Fourier transform spectroscopy has become a standard method for spectral analysis of infrared light. With this method, an interferogram is created by two beam interference which is subsequently Fourier-transformed. Most Fourier transform spectrometers used today provide the interferogram in the temporal domain. In contrast, static Fourier transform spectrometers generate interferograms in the spatial domain.
One example of this type of spectrometer is the static single-mirror Fourier transform spectrometer which offers a high etendue in combination with a simple, miniaturized optics design. As no moving parts are required, it also features a high vibration resistance and high measurement rates. However, it is susceptible to temperature variations. In this paper, we therefore discuss the main sources for temperature-induced errors in static single-mirror Fourier transform spectrometers: changes in the refractive index of the optical components used, variations of the detector sensitivity, and thermal expansion of the housing. As these errors manifest themselves in temperature-dependent wavenumber shifts and intensity shifts, they prevent static single-mirror Fourier transform spectrometers from delivering long-term stable spectra.
To eliminate these shifts, we additionally present a work concept for the thermal stabilization of the spectrometer. With this stabilization, static single-mirror Fourier transform spectrometers are made suitable for infrared process spectroscopy under harsh thermal environmental conditions. As the static single-mirror Fourier transform spectrometer uses the so-called source-doubling principle, many of the mentioned findings are transferable to other designs of static Fourier transform spectrometers based on the same principle.
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.
This paper presents an innovative approach for an automated evaluation of mixture ratios and the detection of impurities in viscous materials. The measurement method is based on fluorescence imaging and works on a non-contact basis. The principle of the measurement setup is that three similar fluorescence images are available in different optical paths. 2D-sensor-arrays having a resolution of 1024 pixel × 1280 pixel are used for the image acquisition. A one-to-one mapping restricts the size of the fluorescence images to 5.3 mm × 6.66 mm. The vertical and horizontal resolution in the images is limited to 5.2 μm; this corresponds to the dimensions of a pixel. Due to the use of an x, y-shifting table in the measurement setup, it is possible to investigate a larger area of the measurement object. To get more information of the measurement object, each image is filtered in a different wavelength range. The center wavelength of the used bandpass filters are 405 nm, 420 nm, and 440 nm. The evaluation of the mixture ratio is realized with an acceptance range in a three-dimensional coordinate system. The determination of the number, positions, areas, and maximum dimensions of contained impurities is implemented by a dedicated threshold algorithm. The minimum detectable impurity size with the used measurement setup is 5.2 μm. Both evaluation approaches work in a real-time and automated process. Advantages of the presented system are the low level of expense for the maintenance and the universality due to the use of optical standard components.
Surface roughness of technical surfaces is an important parameter in, for example, quality control. Speckle interferometry (SI) is a powerful tool for acquiring information about a surface under test. Recent investigations concentrated on deriving analytical functions to describe the dependency of surface roughness on SI, assuming normally distributed surface roughness. The common approach is to use the correlation of the standard deviation of height distribution σ and single speckle-related parameters like fringe visibility V or spectral speckle correlation C to estimate surface roughness. Furthermore, roughness cannot be described clearly using only one parameter (e.g., Ra ), which makes it often necessary to estimate more roughness parameters. A new approach in roughness measurement using SI is presented. A multivariate data analysis for generating a regression model is employed, which may include many speckle-related parameters on one hand and offers the possibility to acquire different roughness parameters on the other hand. Finally, the regression models created for four exemplary roughness parameters Rq , R3z , Rp , and Mr1 are discussed and the accuracy in the prediction of these parameters is indicated.