Please use this identifier to cite or link to this item: https://doi.org/10.1109/VLSITechnologyandCir46769.2022.9830250
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: Umesh Chand 
Mohamed M Sabry Aly
Manohar Lal 
Chen Chun-Kuei
Sonu Hooda
Shih-Hao Tsai
Zihang Fang
Hasita Veluri
Aaron Voon-Yew Thean 
Issue Date: 12-Jun-2022
Publisher: IEEE
Citation: Umesh 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/VLSITechnologyandCir46769.2022.9830250
Rights: CC0 1.0 Universal
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.
Source Title: 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)
URI: https://scholarbank.nus.edu.sg/handle/10635/232258
ISBN: 978-1-6654-9773-2
DOI: 10.1109/VLSITechnologyandCir46769.2022.9830250
Rights: CC0 1.0 Universal
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