EM-Based Monte Carlo Analysis and Yield Prediction of Microwave Circuits Using Linear-Input Neural-Output Space Mapping

A computationally efficient method for highly accurate electromagnetics-based statistical analysis and yield estimation of RF and microwave circuits is described in this paper. The statistical analysis is realized around a space-mapped nominal solution. Our method consists of applying a constrained Broyden-based linear input space-mapping approach to design, followed by an output neural space-mapping modeling process in which not only the responses, but the design parameters and independent variable are used as inputs to the output neural network. The output neural network is trained using reduced sets of training and testing data generated around the space-mapped nominal solution. We illustrate the accuracy and efficiency of our technique through the design and statistical analysis of a classical synthetic problem and a microstrip notch filter with mitered bends