journal article·2026-03-10Open access

Recent Progress of Oxide Semiconductor-Based Artificial Synaptic Transistors

Sein Lee, Jeong-Min Park, Wooho Ham, Junseo Lee, Gyeong-Hun Hwang, Seok Daniel Namgung, Min-Kyu Song, Jang-Yeon Kwon

Abstract

High Resolution Image Download MS PowerPoint Slide Conventional digital computing follows the von Neumann architecture, where memory and processing units are physically separated, resulting in limited throughput caused by data transfer bottlenecks, and high power consumption. In contrast, the human brain integrates computation and memory at synapses, enabling highly parallel and energy-efficient processing with only ∼20 W. Neuromorphic computing aims to replicate this efficiency by merging storage and computation within hardware. Oxide semiconductors (OSs) have emerged as promising candidates for synaptic transistors due to their favorable electrical, optical, and structural characteristics, as well as compatibility with low-temperature and large-area processing. This review outlines the fundamental principles of biological synapses and synaptic plasticity metrics, examines recent developments in OS-based synaptic transistors, and discusses prospects and challenges in implementing OS synaptic devices for neuromorphic hardware and artificial intelligence applications.
Source: openalex_inverted_index · Quality: baseline

Research context

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

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

Affiliations

School of Integrated Technology

Yonsei University

BK21 Graduate Program in Intelligent Semiconductor Technology

School of Electrical Engineering

Korea University

School of Electrical and Electronics Engineering

Chung-Ang University

Funding

National Research Foundation of Korea

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