Graduate courses at UNIST spanning computational science, plasma processes, and computational materials. Click a course for the full outline.
01
Advanced Semiconductor Process and Computational Science
Trends in semiconductor processing, computational methodologies, and simulation hands-on.
Spring 2024Fall 2024Fall 2025
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COURSE 01 · GRADUATE
Advanced Semiconductor Process and Computational Science
Spring 2024Fall 2024Fall 2025
Students gain insights into cutting-edge developments in semiconductor manufacturing and the knowledge to apply computational science to complex industry challenges.
Topics
01
Overview of Semiconductor Processing Advancements
Concise overview of rapid advancements in semiconductor processing and the need for advanced optimization techniques.
02
Introduction to Computational Methodologies
Various computational methodologies used in semiconductor manufacturing and their theoretical underpinnings.
03
Hands-on Experience
Simulation techniques for a simplified semiconductor processing application — applying computational methods to optimize processes.
02
Introduction to Plasma Processes in Semiconductor Manufacturing
From plasma physics fundamentals to plasma–material interaction simulation and case studies.
Spring 2025Spring 2026
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COURSE 02 · GRADUATE
Introduction to Plasma Processes in Semiconductor Manufacturing
Spring 2025Spring 2026
A holistic understanding of plasma processes — from foundational principles to practical applications — equipping students to use plasma processing techniques and computational tools for advanced manufacturing.
Topics
01
Fundamentals of Plasma Physics
Plasma generation, ionization mechanisms, and energy transfer; plasma behavior and its interactions with materials.
02
Simulation Methods for Plasma Processes
Computational methods that model plasma behavior and plasma–material interactions to predict outcomes and optimize processes.
03
Practical Applications & Case Studies
Hands-on exercises applying simulation methods to a simplified plasma processing scenario.
03
Introduction to Computational Materials Science
From numerical fundamentals to atomistic simulation (MD/DFT) and project-based learning.
Fall 2025
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COURSE 03 · GRADUATE
Introduction to Computational Materials Science
Fall 2025
A comprehensive introduction to computational techniques for modeling and analyzing materials behavior, with a focus on numerical methods, optimization algorithms, and modern AI tools.
Topics
01
Numerical Fundamentals
Root-finding, matrix operations, regression, and differential equations — robust modeling with MATLAB or Python.
02
Atomistic-scale Simulation
Classical molecular dynamics (MD) and basic density functional theory (DFT), with hands-on tutorials using LAMMPS.
03
Applications & Final Project
Mechanical, thermal, and electronic property prediction through project-based learning; a final real-world materials project.