| Autor: |
Yun, H. Q., Mei, B., Su, Y. B., Yang, F., Ding, P., Zhang, J. L., Meng, S. H., Zhang, C., Sun, Y., Zhang, H. M., Jin, Z., Zhong, Y. H. |
| Předmět: |
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| Zdroj: |
Journal of Applied Physics; 5/28/2023, Vol. 133 Issue 20, p1-10, 10p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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