Sequential approximate optimisation for statistical analysis and yield optimisation of circularly polarised antennas

Abstract

The study addresses a problem of low-cost statistical analysis and yield optimisation of circularly polarised (CP) microstrip antennas. For the sake of computational efficiency, auxiliary response surface approximation models are utilised to establish a fast representation of the expensive full-wave electromagnetic (EM)-simulation antenna model. The surrogate permits feasible Monte Carlo (MC) analysis using a large number of random samples (necessary to maintain low variance of yield estimation). The sequential approximate optimisation is then utilised to carry out a robust design, where at each iteration, a new surrogate model is established in a relocated domain. The design objectives are set for the in-band axial ratio and impedance bandwidth. For illustration purposes, a planar CP antenna with two feed line stubs is considered. Accurate yield estimation is obtained at the cost of only a hundred of EM antenna simulations despite a large number of independent geometry parameters. Yield improvement from the initial value of 63.5% at the nominal design to 99% is achieved at the cost of 300 EM analyses. Comparison with conventional MC analysis is also provided demonstrating the accuracy of the presented approach.