Materials Science

This course will briefly summarize the fundamental knowledge of linear algebra, statistical probability and distribution theory, python programming language and databases. In addition, this course will introduce the main workflow and concepts in machine learning, including supervised learning, unsupervised learning, regression, classification and some mainstream algorithms. Importantly, some representative examples of machine learning in material science will be introduced. Meanwhile, the importance of quantum chemistry and molecular dynamics simulations in machine learning will be emphasized at the end.

This introductory semiconductor course is specifically designed for interdisciplinary learners. Leveraging professors from STEM (engineering, science, engineering, electrical engineering, and computer science) backgrounds, the course utilizes unit-based thematic teaching to help students understand semiconductor terminology, principles, and forward-looking applications, while also developing their ability to plan for further learning. The course features a holistic overview and does not emphasize related mathematical theories; therefore, it also serves as a general introductory course in contemporary materials science, suitable for students of all backgrounds and academic levels (including graduate students) interested in semiconductor technology. As an introductory course for interdisciplinary learners, this course first provides an overview of the current semiconductor world and how self-learners can modularly understand semiconductor technology. It then explores forward-looking chip applications and AI development to help students understand the importance of semiconductors for future societal development and even human civilization, and why undergraduate and graduate students should study semiconductor general knowledge. The course then progresses through basic solid-state physics and chemistry, materials and components, process equipment, and smart manufacturing modules, concluding with a comprehensive reflection on chip globalization and AI development. There are no exams for this course. Students  integrate the knowledge they have acquired and explore a personalized learning map by submitting handwritten study notes, group discussion assignments, and final forum poster activities.