• JSTS:Journal of Semiconductor Technology and Science
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• v.7 no.2
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• pp.110-119
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• 2007

A new two-dimensional analytical model for dual-material double-gate fully-depleted SOI MOSFET with Pearson-IV type Doping Distribution is presented. An investigation of electrical MOSFET parameters i.e. drain current, transconductance, channel resistance and device capacitance in DM DG FD SOI MOSFET is carried out with Pearson-IV type doping distribution as it is essential to establish proper profiles to get the optimum performance of the device. These parameters are categorically derived keeping view of potential at the center (${\phi}_c$) of the double gate SOI MOSFET as it is more sensitive than the potential at the surface (${\phi}_s$). The proposed structure is such that the work function of the gate material (both sides) near the source is higher than the one near the drain. This work demonstrates the benefits of high performance proposed structure over their single material gate counterparts. The results predicted by the model are compared with those obtained by 2D device simulator ATLAS to verify the accuracy of the proposed model.

• Advances in materials Research
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• v.13 no.1
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• pp.63-86
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• 2024

Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.