Accurate determination of material parameters, such as carrier lifetimes and defect activation energy, is a significant problem in the technology of infrared detectors. Among many different techniques, using the time resolved photoluminescence spectroscopy allows to determine the narrow energy gap materials, as well as their time dynamics. In this technique, it is possible to observe time dynamics of all processes in the measured sample as in a streak camera. In this article, the signal processing for the above technique for Hg(1-x)CdxTe with a composition x of about 0.3 which plays an extremely important role in the mid-infrared is presented. Machine learning algorithms based on the independent components analysis were used to determine components of the analyzed data series. Two different filtering techniques were investigated. In the article, it is shown how to reduce noise using the independent components analysis and what are the advantages, as well as disadvantages, of selected methods of the independent components analysis filtering. The proposed method might allow to distinguish, based on the analysis of photoluminescence spectra, the location of typical defect levels in HgCdTe described in the literature.

In pursuit of increased efficiency and longer operating times of photovoltaic systems, one may encounter numerous difficulties in the form of defects that occur in both individual solar cells and whole modules. The causes of the occurrence range from structural defects to damage during assembly or, finally, wear and tear of the material due to operation. This article provides an overview of modern imaging methods used to detect various types of defects found in photovoltaic cells and panels. The first part reviews typical defects. The second part of the paper reviews imaging methods with examples of the authors’ own test results. The article concludes with recommendations and tables that provide a kind of comprehensive guide to the methods described, depending on the type of defects detected, the range of applicability, etc. The authors also shared their speculations on current trends and the possible path for further development and research in the field of solar cell defect analysis using imaging.

ZnO thin layers were deposited on p-type silicon substrates by the sol-gel spin-coating method and, then, annealed at various temperatures in the range of 573–873 K. Photoluminescence was carried out in the temperature range of 20–300 K. All samples showed two dominant peaks that have UV emissions from 300 nm to 400 nm and visible emissions from 400 nm to 800 nm. Influence of temperature on morphology and chemical composition of fabricated thin layers was examined by XRD, SEM, FTIR, and Raman spectroscopy. These measurements indicate that ZnO structure is obtained for samples annealed at temperatures above 573 K. It means that below this temperature, the obtained thin films are not pure zinc oxide. Thus, annealing temperature significantly affected crystallinity of the thin films.

In the present investigation optical, electro-optical and dielectric properties have been measured for nematic liquid crystal (NLC) material 1550C which consists of 4’-(trans, trans-4-alkylbicyclohexyl) carbonates and 4’-(4-(trans,trans-4-alkyl)-4-cyanobicyclohexane, dispersed with fluorescent dye (Benzo 2,1,3 Thiadiazole) in two different concentrations. Photoluminescence has been enhanced for a dye dispersed system which is the key finding of this investigation. UV absorbance study has also been performed and found to be increased for composite system. Enhanced birefringence after dispersion of dye into pure NLC is also a prominent result of this investigation. Relative permittivity, threshold voltage and dielectric anisotropy have also been measured and found to be increased. The outcome of the present work may be very useful in the construction of liquid crystal displays (LCDs).

The temperature dependence of photoluminescence spectra has been studied for the HgCdTe epilayer. At low temperatures, the signal has plenty of band-tail states and shallow/deep defects which makes it difficult to evaluate the material bandgap. In most of the published reports, the photoluminescence spectrum containing multiple peaks is analyzed using a Gaussian fit to a particular peak. However, the determination of the peak position deviates from the energy gap value. Consequently, it may seem that a blue shift with increasing temperature becomes apparent. In our approach, the main peak was fitted with the expression proportional to the product of the joint density of states and the Boltzmann distribution function. The energy gap determined on this basis coincides in the entire temperature range with the theoretical Hansen dependence for the assumed Cd molar composition of the active layer. In addition, the result coincides well with the bandgap energy determined on the basis of the cut-off wavelength at which the detector response drops to 50% of the peak value.

This paper presents a probabilistic machine learning approach to approximate wavelength values for unmeasured positions on an opto-semiconductor wafer after epitaxy. Insufficient information about optical and opto-electronic properties may lead to undetected specification violations and, consequently, to yield loss or may cause product quality issues. Collection of information is restricted because physical measuring points are expensive and in practice samples are only drawn from 120 specific positions. The purpose of the study is to reduce the risk of uncertainties caused by sampling and measuring inaccuracy and provide reliable approximations. Therefore, a Gaussian process regression is proposed which can determine a point estimation considering measuring inaccuracy and further quantify estimation uncertainty. For evaluation, the proposed method is compared with radial basis function interpolation using wavelength measurement data of 6-inch InGaN wafers. Approximations of these models are evaluated with the root mean square error. Gaussian process regression with radial basis function kernel reaches a root mean square error of 0.814 nm averaged over all wafers. A slight improvement to 0.798 nm could be achieved by using a more complex kernel combination. However, this also leads to a seven times higher computational time. The method further provides probabilistic intervals based on means and dispersions for approximated positions.

The structural, morphological and photoluminescent properties of thermally evaporated neodymium oxide (Nd2O3) thin films deposited onto nanostructured silicon (Si-ns) are reported. Si-ns embedded in silicon nitride (SiN) thin films are prepared by plasma-enhanced chemical vapour deposition (PECVD). SiN and Nd2O3 thin films uniformity and Si-ns formation are confirmed by atomic force microscopy (AFM) and scanning electron microscopy (SEM). The presence of neodymium (Nd), silicon (Si), oxygen (O), and phosphorus (P) is investigated by energy-dispersive spectroscopy (EDS) and secondary ion mass spectrometry (SIMS). Post-annealing SIMS profile indicates an improvement of the homogeneity of activated P distribution in Si bulk. The X-ray diffraction (XRD) combined with Raman spectroscopy and Fourier-transform infrared spectroscopy (FTIR) have been employed to determine amorphous silicon (a-Si), crystalline silicon (c-Si), Nd2O3 and SiN phases present in the Nd2O3-SiN bilayers with their corresponding chemical bonds. After annealing, a Raman shift toward lower wavenumbers is recorded for the Si peak. XPS data reveal the formation of Nd2O3 thin films with Nd-O bonding incorporating trivalent Nd ions (Nd3+). Strong room-temperature photoluminescence is recorded in the visible light range from the Si-ns. Nd-related photoluminescent emission in the near infrared (NIR) range is observed at wavelengths of 1025–1031 nm and 1083 nm, and hence is expected to improve light harvesting of Si-based photovoltaic devices.