Prediction of Statistical Distribution on Nanosheet FET by Geometrical Variability Using Various Machine Learning Models-Reference-Cited by-同舟云学术

Prediction of Statistical Distribution on Nanosheet FET by Geometrical Variability Using Various Machine Learning Models

Published:2023 Issue: Volume:11 Page:125217-125225
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ha Jonghyeon¹^ORCID,Kim Sun Jin²^ORCID,Bang Minji¹^ORCID,Lee Gyeongyeop¹^ORCID,Suh Minki³^ORCID,Shim Minseob³^ORCID,Kim Chong-Eun⁴^ORCID,Kim Jungsik¹^ORCID

Affiliation:

1. Department of Electrical Engineering, Gyeongsang National University (GNU), Jinju, Republic of Korea

2. Smart Structural Safety and Prognosis Research Division, Korea Atomic Energy Research Institute, Daejeon, Republic of Korea

3. Department of Electronic Engineering, Gyeongsang National University (GNU), Jinju, Republic of Korea

4. Department of Railroad Electrical and Electronic Engineering, Korea National University of Transportation, Uiwang, Republic of Korea

Funder

Electronic Design Automation (EDA) Tool Program of the IC Design Education Center (IDEC), Republic of Korea

National Research Foundation of Korea

Korean Government through the Ministry of Science and ICT (MSIT), Republic of Korea

Leaders in INdustry-university Cooperation 3.0 Project funded by the Ministry of Education and NRF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10310203.pdf?arnumber=10310203

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2. FinFET to nanowire transition at 5nm design rules

3. Thermal Resistance Extraction of 14 nm SOI FinFET: A Machine Learning Based Approach

4. Machine Learning Approach for Prediction of Point Defect Effect in FinFET

5. Transistor design for 5nm and beyond: Slowing down electrons to speed up transistors

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