Abstract
The artificial neural networks (ANNs) have been often used for thin-film thickness measurement, whose performance evaluations were only conducted at the level of simple comparisons with the existing analysis methods. However, it is not an easy and simple way to verify the reliability of an ANN based on international length standards. In this article, we propose for the first time a method by which to design and evaluate an ANN for determining the thickness of the thin film with international standards. The original achievements of this work are to choose parameters of the ANN reasonably and to evaluate the training instead of a simple comparison with conventional methods. To do this, ANNs were built in 12 different cases, and then trained using theoretical spectra. The experimental spectra of the certified reference materials (CRMs) used here served as the validation data of each trained ANN, with the output then compared with a certified value. When both values agree with each other within an expanded uncertainty of the CRMs, the ANN is considered to be reliable. We expect that the proposed method can be useful for evaluating the reliability of ANN in the future.
Highlights
ObjectivesThe ultimate goals of this study are (1) reasonable selection of the parameters of ANN algorithms and (2) a performance evaluation based on an international standard instead of a simple comparison with current analysis methods or measurement techniques
A novel method to design and evaluate an ANN algorithm used to determine the thickness of thin films was proposed and demonstrated
With reflectance spectra of the 4 different CRMs obtained by experiments, the thickness values were determined by 12 well-trained ANN algorithms and compared with the corresponding certified values of the CRMs
Summary
The ultimate goals of this study are (1) reasonable selection of the parameters of ANN algorithms and (2) a performance evaluation based on an international standard instead of a simple comparison with current analysis methods or measurement techniques. Because the purpose of this study is not to improve the performance of ANN algorithms, of which basic form is only exploited without any additional techniques among other advanced algorithms
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