Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET-Reference-Cited by-同舟云学术

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Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET

Published:2020 Issue: Volume:8 Page:158237-158242
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lim Jaehyuk^ORCID,Shin Changhwan^ORCID

Funder

National Research Foundation of Korea (NRF) grant

Korea government Ministry of Science and ICT

Future Semiconductor Device Technology Development Program

Ministry of Trade, Industry & Energy (MOTIE) and the Korea Semiconductor Research Consortium

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/09179808.pdf?arnumber=9179808

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. ANN-based framework for modeling process induced variation using BSIM-CMG unified model;Solid-State Electronics;2024-10

2. Analysis and Prediction of Nanowire TFET’s Work Function Variation;JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE;2024-04-30

3. Variations of single event transient induced by line edge roughness (LER) and temperature in FinFET;Microelectronics Journal;2024-02

4. Machine Learning Augmented Compact Modeling for Simultaneous Improvement in Computational Speed and Accuracy;IEEE Transactions on Electron Devices;2024-01

5. Design and Process Variation Analysis of High-performance n and p-channel Insulated-gate Asymmetric-DG MOSFET;Silicon;2023-09-01

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