Chip Appearance Defect Recognition Based on Convolutional Neural Network

To improve the recognition rate of chip appearance defects, an algorithm based on a convolution neural network is proposed to identify chip appearance defects of various shapes and features. Furthermore, to address the problems of long training time and low accuracy caused by redundant input samples...

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Autores principales:	Jun Wang, Xiaomeng Zhou, Jingjing Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	chip appearance defects data cleaning convolutional neural network pattern recognition Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/d3ff6b6109ae4d20bd7ea26a80a5efae
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id	oai:doaj.org-article:d3ff6b6109ae4d20bd7ea26a80a5efae
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spelling	oai:doaj.org-article:d3ff6b6109ae4d20bd7ea26a80a5efae2021-11-11T19:05:40ZChip Appearance Defect Recognition Based on Convolutional Neural Network10.3390/s212170761424-8220https://doaj.org/article/d3ff6b6109ae4d20bd7ea26a80a5efae2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7076https://doaj.org/toc/1424-8220To improve the recognition rate of chip appearance defects, an algorithm based on a convolution neural network is proposed to identify chip appearance defects of various shapes and features. Furthermore, to address the problems of long training time and low accuracy caused by redundant input samples, an automatic data sample cleaning algorithm based on prior knowledge is proposed to reduce training and classification time, as well as improve the recognition rate. First, defect positions are determined by performing image processing and region-of-interest extraction. Subsequently, interference samples between chip defects are analyzed for data cleaning. Finally, a chip appearance defect classification model based on a convolutional neural network is constructed. The experimental results show that the recognition miss detection rate of this algorithm is zero, and the accuracy rate exceeds 99.5%, thereby fulfilling industry requirements.Jun WangXiaomeng ZhouJingjing WuMDPI AGarticlechip appearance defectsdata cleaningconvolutional neural networkpattern recognitionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7076, p 7076 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	chip appearance defects data cleaning convolutional neural network pattern recognition Chemical technology TP1-1185
spellingShingle	chip appearance defects data cleaning convolutional neural network pattern recognition Chemical technology TP1-1185 Jun Wang Xiaomeng Zhou Jingjing Wu Chip Appearance Defect Recognition Based on Convolutional Neural Network
description	To improve the recognition rate of chip appearance defects, an algorithm based on a convolution neural network is proposed to identify chip appearance defects of various shapes and features. Furthermore, to address the problems of long training time and low accuracy caused by redundant input samples, an automatic data sample cleaning algorithm based on prior knowledge is proposed to reduce training and classification time, as well as improve the recognition rate. First, defect positions are determined by performing image processing and region-of-interest extraction. Subsequently, interference samples between chip defects are analyzed for data cleaning. Finally, a chip appearance defect classification model based on a convolutional neural network is constructed. The experimental results show that the recognition miss detection rate of this algorithm is zero, and the accuracy rate exceeds 99.5%, thereby fulfilling industry requirements.
format	article
author	Jun Wang Xiaomeng Zhou Jingjing Wu
author_facet	Jun Wang Xiaomeng Zhou Jingjing Wu
author_sort	Jun Wang
title	Chip Appearance Defect Recognition Based on Convolutional Neural Network
title_short	Chip Appearance Defect Recognition Based on Convolutional Neural Network
title_full	Chip Appearance Defect Recognition Based on Convolutional Neural Network
title_fullStr	Chip Appearance Defect Recognition Based on Convolutional Neural Network
title_full_unstemmed	Chip Appearance Defect Recognition Based on Convolutional Neural Network
title_sort	chip appearance defect recognition based on convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d3ff6b6109ae4d20bd7ea26a80a5efae
work_keys_str_mv	AT junwang chipappearancedefectrecognitionbasedonconvolutionalneuralnetwork AT xiaomengzhou chipappearancedefectrecognitionbasedonconvolutionalneuralnetwork AT jingjingwu chipappearancedefectrecognitionbasedonconvolutionalneuralnetwork
_version_	1718431672575197184

Chip Appearance Defect Recognition Based on Convolutional Neural Network

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