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

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

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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.

    Original languageEnglish
    Title of host publicationOptical Measurement Systems for Industrial Inspection XI
    EditorsPeter Lehmann, Wolfgang Osten, Armando Albertazzi Goncalves
    PublisherSPIE
    ISBN (Electronic)9781510627918
    DOIs
    Publication statusPublished - 2019 Jan 1
    EventOptical Measurement Systems for Industrial Inspection XI 2019 - Munich, Germany
    Duration: 2019 Jun 242019 Jun 27

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    Volume11056
    ISSN (Print)0277-786X
    ISSN (Electronic)1996-756X

    Conference

    ConferenceOptical Measurement Systems for Industrial Inspection XI 2019
    CountryGermany
    CityMunich
    Period19/6/2419/6/27

    Keywords

    • Classification
    • Complex image
    • Defect detection
    • Digital holography
    • Machine learning
    • Optical inspection

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering

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