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.

Machine learning algorithms for data acquisition using automated and autonomous experiments

Machine learning algorithms for understanding material properties and designing new materials

(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.

Machine learning algorithms for achieveing high sensitivity(low noise), high speed, and high resolution data acquisition (e.g. image and spectroscopy data)

The improvement of detection sensitivity of piezoresponse force microscopy, which is one of AFM modes, was demonstrated by combination of band exciation and various machine learning algorithms including deep neural network.

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.

Synthesis of oxide materials

We synthesize various oxide materials (e.g. HfO2, BaTiO3, and TiO2) using different kinds of synthesis methods (e.g. atomic layer deposition (ALD), spin coarter, sputter, and powder process).

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.

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