journal article·2026-02-18

Light-Programmable IGZO Optoelectronic Memristor for Multifunctional Neuromorphic Computing

Heeseong Jang, Seohyeon Ju, Youngseo Lee, Minsu Ko, Chanmin Park, Min-Hwi Kim, Sungjun Kim

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Abstract

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.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Ferroelectric and Negative Capacitance Devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Division of Electronics and Electrical Engineering

Dongguk University

School of Electrical and Electronics Engineering and Department of Intelligent Semiconductor Engineering

Chung-Ang University

Funding

National Research Foundation of Korea

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