Advances and new directions in device modeling and design optimization for microwave CAD

Abstract

Extraordinary advances continue in modeling, optimization and statistical design for microwave CAD. Developments in EM-based modeling capabilities, mixed linear /nonlinear, EM field/circuit simulation, space mapping and knowledge-based artificial neural network (ANN) technology are creating new opportunities for microwave CAD with higher levels of design automation. These technologies address increased complexity of VLSI, RF and microwave circuits to fulfill the industrial demand for faster design cycle and reducing time to market for electronic products. Competition within the fast growing EDA market lead to incorporation of such technologies in future releases of commercial software. An objective of this workshop is a tutorial review of the state of the art and a presentation of implementable methodologies and software. It will be substantially physically and electromagnetically oriented. It will highlight recent advances in ANN and space mapping for modeling and design tasks in RF and microwave CAD. Initiatives in integration of ANN capabilities into statistical design, behavioral modeling, measurement standards and computational electromagnetics are being made. Space mapping optimizations are being performed, for example, by linking full-wave EM simulations with empirical circuit-theory based simulations, or devices under test with suitable simulation surrogates. Exploitation of properly managed space-mapped (surrogate) models promises high efficiency in engineering design optimization practice, as well as in enhancing device models for yield-driven design. We blend methodological aspects of wide applicability, design procedures currently applied in research and development centers, and well-known and widely available full-wave tools for design purposes. The workshop will bring together the foremost practitioners in these fields including microwave component designers, software developers and academic innovators. They will address designers' needs for effective tools for optimal designs, including yield optimization, exploiting accurate, physically-based device and component models, and consider the challenge of real life optimization, i.e., to produce in a short time, with limited resources, a design both competitive and innovative.