Umesh ChandMohamed M Sabry AlyManohar LalChen Chun-KueiSonu HoodaShih-Hao TsaiZihang FangHasita VeluriAaron Voon-Yew TheanELECTRICAL AND COMPUTER ENGINEERINGDEAN'S OFFICE (ENGINEERING)2022-10-122022-10-122022-06-12Umesh Chand, Mohamed M Sabry Aly, Manohar Lal, Chen Chun-Kuei, Sonu Hooda, Shih-Hao Tsai, Zihang Fang, Hasita Veluri, Aaron Voon-Yew Thean (2022-06-12). 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. 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits) : 326-327. ScholarBank@NUS Repository. https://doi.org/10.1109/VLSITechnologyandCir46769.2022.9830250978-1-6654-9773-2https://scholarbank.nus.edu.sg/handle/10635/232258For 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.enCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/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 ComputingConference Paper