Machine Learning in Semiconductor Failure Analysis: Techniques and Case Studies
Machine Learning in Semiconductor Failure Analysis: Techniques and Case Studies
Monday, October 28, 2024: 9:00 AM
202 (Hilton San Diego Bayfront)
Summary:
Since its release, ChatGPT has sparked ongoing fascination for its human-like responses, reflecting the growing interest in AI's potential applications across various fields. In microelectronics failure analysis, a discipline aiming for reliability and performance improvement, machine learning holds promise by aiding defect localization and subsequent analysis through destructive inspection tools. The ability to process large datasets and extract correlated information benefits defect analysis, assisting in identifying root causes. This tutorial introduces machine learning and deep learning to failure analysts covering historical context, data handling, feature extraction, learning algorithms, model evaluation, and optimization. It explores convolutional neural networks for image processing and advanced neural network applications like auto-encoders and natural language processing. Case studies demonstrate ML's potential in failure analysis, such as bill of material generation from optical images, automatic defect detection on PCBs, void segmentation in x-ray images, and SEM image denoising.
Since its release, ChatGPT has sparked ongoing fascination for its human-like responses, reflecting the growing interest in AI's potential applications across various fields. In microelectronics failure analysis, a discipline aiming for reliability and performance improvement, machine learning holds promise by aiding defect localization and subsequent analysis through destructive inspection tools. The ability to process large datasets and extract correlated information benefits defect analysis, assisting in identifying root causes. This tutorial introduces machine learning and deep learning to failure analysts covering historical context, data handling, feature extraction, learning algorithms, model evaluation, and optimization. It explores convolutional neural networks for image processing and advanced neural network applications like auto-encoders and natural language processing. Case studies demonstrate ML's potential in failure analysis, such as bill of material generation from optical images, automatic defect detection on PCBs, void segmentation in x-ray images, and SEM image denoising.