Project Grant 2305437

Award Date 9/1/23
Completion Date 8/31/26
Dollars Obligated $500K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Riverside, CA 92521, USA
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This $500,000 National Science Foundation project grant supports research at the University of California, Riverside to develop novel machine learning-based electromigration analysis and optimization methods for very large-scale integrated circuit design.

Specifically, the university will explore enhanced physics-informed neural network approaches for multi-segment interconnect stress analysis and full-chip electromigration-induced voltage drop modeling. Researchers will also develop efficient neural network-accelerated power grid optimization and dynamic run-time management techniques to identify actual hotspots during processor operation. This work aims to advance understanding and mitigation of electromigration, a growing reliability issue for sub-3nm integrated circuits.

The award falls under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and education across computing, communications, and information fields. Project outcomes could help address circuit lifetime and failure challenges facing continued semiconductor scaling.

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