Abstract
In microwave device and circuit design, many simulations are often needed to find a set of designs that satisfy one or multiple specifications chosen by the designer upfront: the feasible region. A novel Bayesian active learning framework is presented to accurately identify the feasible region with a low number of simulations. The technique leverages on a stochastic model to obtain an efficient and automated procedure. A suitable application example validates the proposed technique and shows its effectiveness to rapidly obtain many suitable designs.
Highlights
Over the last decades, the increase of available computing power has moved electronic designers away from hardware-based prototyping, towards computer aided design (CAD) simulations
Simulations of modern microwave devices and circuits are expensive, both in terms of computational time as well as resources, due to the bandwidth requirements coupled with the complexity of modern microwave systems
This set of design solutions is called the feasible region in the rest of the contribution, while the chosen design specifications are referred to as feasibility conditions
Summary
The increase of available computing power has moved electronic designers away from hardware-based prototyping, towards computer aided design (CAD) simulations. The adopted active learning framework requires only a limited number of expensive CAD simulations (i.e. full-wave simulations) to efficiently identify the feasible region This novel methodology is well suited for design space exploration and reduction. Feasibility conditions are to be defined upon performance metrics that can be computed via CAD simulations These performance metrics, which depend on the values assumed by design parameters, are called objective functions in this contribution. The objective is to individuate the design point that maximizes the information gain on the location of the feasible region
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