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 분회초청강연


     나노매뉴팩쳐링




  김명길 교수 성균관대학교


     ■ Education


2006.9 - 2012.2
Ph. D.
Chemistry (Division of Materials Chemistry)
Northwestern University, Evanston, IL, USA
1999.3 - 2006.8
M.S.
Chemistry (Major) and Applied Mathematics (Minor)
Korea Advanced Institute of Science and Technology(KAIST), Daejon, South Korea




■ Professional Career


2019.9 - present

Associate Professor Research

Sungkyunkwan University 

Research on advanced electronic material, soluble inorganic material precursor, energy and environmental material.

2014.3 - 2019.8

Associate Professor

Chung-Ang University

Research on advanced electronic material, soluble inorganic material precursor, energy and environmental material.

2013.1 - 2014.2

Postdoctoral Fellow

Stanford University

Developed stable and strong charge transfer doping of carbon materials (CNT/Graphene) and polymer semiconductor with oxide semiconductors.

2012.3 - 2012.12

Postdoctoral Fellow

Northwestern University 

Developed novel metal oxide semiconductor based interfacial layer for organic photovoltaics.




Sloution processing of high perfomance inorganic semiconductors 

for complementary electronics


There is increasing demands of material developments for novel electronic materials in emerging large area electronic applications, such as flexible electronics, wearable electronics, IoT devices, and photovoltaics, which require high electrical performance, novel mechanical functionality, high electrical stability, and/or exotic chemical stability under harsh condition. Furthermore, the emerging applications should be realized with low cost and high throughput processing methods. Although the conventional vacuum processed inorganic materials have been successfully utilized for common functionalities, the limited material functionality and high processing cost hinder its further applications in next generation large are electronics. In this talk, I will focus on our approaches to develop the high performance inorganic semiconductor materials, such as metal oxide, chalcogenide, halide, with soft chemical processing strategy. For example, the generalized chalco-gel precursors could achieve high performance TFT with low cost solution processing strategy. The optimized TFT device could exhibit a maximum field-effect mobility greater than 300 cm2V-1s-1 with an on/off current ratio of > 107 and a good operational stability (threshold voltage shift < 0.5 V at positive gate-bias stress of 10 ks). Finally, I will discuss the possibility of metal halides for complementary large area electronic applications.



  김석민 교수 중앙대학교


     ■ Education


2001.02
공학사
연세대학교 기계공학부
2007.02

공학박사

연세대학교 대학원 기계공학과 (석박사 통합과정)





■ Professional Career


2009.3 - 현재
조교수/부교수/교수
중앙대학교 기계공학부
2007.12 ~ 2009.02
Post-doctoral Researcher
Department of Electrical and Computer Engineering
University of Illinois at Urbana-Chamaign, U.S.A
2007.09 ~ 2007.11
박사후 연구원
기계공학부/정보저장기기연구소
연세대학교




Micro/nano hierarchy structured enhanced fluorescence substrate 

for protein microarray chip


Ag nanorods on micropost array was fabricated by the UV-imprinting process and the glancing angle deposition (GLAD) technique as a metal enhanced fluorescence substrate with improved signal-to-background noise ratio (SBR). Micropost structures (50 μm in height, 300 μm in diameter, and 600 μm in pitch) were replicated on glass slides by UV imprinting, and an Ag nanorod structures were formed on top of the micropost structures using GLAD. To maximize the SBR of the proposed Micro/nano hierarchy structured (MNH) MEF substrate, the effects of deposition angles during the GLAD process on the measured fluorescence intensity and SBR of MNH MEF substrates were examined. As a reference, the bare glass substrate and GLAD MEF substrate having GLAD Ag nanorods on bare substrate were fabricated. For fluorescence signal measurement, myeloid progenitor inhibitory factor 1 (MPIF-1) capture antibodies were spotted onto each substrate after amine treatment and their fluorescence intensity were measured after the antibody-antigen reaction. The 71x enhanced fluorescence intensity comparing to the bare glass substrate was obtained from the optimum MNH MEF substrate that had a substrate rotation speed of 5 rpm, d angle of 89°, deposition rate of 0.5 nm/s, and deposition thickness of 500 nm. The SBR of the optimum MNH MEF substrate was 41.0, which was 7 times greater than that form the GLAD substrate and 25 times greater than that from the bare glass substrate.



  최시영 교수 포항공과대학교


     ■ Education


2000.9 - 2004.4
Ph. D.
Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST)
1999.3 - 2000.8
M.S.
Department of Materials Science and Engineering KAIST
1993.3 - 1999.2
B.S.
Department of Ceramic Engineering Pusan National University, Busan, Korea




■ Professional Career


2017.7 - Present
Associate Professor
Department of Materials Science & Engineering, POSTECH
2016.1 - 2017.6
Head of Department
Department of Materials Modeling & Characterization Korea Institute of Materials Science (KIMS)
2013.1 - 2015.1
Principal Researcher, KIMS
2007.12 - 2012.12
Senior Researcher, KIMS
2006 - 2007.12
JSPS Fellow Researcher
Department of Materials Science & Engineering The University of Tokyo




Atomistic analysis via deep machine learning



The use of Scanning Transmission Electron Microscopy (STEM) has led to a much deeper understanding of the materials via the ability to obtain the infinitesimal atomic structure. Of great importance is determining the structure-property relationships, i.e. how the atomic structure gives rise to the measured properties of the material and how structural changes affect these properties. For the last decades, the STEM technique has been drastically improved along with the higher-order aberration corrector and the state-of-art detectors such as pixelate CCD detector, the segmented detector, and so on. In addition, in order to determine more precisely the structure-property relationships, it is necessary to extract the atomic coordinate information and the slight difference in the atomic structure by using deep machine learning. To this end, we introduce the new methods to extract atomistic information at length scales even lower than the current resolution limits of from the instrument itself, which is currently 50-100pm; furthermore, we also propose the analytical method based on deep machine learning to recognize the subtle distinction in the atomic structures and thus to classify the type of octahedral tilt or the point defect. Moreover, the whole procedure is highly automated leading to improved efficiency in addition to a reliable analysis.