Ultraviolet-responsive IGZO synaptic transistors for photoelectric synergistic modulation and applications

Biological visual systems perceive information by processing light signals through the regulation of synaptic weights in the nervous system. Consequently, optoelectronic synaptic devices that directly respond to light stimuli and mimic synaptic plasticity hold immense potential for constructing highly efficient neuromorphic computing systems. This paper reports an optoelectronic synaptic transistor utilizing amorphous indium gallium zinc oxide (IGZO) to serve as the active channel layer of the device. Owing to its wide bandgap, the transistor shows a strong positive photoresponse under ultraviolet (UV) light, leading to substantial photocurrent enhancement. Simultaneously, applying electrical pulses suppressed the current response of the device, achieving negative modulation of synaptic weights, which successfully simulates the dynamic balance mechanism between excitatory and inhibitory effects in biological synapses. Based on this phenomenon, this work defines optoelectronic co-modulation as an operation mode that achieves bidirectional dynamic regulation of channel conductance via ultraviolet light-induced carrier excitation and electrical pulse-induced charge trapping. Leveraging the photoelectric synergistic properties of the device, we successfully simulated key biological synaptic functions including postsynaptic current (PSC), paired-pulse depression (PPD), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). Based on this, we achieved fundamental "AND" and "OR" logic gate functions. Furthermore, when the device was applied to handwritten digit recognition tasks, the neural network achieved an accuracy of 90%.

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Publication Details

Journal
Nanotechnology
Published
2026-07-16
DOI
https://doi.org/10.1088/1361-6528/ae8bb8
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Ultraviolet-responsive IGZO synaptic transistors for photoelectric synergistic modulation and applications

Shuaicai Liu, Jie Su, Shuangqing Fan, Yuqing Cui et al.
Nanotechnology
Advanced Memory and Neural Computing
article

Ultraviolet-responsive IGZO synaptic transistors for photoelectric synergistic modulation and applications

Shuaicai Liu, Jie Su, Shuangqing Fan, Yuqing Cui, Yanqing Wang
article en

Abstract

Biological visual systems perceive information by processing light signals through the regulation of synaptic weights in the nervous system. Consequently, optoelectronic synaptic devices that directly respond to light stimuli and mimic synaptic plasticity hold immense potential for constructing highly efficient neuromorphic computing systems. This paper reports an optoelectronic synaptic transistor utilizing amorphous indium gallium zinc oxide (IGZO) to serve as the active channel layer of the device. Owing to its wide bandgap, the transistor shows a strong positive photoresponse under ultraviolet (UV) light, leading to substantial photocurrent enhancement. Simultaneously, applying electrical pulses suppressed the current response of the device, achieving negative modulation of synaptic weights, which successfully simulates the dynamic balance mechanism between excitatory and inhibitory effects in biological synapses. Based on this phenomenon, this work defines optoelectronic co-modulation as an operation mode that achieves bidirectional dynamic regulation of channel conductance via ultraviolet light-induced carrier excitation and electrical pulse-induced charge trapping. Leveraging the photoelectric synergistic properties of the device, we successfully simulated key biological synaptic functions including postsynaptic current (PSC), paired-pulse depression (PPD), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). Based on this, we achieved fundamental "AND" and "OR" logic gate functions. Furthermore, when the device was applied to handwritten digit recognition tasks, the neural network achieved an accuracy of 90%.

Nanotechnology
Qingdao University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 15%
Advanced Memory and Neural Computing
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