A Reliable All‐2D Materials Artificial Synapse for High Energy‐Efficient Neuromorphic Computing
Year: 2021
Type: article
Abstract: High‐performance artificial synaptic devices are indispensable for developing neuromorphic computing systems with high energy efficiency. However, the reliability and variability issues of existing devices such as nonlinear and asymmetric weight update are the major hurdles in their practical applications for energy‐efficient neuromorphic computing. Here, a two‐terminal floating‐gate memory (2TFGM) based artificial synapse built from all‐2D van der Waals materials is reported. The 2TFGM synaptic device exhibits excellent linear and symmetric weight update characteristics with high reliability and tunability. In particular, the high linearity and symmetric synaptic weight realized by simple programming with identical pulses can eliminate the additional latency and power consumption caused by the peripheral circuit design and achieve an ultralow energy consumption for the synapses in the neural network implementation. A large number of states up to ≈3000, high switching speed of 40 ns and low energy consumption of 18 fJ for a single pulse have been demonstrated experimentally. A high classification accuracy up to 97.7% (close to the software baseline of 98%) has been achieved in the Modified National Institute of Standards and Technology (MNIST) simulations based on the experimental data. These results demonstrate the potential of all‐2D 2TFGM for high‐speed and low‐power neuromorphic computing.
Source: Advanced Functional Materials
Authors: Jian Tang, Congli He, Jianshi Tang, Kun Yue, Qingtian Zhang, Yizhou Liu, Qinqin Wang, Shuopei Wang, Na Li, Cheng Shen, Yanchong Zhao, Jieying Liu, Jiahao Yuan, Zheng Wei, Jiawei Li, Kenji Watanabe, Takashi Taniguchi, Dashan Shang, Shouguo Wang, Wei Yang, Rong Yang, Dongxia Shi, Guangyu Zhang
Institutions: Chinese Academy of Sciences, Institute of Physics, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing Normal University, Institute of Microelectronics, University of Southern California, RIKEN Center for Emergent Matter Science, University of Chinese Academy of Sciences, Songshan Lake Materials Laboratory, National Institute for Materials Science, Research Center for Materials Nanoarchitectonics
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,000
Funders: Ministry of Education, Culture, Sports, Science and Technology, National Natural Science Foundation of China, National Outstanding Youth Science Fund Project of National Natural Science Foundation of China, Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology
OpenAlex ID: W3163773863