YU Xuming, XU Yuehang, WEN Zhang, CHEN Zhikai, HONG Wei. Large Signal Statistical Model Oriented Parameter Extraction Method for GaN High Electron Mobility Transistors[J]. Chinese Journal of Electronics, 2017, 26(6): 1319-1324. doi: 10.1049/cje.2017.09.033
Citation: YU Xuming, XU Yuehang, WEN Zhang, CHEN Zhikai, HONG Wei. Large Signal Statistical Model Oriented Parameter Extraction Method for GaN High Electron Mobility Transistors[J]. Chinese Journal of Electronics, 2017, 26(6): 1319-1324. doi: 10.1049/cje.2017.09.033

Large Signal Statistical Model Oriented Parameter Extraction Method for GaN High Electron Mobility Transistors

doi: 10.1049/cje.2017.09.033
Funds:  This work is supported by the National Natural Science Foundation of China (No.61474020, No.61201004), and the National Key Project of Science and Technology.
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  • Corresponding author: XU Yuehang (corresponding author) was born in Zhejiang Province, China, in 1981. He received the B.S. and M.S. degrees in electromagnetic field and microwave techniques from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2004 and 2007, respectively, and the Ph.D. from the UESTC jointed with Columbia University in the City of New York in 2010. (Email:yuehangxu@uestc.edu.cn)
  • Received Date: 2015-09-10
  • Rev Recd Date: 2015-10-16
  • Publish Date: 2017-11-10
  • Efficient parameter extraction method is essential to establish large signal statistical model. This paper presents an automatic parameter extraction method of I-V model for Gallium nitride (GaN) High electron mobility transistors (HEMTs) large signal statistical model. To accurate modeling the statistical characterization, all of 53 parameters in an I-V model are considered. In order to realize automatic parameter extraction, the model parameters are divided into blocks according to their physical meaning to reduce the complexity of the I-V model. Different parameter blocks are extracted separately by fitting the pulsed I-V transfer characteristic curves of the device at different quiescent bias points. A large signal statistical model for 0.25μm GaN HEMTs process has been established by using the proposed method after measuring 34 GaN HEMTs from 10 batches. The results show that the large-signal performances (Output power and Power added efficiency) can be reproduced with high accuracy by the proposed statistical model.
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