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Öğe An Experimental Study on Artificial Intelligence-Based Prediction of Capacitance-Voltage Parameters of Polymer-Interface 6H-SiC/MEH-PPV/Al Schottky Diodes(Wiley-V C H Verlag Gmbh, 2022) Guzel, Tamer; Colak, Andac BaturHerein, an artificial neural network (ANN) model has been developed to predict the capacitance values of the polymer-interface 6H-SiC/MEH-PPV/Al Schottky diode depending on the frequency. In the training of the feed-forward back-propagation network model with five neurons in its hidden layer, 480 experimental data have been used. Of these, 70% of the data used in the development of the multilayer perceptron network has been used for network training, 15% for validation, and 15% for the test phase. The predictive performance of the network model has been analyzed by comparing the predicted values obtained from the ANN with the experimental data. For the developed ANN, the mean square error value is 4.34E-06, the R-value is 0.99728, and the average margin of deviation value is 0.03%.Öğe An experimental study on determination of the shottky diode current-voltage characteristic depending on temperature with artificial neural network(Elsevier, 2021) Colak, Andac Batur; Guzel, Tamer; Yildiz, Oguzhan; Ozer, MetinShottky diodes are one of the important components of electronic systems. Therefore, it is very important to determine the parameters of the diodes according to the area in which they will be used. One of the most important of these parameters is the current-voltage characteristic of the diode. In this study, firstly, current values of the Schottky diode in the voltage range of -2 V to +3 V are experimentally measured in the temperature range of 100?300 K. In order to estimate the current-voltage characteristic of Shottky diode at different temperatures, a multi-layer perceptron, a feed-forward back-propagation artificial neural network was developed using 362 experimental data obtained. In the artificial neural network where temperature (T) and voltage (V) values are selected as input variables and the hidden layer has 15 neurons, the current (I) value is obtained as output. The results obtained from the artificial neural network have been found to be in good agreement with the experimental data of the Schottky diode.Öğe Artificial intelligence approach on predicting current values of polymer interface Schottky diode based on temperature and voltage: An experimental study(Academic Press Ltd- Elsevier Science Ltd, 2021) Guzel, Tamer; Colak, Andac BaturIn this study, an artificial neural network model has been developed to predict the current values of a 6H?SiC/MEH-PPV Schottky diode with polymer-interface, depending on temperature and voltage. In the training of the multi-layer perceptron network model with 13 neurons in its hidden layer, the experimentally measured current values between 100 and 250 K temperature and -3V to + 3V voltage range have been used. In the input layer of the model developed with a total of 244 experimental data, temperature, and voltage values have been defined and current values were obtained in the output layer. The mean square error value of the artificial neural network is 1.63E-08 and the R-value is 0.99999. The developed model has been able to predict the current values of the polymer-interfaced 6H?SiC/MEH-PPV Schottky diode with an average error rate of -0.15% depending on temperature and voltage, with high accuracy.Öğe Current-voltage characteristics of Ag/TiO2/n-InP/Au Schottky barrier diodes(Amer Inst Physics, 2019) Bilgili, Ahmet Kursat; Guzel, Tamer; Ozer, MetinThe effect of the TiO2 interfacial layer on rectifying junction parameters of Ag/TiO2/n-InP/Au Schottky diodes has been investigated using current-voltage (I-V) measurements in the temperature range of 120-420 K with steps of 20 K. The barrier height is found to be 0.19 eV and 0.68 eV from current-voltage characteristics at 120 K and 420 K, respectively. At 120 K and 420 K, the ideality factor is found to be 3.52 and 1.01 for the Ag/TiO2/n-InP/Au Schottky barrier diode, respectively. These results are gained by the thermionic emission theory at room temperature. Values of series resistances gained from the Cheung-Cheung method are compared with results gained from a modified Norde method. These experimental results indicate that series resistance decreases with an increase in temperature. The current-voltage (I-V) measurements showed that the diode with the TiO2 interfacial layer gave a double Gaussian property in the examined temperature range. The Richardson constant is also calculated from a modified Richardson plot and is found to be very compatible with the theoretical value. Interface state density is also examined by using I-V characteristics.Öğe Determination of the effect of hydrogen peroxide on the structure of graphene produced by electrochemical method(Springer, 2023) Guzel, TamerIt is possible to produce graphene nanosheets with the electrochemical exfoliation method. Despite the many advantages of this method, due to the nature of the process, it causes many defects in graphene. These defects, on the other hand, negatively affect the electronic properties of graphene. For this reason, in this study, a method that can reduce the destructive effects of the electrochemical method on graphene has been proposed. Herein, the electrolyte has been modified with hydrogen peroxide in the electrochemical exfoliation process, and its effect on graphene has been investigated. Structural properties, X-ray diffraction, Raman spectroscopy, energy-dispersive X-ray spectroscopy, and morphological features also were performed using scanning electron microscopy. The results were discussed by comparing them with each other and with the literature. Accordingly, it was found that when the electrolyte, which is one of the components of the electrochemical process, is modified with hydrogen peroxide, there is an effective decline in the defect density of the graphene produced. In addition, a significant decrease in the number of layers of graphene produced in the presence of hydrogen peroxide was detected.Öğe Do Artificial Neural Networks Always Provide High Prediction Performance? An Experimental Study on the Insufficiency of Artificial Neural Networks in Capacitance Prediction of the 6H-SiC/MEH-PPV/Al Diode(Mdpi, 2022) Colak, Andac Batur; Guzel, Tamer; Shafiq, Anum; Nonlaopon, KamsingIn this paper, we study a new model that represents the symmetric connection between capacitance-voltage and Schottky diode. This model has a symmetrical shape towards the horizontal direction. In recent times, works conducted on artificial neural network structure, which is one of the greatest actual artificial intelligence apparatuses used in various fields, stated that artificial neural networks are apparatuses that proposal very high forecast performance by the side of conventional structures. In the current investigation, an artificial neural network structure has been generated to guess the capacitance voltage productions of the Schottky diode with organic polymer edge, contingent on the frequency with a symmetrical shape. Of the dataset, 130 were grouped for training, 28 for validation, and 28 for testing. In order to evaluate the effect of the number of neurons on the prediction accuracy, three different models with different neuron numbers have been developed. This study, in which an artificial neural network model, although well-trained, could not predict the output values correctly, is a first in the literature. With this aspect, the study can be considered as a pioneering study that brings a novelty to the literature.Öğe Effect of Pre-Treatment Time On Graphene Nanosheets Produced by Exfoliation Method(Electrochemical Soc Inc, 2023) Guzel, Tamer; Islek, Yasemin; Yildiz, Oguzhan2D-Materials are the biggest candidates to take today's electronic technology to a new point. In particular, graphene can find its place in many areas due to its unique properties. This has made the investigation of the factors that can affect the quality of graphene production up-to-date. In the present study, effect of pre-treatment time on graphene nanosheets produced was investigated. The structural analyses were carried out by X-ray diffraction spectroscopy (XRD), Raman spectroscopy, and Fourier Transform Infrared Spektrometresi (F-TIR). The morphological analyses of the surface were performed using scanning electron microscopy (SEM). The number of layers and crystallite of graphene nanoparticles was calculated and the results were compared with the literature. The results show that the pre-treatment time affects the structural properties of the graphene nanosheets produced by the exfoliation method and there is a more positive effect on exfoliated graphene quality for 20 min pre-treatment time.Öğe Investigation of inhomogeneous barrier height for Au/n-type 6H-SiC Schottky diodes in a wide temperature range(Academic Press Ltd- Elsevier Science Ltd, 2018) Guzel, Tamer; Bilgili, Ahmet Kursat; Ozer, MetinCurent-Voltage (I-V) properties of Au/6H-SiC/Au Schottky diodes are investigated and results are analised dependent on temperature at 80-400 K range. Fundamental parameters such as ideality factors (n), barrier heights (Phi(bo)), saturation currents (I-o) are calculated for this diode. Also, series resistance (R-s) is calculated with different methods. Richardson curves are plotted for this structure and Richardson constant (A*) is calculated. Results are compared with literature. Gaussian distribution is examined by using barrier inhomogeneity. Parameters belonging to Gaussian disribution are calculated and results are compared with previous studies done by different authors.Öğe Investigation of the usability of extreme temperature changes in pristine graphene production(Elsevier, 2021) Guzel, TamerThe scalable, controllable and sustainable mass production of graphene is still the subject of many studies. Various methods have been proposed and discussed for this. In this study, a low-cost new approach has been reported with purely mechanical methods, environmentally friendly, free of chemical solvents and suitable for mass production. This technique, which aims to separate the layers with the effect of the thermal stress forces created by the volumetric expansion/shrinkage nature caused by extreme temperature change, it differs from other mechanical and thermal exfoliations due to the absence of specific tools and special chemicals. With the approach based on this methodology, two different samples have been produced and their structural and morphological researches have been made. Structural analyzes have been characterized by X-ray diffraction diffractometer, Raman spectrometer, energy dispersive X-ray spectroscopy, while morphological research has been performed using scanning electron microscope. It has been confirmed with characterization devices that the thermomechanical exfoliation method can produce pristine graphene within the limits that can be accepted as quality.Öğe Investigation of the usability of machine learning algorithms in determining the specific electrical parameters of Schottky diodes(Elsevier, 2022) Guzel, Tamer; Colak, Andac BaturSchottky diodes continue to be the favorite of the electronics industry with their ever-expanding usage areas. The electrical parameters that can be obtained by the characterization of Schottky diodes are of high importance as they provide important information in terms of the usage area of the diode. In this study, the usability of the machine learning algorithm has been investigated in the determination of important electrical parameters such as ideality factor, barrier height and resistance of Schottky diodes. Voltage and temperature values were defined in the hidden layer of the multi-layer artificial neural network model, which was developed with a total of 368 data sets, and current values were estimated in the output layer. The developed neural network model was able to predict the electrical parameters of Schottky diodes with an average deviation of 0.11%. Using the data ob-tained from the artificial neural network, the Ideality factor was calculated with an error margin of 1.645, and the resistance value with a margin of error of 5.694.Öğe Investigation of the usability of nitric acid electrolyte in graphene production by electrochemical method(Taylor & Francis Inc, 2021) Guzel, TamerElectrochemical exfoliation method, which is one of the methods of producing graphene, is a suitable technique for the mass production of graphene in the industrial sense. However, the correct selection of electrochemical process inputs (electrolyte, applied voltage, etc.) is very important for graphene formation. Otherwise, thick fragmented graphites are formed. In this study, a method for producing ultrathin layer graphene using dilute nitric acid has been presented. The properties of the produced graphene were characterized. Structural analysis was done with X-Ray diffraction device, Raman Spectroscopy, energy dispersive x-ray spectrometry. The morphological structure of the surface was performed using scanning electron microscopy. The data obtaining were discussed together with the literature and the results were evaluated. In addition to being a good electrolyte in the production of graphene, nitric acid is determined to be a good candidate for the production of graphene oxide in one step with this method with its high oxidation level.Öğe Performance prediction of current-voltage characteristics of Schottky diodes at low temperatures using artificial intelligence(Pergamon-Elsevier Science Ltd, 2023) Guzel, Tamer; Colak, Andac BaturSchottky diodes are still one of the most important elements of electronics. Therefore, investigating the properties of diodes is very important in determining their usage areas. In this study, the performance of the artificial neural network model trained using high temperature data in predicting the current-voltage properties at low temperatures was investigated. An artificial neural network is modeled using the experimentally measured current and voltage values at the temperature range of 80 and 375 K. In the developed network model, temperature and voltage values are defined as input parameters and current values are estimated. LevenbergMarquardt training algorithm was used as the training algorithm in the neural network, which was developed using a total of 1584 data. The current values obtained from the artificial neural network were compared with the experimental current values, and the prediction performance of the network model was extensively analyzed by using various performance parameters. The results showed that the developed artificial neural network can predict current values at low temperatures with high accuracy depending on voltage. In addition, it was found that the current-voltage characteristics of the Schottky diode at low temperatures could be predicted with an error rate of approximately & PLUSMN;7 %. On the other hand, the error rates in the prediction of diode characteristics by artificial intelligence were determined to be independent of temperature.Öğe Pretreatment impact on electrochemically synthesized graphene oxide(Emerald Group Publishing Ltd, 2023) Guzel, TamerThe production of graphene oxide by the electrochemical method is more environmentally friendly, faster and suitable for mass production than the chemical method. Although this technique is very practical, the correct parameters must be selected to produce graphene oxide. Therefore, it is very important to investigate the factors affecting graphene oxide synthesis in the electrochemical process. In this study, the effect of pretreatment on graphene oxide produced by the electrochemical method was investigated. Structural characterization of the produced graphene oxide was carried out using X-ray diffraction, Raman spectroscopy and energy-dispersive X-ray spectroscopy, while morphological research was performed using scanning electron microscopy. Due to the increase in the electrochemical oxidation performance of dilute nitric acid with the effect of the pretreatment process, an effective increase in the oxygen content in the structure of graphene oxide was detected.
