Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 5; p. 1619
• Main Authors: Qiu, Rong-Qing, Tsai, Mu-Lin, Chen, Yu-Wen, Singh, Shivendra Pratap, Lo, Cheng-Yao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2024; MDPI
• Subjects: Artificial intelligence; automatic optical inspection; Cameras; Circuit printing; Efficiency; Electromagnetism; Image processing; Inspection; line edge roughness; machine learning; printed circuit board; Printed circuits; Production management; Python; spiral antenna; User interface; image processing; spiral antenna; printed circuit board; machine learning; line edge roughness; automatic optical inspection
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24051619

An integrated automatic optical inspection (iAOI) system with a procedure was proposed for a printed circuit board (PCB) production line, in which pattern distortions and performance deviations appear with process variations. The iAOI system was demonstrated in a module comprising a camera and lens, showing improved supportiveness for commercially available hardware. The iAOI procedure was realized in a serial workflow of image registration, threshold setting, image gradient, marker alignment, and geometric transformation; furthermore, five operations with numerous functions were prepared for image processing. In addition to the system and procedure, a graphical user interface (GUI) that displays sequential image operation results with analyzed characteristics was established for simplicity. To demonstrate its effectiveness, self-complementary Archimedean spiral antenna (SCASA) samples fabricated via standard PCB fabrication and intentional pattern distortions were demonstrated. The results indicated that, compared with other existing methods, the proposed iAOI system and procedure provide unified and standard operations with efficiency, which result in scientific and unambiguous judgments on pattern quality. Furthermore, we showed that when an appropriate artificial intelligence model is ready, the electromagnetic characteristic projection for SCASAs can be simply obtained through the GUI.