Ultra-high Tunneling Electroresistance Ratio (2 × 104) & Endurance (108) in Oxide Semiconductor-Hafnia Self-rectifying (1.5 × 103) Ferroelectric Tunnel Junction

J. Hwang, Chaeheon Kim, Hunbeom Shin, Hwayoung Kim, S. K. Park, Sanghun Jeon
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Abstract

In this study, we present a remarkable improvement in the performance of hafnia-based ferroelectric tunnel junctions (FTJs) using oxygen scavenging technology and extremely low-damage (ELD) deposition, leading to a significant increase in the tunneling electroresistance ratio $({\mathrm {TER}}) (\gt 2 \times 10^{4})$, on-current density $(\gt 10^{-2}\mathrm{A} /cm^{2})$, and self-rectifying ratio $({\mathrm {RR}}) (\gt 1.5 \times 10^{3})$. First-principles DFT simulations were also used to evaluate how the asymmetric oxygen vacancy (VO) distribution affected FTJs. As an array-level demonstration of the proposed approach, we experimentally built an FTJ-based XNOR synapse array and verified its operation for binary neural networks (BNN).
氧化物半导体-铪自整流(1.5 × 103)铁电隧道结的超高隧穿电阻比(2 × 104)和耐久性(108)
在这项研究中,我们提出了利用氧气清除技术和极低损伤(ELD)沉积显著改善基于铪的铁电隧道结(ftj)的性能,导致隧道电阻比$({\ mathm {TER}}) (\gt 2 \乘以10^{4})$,导通电流密度$(\gt 10^{-2}\ mathm {a} /cm^{2})$和自整流比$({\ mathm {RR}}) (\gt 1.5 \乘以10^{3})$显著增加。第一性原理DFT模拟还用于评估不对称氧空位(VO)分布对ftj的影响。作为该方法的阵列级演示,我们实验构建了一个基于ftj的XNOR突触阵列,并验证了其在二元神经网络(BNN)中的运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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