Machine Learning Approach to Characteristic Fluctuation of Bulk FinFETs Induced by Random Interface Traps

Rajat Butola, Yiming Li*, Sekhar Reddy Kola

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

Interface traps are of particular concern for highly scaled-down semiconductor devices. They cause trapping and de-trapping of charge carriers and have an adverse effect on device characteristics and variability. Therefore, in this work, the influence of randomly generated interface traps (RITs) on device characteristics of 16-nm-gate high-κ/metal gate bulk fin field-effect transistors (FinFETs) is investigated for experimentally validated simulated data. A machine learning (ML) model is proposed here to imitate the device simulation results. The impact of variation of these multi-point defects is analyzed by generating RITs at the interface of gate-oxide and silicon channel of the explored bulk FinFETs. The statistical fluctuations induced by RITs are analyzed by predicting the variations in threshold voltage (VTH), subthreshold slope (SS), drain-induced barrier lowering (DIBL), off-state current (IOFF), and transconductance (gm) using the proposed ML model with high accuracy and small error, in much less computational cost. This work shows the possibility of accelerating the random defects analysis using the technique of machine learning.

原文English
主出版物標題Proceedings of the 23rd International Symposium on Quality Electronic Design, ISQED 2022
發行者IEEE Computer Society
ISBN(電子)9781665494663
DOIs
出版狀態Published - 2022
事件23rd International Symposium on Quality Electronic Design, ISQED 2022 - Santa Jose, United States
持續時間: 6 4月 20227 4月 2022

出版系列

名字Proceedings - International Symposium on Quality Electronic Design, ISQED
2022-April
ISSN(列印)1948-3287
ISSN(電子)1948-3295

Conference

Conference23rd International Symposium on Quality Electronic Design, ISQED 2022
國家/地區United States
城市Santa Jose
期間6/04/227/04/22

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