Literature DB >> 30105324

Ultra-low power Hf0.5Zr0.5O2 based ferroelectric tunnel junction synapses for hardware neural network applications.

Lin Chen1, Tian-Yu Wang, Ya-Wei Dai, Ming-Yang Cha, Hao Zhu, Qing-Qing Sun, Shi-Jin Ding, Peng Zhou, Leon Chua, David Wei Zhang.   

Abstract

Brain-inspired neuromorphic computing has shown great promise beyond the conventional Boolean logic. Nanoscale electronic synapses, which have stringent demands for integration density, dynamic range, energy consumption, etc., are key computational elements of the brain-inspired neuromorphic system. Ferroelectric tunneling junctions have been shown to be ideal candidates to realize the functions of electronic synapses due to their ultra-low energy consumption and the nature of ferroelectric tunneling. Here, we report a new electronic synapse based on a three-dimensional vertical Hf0.5Zr0.5O2-based ferroelectric tunneling junction that meets the full functions of biological synapses. The fabricated three-dimensional vertical ferroelectric tunneling junction synapse (FTJS) exhibits high integration density and excellent performances, such as analog-like conductance transition under a training scheme, low energy consumption of synaptic weight update (1.8 pJ per spike) and good repeatability (>103 cycles). In addition, the implementation of pattern training in hardware with strong tolerance to input faults and variations is also illustrated in the 3D vertical FTJS array. Furthermore, pattern classification and recognition are achieved, and these results demonstrate that the Hf0.5Zr0.5O2-based FTJS has high potential to be an ideal electronic component for neuromorphic system applications.

Year:  2018        PMID: 30105324     DOI: 10.1039/c8nr04734k

Source DB:  PubMed          Journal:  Nanoscale        ISSN: 2040-3364            Impact factor:   7.790


  7 in total

Review 1.  Characterization and Application of PVDF and Its Copolymer Films Prepared by Spin-Coating and Langmuir-Blodgett Method.

Authors:  Zerun Yin; Bobo Tian; Qiuxiang Zhu; Chungang Duan
Journal:  Polymers (Basel)       Date:  2019-12-08       Impact factor: 4.329

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

Authors:  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
Journal:  iScience       Date:  2020-11-30

Review 3.  Competing memristors for brain-inspired computing.

Authors:  Seung Ju Kim; Sang Bum Kim; Ho Won Jang
Journal:  iScience       Date:  2020-12-03

4.  Atomic-scale fatigue mechanism of ferroelectric tunnel junctions.

Authors:  Yihao Yang; Ming Wu; Xingwen Zheng; Chunyan Zheng; Jibo Xu; Zhiyu Xu; Xiaofei Li; Xiaojie Lou; Di Wu; Xiaohui Liu; Stephen J Pennycook; Zheng Wen
Journal:  Sci Adv       Date:  2021-11-24       Impact factor: 14.136

5.  An artificial synaptic transistor using an α-In2Se3 van der Waals ferroelectric channel for pattern recognition.

Authors:  Neha Mohta; Ankit Rao; Nayana Remesh; R Muralidharan; Digbijoy N Nath
Journal:  RSC Adv       Date:  2021-11-17       Impact factor: 4.036

6.  Progressive and Stable Synaptic Plasticity with Femtojoule Energy Consumption by the Interface Engineering of a Metal/Ferroelectric/Semiconductor.

Authors:  Sohwi Kim; Chansoo Yoon; Gwangtaek Oh; Young Woong Lee; Minjeong Shin; Eun Hee Kee; Bae Ho Park; Ji Hye Lee; Sanghyun Park; Bo Soo Kang; Young Heon Kim
Journal:  Adv Sci (Weinh)       Date:  2022-05-24       Impact factor: 17.521

7.  Ferroelectric Tunneling Junctions Based on Aluminum Oxide/ Zirconium-Doped Hafnium Oxide for Neuromorphic Computing.

Authors:  Hojoon Ryu; Haonan Wu; Fubo Rao; Wenjuan Zhu
Journal:  Sci Rep       Date:  2019-12-31       Impact factor: 4.379

  7 in total

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