Roe, Dong Gue Cheon, Sungjoon Im, Seongil Choi, Sinil Kim, Meeree Kim, Subeen Yoo, Youngjae Kim, Jeong Won Ju, Hyunsu Jeong, Sohee Cho, Jeong Ho 2026-01-15T09:30:33Z 2026-01-15T09:30:33Z 2026-01-12 2026-02 https://pubs.kist.re.kr/handle/201004/154027 Major breakthroughs in artificial intelligence software have led to significant transformations across various aspects of life. However, hardware development has lagged behind, primarily due to the inherent constraints of the von Neumann architecture. Although neuromorphic devices that utilize biomimetic parallel and analog computations have emerged, they still face limitations in reducing computational load. Therefore, this study proposes a light-voltage dual-modulating synaptic transistor that can significantly lower computational load through device-level computing. This is realized using a hybrid structure of indium-gallium-zinc-oxide and InAs quantum dots, which enable two distinct memory effects ‒ one induced by light and the other by voltage ‒ within a single device. These dual-modulation capabilities are leveraged to demonstrate traffic signal optimization using a Dueling Deep Q-Network, achieving computation performance comparable to ideal software conditions. These findings highlight the potential of the fabricated device for realizing computing systems that require high energy efficiency and computational density. English Wiley-VCH Verlag Analog Signal Summation for Reinforcement Learning via Simultaneous Light–Voltage Modulation in a Synaptic Device Article 10.1002/advs.202521293 1 Advanced Science, v.13, no.10 Advanced Science 13 10 Y scie scopus 2-s2.0-105024698681 Chemistry, Multidisciplinary Nanoscience & Nanotechnology Materials Science, Multidisciplinary Chemistry Science & Technology - Other Topics Materials Science Article; Early Access ARRAYS artificial intelligence reinforcement learning single-device computation synaptic transistor quantum dot