Large Signal Statistical Model Oriented Parameter Extraction Method for GaN High Electron Mobility Transistors. Issue 6 (1st November 2017)

• Record Type: Journal Article
• Authors: Yu, Xuming; Xu, Yuehang; Wen, Zhang; Chen, Zhikai; Hong, Wei
• Is Part Of: Chinese journal of electronics. Volume 26:Issue 6(2017)
• Page Start: 1319
• Page End: 1324
• Publication Date: 2017-11-01
• Subjects: Statistical model -- Parameter extraction -- GaN HEMTs -- Large signal model; gallium compounds -- high electron mobility transistors -- III‐V semiconductors -- semiconductor device models -- statistical analysis -- wide band gap semiconductors; large signal statistical model oriented parameter extraction method -- gallium nitride high electron mobility transistors -- I‐V model -- pulsed I‐V transfer characteristic curves -- quiescent bias points -- size 0.25 mum -- GaN; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2017.09.033
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.