV-Ozone Treatment

Memristive switching devices with electrically and optically invoked synaptic behaviors show great promise in constructing an artificial biological visual system. Through rational design and integration, 2D materials and their van der Waals (vdW) heterostructures can be applied to realize multifunctional optoelectronic devices. Here, a multifunctional optoelectronic synaptic memtransistor based on a SnSe/MoS₂ vdW p-n heterojunction to simulate the human biological visual system is reported. By employing simple mild UV-ozone treatment, the device exhibits reversible resistive switching (RS) behavior with switching ratio up to 10³ . The retina-like selective response to different input light wavelengths is activated, as well as programmable multilevel resistance states and long-term synaptic plasticity. Moreover, memory and logic functions analogous to those found in the visual cortex of the brain are performed by controlling the optical and electrical input signals. This work proposes a feasible strategy to modulate RS in vdW heterostructures for memristive devices, which show significant potential for neuromorphic processing.

Multifunctional Optoelectronic Synapses Based on Arrayed MoS2 Monolayers Emulating Human Association Memory

Optoelectronic synaptic devices integrating light-perception and signal-storage functions hold great potential in neuromorphic computing for visual information processing, as well as complex brain-like learning, memorizing, and reasoning. Herein, the successful growth of MoS₂ monolayer arrays assisted by gold nanorods guided precursor nucleation is demonstrated. Optical, spectral, and morphology characterizations of MoS₂ prove that arrayed flakes are homogeneous monolayers, and they are further fabricated as optoelectronic devices showing featured photocurrent loops and stable optical responses. Typical synaptic behaviors of photo-induced short-term potentiation, long-term potentiation, and paired pulse facilitation are recorded under different light stimulations of 450, 532, and 633 nm lasers at various excitation powers. A visual sensing system consisting of 5 × 6 pixels is constructed to simulate the light-sensing image mapped by forgetting curves in real time. Moreover, the system presents the ability of utilizing associated images to restore vague and incomplete memories, which successfully mimics human intelligent behaviors of association memory and logical reasoning. The work emulates the brain-like artificial intelligence using arrayed 2D semiconductors, which paves an avenue to achieve smart retina and complex brain-like system.

A neuromorphic bionic eye with filter-free color vision using hemispherical perovskite nanowire array retina

Spherical geometry, adaptive optics, and highly dense network of neurons bridging the eye with the visual cortex, are the primary features of human eyes which enable wide field-of-view (FoV), low aberration, excellent adaptivity, and preprocessing of perceived visual information. Therefore, fabricating spherical artificial eyes has garnered enormous scientific interest. However, fusing color vision, in-device preprocessing and optical adaptivity into spherical artificial eyes has always been a tremendous challenge. Herein, we demonstrate a bionic eye comprising tunable liquid crystal optics, and a hemispherical neuromorphic retina with filter-free color vision, enabled by wavelength dependent bidirectional synaptic photo-response in a metal-oxide nanotube/perovskite nanowire hybrid structure. Moreover, by tuning the color selectivity with bias, the device can reconstruct full color images. This work demonstrates a unique approach to address the color vision and optical adaptivity issues associated with artificial eyes that can bring them to a new level approaching their biological counterparts.

A Bioinspired Ultra Flexible Artificial van der Waals 2D-MoS2 Channel/LiSiOx Solid Electrolyte Synapse Arrays via Laser-Lift Off Process for Wearable Adaptive Neuromorphic Computing

Wearable electronic devices with next-generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast-growing technology. Hardware systems enabling artificial neural networks while consuming low power and processing massive in situ personal data are essential for adaptive wearable neuromorphic edging computing. Herein, the development of an ultraflexible artificial-synaptic array device with concrete-mechanical cyclic endurance consisting of a novel heterostructure with an all-solid-state 2D MoS₂ channel and LiSiO_x (lithium silicate) is demonstrated. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D-MoS₂ channel and LiSiO_x solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. Based on the excellent flexibility of colorless polyimide substrates and thin-layered structures, the fabricated flexible neuromorphic synaptic devices exhibit superior long-term potentiation and long-term depression cyclic endurance performance, even when bent over 700 times or on curved surfaces with a diameter of 10 mm. Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. These results are promising for the development of personalized wearable artificial neural systems in the future.

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.

Flexible Optical Synapses Based on In2Se3/MoS2 Heterojunctions for Artificial Vision Systems in the Near-Infrared Range

Near-infrared (NIR) synaptic devices integrate NIR optical sensitivity and synaptic plasticity, emulating the basic biomimetic function of the human visual system and showing great potential in NIR artificial vision systems. However, the lack of semiconductor materials with appropriate band gaps for NIR photodetection and effective strategies for fabricating devices with synaptic behaviors limit the further development of NIR synaptic devices. Here, a two-terminal NIR synaptic device consisting of the In₂Se₃/MoS₂ heterojunction has been constructed, and it exhibits fundamental synaptic functions. The reduced band gap and potential barrier of In₂Se₃/MoS₂ heterojunctions are essential for NIR synaptic plasticity. In addition, the NIR synaptic properties of In₂Se₃/MoS₂ heterojunctions under strain have been studied systematically. The ΔEPSC of the In₂Se₃/MoS₂ synaptic device can be improved from 38.4% under no strain to 49.0% under a 0.54% strain with a 1060 nm illumination for 1 s at 100 mV. Furthermore, the artificial NIR vision system consisting of a 10 × 10 In₂Se₃/MoS₂ device array has been fabricated, exhibiting image sensing, learning, and storage functions under NIR illumination. This research provides new ideas for the design of flexible NIR synaptic devices based on 2D materials and presents many opportunities in artificial intelligence and NIR vision systems.

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.

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

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

Large-Scale MoS2 Pixel Array for Imaging Sensor

Two-dimensional molybdenum disulfide (MoS₂) has been extensively investigated in the field of optoelectronic devices. However, most reported MoS₂ phototransistors are fabricated using the mechanical exfoliation method to obtain micro-scale MoS₂ flakes, which is laboratory- feasible but not practical for the future industrial fabrication of large-scale pixel arrays. Recently, wafer-scale MoS₂ growth has been rapidly developed, but few results of uniform large-scale photoelectric devices were reported. Here, we designed a 12 × 12 pixels pixel array image sensor fabricated on a 2 cm × 2 cm monolayer MoS₂ film grown by chemical vapor deposition (CVD). The photogating effect induced by the formation of trap states ensures a high photoresponsivity of 364 AW^-1, which is considerably superior to traditional CMOS sensors (≈0.1 AW^-1). Experimental results also show highly uniform photoelectric properties in this array. Finally, the concatenated image obtained by laser lighting stencil and photolithography mask demonstrates the promising potential of 2D MoS₂ for future optoelectrical applications.

Artificial Tactile Recognition Enabled by Flexible Low-Voltage Organic Transistors and Low-Power Synaptic Electronics

The advancement of self-powered intelligent strain systems for human-computer interaction is crucial toward wearable and energy-saving applications. Simultaneously, lowering operating voltage and thus reducing power consumption are of particular interests. A brain-like smart synaptic hardware system is considered as a promising candidate for low-power, parallel computing and learning processes. However, the combination of low-voltage organic transistors and energy efficient smart synapse hardware systems driven by a tactile signal has been hindered by the limited materials and technology. Here, by employing an elastomeric copolymer poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) with a high HFP content of 25 mol %, flexible, low-voltage transistors (|V_G| ≤ 3 V) and a low energy consumption synapse ≤ 9.2 × 10^-17 J are devised simultaneously, along with the lowest quality factor (R = P_w × V_G, 2.76 × 10^-16 J V). Furthermore, based on the low voltage and low power consumption characteristics, flexible artificial tactile recognition system and Morse code recognition are established without any computing supporting. Mechanical flexibility, cycling stability, image contrast enhancement functions, and simulated pattern recognition accuracy of the multilayer perceptron neural network are also simulated. This work recommends a route of exploiting low voltage, low power consumption synaptic systems and smart human-machine interfaces with low energy loss based on flexible organic synaptic transistors.