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 (Pout) of 30.98 dBm at an input power (Formula presented.) 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.