Neural network based modeling of HfO2 thin film characteristics using Latin Hypercube Sampling

In this paper, the neural network based modeling for electrical characteristics of the HfO2 thin films grown by metal organic molecular beam epitaxy was investigated. The accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of the HfO2 dielectric films. The input process parameters were extracted by analyzing the process conditions and the characterization of the films. X-ray diffraction was used to analyze the characteristic variation for the different process conditions. In order to build the process model, the neural network model using the error back-propagation algorithm was carried out and those initial weights and biases are selected by Latin Hypercube Sampling method. This modeling methodology can allow us to optimize the process recipes and improve the manufacturability.