A novel method to design and evaluate artificial neural network for thin film thickness measurement traceable to the length standard

Author:

Lee Joonyoung,Jin Jonghan

Abstract

AbstractThe 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.

Funder

Korea Research Institute of Standards and Science

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A review on optical characterization of refractive index in photonic related devices and applications;Journal of Physics D: Applied Physics;2024-09-10

2. Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework;Sensors;2024-08-24

3. A Review of Thin-film Thickness Measurements using Optical Methods;International Journal of Precision Engineering and Manufacturing;2024-06-22

4. Thin film thickness analysis based on a deep learning algorithm using data augmentation;Metrology, Inspection, and Process Control XXXVIII;2024-04-10

5. Thin-film thickness measurement with normal spectral reflectance;Advanced Sensor Systems and Applications XIII;2023-11-27

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3