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YOLO-DST: MEMS Small-Object Defect Detection Method Based on Dynamic Channel-Spatial Modeling and Multi-Attention

Qianwen Su1, Hanshan Li1

  • 1School of Electronic and Information Engineering, Xi'an Technological University, Xi'an 710021, China.

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Summary
This summary is machine-generated.

This study introduces YOLO-DST, an improved method for detecting small defects in Micro-Electro-Mechanical Systems (MEMS) using dynamic channel-spatial blocks and attention mechanisms. The new approach enhances reliability by accurately identifying bond wire breaks and internal contaminants in metallographic images.

Keywords:
MEMS defect detectionmulti-attention fusionsmall-object detectiontriplet attention

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Vision

Background:

  • Defect detection in Micro-Electro-Mechanical Systems (MEMS) faces challenges with metallographic images, including complex backgrounds, texture interference, and blurred edges.
  • These issues hinder the effective identification of critical defects like bond wire breaks and internal cavity contaminants, impacting overall system reliability.

Purpose of the Study:

  • To propose an advanced defect detection method for MEMS small objects, addressing limitations in current metallographic image analysis.
  • To enhance the accuracy and stability of identifying bond wire breaks and internal cavity contaminants in MEMS devices.

Main Methods:

  • Developed YOLO-DST (Dynamic Channel-Spatial Modeling and Triplet Attention-based YOLO), integrating dynamic channel-space blocks into a YOLOv8s framework.
  • Incorporated multi-attention fusion in the network neck to suppress background interference and utilized a Transformer-based network for small-object perception enhancement.
  • Constructed a dedicated MEMS small-object defect dataset for experimental validation.

Main Results:

  • YOLO-DST demonstrated superior performance compared to existing mainstream detection models.
  • The method achieved significant improvements in key performance indicators, including Precision and mAP@50% on the constructed dataset.

Conclusions:

  • The proposed YOLO-DST method effectively overcomes the challenges of defect detection in MEMS metallographic images.
  • The integration of dynamic channel-spatial blocks and multi-attention mechanisms significantly improves the detection of small defects, enhancing MEMS reliability.