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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Purdue University |
| Country | United States |
| Start Date | Sep 15, 2024 |
| End Date | Aug 31, 2027 |
| Duration | 1,080 days |
| Number of Grantees | 5 |
| Roles | Principal Investigator; Co-Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2425126 |
The project aims to address emerging challenges in the field of materials and computing, specifically focusing on Heterogeneous Integration (HI) in Packaging (HIP). As the density of Interconnected Circuits (ICs) in 2D and 3D packages increases, new defects that affect reliability are emerging. These defects can lead to losses in performance, reliability, and overall lifespan of semiconductor devices.
By developing rapid 3D metrology techniques, and incorporating machine learning, this project aims to accelerate the rate of detection of fabrication defects thereby accelerating the development of next generation of semiconductor packages. The findings of this project have the broader potential to revolutionize the semiconductor industry by improving the reliability and performance of electronic packaging.
Furthermore, the project aims to enhance materials education and public awareness of the importance of semiconductors and HIP, 3D imaging, and computational modeling of semiconductor packages.
The technical aspect of the project involves the development of advanced metrologies and predictive multiscale modeling tools. These tools will be designed to capture defects within HI packages at multiple length scales and predict the most critical defects to device failure. The project proposes a transformational approach for rapid defect detection in next-generation HIPs and the quantification of the impact of these defects on package reliability.
This approach includes a framework for probabilistic and uncertainty analysis to assess the probability of failure of a particular component. The project will involve a rigorous fusion of 3D materials characterization, materials science, mechanics, and machine learning, coupled with numerical and analytical modeling across different length scales. The broader impact includes opportunities for undergraduate students of various backgrounds and mentorship of students who wish to go into academia.
A diverse educational and outreach program is integrated within the research program. This coordinated approach will enable a new paradigm for defect detection and reliability, as well as training the next generation of students for the semiconductor industry.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Purdue University
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