Optoelectronic Synapse Research Articles

An Optoelectronic Synapse Based on Two-Dimensional Violet Phosphorus Heterostructure.

Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light-matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light-to-dark ratio of 106 , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60dB, 128 (7-bit) distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. Thanks to the excellent DR and multi-states, high-precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high-precision neuromorphic computing.

Organic Optoelectronic Synapses for Sound Perception

HighlightsThe organic optoelectronic synapse achieves unprecedented sound perception on volume, tone and timbre simultaneously.The quantitative relationship between the interfacial layers and synaptic performances is clarified.

Carbon Nanotube Optoelectronic Synapse Transistor Arrays with Ultra‐Low Power Consumption for Stretchable Neuromorphic Vision Systems

AbstractHigh‐performance stretchable optoelectronic synaptic transistor arrays are key units for constructing and mimicking simulated neuromorphic vision systems. In this study, ultra‐low power consumption and low‐operation‐voltage stretchable all‐carbon optoelectronic synaptic thin film transistors (TFTs) using sorted semiconducting single‐walled carbon nanotubes (sc‐SWCNTs) modified with CdSe/ZnS quantum dots as active layers on ionic liquid‐based composite elastomer substrates are first reported. The resulting stretchable TFT devices show enhancement‐mode characteristics with excellent electrical properties (such as the record on/off ratios up to 105, negligible hysteresis, and small subthreshold swing), excellent mechanical tensile properties (such as the only 12.4% and 6.4% degradations of the carrier mobility after 20% vertical and horizontal strain stretching), and optoelectronic synaptic plasticity (for the recognition of Morse codes) with ultra‐low power consumptions (15.38 aJ) at the operating voltage from −1 to 0.2 V. At the same time, the designed nonvolatile conductance of the stretchable SWCNT optoelectronic synapse thin film transistors (SSOSTFTs) stimulated by UV light and the bending angle are first used to simulate stretchable neuromorphic vision systems (including the functions of the crystalline lens and optic cone cells as bionic eyes) for detecting the atmospheric environment with a record accuracy of 95.1% as a bionic eye.

Metal-Oxide Heterojunction Optoelectronic Synapse and Multilevel Memory Devices Enabled by Broad Spectral Photocarrier Modulation.

Broad spectral response and high photoelectric conversion efficiency are key milestones for realizing multifunctional, low-power optoelectronic devices such as artificial synapse and reconfigurable memory devices. Nevertheless, the wide bandgap and narrow spectral response of metal-oxide semiconductors are problematic for efficient metal-oxide optoelectronic devices such as photonic synapse and optical memory devices. Here, a simple titania (TiO2 )/indium-gallium-zinc-oxide (IGZO) heterojunction structure is proposed for efficient multifunctional optoelectronic devices, enabling widen spectral response range and high photoresponsivity. By overlaying a TiO2 film on IGZO, the light absorption range extends to red light, along with enhanced photoresponsivity in the full visible light region. By implementing the TiO2 /IGZO heterojunction structure, various synaptic behaviors are successfully emulated such as short-term memory/long-term memory and paired pulse facilitation. Also, the TiO2 /IGZO synaptic transistor exhibits a recognition rate up to 90.3% in recognizing handwritten digit images. Moreover, by regulating the photocarrier dynamics and retention behavior using gate-bias modulation, a reconfigurable multilevel (≥8 states) memory is demonstrated using visible light.

Self‐Powered and Humidity‐Modulable Optoelectronic Synapse

AbstractWith the advantage of high bandwidth, low power consumption, and low crosstalk from optical signal stimulation, optoelectronic synapses are attracting more and more attention. Herein, a self‐powered two‐terminal optoelectronic synapse based on a lead‐free Cs2AgBiBr6 perovskite/P(VDF‐TrFE)/pentacene heterostructure is designed, which shows bidirectional responses to optical stimuli with different wavelengths (λ = 445 nm/660 nm). This feature enables the device to emulate both excitatory and inhibitory synaptic behaviors in a fully optical pathway. Besides optical stimulus, humidity is used as an additional stimulus to modulate the synaptic performance of the device under 445 nm illumination. The device is successfully applied to stimulate the behavior of the weather tree forecast and visual learning and forgetting processes under different mood states via humidity regulation.

Artificial Optoelectronic Synapse with Nanolayered GaN/AlN Periodic Structure for Neuromorphic Computing

Artificial Optoelectronic Synapse with Nanolayered GaN/AlN Periodic Structure for Neuromorphic Computing

Bioinspired activation of silent synapses in layered materials for extensible neuromorphic computing

Activation of silent synapses is of great significance for the extension of neural plasticity related to learning and memory. Inspired by the activation of silent synapses via receptor insertion in neural synapses, we propose an efficient method for activating artificial synapses through the intercalation of Sn in layered α-MoO3. Sn intercalation is capable of switching on the response of layered α-MoO3 to the stimuli of visible and near infrared light by decreasing the bandgap. This mimics the receptor insertion process in silent neural synapses. The Sn-intercalated MoO3 (Sn-MoO3) exhibits persistent photoconductivity due to the donor impurity induced by Sn intercalation. This enables the two-terminal Sn-MoO3 device promising optoelectronic synapse with an ultrahigh paired pulse facilitation (PPF) up to 199.5%. On-demand activation and tunable synaptic plasticity endow the device great potentials for extensible neuromorphic computing. Superior performance of the extensible artificial neural network (ANN) based on the Sn-MoO3 synapses are demonstrated in pattern recognition. Impressively, the recognition accuracy increases from 89.7% to 94.8% by activating more nodes into the ANN. This is consistent with the recognition process of physical neural network during brain development. The intercalation engineering of MoO3 may provide inspirations for the design of high-performance neuromorphic computing architectures.

Optoelectronic artificial synapse based on Si1-xSnx alloyed film

The emergence of big data has increased the requirements for computing and processing systems. Memristors–electrical components that simulate the synapses and neurons of the human brain—have been proposed to help meet these requirements. Optoelectronic synapses are excited by optical signals and have the advantages of low crosstalk, high bandwidth, and low power consumption. Herein, a MOS structure optoelectronic synapse device based on an amorphous silicon (a-Si1-xSnx; x = 0.0457) alloy thin film and an AlOy thin film was fabricated via oxygen vacancy energy band and heterostructure design. Significantly, the device exhibited ultralong decay time of 3576.07 s and a broadband response from the visible to the near-infrared-I region. A series of important synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, short-term memory to long-term memory transition, and learning experience behaviour, was successfully simulated on the device. We simulated a 3 × 3 optoelectronic synapse array, which can simulate the visual memory of images observed by the human eye. The effect of ‘pupil’ scaling on image memory was simulated by adjusting the read voltage. Based on these advantages, the optoelectronic synaptic device developed in this study has significant potential for application in the new generation of artificial synaptic systems.

An artificial optoelectronic synapse based on MoO x film

Artificial optoelectronic synapses have the advantages of large bandwidth, low power consumption and low crosstalk, and are considered to be the basic building blocks of neuromorphic computing. In this paper, a two-terminal optoelectronic synaptic device with ITO-MoO x -Pt structure is prepared by magnetron sputtering. The performance of resistive switching (RS) and the photo plastic properties of the device are analyzed and demonstrated. Electrical characterization tests show that the device has a resistive HRS/LRS ratio of about 90, stable endurance, and retention characteristics of more than 104 s (85 °C). The physical mechanism of the device is elucidated by a conducting filament composed of oxygen vacancies. Furthermore, the function of various synaptic neural morphologies is successfully mimicked using UV light as the stimulation source. Including short-term/long-term memory, paired-pulse facilitation, the transition from short-term to long-term memory, and ‘learning-experience’ behavior. Integrated optical sensing and electronic data storage devices have great potential for future artificial intelligence, which will facilitate the rapid development of retina-like visual sensors and low-power neuromorphic systems.

CsPbBr3/graphene nanowall artificial optoelectronic synapses for controllable perceptual learning

The rapid development of neuromorphic computing has stimulated extensive research interest in artificial synapses. Optoelectronic artificial synapses using laser beams as stimulus signals have the advantages of broadband, fast response, and low crosstalk. However, the optoelectronic synapses usually exhibit short memory duration due to the low lifetime of the photo-generated carriers. It greatly limits the mimicking of human perceptual learning, which is a common phenomenon in sensory interactions with the environment and practices of specific sensory tasks. Herein, a heterostructure optoelectronic synapse based on graphene nanowalls and CsPbBr3 quantum dots was fabricated. The graphene/CsPbBr3 heterojunction and the natural middle energy band in graphene nanowalls extend the carrier lifetime. Therefore, a long half-life period of photocurrent decay - 35.59 s has been achieved. Moreover, the long-term optoelectronic response can be controlled by the adjustment of numbers, powers, wavelengths, and frequencies of the laser pulses. Next, an artificial neural network consisting of a 28 × 28 synaptic array was established. It can be used to mimic a typical characteristic of human perceptual learning that the ability of sensory systems is enhanced through a learning experience. The learning behavior of image recognition can be tuned based on the photocurrent response control. The accuracy of image recognition keeps above 80% even under a low-frequency learning process. We also verify that less time is required to regain the lost sensory ability that has been previously learned. This approach paves the way toward high-performance intelligent devices with controllable learning of visual perception.

Memristors based on NdNiO3 nanocrystals film as sensory neurons for neuromorphic computing.

By mimicking the behavior of the human brain, artificial neural systems offer the possibility to further improve computing efficiency and solve the von Neumann bottleneck. In particular, neural systems with perceptual capability expand the application field and lay a good foundation for the construction of perceptual storage and computational systems. However, research on neurons with perceptual functions is still relatively scarce, with most works focusing on optoelectronic synapses. The neuron is important for neuromorphic computing systems because neurons output excitatory or inhibitory stimuli to regulate the weight of synapses. Therefore, the construction of sensory neurons is crucial to expand the application range of brain-like neural computing. Here, an artificial sensory neuron is proposed, which is constructed using a photosensitive bipolar threshold switching memristor based on NdNiO3 (NNO) nanocrystals. These metallic phase nanocrystals can not only enhance the local electric field, but also act as a reservoir for defects (VoS) to guide the growth of conductive filaments and stabilize the performance of the device. They present stable bipolar threshold switching behavior with a low 120 nW set power, and the operating voltages decreased in light due to photocarrier action. A leaky integrate firing (LIF) neuron has been realized, which achieved key biological neuron functions, such as all-or-nothing spiking, threshold-driven firing, refractory period, and spiking frequency modulation. The LIF neurons receiving optical inputs have the properties of an artificial sensory neuron. It could regulate the spiking output frequency at different light densities, which could be used for a ship approaching a port. This work provides a promising hardware implementation towards constructing high-performance artificial intelligence to assist ships at night in a sensory system.

CuI: An Attractive Material for Constructing Transparent and Metal-electrode-free Optoelectronic Synapse

Fabricating devices with functions of biosynapses is one of attractive highlights in the field of artificial intelligence. Optoelectronic synapses are believed to be an important cornerstone of neuromorphic intelligent systems because they can directly process visual input signals. In this work, a transparent and metal-electrode-free optoelectronic synapse with a structure of CuI/ITO is prepared, in which the CuI layer acts as both an active layer and electrode. Some basic functions of biosynapses are realized by this device, including paired-pulse facilitation (PPF), spike-number-dependent plasticity (SNDP), spike-width-dependent plasticity (SWDP), and ″learning-experience″ behavior. Furthermore, this device exhibits the capability of detecting and processing the integrated optical information, and image recognition and memory. This work illustrates the great application potential of CuI in transparent optoelectronic synapse due to its unique properties.

Optoelectronic synaptic device based on ZnO/HfOx heterojunction for high-performance neuromorphic vision system

Optoelectronic synapses are considered to be important cornerstones in the construction of neuromorphic computing systems because of their low power consumption, high operating speeds, and high scalability. In this work, we demonstrate an optoelectronic synaptic device based on a ZnO/HfOx heterojunction in which optical potentiation/electrical depression behaviors and nonvolatile high current state can be implemented. The heterojunction device exhibits conductance evolution with high linearity. The excellent optoelectronic memristive behavior of the device can be attributed to the interface barrier between ZnO and HfOx, which hinders the recombination of photo-excited electron–hole pairs to increase the carrier lifetime, and realizes the nonvolatile high current state. More importantly, the artificial vision system based on optoelectronic synaptic devices can achieved a high recognition accuracy of 96.1%. Our work provides a feasible pathway toward the development of optoelectronic synaptic devices for use in high-performance neuromorphic vision systems.

Enhanced broadband Si-based optoelectronic synapse for artificial visual applications

Broadband sensibility and efficient image recognition are crucial for perception and memory storage in remote sensing, reconnaissance and environmental monitoring inspired by the human cognitive system. Although the amorphous silicon (a-Si) thin film is compatible to the complementary-metal–oxidesemiconductor (CMOS) technology, implementing in-sensor broadband recognition and memory based on intrinsic a-Si is still a challenge due to the band gap restrictions and the limited spectrum sensing ability. Herein, through doping of Ru (Ruthenium), optoelectronic devices based on the band engineered a-Si:Ru films have been fabricated with a silicon-oxide heterostructure, demonstrating comprehensive broadband image recognition, memory and convolutional processing simultaneously. Upon regulating the trapping and releasing of the photogenerated electron-hole pairs, the devices could mimic various synaptic functions such as excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), transition from short- to long-term potentiation, lower switching energy and adjustable photocurrent analog states. Moreover, the in-sensor broadband convolutional processing has enhanced the high recognition accuracy for multi-band visuals, indicating a significant opportunity for future neuromorphic sensory applications and advanced robot visual systems.

Artificial optoelectronic synapse based on epitaxial Ba0.6Sr0.4TiO3 thin films memristor for neuromorphic computing and image recognition

Electronic synaptic devices with photoelectric sensing function are becoming increasingly important in the development of neuromorphic computing system. Here, we present a photoelectrical synaptic system based on high-quality epitaxial Ba0.6Sr0.4TiO3 (BST) films in which the resistance ramp characteristic of the device provides the possibility to simulate synaptic behavior. The memristor with the Pt/BST/Nb:SrTiO3 structure exhibits reliable I–V characteristics and adjustable resistance modulation characteristics. The device can faithfully demonstrate synaptic functions, such as potentiation and depression, spike time-dependent plasticity, and paired pulse facilitation, and the recognition accuracy of handwritten digits was as high as 92.2%. Interestingly, the functions of visual perception, visual memory, and color recognition of the human eyes have also been realized based on the device. This work will provide a strong candidate for the neuromorphic computing hardware system of photoelectric synaptic devices.

Fiber-shaped artificial optoelectronic synapses for wearable visual-memory systems

<h2>Summary</h2> Biomimetic visual systems that are enabled with receptor-like exquisite visual sensing and synapse-like learning/memory functions offer exciting opportunities for futuristically wearable electronics. However, attempts to develop wearable visual systems remain limited, and present-day challenges in artificial visual systems are unsatisfactory geometric compatibility with irregular surfaces and poor breathability. Here, a fiber-shaped artificial optoelectronic synapse (FAOS) with double-twisted architecture consisting of high-density TiO_2-x and MoS₂ arrays is successfully constructed. Significantly, the proposed FAOS can emulate both electrical and light-induced synaptic functions such as excitatory postsynaptic current, short/long-term plasticity, and "learning-forgetting-relearning" behavior. The FAOS also exhibits excellent flexibility without obvious degradation of optoelectronic synaptic performance during bending. As a proof-of-concept demonstration, multiple FAOSs are woven into commercial textiles to form optoelectronic synapse arrays for perception and memory of image information. This work provides guidance on rational design for advanced FAOSs and accelerates practical application in wearable electronic textiles.

In‐Sensor Reservoir Computing Based on Optoelectronic Synapse

Conventional machine vision systems suffer from great data latency and energy consumption in cognitive tasks due to the separated vision sensors, memory units, and processors. In‐sensor computing based on optoelectronic synapses allows efficient computation by directly sensing and processing optical signals. Herein, an optoelectronic synapse based on Au/ZnO:N/IGZO/TiN structure is proposed. It shows uniform optical SET and electrical RESET behaviors, with various light‐tunable plasticity. Furthermore, a 4‐bit reservoir is experimentally implemented on the device, which is ideal to construct in‐sensor reservoir computing (RC) system. By converting spatiotemporal optical signals to higher dimensional feature space, in‐sensor RC has a great advantage in processing sequential visual information. Simulation results demonstrate that the in‐sensor RC system based on the proposed synapse achieves a considerable recognition accuracy (90.45%) for the MNIST dataset with very limited 36‐30‐10 perceptron network, and a 97.14% accuracy for human action classification from sequential vision data based on the Weizmann dataset. This work proves the low training cost and great efficiency for processing spatiotemporal and sequential optical signals, which may pave a new way for future machine vision applications.

Nanostructured CuAlO2@ZnO optoelectronic device for artificial synaptic applications

Developing a neuromorphic system beyond conventional von Neumann’s architecture is necessary for future artificial intelligence applications and data-intensive computation. Optoelectronic synapses, like biological synapses, are extremely comparable in structure and operating mechanism and can learn directly from the light stimulus, making them a suitable technology for brain-inspired neuromorphic computing. Here, by constructing a core–shell structure of CuAlO2@ZnO oxide heterojunction, we have successfully fabricated the optoelectronic synaptic devices structured as ITO/CuAlO2@ZnO/ITO-glass that can be effectively motivated by light signal at visual light range. Moreover, there is an ultra-high gain of photocurrent generated in the device, resulting in an excitatory postsynaptic current (EPSC) of µA level even at a lower light intensity. In addition, the device could mimic multiple synaptic plasticities at the micro-level such as EPSC, paired-pulse facilitation (PPF), short-term plasticity (STP), spike-number-dependent plasticity (SNDP), and “advanced behaviors” of human-like innate learning and memory functions. The results presented here may serve as a new benchmark for the use of optoelectronic synaptic devices in artificial intelligence.

Wavelength and Polarization Sensitive Synaptic Phototransistor Based on Organic n-type Semiconductor/Supramolecular J-Aggregate Heterostructure.

Human retina- and brain-inspired optoelectronic synapses, which integrate light detection and signal memory functions for data processing, have significant interest because of their potential applications for artificial vision technology. In nature, many animals such as mantis shrimp use polarized light information as well as scalar information including wavelength and intensity; however, a spectropolarimetric organic optoelectronic synapse has been seldom investigated. Herein, we report an organic synaptic phototransistor, consisting of a charge trapping liquid-crystalline perylene bisimide J-aggregate and a charge transporting crystalline dichlorinated naphthalene diimide, that can detect both wavelength and polarization information. The device shows persistent positive and negative photocurrents under low and high voltage conditions, respectively. Furthermore, the aligned organic heterostructure in the thin-film enables linearly polarized light to be absorbed with a dichroic ratio of 1.4 and 3.7 under transverse polarized blue and red light illumination, respectively. These features allow polarized light sensitive postsynaptic functions in the device. Consequently, a simple polarization imaging sensor array is successfully demonstrated using photonic synapses, which suggests that a supramolecular material is an important candidate for the development of spectropolarimetric neuromorphic vision systems.

An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computing

An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computing