{"title":"A novel BP-GA based autofocus method for detection of circuit board components","authors":"Guangyi Zhu , Siyuan Wang, Lilin Wang","doi":"10.1016/j.optcom.2024.131246","DOIUrl":null,"url":null,"abstract":"<div><div>Optical micro-inspection systems use different focusing methods depending on the inspection requirements of different scenarios. In practical industrial micro-inspection, grid samples have a wide variety of components, for example, electronic components on circuit boards and transistors on electronic chips. Changes in the surrounding environment (e.g., brightness of light, flatness of the platform, and temperature, etc.) during the inspection of these processed parts may lead to out-of-focus of the object under the microscope. Therefore, this paper proposes an autofocus algorithm to cope with the complex environment during inspection. The algorithm is based on feature vectors reflecting the external geometry of the spot and the internal energy distribution, and is combined with a back-propagation neural network with a genetic algorithm (GA) to enhance the focusing capability of the optical microscope. Preliminary numerical test results show that because of the bias problem in the focusing system, the accuracy of the neural network in calculating the defocused amount (DA) is significantly improved, despite the pitfalls of its generalization ability and the possibility of endless loops during the focusing process. In order to further solve the pitfalls of neural networks, this paper introduces a full reference image evaluation model into the optical microscope system and finally develops the autofocus software. Focusing tests using the developed software for the inspection of real components demonstrate that the introduced full reference image evaluation model not only expands the focusing distance of the inspection system, but also prevents the autofocus algorithm from falling into a dead loop.</div></div>","PeriodicalId":19586,"journal":{"name":"Optics Communications","volume":"575 ","pages":"Article 131246"},"PeriodicalIF":2.2000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics Communications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030401824009830","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Optical micro-inspection systems use different focusing methods depending on the inspection requirements of different scenarios. In practical industrial micro-inspection, grid samples have a wide variety of components, for example, electronic components on circuit boards and transistors on electronic chips. Changes in the surrounding environment (e.g., brightness of light, flatness of the platform, and temperature, etc.) during the inspection of these processed parts may lead to out-of-focus of the object under the microscope. Therefore, this paper proposes an autofocus algorithm to cope with the complex environment during inspection. The algorithm is based on feature vectors reflecting the external geometry of the spot and the internal energy distribution, and is combined with a back-propagation neural network with a genetic algorithm (GA) to enhance the focusing capability of the optical microscope. Preliminary numerical test results show that because of the bias problem in the focusing system, the accuracy of the neural network in calculating the defocused amount (DA) is significantly improved, despite the pitfalls of its generalization ability and the possibility of endless loops during the focusing process. In order to further solve the pitfalls of neural networks, this paper introduces a full reference image evaluation model into the optical microscope system and finally develops the autofocus software. Focusing tests using the developed software for the inspection of real components demonstrate that the introduced full reference image evaluation model not only expands the focusing distance of the inspection system, but also prevents the autofocus algorithm from falling into a dead loop.
期刊介绍:
Optics Communications invites original and timely contributions containing new results in various fields of optics and photonics. The journal considers theoretical and experimental research in areas ranging from the fundamental properties of light to technological applications. Topics covered include classical and quantum optics, optical physics and light-matter interactions, lasers, imaging, guided-wave optics and optical information processing. Manuscripts should offer clear evidence of novelty and significance. Papers concentrating on mathematical and computational issues, with limited connection to optics, are not suitable for publication in the Journal. Similarly, small technical advances, or papers concerned only with engineering applications or issues of materials science fall outside the journal scope.