Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications

Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications

Conventional techniques for extracting physics‐based model parameters are inherently slow processes and often yield less accurate model parameters because of the inflexibility of physical equations. This study presents a novel machine learning–based method to accelerate and enhance the accuracy of c...

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Bibliographic Details
Main Authors: Seungjoon Eom, Seunghwan Lee, Hyeok Yun, Kyeongrae Cho, Soomin Kim, Rockhyun Baek
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Advanced Intelligent Systems
Subjects:
compact models
hypernetworks
machine learning
nanosheet field‐effect transistors
variabilities
Online Access:https://doi.org/10.1002/aisy.202400571
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