Enhancement and Expansion of the Neural Network-Based Compact Model Using a Binning Method-Reference-Cited by-同舟云学术

Enhancement and Expansion of the Neural Network-Based Compact Model Using a Binning Method

Published:2024 Issue: Volume:12 Page:65-73
ISSN:2168-6734
Container-title:IEEE Journal of the Electron Devices Society
language:
Short-container-title:IEEE J. Electron Devices Soc.

Author:

Choi Jinyoung¹^ORCID,Jeong Hyunjoon²^ORCID,Woo Sangmin²^ORCID,Cho Hyungmin¹^ORCID,Kim Yohan³^ORCID,Kong Jeong-Taek⁴,Kim Soyoung⁴^ORCID

Affiliation:

1. Department of Semiconductor and Display Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Republic of Korea

2. Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Republic of Korea

3. Computational Science and Engineering Team, Innovation Center, Samsung Electronics, Suwon, Republic of Korea

4. Department of Semiconductor Systems Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Republic of Korea

Funder

Institute of Information and Communications Technology Planning and Evaluation

Korean Government

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/6245494/10416702/10371311.pdf?arnumber=10371311

Reference18 articles.

1. Neural Network-Based BSIM Transistor Model Framework: Currents, Charges, Variability, and Circuit Simulation

2. BSIM-CMG: Standard FinFET compact model for advanced circuit design

3. BSIM-IMG: A Compact Model for Ultrathin-Body SOI MOSFETs With Back-Gate Control

4. Artificial Neural Network Surrogate Models for Efficient Design Space Exploration of 14-nm FinFETs

5. Sensitivity Analysis Based on Neural Network for Optimizing Device Characteristics