This National Science Foundation (NSF) Engineering program (CFDA 47.041) project grant award of $179,460 supports fundamental research to develop a novel machine learning framework for predicting and preventing cracking in semiconductor materials, specifically at silicon carbide/aluminum nitride (SiC/AlN) interfaces during the cooling process. The research aims to use advanced machine learning and simulation techniques to identify the mechanisms of cracking and proactively prevent it, which could revolutionize the design and production of semiconductor materials. Additionally, the project will establish a widely accessible virtual mechanical testing lab to educate students, with a focus on engaging underrepresented groups in STEM fields. The award period is from Jul 1, 2024 to Mar 31, 2026, and there are no planned sub-awards.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $179.5k | 8/5/24 |