Inline hyperspectral thickness determination of thin films using neural networks

Anton J. Tremmel; Roman Weiss; Michael Schardt; Alexander W. Koch

doi:10.1117/12.2262070

28 April 2017 Inline hyperspectral thickness determination of thin films using neural networks

Anton J. Tremmel, Roman Weiss, Michael Schardt, Alexander W. Koch

Proceedings Volume 10213, Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017; 102130G (2017) https://doi.org/10.1117/12.2262070
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

Combining reflectometry and hyperspectral imaging allows mapping of thin film thickness. Therefore, layer thickness is calculated by comparing a dataset of simulated spectra with the measured data. Utilizing the maximum frame rate of the hyperspectral imager, the pixel wise spectra comparing procedure cannot be performed using a standard computer due to the processing load. In this work, a method using neural networks for calculating layer thickness is presented. By the use of the nonlinear equation as result of a trained neural network, thickness data can be determined with a measurement rate matching the maximum frame rate of the hyperspectral imager.

Citation Download Citation

Anton J. Tremmel, Roman Weiss, Michael Schardt, and Alexander W. Koch "Inline hyperspectral thickness determination of thin films using neural networks", Proc. SPIE 10213, Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017, 102130G (28 April 2017); https://doi.org/10.1117/12.2262070

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