Machine Learning Driven Channel Thickness Optimization in Dual‐Layer Oxide Thin‐Film Transistors for Advanced Electrical Performance

Machine Learning Driven Channel Thickness Optimization in Dual‐Layer Oxide Thin‐Film Transistors for Advanced Electrical Performance

Abstract Machine learning (ML) provides temporal advantage and performance improvement in practical electronic device design by adaptive learning. Herein, Bayesian optimization (BO) is successfully applied to the design of optimal dual‐layer oxide semiconductor thin film transistors (OS TFTs). This...

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Bibliographic Details
Main Authors: Jiho Lee, Jae Hak Lee, Chan Lee, Haeyeon Lee, Minho Jin, Jiyeon Kim, Jong Chan Shin, Eungkyu Lee, Youn Sang Kim
Format: Article
Language:English
Published: Wiley 2023-12-01
Series:Advanced Science
Subjects:
Bayesian optimization
design of experiment
dual‐layer channel
machine learning
oxide semiconductors
thin film transistors
Online Access:https://doi.org/10.1002/advs.202303589

Internet

https://doi.org/10.1002/advs.202303589

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