Physics-informed neural network assisted automated design of power amplifier by user defined specifications

This article presents a model that can automatically produce a power amplifier's (PA) design parameters, that is, transmission lines (TLs) dimension, from a dataset of user-specified design goals like gain, efficiency, linearity, and scattering (S-) parameters. Based on the applied boundary conditions, a synthetic dataset is generated with the best range of design parameters (W and L). This dataset is utilized for training the physics-informed neural network (PINN) model with user-specified design goals as input and design parameters as target to produce the optimum value of W and L as the resultant output. Furthermore, utilizing the obtained dimensions, design, simulation, fabrication, and measurement of a PA are performed to validate our proposed model. The results of large signal measurements of PA are drain efficiency (DE) of 26.9%, power added efficiency (PAE) of 24.7%, output power (P_out) of 30.98 dBm at an input power $\left(P_{\text{in}}\right)$ of 19 dBm, and gain of 12.41 dB at an operating frequency of 1.625 GHz. It has been observed that the design parameters produced by the model have a significant agreement with the validated output. Also, the statistical error analysis is done by calculating the error metrics between the validated output and the actual output of the PA design.