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Convolutional neural network model based on terahertz imaging for integrated circuit defect detections.

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    Terahertz imaging combined with a novel convolutional neural network (CNN) improves integrated circuit (IC) defect detection. This nondestructive method offers a faster, more accurate alternative to traditional techniques for IC manufacturing quality control.

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    Area of Science:

    • Materials Science and Engineering
    • Electrical Engineering
    • Computer Science

    Background:

    • Integrated circuit (IC) defect detection is critical in manufacturing.
    • Conventional methods like scanning electron microscopy and X-ray imaging are time-consuming and destructive.
    • Terahertz (THz) imaging presents a label-free, nondestructive alternative.

    Purpose of the Study:

    • To enhance terahertz imaging-based IC defect detection using a convolutional neural network (CNN).
    • To develop and optimize a CNN model for improved accuracy in THz imaging defect identification.

    Main Methods:

    • Construction and analysis of a terahertz imaging integrated circuit dataset.
    • Proposal of a novel CNN architecture based on the VGG16 network.
    • Optimization of the CNN structure and dropout rate for performance enhancement.

    Main Results:

    • The proposed CNN model significantly improves the accuracy of integrated circuit defect detection using terahertz imaging.
    • The optimized CNN structure and dropout rate contribute to enhanced detection performance.

    Conclusions:

    • The developed CNN-enhanced terahertz imaging technique offers a more accurate and efficient method for IC defect detection.
    • This research promotes the practical application of terahertz imaging in semiconductor manufacturing.
    • Provides a foundation for future advancements in non-destructive testing and analysis within the field.