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An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS.

Amin Amini1, Jamil Kanfoud1, Tat-Hean Gan1

  • 1Brunel Innovation Centre, Brunel University London, Uxbridge UB8 3PH, UK.

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Summary

This study introduces an automated defect recognition system for micro-electro-mechanical systems (MEMS) manufacturing. The novel approach enhances quality control and production yield through accurate, machine-learning-based defect detection.

Keywords:
CNNMEMSdeep-learningdefect detectionmachine-learning

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Computer Science

Background:

  • Miniaturization in electronics and the widespread use of micro-electro-mechanical systems (MEMS) necessitate improved manufacturing quality control.
  • Current defect detection methods in MEMS production can be labor-intensive and may not identify issues early enough to optimize yield.

Purpose of the Study:

  • To develop an automated defect recognition (ADR) system for early-stage detection of surface defects in MEMS production.
  • To enhance manufacturing yield and product quality by implementing an accurate and efficient defect detection solution.

Main Methods:

  • Development of an automated defect recognition (ADR) system utilizing a unique plenoptic camera.
  • Application of a machine-learning approach for analyzing MEMS wafer images.
  • Algorithm designed for defect detection at both the whole wafer and individual component levels.

Main Results:

  • The developed ADR system achieved an average F1 score of 0.81 for true positive defect detection.
  • The system demonstrated a processing time of 18 seconds per image.
  • Validation was performed on 6 sample images with 371 labeled defects.

Conclusions:

  • The ADR system offers a high-accuracy, automated solution for identifying surface defects in MEMS manufacturing.
  • The technology can be integrated at various production stages, improving efficiency and reducing material waste.
  • This machine-learning-based approach significantly contributes to quality assurance in MEMS production.