数码产品
计算机科学
自动光学检测
软件
人工智能
预处理器
质量(理念)
自动X射线检查
电子元件
特征提取
计算机硬件
图像处理
工程类
电气工程
图像(数学)
哲学
认识论
程序设计语言
作者
Abd Al Rahman M. Abu Ebayyeh,Ali Mousavi
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:8: 183192-183271
被引量:195
标识
DOI:10.1109/access.2020.3029127
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.
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