Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing

Shengliang Cheng; Zhen Fan; Jingjing Rao; Lanqing Hong; Qicheng Huang; Ruiqiang Tao; Zhipeng Hou; Minghui Qin; Min Zeng; Xubing Lu; Guofu Zhou; Guoliang Yuan; Xingsen Gao; Jun-Ming Liu

doi:10.1016/j.isci.2020.101874

Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing

iScience. 2020 Nov 30;23(12):101874. doi: 10.1016/j.isci.2020.101874. eCollection 2020 Dec 18.

Authors

Shengliang Cheng^{1

2}, Zhen Fan^{1

2}, Jingjing Rao¹, Lanqing Hong³, Qicheng Huang¹, Ruiqiang Tao¹, Zhipeng Hou¹, Minghui Qin¹, Min Zeng¹, Xubing Lu¹, Guofu Zhou^{2

4}, Guoliang Yuan⁵, Xingsen Gao¹, Jun-Ming Liu^{1

6}

Affiliations

¹ Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
² Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
³ Department of Industrial Systems Engineering and Management, National University of Singapore, 117576, Singapore.
⁴ National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China.
⁵ School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
⁶ Laboratory of Solid State Microstructures and Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.

Abstract

Ferroelectric synapses using polarization switching (a purely electronic switching process) to induce analog conductance change have attracted considerable interest. Here, we propose ferroelectric photovoltaic (FePV) synapses that use polarization-controlled photocurrent as the readout and thus have no limitations on the forms and thicknesses of the constituent ferroelectric and electrode materials. This not only makes FePV synapses easy to fabricate but also reduces the depolarization effect and hence enhances the polarization controllability. As a proof-of-concept implementation, a Pt/Pb(Zr_0.2Ti_0.8)O₃/LaNiO₃ FePV synapse is facilely grown on a silicon substrate, which demonstrates continuous photovoltaic response modulation with good controllability (small nonlinearity and write noise) enabled by gradual polarization switching. Using photovoltaic response as synaptic weight, this device exhibits versatile synaptic functions including long-term potentiation/depression and spike-timing-dependent plasticity. A simulated FePV synapse-based neural network achieves high accuracies (>93%) for image recognition. This study paves a new way toward highly controllable and silicon-compatible synapses for neuromorphic computing.

Keywords: Circuit Systems; Devices; Electrical Engineering; Materials Science; Semiconductor Manufacturing.