Sequential model-based design of experiments for development of mathematical models for thin film deposition using chemical vapor deposition process

Abstract

Experimental data is required to estimate unknown parameters for a mathematical model describing thin film growth and to optimize the chemical vapor deposition (CVD) process. The most informative experimental data can be obtained by sequential model-based design of experiments (MBDoE), but this requires significant effort to implement compared to factorial design of experiments (DoE). For CVD processes it is not clear which approach is better or more efficient. Here, we compare the effectiveness of sequential A-optimal (MBDoE) with a factorial (DoE) using a case study involving a mechanistic model of thin-film deposition. The sequential A-optimal MBDoE and factorial DoE are compared based on their effectiveness in obtaining accurate parameter estimates and model predictions. Synthetic experimental data is generated from simulations with the true parameters plus noise. The results suggest that the A-optimal MBDoE is slightly better than factorial DoE for estimating model parameters. The parameter estimates from each experimental design approach are used to solve a model-based optimization problem. The results indicate that model-based optimization from the model with parameter estimates obtained by the A-optimal design gives a significantly better estimate of the optimal process conditions than factorial DoE.