| Title |
Modeling High Power Semiconductor Device Using Backpropagation Neural Network |
| Authors |
김병환(Kim, Byung-Whan) ; 김성모(Kim, Sung-Mo) ; 이대우(Lee, Dae-Woo) ; 노태문(Roh, Tae-Moon) ; 김종대(Kim, Jong-Dae) |
| Keywords |
Model ; high power semiconductor device ; backpropagation neural network |
| Abstract |
Using a backpropagation neural network (BPNN), a high power semiconductor device was empirically modeled. The device modeled is a n-LDMOSFET and its electrical characteristics were measured with a HP4156A and a Tektronix curve tracer 370A. The drain-source current (I_{DS}) was measured over the drain-source voltage (V_{DS}) ranging between 1 V to 200 V at each gate-source voltage (V_{GS}). For each V_{GS}, the BPNN was trained with 100 training data, and the trained model was tested with another 100 test data not pertaining to the training data. The prediction accuracy of each V_{GS} model was optimized as a function of training factors, including training tolerance, number of hidden neurons, initial weight distribution, and two gradients of activation functions. Predictions from optimized models were highly consistent with actual measurements. |