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선택적 캐리어 수집을 위한 터널 산화막을 이용한 결정질 실리콘 태양전지
한상욱(Sanguk Han),심경배(Gyungbae Shim),박수영(Sooyoung Park),안시현(Shihyun Ahn),박철민(Cheolmin Park),조영현(Younghyun Cho),김현후(Hyunhoo Kim),이준신(Junsin Yi) 한국신재생에너지학회 2017 신재생에너지 Vol.13 No.3
In silicon solar cells, the doping process is performed to form a Back Surface Field (BSF) layer and is followed by many other processes. In this study, phosphorus doped a-Si:H doped at a high concentration in the tunnel oxide layer was crystallized through furnace annealing and Excimer Laser Annealing (ELA), in order to apply it to the Polycrystalline (Poly) - BSF layer in the Tunnel Oxide Passivated Contact (TOPCon) structure. In the excimer laser annealing fabrication process, an XeCl excimer laser with a wavelength of 308 nm was used, and the thickness of the a-Si layer and energy density of the laser were varied from 20 to 40 nm and from 390 to 450 mJ/cm², respectively. The highest carrier lifetime and implied VOC were found to be 588 ㎲ and 697 mV, respectively, at an a-Si thickness of 20 nm and energy density of the laser of 450 mJ/cm². The TOPCon cell was fabricated using wet oxidation and plasma oxidation. Its efficiency and FF were found to be higher when fabricated using the wet process, with values of 19.41% and 74.8%, respectively, while its VOC and JSC values were higher when it was fabricated using plasma oxidation, with values of 41.04 mJ/cm² and 644 mV, respectively. Therefore, if the conditions providing for a high implied VOC and carrier lifetime and sufficient crystallization were found, the efficiency of n-type TOPCon solar cells could be increased.
머신러닝 기반 아파트 주동형상 자동 판별 모형 개발 및 적용 - 주동형상에 따른 아파트 개발 특성분석을 중심으로 -
한상욱 ( Sanguk Han ),서정석 ( Jungseok Seo ),( Sri Utami Purwaningati ),오지훈 ( Jihun Oh ),김정섭 ( Jeongseob Kim ) 한국지리정보학회 2023 한국지리정보학회지 Vol.26 No.2
본 연구의 목적은 GIS와 머신러닝 알고리즘을 활용하여 아파트 단지의 주동형상을 자동으로 판별해주는 모형을 개발하고, 이를 주동형상과 단지특성 관의 관계 분석에 적용하는 것이다. 지리정보데이터를 사용하여 아파트단지별 주동 데이터베이스를 구축하고 랜덤포레스트 알고리즘을 활용하여 단지 내 개별동을 형태에 따라 판상형, 탑상협, 혼합형으로 분류하였다. 또한, 아파트단지별 주동형상별 비중과 개발밀도, 층수 등 단지특성 정보간의 관계를 분석하여 부동산 분야 지리정보응용 가능성을 제안하였다. 본 연구는 인공지능 기반 건축물 유형 분류와 관련한 기초연구로서 다양한 공간분석 및 부동산 분석에 활용될 것으로 예상한다. This study aims to develop a model that can automatically identify the rooftop shape of apartment buildings using GIS and machine learning algorithms, and apply it to analyze the relationship between rooftop shape and characteristics of apartment complexes. A database of rooftop data for each building in an apartment complex was constructed using geospatial data, and individual buildings within each complex were classified into flat type, tower type, and mixed types using the random forest algorithm. In addition, the relationship between the proportion of rooftop shapes, development density, height, and other characteristics of apartment complexes was analyzed to propose the potential application of geospatial information in the real estate field. This study is expected to serve as a basic research on AI-based building type classification and to be utilized in various spatial and real estate analyses.