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

半导体材料的性能和质量对先进技术的广泛应用至关重要。生产这些材料的一个重大挑战是冷却过程。在生产阶段，半导体材料在冷却时容易破裂。这些裂纹可能会导致最终产品出现故障、可靠性降低和制造成本上升。该工程研究启动奖(ERI)支持旨在防止半导体冷却过程中裂纹形成的基础研究。这个项目的目标是开发一种新的方法，将机器学习技术与力学基本原理相结合来预测裂纹的形成。这项研究将促进高质量半导体材料的生产。该项目还将为STEM教育领域做出重大贡献。将建立一个可广泛使用的虚拟机械测试实验室，该实验室将使用交互式虚拟工具来教育学生有关测试材料的知识。这项研究的目标是开发一个力学信息的机器学习框架来预测和量化半导体材料中的界面裂纹，特别是在冷却过程中的碳化硅/氮化铝(SiC/AlN)界面。认识到界面缺陷和残余应力是裂纹的关键因素，研究目的是利用先进的机器学习和模拟技术来识别裂纹的机理并积极预防。机器学习模型将使用破裂行为的原子模拟进行训练，为半导体材料的设计提供创新的见解。这项研究的潜在贡献很多，不仅旨在减轻半导体接口的损伤，从而彻底改变其设计和生产，而且还旨在开发一个具有不确定性量化的集成机器学习框架，在预测其他材料的行为和性能方面具有更广泛的适用性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.