U. Chand, M. Aly, Manohar Lal, Chen Chun-Kuei, S. Hooda, Shih-Hao Tsai, Zihang Fang, H. Veluri, A. Thean
{"title":"亚10nm超薄ZnO沟道场效应管,在VDS 1V下具有创纪录的561 μ A/ μ m离子,高μ -84 cm2/V-s和1t - 1rram存储单元,证明了节能深度学习计算的存储意义","authors":"U. Chand, M. Aly, Manohar Lal, Chen Chun-Kuei, S. Hooda, Shih-Hao Tsai, Zihang Fang, H. Veluri, A. Thean","doi":"10.1109/vlsitechnologyandcir46769.2022.9830250","DOIUrl":null,"url":null,"abstract":"For the first time, we investigated ultra-short-channel ZnO thin-film FETs with Lch = 8 nm with extremely scaled channel thickness tZnO of 3nm, the device exhibits ultra-low sub-pA/µm off leakage (1.2 pA/µm), high electron mobility (µeff = 84 cm2/V•s) with record peak transconductance (Gm,) of 254 μS/μm at VDS = 1 V wrt. reported oxide-based transistors, to date, leading to high on-state current (ION) of 561 μA/μm. We demonstrated the integration of a ZnO access transistor with Al2O3 RRAM to enable a 1T-1R memory cell, suitable for BEOL-embedded memory. We evaluate the system-level benefits of a hardware accelerator for deep learning to employ FET-RRAM as working memory—up to 10X energy-efficiency benefits can be achieved over current baseline configurations.","PeriodicalId":332454,"journal":{"name":"2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Sub-10nm Ultra-thin ZnO Channel FET with Record-High 561 µA/µm ION at VDS 1V, High µ-84 cm2/V-s and1T-1RRAM Memory Cell Demonstration Memory Implications for Energy-Efficient Deep-Learning Computing\",\"authors\":\"U. Chand, M. Aly, Manohar Lal, Chen Chun-Kuei, S. Hooda, Shih-Hao Tsai, Zihang Fang, H. Veluri, A. Thean\",\"doi\":\"10.1109/vlsitechnologyandcir46769.2022.9830250\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For the first time, we investigated ultra-short-channel ZnO thin-film FETs with Lch = 8 nm with extremely scaled channel thickness tZnO of 3nm, the device exhibits ultra-low sub-pA/µm off leakage (1.2 pA/µm), high electron mobility (µeff = 84 cm2/V•s) with record peak transconductance (Gm,) of 254 μS/μm at VDS = 1 V wrt. reported oxide-based transistors, to date, leading to high on-state current (ION) of 561 μA/μm. We demonstrated the integration of a ZnO access transistor with Al2O3 RRAM to enable a 1T-1R memory cell, suitable for BEOL-embedded memory. We evaluate the system-level benefits of a hardware accelerator for deep learning to employ FET-RRAM as working memory—up to 10X energy-efficiency benefits can be achieved over current baseline configurations.\",\"PeriodicalId\":332454,\"journal\":{\"name\":\"2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/vlsitechnologyandcir46769.2022.9830250\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/vlsitechnologyandcir46769.2022.9830250","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sub-10nm Ultra-thin ZnO Channel FET with Record-High 561 µA/µm ION at VDS 1V, High µ-84 cm2/V-s and1T-1RRAM Memory Cell Demonstration Memory Implications for Energy-Efficient Deep-Learning Computing
For the first time, we investigated ultra-short-channel ZnO thin-film FETs with Lch = 8 nm with extremely scaled channel thickness tZnO of 3nm, the device exhibits ultra-low sub-pA/µm off leakage (1.2 pA/µm), high electron mobility (µeff = 84 cm2/V•s) with record peak transconductance (Gm,) of 254 μS/μm at VDS = 1 V wrt. reported oxide-based transistors, to date, leading to high on-state current (ION) of 561 μA/μm. We demonstrated the integration of a ZnO access transistor with Al2O3 RRAM to enable a 1T-1R memory cell, suitable for BEOL-embedded memory. We evaluate the system-level benefits of a hardware accelerator for deep learning to employ FET-RRAM as working memory—up to 10X energy-efficiency benefits can be achieved over current baseline configurations.