Modelling of Random Textured Tandem Silicon Solar Cells Characteristics: Decision Tree Approach

We report decision tree (DT) modeling of randomly textured tandem silicon solar cells characteristics. The photovoltaic modules of silicon-based solar cells are extremely popular due to their high efficiency and longer lifetime. Decision tree model is one of the most common data mining models can be used for predictive analytics. The reported investigation depicts optimum decision tree architecture achieved by tuning parameters such as Min split, Min bucket, Max depth and Complexity. DT model, thus derived is easy to understand and entails recursive partitioning approach implemented in the “rpart” package. Moreover the performance of the model is evaluated with reference Mean Square Error (MSE) estimate of error rate. The modeling of the random textured silicon solar cells reveals strong correlation of efficiency with “Fill factor” and “thickness of a-Si layer

Numerical Investigation of Spatial Effects on the Silicon Solar Cell

Investigating the effect of device dimension on the silicon solar cell, by using the PC1D numerical simulation environment, we report strong correlation of efficiency of the silicon solar cell with its size. The results showcase finer efficiency at the lower n-type thickness and higher p-type thickness. The internal quantum efficiency (IQE) and external quantum efficiency (EQE) too exhibit variation with the device size. As a whole, based on the statistical analysis, especially regression, variance, and best subsets selection, the paper depicts that the p-type thickness, ISC and VOC are the preeminent parameters to model the silicon solar cell

Piecewise Linear and Nonlinear Window Functions for Modelling of Nanostructured Memristor Device

The present paper reports two new window functions viz. piecewise linear window function and nonlinear window function for modelling of the nanostructured memristor device. The piecewise linear window function can be used for modelling of symmetric pinched hysteresis loop in I-V plane (for digital memory applications) and the nonlinear window function can be used for modelling of nonlinear pinched hysteresis loop in I-V plane (for analog memory applications). Flexibility in the parameter selection is the main attractive feature of these window functions

Piecewise Linear and Nonlinear Window Functions for Modelling of Nanostructured Memristor Device

The present paper reports two new window functions viz. piecewise linear window function and nonlinear window function for modelling of the nanostructured memristor device. The piecewise linear window function can be used for modelling of symmetric pinched hysteresis loop in I-V plane (for digital memory applications) and the nonlinear window function can be used for modelling of nonlinear pinched hysteresis loop in I-V plane (for analog memory applications). Flexibility in the parameter selection is the main attractive feature of these window functions

Artificial Neural Network Modeling of NixMnxOx based Thermistor for Predicative Synthesis and Characterization

As foremost sensors of ambient conditions, temperature sensors are regarded as the most vital ones in wide-ranging applications touching the societal life. Amongst the temperature sensors, NTC thermistors have captured their unique place due to the favorable metrics such as highest sensitivity, low cost, and ease of deployment. Transition metal oxides especially the NixMnxOx are widely used for thermistor synthesis in spite of the main difficulty of predicting the final sensor characteristics before the actual synthesis. In view of the above, we report an Artificial Neural Network (ANN) technique to accomplish the synthesis with predictable results saving valuable resources. In the said ANN modeling we use hyperbolic tangent sigmoid transfer function for input layer and linear transfer function for the output layer. Levenberg-Marquardt feed-forward algorithm trains the neural net. We measure the performance of the ANN model with regard to mean square error (MSE) and the correlation coefficient between expected output and output provided by the network. Moreover, we uniquely model the resistance-temperature (R-T) characteristics of different thermistor samples using optimized ANN structure. To model such sort of behavior, we provide nickel content, room temperature resistance, and concentration of oxalic acid as an input data to the network and predict the nickel acetate and manganese acetate concentration. The accomplished ANN modeling evidences a lower number of hidden neuron architecture exhibiting optimum performance as regards to prediction accuracy. The lower number of hidden neurons signifies a lesser amount of memory required for prediction of different chemical composition. Thus, we demonstrate exploitation of modeling, simulation and soft computational approaches for predicting the best suitable chemical composition and thus establish the synergy between the materials science and soft computing paradigm

A Processing in Memory Realization Using Quantum Dot Cellular Automata (QCA): Proposal and Implementation

Processing in Memory (PIM) is a computing paradigm that promises enormous gain in processing speed by eradicating latencies in the typical von Neumann architecture. It has gained popularity owing to its throughput by embedding storage and computation of data in a single unit. We portray implementation of Akers array architecture endowed with PIM computation using Quantum-dot Cellular Automata (QCA). We present the proof of concept of PIM with its realization in the QCA designer paradigm. We illustrate implementation of Ex-OR gate with the help of QCA based Akers Array and put forth many interesting potential possibilities

Investigating the Temperature Effects on ZnO, TiO2, WO3 and HfO2 Based Resistive Random Access Memory (RRAM) Devices

In this paper, we report the effect of filament radius and filament resistivity on the ZnO, TiO2, WO3 and HfO2 based Resistive Random Access Memory (RRAM) devices. We resort to the thermal reaction model of RRAM for the present analysis. The results substantiate decrease in saturated temperature with increase in the radius and resistivity of filament for the investigated RRAM devices. Moreover, a sudden change in the saturated temperature at a lower value of filament radius and resistivity is observed as against the steady change at the medium and higher value of the filament radius and resistivity. Results confirm the dependence of saturated temperature on the filament size and resistivity in RRAM

Investigating the Temperature Effects on ZnO, TiO2, WO3 and HfO2 Based Resistive Random Access Memory (RRAM) Devices

In this paper, we report the effect of filament radius and filament resistivity on the ZnO, TiO2, WO3 and HfO2 based Resistive Random Access Memory (RRAM) devices. We resort to the thermal reaction model of RRAM for the present analysis. The results substantiate decrease in saturated temperature with increase in the radius and resistivity of filament for the investigated RRAM devices. Moreover, a sudden change in the saturated temperature at a lower value of filament radius and resistivity is observed as against the steady change at the medium and higher value of the filament radius and resistivity. Results confirm the dependence of saturated temperature on the filament size and resistivity in RRAM