Digital holographic imaging for optical inspection in learning-based pattern classification

Han-Yen Tu; Kuang-Che Chang Chien; Chau-Jern Cheng

doi:10.1117/12.2525946

21 June 2019 Digital holographic imaging for optical inspection in learning-based pattern classification

Han-Yen Tu, Kuang-Che Chang Chien, Chau-Jern Cheng

Proceedings Volume 11056, Optical Measurement Systems for Industrial Inspection XI; 1105606 (2019) https://doi.org/10.1117/12.2525946
Event: SPIE Optical Metrology, 2019, Munich, Germany

Abstract

High demand of optical inspection is increased to guarantee manufacture and product quality in industries. To overcome limitations of the manual defect inspection, machine vision inspection is needed to efficiently and accurately screen the undesired defects on various products. Recently, the transparent substrate is becoming widely used for manufacturing optics and electronics products. For high-grade transparent substrates, development of machine vision inspection has increased its importance for inspecting defects after production. To perform machine vision inspection for the transparent substrate, the exposure procedure and analysis of the capturing image are critical challenges due to its properties of reflection and transparency. However, conventional machine vision systems are performed for optical inspection based on two-dimensional (2D) intensity images from the camera-based photography without phase and depth information, and may decrease inspection accuracy as well as defect classification. Conversely, instead of the 2D intensity image by camera-based photography with complicated algorithms and time-consuming computation, digital holography is a novel three-dimensional (3D) imaging technique to rapidly access the whole wavefront information of the target sample for optical inspection and complex defect analysis. In this study, we propose digital holographic imaging of transparent target sample for optical inspection in learning-based pattern classification, which a novel complex defect inspection model is presented for multiple defects identification of the transparent substrate based on 3D diffraction characteristics and machine learning algorithm. Both theoretical and experimental results will be presented and analyzed to verify the effective inspection and high accuracy.

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Han-Yen Tu, Kuang-Che Chang Chien, and Chau-Jern Cheng "Digital holographic imaging for optical inspection in learning-based pattern classification", Proc. SPIE 11056, Optical Measurement Systems for Industrial Inspection XI, 1105606 (21 June 2019); https://doi.org/10.1117/12.2525946

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KEYWORDS

Defect detection

Digital holography

Diffraction

Image classification

3D image reconstruction

Optical inspection

Glasses

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