Modelling the spice parameters of SOI MOSFET using a combinational algorithm

Progress in the technology of submicron semiconductor device, which makes a short-channel and quantum effects, having equations of nonlinear modelling, leads to complicated and time-consuming calculations. In order to control these complexities and obtain the device characteristics according to device parameters, a faster method is needed. In this paper, a combinational algorithm is proposed for modelling a nano silicon-on-insulator metal–oxide–semiconductor field effect transistor (SOI MOSFET) characteristic. The proposed method shows the same device characteristics with lower input parameters. In this method, a combination of genetic algorithm (GA) and artificial neural network are used. Then quantum evolutionary algorithm (QEA) is employed instead of genetic algorithm (GA) for comparing and modifying algorithm. Results show that the algorithm’s accuracy is 95% and 98% for test data of GA and QGA, respectively. Moreover, the reduction percentage of input parameters are 11% and 52% for GA and QEA, respectively. The simulation results represent that the implemented quantum genetic algorithm for prediction of device characteristics is more effective and accurate than GA.