Multi-Task Learning for Real-Time BSIM-CMG Parameter Extraction of NSFETs With Multiple Structural Variations

Multi-Task Learning for Real-Time BSIM-CMG Parameter Extraction of NSFETs With Multiple Structural Variations

We present a novel multi-task learning (MTL) approach with shared representation for the real-time extraction of Berkeley Short-channel IGFET Model-Common Gate (BSIM-CMG) parameters in nanosheet field-effect transistors (NSFETs) with multiple structural variations. An innovative artificial neural ne...

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Bibliographic Details
Main Authors: Seunghwan Lee, Seungjoon Eom, Jinsu Jeong, Junjong Lee, Sanguk Lee, Hyeok Yun, Yonghwan Ahn, Rock-Hyun Baek
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Additive noise
artificial neural network (ANN)
Berkeley short-channel IGFET model common multi-gate (BSIM-CMG)
compact model
multi-task learning (MTL)
nanosheet field-effect transistor (NSFET)
Online Access:https://ieeexplore.ieee.org/document/10781410/
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https://ieeexplore.ieee.org/document/10781410/

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