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ATNet: A Defect Detection Framework for X-ray Images of DIP Chip Lead Bonding.

Renbin Huang¹, Daohua Zhan¹, Xiuding Yang¹

¹School of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China.

|July 29, 2023

Summary

This study introduces a new deep learning model, ATNet, for enhanced chip defect detection using X-ray imaging. ATNet significantly improves accuracy and speed in identifying defects, boosting production quality.

Keywords:

X-ray images chips deep learning defects

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Area of Science:

Artificial Intelligence
Computer Vision
Materials Science

Background:

X-ray nondestructive imaging is crucial for chip defect detection in quality control.
Traditional defect detection algorithms struggle with the high accuracy, speed, and real-time demands of industrial production.

Purpose of the Study:

To develop an advanced deep learning model for accurate and rapid chip defect detection.
To improve the production quality and qualification rate of chips through enhanced defect identification.

Main Methods:

Proposed a novel multi-scale feature fusion module (ATSPPF) based on convolutional neural networks.
Designed a deep learning model (ATNet) incorporating the ATSPPF module for chip lead defect detection.

Main Results:

ATNet achieved high average accuracy: mAP_0.5 of 99.4% and mAP_0.5-0.95 of 69.3%.
The model operates at 146 frames per second (FPS) with 8.2 giga floating point operations (GFLOPs).
Achieved a detection speed nearly 50% faster than the baseline yolov5s.

Conclusions:

The proposed ATNet model effectively extracts multi-scale semantic information for superior chip defect detection.
ATNet offers a promising solution for real-time, high-accuracy industrial chip quality inspection.