Optoelectronic Artificial Synaptic Device Based on Amorphous InAlZnO Films for Learning Simulations

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

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

High-Precision Attention Mechanism for Machine Vision Enabled by an Artificial Optoelectronic Memristor Synapse

The rapid advancement of artificial intelligence has facilitated the broad application of machine vision systems in diverse industries. However, these systems are often confronted with computational challenges stemming from an overwhelming amount of data. Here, we have developed a novel optoelectronic memristor synapse constructed from an ITO/Nb:SrTiO3 heterostructure, which synergistically integrates light signal detection with information processing and memory functions. Notably, we have achieved precise decoupling of the interactions between light power and wavelength at the hardware level, significantly enhancing the accuracy and efficiency of image processing. Furthermore, by incorporating an attention mechanism analogous to that of human vision, we have enabled the device to weight key information and filter out irrelevant data. Experimental results demonstrate that this attention mechanism can increase the accuracy of facial recognition by 13% while reducing the data load by 35-65%. This work is expected to advance the development of optoelectronic synapses in machine vision.

Dye Molecule-Induced Optoelectronic Synaptic Behaviors of Monolayer MoSe2

Although MoSe2-based photodetectors have achieved excellent performance, the ultrafast photoresponse has limited their application as an optoelectronic synapse. In this paper, the enhancement of the rhodamine 6G molecule on the memory time of MoSe2 is reported. It is found that the memory time of monolayer MoSe2 can be obviously enhanced after assembly with rhodamine 6G exhibiting synaptic characteristics in comparison to pristine MoSe2. Furthermore, the synaptic functions, including excitatory postsynaptic current, pair-pulse facilitation, short-term memory, long-term memory, "learning-experience" behavior, and tunable learning and forgetting process, can be achieved successfully. The distinctive energy-level structure of R6G and its excellent light absorption properties give MoSe2 access to an additional source of electrons, thus enabling the proposed MoSe2/rhodamine 6G hybrid heterostructure optoelectronic synapse to provide an efficient protocol for realizing optoelectronic neuromorphic computing and enormously facilitate the advancement of neuromorphic electronics.

Molecular Reconfiguration of Disordered Tellurium Oxide Transistors with Biomimetic Spectral Selectivity

Reconfigurable devices with field-effect transistor features and neuromorphic behaviors are promising for enhancing data processing capability and reducing power consumption in next-generation semiconductor platforms. However, commonly used 2D materials for reconfigurable devices require additional modulation terminals and suffer from complex and stringent operating rules to obtain specific functionalities. Here, a p-type disordered tellurium oxide is introduced that realizes dual-mode reconfigurability as a logic transistor and a neuromorphic device. Due to the disordered film surface, the enhanced adsorption of oxygen molecules and laser-induced desorption concurrently regulate the carrier concentration in the channel. The device exhibits high-performance p-type characteristics with a field-effect hole mobility of 10.02 cm2 V-1 s-1 and an Ion/Ioff ratio exceeding 106 in the transistor mode. As a neuromorphic device, the vision system exhibits biomimetic bee vision, explicitly responding to the blue-to-ultraviolet light. Finally, in-sensor denoising and invisible image recognition in static and dynamic scenarios are achieved.

MXene/WO3 Sensor Array with Improved SNN Algorithm for Accurate Identification of Toxic Gases

Gas sensing is pivotal in critical areas such as industrial production and food safety. This study explores the gas classification capabilities of MXene-based gas sensors. Pure V2CTx MXene and an MXene/WO3 nanocomposite were synthesized, and MXene-based gas sensors were integrated into a 2 × 2 rudimentary electronic nose array. The tests on gas sensitivity revealed that the inclusion of WO3 nanoparticles (NPs) boosted the sensor's response to 10 ppm of NO2 from 2.82 to 3.45 at room temperature. Moreover, the sensor showcased a rapid response/recovery duration of 74.5/149.0 s, excellent environmental stability, and long-term reliable sensing performance. Furthermore, we have improved the method of accurately identifying four toxic gases detected by an MXene-based sensor array using a spiking neural network (SNN) based on the memristive system. Also, the performance of this identification method revealed that the method achieved 95.83% accuracy in the identification of the four gases. Notably, the improved SNN demonstrated approximately 5% higher accuracy than the other gas recognition algorithm. These results highlight the potential of SNN as a powerful tool to accurately and reliably identify toxic gases based on the gas sensor array.

IGZO/PVP Composite Nanofiber Neuromorphic Transistors with Optoelectronic Synapse Emulation and Reservoir Computing

Nanofiber neuromorphic transistors are regarded as promising candidates for mimicking brain-like learning and advancing high-performance computing. Composite nanofibers (CNFs) typically exhibit enhanced optoelectronic and mechanical properties. In this study, indium-gallium-zinc oxide (IGZO)/polyvinylpyrrolidone (PVP) CNFs were synthesized, and the neuromorphic transistor was integrated on both rigid and flexible substrates. The learning behavior, characterized by the transition from short-term plasticity (STP) to long-term plasticity, was achieved through photoelectric stimulation of the rigid neuromorphic transistor. The nonlinear STP was simulated by the flexible neuromorphic transistor through electrical pulses, matching effectively with a reservoir computing (RC) system. Hand gesture recognition with little energy consumption (49 pJ per reservoir state) and a maximum accuracy of 92.86% has been achieved by the RC system, proving the substantial potential of the IGZO/PVP CNF neuromorphic transistor for wearable intelligent processing tasks.

Low-Cost, High-Efficiency Aluminum Zinc Oxide Synaptic Transistors: Blue LED Stimulation for Enhanced Neuromorphic Computing Applications

Neuromorphic devices are electronic devices that mimic the information processing methods of neurons and synapses, enabling them to perform multiple tasks simultaneously with low power consumption and exhibit learning ability. However, their large-scale production and efficient operation remain a challenge. Herein, we fabricated an aluminum-doped zinc oxide (AZO) synaptic transistor via solution-based spin-coating. The transistor is characterized by low production costs and high performance. It demonstrates high responsiveness under UV laser illumination. In addition, it exhibits effective synaptic behaviors under blue LED illumination, indicating high-efficiency operation. The paired-pulse facilitation (PPF) index measured from optical stimulus modulation was 179.6%, indicating strong synaptic connectivity and effective neural communication and processing. Furthermore, by modulating the blue LED light pulse frequency, an excitatory postsynaptic current gain of 4.3 was achieved, demonstrating efficient neuromorphic functionality. This study shows that AZO synaptic transistors are promising candidates for artificial synaptic devices.

Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction

The development of neuromorphic hardware capable of detecting and recognizing moving targets through an in-sensor computing strategy is considered to be an important component of the construction of edge computing systems with distributed computation. In addition to responsiveness to visible light, the implementation of neuromorphic hardware should also demonstrate the ability to sense and process nonvisible light, which is essential for tracking target object trajectories in specialized environments. In this work, we fabricated an organic synaptic transistor with a near-infrared (NIR) response by incorporating doped LaF3: Yb/Ho upconversion quantum dots (UCQDs) into the channel of a Poly3-hexylthiophene (P3HT)-based organic field effect transistor (FET), serving as charge trapping and infrared sensing sites. The obtained synaptic transistor not only replicates common synaptic behaviors when exposed to NIR illumination but also demonstrates potential applications for the dynamic trajectory recognition of animals in the dark. Compared to other monitoring technologies, P3HT transistors doped with LaF3: Yb/Ho UCQDs exhibit distinct advantages, including a NIR response, high-efficiency computing, and sensitivity, which provide an experimental foundation and a design reference for the development of next-generation intelligent dynamic image recognition systems.

Optoelectronic synapses based on a triple cation perovskite and Al/MoO3 interface for neuromorphic information processing

Optoelectronic synaptic transistors are attractive for applications in next-generation brain-like computation systems, especially for their visible-light operation and in-sensor computing capabilities. However, from a material perspective, it is difficult to build a device that meets expectations in terms of both its functions and power consumption, prompting the call for greater innovation in materials and device construction. In this study, we innovatively combined a novel perovskite carrier supply layer with an Al/MoO3 interface carrier regulatory layer to fabricate optoelectronic synaptic devices, namely Al/MoO3/CsFAMA/ITO transistors. The device could mimic a variety of biological synaptic functions and required ultralow-power consumption during operation with an ultrafast speed of >0.1 μs under an optical stimulus of about 3 fJ, which is equivalent to biological synapses. Moreover, Pavlovian conditioning and visual perception tasks could be implemented using the spike-number-dependent plasticity (SNDP) and spike-rate-dependent plasticity (SRDP). This study suggests that the proposed CsFAMA synapse with an Al/MoO3 interface has the potential for ultralow-power neuromorphic information processing.

Gradual conductance modulation by defect reorganization in amorphous oxide memristors

Amorphous oxides show great prospects in revolutionizing memristors benefiting from their abundant non-stoichiometric composition. However, an in-depth investigation of the memristive characteristics in amorphous oxides is inadequate and the resistive switching mechanism is still controversial. In this study, aiming to clearly understand the gradual conductance modulation that is deeply bound to the evolution of defects-mainly oxygen vacancies, forming-free memristors based on amorphous ZnAlSnO are fabricated, which exhibit high reproducibility with an initial low-resistance state. Pulse depression reveals the logarithmic-exponential mixed relaxation during RESET owing to the diffusion of oxygen vacancies in orthogonal directions. The remnants of conductive filaments formed through aggregation of oxygen vacancies induced by high-electric-field are identified using ex situ TEM. Especially, the conductance of the filament, including the remnant filament, is larger than that of the hopping conductive channel derived from the diffusion of oxygen vacancies. The Fermi level in the conduction band rationalizes the decay of the high resistance state. Rare oxidation-migration of Au occurs upon device failure, resulting in numerous gold nanoclusters in the functional layer. These comprehensive revelations on the reorganization of oxygen vacancies could provide original ideas for the design of memristors.

Light-Stimulated IGZO Transistors with Tunable Synaptic Plasticity Based on Casein Electrolyte Electric Double Layer for Neuromorphic Systems

In this study, optoelectronic synaptic transistors based on indium-gallium-zinc oxide (IGZO) with a casein electrolyte-based electric double layer (EDL) were examined. The casein electrolyte played a crucial role in modulating synaptic plasticity through an internal proton-induced EDL effect. Thus, important synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, and spike rate-dependent and spike number-dependent plasticity, were successfully implemented by utilizing the persistent photoconductivity effect of the IGZO channel stimulated by light. The synergy between the light stimulation and the EDL effect allowed the effective modulation of synaptic plasticity, enabling the control of memory levels, including the conversion of short-term memory to long-term memory. Furthermore, a Modified National Institute of Standards and Technology digit recognition simulation was performed using a three-layer artificial neural network model, achieving a high recognition rate of 90.5%. These results demonstrated a high application potential of the proposed optoelectronic synaptic transistors in neuromorphic visual systems.