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A Machine Learning Approach for Optimization of Channel Geometry and Source/Drain Doping Profile of Stacked Nanosheet Transistors

文献类型：期刊论文

作者Xu, Haoqing3,4; Gan, Weizhuo3,4; Cao, Lei3,4; Yang, Cheng3,4; Wu, Jiahao5,6; Zhou, Mi2; Qu, Hengze1; Zhang, Shengli1; Yin, Huaxiang3,4; Wu, Zhenhua3,4
刊名IEEE TRANSACTIONS ON ELECTRON DEVICES
出版日期2022-05-24
页码7
ISSN号0018-9383
关键词Performance evaluation Doping Optimization Geometry Semiconductor process modeling Training Logic gates Machine learning multi-objective optimization (MOO) nanosheet technology computer-aided design (TCAD) simulation
DOI10.1109/TED.2022.3175708
英文摘要Complex nonlinear dependence of ultra-scaled transistor performance on its channel geometry and source/drain (S/D) doping profile bring obstacles in the advanced technology path-finding and optimization. A machine learning-based multi-objective optimization (MOO) workflow is proposed to optimize the sub-3-nm node gate-all-around (GAA) three-layer-stacked nanosheet transistors (NSFETs) accounting for the key performance knob of channel geometry and S/D doping profile. The artificial neural network (ANN) is trained to learn the compact current-voltage (I-V) relationship of NSFETs from 3-D technology computer-aided design (TCAD) simulation results. Based on the artificial neural network (ANN) model, MOO between threshold swing, on-off ratio, and on-state current of NSFETs is performed with adaptive weighted sum theory. The proposed workflow efficiently suggests an optimized design window of channel geometry and doping profile of NSFETs. The proposed devices satisfy the 2025 International Roadmap for Devices and Systems (IRDSs) target in terms of electrical characteristics for digital circuits.
资助项目International Partnership Program of the Chinese Academy of Sciences[E1YH01X] ; MOST[2021YFA1200502] ; NSFC[91964202]
WOS研究方向Engineering ; Physics
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000800781900001
源URL[http://119.78.100.204/handle/2XEOYT63/19578]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Shengli; Wu, Zhenhua
作者单位1.Nanjing Univ Sci & Technol, Coll Mat Sci & Engn, Nanjing 210094, Peoples R China
2.Open Univ Sichuan, Informat Technol Ctr, Chengdu 610072, Peoples R China
3.Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China
4.Univ Chinese Acad Sci, Sch Integrated Circuits, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Haoqing,Gan, Weizhuo,Cao, Lei,et al. A Machine Learning Approach for Optimization of Channel Geometry and Source/Drain Doping Profile of Stacked Nanosheet Transistors[J]. IEEE TRANSACTIONS ON ELECTRON DEVICES,2022:7.
APA Xu, Haoqing.,Gan, Weizhuo.,Cao, Lei.,Yang, Cheng.,Wu, Jiahao.,...&Wu, Zhenhua.(2022).A Machine Learning Approach for Optimization of Channel Geometry and Source/Drain Doping Profile of Stacked Nanosheet Transistors.IEEE TRANSACTIONS ON ELECTRON DEVICES,7.
MLA Xu, Haoqing,et al."A Machine Learning Approach for Optimization of Channel Geometry and Source/Drain Doping Profile of Stacked Nanosheet Transistors".IEEE TRANSACTIONS ON ELECTRON DEVICES (2022):7.

入库方式： OAI收割

来源：计算技术研究所

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