PCA-based network modeling using standardized X-ray diffraction data for the electrical characteristics of the HfO2 thin films grown by MOMBE

The PCA-based neural network process models of the HfO
₂
thin films are investigated. The input process parameters are extracted by analyzing the process conditions and the accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of the HfO
₂
dielectric films. Here, X-ray diffraction data that are standardized with mean and standard deviation. PCA is then carried out to reduce the dimension of the standardized two types of XRD data that are compressed into a small number of principal components. Those are used to analyze the characteristic variation for the different process conditions and predict the crystallinity-based the response models for the electrical characteristics. The compressed data are trained using the neural networks.