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

Barrier Polarity Reversal Based on Interfacial Modification of Au Nanoparticles for Nonvolatile Multilevel Memory and Optoelectronic Synapses

Optoelectronic synaptic devices, integrating light sensing and information processing capabilities, have emerged as advantageous tools for the implementation of visual neuromorphic computing. However, the transient light-triggered response characteristic typically results in unstable memory retention times and restricted current response ranges, posing significant challenges to the development and practical application of neural network systems. In response to these limitations, this study developed a nonvolatile optoelectronic memory based on the indium tin oxide (ITO)/Au nanoparticles (NPs)/amorphous Ga2O3 (a-Ga2O3)/Pt structure. Unlike conventional optoelectronic memories, this device features a modification with Au NPs that markedly enhances the Schottky barrier height at the interface. Au NPs function as a charge-trapping layer for sensitive and large-scale modulation of the barrier by the light field, thereby enabling the nonvolatile reversal of the device's barrier polarity. This innovative approach enables controllable multilevel data storage with an ultra large on/off ratio (∼104) and excellent retention capability exceeding 12,000 s. Additionally, the device emulates essential synaptic functions and demonstrates potential application values in visual weak signal perception and image memory. This study introduces a mechanism for Schottky barrier polarity control and presents a promising strategy for the development of future high-performance integrated devices and optoelectronic synaptic elements.

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.

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.

Human somatosensory systems based on sensor-memory-integrated technology

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

A bionic self-driven retinomorphic eye with ionogel photosynaptic retina

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

Förster-free Effect Fluorescence Quenching of 5,10,15,20-tetraphenylporphyrin in Solution

The absorption of exciting light by an inner filter may be the cause of large errors in estimating the efficiency of fluorescence quenching. We performed fluorescence measurements of 5,10,15,20-tetraphenylporphyrin toluene solution in a wide concentration range. We have shown a Förster-free fluorescence quenching in a right-angle geometry experiment, caused by second order inner filter. We proposed to perform measurements in a front-surface geometry to investigate the nature of quenching not related to the inner filters. We have shown that concentration effects in tetraphenylporphyrin solutions in toluene are not accompanied by a decrease in the fluorescence intensity in a front-surface geometry at concentrations of 10- 3-10- 7 mol/l. It was possible to separate the phenomena of the internal filters from the processes occurring in a liquid medium. Our results are of great importance due to the widespread fundamental research of porphyrin-based dyes properties.

Towards Artificial Visual Sensory System: Organic Optoelectronic Synaptic Materials and Devices

Developing an artificial visual sensory system requires optoelectronic materials and devices that can mimic the behavior of biological synapses. Organic/polymeric semiconductors have emerged as promising candidates for optoelectronic synapses due to their tunable optoelectronic properties, mechanic flexibility, and biological compatibility. In this review, we discuss the recent progress in organic optoelectronic synaptic materials and devices, including their design principles, working mechanisms, and applications. We also highlight the challenges and opportunities in this field and provide insights into potential applications of these materials and devices in next-generation artificial visual systems. By leveraging the advances in organic optoelectronic materials and devices, we can envision its future development in artificial intelligence.

Superlow Power Consumption Memristor Based on Borphyrin-Deoxyribonucleic Acid Composite Films as Artificial Synapse for Neuromorphic Computing

Memristor synapses based on green and pollution-free organic materials are expected to facilitate biorealistic neuromorphic computing and to be an important step toward the next generation of green electronics. Metalloporphyrin is an organic compound that widely exists in nature with good biocompatibility and stable chemical properties, and has already been used to fabricate memristors. However, the application of metalloporphyrin-based memristors as synaptic devices still faces challenges, such as realizing a high switching ratio, low power consumption, and bidirectional conductance modulation. We developed a memristor that improves the resistive switching (RS) characteristics of Zn(II)meso-tetra(4-carboxyphenyl) porphine (ZnTCPP) by combining it with deoxyribonucleic acid (DNA) in a composite film. The as-fabricated ZnTCPP-DNA-based device showed excellent RS memory characteristics with a sufficiently high switching ratio of up to ∼104, super low power consumption of ∼39.56 nW, good cycling stability, and data retention capability. Moreover, bidirectional conductance modulation of the ZnTCPP-DNA-based device can be controlled by modulating the amplitudes, durations, and intervals of positive and negative pulses. The ZnTCPP-DNA-based device was used to successfully simulate a series of synaptic functions including long-term potentiation, long-term depression, spike time-dependent plasticity, paired-pulse facilitation, excitatory postsynaptic current, and human learning behavior, which demonstrates its potential applicability to neuromorphic devices. A two-layer artificial neural network was used to demonstrate the digit recognition ability of the ZnTCPP-DNA-based device, which reached 97.22% after 100 training iterations. These results create a new avenue for the research and development of green electronics and have major implications for green low-power neuromorphic computing in the future.

Enhanced Multiwavelength Response of Flexible Synaptic Transistors for Human Sunburned Skin Simulation and Neuromorphic Computation

In biological species, optogenetics and bioimaging work together to regulate the function of neurons. Similarly, the light-controlled artificial synaptic system not only enhances computational speed but also simulates complex synaptic functions. However, reported synaptic properties are mainly limited to mimicking simple biological functions and single-wavelength responses. Therefore, the development of flexible synaptic devices with multiwavelength optical signal response and multifunctional simulation remains a challenge. Here, flexible organic light-stimulated synaptic transistors (LSSTs) enabled by alumina oxide (AlOX ), with a simple fabrication process, are reported. By embedding AlOX nanoparticles, the excitons separation efficiency is improved, allowing for multiple wavelength responses. Optimized LSSTs can respond to multiple optical and electrical signals in a highly synaptic manner. Multiwavelength optical synaptic plasticity, electrical synaptic plasticity, sunburned skin simulation, learning efficiency model controlled by photoelectric cooperative stimulation, neural network computing, "deer" picture learning and memory functions are successfully proposed, which promote the development for future artificial intelligent systems. Furthermore, as prepared flexible transistors exhibit mechanical flexibility with bending radius down to 2.5 mm and improved photosynaptic plasticity, which facilitating development of neuromorphic computing and multifunction integration systems at the device-level.

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.

Sustained Flexible Photonic Transistor Memories Based on Fully Natural Floating Gate Electrets

Photonic transistor memory with high-speed communication and energy-saving capabilities has emerged as a new data storage technology. However, most floating-gate electrets are composed of quantum dots derived from petroleum or metals, which are either toxic or harmful to the environment. In this study, an environmentally friendly floating-gate electret made entirely from biomass-derived materials was designed for photonic memories. The results show that the photosensitive hemin and its derivative protoporphyrin IX (PPIX) were successfully embedded in a polylactic acid (PLA) matrix. Correspondingly, their disparate photochemistry and core structure strongly affected the photosensitivity and charge-trapping capacity of the prepared electrets. With an appropriate energy-level alignment, the interlayer exciton formed with the correct alignment of energy levels within the PPIX/PLA electret. In addition, the demetallized core offered a unique relaxation dynamic and additional trapping sites to consolidate the charges. Correspondingly, the as-prepared device exhibited a memory ratio of up to 2.5 × 10⁷ with photo-writing-electrical-erasing characteristics. Conversely, hemin demonstrated self-charge transfer during relaxation, making it challenging for the device to store the charges and exhibit a photorecovery behavior. Furthermore, the effect of trapping site discreteness on memory performance was also investigated. The photoactive components were effectively distributed due to the high dipole-dipole interaction between the PLA matrix and PPIX, resulting in a sustained memory performance for at least 10⁴ s after light removal. The photonic memory was also realized on a bio-derived dielectric flexible substrate. Accordingly, a reliable photorecording behavior was observed, wherein, even after 1000 cycles of bending under a 5 mm bending radius, the data was retained for more than 10⁴ s. To our knowledge, it is the first time that a two-pronged approach has been used to improve the performance of photonic memories while addressing the issue of sustainability with a biodegradable electret made entirely from natural materials.

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

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

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

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

Artificial synapse based on 1,4-diphenylbutadiyne with femtojoule energy consumption

Memristors as electronic artificial synapses have attracted increasing attention in neuromorphic computing. Especially, organic small molecule artificial synapses show great promise for low-energy neuromorphic devices. In this study, the basic functions of biological synapses including paired-pulse facilitation/paired-pulse depression (PPF/PPD), spike rate-dependent plasticity (SRDP) and fast Bienenstock-Cooper-Munro learning rules (BCM) have been successfully simulated in the 1,4-diphenylbutadiyne (DPDA) memristor device. Furthermore, ultra-low energy consumption (∼25 fJ per spike), linear and large conductance changes have been obtained in the small molecule DPDA device. This work makes a great contribution to improve the accuracy, speed and to reduce the energy consumption for neuromorphic computing.

Multimodal-Synergistic-Modulation Neuromorphic Imaging Systems for Simulating Dry Eye Imaging

Inspired by human eyes, the neuromorphic visual system employs a highly efficient imaging and recognition process, which offers tremendous advantages in image acquisition, data pre-processing, and dynamic storage. However, it is still an enormous challenge to simultaneously simulate the structure, function, and environmental adaptive behavior of the human eye based on one device. Here, a multimodal-synergistic-modulation neuromorphic imaging system based on ultraflexible synaptic transistors is successfully presented and firstly simulates the dry eye imaging behavior at the device level. Moreover, important functions of the human visual system in relation to optoelectronic synaptic plasticity, image erasure and enhancement, real-time preprocessing, and dynamic storage are simulated by versatile devices. This work not only simplifies the complexity of traditional neuromorphic visual systems, but also plays a positive role in the publicity of biomedical eye care.

Interface Influence on the Photoelectric Performance of Transition Metal Dichalcogenide Lateral Heterojunctions

The ultrathin feature of two-dimensional (2D) transition metal dichalcogenides (TMDs) has brought special performance in electronic and optoelectronic fields. When vertical and lateral heterojunctions are made using different TMD combinations, the original properties of premier TMDs can be optimized. Especially for lateral heterojunctions, their sharp interface signifies a narrow space charge region, leading to a strong in-plane built-in electric field, which may contribute to high separation efficiency of photogenerated carriers, good rectification behavior, self-powered photoelectric device construction, etc. However, due to the poor controllability over the synthesis process, obtaining a clean and sharp interface of the lateral heterojunction is still a challenge. Herein, we propose a simple chemical vapor deposition (CVD) method, which can effectively separate the growth process of different TMDs, thus resulting in good regulation of the composition change at the junction region. By this method, MoS₂-WS₂ lateral heterojunctions with sharp interfaces have been obtained with good rectification characteristics, ∼10⁵ on/off ratio, 1874% external quantum efficiency, and ∼120 ms photoresponse speed, exhibiting a better photoelectric performance than that of the lateral ones with graded junctions.

Advances in Emerging Photonic Memristive and Memristive-Like Devices

Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive-like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive-like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive-like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive-like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive-like devices, and lay a foundation for academic research and commercial applications.

Co-assembled perylene/graphene oxide photosensitive heterobilayer for efficient neuromorphics

Neuromorphic electronics, which use artificial photosensitive synapses, can emulate biological nervous systems with in-memory sensing and computing abilities. Benefiting from multiple intra/interactions and strong light-matter coupling, two-dimensional heterostructures are promising synaptic materials for photonic synapses. Two primary strategies, including chemical vapor deposition and physical stacking, have been developed for layered heterostructures, but large-scale growth control over wet-chemical synthesis with comprehensive efficiency remains elusive. Here we demonstrate an interfacial coassembly heterobilayer films from perylene and graphene oxide (GO) precursors, which are spontaneously formed at the interface, with uniform bilayer structure of single-crystal perylene and well-stacked GO over centimeters in size. The planar heterostructure device exhibits an ultrahigh specific detectivity of 3.1 × 10¹³ Jones and ultralow energy consumption of 10⁻⁹ W as well as broadband photoperception from 365 to 1550 nm. Moreover, the device shows outstanding photonic synaptic behaviors with a paired-pulse facilitation (PPF) index of 214% in neuroplasticity, the heterosynapse array has the capability of information reinforcement learning and recognition.

Layered heterostructures are promising photosensitive materials for advanced optoelectronics. Here, the authors introduce an interfacial coassembly method to construct large-scale perylene/grahene oxide (GO) heterobilayer for broadband photoreception and efficient neuromorphics.

Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing

In order to imitate brain-inspired biological information processing systems, various neuromorphic computing devices have been proposed, most of which were prepared on rigid substrates and have energy consumption levels several orders of magnitude higher than those of biological synapses (∼10 fJ per spike). Herein, a new type of wearable organic ferroelectric artificial synapse is proposed, which has two modulation modes (optical and electrical modulation). Because of the high photosensitivity of organic semiconductors and the ultrafast polarization switching of ferroelectric materials, the synaptic device has an ultrafast operation speed of 30 ns and an ultralow power consumption of 0.0675 aJ per synaptic event. Under combined photoelectric modulation, the artificial synapse realizes associative learning. The proposed artificial synapse with ultralow power consumption demonstrates good synaptic plasticity under different bending strains. This provides new avenues for the construction of ultralow power artificial intelligence system and the development of future wearable devices.

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

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

Leaving-group controlled N- or C-cyclization of azadienes via domino sequences: synthesis, mechanism, and chemoselectivity

We reported a leaving-group controlled N- or C-cyclization of azadienes bearing a benzothiophene moiety. Density functional calculations revealed that the competition of two cyclizations was governed by the nature of the leaving group on the partner.

The Photodetectors Based on Lateral Monolayer MoS2/WS2 Heterojunctions

Monolayer transition metal dichalcogenides (TMDs) show promising potential for next-generation optoelectronics due to excellent light capturing and photodetection capabilities. Photodetectors, as important components of sensing, imaging and communication systems, are able to perceive and convert optical signals to electrical signals. Herein, the large-area and high-quality lateral monolayer MoS2/WS2 heterojunctions were synthesized via the one-step liquid-phase chemical vapor deposition approach. Systematic characterization measurements have verified good uniformity and sharp interfaces of the channel materials. As a result, the photodetectors enhanced by the photogating effect can deliver competitive performance, including responsivity of ~ 567.6 A/W and detectivity of ~ 7.17 × 1011 Jones. In addition, the 1/f noise obtained from the current power spectrum is not conductive to the development of photodetectors, which is considered as originating from charge carrier trapping/detrapping. Therefore, this work may contribute to efficient optoelectronic devices based on lateral monolayer TMD heterostructures.

Inorganic Perovskite Quantum Dot-Based Strain Sensors for Data Storage and In-Sensor Computing

Although remarkable improvement has been achieved in stretchable strain sensors, challenges still exist in aspects including intelligent sensing, simultaneous data processing, and scalable fabrication techniques. In this work, a strain-sensitive device is presented by fabricating a CsPbBr₃ quantum dots (QDs) floating-gate field-effect transistor (FET) sensing array on thin polyimide (PI) films. The FET exhibits an excellent on/off ratio (>10³) and a large memory window (>2 V). With the introduction of CsPbBr₃ QDs as the trapping layer, an additional UV response is obtained because of the photogenerated charge carriers that significantly enhance the source-drain current (I_DS) of the device. At each electrical state, the I_DS varies with the strains and the sensing range is from compressive +12.5% to tensile -10.8%. Excellent data retainability and mechanical durability demonstrate the high quality and reliability of the fabricated sensors. Furthermore, synapse functions including long-term potentiation (LTP), long-term depression (LTD), etc., are emulated at the device level. Linearity factor changes of LTP/LTD in different sensing scenarios demonstrate the reliability of the device and further confirm the different sensing mechanisms with/without UV illumination. Our results exhibit the potential of transistor-based devices for multifunctional intelligent sensing.

Complementary Photo-Synapses Based on Light-Stimulated Porphyrin-Coated Silicon Nanowires Field-Effect Transistors (LPSNFET)

Neuromorphic computing has emerged as the high-energy-efficiency and intelligent solution for processing sensory data. As a potential alternative to neuromorphic computing, photo-excited synaptic systems can integrate the functions of optoelectronic sensing and synaptic computing to realize the low-power and high-performance visual perception. However, one major challenge in high-efficient photo-excited synaptic system is to realize the complementarily enhanced and inhibited synaptic behaviors with small hardware cost as possible. Another challenge is to fabricate the photo-synapse devices with complementary metal oxide semiconductor (CMOS)-compatible process to achieve high enough integration density for practical application. Here, a CMOS-compatible Light-stimulated Porphyrin-coated Silicon Nanowire Field Effect Transistor (LPSNFET) technology is proposed and developed to form the complementary photo-synapses with only two CMOS-like transistors. LPSNFET exhibits fivefold improvement in photo-sensitivity compared to the bare silicon nanowire (SiNW) devices, and can still show obvious responses when incident illumination power is as low as 0.1 mW cm^-2 . Moreover, it enables tunable dynamic synaptic plasticity and versatile synaptic functions. Especially, the complementarily enhanced and inhibited behaviors can be realized by modulating SiNW/porphyrin interface via simply changing the MOS type of LPSNFET, which acts like the photonic counterpart of CMOS technology to provide the basic brick for building complex neuromorphic circuits efficiently and economically. Finally, the CMOS process compatibility of LPSNFET provides potential application in future large scale in-sensor computing.

Retina-Inspired Organic Heterojunction-Based Optoelectronic Synapses for Artificial Visual Systems

For the realization of retina-inspired neuromorphic visual systems which simulate basic functions of human visual systems, optoelectronic synapses capable of combining perceiving, processing, and memorizing in a single device have attracted immense interests. Here, optoelectronic synaptic transistors based on tris(2-phenylpyridine) iridium (Ir(ppy)₃) and poly(3,3-didodecylquarterthiophene) (PQT-12) heterojunction structure are presented. The organic heterojunction serves as a basis for distinctive synaptic characteristics under different wavelengths of light. Furthermore, synaptic transistor arrays are fabricated to demonstrate their optical perception efficiency and color recognition capability under multiple illuminating conditions. The wavelength-tunability of synaptic behaviors further enables the mimicry of mood-modulated visual learning and memorizing processes of humans. More significantly, the computational dynamics of neurons of synaptic outputs including associated learning and optical logic functions can be successfully demonstrated on the presented devices. This work may locate the stage for future studies on optoelectronic synaptic devices toward the implementation of artificial visual systems.