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Controlled Memory and Threshold Switching Behaviors in a Heterogeneous Memristor for Neuromorphic Computing
Release time:2023-08-21  Hits:

Indexed by: Journal paper

First Author: yuanjunhui,黄晓弟,李灏阳

Correspondence Author: 缪向水

Co-author: Xue Kanhao,He Yuhui,TONG HAO,李祎,万天晴,卢一帆

Journal: Advanced Electronic Materials

Affiliation of Author(s): 华中科技大学

Discipline: Engineering

First-Level Discipline: Electronic Science And Technology

Document Type: J

Volume: 6

Issue: 8

Page Number: 2000309

Key Words: conductive filaments memory switching memristors neuromorphic computing threshold switching

DOI number: 10.1002/aelm.202000309

Date of Publication: 4402-07-01

Abstract: The fully memristive neural network is emerging as a game-changer in the artificial intelligence competition. Artificial synapses and neurons, as two fundamental elements for hardware neural networks, have been substantially implemented by different devices with memory and threshold switching (TS) behaviors, respectively. However, obtaining controllable memory and TS behaviors in the same memristive material system is still a considerable challenge that holds great potential for realizing compatible artificial neurons and synapses. Here, a heterogeneous bilayer conductive filamentary memristor comprising two different electrolytes with distinct copper ion mobility is reported: Cu/GeTe/Al2O3/Pt, which can demonstrate the governance of switching types. Experimentally, when the thickness of the Al2O3 layer is 3 nm, stable nonvolatile multilevel memory switching (MS) is observed and employed to mimic the synaptic plasticity. With increasing oxide thickness, the switching behavior under the same compliance current alters from MS to volatile TS and is used to emulate the integrate-and-fire neuron function. The controllable switching stems from the change in the metal filament morphology within the Al2O3 layer, which is supported by ab initio calculation results. This method enables a new pathway for constructing functionally reconfigurable neuromorphic devices for intelligence neuromorphic systems.

Links to published journals: https://onlinelibrary.wiley.com/doi/abs/10.1002/aelm.202000309