基于SnO<sub>2</sub>∶Sb薄膜沉积工艺参数优化的支持向量回归分析

doi:10.11896/cldb.21120097

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Abstract Developing the low-cost transparent conductive oxide film material with high quality is of vital important for the development of modern optoelectronic devices. Upon facing the multi-dimensional thin film growth parameter space, how to reduce the time and material cost effectively in the process of seeking the optimal thin film is an urgent concern for researchers. Based on this, a process parameter optimization approach is proposed focusing on the process of growing SnO₂∶Sb thin films on quartz substrates using the mist-CVD method. The mist-CVD system is adopted because its environmentally friendly process to prepare metal oxide of high quality with simple equipment configuration, and shows potential for industrial development. Firstly, A experimental design methods is adopted to design experiments for preparing the transparent conductive thin films considering process parameters of doping concentration, deposition temperature, HCl content and deposition time. Furthermore, the value of figures of merits is introduced to evaluate the transparent conducting property of SnO₂∶Sb film. Moreover, to establish the effective prediction model of the transparent conductivity of SnO₂∶Sb film under different process parameters, the support vector regression method based on Bayesian optimization is used. The two-dimensional cloud image distribution results show the effective prediction results of the influence of preparation parameters on properties of SnO₂∶Sb films, which including 4-dimensional parameter space with a limited combination of 27 sets of experimental results. Finally, the prominent process parameters preparing SnO₂∶Sb films with high figures of merits is predicted from the two-dimensional cloud image. The SnO₂∶Sb films with high transparency and conductivity can be obtained based on the mist-CVD method adopting the optimized process parameter. The average thickness of the SnO₂∶Sb films is about 380 nm, the average visible light transmittance of the sample can reach 86.61%, and the sheet resistance is 21.1 Ω·□^-1. This work proposes an effective method for optimizing process parameters during exploring materials or device, which can speed up the development of materials or devices, and save time and materials cost meanwhile.

Key words： support vector regression SnO₂∶Sb thin film process parameters optimization mist-CVD

Published: 10 June 2023 Online: 2023-06-19

CLC number:

TB303

Fund:Chongqing Municipal Education Commission (KJQN202201137), Chongqing Natural Science Foundation (Postdoctoral Foundation)(cstc2021jcyj-bshX0219), High Quality Development Project of Graduate Education (gzljg2023315) and Graduate Student Innovation Project of Chongqing University of Technology (clgycx20203141).

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	Articles by authors
	CHEN Yuanhao
	XIAO Li
	LIANG Changxing
	LUO Yueting
	GONG Hengxiang

Cite this article:

CHEN Yuanhao,XIAO Li,LIANG Changxing, et al. Support Vector Regression Analysis Based on the Process Parameter Optimization of SnO₂∶Sb Thin Film Deposition[J]. Materials Reports, 2023, 37(11): 21120097-6.

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http://www.mater-rep.com/EN/10.11896/cldb.21120097 OR http://www.mater-rep.com/EN/Y2023/V37/I11/21120097

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