Premium
Emulation of Learning and Memory Behaviors by Memristor Based on Ag Migration on 2D MoS 2 Surface
Author(s) -
Yin Siqi,
Luo Zhaochu,
Li Qian,
Xiong Chengyue,
Liu Yunlong,
Singh Rajan,
Zeng Fei,
Zhong Yi,
Zhang Xiaozhong
Publication year - 2019
Publication title -
physica status solidi (a)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.532
H-Index - 104
eISSN - 1862-6319
pISSN - 1862-6300
DOI - 10.1002/pssa.201900104
Subject(s) - memristor , neuromorphic engineering , materials science , emulation , nanotechnology , semiconductor , protein filament , optoelectronics , conductance , electronic engineering , artificial neural network , computer science , condensed matter physics , composite material , engineering , physics , artificial intelligence , economic growth , economics
Electrochemical metallization memories (ECM)‐based memristors are widely regarded as potential electronic devices for neuromorphic computing. However, in ECM‐based memristors, the formation of metallic conducting filament in insulating layer will cause an abrupt current increase, making it hard for analog neuromorphic emulation. Here, a memristor fabricated by using two‐dimensional (2D) semiconductor MoS 2 that can provide atomically smooth and semi‐insulating surface as the medium for electric‐field‐driven migration of conducting filaments is proposed. This memristor based on the Ag ions migration on 2D MoS 2 surface exhibits gradual conductance change behavior. Microstructure characterization shows that such gradual conductance change behavior can be attributed to the formation of conducting filament composed of a chain of metallic Ag nanoparticles of ≈5 nm at ON‐state device. By comparing with biological experimental data, it is found that our device can well mimic the learning behavior of Drosophila . Finding shows the potential to realize stable analog ECM‐based memristors and paves the way for fabricating large‐scale memristor network.