Equivalent small-signal model of InP-based HEMTs with accurate radiation effects characterization
| Autor: | H. Q. Yun, B. Mei, Y. B. Su, F. Yang, P. Ding, J. L. Zhang, S. H. Meng, C. Zhang, Y. Sun, H. M. Zhang, Z. Jin, Y. H. Zhong |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of Applied Physics. 133 |
| ISSN: | 1089-7550 0021-8979 |
| DOI: | 10.1063/5.0150647 |
| Popis: | In this paper, an effective equivalent modeling technique has been proposed to describe small-signal characteristics of InP-based high electron mobility transistors (HEMTs) after proton radiation, which is composed of an artificial neural network and equivalent-circuit models. Small-signal intrinsic parameters of InP-based HEMTs are extracted from S-parameters before and after 2 MeV proton radiation as modeling objects. The deep learning model of a generative adversarial network has been explored to expand the measured finite data samples. Four feedforward neural networks are incorporated to equivalent-circuit topology to form the equivalent model, which are trained to accurately predict the radiation-induced variations of Cgs, Cgd, Rds, and gm, respectively. The prediction accuracy of the developed equivalent model has been well verified in terms of the broad-band S-parameters under radiation fluence of 1 × 1014 and 5 × 1013 H+/cm2. This equivalent modeling method with characterization of radiation damage effects could provide significant guidance for the aerospace monolithic millimeter-wave integrated circuit design. |
| Databáze: | OpenAIRE |
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