In this paper, a deep neural network (DNN) model is proposed for the behavioral modeling of nonlinear power amplifiers with supply dependency. Although the conventional nonlinear model, such as the Volterra series, has high accuracy, it is not commonly implemented because of its complexity. However, with manageable complexity, the multidimensional input parameters of the proposed model ensure the modeling of the nonlinear behavior of power amplifiers with supply voltage dependency. The proposed model is trained by multi-tone signals on a 10-W power amplifier and validated by comparing the output spectrum and the third-order intermodulation (IMD3) of the model versus the measured data. The output spectrum shows less than 0.38 dB of error over a bandwidth of 10 MHz and input power from 11 dBm to 17 dBm, and the IMD3 error is less than 0.1 dB over the output power range.
In this study, we propose an algorithm that complements the frequency dependence of the relative permittivity conversion equation applied for the measurement of relative permittivity through a ring resonator. The broadband electrical characteristics of an epoxy molding compound (EMC) used in the fan-out wafer-level packaging (FOWLP) process were measured using our algorithm. By proposing a formula for calculating the permittivity that reflects the current distribution at the multiplied frequency of the microstrip ring resonator, the effective range of the permittivity of the sample was extended to the multiplied frequency region. Thus, a more sophisticated and effective complex permittivity was calculated over a wide band. The relative permittivity of the EMC measured by the proposed technique was 3.836 and the dielectric loss tangent was 0.022. This technique improved the relative permittivity measurement error rate by 3.95 % and the measurement error rate of the dielectric loss tangent by 8.4 %p compared to the conventional method.
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