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Improved sensitivity of atomic force microscopy images by machine learning

김윤석 교수

성균관대학교 신소재공학과

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yunseokkim@skku.edu

Improved sensitivity of atomic force microscopy images by machine learning

Yunseok Kim^1,*

¹School of Advanced Materials Science and Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea

*yunseokkim@skku.edu

Atomic force microscopy (AFM) becomes a quasi-essential tool for exploring microstructural, physical, and chemical properties of materials at the nanoscale. In particular, as the research focus on the sizes or dimensions has shifted from bulk to nanosized materials, the AFM techniques with better signal are continuously developing. Nonetheless, the interpretation of the AFM data is still insufficient to fully understand materials properties due to technically limited signal. Recently, machine learning based analysis has received considerable attention because it allows overcoming the current technical drawbacks. The improved sensitivity of AFM techniques could allow better interpretation of the AFM data and, eventually, allow better understanding of material properties. In this presentation, I will discuss about application of machine learning for improving sensitivity of AFM images. In particular, I will focus on the application of deep learning to the piezoresponse force microscopy, which can be used to observe piezoelectric, ferroelectric, and electrochemical properties, for improving sensitivity and spatial resolution.

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