Use of Neural Network to Control a Refractive Index of SiN Film Deposited by Plasma Enhanced Chemical Vapor Deposition

Kim, Byungwhan; Kim, Sungmo; Hong, Wan‐Shick

doi:10.1023/b:pcpp.0000004880.73226.1f

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Numerous procedures have been industrialized, such as texturing Si structures using dry or wet etching processes or fabricating antireflective composite thin films [6,7,8]. However, the majority of these common texturing methods are either too expensive or possess too low replicability to advance the progress of solar cells in an economically reasonable way [5,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Influence of Refractive Index on Antireflectance Efficiency of Thin Films

et al. 2019

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In today’s world, scientific development is tremendously strengthened by imitating natural processes. This development remarkably validates progressive and efficient operation of multifunctional thin films in variable ecological circumstances. We use TFCalc thinfilm software, a reliable and trustworthy simulation tool, to design antireflective (AR) coatings for solar cells that can operate in varying environmental conditions and can be functional according to user-defined conditions. Silicon nearly reflects 36% light in the 550 nm wavelength region, causing a significant loss in solar cell efficiency. We used silicon as the substrate on which we designed and fabricated a trilayer inorganic oxide AR thin films, and this reduced it reflectance to <4% in the 300~800 nm wavelength range. Because of their distinguishing physical physiognomies, we used a combination of different inorganic oxides, comprising high-, low-, and medium-refractive indices, to model AR coatings in the desired wavelength range. Experimental implementation of the designed AR thin films in the present study unlocks new techniques for production of competent, wideband-tunable AR coatings that are applicable in high-performance photovoltaic applications.

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Section: Introductionmentioning

confidence: 99%

Influence of Refractive Index on Antireflectance Efficiency of Thin Films

et al. 2019

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“…This is supported by the refractive index variation at the same plasma conditions. 18 Actually, the refractive index increasing with the pressure was considerably decreased at higher pressures. This is a clear indicative of a larger concentration of N relatively than Si.…”

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confidence: 98%

Radio Frequency Power Effect on Charge Density of PECVD-SiN Thin Films

Kim

2007

J. Ceram. Soc. Japan

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Charge density is an important film characteristic that determines the quality of film surface passivation. In this study, a prediction model of charge density was constructed by using a neural network and generic algorithm. For a systematic modeling, plasma enhanced chemical vapor deposition process of silicon nitride films was characterized by means of a statistical experimental design. Effects of parameters under various radio frequency rf powers were examined under various plasma conditions. An increase in charge density was observed with increasing rf power over the entire range of SiH 4 flow rate as well as at lower N 2 flow rates or pressures. The effect of SiH 4 flow rate on charge density was opposite to that for N 2 flow rate given rf powers. For the variations in N 2 or SiH 4 flow rate, charge density seemed to be strongly correlated to NSi ratio. Under the variations in pressure or N 2 flow rate, maintaining a lower pressure was beneficial in achieving a higher charge density. A pronounced rf power effect at smaller grain size was expected.

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Machine Learning (ML)‐Assisted Design and Fabrication for Solar Cells

Fan

Peng

Zuo

et al. 2019

Energy & Environ Materials

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Photovoltaic (PV) technologies have attracted great interest due to their capability of generating electricity directly from sunlight. Machine learning (ML) is a technique for computer to learn how to perform a specific task using known data. It can be used in many areas and has become a hot research topic recently due to the rapid accumulation of data and advancement of computer hardware. The application of ML techniques in the design and fabrication of solar cells started slowly but has recently gained tremendous momentum. An exhaustive compilation of the literatures indicates that all the major aspects in the research and development of solar cells can be effectively assisted by ML techniques. If combined with other tools and fed with additional theoretical and experimental data, more accurate and robust results can be achieved from ML techniques. The aspects can be grouped into four categories: prediction of material properties, optimization of device structures, optimization of fabrication processes, and reconstruction of measurement data. A statistical analysis of the literatures shows that artificial neural network (ANN) and genetic algorithm (GA) are the two most applied ML techniques and the topics in the optimization of device structures and optimization of fabrication processes are more popular. This article can be used as a reference by all PV researchers who are interested in ML techniques.

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Use of Neural Network to Control a Refractive Index of SiN Film Deposited by Plasma Enhanced Chemical Vapor Deposition

Cited by 4 publications

References 12 publications

Influence of Refractive Index on Antireflectance Efficiency of Thin Films

Influence of Refractive Index on Antireflectance Efficiency of Thin Films

Radio Frequency Power Effect on Charge Density of PECVD-SiN Thin Films

Machine Learning (ML)‐Assisted Design and Fabrication for Solar Cells

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