Microcrack Detection And Noise Reduction In Integrated Circuit Packages

The rise in consumption of electronic products in the recent years have subsequently led to an increase in manufacturing of integrated circuits (ICs) to meet consumers’ demands. Thus, it is vital that each IC is inspected for defects that compromises its quality and usability. This ensures that no d...

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Main Author: Koh, Ye Sheng
Format: Monograph
Language:en
Published: Universiti Sains Malaysia 2018
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Online Access:http://eprints.usm.my/53577/1/Microcrack%20Detection%20And%20Noise%20Reduction%20In%20Integrated%20Circuit%20Packages_Koh%20Ye%20Sheng_E3_2018.pdf
http://eprints.usm.my/53577/
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author Koh, Ye Sheng
author_facet Koh, Ye Sheng
author_sort Koh, Ye Sheng
building Hamzah Sendut Library
collection Institutional Repository
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
continent Asia
country Malaysia
description The rise in consumption of electronic products in the recent years have subsequently led to an increase in manufacturing of integrated circuits (ICs) to meet consumers’ demands. Thus, it is vital that each IC is inspected for defects that compromises its quality and usability. This ensures that no defective ICs are used in conjunction with the manufacturing of electronic products which may severely impact the end product’s performance and lifespan. One of the common defects is microcrack on the IC’s package. Image processing is used to detect the presence of microcracks on the IC and the method currently employed to achieve this is by convolution with multiple kernels with different configurations. However, this method is time consuming due to the multiple configurations needed to be tuned and is also susceptible to image noise which lowers the accuracy of the detected microcracks. Therefore, a better algorithm is desired to improve the detection performance in terms of time and accuracy. Three algorithms are tested and evaluated in terms of microcrack detection and noise reduction which are probability based thresholding, histogram equalization, and modified Perona-Malik’s anisotropic diffusion methods. The first algorithm, probability based thresholding method consists of two stages, (i) image crack segmentation where the crack regions are analysed to obtain a suitable thresholding value, and (ii) image denoising where morphological closing is performed on the image. For the second algorithm, histogram equalization method has three stages, (i) image contrast enhancement through histogram equalization, (ii) image crack segmentation which subtracts the histogram equalized image with the image before histogram equalization process before merging the images using bitwise operation, and (iii) image denoising using morphological opening. The third algorithm, modified Perona-Malik’s anisotropic diffusion method consists of three stages, (i) image crack enhancement which separates the image into its red, green, and blue channels and enhances the crack features using modified Perona-Malik’s anisotropic diffusion, (ii) image crack segmentation which subtracts the diffused image with the pre-diffused image before summing the grey values of the images together, and (iii) image denoising using morphological opening and median filter. Images processed using modified Perona-Malik’s anisotropic diffusion method produces images with less noise compared to probability based thresholding method and histogram equalization method. The method has detected cracks present in three samples out of the five samples tested. The modified Perona-Malik’s anisotropic diffusion method is thus proven to produce relatively better performance compared to the other tested methods.
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spelling my.usm.eprints.53577 http://eprints.usm.my/53577/ Microcrack Detection And Noise Reduction In Integrated Circuit Packages Koh, Ye Sheng T Technology TK Electrical Engineering. Electronics. Nuclear Engineering The rise in consumption of electronic products in the recent years have subsequently led to an increase in manufacturing of integrated circuits (ICs) to meet consumers’ demands. Thus, it is vital that each IC is inspected for defects that compromises its quality and usability. This ensures that no defective ICs are used in conjunction with the manufacturing of electronic products which may severely impact the end product’s performance and lifespan. One of the common defects is microcrack on the IC’s package. Image processing is used to detect the presence of microcracks on the IC and the method currently employed to achieve this is by convolution with multiple kernels with different configurations. However, this method is time consuming due to the multiple configurations needed to be tuned and is also susceptible to image noise which lowers the accuracy of the detected microcracks. Therefore, a better algorithm is desired to improve the detection performance in terms of time and accuracy. Three algorithms are tested and evaluated in terms of microcrack detection and noise reduction which are probability based thresholding, histogram equalization, and modified Perona-Malik’s anisotropic diffusion methods. The first algorithm, probability based thresholding method consists of two stages, (i) image crack segmentation where the crack regions are analysed to obtain a suitable thresholding value, and (ii) image denoising where morphological closing is performed on the image. For the second algorithm, histogram equalization method has three stages, (i) image contrast enhancement through histogram equalization, (ii) image crack segmentation which subtracts the histogram equalized image with the image before histogram equalization process before merging the images using bitwise operation, and (iii) image denoising using morphological opening. The third algorithm, modified Perona-Malik’s anisotropic diffusion method consists of three stages, (i) image crack enhancement which separates the image into its red, green, and blue channels and enhances the crack features using modified Perona-Malik’s anisotropic diffusion, (ii) image crack segmentation which subtracts the diffused image with the pre-diffused image before summing the grey values of the images together, and (iii) image denoising using morphological opening and median filter. Images processed using modified Perona-Malik’s anisotropic diffusion method produces images with less noise compared to probability based thresholding method and histogram equalization method. The method has detected cracks present in three samples out of the five samples tested. The modified Perona-Malik’s anisotropic diffusion method is thus proven to produce relatively better performance compared to the other tested methods. Universiti Sains Malaysia 2018-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53577/1/Microcrack%20Detection%20And%20Noise%20Reduction%20In%20Integrated%20Circuit%20Packages_Koh%20Ye%20Sheng_E3_2018.pdf Koh, Ye Sheng (2018) Microcrack Detection And Noise Reduction In Integrated Circuit Packages. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted)
spellingShingle T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
Koh, Ye Sheng
Microcrack Detection And Noise Reduction In Integrated Circuit Packages
title Microcrack Detection And Noise Reduction In Integrated Circuit Packages
title_full Microcrack Detection And Noise Reduction In Integrated Circuit Packages
title_fullStr Microcrack Detection And Noise Reduction In Integrated Circuit Packages
title_full_unstemmed Microcrack Detection And Noise Reduction In Integrated Circuit Packages
title_short Microcrack Detection And Noise Reduction In Integrated Circuit Packages
title_sort microcrack detection and noise reduction in integrated circuit packages
topic T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
url http://eprints.usm.my/53577/1/Microcrack%20Detection%20And%20Noise%20Reduction%20In%20Integrated%20Circuit%20Packages_Koh%20Ye%20Sheng_E3_2018.pdf
http://eprints.usm.my/53577/
url_provider http://eprints.usm.my/