This paper presents an equation-free, data-driven approach for reduced order modeling of a Chemical Vapor Deposition (CVD) process. The proposed approach is based on process information provided by detailed, high-fidelity models, but can also use spatio-temporal measurements. The Reduced Order Model (ROM) is built using the method-of-snapshots variant of the Proper Orthogonal Decomposition (POD) method and Artificial Neural Networks (ANN) for the identification of the time-dependent coefficients. The derivation of the model is completely equation-free as it circumvents the projection of the actual equations onto the POD basis. Prior to building the model, the Support Vector Machine (SVM) supervised classification algorithm is used in order to identify clusters of data corresponding to (physically) different states that may develop at the same operating conditions due to the inherent nonlinearity of the process. The different clusters are then used for ANN training and subsequent development of the ROM. The results indicate that the ROM is successful at predicting the dynamic behavior of the system in windows of operating parameters where steady states are not unique.

本文提出了一种无方程式，数据驱动的方法，用于化学气相沉积（CVD）过程的降阶建模。所提出的方法基于详细的高保真模型提供的过程信息，但也可以使用时空测量。使用适当的正交分解（POD）方法和人工神经网络（ANN）的快照方法变体构建了降阶模型（ROM），以识别随时间变化的系数。模型的推导完全没有方程式，因为它避免了将实际方程式投影到POD上。在建立模型之前，使用支持向量机（SVM）监督分类算法，以识别与（物理上）不同状态相对应的数据簇，这些过程由于过程固有的非线性而可能在相同的操作条件下发展。然后，将不同的群集用于ANN训练和ROM的后续开发。结果表明，ROM在稳态不是唯一的运行参数窗口中可以成功地预测系统的动态行为。