Implicit Space Mapping Optimization Exploiting Preassigned Parameters

TLDR

The paper introduces implicit space mapping (ISM) as a general framework that relates to existing explicit coarse‑to‑fine model mapping and proposes a unified concept. The authors implement a simple ISM‑based algorithm that extracts preassigned parameters to align a calibrated coarse surrogate with a fine model, then re‑optimizes the surrogate; the method is demonstrated on a toy cheese‑cutting problem, microwave design, and a high‑temperature superconducting filter in Agilent ADS. The technique is easy to implement because the mapping is embedded in the calibrated coarse model and automatically updated during parameter extraction.

Abstract

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.