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Cheng S, Fan Z, Rao J, Hong L, Huang Q, Tao R, Hou Z, Qin M, Zeng M, Lu X, Zhou G, Yuan G, Gao X, Liu JM. Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing. iScience 2020; 23:101874. [PMID: 33344918 PMCID: PMC7736912 DOI: 10.1016/j.isci.2020.101874] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/29/2020] [Accepted: 11/24/2020] [Indexed: 11/19/2022] Open
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(Zr0.2Ti0.8)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.
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Affiliation(s)
- Shengliang Cheng
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Zhen Fan
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Corresponding author
| | - Jingjing Rao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Lanqing Hong
- Department of Industrial Systems Engineering and Management, National University of Singapore, 117576, Singapore
| | - Qicheng Huang
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Ruiqiang Tao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Zhipeng Hou
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Minghui Qin
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Min Zeng
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Xubing Lu
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Guofu Zhou
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Guoliang Yuan
- School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Xingsen Gao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Jun-Ming Liu
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Laboratory of Solid State Microstructures and Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
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Shaikh NF, Doye DD. An Adaptive Central Force Optimization (ACFO) and Feed Forward Back Propagation Neural Network (FFBNN) based iris recognition system. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2016. [DOI: 10.3233/ifs-151921] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Nuzhat F. Shaikh
- Department of Computer Engineering, Modern Education Society’s College of Engineering, Pune
| | - Dharmpal D. Doye
- Department of Electronics and Telecommunication, SGGSIET, Nanded
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CHEN ZHENXUE, CHANG FALIANG, LIU CHUNSHENG. CHINESE LICENSE PLATE RECOGNITION BASED ON HUMAN VISION ATTENTION MECHANISM. INT J PATTERN RECOGN 2013. [DOI: 10.1142/s0218001413500249] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
License plate recognition (LPR) is one of the most important elements affecting intelligent transportation systems. A number of LPR techniques have been proposed. Humans are good target recognition systems. In other words, humans easily recognize common objects. In this paper, the researchers present a novel method of recognizing Chinese license plates. The method is based on the Human Vision Attention Mechanism (HVAM) and uses Chinese license plates as the targets. The research consists of three stages. The first stage involved finding and identifying license plates in videos of moving vehicles. The second stage separated each license plate into the seven characters. In the third stage, the character recognizer extracted some salient features of Chinese characters and used a multi-stage classifier to recognize each character on the license plate. In the experiment locating license plates, 1176 images taken from various scenes and conditions were employed. The method failed to identify the license plates in only 27 of the images; resulting in a license plate location rate of success of 97.7%. In the experiment for identifying license characters, 1149 images were used, from which license plates had been successfully located. The method failed to identify the characters in 45 of these images giving a success rate of 96.1%. Combining the above two rates, the overall rate of success for our LPR is 93.9%.
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Affiliation(s)
- ZHENXUE CHEN
- Shandong University, School of Control Science and Engineering, Jinan 250061, P. R. China
| | - FALIANG CHANG
- Shandong University, School of Control Science and Engineering, Jinan 250061, P. R. China
| | - CHUNSHENG LIU
- Shandong University, School of Control Science and Engineering, Jinan 250061, P. R. China
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