The 3D Stellarator and Tokamak (3DST) Laboratory is dedicated to developing optimized solutions for magnetically confined fusion systems. To achieve this, the lab focuses on advancing theoretical models and computational frameworks across multiple scales to establish a validated integrated predictive model. The newly developed modeling framework incorporates 3D constraints and boundary conditions, enabling system-level analysis and prediction across diverse scales—from large, fast magnetohydrodynamic (MHD) instabilities to small-scale kinetic transport and turbulence phenomena. Currently, the lab is enhancing 3D plasma equilibrium models by integrating kinetic effects and drift-MHD modeling. This research aims to develop novel methodologies for simultaneously predicting key instabilities—interchange, locking, tearing, peeling, and ballooning modes—along with their associated transport behaviors in toroidal magnetic confinement systems. To validate predictions and implement adaptive control strategies, the lab collaborates closely with domestic and international fusion institutions. By leveraging these predictive and control capabilities, the 3DST Laboratory seeks to optimize fusion system configurations and scenarios, ultimately driving the development of innovative integrated solutions that enhance the performance and reliability of future fusion reactors.

Plasma Laboratory for Advanced Research conducts research to sustain stable and continuous fusion reactions in tokamak fusion reactors. The laboratory develops tokamak operation scenarios and performs integrated computational simulation analyses to achieve high-performance plasma in devices such as VEST and KSTAR. The laboratory also focuses on plasma control technology and the design of next-generation fusion devices using a virtual tokamak simulator. In addition, the laboratory explores various fusion-related topics, including plasma discharge, transport, instabilities, heating, and current drive, in an integrated manner. Furthermore, the laboratory collaborates with the Korea Institute of Fusion Energy as well as leading fusion research groups worldwide on joint research projects.

The Nuclear Materials Modeling Laboratory (NMML) studies nuclear materials using computational simulations such as molecular dynamics and first-principles calculations, which can analyze the motion of atoms, and theoretical methods such as thermodynamics and kinetics. (1) To enhance the safety and economics of using liquid metals and molten salts as coolants in fast reactors and tritium breeding materials in fusion reactors, it is necessary to suppress material corrosion and fire caused by high chemical reactivity. We apply computational simulations and theories to accurately evaluate the chemical reactivity and develop a method to effectively control the factors that determine the reactivity of these liquid materials, as well as discover new liquid materials through a materials informatics approach that integrates atomistic simulations and machine learning methods. (2) Tritium, a hydrogen isotope and the fuel for nuclear fusion reactors, is radioactive and rarely found in nature. Therefore, to realize a safe and economical nuclear fusion reactor, we need to thoroughly understand and control the behavior of tritium in various nuclear fusion reactor materials. We are addressing this issue by combining atomistic simulations and experiments performed through national and international collaborations. (3) Nuclear materials are exposed to energetic particles such as fast neutrons, which create a variety of defects and degrade the performance of the material through complex reactions that do not occur under normal conditions. By combining atomic simulations and machine learning techniques, we are building an accurate radiation damage prediction model.

The Plasma & Ion Beam Laboratory has built and operates the Versatile Experiment Spherical Torus (VEST), the only experimental spherical tokamak in Korea. Through actual fusion plasma experiments using the VEST, we are conducting research on all aspects of fusion plasma, including plasma discharge, heating, diagnosis, control, and disruption, with the ultimate goal of realizing fusion power as a viable energy source for the future.
In particular, we have developed an effective plasma discharge scenario using a Trapped Particle Configuration (TPC) magnetic structure and successfully installed and operated a Neutral Beam Injection (NBI) heating system, which is widely used for heating fusion plasma. In addition, we are developing and upgrading various diagnostic instruments such as magnetic diagnostics, Thomson scattering diagnostics, interferometers, photonic phase and neutral pressure gauges to measure various parameters of the fusion plasma, and developing fusion plasma equilibrium and prediction codes based on these data to conduct research on plasma control and decay mechanisms.
Based on these studies, we are working with the simulation team to solve challenging problems in fusion reactors, such as stable operation and plasma instability analysis. We also plan to build a virtual fusion reactor that can operate a high-performance spherical tokamak using artificial intelligence techniques, with the ultimate goal of realizing fusion power generation.

SNUPAL conducts research on “Data-driven Plasma Science” across diverse fields, including plasma processes for semiconductor and display manufacturing, fusion plasma diagnostics and monitoring, and plasma application physics.

We extract plasma physics information from large-scale data generated in world-leading semiconductor and display manufacturing environments, and integrate it with artificial intelligence to develop Plasma Information-based Virtual Metrology (PI-VM), Plasma Information-based Advanced Process Control (PI-APC), and a broad range of Plasma Information-based Artificial Intelligence (PI-AI) technologies.

Our research leads the application of PI-AI to various aspects of semiconductor and display processes, including outcome prediction, root-cause analysis, process design, and mass production control. Based on these efforts, we develop novel and next-generation processes for semiconductor and display manufacturing.

Leveraging flexible PI-AI methodologies, deep understanding of low-temperature plasma physics, kinetics modeling techniques, advanced plasma diagnostics, and modeling of plasma–surface interactions, we conduct research on process optimization for new pattern architectures, development of novel material processes, and next-generation plasma processes such as cryogenic processes, atomic layer etching (ALE), and atomic layer deposition (ALD), as well as conventional plasma processes including etching, PECVD, and sputtering.

In particular, we focus on interpreting complex process plasmas based on fundamental kinetics modeling, and we carry out in-depth research on plasma sheath dynamics, which remains one of the most challenging open problems in the field of low-temperature plasma physics.