Random-Space Dimensionality Reduction for Expedient Yield Estimation of Passive Microwave Structures

Abstract

A methodology is presented for the expedient statistical analysis of the electromagnetic attributes of passive microwave structures exhibiting manufacturing uncertainty in geometric and material parameters. In the proposed approach, the computational complexity stemming from the high dimensionality of the random space that describes the uncertainty in the electromagnetic analysis of the structure is mitigated by employing a principal component analysis with sensitivity assessment in combination with an adaptive sparse grid collocation scheme. The method exploits the inherent dependencies between random parameters to reduce the number of simulations needed to extract the statistics of the desired output response. This leads to the expedient estimation of production yield by means of the cross-entropy algorithm, which provides for fast calculation of the failure probability for a given functionality criterion. The proposed methodology is demonstrated through its application to the analysis of crosstalk in coupled microstrip lines exhibiting manufacturing variability and the investigation of the variation the bandwidth characteristics of a bandpass filter in the presence of uncertainty in geometric and/or material parameters.