A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

IEEE Access

Alireza Mousavi¹  Abd Al Rahman M. Abu Ebayyeh¹ 

[1]Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, U.K.

关键词: Automatic optical inspection;    classification algorithms;    electronics industry;    feature extraction;    image processing;    image sensor;   

DOI  :  10.1109/ACCESS.2020.3029127

来源: DOAJ

【 摘 要 】

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

【 授权许可】

Unknown