Rapid design optimization of microwave filters using variable-fidelity EM simulations and adjoint sensitivity

A robust and computationally efficient technique for microwave design optimization is presented. Our approach exploits variable-fidelity electromagnetic (EM) simulation models and adjoint sensitivities. The low-fidelity EM model correction is realized by means of space mapping (SM). In the optimization process, the SM parameters are optimized together with the design itself, which allows us to keep the number of both low- and high-fidelity simulations at low levels. The algorithm is demonstrated using two examples. Experimental validation is also provided.

Fast Optimization of Integrated Photonic Components Using Response Correction and Local Approximation Surrogates

The paper addresses simulation-driven design optimization of compact antennas involving variable-fidelity electromagnetic (EM) simulation models. Comprehensive investigations are carried out concerning selection of the coarse model discretization density. The effects of the low-fidelity model setup on the reliability and computational complexity of the optimization process are determined using a benchmark set of three ultra-wideband antennas designed for the best matching and for the minimum size. The optimization algorithm of choice is trust-region gradient search with low-fidelity model correction realized through frequency scaling and multiplicative output space mapping. The results indicate that appropriate low-fidelity model setup can be assessed using a correlation analysis based on the selected characteristic points of the antenna responses and sparsely allocated test designs, and the results are consistent across the considered benchmark set.

A simple yet robust methodology for rapid multiobjective design optimization of antenna structures has been presented. The key component of our approach is sequential domain patching of the design space, which is a stencil-based search that aims at creating a path that connects the extreme Pareto-optimal designs, obtained by means of single-objective optimization runs. The patching process yields the initial approximation of the Pareto set representing the best possible tradeoffs between conflicting design objectives. It is realized-for the sake of computational efficiency-at the level of coarse-discretization electromagnetic (EM) simulation model. The final Pareto front is subsequently obtained using surrogate-assisted optimization where the EM simulation data acquired at the initial design stage is reused.

Purpose Development of techniques for expedited design optimization of complex and numerically expensive electromagnetic (EM) simulation models of antenna structures validated both numerically and experimentally. The paper aims to discuss these issues. Design/methodology/approach The optimization task is performed using a technique that combines gradient search with adjoint sensitivities, trust region framework, as well as EM simulation models with various levels of fidelity (coarse, medium and fine). Adaptive procedure for switching between the models of increasing accuracy in the course of the optimization process is implemented. Numerical and experimental case studies are provided to validate correctness of the design approach. Findings Appropriate combination of suitable design optimization algorithm embedded in a trust region framework, as well as model selection techniques, allows for considerable reduction of the antenna optimization cost compared to conventional methods. Research limitations/implications The study demonstrates feasibility of EM-simulation-driven design optimization of antennas at low computational cost. The presented techniques reach beyond the common design approaches based on direct optimization of EM models using conventional gradient-based or derivative-free methods, particularly in terms of reliability and reduction of the computational costs of the design processes. Originality/value Simulation-driven design optimization of contemporary antenna structures is very challenging when high-fidelity EM simulations are utilized for performance utilization of structure at hand. The proposed variable-fidelity optimization technique with adjoint sensitivity and trust regions permits rapid optimization of numerically demanding antenna designs (here, dielectric resonator antenna and compact monopole), which cannot be achieved when conventional methods are of use. The design cost of proposed strategy is up to 60 percent lower than direct optimization exploiting adjoint sensitivities. Experimental validation of the results is also provided.

Numerical optimization has been ubiquitous in antenna design for over a decade or so. It is indispensable in handling of multiple geometry/material parameters, performance goals, and constraints. It is also challenging as it incurs significant CPU expenses, especially when the underlying computational model involves full-wave electromagnetic (EM) analysis. In most practical cases, the latter is imperative to ensure evaluation reliability. The numerical challenges are even more pronounced when global search is required, which is most often carried out using nature-inspired algorithms. Population-based procedures are known for their ability to escape from local optima, yet their computational efficiency is poor, which makes them impractical when applied directly to EM models. A common workaround is the utilization of surrogate modeling techniques, typically in the form of iterative prediction-correction schemes, where the accumulated EM simulation data is used to identify the promising regions of the parameter space and to refine the surrogate model predictive power at the same time. Notwithstanding, implementation of surrogate-assisted procedures is often intricate, whereas their efficacy may be hampered by the dimensionality issues and considerable nonlinearity of antenna characteristics. This work investigates the benefits of incorporating variable-resolution EM simulation models into nature-inspired algorithms for optimization of antenna structures, where the model resolution pertains to the level of discretization density of an antenna structure in the full-wave simulation model. The considered framework utilizes EM simulation models which share the same physical background and are selected from a continuous spectrum of allowable resolutions. The early stages of the search process are carried out with the use of the lowest fidelity model, which is subsequently automatically increased to finally reach the high-fidelity antenna representation (i.e., considered as sufficiently accurate for design purposes). Numerical validation is executed using several antenna structures of distinct types of characteristics, and a particle swarm optimizer as the optimization engine. The results demonstrate that appropriate resolution adjustment profiles permit considerable computational savings (reaching up to eighty percent in comparison to high-fidelity-based optimization) without noticeable degradation of the search process reliability. The most appealing features of the presented approach—apart from its computational efficiency—are straightforward implementation and versatility.

In this paper, a novel structure of a compact UWB slot antenna and its design optimization procedure has been presented. In order to achieve a sufficient number of degrees of freedom necessary to obtain a considerable size reduction rate, the slot is parameterized using spline curves. All antenna dimensions are simultaneously adjusted using numerical optimization procedures. The fundamental bottleneck here is a high cost of the electromagnetic (EM) simulation model of the structure that includes (for reliability) an SMA connector. Another problem is a large number of geometry parameters (nineteen). For the sake of computational efficiency, the optimization process is therefore performed using variable-fidelity EM simulations and surrogate-assisted algorithms. The optimization process is oriented towards explicit reduction of the antenna size and leads to a compact footprint of 199 mm2 as well as acceptable matching within the entire UWB band. The simulation results are validated using physical measurements of the fabricated antenna prototype.

Mykonos is a linear transformer driver (LTD) pulsed power accelerator currently undergoing testing at Sandia National Laboratories. Mykonos-2, the initial configuration, includes two 1-MA, 200-kV LTD cavities driving a water-filled transmission line terminated by a resistive load. Transmission line and 3D electromagnetic (EM) simulation models of high-current LTD cavities have been developed [D.V. Rose et al. Phys. Rev. ST Accel. Beams 13, 90401 (2010)]. These models have been used to develop an equivalent two-cavity transmission line model of Mykonos-2 using the BERTHA transmission line code. The model explicitly includes 40 bricks per cavity and detailed representations of the water-filled transmission line and resistive load. (A brick consists of two capacitors and a switch connected in series.) This model is compared to 3D EM simulations of the entire accelerator including detailed representations of the individual capacitors and switches in each cavity. Good agreement is obtained between the two simulation models and both models are in good agreement with preliminary data from Mykonos-2.

Full-wave electromagnetic (EM) simulation models are ubiquitous in carrying out design closure of antenna structures. Yet, EM-based design is expensive due to a large number of analyses necessary to yield an optimized design. Computational savings can be achieved using, for example, adjoint sensitivities, surrogate-assisted procedures, design space dimensionality reduction, or similar sophisticated means. In this article, a simple modification of a rudimentary trust-region-embedded gradient search with numerical derivatives is proposed for reduced-cost optimization of input characteristics of wideband antennas. The approach exploits information and history of relative changes of the design (as compared with the trust region size) during algorithm iterations to control the updates of components of the antenna response Jacobian, specifically, to execute them only if necessary. It is demonstrated that the proposed framework may lead to over 50% savings over the reference algorithm with only minor degradation of the design quality, specifically, up to 0.3 dB (or <3%). Numerical results are supported by experimental validation of the optimized antenna designs. The presented algorithm can be utilized as a stand-alone optimization routine or as a building block of surrogate-assisted procedures.

Information regarding the best possible design trade‐offs of an antenna structure can be obtained through multi‐objective optimisation (MO). Unfortunately, MO is extremely challenging if full‐wave electromagnetic (EM) simulation models are used for performance evaluation. Yet, for the majority of contemporary antennas, EM analysis is the only tool that ensures reliability. This study introduces a procedure for accelerated MO design of antennas that exploits cheap data‐driven surrogates, as well as MO evolutionary algorithm to yield an initial approximation of the Pareto set. The final Pareto set is found using output space mapping. The major contribution of this work is a further reduction of the computational cost of surrogate model construction using a design space segmentation mechanism, where a part of the space containing the Pareto front is first identified by means of single‐objective optimisation runs and subsequently represented by a set of adjacent compartments with separate surrogate models established within them. Segmentation allows for significant reduction of the number of training points necessary to build a reliable surrogate and thus the overall optimisation cost. The presented technique is demonstrated using two antenna structures and compared with a state‐of‐the‐art surrogate‐assisted techniques. Experimental validation is also provided.

A computationally efficient procedure for design optimization of slot antennas is presented. We use space mapping as the main optimization engine, the underlying coarse model being coarse-discretization electromagnetic (EM) simulation data of the antenna structure of interest (low-fidelity model). In order to speed up the design process, the low-fidelity model is not used directly in the process; instead, the coarse-discretization simulation data - sampled only in the vicinity of their approximate optimum - are used to create an auxiliary response surface model through Bayesian support vector regression. The latter - after suitable space-mapping-based correction - serves as a prediction tool to find an accurate optimum design of the antenna. The proposed procedure is illustrated using two examples of slot antennas.

Accurate responses of antennas, in many cases, can be obtained only with discrete full-wave electromagnetic (EM) simulations. Therefore, contemporary antenna design strongly relies on these EM simulations. On the other hand, direct use of high-fidelity EM simulations in the design process, particularly for automated parameter optimization, often results in prohibitive computational costs. In this chapter, we illustrate how the designs of various antennas can be obtained efficiently using an automated surrogate-based optimization (SBO) methodology. The SBO techniques considered here include the adaptive design specification technique, variable-fidelity simulation-driven optimization, and shape-preserving response prediction. The essence of these techniques resides in shifting the optimization burden to a fast surrogate of the antenna structure, and using coarse-discretization EM models to configure the surrogate. A properly created and handled surrogate serves as a reliable prediction tool allowing satisfactory designs to be obtained at the cost of a few simulations of the high-fidelity antenna model. We also demonstrate the effect of the coarse-discretization model fidelity on the final design quality and the computational cost of the design process. Finally, we give an example of automatic management of the coarse model quality. Recommendations concerning the application of specific SBO techniques to antenna design are also presented.

Test fixtures with high power capacity and an impedance matching network are generally chosen for measurements of high-power gallium nitride high electron mobility transistors. To make interconnection of the test fixtures and devices under test, wire bonding is an effective assembly method. Bonding wires become dominant parasitic elements, especially in the S parameter measurement and loadpull measurement, which should be taken into account and accurately de-embedded for device measurements and modeling. In this paper, test fixtures and through-reflect-line calibration kits are designed to achieve the S parameter measurements of the bonding wire with a vector network analyzer. Equivalent inductances of the bonding wire can be obtained with the electromagnetic simulation model and compact circuit model proposed based on the test fixture. The good agreement of the equivalent inductances extracted with the test fixture and models verified that the way to characterize bonding wire interconnection is effective and accurate. Cree's CGHV1J006D is chosen to take the S parameter measurement for proving the accurate model of the bonding wire. Finally, the equivalent inductance of the 2mm gate-width transistor is obtained with the electromagnetic model. The drain impedance is accurately calculated after the loadpull measurement with bonding wire effects de-embedded, which matches the loadline theory.

We inferred the effective sheath width using the cutoff probe and incorporating a full-wave three-dimensional electromagnetic (EM) simulation. The EM simulation reproduced the experimentally obtained plasma-sheath resonance (PSR) on the microwave transmission (S21) spectrum well. The PSR frequency has a one-to-one correspondence with the width of the vacuum layer assumed to be the effective sheath in the EM simulation model. The sheath width was estimated by matching the S21 spectra of the experiment and the EM simulation for different widths of the sheath. We found that the inferred sheath widths quantitatively and qualitatively agree with the sheath width measured by incorporating an equivalent circuit model. These results demonstrate the excellent potential of the cutoff probe for inferring the effective sheath width from its experimental spectrum data.

Reducing trainee mistakes. Better performance with changing to a high-fidelity simulation system?