神经形态工程学
记忆电阻器
MNIST数据库
材料科学
计算机科学
概率逻辑
卷积神经网络
人工神经网络
电子工程
随机计算
尖峰神经网络
人工智能
光电子学
光开关
突触重量
兴奋性突触后电位
调制(音乐)
突触后电流
突触后电位
计算机体系结构
电压
光学计算
玻尔兹曼机
电阻随机存取存储器
斑点图案
深度学习
光子学
等离子体子
作者
Hyun-Su Jang,Seohyeon Ju,Youngseo Lee,Minsu Ko,C. S. Park,Min-Hwi Kim,S Kim
标识
DOI:10.1021/acsami.5c24346
摘要
Next-generation neuromorphic systems require hardware platforms that seamlessly integrate sensing, memory, and computation. Here, we present a light-programmable optoelectronic memristor based on an ITO/IGZO/W structure, capable of emulating a broad spectrum of synaptic and neuronal functions under purely optical stimulation through the transparent ITO top electrode. The device exhibits short-term plasticity, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and spike-dependent learning behaviors (SADP, SWDP, SNDP). It also replicates nociceptive responses such as threshold activation, no adaptation, relaxation, and sensitization. Pavlovian associative learning is demonstrated using optical stimuli, showing acquisition, extinction, and recovery behaviors driven by persistent photoconductivity. Furthermore, a 4-bit optical pulse-driven reservoir computing architecture achieves 97.005% MNIST classification accuracy through a convolutional neural network readout. A light-induced stochastic activation function, extracted from threshold-switching behavior, is applied in a Restricted Boltzmann Machine to model probabilistic neurons, reaching 96.35% image reconstruction accuracy. Postforming optical modulation enables light-intensity-dependent trap/detrap dynamics and fine-tuning of the conductive filament. These results highlight the proposed IGZO-based optoelectronic memristor as a versatile and energy-efficient platform for multifunctional neuromorphic computing, combining sensory, deterministic, and probabilistic intelligence in a single reconfigurable device.
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