Implicit space mapping optimization exploiting preassigned parameters

We introduce the idea of implicit space mapping (ISM) and show how it relates to the well-established (explicit) space mapping between coarse and fine device models. Through comparison, a general space mapping concept is proposed. A simple algorithm based on the novel ISM concept is implemented. It is illustrated on a contrived "cheese-cutting problem" and is applied to electromagnetics-based microwave modeling and design. An auxiliary set of parameters (selected preassigned parameters) is extracted to match the coarse model with the fine model. The calibrated coarse model (the surrogate) is then (re)optimized to predict a better fine model solution. This is an easy space mapping technique to implement since the mapping itself is embedded in the calibrated coarse model and updated automatically in the procedure of parameter extraction. We illustrate our approach through optimization of a high-temperature superconducting filter using Agilent ADS with Momentum and Agilent ADS with Sonnet's em.