Analog Signal Summation for Reinforcement Learning via Simultaneous Light–Voltage Modulation in a Synaptic Device

Abstract

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.