Abstract
GaN HEMT switches has become more and more important in RF front-end. This article proposes a circuit-based neuro-SM technique for the small-signal modeling of multi-gate GaN HEMT switches for the first time. A general equivalent circuit model for multi-gate switch HEMT is proposed using the cascaded single-gate switch HEMT model to represent the tendency rather than the exact behaviors of the small-signal responses. We utilize this proposed equivalent circuit model as the coarse model and propose a novel circuit-based neuro-SM modeling technique. Neural networks are incorporated to learn the difference between the coarse model and the fine device data, improving the neuro-SM model accuracy. After being trained, the obtained neuro-SM model can be used for high-level circuit design to increase the speed and accuracy of circuit design. Compared to the existing modeling techniques for multi-gate switches, the proposed neuro-SM achieves better model accuracy. Examples of a dual-gate GaN HEMT switch and a triple-gate GaN HEMT switch have been examined.
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