AI-Driven Thermal Prediction for Enhanced Reliability in 3D HBM Chiplets
In this talk, Dr. Aladwani presents a data-driven methodology that leverages neural networks trained on finite-element simulations to predict junction temperatures and hotspot locations in 3D HBM chiplets. This approach significantly reduces simulation time while maintaining accuracy, enabling faster, reliability-aware design of advanced 3D IC systems.
About Dr. Tahani Aladwani:
Dr. Tahani Aladwani is a Research Assistant at the University of Glasgow. She earned her PhD in Computer Science (2024) and specializes in federated learning, data representation, and meta-learning techniques. Her research focuses on addressing challenges such as label inconsistencies, data imbalance, and improving model performance.
This talk was part of COIN3D, a Horizon Europe Twinning project, co-funded by the European Union.
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