Towards neural network-based design of radiofrequency low-noise amplifiers

The preliminary work on a new methodology to design low noise amplifiers (LNAs) for use in radiofrequency (RF) wireless systems is presented. The methodology aims to find the relevant design parameters faster than current analytical models and optimization procedures. To reach this goal, an artificial neural network (ANN) is used to learn the design task by being exposed to successful design examples. Our preliminary results, using a training set of two hundred design examples, show that a radial basis functions ANN can learn the provided designs perfectly, but a larger training set is required for definite conclusions regarding the prediction of component values for new designs.