Artificial Intelligence in Multimodal Microscopy Workflows for Failure Analysis

In this article, we explore how artificial intelligence (AI) is transforming the way image data is processed and analyzed in the context of failure analysis (FA) for electronics and semiconductors. We highlight the core benefits of AI-based approaches, examine practical applications, and discuss the future implications for research, quality assurance, and industrial reliability.

Key Learnings:

• Advancements in Image Processing: The integration of AI, machine learning (ML), and deep learning (DL) has revolutionized image processing and analysis, enabling high-resolution, non-destructive imaging for failure analysis (FA) in electronics and semiconductors.
• AI-Powered Image Segmentation: AI-driven segmentation techniques improve the precision and scalability of defect detection, addressing challenges posed by complex and high-dimensional image datasets.
• Future of FA with AI and Data Management: The adoption of cloud-based training, multimodal pipelines, and robust data management systems is paving the way for automated, efficient, and predictive failure analysis workflows.