Research

We study material properties in functional materials and devices using various imaging techniques combined with machine learning algorithms. In particular, we study material properties in the applications of information and energy technologies by advanced atomic force microscopy (AFM, 원자힘현미경) techniques combined with machine learning algorithms. 

Uderstanding material properties using AI (AI를 활용한 물성 이해)

(Left) Singular value decomposition and k-means clustering allow identifying each local microstructure of carbon steel. (Right) Further, the application of clustering to the 4D I-V data-set allows visualizing distribution of nanoscale components in fuel cells.

Local evaluation of ferroelectricity and electrochemistry in oxides (e.g. HfO2, BaTiO3) and metals (e.g. steel, Al, Cu)

We explore local ferroelectricity in various materials systems (e.g. HfO2 and BaTiO3) for multiple applications (e.g. negative cap, FET, and MLCC). Furhter, we explore local electrochemistry in oxide and metal surfaces (e.g. steel, Al, hydrogen storage materials, TiO2, Li ion-based materials) for multiple applications (e.g. fuel cells, corroson, memories). To explore local ferroelectricity and electrochemistry, we apply machine learning algorithms to the AFM data-set combined with the advanced AFM techniques.

Applications

We also study vaiours materials such as piezoelectrics and ferroelectrics for sensors, memories and capacitors by analyzing piezoelectric, ferroelectric and capacitive properites and analyze image and spectroscopy based on the machine learning algorithms.