Synergetic Effect of Gas Compositions on Material Properties of N-Type Nanocrystalline Silicon Oxide Prepared by Plasma-Enhanced Chemical Vapor Deposition
SOLAR RRL（2024）
Forschungszentrum Julich GmbH
Abstract
Hydrogenated nanocrystalline silicon oxide (nc-SiOx:H) has drawn extensive attention during the past years for the utilization of silicon heterojunction (SHJ) solar cells. This material is demonstrated to be the key to the performance improvement of SHJ solar cells. Herein, the influence of the process gas compositions on the optical, electronic, chemical, and structural properties of n-type nc-SiOx:H films at the thickness of 38 nm is systematically investigated. The synergetic effect of CO2 and PH3 gas flow fraction (fCO2$f_{\left(\text{CO}\right)_{2}}$ and fPH3$f_{\left(\text{PH}\right)_{3}}$) and the synergetic effect of CO2 and SiH4 gas flow fraction (fCO2$f_{\left(\text{CO}\right)_{2}}$ and fSiH4$f_{\left(\text{SiH}\right)_{4}}$) on the growth of nc-SiOx:H (n) films are demonstrated. Creatively combining the Fourier-transform infrared, UV-Raman scattering spectroscopy, spectroscopy ellipsometry, and photo-thermal deflection spectroscopy measurement, the mechanism behind the synergetic effect of the gas compositions on the material properties is analyzed. It was demonstrated that the utilization of PH3 or SiH4 would weaken the oxygen incorporation in the hydrogenated nanocrystalline silicon oxide (nc-SiOX:H) films moderately and promote the transition of amorphous silicon oxide (a-SiOX:H) phases to amorphous silicon (a-Si:H) or nanocrystalline silicon (nc-Si:H) phases, which become more obvious at larger CO2 gas flow fraction (fCO2).image (c) 2024 WILEY-VCH GmbH
MoreTranslated text
Key words
nanocrystalline silicon oxide,plasma-enhanced chemical vapor deposition,random mixture model,synergetic effect
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined