Predictive modeling and analysis of HfO2 thin film process based on Bayesian information criterion using PCA‐based neural networks |
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| Authors: | Young‐Don Ko Pyung Moon Chang Eun Kim Moon‐Ho Ham Myong‐Kee Jeong Alberto Garcia‐Diaz Jae‐Min Myoung Ilgu Yun |
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| Affiliation: | 1. Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, , Toronto, Ontario, Canada;2. Institute of Biomaterials and Biomedical Engineering, University of Toronto, , Toronto, Ontario, Canada;3. Department of Electrical and Electronic Engineering, Yonsei University, , Seoul, 120‐749 Korea;4. School of Materials Science and Engineering and Department of Nanobio Materials and Electronics, Gwangju Institute of Science and Technology, , Gwangju, 500‐712 Korea;5. Department of Industrial and Systems Engineering, Rutgers Center for Operations Research, Rutgers, The State University of New Jersey, , Piscataway, NJ, 08854 USA;6. Department of Industrial and Information Engineering, University of Tennessee, , Knoxville, TN, 37996 USA;7. Department of Materials Science and Engineering, Yonsei University, , Seoul, 120‐749 Korea |
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| Abstract: | 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. Copyright © 2013 John Wiley & Sons, Ltd. |
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| Keywords: | HfO2 neural networks Latin hypercube sampling standardized X‐ray diffraction data Bayesian information criterion |
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