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논문 기본 정보

자료유형
학술저널
저자정보
Tae Hyun Hwang (LS Electric) Sangwan Kim (Sogang University) Garam Kim (Myongji University) Hyunwoo Kim (Konkuk University) Jang Hyun Kim (Ajou University)
저널정보
대한전자공학회 JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE Journal of Semiconductor Technology and Science Vol.24 No.2
발행연도
2024.4
수록면
96 - 104 (9page)
DOI
10.5573/JSTS.2024.24.2.96

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초록· 키워드

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The research investigates the electrical effect of Work Function Variation (WFV) in Tunnel Field-Effect Transistors (TFETs), with Titanium Nitride (TiN) gate as a common Metal Gate material. Employing advanced Machine Learning (ML) techniques, this study seeks to establish causal relationships among various parameters, optimize ML models, and predict exceptional scenarios. Through an in-depth analysis of diverse data, the study uncovers insights into TFET’s performance variations. The ML model was optimized using the elimination method, checking each R² value. After discovering the relevant output parameters (e.g., turn-on voltage (V<SUB>on</SUB>), threshold voltage (V<SUB>th</SUB>)), it was observed that WFV at particular gate regions heavily affects current variation. Furthermore, ML demonstrated the ability to predict output parameters for exceptional cases, not present in the training data, such as gates composed of the 4.4-eV grain, which exhibited a high R² value (0.9927).

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Abstract
I. INTRODUCTION
II. DEVICE DESIGN AND METHODOLOGY
III. FINDING THE DETAILED CAUSE
IV. MULTIPLE CAUSES OF VARIATION
V. PREDICTION OF EXCEPTIONAL SITUATION
VI. CONCLUSIONS
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