Photo‐Assisted Ferroelectric Domain Control for α‐In2Se3 Artificial Synapses Inspired by Spontaneous Internal Electric Fields

Author:

Kang Seok‐Ju12,Jung Wonzee13,Gwon Oh Hun1,Kim Han Seul4,Byun Hye Ryung12,Kim Jong Yun12,Jang Seo Gyun1,Shin BeomKyu1,Kwon Ojun4,Cho Byungjin4,Yim Kanghoon3,Yu Young‐Jun12ORCID

Affiliation:

1. Department of Physics Chungnam National University 99 Daehak‐ro, Yuseong‐gu Daejeon 34134 Republic of Korea

2. Institute of Quantum Systems Chungnam National University 99 Daehak‐ro, Yuseong‐gu Daejeon 34134 Republic of Korea

3. Energy AI & Computational Science Laboratory Korea Institute of Energy Research Daejeon 34129 Republic of Korea

4. Department of Advanced Material Engineering Chungbuk National University Chungdae‐ro 1, Seowon‐Gu Cheongju Chungbuk 28644 Republic of Korea

Abstract

Abstractα‐In2Se3 semiconductor crystals realize artificial synapses by tuning in‐plane and out‐of‐plane ferroelectricity with diverse avenues of electrical and optical pulses. While the electrically induced ferroelectricity of α‐In2Se3 shows synaptic memory operation, the optically assisted synaptic plasticity in α‐In2Se3 has also been preferred for polarization flipping enhancement. Here, the synaptic memory behavior of α‐In2Se3 is demonstrated by applying electrical gate voltages under white light. As a result, the induced internal electric field is identified at a polarization flipped conductance channel in α‐In2Se3/hexagonal boron nitride (hBN) heterostructure ferroelectric field effect transistors (FeFETs) under white light and discuss the contribution of this built‐in electric field on synapse characterization. The biased dipoles in α‐In2Se3 toward potentiation polarization direction by an enhanced internal built‐in electric field under illumination of white light lead to improvement of linearity for long‐term depression curves with proper electric spikes. Consequently, upon applying appropriate electric spikes to α‐In2Se3/hBN FeFETs with illuminating white light, the recognition accuracy values significantly through the artificial learning simulation is elevated for discriminating hand‐written digit number images.

Funder

National Research Foundation of Korea

Korea Institute for Advancement of Technology

Korea Institute of Energy Research

Publisher

Wiley

Subject

Biomaterials,Biotechnology,General Materials Science,General Chemistry

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