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Predictive modeling and analysis of HfO 2 thin film process based on Bayesian information criterion using PCA‐based neural networks
Author(s) -
Ko YoungDon,
Moon Pyung,
Kim Chang Eun,
Ham MoonHo,
Jeong MyongKee,
GarciaDiaz Alberto,
Myoung JaeMin,
Yun Ilgu
Publication year - 2013
Publication title -
surface and interface analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 90
eISSN - 1096-9918
pISSN - 0142-2421
DOI - 10.1002/sia.5286
Subject(s) - principal component analysis , artificial neural network , bayesian information criterion , materials science , dimensionality reduction , multivariate statistics , computer science , artificial intelligence , bayesian probability , pattern recognition (psychology) , data mining , machine learning
Principal component analysis (PCA)‐based neural network (NNet) models of HfO 2 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 HfO 2 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. Copyright © 2013 John Wiley & Sons, Ltd.