Defect passivation of hafnium oxide ferroelectric tunnel junction using forming gas annealing for neuromorphic applications

IF 13.4 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Nano Convergence Pub Date : 2025-03-24 DOI:10.1186/s40580-025-00481-6

Manh-Cuong Nguyen, Kyung Kyu Min, Wonjun Shin, Jiyong Yim, Rino Choi, Daewoong Kwon

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引用次数: 0

Abstract

Forming gas annealing (FGA) is applied to HfO_x ferroelectric tunnel junction (FTJ) synaptic devices to passivate defects and reduce trap-assisted-tunneling (TAT). Without FGA, TAT caused by defects in metal–ferroelectric–insulator–semiconductor (MFIS) FTJ stack dominates the conduction mechanism in FTJs and results in no memory window (MW). The reduction of defects or TAT after FGA reveals the effect of polarization switching on the FTJ performance. Consequently, linear/symmetric potentiation and depression (P/D) characteristics of FTJ after FGA with stable repeatability are obtained. Owing to the FGA-induced linearity and symmetricity of P/D, a learning accuracy of approximately 90% is achieved via pattern recognition simulations utilizing HfO_x FTJ crossbar.

Graphical Abstract

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神经形态应用中氧化铪铁电隧道结的形成气体退火缺陷钝化

将成形气体退火（FGA）应用于HfOx铁电隧道结（FTJ）突触器件中，以钝化缺陷和减少陷阱辅助隧道效应（TAT）。在没有FGA的情况下，由金属-铁电-绝缘体-半导体（MFIS） FTJ堆叠缺陷引起的TAT主导了FTJ的传导机制，导致没有记忆窗口（MW）。FGA后缺陷或TAT的减少揭示了极化开关对FTJ性能的影响。因此，FGA后FTJ的线性/对称增强和抑制（P/D）特性具有稳定的重复性。由于fga诱导的P/D线性和对称性，通过使用HfOx FTJ交叉棒的模式识别模拟，实现了约90%的学习精度。图形抽象

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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来源期刊

Nano Convergence Engineering-General Engineering

CiteScore

15.90

自引率

2.60%

发文量

审稿时长

13 weeks

期刊介绍： Nano Convergence is an internationally recognized, peer-reviewed, and interdisciplinary journal designed to foster effective communication among scientists spanning diverse research areas closely aligned with nanoscience and nanotechnology. Dedicated to encouraging the convergence of technologies across the nano- to microscopic scale, the journal aims to unveil novel scientific domains and cultivate fresh research prospects. Operating on a single-blind peer-review system, Nano Convergence ensures transparency in the review process, with reviewers cognizant of authors' names and affiliations while maintaining anonymity in the feedback provided to authors.