Sept. 11, 2020
AI research to predict and refine commercial use of diamonds
Large silicon crystals are the basis for semiconductor computer chips and switching devices for electric grid applications. The efficiencies of today’s electronic devices are dependent on a crystal’s perfection and its ability to better control electrons without loss.
Diamond semiconductor crystals outperform silicon in electric grid and high power applications by orders of magnitude but are essentially unavailable for commercial use in the current marketplace.
What if artificial intelligence (AI) could improve process efficiencies and grow large-area defect-free crystals for manufacturing?
That’s what Elias Garratt of Michigan State University will explore with the help of a two-year, $500,000 grant that begins this month from the National Science Foundation’s Future Manufacturing Program.
The assistant professor of electrical and computer engineering and materials science is collaborating with Fraunhofer USA Center for Coatings and Diamond Technologies (CCD) and Fraunhofer USA Center for Experimental Software Engineering (CESE) to develop AI technology to predict and refine diamond growth for manufacturing.
The research team hopes to leverage the vast amounts of data generated during the crystal-growth process instead of analyzing data once experiments are completed.
“Development and integration of deep learning artificial intelligence architectures in the Chemical Vapor Deposition process will make growth predictions more accurate and add defect assessment to the prediction for manufacturing of diamond materials,” he explained. “Outcomes of the project will accelerate the development cycles and reduce costs for manufacturing processes – making them adaptable to a broad range of crystal-growth processes for electronics,” he added.
The team at MSU and Fraunhofer USA will also develop a course in AI-based manufacturing aimed at preparing vocational workers to run crystal deposition/growth equipment in an AI-driven manufacturing setting. Concepts developed in the project will also be integrated into existing courses, capstone projects for students, and education modules for training operators.
For more on the project, read the NSF abstract and ECE website.