Automatic printed circuit board inspection: a comprehensible survey

Author:

Fonseca Luis Augusto Libório Oliveira,Iano Yuzo,Oliveira Gabriel Gomes de,Vaz Gabriel Caumo,Carnielli Giulliano Paes,Pereira Júlio César,Arthur Rangel

Abstract

AbstractThe printed circuit board (PCB) plays a critical role in any electronic product, and its manufacturing quality assurance is responsible for substantially impacting the final product’s price. Hence, research and development (R&D) for better inspection methods have been an internationally growing subject. Regarding the multiple PCB inspection paradigms, the visual investigation of these boards represents the majority of modern techniques. Thus, the present survey collects the most impacting studies over the last 25 years, highlighting their operation strategies and how they evolved until recently, including introducing artificial intelligence (AI) approaches to increase their overall performance and lower operating costs. Furthermore, this work calls attention to the importance of public PCB datasets to contribute to developing such methods and provide benchmarking references. Finally, the most recent challenges in the field are described and discussed.

Publisher

Springer Science and Business Media LLC

Reference56 articles.

1. Precedence Research. Electronic contract manufacturing and design services market. https://www.precedenceresearch.com/electronic-contract-manufacturing-and-design-services-market. Accessed 3 Aug 2023.

2. Lee YT, Kumaraguru S, Jain S, Robinson S, Helu M, Hatim QY, et al. A classification scheme for smart manufacturing systems’ performance metrics. Smart Sustain Manuf Syst. 2017;1(1):52–74. https://doi.org/10.1520/SSMS20160012.

3. Smart Manufacturing Leadership Coalition. SMLC forum: priorities, infrastructure, and collaboration for implementation of smart manufacturing. https://smartmanufacturingcoalition.org/sites/default/files/smlc_forum_report_vf_0.pdf. Accessed 3 Aug 2023.

4. Powell D, Magnanini MC, Colledani M, Myklebust O. Advancing zero defect manufacturing: a state-of-the-art perspective and future research directions. Comput Ind. 2022;136: 103596. https://doi.org/10.1016/j.compind.2021.103596.

5. Bhattacharya A, Cloutier SG. End-to-end deep learning framework for printed circuit board manufacturing defect classification. Sci Rep. 2022;12(1):1–13. https://doi.org/10.1038/s41598-022-16302-3.

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