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Fang Song

Researcher at Xidian University

Publications -  14
Citations -  286

Fang Song is an academic researcher from Xidian University. The author has contributed to research in topics: Memristor & Resistive random-access memory. The author has an hindex of 7, co-authored 12 publications receiving 187 citations.

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A bio-inspired physically transient/biodegradable synapse for security neuromorphic computing based on memristors.

TL;DR: This work presents the first presentation of fully degradable biomimetic synaptic devices based on a W/MgO/ZnO/Mo memristor on a silk protein substrate, which show remarkable information storage and synaptic characteristics including long-term potentiation (LTP), long- term depression (LTD) and spike timing dependent plasticity (STDP) behaviors.
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Physically Transient Threshold Switching Device Based on Magnesium Oxide for Security Application.

TL;DR: A dissolvable and flexible threshold switching (TS) device with a vertically crossed structure is introduced, which exhibits a high selectivity of 107 and is capable of 107 Gb memory implementation, indicating that the transient TS device could provide great opportunities to achieve highly integrated transient memory arrays.
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ZnO-Based Physically Transient and Bioresorbable Memory on Silk Protein

TL;DR: In this article, physically transient and fully bioresorbable resistive switching devices based on ZnO are proposed, with excellent resistive switch memory performance shown in Mg/ZnO/W and Mg+W/Mg devices.
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Dissolvable and Biodegradable Resistive Switching Memory Based on Magnesium Oxide

TL;DR: In this article, dissolvable and biodegradable resistive switching devices with a cell structure of Mg/MgO/mg were demonstrated, and they were able to completely dissolve as fast as 30 min while immersed in deionized (DI) water and in phosphate-buffered saline solution.
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Physically Transient Memristor Synapse Based on Embedding Magnesium Nanolayer in Oxide for Security Neuromorphic Electronics

TL;DR: In this paper, a fully physically transient artificial synapse based on W/MgO/mg/MgaO/W memristor was realized for the first time by embedding magnesium nanolayer in MgO switching layer, multilevel and long-term memory with precise tuning ability.