Towards Neural Network Classification of Terahertz Measurements for Determining the Number of Coating Layers
| Autor: | N. Rohde, Bernd Tibken, Hartmut Haehnel, Volker K. S. Feige, Simon Christmann, Imke Busboom |
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| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Artificial neural network Terahertz radiation Computer science 020208 electrical & electronic engineering 02 engineering and technology engineering.material 01 natural sciences Statistical classification Coating Surface wave 0103 physical sciences 0202 electrical engineering electronic engineering information engineering engineering Feedforward neural network Layer (object-oriented design) Biological system Neural network classification |
| Zdroj: | 2020 45th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz). |
| DOI: | 10.1109/irmmw-thz46771.2020.9370440 |
| Popis: | To determine layer thicknesses with terahertz time-domain spectroscopy, the number of layers must usually be known. However, in some applications the number of layers varies along the surface, so that the number of layers at a specific measuring location can be unknown. Our approach is to use an artificial deep neural network for estimating the number of layers at a preliminary stage for common terahertz algorithms. This work describes the selection and evaluation of a feedforward neural network. This neural network allows a good estimation of the number of layers confirming the usefulness of the proposed approach. |
| Databáze: | OpenAIRE |
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