AI Applications in Semiconductor Packaging

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Course Description

This two-hour live session examines how AI is reshaping semiconductor packaging reliability. It compares conventional approaches with advanced methods for predicting performance. Additionally, it analyzes failures and optimizes lifecycle outcomes.

Course Objectives

• Compare traditional and AI-driven approaches to semiconductor packaging reliability, gaining insight into how artificial intelligence transforms performance prediction and failure analysis.
• Understanding key categories of AI, including machine learning (ML), deep learning, and generative AI–with distinctions between supervised, unsupervised, and generative models.
• Explore neural networks for Semiconductor Packaging, specifically their components, including activation functions and neuron models, through analogies to human cognition and decision-making.
• Discover select AI/ML techniques, such as Support Vector Machines, K-means clustering, Self-Organizing Maps, and Long-Short-Term Memory networks, and how they apply to packaging reliability.
• Apply AI methods to real-world challenges in Semiconductor Packaging, including anomaly detection, machine state assessment, digital twin modeling, and forecasting the timing of future failures.

Authors and Instructors