Rapid design closure of microwave components by means of feature-based optimization and adjoint sensitivities

Slawomir Koziel; Adrian Bekasiewicz

doi:10.1002/mmce.21182

Rapid design closure of microwave components by means of feature-based optimization and adjoint sensitivities

International Journal of RF and Microwave Computer-Aided Engineering ◽

10.1002/mmce.21182 ◽

2017 ◽

Vol 28 (2) ◽

pp. e21182 ◽

Cited By ~ 1

Author(s):

Slawomir Koziel ◽

Adrian Bekasiewicz

Keyword(s):

Rapid Design ◽

Microwave Components ◽

Feature Based ◽

Design Closure

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Fast Design Closure of Compact Microwave Components by Means of Feature-Based Metamodels

Electronics ◽

10.3390/electronics10010010 ◽

2020 ◽

Vol 10 (1) ◽

pp. 10

Author(s):

Anna Pietrenko-Dabrowska ◽

Slawomir Koziel

Keyword(s):

Network Models ◽

Gradient Estimation ◽

Passive Components ◽

Simulation Tools ◽

Design Procedures ◽

Warm Start ◽

Microwave Components ◽

Feature Based ◽

And Performance ◽

Design Closure

Precise tuning of geometry parameters is an important consideration in the design of modern microwave passive components. It is mandatory due to limitations of theoretical design methods unable to quantify certain phenomena that are important for the operation and performance of the devices (e.g., strong cross-coupling effects in miniaturized layouts). Consequently, the initial designs obtained using analytical or equivalent network models require further adjustment. For reliability reasons, it has to be conducted using electromagnetic (EM) simulation tools, which entails considerable computational expenses whenever conventional numerical optimization algorithms are employed. Accelerating EM-driven design procedures is therefore highly desirable. This work discusses a surrogate-based algorithm for fast design closure and dimension scaling of miniaturized microwave passives. Our approach involves a small database of previously obtained designs as well as two metamodels, an inverse one, employed to yield a high-quality initial design, and the forward surrogate that provides predictions of the system sensitivities. The second model is constructed at the level of response features, which enables a more accurate gradient estimation and leads to improved reliability and a faster convergence of the optimization process. The presented technique is validated using two compact microstrip couplers and benchmarked against the state-of-the-art warm-start optimization frameworks.

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