Aung, A. P. P., Lum, L., Shi, Z., Qiu, W., Zee, B., Chin, J. M., Lim, Y. K., & Jayavelu, S. (2026). Physics-informed generative models for magnetic field images. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2026). IEEE.
Abstract:
In semiconductor manufacturing, defect detection and localization are critical to ensuring product quality and yield. While X-ray imaging is a reliable non-destructive testing method, it is memory-intensive and time-consuming for large-scale scanning, Magnetic Field Imaging (MFI) offers a more efficient means to localize regions of interest (ROI) for targeted X-ray scanning. However, the limited availability of MFI datasets due to proprietary concerns presents a significant bottleneck for training machine learning (ML) models using MFI. To address this challenge, we propose Physics Informed Generative Models for Magnetic Field Images (PI-GenMFI) to generate synthetic MFI samples by integrating specific physical information. We generate MFI images for the most common defect types: power shorts. These synthetic images will serve as training data for ML algorithms designed to localize defect areas efficiently. We present domain experts, qualitative and quantitative evaluation using various metrics used for image generation and signal processing, showing promising results to optimize the defect localization process.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Industry Alignment Fund - Pre-Positioning: Machine Learning Guided Failure Analysis & Diagnostic Capability Development for Next Generation 3D-IC Packaging
Grant Reference no. : M23K8a0050