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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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A Lightweight Method for Detecting IC Wire Bonding Defects in X-ray Images.

Daohua Zhan1,2, Jian Lin1,2, Xiuding Yang1,2

  • 1State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangzhou 510006, China.

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Summary
This summary is machine-generated.

A new convolutional neural network (CNN) framework with a Spatial Convolution Attention (SCA) module improves integrated circuit (IC) wire bonding defect detection. The lightweight Light and Mobile Network (LMNet) offers high accuracy with efficient performance.

Keywords:
X-ray imagesconvolutional neural networklightweight networkwire bonding defects

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Materials Science

Background:

  • Integrated circuit (IC) wire bonding inspection is vital for product quality.
  • Current defect detection methods suffer from slow speeds and high energy consumption.

Purpose of the Study:

  • To develop an efficient and accurate CNN-based framework for IC wire bonding defect detection.
  • To introduce a lightweight network (LMNet) for practical industrial applications.

Main Methods:

  • Proposed a CNN framework incorporating a Spatial Convolution Attention (SCA) module.
  • Developed a lightweight network, LMNet, integrating the SCA module for multi-scale feature analysis and adaptive weighting.
  • Evaluated the network's performance using metrics like mean average precision (mAP50), GFLOPs, and FPS.

Main Results:

  • The LMNet achieved a mean average precision (mAP50) of 99.2%.
  • The network demonstrated efficient performance with 1.5 GFLOPs and 108.7 FPS.
  • The proposed framework balances high performance with low energy consumption.

Conclusions:

  • The LMNet framework effectively detects wire bonding defects in IC chips.
  • The SCA module enhances feature integration and adaptive weighting for improved accuracy.
  • LMNet presents a practical solution for industrial IC inspection, offering a favorable performance-consumption trade-off.