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Vertically Aligned WS 2 Layers for High‐Performing Memristors and Artificial Synapses
Author(s) -
Kumar Mohit,
Ban DongKyun,
Kim Sang Moon,
Kim Joondong,
Wong ChingPing
Publication year - 2019
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.201900467
Subject(s) - neuromorphic engineering , memristor , materials science , resistive random access memory , synapse , computer science , nanotechnology , artificial intelligence , electronic engineering , artificial neural network , voltage , neuroscience , electrical engineering , engineering , biology
Inspired by the human brain, the quest for high‐performing neuromorphic architecture has recently gained more attention, which can be achieved by two‐terminal memristors. However, due to random and uncontrolled filament formation during a typical switching process, conventional memristors suffer from severe shortcomings such as temporal/spatial reproducibility as well as trivial sensitivity against applied spikes, however all these properties are crucial for accurate and quick information processing. Here, reproducible and robust two serially connected memristors comprised of ZnO and vertically grown WS 2 layers are reported. The device demonstrates remarkable tunable dynamic range along with comprehensive synaptic functions, including short‐ to long‐term plasticity, paired‐pulse facilitation, and spike‐timing‐dependent plasticity. Vertically aligned WS 2 layers confine the conduction along 1D channel and drastically enhance the performance. The dynamic processes of memorizing and forgetting are mimicked through a 3 × 3 memristive synapse array. A unique platform to design high‐performing and reproducible artificial synapses for neuromorphic computing is provided.