Advanced Optoelectronic Devices for Neuromorphic Analog Based on Low‐Dimensional Semiconductors

Abstract

Neuromorphic systems can parallelize the perception and computation of information, making it possible to break through the von Neumann bottleneck. Neuromorphic engineering has been developed over a long period of time based on Hebbian learning rules. The optoelectronic neuromorphic analog device combines the advantages of electricity and optics, and can simulate the biological visual system, which has a very strong development potential. Low‐dimensional materials play a very important role in the field of optoelectronic neuromorphic devices due to their flexible bandgap tuning mechanism and strong light‐matter coupling efficiency. This review introduces the basic synaptic plasticity of neuromorphic devices. According to the different number of terminals, two‐terminal neuromorphic memristors, three‐terminal neuromorphic transistors and artificial visual system are introduced from the aspects of the action mechanism and device structure. Finally, the development prospect of optoelectronic neuromorphic analog devices based on low‐dimensional materials is prospected.

Weak Light‐Stimulated Synaptic Transistors Based on MoS2/Organic Semiconductor Heterojunction for Neuromorphic Computing

“…After the removal of light, the photogenerated holes and electrons cannot recombine immediately owing to part of the photogenerated electrons captured inside the charge‐trapping sites at the heterojunction interface, bringing about a slow decay in the EPSC. [ 45 ] Moreover, the EPSC could be efficiently modulated by the width, frequency, and number of optical pulses (Figure S5, Supporting Information). Regulations in synaptic weights are described as synaptic plasticity, which is fundamental and essential for various cognitive processes in human brains.…”

Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality

“…In brain cognitive behaviors, the SRDP, also termed spike-driven rate-based plasticity, is a crucial synaptic learning mechanism since the transfer of information in biological neural networks is directly connected to the average action potential firing rate. [25,26] To deeply investigate the SRDP learning rule and verify the effect of presynaptic spiking rate, consecutive spike pulse trains with a fixed amplitude (200 mV) and width (100 ms) were applied to the synaptic device with the following intervals of 10, 50, 100, 200, 500, and 1000 ms. Initially, the devices are in an HRS state during the measurement to minimize current and reduce power consumption.…”

Biological UV Photoreceptors‐Inspired Sn‐Doped Polycrystalline β‐Ga2O3 Optoelectronic Synaptic Phototransistor for Neuromorphic Computing

“…[8][9][10][11] More recently, complex biological activities and perceptions, such as memory and forgetting processes, classically conditioned learning experiments, and artificial sensory functions, have been emulated using neuromorphic artificial synaptic devices, including memristors and FETs. [8,9] The biological nervous system represents associative learning through sensory systems (vision, hearing, touch, taste, smell, and balance). [12] To comprehensively mimic brain functions, artificial synaptic devices that integrate sensing and processing functions must be developed.…”