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Analysis And Design Of Autonomous Microwave Cir...

Developing these systems requires specialized simulation/measurements, high-frequency device models and design automation found in the Cadence AWR RF/microwave electronic design software. This RF/microwave simulation software enables design teams to effectively manage the complex design and integration challenges associated with developing these high-speed and RF-enabled networks. Through proper analysis and design automation software from Cadence, engineering teams can accelerate the design of robust devices to meet performance, size, weight, and cost requirements while reducing time to market.

Analysis and Design of Autonomous Microwave Cir...

The RF Module helps you optimize designs by investigating effects such as electromagnetic wave propagation and resonance effects in high-frequency applications. Use the RF Module for understanding and predicting the performance of devices used in the RF, microwave, and millimeter-wave industries.

Designers of RF and microwave devices need to ensure that the electromagnetics-based products are reliable and robust. Traditional electromagnetics modeling lets you examine electromagnetic wave effects alone, but no real-world product operates under ideal operational conditions. To see how other physics phenomena affect the design, you need multiphysics modeling, which allows extending your analysis to include effects such as temperature rise and structural deformations.

With the RF Module add-on to the COMSOL Multiphysics simulation platform, you can analyze RF designs in ideal or multiphysics scenarios, including microwave and RF heating, all within the same software environment.

It is also important to use analysis software to evaluate RF interference and compatibility in wireless communication platforms for the seamless operation of your products for developing applications, including wearable devices, autonomous vehicles, and state-of-the-art microwave and RF products.

The RF Module relies heavily on the proven finite element method (FEM) for standard high-frequency electromagnetics analyses, and also includes alternate methods and solvers for specific types of analysis. The default solvers built into the RF Module help you feel confident that your analysis is correct and the design is backed up by solid numerical solutions. FEM is used for frequency-domain and transient analysis, with vector/edge elements of order 1, 2, or 3 that adapt to the curvature of CAD surfaces. There are tetrahedral, hexahedral, prismatic, and pyramidal mesh elements, as well as automatic and adaptive meshing.

While transient analyses are useful for TDR to handle signal integrity (SI) problems, many RF and microwave examples are addressed using frequency-domain simulations that generate S-parameters. By performing the frequency-to-time fast Fourier transform (FFT) after the conventional frequency domain study, TDR analysis is feasible. This type of analysis helps in identifying physical discontinuities and impedance mismatches on a transmission line by investigating the signal fluctuation in the time domain.

For modeling real-world phenomena, COMSOL Multiphysics, the RF Module, and other add-on products allow for a variety of multiphysics analyses. Thermal analysis and stress deformation are important considerations for filter designs. For example, cavity filters are typically made out of both dielectric and metallic materials. The conductivity of metals varies with temperature, which affects the losses in the device and dissipates heat. The dissipation of heat leads to a rise in temperature, and a variation in temperature will cause materials to expand or contract. Thus, when a cavity filter undergoes a high power load or an extreme thermal environment, drift may occur. Multiphysics analysis will help you consider such effects in device optimization.

Ansys HFSS is a 3D electromagnetic (EM) simulation software for designing and simulating high-frequency electronic products such as antennas, antenna arrays, RF or microwave components, high-speed interconnects, filters, connectors, IC packages and printed circuit boards. Engineers worldwide use Ansys HFSS software to design high-frequency, high-speed electronics found in communications systems, advanced driver assistance systems (ADAS), satellites, and internet-of-things (IoT) products.

Ansys HFSS is a 3D electromagnetic simulation software solution for designing and simulating high-frequency electronic products such as antennas, RF and microwave components, high-speed interconnects, filters, connectors, IC components and packages and printed circuit boards.

Get step-by-step instructions on designing antennas in Ansys HFSS in this video, which demonstrates how to create the geometry of the dipole antenna and discusses features in HFSS for antenna analysis. "How to Design Antennas in Ansys HFSS."

A candidate array design can examine input impedances of all elements under any beam scan condition. Phased array antennas can be optimized for performance at the element, subarray or complete array level based on element match (passive or driven) far-field and near-field pattern behavior over any scan condition of interest. Infinite array modeling involves one or more antenna elements placed within a unit cell. The cell contains periodic boundary conditions on the surrounding walls to mirror fields, creating an infinite number of elements. Element scan impedance and embedded element radiation patterns can be computed, including all mutual coupling effects. The method is especially useful for predicting array-blind scan angles that can occur under certain array beam steering conditions. Finite array simulation technology leverages domain decomposition with the unit cell to obtain a fast solution for large finite-sized arrays. This technology makes it possible to perform complete array analysis to predict all mutual coupling, scan impedance, element patterns, array patterns and array edge effects.

HFSS multipaction solver is based on a finite-element particle-in-cell (PIC) method. HFSS provides the multipaction analysis as a postprocessing of the frequency-domain field solutions. With few steps to set up the excitations and boundary conditions for charged particle simulation, you can check whether your design meets the standard for multipaction breakdown prevention.

The importance of numerical optimization techniques has been continually growing in the design of microwave components over the recent years. Although reasonable initial designs can be obtained using circuit theory tools, precise parameter tuning is still necessary to account for effects such as electromagnetic (EM) cross coupling or radiation losses. EM-driven design closure is most often realized using gradient-based procedures, which are generally reliable as long as the initial design is sufficiently close to the optimum one. Otherwise, the search process may end up in a local optimum that is of insufficient quality. Furthermore, simulation-based optimization incurs considerable computational expenses, which are often impractically high. This paper proposes a novel parameter tuning procedure, combining a recently reported design specification management scheme, and variable-resolution EM models. The former allows for iteration-based automated modification of the design goals to make them accessible in every step of the search process, thereby improving its immunity to poor starting points. The knowledge-based procedure for the adjustment of the simulation model fidelity is based on the convergence status of the algorithm and discrepancy between the current and the original performance specifications. Due to using lower-resolution EM simulations in early phase of the optimization run, considerable CPU savings can be achieved, which are up to 60 percent over the gradient-based search employing design specifications management and numerical derivatives. Meanwhile, as demonstrated using three microstrip circuits, the computational speedup is obtained without design quality degradation.

Geometries of microwave passive components become continuously more involved to fulfil the performance requirements of various application areas (wireless communications including 5G and 6G1,2, internet of things3, wireless sensing4, microwave imaging5, wearable devices6, autonomous vehicles7, etc.). In particular, many of these applications require specific functionalities such as multi-band operation8, harmonic suppression9, customized phase characteristics10, reconfigurability11, often combined with limitations on the physical size of the devices12,13,14,15. Due to their inherent complexity, microwave structures designed to meet these and other requirements typically feature considerably increased numbers of design variables than conventional circuits, whereas their design process has to account for several objectives and constraints imposed on their electrical characteristics. Adequate parameter tuning of such circuits requires rigorous numerical optimization16,17,18,19,20. At the same time, the adjustment process has to be carried out at the level of full-wave electromagnetic (EM) simulations to account for effects such as EM cross-couplings, or dielectric and radiation losses. These cannot be properly quantified using analytical or equivalent network models, yet are important for the operation of modern circuits implemented using techniques such as transmission line folding21, defected ground structures22, the employment of slow-wave phenomena (e.g., compact microstrip resonant cells, CMRCs23), or the incorporation of geometrical modifications (stubs24, slots25, shorting pins26). Although imperative from the standpoint of ensuring design quality, EM-driven design optimization is computationally expensive, even in the case of local tuning27, let alone global28 or multi-objective search29, or statistical design30.

Computational models of microwave devices can be implemented using full-wave EM analysis71, or circuit theory tools (equivalent networks72, analytical descriptions73). In this work, it is assumed that the primary (high-fidelity) representation of the circuit of interest is in the form of (high-fidelity) EM simulation, whereas lower-fidelity models are obtained through EM analysis executed at lower discretization density of the structure. This is a versatile and easy to control way, which also ensures a sufficiently good correlation between the models of different resolutions. Other simplification factors (e.g., neglecting losses, reducing computational domain74) will not be considered here. Utilization of multi-fidelity models can be beneficial for computational efficiency of the CAD procedures, e.g., space mapping75, response correction45,46, co-kriging76. Typically, two levels of fidelity are employed (coarse/low-fidelity, fine/high-fidelity)77), which raises some practical issues related to appropriate model selection and setup78. 041b061a72


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