Projects : Electronic
Memory
Full-Cycle AI Technology for Developing Ferroelectrics toward In-Memory Computing
In-memory computing (IMC) is a computing technology that performs data storage and computation within the system’s main memory, overcoming the von Neumann bottleneck. The IMC systems require superior memory devices with both scalable, non-volatile storage and low-power, high-endurance, high-speed computing properties. Ferroelectric RAM (FeRAM) is a promising device for IMC systems, which is non-volatile and low power operation, but its large cell-size and weak endurance are still in challenges. A full-cycle AI-based materials design integrates materials, process, microstructure, device, and circuit level properties through AI-driven generation, prediction, and optimization. Using an automated data crawling system based on natural language processing (NLP) with experimental validation process, this design system can rapidly predict optimal recipes for superior FeRAM devices, addressing the shortcomings in current IMC applications.
Understanding the domain walls movement of ferroelectric HfO2 with machine learning potential
HfO2-based FeRAM has been spotlighted as a key material for future semiconductor technologies such as logic-in-memory, neuromorphic computing, and negative capacitance FET, but it is facing difficulties in practical use due to its high coercive field and low domain wall velocity. Therefore, this study aims to understand and investigate the phenomenon related to the movement of domain walls under the electric field of HfO2-based ferroelectric materials at the atomic level through machine learning and molecular dynamics simulation.
Optics
Assessing Additive Compositions for Glass Using MLP, MD, and DFT-based Simulations
Amorphous silicon dioxide (SiO₂) is a fundamental oxide material with extensive scientific and commercial applications, attributed to its excellent thermal properties, dielectric characteristics, and optical transmission performance. To enhance the transparency of silica glass—the amorphous form of SiO₂—dopants such as germanium (Ge) or titanium (Ti) are often introduced to increase optical light transmission.
Using ab initio molecular dynamics, potential datasets are generated on Ge- and Ti-doped amorphous SiO₂ to train machine learning potentials. These trained models enable the reproduction of amorphous structures with quantum-level accuracy. The dielectric constant, and consequently the refractive index, of the Ge/Ti-doped amorphous SiO₂ is then calculated to assess the system’s transmission coefficient.
END.
Completed projects
Understanding of the rapid growth of tungsten thin film on TiN adhesion layer for the semiconducting applications
TiN is a well-known material used for the adhesion layer in the fabrication of semiconductors. However, the rapid growth of W on the TiN layer has been a mystery, as the underlying mechanism is not well-understood. To address this issue, we conducted an investigation using first-principles calculations to determine the growth mechanism of W on the TiN layer. Our research has revealed that W growth on the TiN layer is driven by a combination of the energetics of the system and surface tension, leading to a greater understanding of the growth process. Our findings provide valuable information for future research on this topic.
Development of transition metal oxides for electrolyte forming low voltage filament
Compared to conventional flash memory, RRAM has advantages such as scalability, durability, and cost, so it is attracting attention as a next-generation memory. The performance of RRAM is greatly influenced by the filament behaviors in the electrolyte, and understanding of the filament behaviors is possible through first-principles calculations. In this project, we predict the behavior of the filaments through first-principles calculations of the change in the properties of defects constituting the filaments and propose an optimal material and composition.
Development of novel resistance switching materials by using first principle-based machine learning platform
AIM-HS(Ab Initio calculation based Machine learning High-throughput Screening) platform is Ab initio calculation high-throughput screening system based on multi-dimensional machine learning analysis. This project is purposed to detect hidden correlation between calculated properties and make criterion based on big data of Ab initio calculation. The final goal is material designing based on needs for material property.
Synthesis of wafer-scale single-grain Si2Te3 2D thin films and fabrication of high-performance atomic switches
Using a new single crystal synthesis mechanism, Te is reacted on a silicon substrate to synthesize a wafer-scale single crystal Si2Te3 thin film. We intend to develop a highly integrated atomic switch device using no-transcription process that overcomes the difficulties of practical use of 2D thin film materials.To this end atomic switching mechanism and electrical properties and device stability of Si2Te3 thin film are investigated by the first principle calculations.
Understanding the resistance switching mechanism of single-element-based devices using first principle calculation
Collaborating with POSCTECH SIDP Laboratory
Atom-based devices can exhibit memory, switch, and battery characteristics and depend on switching conditions. In addition, it is a clue to overcome various problems of IoT devices. In this project, through the calculation of the first principle, the mechanism for various properties of ions behaving in atom-based device will be identified, and based on this, a method for maximizing the performance of the device will be sought.
Discovery of ultralow operating voltage memristor materials using high-throughput calculations
Collaborating with Kookmin university
Finding out new composition molecular-motion ionic crystal by using high-throughput screening calculations.Selecting new composition material group for memristor by using high-throughput screening calculation
Atomic analysis and understanding of multilayer thin film structure and composition through first principles calculation
Collaborating with NINT(National Institute for Nanomaterials Technology) of POSTECH
APT(atom probe tomography) is a powerful method to analysis chemical distribution within a material at an atomic scale.However, accuracy in hetero-structure is low due to difference of the electric field evaporation intensity between elements. Therefore, in order to improve the accuracy of ATP analysis in hetero-structure, it is necessary to predict the changes in structure and composition by using the first principles calculation method and to elucidate the mechanism of atomic behavior change in the electric field evaporation process.
Understanding physical origin of abnormal optical and magnetic properties of rare-earth doped GaN for optoelectronics or spintronics applications
Collaborating with Lehigh University
Elucidating thermodynamics and kinetics of various defect complexes under differnet treatments in Re:GaN. Identifying efficient energy transfer mechanism for enhanced luminescence in various defect centers. Understanding the physical origin of superparamagnetism in Re:GaN
Exchange interaction of paramagentic noises in superconducting qubit
Collaborating with Lawrence Livermore National Laboratory
Understanding the mechanism of exchange interaction of paramagnetic noise in superconducting qubit. Investigating atomistic origin of magnetic noise at superconductor/substrate interfaces. Developing methods to reduce magnetic noise sources in superconducting qubit