TY - GEN
T1 - A ZnO Nanowire Based Memristive Device and Its Application in Artificial Synapse
AU - Liu, Yiming
AU - Zhang, Mingxuan
AU - Xia, Yizhang
AU - Zhao, Yinchao
AU - Lu, Qifeng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - With the advent of the Big Data era, there is a growing demand for artificial intelligence, the Internet of Things, and machine learning. However, traditional computers based on von Neumann architecture face a significant challenge in the processing of vast amounts of data due to the separation of memory and the central processing unit. The hardware-based neuromorphic computing, which is inspired by the human brain, has the potential to significantly reduce energy consumption and realize a wide range of applications. Although a great achievement has been obtained, the uniformity in the electrical characteristics is still a challenge. Therefore, we designed and fabricated a memristor using ZnO nanowires (NWs) to achieve consistent modulation of conductive filaments. This memristor can emulate various synaptic functions, including paired-pulse facilitation, and short-term and long-term plasticity. As a proof of concept, the convolutional neural network simulation for MNIST recognition with 98.87% accuracy was achieved by the ZnO NWs-based memristors.
AB - With the advent of the Big Data era, there is a growing demand for artificial intelligence, the Internet of Things, and machine learning. However, traditional computers based on von Neumann architecture face a significant challenge in the processing of vast amounts of data due to the separation of memory and the central processing unit. The hardware-based neuromorphic computing, which is inspired by the human brain, has the potential to significantly reduce energy consumption and realize a wide range of applications. Although a great achievement has been obtained, the uniformity in the electrical characteristics is still a challenge. Therefore, we designed and fabricated a memristor using ZnO nanowires (NWs) to achieve consistent modulation of conductive filaments. This memristor can emulate various synaptic functions, including paired-pulse facilitation, and short-term and long-term plasticity. As a proof of concept, the convolutional neural network simulation for MNIST recognition with 98.87% accuracy was achieved by the ZnO NWs-based memristors.
KW - conductive filaments mechanism
KW - convolutional neural networks
KW - memristor
KW - neuromorphic computing
KW - ZnO nanowires
UR - http://www.scopus.com/inward/record.url?scp=85210841943&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70684-4_9
DO - 10.1007/978-3-031-70684-4_9
M3 - Conference Proceeding
AN - SCOPUS:85210841943
SN - 9783031706837
T3 - Lecture Notes in Networks and Systems
SP - 108
EP - 115
BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
A2 - Abdul Majeed, Anwar P.P.
A2 - Yap, Eng Hwa
A2 - Liu, Pengcheng
A2 - Huang, Xiaowei
A2 - Nguyen, Anh
A2 - Chen, Wei
A2 - Kim, Ue-Hwan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
Y2 - 6 December 2023 through 8 December 2023
ER -
