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In this work, the kink effect (KE), typically visible in S22, is analyzed and modeled. Two different modeling techniques: equivalent circuit modeling (ECM) method and machine learning method which based on support vector regression (SVR) technique are presented and compared, when applied to the S22 behavior of a Gallium Nitride (GaN) high electron mobility transistor (HEMT). The device under test (DUT) has a width of 8 Â 125 μm, with a gate feature size of 0.25 μm. The proposed method identifies the effect that the bias voltage and extrinsic elements have on the S22 kink shape. Additionally, compared to ECM, the SVR model attains a superior fitting accuracy across the complete frequency band.

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