Achieving reinforcement learning in a three-active-terminal neuromorphic device based on a 2D vdW ferroelectric material

Feng Guo, Weng Fu Io, Zhaoying Dang, Ran Ding, Sin Yi Pang, Yuqian Zhao, Jianhua Hao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Currently, for most three-terminal neuromorphic devices, only the gate terminal is active. The inadequate modes and freedom of modulation in such devices greatly hinder the implementation of complex neural behaviors and brain-like thinking strategies in hardware systems. Taking advantage of the unique feature of co-existing in-plane (IP) and out-of-plane (OOP) ferroelectricity in two-dimensional (2D) ferroelectric α-In2Se3, we construct a three-active-terminal neuromorphic device where any terminal can modulate the conductance state. Based on the co-operation mode, controlling food intake as a complex nervous system-level behavior is achieved to carry out positive and negative feedback. Specifically, reinforcement learning as a brain-like thinking strategy is implemented due to the coupling between polarizations in different directions. Compared to the single modulation mode, the chance of the agent successfully obtaining the reward in the Markov decision process is increased from 68% to 82% under the co-operation mode through the coupling effect between IP and OOP ferroelectricity in 2D α-In2Se3 layers. Our work demonstrates the practicability of three-active-terminal neuromorphic devices in handling complex tasks and advances a significant step towards implementing brain-like learning strategies based on neuromorphic devices for dealing with real-world challenges.

Original languageEnglish
Pages (from-to)3719-3728
Number of pages10
JournalMaterials Horizons
Volume10
Issue number9
DOIs
Publication statusPublished - 1 Jul 2023

ASJC Scopus subject areas

  • General Materials Science
  • Mechanics of Materials
  • Process Chemistry and Technology
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Achieving reinforcement learning in a three-active-terminal neuromorphic device based on a 2D vdW ferroelectric material'. Together they form a unique fingerprint.

Cite this