Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors

The semiconductor manufacturing processes have been evolved to improve the yield rate. Here, we studied a sequential and comprehensive algorithm that could be used for fault detection and classification (FDC) of the semiconductor chips. A statistical process control (SPC) method is employed for insp...

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Autores principales:	Hirak Mazumdar, Tae Hyeon Kim, Jong Min Lee, Euiseok Kum, Seungho Lee, Suho Jeong, Bong Geun Chung
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	defect classification machine learning Voronoi diagram statistical process control Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/0a4ed4f981044769bafd66ffef6a9a1d
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spelling	oai:doaj.org-article:0a4ed4f981044769bafd66ffef6a9a1d2021-11-11T15:24:07ZSequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors10.3390/app1121104192076-3417https://doaj.org/article/0a4ed4f981044769bafd66ffef6a9a1d2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10419https://doaj.org/toc/2076-3417The semiconductor manufacturing processes have been evolved to improve the yield rate. Here, we studied a sequential and comprehensive algorithm that could be used for fault detection and classification (FDC) of the semiconductor chips. A statistical process control (SPC) method is employed for inspecting whether sensors used in the semiconductor manufacturing process become stable or not. When the sensors are individually stable, the algorithm conducts the relational inspection to identify the relationship between two sensors. The key factor here is the coefficient of determination (R<sup>2</sup>). If R<sup>2</sup> is calculated as more than 0.7, their relationship is analyzed through the regression analysis, while the algorithm conducts the clustering analysis to the sensor pair with R<sup>2</sup> less than 0.7. This analysis also provided the capability to determine whether the newly generated data are defective or defect-free. Therefore, this study is not only applied to the semiconductor manufacturing process but can also be to the various research fields where the big data are treated.Hirak MazumdarTae Hyeon KimJong Min LeeEuiseok KumSeungho LeeSuho JeongBong Geun ChungMDPI AGarticledefect classificationmachine learningVoronoi diagramstatistical process controlTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10419, p 10419 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	defect classification machine learning Voronoi diagram statistical process control Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	defect classification machine learning Voronoi diagram statistical process control Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Hirak Mazumdar Tae Hyeon Kim Jong Min Lee Euiseok Kum Seungho Lee Suho Jeong Bong Geun Chung Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
description	The semiconductor manufacturing processes have been evolved to improve the yield rate. Here, we studied a sequential and comprehensive algorithm that could be used for fault detection and classification (FDC) of the semiconductor chips. A statistical process control (SPC) method is employed for inspecting whether sensors used in the semiconductor manufacturing process become stable or not. When the sensors are individually stable, the algorithm conducts the relational inspection to identify the relationship between two sensors. The key factor here is the coefficient of determination (R<sup>2</sup>). If R<sup>2</sup> is calculated as more than 0.7, their relationship is analyzed through the regression analysis, while the algorithm conducts the clustering analysis to the sensor pair with R<sup>2</sup> less than 0.7. This analysis also provided the capability to determine whether the newly generated data are defective or defect-free. Therefore, this study is not only applied to the semiconductor manufacturing process but can also be to the various research fields where the big data are treated.
format	article
author	Hirak Mazumdar Tae Hyeon Kim Jong Min Lee Euiseok Kum Seungho Lee Suho Jeong Bong Geun Chung
author_facet	Hirak Mazumdar Tae Hyeon Kim Jong Min Lee Euiseok Kum Seungho Lee Suho Jeong Bong Geun Chung
author_sort	Hirak Mazumdar
title	Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
title_short	Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
title_full	Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
title_fullStr	Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
title_full_unstemmed	Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
title_sort	sequential and comprehensive algorithm for fault detection in semiconductor sensors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0a4ed4f981044769bafd66ffef6a9a1d
work_keys_str_mv	AT hirakmazumdar sequentialandcomprehensivealgorithmforfaultdetectioninsemiconductorsensors AT taehyeonkim sequentialandcomprehensivealgorithmforfaultdetectioninsemiconductorsensors AT jongminlee sequentialandcomprehensivealgorithmforfaultdetectioninsemiconductorsensors AT euiseokkum sequentialandcomprehensivealgorithmforfaultdetectioninsemiconductorsensors AT seungholee sequentialandcomprehensivealgorithmforfaultdetectioninsemiconductorsensors AT suhojeong sequentialandcomprehensivealgorithmforfaultdetectioninsemiconductorsensors AT bonggeunchung sequentialandcomprehensivealgorithmforfaultdetectioninsemiconductorsensors
_version_	1718435349501313024

Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors

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