神经形态工程学
材料科学
肖特基势垒
接口(物质)
可靠性(半导体)
肖特基二极管
人工神经网络
MNIST数据库
光电子学
计算机科学
人工智能
物理
功率(物理)
二极管
毛细管数
量子力学
毛细管作用
复合材料
作者
Sundar Kunwar,Zachary Jernigan,Zach Hughes,Chase Somodi,Michael Saccone,Francesco Caravelli,Pinku Roy,Di Zhang,Haiyan Wang,Q. X. Jia,Judith L. MacManus‐Driscoll,Garrett T. Kenyon,Andrew Sornborger,Wanyi Nie,Aiping Chen
标识
DOI:10.1002/aisy.202300035
摘要
Interface‐type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament‐type (FT) devices for neuromorphic computing because of their uniform, filament‐free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface‐dominated memristive device is demonstrated using a simple Au/Nb‐doped SrTiO 3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface‐controlled RS, the proposed device shows low device‐to‐device, cell‐to‐cell, and cycle‐to‐cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next‐generation neuromorphic computing.
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