Artificial bee colony algorithm: a novel strategy for optical constants and thin film thickness extraction using only optical transmittance spectra for photovoltaic applications

An effective approach to determine thin film thickness (d) and optical constants (n, k, α) from transmittance spectrum with interference fringes is proposed. The developed strategy is based on applying the artificial bee colony (ABC) algorithm and Cauchy dispersion model. The accuracy test of this method has been assessed by using simulated and real tests. Simulated test is used to check the ability of ABC algorithm to determine the parameters of simulated transmittance spectra. Real test deals with the investigation of the determination approach on experimental measured transmittance spectra. Those spectra were measured from six elaborated samples of amorphous hydrogenated silicon (a-Si:H) thin films with different thicknesses, which will be used as an eco-friendly layer for solar cell applications. The obtained results noticeably show the high effectiveness of the developed strategy to accurately determine the thin film thickness and optical constants

Investigation of structural and electrical properties of ITO thin films and correlation to optical parameters extracted using novel method based on PSO algorithm

Thermally annealed DC sputtered indium tin oxide (ITO) thin films were investigated for improvement in properties. The structural and optoelectronic characteristics of as-grown and air annealed films were studied and correlated to the film deposition time. Raman spectroscopy analysis showed low crystalline quality films for as-grown films and were significantly improved after annealing. X-ray diffraction analysis confirmed the crystallinity of samples with (222)preferential orientation. The 30-min ITO films showed a peak at (400). The films optical study shows an increased transmittance (in the transparency region) with decreasing deposition time, yielding a high transparency of 90% for the 5- and 15-min ITO films annealed at 400°C. The films thickness and optical constants were determined from optical transmission only without interference fringe using a novel method based on particle swarm optimization (PSO) algorithm. The absorption coefficient and calculated refractive index decreased with increasing deposition time and their valuereduced further after annealing treatment. The 30-min ITO films showed a comparable low resistivity of 4 9 10-3 X cm before and after annealing as determined by Hall effect measurements. This observation confirms their non-sensitivity to the oxygen post-contamination that resulted from (400) orientation. A shift of the absorption edge towards shorter wavelengths accompanied with an increase in the optical bandgap before and after annealing with decreasing thickness were observed. We have demonstrated that the optical parameters such as the optical gap depend mainly on the electrical parameters such as the carrier concentration

Investigation of the effects of thermal annealing on the structural, morphological and optical properties of nanostructured Mn doped ZnO thin films

The control of the optical properties of ZnO nanostructured thin films by using different dopant elements paves the way for the development of potential materials for photonic and optoelectronic applications. In this work manganese (Mn) doped ZnO thin films were fabricated by rapid thermal evaporation method on a glass substrate having the same Mn content level of ~10% and annealed at different temperatures. XRD analysis showed that the annealed layers have hexagonal wurtzite structure, however, the unannealed layers showed only Zn peaks without any preferential direction. The elemental analysis of the films has been investigated by XPS, which revealed the presence of Mn and oxygen atoms for all layers. In addition, it was observed by FIB-SEM that the morphology of thin films changed with the annealing temperature. For an anneal at 500 °C nanoneedles appeared. Raman spectroscopy showed E1 (TO) mode in the sample annealed at 500 °C which was attributed with the formation of nanoneedles structures. The optical transmission of the annealed films was in the range of 75–77% and the optical bandgap varied from 3.97 to 3.72 eV. These variations are related to the structural and morphological changes of the thin films with annealing temperature

Optimal Identification of Be-Doped Al0.29Ga0.71As Schottky Diode Parameters Using Dragonfly Algorithm: A Thermal Effect Study

In this work, a recent heuristic method called Dragonfly Algorithm (DA) has been employed for the first time to investigate the temperature effect on the Schottky diode electrical parameters. Beryllium-doped Al0.29Ga0.71As Schottky diodes grown by molecular beam epitaxy (MBE) have been used to validate the suggested method. The proposed approach is based on the analysis of current-voltage-temperature (I–V-T) and capacitance-voltage (C–V) characteristics. Furthermore, the interface state density (Nss) as function of the difference between the surface state energy and valence band energy (Ess – Ev) was determined. The obtained results demonstrate the high efficiency of this strategy to accurately determine the electrical parameters and investigate their temperature dependency. This efficiency can be clearly remarked from the well fit between both predicted and measured current characteristics

Machine learning-based method for predicting C-V-T characteristics and electrical parameters of GaAs/AlGaAs Multi-Quantum Wells Schottky diodes

In this work, two models of artificial neural networks are developed to predict the electrical parameters and capacitance-voltage characteristics of GaAs/AlGaAs multi-quantum wells Schottky diodes at different temperatures. Capacitance-Voltage-Temperature (C-V-T) characteristics for voltages and temperatures in the ranges (-4V to 0V) and (20K to 400K), respectively, were used to assess the effectiveness of the proposed approach. The first model (Model 1) is used to evaluate how well the neural network predicts the C-V-T characteristics. The second simulation, known as Model 2, was constructed to simultaneously overcome the problems of determining the electrical parameters and predicting C-V-T characteristics. Model 2 allows the calculation of the built-in voltage, effective density, and capacitance. Three-fold cross-validation and mean square error are used to assess the effectiveness of the developed models. The results clearly demonstrate the high prediction accuracy of the electrical parameters and C-V characteristics at all temperatures. After training, Model 1 the Mean Square Error performance is at 1450 epochs, whereas Model 2 MSE is at 642 epochs. According to the error distribution frequency histogram, about 95% of errors for Model 1 and Model 2 lie between [0.00535 and 0.005608] and [0.00328 and 0.00333], respectively. The R-values that correspond to the training and validation datasets for both models are close to one (0.9999). Parameters determination results have been compared against those obtained using ant lion optimizer based method. It was found that the results obtained from the neural networks models strongly agree with the experimental data