DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Park, Steve | - |
dc.contributor.advisor | 박스티브 | - |
dc.contributor.author | Woo, Jun Hee | - |
dc.date.accessioned | 2023-06-23T19:32:11Z | - |
dc.date.available | 2023-06-23T19:32:11Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=997708&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308966 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 신소재공학과, 2022.2,[v, 35 p. :] | - |
dc.description.abstract | Solution-based thin-film processing is a widely utilized technique for the fabrication of various devices. In particular, the tunability of the ink composition and coating condition allow precise control of thin-film properties and device performance. Despite the advantage of having such tunability, the sheer number of possible combinations of parameters render it infeasible to efficiently optimize device performance and analyze the correlation between experimental parameters and device performance. In this work, microfluidic screening-embedded thin-film processing technique is developed, through which thin-films of varying ratios of small molecule semiconductor:polymer blend were simultaneously generated and screened in a time- and resource-efficient manner. Moreover, utilizing the thin-films of varying combinations of experimental parameters, machine learning models were trained to predict the transistor performance. Gaussian Process Regression (GPR) algorithms tuned by Bayesian optimization showed the best predictive accuracy amongst the trained models, which enables narrowing down of the combinations of experimental parameters and investigation of the degree of vertical phase separation under the predicted parameter space. Our technique can serve as a guideline for elucidating the underlying complex parameter-property-performance correlations in solution-based thin-film processing, thereby accelerating the optimization of various thin-film devices in the future. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.title | Microfluidic screening-assisted machine learning to investigate vertical phase separation of small molecule | - |
dc.title.alternative | 미세 유체 스크리닝과 기계 학습을 통한 단분자:고분자 혼합 박막의 수직 상 분리 현상 분석 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :신소재공학과, | - |
dc.contributor.alternativeauthor | 우준희 | - |
dc.title.subtitle | polymer blend | - |
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