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AI Applications in Semiconductor Packaging
Explore how AI is reshaping semiconductor packaging reliability in this two-hour recorded training.
- 0.2 CEU / 2 PDH credits
- Launched 2025
- 1 course
- 2 hours
Course Description
This two-hour live session examines how AI is reshaping semiconductor packaging reliability, comparing conventional approaches with advanced methods for predicting performance, analyzing failures, and optimizing 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
