A Reconfigurable Optoelectronic Synaptic Transistor with Stable Zr-CsPbI3 Nanocrystals for Visuomorphic Computing

2D MoTe2 memristors for energy-efficient artificial synapses and neuromorphic applications

The potential of two-dimensional (2D) transition metal dichalcogenides (TMDs), especially molybdenum telluride (MoTe2), in sophisticated electrical and low-energy neuromorphic applications, has attracted a lot of interest. The creation, characteristics, and uses of MoTe2-based memristive devices are summarized in this review paper, with an emphasis on their potential as artificial synapses for neuromorphic computing. We thoroughly examine the special properties of MoTe2, such as its remarkable resistance switching response, excellent linearity in synaptic potentiation, and customizable phase states. These characteristics make it possible to implement basic computational functions with minimal energy consumption, including decimal arithmetic operations and the commutative principles of addition and multiplication. In addition to simulating intricate synaptic processes such as long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP), the article emphasizes the experimental performances of MoTe2 memristors, which include their capacity to execute exact decimal arithmetic operations. The demonstration of centimeter-scale 2D MoTe2 film-based memristor arrays attaining over 90% recognition accuracy in handwritten digit identification tests further demonstrates the devices' great scalability, stability, and incorporation capabilities. Notwithstanding these developments, issues such as poor environmental robustness, phase transition sensitivity, and low thermal stability still exist. The creation of hybrid or composite materials, doping, and structural alteration are some of the methods to get beyond these obstacles that are covered in the paper. The need for scalable, economical synthesis techniques and a better comprehension of the material's mechanical, optical, and electrical properties through modeling and experiments are emphasized.

Suppression of Tin Oxidation via Sn→B Bonding Interactions for High-Resolution Lead-Free Perovskite Neuromorphic Imaging Sensors

Lead-free tin-based perovskites, specifically (4-Cl-PEA)2SnI4, possess significant potential for the development of high-performance, robust neuromorphic imaging sensors, owing to their superior optoelectronic properties and compatibility with conventional complementary metal-oxide-semiconductor fabrication techniques and silicon-based readout circuits. However, the excessive oxidation of Sn2+ remains a significant obstacle, leading to suboptimal synaptic performance and low resolution in the neuromorphic imaging sensors due to increased recombination losses and poor film uniformity. This study first demonstrates that the introduction of novel Sn→B donor-acceptor bonding interactions effectively suppresses Sn2+ oxidation, enhancing uniformity, reducing nonradiative recombination, and improving synaptic plasticity. A vertical optoelectronic synapse demonstrates diverse synaptic behaviors, attributed to hole trapping and detrapping at the device interface. Additionally, the device enables applications in associative learning, neuromorphic computation, letter encoding, and handwritten digit recognition. Ultimately, integration with silicon circuits results in a high-resolution (32 × 32) neuromorphic imaging array, one of the highest reported resolutions for perovskite optoelectronic synapse arrays. The improved uniformity of boric acid-added (4-Cl-PEA)2SnI4 perovskite films significantly reduces photo response non-uniformity, enhances resolution, and improves memory capabilities. This neuromorphic imaging array successfully integrates sensing, storage, and computation, enabling advanced functionalities like letter recognition, memory, and processing, surpassing conventional image sensors.

Chelated tin halide perovskite for near-infrared neuromorphic imaging array enabling object recognition and motion perception

Neuromorphic imaging arrays integrate sensing, memory, and processing for efficient spatiotemporal fusion, enabling intelligent object and motion recognition in autonomous and surveillance systems. Halide perovskites offer potential for neuromorphic imaging by regulating photogenerated ions and charges, but lead toxicity and limited response range remain key limitations. Here, we present lead-free non-toxic formamidinium tin triiodide perovskites functionalized with bio-friendly quercetin molecules via a multi-site chelate strategy, achieving favorable near-infrared response and optoelectronic properties. Leveraging a non-equilibrium photogenerated carrier strategy, the formamidinium tin triiodide-quercetin based near-infrared optoelectronic synapses exhibit key synaptic features for practical applications, including quasi-linear time-dependent photocurrent generation, prolonged photocurrent decay, high stability, and low energy consumption. Ultimately, a 12 × 12 real-time neuromorphic near-infrared imaging array is constructed on thin-film transistor backplanes, enabling hardware-level spatiotemporal fusion for robust object recognition and motion perception in complex environments for autonomous and surveillance systems.

Advances in Single-Crystal Films: Synergistic Insights from Perovskites and Organic Molecules for High-Performance Optoelectronics

Semiconductor single-crystal thin films are crucial for the advancement of high-performance optoelectronic devices. Despite significant progress in fabricating perovskite and organic single-crystal films, interdisciplinary insights between these domains remain unexplored. This review aims to bridge this gap by summarizing recent advances in fabrication strategies for perovskite and organic molecular single-crystal films. Five preparation methods-solution-phase epitaxy, solid-phase epitaxy, meniscus-induced crystallization, antisolvent-induced crystallization, and space-confined growth-are analyzed with a focus on their principles, functional properties, and distinct advantages. By comparing these approaches across material systems, this review identifies transferable insights that can drive the development of large-scale, high-quality single-crystal films. Furthermore, the optoelectronic applications of these films are explored, including solar cells, photodetectors, light-emitting devices, and transistors, while addressing challenges such as scalability, defect control, and integration. This work highlights the importance of cross-disciplinary innovation and provides an effective pathway for integrating perovskite and organic molecular processing to advance the next generation of single-crystal film technologies.

All-Solution-Processed IGZO Optoelectronic Synaptic Transistor with Dual-Mode Operation toward Artificial Vision Applications

Realizing the dual-mode electric and optical synaptic plasticity within one neuromorphic device is impressive for the construction of a compact artificial visual system. Here, we proposed indium gallium zinc oxide (IGZO) photoelectric synaptic transistors utilizing all-solid-state electrolytes (Li-doped ZrO2) as gate dielectric layers. The device was fabricated by using a facile and cost-effective all-solution method. The synaptic transistor exhibited dual-mode electric and optical synaptic plasticity. Meanwhile, the tunable conductance is achieved through electric potentiation and depression processes, demonstrating the potential for realizing neuromorphic computing. Based on this, a simulated convolutional neural network was designed to realize handwriting digit recognition, achieving an accuracy of 96.8%. Additionally, sophisticated neuromorphic applications such as logic operations, Pavlov's classical experiment, and pupillary reflex simulation were successfully realized. Therefore, the designed transistor demonstrates significant potential for future applications in artificial vision.

An optoelectrochemical synapse based on a single-component n-type mixed conductor

Organic mixed ionic-electronic conductors (OMIECs) are materials that can be used to build bio-inspired electronic devices as they emulate ion-based cellular communication through doping with aqueous ionic charges. The integration of charge photogeneration and electrochemical doping processes in the polymer film enables optoelectronic applications that involve synaptic transistors. However, no OMIEC has yet been implemented to create a miniaturized photoactive platform capable of perceiving and processing multi-spectral visual information. Here, we present a materials and device design concept in which an n-type OMIEC film is incorporated into the micron-scale channel of an electrochemical transistor operating directly in an aqueous electrolyte under ambient conditions. The conjugated polymer channel, consisting of a fluorinated bisistain-lactone-bithiazole acceptor, has a current modulated in response to both electrical and optical stimuli, emulating the multimodal function of the visual nervous system. Our optoelectrochemical synapse achieves multilevel conductance states as well as transduction of visual information covering ultraviolet, visible, and near-infrared regions of the spectrum - a range beyond that of the human visual system's perception. The resulting transistor active-matrix array is capable of adaptive sensing, memory, and pre-processing of visual information, demonstrating an efficient optoelectronic neuromorphic system with multi-task learning capability.

Molecular Cocrystal Strategy for Retinamorphic Vision with UV-Vis-NIR Perception and Fast Recognition

Neuromorphic vision sensors capable of multispectral perception and efficient recognition are highly desirable for bioretina emulation, but their realization is challenging. Here, we present a cocrystal strategy for preparing an organic nanowire retinamorphic vision sensor with UV-vis-NIR perception and fast recognition. By leveraging molecular-scale donor-acceptor interpenetration and charge-transfer interfaces, the cocrystal nanowire device exhibits ultrawide photoperception ranging from 350 to 1050 nm, fast photoresponse of 150 ms, high specific detectivity of 8.2 × 1012 Jones, and responsivity of 15 A W-1, as well as retina-like photosynaptic plasticity behaviors. Utilizing the sensor nerve and convolutional neural network, the architecture achieves 90% accuracy in recognizing colorful images. The cocrystal design offers an effective method for constructing nanowire photosynases with high performance in artificial visual systems.

Adaptive In-Sensor Computing for Enhanced Feature Perception and Broadband Image Restoration

Traditional imaging systems struggle in weak or complex lighting environments due to their fixed spectral responses, resulting in spectral mismatches and degraded image quality. To address these challenges, a bioinspired adaptive broadband image sensor is developed. This innovative sensor leverages a meticulously designed type-I heterojunction alignment of 0D perovskite quantum dots (PQDs) and 2D black phosphorus (BP). This configuration enables efficient carrier injection control and advanced computing capabilities within an integrated phototransistor array. The sensor's unique responses to both visible and infrared (IR) light facilitate selective enhancement and precise feature extraction under varying lighting conditions. Furthermore, it supports real-time convolution and image restoration within a convolutional autoencoder (CAE) network, effectively countering image degradation by capturing spectral features. Remarkably, the hardware responsivity weights perform comparably to software-trained weights, achieving an image restoration accuracy of over 85%. This approach offers a robust and versatile solution for machine vision applications that demand precise and adaptive imaging in dynamic lighting environments.

Emerging Artificial Synaptic Devices Based on Organic Semiconductors: Molecular Design, Structure and Applications

In modern computing, the Von Neumann architecture faces challenges such as the memory bottleneck, hindering efficient processing of large datasets and concurrent programs. Neuromorphic computing, inspired by the brain's architecture, emerges as a promising alternative, offering unparalleled computational power while consuming less energy. Artificial synaptic devices play a crucial role in this paradigm shift. Various material systems, from organic to inorganic, have been explored for neuromorphic devices, with organic materials attracting attention for their excellent photoelectric properties, diverse material choices, and versatile preparation methods. Organic semiconductors, in particular, offer advantages over transition-metal dichalcogenides, including ease of preparation and flexibility, making them suitable for large-area organic films. This review focuses on emerging artificial synaptic devices based on organic semiconductors, discussing different branches within the organic semiconductor material system, various fabrication methods, device structure designs, and applications of organic artificial synapse. Critical considerations and challenges for achieving truly human-like dynamic perception in artificial systems based on organic semiconductors are also outlined, reflecting the ongoing evolution of neuromorphic computing.

Chiroptical Synaptic Perovskite Memristor as Reconfigurable Physical Unclonable Functions

Physical unclonable functions (PUFs), often referred to as digital fingerprints, are emerging as critical elements in enhancing hardware security and encryption. While significant progress has been made in developing optical and memory-based PUFs, integrating reconfigurability with sensitivity to circularly polarized light (CPL) remains largely unexplored. Here, we present a chiroptical synaptic memristor (CSM) as a reconfigurable PUF, leveraging a two-dimensional organic-inorganic halide chiral perovskite. The device combines CPL sensitivity with photoresponsive electrical behavior, enabling its application in optoneuromorphic systems, as demonstrated by its ability to perform image categorization tasks within neuromorphic computing. Furthermore, by leveraging a 10 × 10 crossbar array of the CSMs, we develop a PUF capable of generating reconfigurable cryptographic keys based on the combination of neuromorphic potentiation and polarized light conditions. This work demonstrates an integrated approach to optoneuromorphic functionality, data storage, and encryption, providing an alternative approach for reconfigurable memristor-based PUFs.

All-Optically Controlled Memristive Device Based on Cu2O/TiO2 Heterostructure Toward Neuromorphic Visual System

The optoelectronic memristor integrates the multifunctionalities of image sensing, storage, and processing, which has been considered as the leading candidate to construct novel neuromorphic visual system. In particular, memristive materials with all-optical modulation and complementary metal oxide semiconductor (CMOS) compatibility are highly desired for energy-efficient image perception. As a p-type oxide material, Cu2O exhibits outstanding theoretical photoelectric conversion efficiency and broadband photoresponse. In this work, an all-optically controlled memristor based on the Cu2O/TiO2/sodium alginate nanocomposite film is developed. Optical potentiation and depression behaviors have been implemented by utilizing visible (680 nm) and ultraviolet (350 nm) light. Furthermore, a 7 × 9 optoelectronic memristive array with satisfactory device variation and environment stability is constructed to emulate the image preprocessing function in biological retina. The random noise can be reduced effectively by utilizing bidirectional optical input. Beneficial from the image preprocessing function, the accuracy of handwritten digit classification increases more than 60%. Our work presents a pathway toward high-efficient neuromorphic visual system and promotes the development of artificial intelligence technology.

Engineering Steep Subthreshold Swings in High-Performance Organic Field-Effect Transistor Sensors

Organic field-effect transistor (OFET)-based sensors have gained considerable attention for information perception and processing in developing artificial intelligent systems owing to their amplification function and multiterminal regulation. Over the last few decades, extensive research has been conducted on developing OFETs with steep subthreshold swings (SS) to achieve high-performance sensing. In this review, based on an analysis of the critical factors that are unfavorable for a steep SS in OFETs, the corresponding representative strategies for achieving steep SS are summarized, and the advantages and limitations of these strategies are comprehensively discussed. Furthermore, a bridge between SS and OFET sensor performance is established. Subsequently, the applications of OFETs with steep SS in sensor systems, including pressure sensors, photosensors, biochemical sensors, and electrophysiological signal sensors. Lastly, the challenges faced in developing OFET sensors with steep SS are discussed. This study provides insights into the design and application of high-performance OFET sensor systems.

An Organic Optoelectronic Synapse with Multilevel Memory Enabled by Gate Modulation

Artificial synaptic devices are emerging as contenders for next-generation computing systems due to their combined advantages of self-adaptive learning mechanisms, high parallel computation capabilities, adjustable memory level, and energy efficiency. Optoelectronic devices are particularly notable for their responsiveness to both voltage inputs and light exposure, making them attractive for dynamic modulation. However, engineering devices with reconfigurable synaptic plasticity and multilevel memory within a singular configuration present a fundamental challenge. Here, we have established an organic transistor-based synaptic device that exhibits both volatile and nonvolatile memory characteristics, modulated through gate voltage together with light stimuli. Our device demonstrates a range of synaptic behaviors, including both short/long-term plasticity (STP and LTP) as well as STP-LTP transitions. Further, as an encoding unit, it delivers exceptional read current levels, achieving a program/erase current ratio exceeding 105, with excellent repeatability. Additionally, a prototype 4 × 4 matrix demonstrates potential in practical neuromorphic systems, showing capabilities in the perception, processing, and memory retention of image inputs.

Visible-Light-Stimulated Optoelectronic Neuromorphic Transistor Based on Indium-Gallium-Zinc Oxide via Bi2Te3 Light Absorption Layer

To emulate a visual perception system, a bismuth telluride (Bi2Te3)/indium-gallium-zinc oxide (IGZO) heterostructure is introduced for optoelectronic neuromorphic transistors (ONTs). Amorphous IGZO is applied as a channel layer to exhibit low off-current, high mobility, and persistent photoconductivity, enabling light-stimulated neuromorphic characteristics. The atomic ratio of In/Ga/Zn was 9.4:9.8:5.2. For a light absorption layer, Bi2Te3 is applied due to a small bandgap, high photoresponse, and carrier concentration. However, conventional optoelectronic devices using Bi2Te3 exhibit insufficient performance owing to their excessive conductivity. To resolve the constraint, the oxidation of Bi2Te3 is performed to suppress its electrical conductivity. Finally, the IGZO ONT with a Bi2Te3 layer exhibits optoelectronic characteristics under visible-light irradiation. Under red-light irradiation having a light intensity of 5 mW mm-2, it exhibits enhanced optoelectronic characteristics including photoresponsivity, photosensitivity, and detectivity from 19.6 to 3.46 × 102 A/W, 4.95 to 1.46 × 108, and 1.45 × 107 to 2.13 × 1012 Jones compared to that without a Bi2Te3 layer, respectively. To confirm reproducibility and uniformity, we fabricated and compared the optoelectronic characteristics of 5 samples from batch to batch. Regarding optoelectronic neuromorphic characteristics, the ONT exhibits short- and long-term memory and a highly linear relationship between peak synaptic current and pulse number under red-light pulses. The paired-pulse facilitation index was 208%. To confirm the applicability of artificial visual perception, a 6 × 6 ONT array demonstrates long-term memory characteristic successfully after red-light irradiation with a Y-shaped pattern. In view of stability and reliability, we conducted three potentiation processes and compared the peak synaptic currents of each potentiation process.

Wafer-Scale Ag2S-Based Memristive Crossbar Arrays with Ultra-Low Switching-Energies Reaching Biological Synapses

Memristive crossbar arrays (MCAs) offer parallel data storage and processing for energy-efficient neuromorphic computing. However, most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor (CMOS) technology still suffer from substantially larger energy consumption than biological synapses, due to the slow kinetics of forming conductive paths inside the memristive units. Here we report wafer-scale Ag2S-based MCAs realized using CMOS-compatible processes at temperatures below 160 °C. Ag2S electrolytes supply highly mobile Ag+ ions, and provide the Ag/Ag2S interface with low silver nucleation barrier to form silver filaments at low energy costs. By further enhancing Ag+ migration in Ag2S electrolytes via microstructure modulation, the integrated memristors exhibit a record low threshold of approximately - 0.1 V, and demonstrate ultra-low switching-energies reaching femtojoule values as observed in biological synapses. The low-temperature process also enables MCA integration on polyimide substrates for applications in flexible electronics. Moreover, the intrinsic nonidealities of the memristive units for deep learning can be compensated by employing an advanced training algorithm. An impressive accuracy of 92.6% in image recognition simulations is demonstrated with the MCAs after the compensation. The demonstrated MCAs provide a promising device option for neuromorphic computing with ultra-high energy-efficiency.

Electro-Optically Configurable Synaptic Transistors With Cluster-Induced Photoactive Dielectric Layer for Visual Simulation and Biomotor Stimuli

The integration of visual simulation and biorehabilitation devices promises great applications for sustainable electronics, on-demand integration and neuroscience. However, achieving a multifunctional synergistic biomimetic system with tunable optoelectronic properties at the individual device level remains a challenge. Here, an electro-optically configurable transistor employing conjugated-polymer as semiconductor layer and an insulating polymer (poly(1,8-octanediol-co-citrate) (POC)) with clusterization-triggered photoactive properties as dielectric layer is shown. These devices realize adeptly transition from electrical to optical synapses, featuring multiwavelength and multilevel optical synaptic memory properties exceeding 3 bits. Utilizing enhanced optical memory, the images learning and memory function for visual simulation are achieved. Benefiting from rapid electrical response akin to biological muscle activation, increased actuation occurs under increased stimulus frequency of gate voltage. Additionally, the transistor on POC substrate can be effectively degraded in NaOH solution due to degradation of POC. Pioneeringly, the electro-optically configurability stems from light absorption and photoluminescence of the aggregation cluster in POC layer after 200 °C annealing. The enhancement of optical synaptic plasticity and integration of motion-activation functions within a single device opens new avenues at the intersection of optoelectronics, synaptic computing, and bioengineering.

Single-Crystal Perovskite for Solar Cell Applications

The advent of organic-inorganic hybrid metal halide perovskites has revolutionized photovoltaics, with polycrystalline thin films reaching over 26% efficiency and single-crystal perovskite solar cells (IC-PSCs) demonstrating ≈24%. However, research on single-crystal perovskites remains limited, leaving a crucial gap in optimizing solar energy conversion. Unlike polycrystalline films, which suffer from high defect densities and instability, single-crystal perovskites offer minimal defects, extended carrier lifetimes, and longer diffusion lengths, making them ideal for high-performance optoelectronics and essential for understanding perovskite material behavior. This review explores the advancements and potential of IC-PSCs, focusing on their superior efficiency, stability, and role in overcoming the limitations of polycrystalline counterparts. It covers device architecture, material composition, preparation methodologies, and recent breakthroughs, emphasizing the importance of further research to propel IC-PSCs toward commercial viability and future dominance in photovoltaic technology.

A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression

Physical reservoir-based reservoir computing (RC) systems for intelligent perception have recently gained attention because they require fewer computing resources. However, the system remains limited in infrared (IR) machine vision, including materials and physical reservoir expression power. Inspired by biological visual perception systems, the study proposes a near-infrared (NIR) retinomorphic device that simultaneously perceives and encodes narrow IR spectral information (at ≈980 nm). The proposed device, featuring core-shell upconversion nanoparticle/poly (3-hexylthiophene) (P3HT) nanocomposite channels, enables the absorption and conversion of NIR into high-energy photons to excite more photo carriers in P3HT. The photon-electron-coupled dynamics under the synergy of photovoltaic and photogating effects influence the nonlinearity and high dimensionality of the RC system under narrow-band NIR irradiation. The device also exhibits multilevel data storage capability (≥8 levels), excellent stability (≥2000 s), and durability (≥100 cycles). The system accurately identifies NIR static and dynamic handwritten digit images, achieving recognition accuracies of 91.13% and 90.07%, respectively. Thus, the device tackles intricate computations like solving second-order nonlinear dynamic equations with minimal errors (normalized mean squared error of 1.06 × 10⁻3 during prediction).

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.

Enhancing the performance of organic phototransistors using a sandwich-heterostructure

To weaken the adverse effects of traditional planar heterojunctions on phototransistors, an effective strategy for achieving low dark-current and high photoresponsive organic phototransistors via constructing a sandwich heterostructure is demonstrated. This work offers a new route for the design and development of high-performance phototransistor devices.

Perovskite-Doped Modulated Color-Selective Photosynaptic Transistors for Target Object Recognition

The processing of multicolor noisy images in visual neuromorphic devices requires selective absorption at specific wavelengths; however, it is difficult to achieve this because the spectral absorption range of the device is affected by the type of material. Surprisingly, the absorption range of perovskite materials can be adjusted by doping. Herein, a CdCl2 co-doped CsPbBr3 nanocrystal-based photosensitive synaptic transistor (PST) is reported. By decreasing the doping concentration, the response of the PST to short-wavelength light is gradually enhanced, and even weak light of 40 μW·cm-2 can be detected. Benefiting from the excellent color selectivity of the PST device, the device array is applied to feature extraction of target blue items and removal of red and green noise, which results in the recognition accuracy of 95% for the noisy MNIST data set. This work provides new ideas for the application of novel transistors integrating sensors and storage computing.

Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception

Neuromorphic visual systems can emulate biological retinal systems to perceive visual information under different levels of illumination, making them have considerable potential for future intelligent vehicles and vision automation. However, the complex circuits and high operating voltages of conventional artificial vision systems present great challenges for device integration and power consumption. Here, bioinspired synaptic transistors based on organic single crystal phototransistors are reported, which exhibit excitation and inhibition synaptic plasticity with time-varying. By manipulating the charge dynamics of the trapping centers of organic crystal-electret vertical stacks, organic transistors can operate below 1 V with record high on/off ratios close to 108 and sharp switching with a subthreshold swing of 59.8 mV dec-1. Moreover, the approach offers visual adaptation with highly localized modulation and over 98.2% recognition accuracy under different illumination levels. These bioinspired visual adaptation transistors offer great potential for simplifying the circuitry of artificial vision systems and will contribute to the development of machine vision applications.

Photoprogrammed Multifunctional Optoelectronic Synaptic Transistor Arrays Based on Photosensitive Polymer-Sorted Semiconducting Single-Walled Carbon Nanotubes for Image Recognition

The development of neuromorphic optoelectronic systems opens up the possibility of the next generation of artificial vision. In this work, the novel broadband (from 365 to 940 nm) and multilevel storage optoelectronic synaptic thin-film transistor (TFT) arrays are reported using the photosensitive conjugated polymer (poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-(bithiophene)], F8T2) sorted semiconducting single-walled carbon nanotubes (sc-SWCNTs) as channel materials. The broadband synaptic responses are inherited to absorption from both photosensitive F8T2 and sorted sc-SWCNTs, and the excellent optoelectronic synaptic behaviors with 200 linearly increasing conductance states and long retention time > 103 s are attributed to the superior charge trapping at the AlOx dielectric layer grown by atomic layer deposition. Furthermore, the synaptic TFTs can achieve IOn/IOff ratios up to 106 and optoelectronic synaptic plasticity with the low power consumption (59 aJ per single pulse), which can simulate not only basic biological synaptic functions but also optical write and electrical erase, multilevel storage, and image recognition. Further, a novel Spiking Neural Network algorithm based on hardware characteristics is designed for the recognition task of Caltech 101 dataset and multiple features of the images are successfully extracted with higher accuracy (97.92%) of the recognition task from the multi-frequency curves of the optoelectronic synaptic devices.

Dual-Adaptive Heterojunction Synaptic Transistors for Efficient Machine Vision in Harsh Lighting Conditions

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes.

Stability Strategies and Applications of Iodide Perovskites

Iodide perovskites have demonstrated their unprecedented high efficiency and commercialization potential, and their superior optoelectronic properties, such as high absorption coefficient, high carrier mobility, and narrow direct bandgap, have attracted much attention, especially in solar cells, photodetectors, and light-emitting diodes (LEDs). However, whether it is organic iodide perovskite, organic-inorganic hybrid iodide perovskite or all-inorganic iodide perovskite the stability of these iodide perovskites is still poor and the contamination is high. In recent years, scholars have studied more iodide perovskites to improve their stability as well as optoelectronic properties from various angles. This paper systematically reviews the strategies (component engineering, additive engineering, dimensionality reduction engineering, and phase mixing engineering) used to improve the stability of iodide perovskites and their applications in recent years.

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.

In2O3/ZnO heterojunction thin film transistor for high recognition accuracy neuromorphic computing and optoelectronic artificial synapses

Solid electrolyte-gated transistors exhibit improved chemical stability and can fulfill the requirements of microelectronic packaging. Typically, metal oxide semiconductors are employed as channel materials. However, the extrinsic electron transport properties of these oxides, which are often prone to defects, pose limitations on the overall electrical performance. Achieving excellent repeatability and stability of transistors through the solution process remains a challenging task. In this study, we propose the utilization of a solution-based method to fabricate an In2O3/ZnO heterojunction structure, enabling the development of efficient multifunctional optoelectronic devices. The heterojunction's upper and lower interfaces induce energy band bending, resulting in the accumulation of a large number of electrons and a significant enhancement in transistor mobility. To mimic synaptic plasticity responses to electrical and optical stimuli, we utilize Li+-doped high-k ZrOxthin films as a solid electrolyte in the device. Notably, the heterojunction transistor-based convolutional neural network achieves a high accuracy rate of 93% in recognizing handwritten digits. Moreover, our research involves the simulation of a typical sensory neuron, specifically a nociceptor, within our synaptic transistor. This research offers a novel avenue for the advancement of cost-effective three-terminal thin-film transistors tailored for neuromorphic applications.

Spectrally Tunable Lead-Free Perovskite Rb2ZrCl6:Te for Information Encryption and X-ray Imaging

A series of lead-free Rb2ZrCl6:xTe4+ (x = 0%, 0.1%, 0.5%, 1.0%, 2.0%, 3.0%, 5.0%, 10.0%) perovskite materials were synthesized through a hydrothermal method in this work. The substitution of Te4+ for Zr in Rb2ZrCl6 was investigated to examine the effect of Te4+ doping on the spectral properties of Rb2ZrCl6 and its potential applications. The incorporation of Te4+ induced yellow emission of triplet self-trapped emission (STE). Different luminescence wavelengths were regulated by Te4+ concentration and excitation wavelength, and under a low concentration of Te4+ doping (x ≤ 0.1%), different types of host STE emission and Te4+ triplet state emission could be achieved through various excitation energies. These luminescent properties made it suitable for applications in information encryption. When Te4+ was doped at high concentrations (x ≥ 1%), yellow triplet state emission of Te4+ predominated, resulting in intense yellow emission, which stemmed from strong exciton binding energy and intense electron-phonon coupling. In addition, a Rb2ZrCl6:2%Te4+@RTV scintillating film was fabricated and a spatial resolution of 3.7 lp/mm was achieved, demonstrating the potential applications of Rb2ZrCl6:xTe4+ in nondestructive detection and bioimaging.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Strain-insensitive viscoelastic perovskite film for intrinsically stretchable neuromorphic vision-adaptive transistors

Stretchable neuromorphic optoelectronics present tantalizing opportunities for intelligent vision applications that necessitate high spatial resolution and multimodal interaction. Existing neuromorphic devices are either stretchable but not reconcilable with multifunctionality, or discrete but with low-end neurological function and limited flexibility. Herein, we propose a defect-tunable viscoelastic perovskite film that is assembled into strain-insensitive quasi-continuous microsphere morphologies for intrinsically stretchable neuromorphic vision-adaptive transistors. The resulting device achieves trichromatic photoadaptation and a rapid adaptive speed (<150 s) beyond human eyes (3 ~ 30 min) even under 100% mechanical strain. When acted as an artificial synapse, the device can operate at an ultra-low energy consumption (15 aJ) (far below the human brain of 1 ~ 10 fJ) with a high paired-pulse facilitation index of 270% (one of the best figures of merit in stretchable synaptic phototransistors). Furthermore, adaptive optical imaging is achieved by the strain-insensitive perovskite films, accelerating the implementation of next-generation neuromorphic 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.

Large-Area Fabrication of Hexaazatrinaphthylene-Based 2D Metal-Organic Framework Films for Flexible Photodetectors and Optoelectronic Synapses

2D conjugated metal-organic frameworks (c-MOFs) have emerged as promising materials for (opto)electronic applications due to their excellent charge transport properties originating from the unique layered-stacked structures with extended in-plane conjugation. The further advancement of MOF-based (opto)electronics necessitates the development of novel 2D c-MOF thin films with high quality. Cu-HHHATN (HHHATN: hexahydroxyl-hexaazatrinaphthylene) is a recently reported 2D c-MOF featuring high in-plane conjugation, strong interlayer π-π stacking, and multiple coordination sites, while the production of its thin-film form has not yet been reported. Herein, large-area Cu-HHHATN thin films with preferential orientation, high uniformity, and smooth surfaces are realized by using a convenient layer-by-layer growth method. Flexible photodetectors are fabricated, showing broadband photoresponse ranging from UV to short-wave infrared (370 to 1450 nm). The relatively long relaxation time of photocurrent, which arises from the trapping of photocarriers, renders the device's synaptic plasticity similar to that of biological synapses, promising its use in neuromorphic visual systems. This work demonstrates the great potential of Cu-HHHATN thin films in flexible optoelectronic devices for various applications.

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.

An in-sensor humidity computing system for contactless human-computer interaction

Being capable of processing large amounts of redundant data and decreasing power consumption, in-sensor computing approaches play significant roles in neuromorphic computing and are attracting increasing interest in perceptual information processing. Herein, we proposed a high performance humidity-sensitive memristor based on a Ti/graphene oxide (GO)/HfOx/Pt structure and verified its potential for application in remote health management and contactless human-machine interfaces. Since GO possesses abundant hydrophilic groups (carbonyl, epoxide, and hydroxyl), the memristor shows a high humidity sensitivity, fast response, and wide response range. By utilizing the proton-modulated redox reaction, humidity exposure to the memristor induces a dynamic change in the switching between high and low resistance states, ensuring essential synaptic learning functions, such as paired-pulse facilitation, spike number-dependent plasticity, and spike amplitude-dependent plasticity. More importantly, based on the humidity-induced salient features originating from the abundant hydrophilic functional groups in GO, we have implemented a noncontact human-machine interface utilizing the respiratory mode in humans, demonstrating the potential of promoting health monitoring applications and effectively blocking virus transmission. In addition, the high recognition accuracy of contactless handwriting in a 5 × 5 array artificial neural network was successfully achieved, which is attributed to the excellent emulated synaptic behaviors. This study provides a feasible method to develop an excellent humidity-sensitive memristor for constructing efficient in-sensor computing for application in health management and contactless human-computer interaction.

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.

Energy-Efficient ReS2-Based Optoelectronic Synapse for 3D Object Reconstruction and Recognition

The neuromorphic vision system (NVS) equipped with optoelectronic synapses integrates perception, storage, and processing and is expected to address the issues of traditional machine vision. However, owing to their lack of stereo vision, existing NVSs focus on 2D image processing, which makes it difficult to solve problems such as spatial cognition errors and low-precision interpretation. Consequently, inspired by the human visual system, an NVS with stereo vision is developed to achieve 3D object recognition, depending on the prepared ReS2 optoelectronic synapse with 12.12 fJ ultralow power consumption. This device exhibits excellent optical synaptic plasticity derived from the persistent photoconductivity effect. As the cornerstone for 3D vision, color planar information is successfully discriminated and stored in situ at the sensor end, benefiting from its wavelength-dependent plasticity in the visible region. Importantly, the dependence of the channel conductance on the target distance is experimentally revealed, implying that the structure information on the object can be directly captured and stored by the synapse. The 3D image of the object is successfully reconstructed via fusion of its planar and depth images. Therefore, the proposed 3D-NVS based on ReS2 synapses for 3D objects achieves a recognition accuracy of 97.0%, which is much higher than that for 2D objects (32.6%), demonstrating its strong ability to prevent 2D-photo spoofing in applications such as face payment, entrance guard systems, and others.

Portable, Intelligent Fluorescence Sensing Platform for Dense Convolutional Network-Capable Detection of Indophenol Sulfate and Methylmalonic Acid Using a Luminescent Eu@HOF Film

Indophenol sulfate (IS) and methylmalonic acid (MMA) are biomarkers of chronic kidney disease (CKD) and diabetes polyneuropathy (DPN), respectively. Portable and accurate monitoring of IS and MMA is very important to ensuring human health. The dense convolutional network (DenseNet) with image recognition has great potential in fluorescence sensing, but developing a platform with high precision and portability to diagnose the disease still faces huge challenges. Herein, we developed a high-sensitivity platform with a fluorescence material, a smartphone, and the DenseNet to monitor IS and MMA. A red-emitting Eu@PFC-13 (1) is prepared, and 1 shows high selectivity and low detection limits (DLs) to detect IS and MMA. The sensing mechanism of 1 toward IS and MMA is investigated by experiments and theoretical calculation. For detecting IS and MMA in serum and urine, 1 is fabricated into an Eu@PFC-13/AG (2) film with DLs of 1.4 and 1.6 μM, respectively. In addition, a portable smartphone platform is designed to monitor IS and MMA with high precision. Moreover, the DenseNet is constructed by Python, which can output the concentration of analytes by identifying fluorescence images and judge whether any is in a dangerous range. This work not only proposes a novel method that integrates a fluorescence material, a smartphone, and deep learning to detect analytes but also opens a new way for the diagnosis of CKD and DPN.

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.

Optically Readable Organic Electrochemical Synaptic Transistors for Neuromorphic Photonic Image Processing

Optically readable organic synaptic devices have great potential in both artificial intelligence and photonic neuromorphic computing. Herein, a novel optically readable organic electrochemical synaptic transistor (OR-OEST) strategy is first proposed. The electrochemical doping mechanism of the device was systematically investigated, and the basic biological synaptic behaviors that can be read by optical means are successfully achieved. Furthermore, the flexible OR-OESTs are capable of electrically switching the transparency of semiconductor channel materials in a nonvolatile manner, and thus the multilevel memory can be achieved through optical readout. Finally, the OR-OESTs are developed for the preprocessing of photonic images, such as contrast enhancement and denoising, and feeding the processed images into an artificial neural network, achieving a recognition rate of over 90%. Overall, this work provides a new strategy for the implementation of photonic neuromorphic systems.