Tetrachromatic vision-inspired neuromorphic sensors with ultraweak ultraviolet detection

High-Performance Self-Powered Organic Photodetectors for Near-Infrared Weak Light Detection

Near-infrared (NIR) organic photodetectors (OPDs) have significant potential in the development of night vision, optical communication, and image-sensing systems. However, most of them require external energy consumption, and particularly the investigation focuses on weak light detection in the NIR region at or beyond 1000 nm remains limited. In this study, self-powered OPDs with a PCE10:COTIC-4F organic bulk heterojunction as the photoactive layer are designed, which are capable of responding to an ultra-weak light signal of 6.3 pW cm-2 at 1000 nm, demonstrating a significantly low level in comparison to currently reported OPDs. In addition, the OPDs also exhibit other outstanding photodetection performance, including large Ilight/Idark ratio of 3.47 × 106, high responsivity of 1.50 A W-1, and detectivity of 3.17 × 1013/1.80 × 1011 Jones (evaluated by dark/noise current methods). Furthermore, the unencapsulated OPDs demonstrate almost no obvious attenuation in the air during a 224-day test and in the aging environment during a 67-day test. More importantly, the self-powered OPDs demonstrate the potential for flexible electronics, NIR imaging, and NIR selectivity with visible-blind characteristic. The development of self-powered OPDs provides an accessible and viable route for advancing weak NIR detection.

Neuromorphic Visual Computing with ZnMgO QDs-Based UV-Responsive Optoelectronic Synaptic Devices for Image Encryption and Recognition

Retina-inspired optoelectronic neuromorphic devices integrating optical sensing and computation are the key components in realizing neuromorphic visual computing. In particular, UV-responsive optoelectronic synaptic devices hold significant value for advanced neuromorphic vision systems, as they can expand human visual perception. Herein, we demonstrate a UV-responsive optoelectronic synaptic device based on ZnMgO quantum dots (QDs) designed for in-sensor computing in neuromorphic vision applications. The device demonstrates voltage-driven short-term and long-term synaptic plasticity, as well as multiple photoinduced synaptic functions. Based on this device, an in-sensor image-blending encryption method has been designed, which can effectively reduce the risk of data leakage during transmission. Furthermore, an in-sensor reservoir computing (RC) system with image processing functions is constructed, which integrates a photonic reservoir layer (PRL) for image preprocessing and a multilayer perceptron (MLP) capable of image recognition. The system achieves 98.6% accuracy in recognizing Fashion-MNIST images and maintains 83% accuracy under 60% random noise, showcasing its robustness. This work introduces a novel approach for developing UV-responsive optoelectronic synaptic devices equipped with dual-mode modulation of both electrical and optical signals, offering new perspectives and solutions for integrated applications in neuromorphic vision systems.

High-Performance Broadband Mixed-Dimensional Phototransistors Based on the Boron Nitride Quantum Dots/MoSe2 Heterostructure with Enhanced UV Sensitivity

Two-dimensional (2D) semiconductors have been of great interest in phototransistors in recent years due to their unique optoelectronic and electronic properties. However, their discernible spectral range and the efficiency of light absorption are usually restricted. Here, we present phototransistors based on mixed-dimensional heterostructures formed by zero-dimensional (0D) boron nitride quantum dots (BNQDs) and molybdenum diselenide (MoSe2), which have high responsivity (R), specific detectivity (D*), and external quantum efficiency (EQE), especially in the ultraviolet (UV) spectral range. The heterostructure phototransistors showed a 440% increase in R at 375 nm (from 5.6 to 24.7 A/W) and a 260% increase in D* (from 3.3 to 8.7 × 1011 Jones) compared to bare MoSe2 at the wavelength of 375 nm and a bias of 1 V. A series of characterization and comparison experiments show that charge transfer on BNQDs/MoSe2 results in the photogating effect and optical gain. Meanwhile, the high-performance BNQDs/MoSe2 heterostructure phototransistors exhibit broadband imaging capabilities and thus hold great promise for ultrasensitive light detection, neuromorphic visual sensing, and in-sensor computing applications.

Physisorption-assistant optoelectronic synaptic transistors based on Ta2NiSe5/SnS2 heterojunction from ultraviolet to near-infrared

Neuromorphic computing vision is the most promising technological solution to overcome the arithmetic bottleneck in machine vision applications. All-in-one neuromorphic sensors have been attracting increased attention because they can integrate visual perception, processing, and memory functionalities into one single device. However, the limited responsivity and data retention time of all-in-one neuromorphic sensors usually hinder their potential in multispectral machine vision, especially in the near-infrared (NIR) band which contains critical information for pattern recognition. Here, we demonstrate physisorption-assistant optoelectronic synaptic transistors based on Ta2NiSe5/SnS2 heterojunction, which present tunable synaptic functionality in broadband (375-1310 nm). We propose a strategy about the physisorption-assistant persistent photoconductivity (PAPPC) effect to effectively solve the problem in detecting and storing the NIR light information. Under this strategy, the responsivity and data retention time of our devices were significantly enhanced and prolonged in broadband from 375 to 1310 nm. Further, the devices realize multilevel non-volatile optoelectronic memory through the modulation of several optical and back-gate signals to simulate emotion-controlled learning and memory processes, optical writing-electric erasing, and associative learning. Moreover, we developed a simplified human visual system to simulate color-cognitive perception and memory functions. Our approach offers a route for creating advanced all-in-one neuromorphic sensors and developing neuromorphic computing vision.

Tuning Absorption State and Intermolecular Potential of Organic Semiconductors for Narrowband Ultraviolet Photodetection

Narrowband response of organic semiconductors determines the band selectivity and anti-interference of the organic photodetectors, which are pursued for a long time but have not yet been resolved in the UV band. Herein, a feasible strategy is developed to realize narrowband UV response by tuning the absorption state and intermolecular potential of organic semiconductors. The as-designed non-Donor-Acceptor molecule, 2,5-diphenylthieno[3,2-b]thiophene (2,5-DPTT), exhibits narrowband absorption by fully suppressing the charge transfer state absorption. Simultaneously, the intermolecular potential is significantly enhanced (to ≈90 KJ mol-1) by modulating the molecular planarity. Consequently, the UV photodetector based on 2,5-DPTT achieves excellent narrowband response at 310 nm wavelength and a record-breaking photosensitivity (P = 1.21 × 106) in the deep UV range. In the demonstration application of flame alarm, the flame detector based on 2,5-DPTT single crystal exhibits excellent anti-interference capability. This work provides the inspiration for designing narrowband responsive organic semiconductors and building up multifunctional optoelectronic devices.

Organic Synaptic Transistors Based on a Semiconductor Heterojunction for Artificial Visual and Neuromorphic Functions

Visual acuity is the ability of the biological retina to distinguish images. High-sensitivity image acquisition improves the quality of visual perception, making images more recognizable for the visual system. Therefore, developing synaptic phototransistors with enhanced photosensitivity is crucial for high-performance artificial vision. Here, organic synaptic phototransistors (OSPs) based on p-n type semiconductor heterojunctions are presented, which demonstrate improved photoresponses and light storage characteristics. As many as 800 potentiation-depression states can be obtained, and the nonlinearity extracted from the long-term potentiation curve is only 0.08. Furthermore, by utilizing light-adjustable synapse-like behaviors, the phototransistors realize a noise reduction function and logic gate transformation. Benefiting from the enhanced photosensitivity of the OSPs, an artificial neural network constructed based on the OSPs shows the recognition accuracy of ∼93% for both handwritten numbers and electrocardiography signals. This research provides an effective path for developing OSPs with enhanced photoelectric performance to advance artificial visual systems.

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.

Retina-Inspired Flexible Visual Synaptic Device for Dynamic Image Processing

Exploiting biomimetic perception of invisible spectra in flexible artificial human vision systems (HVSs) is crucial for real-time dynamic information processing. Nevertheless, the fast processing of motion objects in natural environments poses a challenge, necessitating that these artificial HVSs simultaneously have swift photoresponse and nonvolatile memory. Here, inspired by the human retina, we propose a flexible UV neuromorphic visual synaptic device (NeuVSD) based on GaOx@GaN-composited nanowires for dynamic visual perception. Benefiting from the combined action of oxygen adsorption kinetics on the GaOx shell and photoelectronic excitation in the GaN core, the NeuVSD can achieve rapid photoresponse and long-term plasticity simultaneously, a feature that surpasses traditional devices based on persistent photoconductivity mechanisms. Beyond the optoelectronic synaptic plasticity, the flexible NeuVSD on the PET/PI substrate exhibits good performance stability after 1000 bending cycles, profiting from the high adaptability of nanowires. Furthermore, target recognition and motion detection with weak light intensities are achieved through the establishment of neuromorphic visual neural networks, relying on dynamic exposures and edge information processing, respectively. Real-time edge detection can still be realized under a 40% noise factor and effectively remove the noise background. The flexible NeuVSD array-based artificial visual system can enhance the capability of dynamic visual information processing in low-light and high-noise conditions, thereby fostering the evolution of in-sensor computing and artificial intelligence.

High External Quantum Efficiency and Ultra-Narrowband Organic Photodiodes Using Single-Component Photoabsorber With Multiple-Resonance Effect

Organic photodiodes (OPDs) that utilize wavelength-selective absorbing molecules offer a direct approach to capturing specific wavelengths of light in multispectral sensors/imaging systems without filters. However, they exhibit broad response bandwidths, low external quantum efficiency (EQE), and often require compromises in two-component photoactive materials. Herein, the first utility of boron-nitrogen (BN) single-component photoabsorbers, leveraging a multi-resonance effect are introduced to attain OPDs with both record-high EQE of 33.77% and ultra-small full-width half-maximum (FWHM) of 36 nm in the reported narrowband OPDs using single-component photoabsorbers. It is found that the outstanding performance of these narrowband OPDs can be attributed to the ultra-small FWHM, slow charge recombination, low activation energy, and balanced bipolar charge transport within the para-tert-butyl substituted B,N-embedded rigid polycyclic molecule (BNCz) film. Furthermore, BN derivatives such as BN(p)SCH3, BN(p)SO2CH3, and pyBN-m-H have also shown high EQE, minimal FWHM, and tunable photoresponse peaks ranging from blue-violet to blue-turquoise, highlighting the potential of BN molecules and molecular engineering in the development of novel narrowband absorbers for advanced wavelength-selective OPDs. Such pioneering working can provide a class of novel narrowband absorbers to propel the advancement of high-performance wavelength-selective OPDs.

Hardware-Feasible and Efficient N-Type Organic Neuromorphic Signal Recognition via Reservoir Computing

Organic electrochemical synaptic transistors (OESTs), inspired by the biological nervous system, have garnered increasing attention due to their multifunctional applications in neuromorphic computing. However, the practical implementation of OESTs for signal recognition-particularly those utilizing n-type organic mixed ionic-electronic conductors (OMIECs)-still faces significant challenges at the hardware level. Here, a state-of-the-art small-molecule n-type OEST integrated within a physically simple and hardware feasible reservoir-computing (RC) framework for practical temporal signal recognition is presented. This integration is achieved by leveraging the adjustable synaptic properties of the n-OEST, which exhibits tunable nonlinear short-term memory, transitioning from volatility to nonvolatility, and demonstrating adaptive temporal specificity. Additionally, the nonvolatile OEST offers 256 conductance levels and a wide dynamic range (≈147) in long-term potentiation/depression (LTP/LTD), surpassing previously reported n-OESTs. By combining volatile n-OESTs as reservoirs with a single-layer perceptron readout composed of nonvolatile n-OEST networks, this physical RC system achieves substantial recognition accuracy for both handwritten-digit images (94.9%) and spoken digit (90.7%), along with ultrahigh weight efficiency. Furthermore, this system demonstrates outstanding accuracy (98.0%) by grouped RC in practical sleep monitoring, specifically in snoring recognition. Here, a reliable pathway for OMIEC-driven computing is presented to advance bioinspired hardware-based neuromorphic computing in the physical world.

Advances in Metal Halide Perovskite Memristors: A Review from a Co-Design Perspective

The memristor has recently demonstrated considerable potential in the field of large-scale data information processing. Metal halide perovskites (MHPs) have emerged as the leading contenders for memristors due to their sensitive optoelectronic response, low power consumption, and ability to be prepared at low temperatures. This work presents a comprehensive enumeration and analysis of the predominant research advancements in mechanisms of resistance switch (RS) behaviors in MHPs-based memristors, along with a summary of useful characterization techniques. The impact of diverse optimization techniques on the functionality of perovskite memristors is examined and synthesized. Additionally, the potential of MHPs memristors in data processing, physical encryption devices, artificial synapses, and brain-like computing advancement of MHPs memristors is evaluated. This review can prove a valuable reference point for the future development of perovskite memristors applications. In conclusion, the current challenges and prospects of MHPs-based memristors are discussed in order to provide insights into potential avenues for the development of next-generation information storage technologies and biomimetic applications.

Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

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.

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).

Self-powered visualized tactile-acoustic sensor for accurate artificial perception with high brightness and record-low detection limit

The growth of the Internet of Things has focused attention on visualized sensors as a key technology. However, it remains challenging to achieve high sensing accuracy and self-power ability. Here, we propose a self-powered visualized tactile-acoustic sensor (SVTAS) based on an elaborated triboelectrification-induced electroluminescence (TIEL) unit. To date, it features a high brightness of 0.5 mW cm-2 (32 cd m-2) and a record-low detection limit of 0.5 kPa in horizontal-sliding mode. Meanwhile, the SVTAS is applicable to convert acoustic waves into TIEL signals in contact-separation mode, showing the highest response to the 44.07 Hz sound, a high signal-to-noise ratio of 8.7 dB-1, and an ultrafast response time of 0.8 ms. Furthermore, advanced artificial visualized perception systems are constructed with excellent performance in recognizing motion trajectories and human speech with different words/sentences. This work paves the way for the highly efficient and sustainable development of new-generation self-powered visualized perception systems, contributing a solution to wireless communication free from electromagnetic interference.

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.

All-In-One Optoelectronic Transistors for Bio-Inspired Visual System

Visual perception has profound effects on human decision-making and emotional responses. Replicating the functions of the human visual system through device development has been a constant pursuit in recent years. However, to fully simulate the various functions of the human visual system, it is often necessary to integrate multiple devices with different functions, resulting in complex, large-volume device structures and increased power consumption. Here, an optoelectronic transistor with comprehensive visual functions is introduced. By coupling diverse photoreceptive properties of the channel and electrical regulation through charge injection/ferroelectric switching from the hafnium-based gate, the devices can simulate functions of both photoreceptors in the retina and synapses in the visual cortex. A device array is constructed to confirm the perceptual functions of cone and rod cells. Subsequently, color discrimination and recognition for color images are achieved by combining the tunable perception and synapse functions. Then an intelligent traffic judgment system with this all-in-one device is developed, which is capable of making judgments and decisions regarding traffic signals and pedestrian movements. This work provides a potential solution for developing compact and efficient devices for the next-generation bio-inspired visual system.

Bioinspired Flexible and Low-Voltage Organic Synaptic Transistors for UV Light-Driven Vision Systems

Neuromorphic vision systems, particularly those stimulated by ultraviolet (UV) light, hold great potential applications in portable electronics, wearable technology, biological analysis, military surveillance, etc. Organic artificial synaptic devices hold immense potential in this field due to their ease of processing, flexibility, and biocompatibility. In this work, we have fabricated a flexible organic field-effect transistor (OFET) that utilizes chitosan-silver nanoparticles (AgNPs) composite material as the active dielectric material. During UV light illumination, both silver nanoparticles and the pentacene layer generate a large number of charge carriers. The photogenerated carriers lead to a more significant hole accumulation at the pentacene interface, resulting in a current rise. In the absence of light, the trapped electron in the silver nanoparticles persists for a longer duration, preventing the instant recombination with holes. This extended retention of electrons leads to the observed synaptic performance of the transistor. The use of aluminum oxide (Al2O3) as one of the dielectric layers enables the device to operate effectively at low voltage (<1 V). The device mimics various crucial synaptic properties of the brain, including short-term potentiation and long-term potentiation (STP and LTP), paired-pulse facilitation (PPF), spike-duration dependent plasticity (SDDP), spike-number dependent plasticity (SNDP), and spike-rate dependent plasticity (SRDP), etc. This work introduces an approach to develop flexible organic synaptic transistors that operate efficiently at low voltages, paving the way toward high-performance, UV light-driven neuromorphic vision systems.

Monolithically integrated neuromorphic electronic skin for biomimetic radiation shielding

Melanogenesis, a natural responsive mechanism of human skin to harmful radiation, is a self-triggered defensive neural activity safeguarding the body from radiation exposure in advance. With the increasing significance of radiation shielding in diverse medical health care and wearable applications, a biomimetic neuromorphic optoelectronic system with adaptive radiation shielding capability is often needed. Here, we demonstrate a transparent and flexible metal oxide-based photovoltaic neuromorphic defensive system. By using a monolithically integrated ultraflexible optoelectronic circuitry and electrochromic device, seamless neural processing for ultraviolet (UV) radiation shielding including history-based sensing, memorizing, risk recognition, and blocking can be realized with piling the entire signal chain into the flexible devices. The UV shielding capability of the system can be evaluated as autonomous blocking up to 97% of UV radiation from 5 to 90 watts per square meter in less than 16.9 seconds, demonstrating autonomously modulated sensitivity and response time corresponding to UV environmental conditions and supplied bias.

An Active-Matrix Synaptic Phototransistor Array for In-Sensor Spectral Processing

The human retina perceives and preprocesses the spectral information of incident light, enabling fast image recognition and efficient chromatic adaptation. In comparison, it is reluctant to implement parallel spectral preprocessing and temporal information fusion in current complementary metal-oxide-semiconductor (CMOS) image sensors, requiring intricate circuitry, frequent data transmission, and color filters. Herein, an active-matrix synaptic phototransistor array (AMSPA) is developed based on organic/inorganic semiconductor heterostructures. The AMSPA provides wavelength-dependent, bidirectional photoresponses, enabling dynamic imaging and in-sensor spectral preprocessing functions. Specifically, near-infrared light induces inhibitory photoresponse while UV light results in exhibitory photoresponse. With rational structural design of the organic/inorganic hybrid heterostructures, the current dynamic range of phototransistor is improved to over 90 dB. Finally, a 32 × 64 AMSPA (128 pixels per inch) is demonstrated with one-switch-transistor and one-synaptic phototransistor (1-T-1-PT) structure, achieving spatial chromatic enhancement and temporal trajectory imaging. These results reveal the feasibility of AMSPA for constructing artificial vision systems.

All-in-One 2D Molecular Crystal Optoelectronic Synapse for Polarization-Sensitive Neuromorphic Visual System

Neuromorphic visual systems (NVSs) hold the potential to not only preserve but also enhance human visual capabilities. One such augmentation lies in harnessing polarization information from light reflected or scattered off surfaces like bees, which can disclose unique characteristics imperceptible to the human eyes. While creating polarization-sensitive optoelectronic synapses presents an intriguing avenue for equipping NVS with this capability, integrating functions like polarization sensitivity, photodetection, and synaptic operations into a singular device has proven challenging. This integration typically necessitates distinct functional components for each performance metric, leading to intricate fabrication processes and constraining overall performance. Herein, a pioneering linear polarized light sensitive synaptic organic phototransistor (OPT) based on 2D molecular crystals (2DMCs) with highly integrated, all-in-one functionality, is demonstrated. By leveraging the superior crystallinity and molecular thinness of 2DMC, the synaptic OPT exhibits comprehensive superior performance, including a linear dichroic ratio up to 3.85, a high responsivity of 1.47 × 104 A W-1, and the adept emulation of biological synapse functions. A sophisticated application in noncontact fingerprint detection achieves a 99.8% recognition accuracy, further highlights its potential. The all-in-one 2DMC optoelectronic synapse for polarization-sensitive NVS marks a new era for intelligent perception systems.

Birdlike broadband neuromorphic visual sensor arrays for fusion imaging

Wearable visual bionic devices, fueled by advancements in artificial intelligence, are making remarkable progress. However, traditional silicon vision chips often grapple with high energy losses and challenges in emulating complex biological behaviors. In this study, we constructed a van der Waals P3HT/GaAs nanowires P-N junction by carefully directing the arrangement of organic molecules. Combined with a Schottky junction, this facilitated multi-faceted birdlike visual enhancement, including broadband non-volatile storage, low-light perception, and a near-zero power consumption operating mode in both individual devices and 5 × 5 arrays on arbitrary substrates. Specifically, we realized over 5 bits of in-memory sensing and computing with both negative and positive photoconductivity. When paired with two imaging modes (visible and UV), our reservoir computing system demonstrated up to 94% accuracy for color recognition. It achieved motion and UV grayscale information extraction (displayed with sunscreen), leading to fusion visual imaging. This work provides a promising co-design of material and device for a broadband and highly biomimetic optoelectronic neuromorphic system.

Versatile Papertronics: Photo-Induced Synapse and Security Applications on Papers

Paper is a readily available material in nature. Its recyclability, eco-friendliness, portability, flexibility, and affordability make it a favored substrate for researchers seeking cost-effective solutions. Electronic devices based on solution process are fabricated on paper and banknotes using PVK and SnO2 nanoparticles. The devices manufactured on paper substrates exhibit photosynaptic behavior under ultraviolet pulse illumination, stemming from numerous interactions on the surface of the SnO2 nanoparticles. A light-modulated artificial synapse device is realized on a paper at a low voltage bias of -0.01 V, with an average recognition rate of 91.7% based on the Yale Face Database. As a security device on a banknote, 400 devices in a 20 × 20 array configuration exhibited random electrical characteristics owing to the local morphology of the SnO2 nanoparticles and differences in the depletion layer width at the SnO2/PVK interface. The security Physically Unclonable Functions (PUF) key based on the current distribution extracted at -1 V show unpredictable reproducibility with 50% uniformity, 48.7% inter-Hamming distance, and 50.1% bit-aliasing rates. Moreover, the device maintained its properties for more than 210 days under a curvature radius of 8.75 mm and bias and UV irradiation stress conditions.

Negative Photoconductivity Transistors for Visuomorphic Computing

Visuomorphic computing aims to simulate and potentially surpass the human retina by mimicking biological visual perception with an artificial retina. Despite significant progress, challenges persist in perceiving complex interactive environments. Negative photoconductivity transistors (NPTs) mimic synaptic behavior by achieving adjustable positive photoconductivity (PPC) and negative photoconductivity (NPC), simulating "excitation" and "inhibition" akin to sensory cell signals. In complex interactive environments, NPTs are desired for visuomorphic computing that can achieve a better sense of information, lower power consumption, and reduce hardware complexity. In this review, it is started by introducing the development process of NPTs, while placing a strong emphasis on the device structures, working mechanisms, and key performance parameters. The common material systems employed in NPTs based on their functions are then summarized. Moreover, it is proceeded to summarize the noteworthy applications of NPTs in optoelectronic devices, including advanced multibit nonvolatile memory, optoelectronic logic gates, optical encryption, and visual perception. Finally, the challenges and prospects that lie ahead in the ongoing development of NPTs are addressed, offering valuable insights into their applications in optoelectronics and a comprehensive understanding of their significance.

Interspecies-chimera machine vision with polarimetry for real-time navigation and anti-glare pattern recognition

Cutting-edge humanoid machine vision merely mimics human systems and lacks polarimetric functionalities that convey the information of navigation and authentic images. Interspecies-chimera vision reserving multiple hosts' capacities will lead to advanced machine vision. However, implementing the visual functions of multiple species (human and non-human) in one optoelectronic device is still elusive. Here, we develop an optically-controlled polarimetry memtransistor based on a van der Waals heterostructure (ReS2/GeSe2). The device provides polarization sensitivity, nonvolatility, and positive/negative photoconductance simultaneously. The polarimetric measurement can identify celestial polarizations for real-time navigation like a honeybee. Meanwhile, cognitive tasks can be completed like a human by sensing, memory, and synaptic functions. Particularly, the anti-glare recognition with polarimetry saves an order of magnitude energy compared to the traditional humanoid counterpart. This technique promotes the concept of interspecies-chimera visual systems that will leverage the advances of autonomous vehicles, medical diagnoses, intelligent robotics, etc.

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.

Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors

Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.

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.

Highly Stretchable and Oriented Wafer-Scale Semiconductor Films for Organic Phototransistor Arrays

Stretchable organic phototransistor arrays have potential applications in artificial visual systems due to their capacity to perceive ultraweak light across a broad spectrum. Ensuring uniform mechanical and electrical performance of individual devices within these arrays requires semiconductor films with large-area scale, well-defined orientation, and stretchability. However, the progress of stretchable phototransistors is primarily impeded by their limited electrical properties and photodetection capabilities. Herein, wafer-scale and well-oriented semiconductor films were successfully prepared using a solution shearing process. The electrical properties and photodetection capabilities were optimized by improving the polymer chain alignment. Furthermore, a stretchable 10 × 10 transistor array with high device uniformity was fabricated, demonstrating excellent mechanical robustness and photosensitive imaging ability. These arrays based on highly stretchable and well-oriented wafer-scale semiconductor films have great application potential in the field of electronic eye and artificial visual systems.

Multidimensional vision sensors for information processing

The visual scene in the physical world integrates multidimensional information (spatial, temporal, polarization, spectrum and so on) and typically shows unstructured characteristics. Conventional image sensors cannot process this multidimensional vision data, creating a need for vision sensors that can efficiently extract features from substantial multidimensional vision data. Vision sensors are able to transform the unstructured visual scene into featured information without relying on sophisticated algorithms and complex hardware. The response characteristics of sensors can be abstracted into operators with specific functionalities, allowing for the efficient processing of perceptual information. In this Review, we delve into the hardware implementation of multidimensional vision sensors, exploring their working mechanisms and design principles. We exemplify multidimensional vision sensors built on emerging devices and silicon-based system integration. We further provide benchmarking metrics for multidimensional vision sensors and conclude with the principle of device-system co-design and co-optimization.

Human somatosensory systems based on sensor-memory-integrated technology

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

All-Photolithography Fabrication of Ion-Gated Flexible Organic Transistor Array for Multimode Neuromorphic Computing

Organic ion-gated transistors (OIGTs) demonstrate commendable performance for versatile neuromorphic systems. However, due to the fragility of organic materials to organic solvents, efficient and reliable all-photolithography methods for scalable manufacturing of high-density OIGT arrays with multimode neuromorphic functions are still missing, especially when all active layers are patterned in high-density. Here, a flexible high-density (9662 devices per cm2) OIGT array with high yield and minimal device-to-device variation is fabricated by a modified all-photolithography method. The unencapsulated flexible array can withstand 1000 times' bending at a radius of 1 mm, and 3 months' storage test in air, without obvious performance degradation. More interesting, the OIGTs can be configured between volatile and nonvolatile modes, suitable for constructing reservoir computing systems to achieve high accuracy in classifying handwritten digits with low training costs. This work proposes a promising design of organic and flexible electronics for affordable neuromorphic systems, encompassing both array and algorithm aspects.

Multicolor vision perception of flexible optoelectronic synapse with high sensitivity for skin sunburn warning

The development of flexible synaptic devices with multicolor signal response is important to exploit advanced artificial visual perception systems. The Sn vacancy-dominant memory and narrow gap characteristics of PEA2SnI4 make it suitable as a functional layer in ultraviolet-visible (UV-Vis) light-stimulated synaptic devices. However, such device tends to have high dark current and poor sensitivity, which is not conducive to subsequent information processing. Here, we proposed a self-powered flexible optoelectronic synapse based on PEA2SnI4 films. By introducing the electron transport layer (ETL), the dark current of the device is decreased by 5 orders of magnitude as compared to the Au/PEA2SnI4/ITO device, and the sensitivity is increased from 10.3% to 99.2% at 1.25 mW cm-2 light illumination (520 nm), indicating the vital role of the introduced ETL in promoting the separation of excitons in the interface and inhibiting the free carrier transfer. On this basis, the optoelectronic synaptic functions with integrated sensing, recognition, and memory features were realized. The array device exhibits UV-Vis light sensitivity and tunable synaptic plasticity, enabling its application for multicolor visual sensing and skin sunburn warning. This work provides an effective strategy for fabricating multicolor intelligent sensors and artificial vision systems, which facilitate the practical application of artificial optoelectronic synapses.

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.

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.

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.

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

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

Designing Ordered Organic Small-Molecule Domains for Ultraviolet Detection and Micrometer-Sized Flexible Imaging

Photodetectors displaying an ultraviolet (UV) spectral response window are typically based on wide-bandgap semiconductors that have long been dominated by inorganic materials that suffer from bottlenecks of low flexibility and a limited material family. Here, we synthesized a novel organic small molecule and controlled its crystallization to suppress leakage currents and facilitate separation of the carriers, and the relationship between the nanoscale phase separation morphology and the optoelectrical performance of the photodetectors is disclosed. Our optimized organic photodetector (OPD) presents a UV spectral response window, with superior self-powered responsivities of 45 mA/W (under 250 nm light) and 70 mA/W (under 300 nm light), outperforming the Si photodiode and rivaling other reported UV self-powered photodetectors. Finally, an imaging system was constructed to demonstrate the application potential of the OPD in UV flexible imaging with high-resolution arrays of 400 pixels × 400 pixels (5 μm × 5 μm per pixel), which could work in bent states and successfully output images of micrometer-sized objects.