铁电存储器件中随机性与过程损伤的物理关系

IF 11 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Ryun-Han Koo, Seungwhan Kim, Jiseong Im, Sangwoo Ryu, Kangwook Choi, Sung-Ho Park, Jonghyun Ko, Jongho Ji, Mingyun Oh, Jangsaeng Kim, Gyuweon Jung, Sung-Tae Lee, Daewoong Kwon, Wonjun Shin, Jong-Ho Lee
{"title":"铁电存储器件中随机性与过程损伤的物理关系","authors":"Ryun-Han Koo,&nbsp;Seungwhan Kim,&nbsp;Jiseong Im,&nbsp;Sangwoo Ryu,&nbsp;Kangwook Choi,&nbsp;Sung-Ho Park,&nbsp;Jonghyun Ko,&nbsp;Jongho Ji,&nbsp;Mingyun Oh,&nbsp;Jangsaeng Kim,&nbsp;Gyuweon Jung,&nbsp;Sung-Tae Lee,&nbsp;Daewoong Kwon,&nbsp;Wonjun Shin,&nbsp;Jong-Ho Lee","doi":"10.1186/s40580-025-00505-1","DOIUrl":null,"url":null,"abstract":"<div><p>This study investigates the influence of sputtering plasma-induced damage on stochastic characteristics in HfZrO₂ (HZO)-based ferroelectric tunnel junctions (FTJs), with an emphasis on memory and neuromorphic device optimization. Variation of the sputtering plasma power during top electrode deposition introduces distinct levels of trap within the HZO layer. Low-frequency noise (LFN) spectroscopy and temperature-dependent electrical measurements confirm that higher plasma power generates additional shallow-level traps, thereby promoting Poole-Frenkel conduction while simultaneously increasing current noise magnitude. Although the resulting enhancements in on-current density and ferroelectric tunnel electroresistance (TER) ratio are beneficial for high-density memory integration, these conditions also elevate stochastic fluctuations, potentially degrading read margins and long-term endurance. Furthermore, the observed increase in stochasticity negatively affects neuromorphic inference accuracy, particularly after endurance cycling stress. These results demonstrate the critical interplay among plasma process conditions, trap density, and LFN in FTJs. By systematically engineering sputtering process parameters, we optimize the electrical performance with minimized stochastic noise. This approach provides guidelines for the development of next-generation ferroelectric-based memories and neuromorphic systems with consideration of stochasticity, where robust performance and reliability are imperative for large-scale integration.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":712,"journal":{"name":"Nano Convergence","volume":"12 1","pages":""},"PeriodicalIF":11.0000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://nanoconvergencejournal.springeropen.com/counter/pdf/10.1186/s40580-025-00505-1","citationCount":"0","resultStr":"{\"title\":\"Physical correlation between stochasticity and process-induced damage in ferroelectric memory devices\",\"authors\":\"Ryun-Han Koo,&nbsp;Seungwhan Kim,&nbsp;Jiseong Im,&nbsp;Sangwoo Ryu,&nbsp;Kangwook Choi,&nbsp;Sung-Ho Park,&nbsp;Jonghyun Ko,&nbsp;Jongho Ji,&nbsp;Mingyun Oh,&nbsp;Jangsaeng Kim,&nbsp;Gyuweon Jung,&nbsp;Sung-Tae Lee,&nbsp;Daewoong Kwon,&nbsp;Wonjun Shin,&nbsp;Jong-Ho Lee\",\"doi\":\"10.1186/s40580-025-00505-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study investigates the influence of sputtering plasma-induced damage on stochastic characteristics in HfZrO₂ (HZO)-based ferroelectric tunnel junctions (FTJs), with an emphasis on memory and neuromorphic device optimization. Variation of the sputtering plasma power during top electrode deposition introduces distinct levels of trap within the HZO layer. Low-frequency noise (LFN) spectroscopy and temperature-dependent electrical measurements confirm that higher plasma power generates additional shallow-level traps, thereby promoting Poole-Frenkel conduction while simultaneously increasing current noise magnitude. Although the resulting enhancements in on-current density and ferroelectric tunnel electroresistance (TER) ratio are beneficial for high-density memory integration, these conditions also elevate stochastic fluctuations, potentially degrading read margins and long-term endurance. Furthermore, the observed increase in stochasticity negatively affects neuromorphic inference accuracy, particularly after endurance cycling stress. These results demonstrate the critical interplay among plasma process conditions, trap density, and LFN in FTJs. By systematically engineering sputtering process parameters, we optimize the electrical performance with minimized stochastic noise. This approach provides guidelines for the development of next-generation ferroelectric-based memories and neuromorphic systems with consideration of stochasticity, where robust performance and reliability are imperative for large-scale integration.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>\",\"PeriodicalId\":712,\"journal\":{\"name\":\"Nano Convergence\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":11.0000,\"publicationDate\":\"2025-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://nanoconvergencejournal.springeropen.com/counter/pdf/10.1186/s40580-025-00505-1\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nano Convergence\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1186/s40580-025-00505-1\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nano Convergence","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1186/s40580-025-00505-1","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

本文研究了溅射等离子体诱导损伤对HfZrO (HZO)基铁电隧道结(FTJs)随机特性的影响,重点研究了记忆和神经形态器件优化。在顶部电极沉积过程中,溅射等离子体功率的变化在HZO层内引入了不同程度的陷阱。低频噪声(LFN)光谱和温度相关的电学测量证实,更高的等离子体功率会产生额外的浅层陷阱,从而促进普尔-弗伦克尔传导,同时增加电流噪声量级。虽然导通电流密度和铁电隧道电阻(TER)比的增强有利于高密度存储器集成,但这些条件也会增加随机波动,潜在地降低读取裕量和长期耐用性。此外,观察到的随机性的增加对神经形态推理的准确性产生负面影响,特别是在耐力循环应激后。这些结果证明了等离子体工艺条件、陷阱密度和ftj中的LFN之间的关键相互作用。通过系统地设计溅射工艺参数,使随机噪声最小化,优化电性能。这种方法为考虑随机性的下一代铁电存储器和神经形态系统的开发提供了指导方针,其中强大的性能和可靠性对于大规模集成是必不可少的。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。
Physical correlation between stochasticity and process-induced damage in ferroelectric memory devices

This study investigates the influence of sputtering plasma-induced damage on stochastic characteristics in HfZrO₂ (HZO)-based ferroelectric tunnel junctions (FTJs), with an emphasis on memory and neuromorphic device optimization. Variation of the sputtering plasma power during top electrode deposition introduces distinct levels of trap within the HZO layer. Low-frequency noise (LFN) spectroscopy and temperature-dependent electrical measurements confirm that higher plasma power generates additional shallow-level traps, thereby promoting Poole-Frenkel conduction while simultaneously increasing current noise magnitude. Although the resulting enhancements in on-current density and ferroelectric tunnel electroresistance (TER) ratio are beneficial for high-density memory integration, these conditions also elevate stochastic fluctuations, potentially degrading read margins and long-term endurance. Furthermore, the observed increase in stochasticity negatively affects neuromorphic inference accuracy, particularly after endurance cycling stress. These results demonstrate the critical interplay among plasma process conditions, trap density, and LFN in FTJs. By systematically engineering sputtering process parameters, we optimize the electrical performance with minimized stochastic noise. This approach provides guidelines for the development of next-generation ferroelectric-based memories and neuromorphic systems with consideration of stochasticity, where robust performance and reliability are imperative for large-scale integration.

Graphical abstract

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Nano Convergence
Nano Convergence Engineering-General Engineering
CiteScore
15.90
自引率
2.60%
发文量
50
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信