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.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $500.0k | 5/19/23 |