阈值电压
材料科学
薄膜晶体管
晶体管
场效应
光电子学
图层(电子)
计算机科学
电压
电子工程
纳米技术
电气工程
工程类
作者
Jiho Lee,Jae‐Hak Lee,Chan Lee,Haeyeon Lee,Minho Jin,Jiyeon Kim,Jong Chan Shin,Eungkyu Lee,Youn Sang Kim
标识
DOI:10.1002/advs.202303589
摘要
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 approach effectively manages the complex correlation and interdependency between two oxide semiconductor layers, resulting in the efficient design of experiment (DoE) and reducing the trial‐and‐error. Considering field effect mobility (𝜇) and threshold voltage ( V th ) simultaneously, the dual‐layer structure designed by the BO model allows to produce OS TFTs with remarkable electrical performance while significantly saving an amount of experimental trial (only 15 data sets are required). The optimized dual‐layer OS TFTs achieve the enhanced field effect mobility of 36.1 cm 2 V −1 s −1 and show good stability under bias stress with negligible difference in its threshold voltage compared to conventional IGZO TFTs. Moreover, the BO algorithm is successfully customized to the individual preferences by applying the weight factors assigned to both field effect mobility (𝜇) and threshold voltage ( V th ).
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