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An analytical model to predict the surface roughness for the plasma-enhanced chemical vapor deposition (PECVD) process over a large range of temperature values is still nonexistent. By using an existing prediction model, the surface roughness can directly be calculated instead of repeating the experimental processes, which can largely save time and resources. This research work focuses on the investigation and analytical modeling of surface roughness of SiO2 deposition using the PECVD process for almost the whole range of operating temperatures, i.e., 80 to 450 °C. The proposed model is based on experimental data of surface roughness against different temperature conditions in the PECVD process measured using atomic force microscopy (AFM). The quality of these SiO2 layers was studied against an isolation layer in a microelectromechanical system (MEMS) for light steering applications. The analytical model employs different mathematical approaches such as linear and cubic regressions over the measured values to develop a prediction model for the whole operating temperature range of the PECVD process. The proposed prediction model is validated by calculating the percent match of the analytical model with experimental data for different temperature ranges, counting the correlations and error bars.

Prediction of Surface Roughness as a Function of Temperature for SiO2 Thin-Film in PECVD Process