Millimeter wave RF front end design using neuro-genetic algorithms

Abstract

This paper presents neural network and genetic algorithm based modeling and design of millimeter wave RF front end circuits. The neuro-genetic design methodology is composed of two stages. Stage one consists of the development of an accurate neural network model for the microwave filters from the measured data. This model can be used to perform sensitivity analysis and derive response surfaces. In the second stage, the neural network model is used in conjunction with genetic algorithms to synthesize millimeter wave devices with desired electrical specifications. The synthesis methodology uses an accurate model that accounts for the manufacturing variations and parameter indeterminacy issues. Furthermore, the genetic synthesis algorithm uses a priority scheme to account for tradeoffs among various electrical characteristics to provide the best design. This method has been used to synthesize mm-wave low pass and band pass filters. The electrical response obtained from the layout parameters predicted by the method matches the desired electrical characteristics within 5%. The generic nature of the technique suggests potential extension to other mm-wave front ends, such as antennas, diplexers and baluns.