TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration

This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCAD-ML) framework to assist the analysis of device-to-device variation and operating (ambient) temperature without the need of physical quantities extraction. The ML algorithm used in this work is the Principal Comp...

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Autores principales:	Hiu Yung Wong, Ming Xiao, Boyan Wang, Yan Ka Chiu, Xiaodong Yan, Jiahui Ma, Kohei Sasaki, Han Wang, Yuhao Zhang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	TCAD simulation machine learning variation principal component analysis ultra-wide bandgap gallium oxide Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/9a791aae934746e48454420b76d59832
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spelling	oai:doaj.org-article:9a791aae934746e48454420b76d598322021-11-19T00:01:35ZTCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration2168-673410.1109/JEDS.2020.3024669https://doaj.org/article/9a791aae934746e48454420b76d598322020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9199850/https://doaj.org/toc/2168-6734This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCAD-ML) framework to assist the analysis of device-to-device variation and operating (ambient) temperature without the need of physical quantities extraction. The ML algorithm used in this work is the Principal Component Analysis (PCA) followed by third order polynomial regression. After calibrated to limited ‘expensive’ experimental data, ‘low cost’ TCAD simulation is used to generate a large amount of device data to train the ML model. The ML was then used to identify the root cause of device variation and operating temperature from any given experimental current-voltage (I-V) characteristics. We applied this framework to study the ultra-wide-bandgap gallium oxide (Ga<sub>2</sub>O<sub>3</sub>) Schottky barrier diode (SBD), an emerging device technology that holds great promise for temperature sensing, RF, and power applications in harsh environments. After calibration, over 150,000 electrothermal TCAD simulations are performed with random variation of physical parameters (anode effective work function, drift layer doping, and drift layer thickness) and operating temperature. An ML model is trained using these TCAD data and we found 1,000–10,000 TCAD data can train an accurate machine. We show that without physical quantities extraction, performing PCA is essential for the TCAD trained ML model to be applicable to analyze experimental characteristics. The physical parameters and temperatures predicted by the ML model show good agreement with experimental analysis. Our TCAD-ML framework shows great promise to accelerate the development of new device technologies with a significantly more efficient process of material and device experimentation.Hiu Yung WongMing XiaoBoyan WangYan Ka ChiuXiaodong YanJiahui MaKohei SasakiHan WangYuhao ZhangIEEEarticleTCAD simulationmachine learningvariationprincipal component analysisultra-wide bandgapgallium oxideElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Journal of the Electron Devices Society, Vol 8, Pp 992-1000 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	TCAD simulation machine learning variation principal component analysis ultra-wide bandgap gallium oxide Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	TCAD simulation machine learning variation principal component analysis ultra-wide bandgap gallium oxide Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hiu Yung Wong Ming Xiao Boyan Wang Yan Ka Chiu Xiaodong Yan Jiahui Ma Kohei Sasaki Han Wang Yuhao Zhang TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration
description	This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCAD-ML) framework to assist the analysis of device-to-device variation and operating (ambient) temperature without the need of physical quantities extraction. The ML algorithm used in this work is the Principal Component Analysis (PCA) followed by third order polynomial regression. After calibrated to limited ‘expensive’ experimental data, ‘low cost’ TCAD simulation is used to generate a large amount of device data to train the ML model. The ML was then used to identify the root cause of device variation and operating temperature from any given experimental current-voltage (I-V) characteristics. We applied this framework to study the ultra-wide-bandgap gallium oxide (Ga<sub>2</sub>O<sub>3</sub>) Schottky barrier diode (SBD), an emerging device technology that holds great promise for temperature sensing, RF, and power applications in harsh environments. After calibration, over 150,000 electrothermal TCAD simulations are performed with random variation of physical parameters (anode effective work function, drift layer doping, and drift layer thickness) and operating temperature. An ML model is trained using these TCAD data and we found 1,000–10,000 TCAD data can train an accurate machine. We show that without physical quantities extraction, performing PCA is essential for the TCAD trained ML model to be applicable to analyze experimental characteristics. The physical parameters and temperatures predicted by the ML model show good agreement with experimental analysis. Our TCAD-ML framework shows great promise to accelerate the development of new device technologies with a significantly more efficient process of material and device experimentation.
format	article
author	Hiu Yung Wong Ming Xiao Boyan Wang Yan Ka Chiu Xiaodong Yan Jiahui Ma Kohei Sasaki Han Wang Yuhao Zhang
author_facet	Hiu Yung Wong Ming Xiao Boyan Wang Yan Ka Chiu Xiaodong Yan Jiahui Ma Kohei Sasaki Han Wang Yuhao Zhang
author_sort	Hiu Yung Wong
title	TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration
title_short	TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration
title_full	TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration
title_fullStr	TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration
title_full_unstemmed	TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration
title_sort	tcad-machine learning framework for device variation and operating temperature analysis with experimental demonstration
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/9a791aae934746e48454420b76d59832
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TCAD-Machine Learning Framework for Device Variation and Operating Temperature Analysis With Experimental Demonstration

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