Principal component analysis (PCA)-based neural network (NNet) models of HfO2 thin films are used to study the process of efficient model selection and develop an improved model by using multivariate functional data such as X-ray diffraction data (XRD). The accumulation capacitance and the hysteresis index input parameters, both characteristic of HfO2 dielectric films, were selected for the inclusion in the model by analyzing the process conditions. Standardized XRD were used to analyze the characteristic variations for different process conditions; the responses and the electrical properties were predicted by NNet modeling using crystallinity-based measurement data. A Bayesian information criterion (BIC) was used to compare the model efficiency and to select an improved model for response prediction. Two conclusions summarize the results of the research documented in this paper: (i) physical or material properties can be predicted by the PCA-based NNet model using large-dimension data, and (ii) BIC can be used for the selection and evaluation of predictive models in semiconductor manufacturing processes.
All Science Journal Classification (ASJC) codes
- Condensed Matter Physics
- Surfaces and Interfaces
- Surfaces, Coatings and Films
- Materials Chemistry