ERI: A Machine Learning Framework for Preventing Cracking in Semiconductor Materials

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The performance and quality of semiconductor materials are critical to advanced technologies for a wide range of applications. A significant challenge in the production of these materials is the cooling process. During the production phase, semiconductor materials are prone to cracking as they cool. These cracks can lead to failures in the final products, decreased reliability, and higher manufacturing costs. This Engineering Research Initiation (ERI) award supports fundamental research aiming to prevent the formation of cracks during the semiconductor cooling process. The objective of this project is to develop a novel method that integrates machine learning techniques with fundamental principles of mechanics to predict crack formation. This research will enhance production of high-quality semiconductor materials. This project will also make significant contributions to the field of STEM education. A widely accessible Virtual Mechanical Testing Lab will be established, which will use interactive virtual tools to educate students about testing materials. Special efforts will also be made to engage students who have historically been underrepresented in STEM fields in this research.The goal of this project is to develop a mechanics-informed machine learning framework to predict and quantify interfacial cracking in semiconductor materials, specifically at silicon carbide/aluminum nitride (SiC/AlN) interfaces during the cooling process. Recognizing that interfacial defects and residual stresses are critical factors in cracking, the research aim is to use advanced machine learning and simulation techniques to identify the mechanisms of cracking and proactively prevent it. The machine learning model will be trained using atomistic simulations of cracking behaviors, providing innovative insights into the design of semiconductor materials. The potential contributions of this research are numerous, aiming not only to mitigate damage in semiconductor interfaces, thereby revolutionizing their design and production, but also to develop an integrated machine learning framework with uncertainty quantification, which will have broader applicability in predicting behaviors and properties of other materials.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.