Work

Thin Film Thickness Analysis Using a Deep Learning Algorithm with a Consideration of Reflectance Fluctuation

Year: 2025

Type: article

Abstract: A deep learning algorithm for thin film thickness analysis based on spectral reflectometry, using a dataset that reflects experimental conditions, has been proposed and implemented. This study extends our previous research, in which we designed an artificial neural network (ANN) algorithm using theoretical reflectance spectrum datasets and quantitatively evaluated it according to the international standard traceability system. The evaluation results indicated that one of the major sources of uncertainty was the offset between the outputs of the ANN algorithm and the certified values of certified reference materials (CRMs). In this study, we focused on how much the uncertainty factor related to the offset is affected by using a dataset that reflects experimental conditions instead of theoretical reflectance spectrum datasets. By applying the fluctuations in reflectance obtained from experiments to the theoretical reflectance spectrum, we created a dataset to train the ANN algorithm under the same conditions as in our previous studies for comparison. As a result, the major uncertainty factor related to the offset improved by about 30%. This study demonstrates the importance of having datasets that accurately reflect real-world conditions for training ANN algorithms.

Source: International Journal of Precision Engineering and Manufacturing-Smart Technology

DOI: 10.57062/ijpem-st.2024.00164

Authors: Joonyoung Lee, Jonghan Jin

Institutions: Korea University of Science and Technology, Korea Research Institute of Standards and Science

Language: English

Topic: Scientific Measurement and Uncertainty Evaluation

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funder: Korea Research Institute of Standards and Science

Award: G3564738072