Study on the Sodium-Doped Titania Interface-Type Memristor

材料科学 记忆电阻器 电极 肖特基势垒 光电子学 神经形态工程学 兴奋剂 肖特基二极管 掺杂剂 纳米技术 电子工程 计算机科学 化学 机器学习 工程类 物理化学 二极管 人工神经网络
作者
Minjae Kim,Sang‐Jun Lee,Seung Ju Kim,Byeong Min Lim,Byeong-Soo Kang,Hong‐Sub Lee
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:16 (13): 16453-16461 被引量:17
标识
DOI:10.1021/acsami.3c19531
摘要

Memristors integrated into a crossbar-array architecture (CAA) are promising candidates for analog in-memory computing accelerators. However, the relatively low reliability of the memristor device and sneak current issues in CAA remain the main obstacles. Alkali ion-based interface-type memristors are promising solutions for implementing highly reliable memristor devices and neuromorphic hardware. This interface-type device benefits from self-rectifying and forming-free resistive switching (RS), and exhibits relatively low variation from device to device and cycle to cycle. In a previous report, we introduced an in situ grown Na/TiO2 memristor using atomic layer deposition (ALD) and proposed a RS mechanism from experimentally measured Schottky barrier modulation. Self-rectifying RS characteristics were observed by the asymmetric distribution of Na dopants and oxygen vacancies as the Ti metal used as the adhesion layer for the bottom electrode diffuses over the Pt electrode at 250 °C during the ALD process and is doped into the TiO2 layer. Here, we theoretically verify the modulation of the Schottky barrier at the TiO2/Pt electrode interface by Na ions. This study fabricated a Pt/Na/TiO2/Pt memristor device and confirmed its self-rectifying RS characteristics and stable retention characteristics for 24 h at 85 °C. Additionally, this device exhibited relative standard deviations of 27 and 7% in the high and low resistance states, respectively, in terms of cycle-to-cycle variation. To verify the RS mechanism, we conducted density functional theory simulations to analyze the impact of Na cations at interstitial sites on the Schottky barrier. Our findings can contribute to both fundamental understanding and the design of high-performance memristor devices for neuromorphic computing.
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