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Artificial Synapse Based on a 2D-SnO2Memtransistor with Dynamically Tunable Analog Switching for Neuromorphic Computing
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Artificial Synapse Based on a 2D-SnO2Memtransistor with Dynamically Tunable Analog Switching for Neuromorphic Computing

Chi-Hsin Huang, Hsuan Chang, Tzu-Yi Yang, Yi-Chung Wang, Yu-Lun ChuehKenji Nomura
ACS Applied Materials and Interfaces, 卷.13(44), 頁碼.52822-52832
11/2021

摘要

analog switching memtransistor neuromorphic computing tin oxide two-dimensional oxide nanosheet Materials Science (all)
A new type of two-dimensional (2D) SnO2 semiconductor-based gate-tunable memristor, that is, a memtransistor, an integrated device of a memristor and a transistor, was demonstrated to advance next-generation neuromorphic computing technology. The polycrystalline 2D-SnO2 memristors derived from a low-temperature and vacuum-free liquid metal process offer several interesting resistive switching properties such as excellent digital/analog resistive switching, multistate storage, and gate-tunability function of resistance switching states. Significantly, the gate tunability function that is not achievable in conventional two-terminal memristors provides the capability to implement heterosynaptic analog switching by regulating gate bias for enabling complex neuromorphic learning. We successfully demonstrated that the gate-tunable synaptic device dynamically modulated the analog switching behavior with good linearity and an improved conductance change ratio for high recognition accuracy learning. The presented gate-tunable 2D-oxide memtransistor will advance neuromorphic device technology and open up new opportunities to design learning schemes with an extra degree of freedom.

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