Complex defect inspection for transparent substrate by combining digital holography with machine learning

CCUR > College of Natural Science and Engineering > College of Engineering > Department of Electrical Engineering > journal articles > Item 987654321/45524

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/45524

Title:	Complex defect inspection for transparent substrate by combining digital holography with machine learning
Authors:	Chien, KCC (Chien, Kuang-Che Chang) Tu, HY (Tu, Han-Yen)
Contributors:	電機系
Keywords:	digital holography machine learning defect inspection
Date:	2019-08
Issue Date:	2019-12-24 09:47:09 (UTC+8)
Abstract:	We proposed a complex defect inspection (CDI) technique for quality control of transparent substrates that uses the diffraction characteristics of digital holograms and a machine learning algorithm. A complex pattern diffraction model was built to provide two diffraction criteria, the least separation of confusion and the effective diffraction distance, to extend depth of focus in the effective diffraction regime for numerical reconstruction. On the basis of an analysis of three-dimensional diffraction characteristics of complex images, defect identification was performed to detect and classify defects (cracks, dusts, and watermarks) in transparent substrates using region-based segmentation and a machine learning algorithm. The experimental results indicated that the defect detection performance of the proposed CDI system was recall = 96.3% and precision = 92.8%. Moreover, overall multiclass classification accuracy = 95.3%, resulting in a discrimination area under the receiver operating characteristic curve (Az) of 0.96.
Relation:	JOURNAL OF OPTICS 卷冊: 21 期: 8 文獻號碼: 085701
Appears in Collections:	[Department of Electrical Engineering ] journal articles

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	236	View/Open

View Licence