Introduction

Committing the future of advanced manufacturing

Introduction

TOP

About us

Chaotic Transport

  • Computation of stretching and its efficiency in chaotic systems
  • Mixing, reaction and chaos in multi-dimensional flows
  • Periodic point and invariant manifold in chaotic dynamics
Chaotic cavity flow

Chaotic cavity flow

(Source : Ho Jun Kim and Ali Beskok 2007 J. Micromech. Microeng. 17 2197)

Plasma Discharge

  • Process plasma (CCP) simulations: PE-TEOS, PE-SiN, PE-SiON, ACL, PE-ALD
  • Process plasma (ICP) simulations: HDP-SiO2, HDP-SiN, HDP-ACL
  • Remote plasma: fluorine chemistry, NF3 plasma dissociation, fluorine transport
Capacitively Coupled Plasma Reactor

Capacitively Coupled Plasma Reactor

(Source: Journal of Applied Physics 118, 043304 (2015))

Surface Chemistry

  • Molecular dynamics: deposition, etching
  • Ab initio calculation: reaction rate, activation energy
  • Computational chemistry: reaction rate, reaction pathway
Molecule dissociation and deposition of Si2H6

Molecule dissociation and deposition of Si2H6

(Source: Applied Surface Science Volume 496, 1 December 2019, 143728)
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Chaotic Systems and Plasma Physics Laboratory is a research group within the Department of Mechanical Engineering specializing in advanced computational fluid dynamics (CFD), molecular dynamics (MD), and plasma simulations for semiconductor and display processing equipment. Our lab employs a wide range of state-of-the-art modeling techniques—including 2D/3D numerical simulations, Particle-in-Cell/Monte Carlo Collision (PIC-MCC) methods, Molecular Dynamics (classical MD and Reactive MD), and Density Functional Theory (DFT)—to precisely analyze the complex fluid dynamics and chemical reactions occurring in plasma processing environments. Through these approaches, we systematically investigate microscale phenomena such as plasma distribution, transport of reactive species, thin-film deposition mechanisms, surface reactions, and etching behavior inside plasma reactors.

We place a strong emphasis on optimizing key materials and structural components within processing equipment—such as wafers, electrodes, and chamber sidewalls—to meet the demands for high precision and high efficiency in semiconductor and display manufacturing. By quantitatively evaluating how material properties (e.g., dielectric constant, thermal conductivity) and geometric configurations influence plasma characteristics and film-thickness uniformity, we develop practical design guidelines that can be readily applied in industrial settings. Simulation results are rigorously validated through comparison with experimental data to ensure accuracy and reliability.

Recently, the laboratory has expanded its research into data-driven approaches by integrating machine learning with physics-based simulations. Using large-scale datasets generated from DFT calculations, we develop machine-learning models capable of rapidly predicting the activity and stability of catalysts for key energy-conversion reactions such as the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). This combined computational–data-driven framework accelerates the discovery and optimization of novel materials, contributing both to clean-energy technologies and next-generation semiconductor processes.

The Chaotic Systems and Plasma Physics Laboratory actively collaborates with government agencies—including the Ministry of Trade, Industry and Energy and the Ministry of Science and ICT—as well as national research institutes and industrial partners. Through these collaborations, we address real-world engineering challenges and drive technological innovation. Our researchers routinely present their findings at major domestic and international conferences, maintaining a leading role in advancing the field.

Ultimately, the Chaotic Systems and Plasma Physics Laboratory aims to pioneer the future of semiconductor and display industries by combining cutting-edge CFD, plasma simulation, molecular dynamics, and data-driven materials design. By integrating experiments, simulations, and data science, we are committed to developing high-value processing technologies and breakthrough materials that will shape the next era of advanced manufacturing.

Chaotic Systems and Plasma Physics Laboratory Logo txt

In our Lab, We focus on industry-sponsored research related to new equipment development.

  • CCP Plasma Deposition
  • Magnetron Sputter
  • CCP Plasma Deposition
  • Plasma Deposition
  • Chemical Vapor Deposition
  • Plasma Cleaning
  • Chemical Vapor Deposition
  • ICP Plasma Deposition
  • CCP Plasma Deposition
  • ICP Plasma Deposition
  • Plasma Deposition
  • Chemical Vapor Deposition
  • Plasma Cleaning
  • Visiting Professor
  • Plasma Equipment
  • Digital Twin
  • ICP + CCP Plasma Deposition
  • World First Concept
  • CCP Plasma Deposition
  • Visiting Professor
Rank
2016
Rank
2018
Company Share(%)
2018
1 1 Applied Materials 20.9
2 2 Lam Research 15.9
4 3 Tokyo Electron 14.1

Source : Gartner (April, 2019)

Importance of understanding plasma behavior

Plasma Process Flow | This diagram illustrates the Input-Process-Output relationship in plasma manufacturing:
					- Inputs (External Parameters): The process begins with controllable settings on the left, such as plasma power, gas composition, and pressure.
					- Process (Plasma Interaction): These parameters dictate the behavior of the plasma generated inside the chamber, which reacts with the substrate (wafer).
					- Outputs (Process Results): The plasma's behavior determines the final quality metrics on the right, including deposition rate, thickness, and uniformity.
					- Core Message: Precise control of external parameters is required to optimize plasma behavior and achieve the desired processing results.

Experimental limitations

Costly, time-consuming recipe experiments

Quantitative analysis recipe experiments

Process optimization through condition analysis

Growing demand for Multiscale

Accuracy limits of single-scale analysis

Growing need for plasma-wafer coupled analysis

Expanded use of simulations for recipe optimization

Need for simulation technology to continuously analyze from plasma behavior in equipment to thin film profiles

Key Technology

  • Process-validated plasma chemistry
  • Real process plasma & complex geometry equipment data
  • Multi-dimensional simulation methodology
Experimental data: Korea Institute of Fusion Energy

Experimental data: Korea Institute of Fusion Energy

Plasma distribution under parameters

Reactor design improvements

Providing process results

Key Technologies

  • Wafer processing equipment for thin-film deposition
  • ioneering PEALD for atomic-scale thin films
  • dvanced semiconductor products, services & materials

Project demand

  • Spatial distribution of reactive species beyond simple fluid simulations
  • Enhanced uniformity in next-generation deposition equipment
  • dentifying and solving causes of process nonuniformity
Evaluation of how process parameters affect thin-film uniformit