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Die-level defects classification using region-based convolutional neural network

Visual inspection process on semiconductors is usually performed by human experts. These inspection tasks require extreme concentration, and the time an inspector could continue the inspection tasks is limited. An automated die-level defects classification system is presented in this paper to replac...

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Main Authors: You, Kwong Ming, Sheikh, Usman Ullah, Alias, Nurul Ezaila
Format: Conference or Workshop Item
Published: 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/98693/
http://dx.doi.org/10.1109/ICSE56004.2022.9863135
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id my.utm.98693
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spelling my.utm.986932023-02-02T05:46:19Z http://eprints.utm.my/id/eprint/98693/ Die-level defects classification using region-based convolutional neural network You, Kwong Ming Sheikh, Usman Ullah Alias, Nurul Ezaila TK Electrical engineering. Electronics Nuclear engineering Visual inspection process on semiconductors is usually performed by human experts. These inspection tasks require extreme concentration, and the time an inspector could continue the inspection tasks is limited. An automated die-level defects classification system is presented in this paper to replace human experts in inspection tasks. The proposed system utilizes a Region-based Convolutional Neural Network on die-level images for defect detection and classification. Four defect classes are considered, blob, die crack, pinhole, and underfill. The proposed method achieved 88.5% and 71.4% accuracy in detection and defect classification, which is equivalent to that performed by human inspectors of between 60 - 80%. 2022 Conference or Workshop Item PeerReviewed You, Kwong Ming and Sheikh, Usman Ullah and Alias, Nurul Ezaila (2022) Die-level defects classification using region-based convolutional neural network. In: 2022 IEEE International Conference on Semiconductor Electronics, ICSE 2022, 15 - 17 August 2022, Virtual, Kuala Lumpur. http://dx.doi.org/10.1109/ICSE56004.2022.9863135
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
You, Kwong Ming
Sheikh, Usman Ullah
Alias, Nurul Ezaila
Die-level defects classification using region-based convolutional neural network
description Visual inspection process on semiconductors is usually performed by human experts. These inspection tasks require extreme concentration, and the time an inspector could continue the inspection tasks is limited. An automated die-level defects classification system is presented in this paper to replace human experts in inspection tasks. The proposed system utilizes a Region-based Convolutional Neural Network on die-level images for defect detection and classification. Four defect classes are considered, blob, die crack, pinhole, and underfill. The proposed method achieved 88.5% and 71.4% accuracy in detection and defect classification, which is equivalent to that performed by human inspectors of between 60 - 80%.
format Conference or Workshop Item
author You, Kwong Ming
Sheikh, Usman Ullah
Alias, Nurul Ezaila
author_facet You, Kwong Ming
Sheikh, Usman Ullah
Alias, Nurul Ezaila
author_sort You, Kwong Ming
title Die-level defects classification using region-based convolutional neural network
title_short Die-level defects classification using region-based convolutional neural network
title_full Die-level defects classification using region-based convolutional neural network
title_fullStr Die-level defects classification using region-based convolutional neural network
title_full_unstemmed Die-level defects classification using region-based convolutional neural network
title_sort die-level defects classification using region-based convolutional neural network
publishDate 2022
url http://eprints.utm.my/id/eprint/98693/
http://dx.doi.org/10.1109/ICSE56004.2022.9863135
_version_ 1758578006907944960
score 13.211869

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