Teaching

(1) Overview of Semiconductor Processing Advancements: this lecture provides a concise overview of the rapid advancements in semiconductor processing, highlighting the need for advanced optimization techniques.

(2) Introduction to Computational Methodologies: this lecture introduces various computational methodologies used in semiconductor manufacturing, offering a fundamental understanding of their theoretical underpinnings.

(3) Hands-on Experience: Students will have the opportunity for hands-on experience with simulation techniques for a simplified semiconductor processing application. This practical exercise will enhance their understanding of how computational methods can be applied to optimize semiconductor processes.

Overall, this lecture will enable students to gain insights into the cutting-edge developments in semiconductor manufacturing and equip them with the knowledge and skills needed to apply computational science to address complex challenges in the industry.

(1) Fundamentals of Plasma Physics: Students will be introduced to the core principles of plasma physics, including plasma generation, ionization mechanisms, and energy transfer. This theoretical foundation will help them grasp the behavior of plasma under different conditions and its interactions with materials.

(2) Simulation Methods for Plasma Processes: Students will learn about computational methodologies and simulation techniques that model plasma behavior and plasma-material interactions. Through these simulations, they will understand the application of computational tools in predicting outcomes and optimizing plasma processes for semiconductor manufacturing.

(3) Practical Applications and Case Studies: The course will include hands-on exercises and case studies, where students apply simulation methods to a simplified plasma processing scenario. This practical experience aims to deepen their understanding of how theoretical concepts translate into real-world applications in semiconductor fabrication.

Through this lecture, students will gain a holistic understanding of plasma processes, from foundational principles to practical applications. They will leave with the skills and knowledge to utilize plasma processing techniques and computational tools for advanced manufacturing challenges in the semiconductor industry.

This course provides a comprehensive introduction to computational techniques for modeling and analyzing materials behavior, with a focus on numerical methods, optimization algorithms, and modern AI tools. The first half of the course emphasizes numerical fundamentals such as root-finding, matrix operations, regression, and differential equations, enabling students to develop robust modeling skills using MATLAB or Python.

In the latter part of the course, students are introduced to atomistic-scale simulation techniques, including classical molecular dynamics (MD) and basic principles of density functional theory (DFT), along with hands-on tutorials using tools like LAMMPS. Key applications to materials systems—including mechanical, thermal, and electronic property prediction—are explored through project-based learning. The course culminates in a final project where students apply learned methodologies to solve real-world materials science problems.