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Related Experiment Video

Updated: Sep 25, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Artificial intelligence deep learning for 3D IC reliability prediction.

Po-Ning Hsu1,2, Kai-Cheng Shie1,2, Kuan-Peng Chen3

  • 1Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan, ROC.

Scientific Reports
|April 26, 2022
PubMed
Summary

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This study uses AI deep learning with 3D X-ray imaging to non-destructively analyze three-dimensional integrated circuit (3D IC) solder interconnects, achieving 89.9% accuracy in predicting failures.

Area of Science:

  • Materials Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Three-dimensional integrated circuits (3D ICs) are crucial due to limitations in traditional 2D scaling.
  • Reliability challenges in 3D ICs, particularly interconnect failures, necessitate advanced analysis methods.
  • Current testing methods for 3D IC reliability are slow and subjective.

Purpose of the Study:

  • To develop a non-destructive method for analyzing the reliability of solder interconnects in 3D ICs.
  • To leverage artificial intelligence (AI) and 3D X-ray imaging for automated failure prediction.
  • To identify key features indicative of interconnect failure in 3D ICs.

Main Methods:

  • Utilized 3D X-ray tomographic imaging to capture detailed images of solder interconnects.

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  • Employed a convolutional neural network (CNN), a type of AI deep learning, for image analysis.
  • Trained the AI model using a curated database of images to recognize failure patterns.

    • Main Results:

    • Achieved an accuracy of up to 89.9% in detecting and predicting interconnect faults.
    • Successfully identified critical features, such as area loss percentage, that correlate with interconnect failure.
    • Demonstrated the capability of AI to rapidly assess 3D IC reliability from non-destructive imaging.

    Conclusions:

    • AI-powered 3D X-ray analysis offers a fast and accurate solution for 3D IC reliability assessment.
    • This approach overcomes the limitations of traditional, time-consuming testing methods.
    • The findings contribute to enhancing the manufacturing yield and operational lifespan of 3D ICs.