Accurate modeling of pHEMT output current derivatives over a wide temperature range

作者：Zhu, Yuan-Yuan; Ma, Jian-Guo; Fu, Hai-Peng^*; Zhang, Qi-Jun; Cheng, Qian-Fu; Lin, Qian

来源：International Journal of Numerical Modelling: Electronic Networks, Devices and Fields , 2017, 30(3-4): e2185.

DOI：10.1002/jnm.2185

• pHEMT modeling
• derivative
• artificial neural network
• temperature
• nonlinear

In this paper, the bias-dependent current-voltage (I-V) characteristics and their high-order derivatives of GaAs pseudomorphic high electron mobility transistors (pHEMTs) have been modeled over a wide temperature range. To simulate these characteristics at different temperatures, the model is developed considering the dependence on the ambient temperature. It is the first time that the temperature-dependent high-order derivatives of I-V characteristics of pHEMT are predicted, which can guarantee their accuracy under different bias conditions. The artificial neural networks are employed with the temperature as one of the input variables. The validity of this model has been demonstrated by comparing the measured and modeled I-ds and its derivatives (g(m), g(m2) and g(m3), derived from the I-V characteristics numerically) of a GaAs pHEMT at different temperature range (250-400K, with step of 50K). The results show that the proposed model has a better agreement of high-order derivatives than the popularly used Angelov model, especially for the third-order derivative.

• 出版日期2017-8
• 单位天津大学