J. Hwang, Chaeheon Kim, Hunbeom Shin, Hwayoung Kim, S. K. Park, Sanghun Jeon
{"title":"Ultra-high Tunneling Electroresistance Ratio (2 × 104) & Endurance (108) in Oxide Semiconductor-Hafnia Self-rectifying (1.5 × 103) Ferroelectric Tunnel Junction","authors":"J. Hwang, Chaeheon Kim, Hunbeom Shin, Hwayoung Kim, S. K. Park, Sanghun Jeon","doi":"10.23919/VLSITechnologyandCir57934.2023.10185231","DOIUrl":null,"url":null,"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).","PeriodicalId":317958,"journal":{"name":"2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSITechnologyandCir57934.2023.10185231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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).