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.

需要实验数据来估计描述薄膜生长的数学模型的未知参数，并优化化学气相沉积（CVD）工艺。可以通过基于顺序模型的实验设计（MBDoE）获得最有用的实验数据，但是与析因设计实验（DoE）相比，这需要大量的精力来实施。对于CVD工艺，尚不清楚哪种方法更好或更有效。在这里，我们使用涉及薄膜沉积机理模型的案例研究，比较了连续A最优（MBDoE）与阶乘（DoE）的有效性。根据顺序A最优MBDoE和阶乘DoE的比较，基于它们在获取准确参数估计和模型预测中的有效性。合成的实验数据是通过模拟生成的，其中包含真实参数加噪声。结果表明，在估计模型参数方面，A最优MBDoE优于阶乘DoE。每个实验设计方法的参数估计值用于解决基于模型的优化问题。结果表明，基于模型的基于模型的优化（通过A最优设计获得的参数估计值）比因数DOE能够更好地估计最佳工艺条件。