A Machine Learning Approach for Optimization of Channel Geometry and Source/Drain Doping Profile of Stacked Nanosheet Transistors-Reference-Cited by-同舟云学术

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

Published:2022-07 Issue:7 Volume:69 Page:3568-3574
ISSN:0018-9383
Container-title:IEEE Transactions on Electron Devices
language:
Short-container-title:IEEE Trans. Electron Devices

Author:

Xu Haoqing¹^ORCID,Gan Weizhuo¹^ORCID,Cao Lei¹^ORCID,Yang Cheng¹,Wu Jiahao²,Zhou Mi³,Qu Hengze⁴^ORCID,Zhang Shengli⁴^ORCID,Yin Huaxiang¹^ORCID,Wu Zhenhua¹^ORCID

Affiliation:

1. Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China

2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

3. The Information Technology Center, Open University of Sichuan, Chengdu, China

4. College of Material Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

Funder

International Partnership Program of the Chinese Academy of Sciences

MOST

NSFC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/16/9802454/09780572.pdf?arnumber=9780572

Reference42 articles.

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2. Improved Adam Optimizer for Deep Neural Networks

3. Physical modeling - A new paradigm in device simulation

4. Multiobjective Design of 2-D-Material-Based Field-Effect Transistors With Machine Learning Methods

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