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

Rong-Qing Qiu1, Mu-Lin Tsai2, Yu-Wen Chen1

  • 1Institute of NanoEngineering and MicroSystems, National Tsing Hua University, Hsinchu 300044, Taiwan.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
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A new integrated automatic optical inspection (iAOI) system and procedure were developed for printed circuit board (PCB) manufacturing. This system offers efficient, unambiguous pattern quality judgments, aiding in defect detection during production.

Area of Science:

  • Electrical Engineering
  • Materials Science
  • Computer Vision

Background:

  • Printed circuit board (PCB) manufacturing is susceptible to process variations leading to pattern distortions.
  • Ensuring pattern quality and performance consistency in PCBs is critical for electronic device reliability.
  • Existing inspection methods may lack standardization and efficiency in addressing process-induced defects.

Purpose of the Study:

  • To propose an integrated automatic optical inspection (iAOI) system and procedure for PCB production lines.
  • To enhance the defect detection and pattern quality assessment capabilities in PCB manufacturing.
  • To provide a unified and efficient solution for identifying pattern distortions and performance deviations.

Main Methods:

  • Development of an iAOI system module with camera and lens, supporting commercial hardware.
Keywords:
automatic optical inspectionimage processingline edge roughnessmachine learningprinted circuit boardspiral antenna

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  • Implementation of a serial workflow including image registration, threshold setting, image gradient, marker alignment, and geometric transformation.
  • Creation of a user-friendly graphical user interface (GUI) for displaying image processing results and analyzed characteristics.

    • Main Results:

    • The iAOI system demonstrated improved supportiveness for commercial hardware.
    • The proposed procedure enabled efficient and standardized image processing operations.
    • Effectiveness was validated using self-complementary Archimedean spiral antenna (SCASA) samples with intentional distortions.
    • The system provided scientific and unambiguous judgments on pattern quality, outperforming existing methods.

    Conclusions:

    • The developed iAOI system and procedure offer an efficient and standardized approach for PCB quality inspection.
    • The system facilitates unambiguous judgments on pattern quality, crucial for identifying process variations.
    • Future integration with artificial intelligence models can enable electromagnetic characteristic projection for components like SCASAs via the GUI.