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

## TL;DR

A new synaptic device uses light and voltage to improve reinforcement learning efficiency, potentially leading to more energy-efficient computing systems.

## Contribution

The study introduces a light-voltage dual-modulating synaptic transistor that enables analog signal summation for reinforcement learning at the device level.

## Key findings

- The hybrid IGZO/InAs quantum dot structure enables dual modulation via light and voltage in a single device.
- The device successfully demonstrated traffic signal optimization using a Dueling Deep Q-Network.
- The proposed transistor reduces computational load, energy consumption, and complexity by eliminating peripheral circuits.

## 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.

To overcome limitations of conventional AI hardware, a light‐voltage dual‐modulating synaptic (LVDS) transistor using an IGZO/InAs quantum dot hybrid structure is proposed. LVDS transistor enables analog summation for Dueling Deep Q‐Networks by independently modulating memory via optical and electrical stimuli. This device‐level computing potentially reduces computational loads, energy, and complexity by eliminating peripheral circuits.