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A DoE–TOPSIS meta-model for parametric optimization of silicon carbonitride (SiCN) thin film deposition process

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Silicon carbonitride (SiCN) thin films were deposited on p-type Si(100) substrate by thermal chemical vapour deposition process using SiC and Si3N4 nanopowder as the source, and mixture of N2 and… Click to show full abstract

Silicon carbonitride (SiCN) thin films were deposited on p-type Si(100) substrate by thermal chemical vapour deposition process using SiC and Si3N4 nanopowder as the source, and mixture of N2 and H2 as precursor gases. For the experimental design, Taguchi’s L25 orthogonal array was used considering H2 flow rate, N2 flow rate and deposition temperature as the input deposition parameters, while taking into account, ID/IG ratio, hardness and Young’s modulus as the responses. Design of experiments integrated with technique for order of preference by similarity to ideal solution (TOPSIS) method was applied to develop a corresponding meta-model so as to obtain the optimal parametric combination of the deposition process with respect to the computed TOPSIS score. It was found that optimal parametric combination of H2 flow rate as 60 sccm, N2 flow rate as 100 sccm and deposition temperature as 1300 °C results in improved values of ID/IG ratio, hardness and Young’s modulus. The microstructure, chemical bonding and mechanical properties of SiCN thin films were characterized using Atomic force microscopy, Raman spectroscopy and nano-indentation techniques. As the developed meta-model is solely based on the analysis of the data obtained from experimentation, it is more practical and immune of introducing any additional parameter in the analysis. Moreover, the developed surface plots will further help the process engineer in selecting the most suitable combination of deposition parameters for achieving desirable responses.

Keywords: deposition; topsis; sicn thin; deposition process; meta model

Journal Title: Journal of the Brazilian Society of Mechanical Sciences and Engineering
Year Published: 2019

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