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

doi:10.1109/TED.2022.3175708

	A Machine Learning Approach for Optimization of Channel Geometry and Source/Drain Doping Profile of Stacked Nanosheet Transistors
	Xu, Haoqing 1,2; Gan, Weizhuo 1,2; Cao, Lei 1,2; Yang, Cheng 1,2; Wu, Jiahao 3,4; Zhou, Mi 5; Qu, Hengze 6; Zhang, Shengli 6; Yin, Huaxiang 1,2; Wu, Zhenhua 1,2
	2022-05-24
发表期刊	IEEE TRANSACTIONS ON ELECTRON DEVICES
ISSN	0018-9383
页码	7
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.
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
DOI	10.1109/TED.2022.3175708
收录类别	SCI
语种	英语
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
WOS类目	Engineering, Electrical & Electronic ; Physics, Applied
WOS记录号	WOS:000800781900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19579
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.Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China 2.Univ Chinese Acad Sci, Sch Integrated Circuits, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 5.Open Univ Sichuan, Informat Technol Ctr, Chengdu 610072, Peoples R China 6.Nanjing Univ Sci & Technol, Coll Mat Sci & Engn, Nanjing 210094, 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.