Mitigating Field Enhancement in Metasurfaces and Metamaterials for High-Power Microwave Applications

Bossard, Jeremy A.; Scarborough, Clinton P.; Wu, Qi; Campbell, Sawyer D.; Werner, Douglas H.; Werner, Pingjuan L.; Griffiths, Scott F.; Ketner, Matthew

doi:10.1109/tap.2016.2623643

Cited by 16 publications

(10 citation statements)

References 27 publications

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“…A genetic algorithm evaluated negative and low-index metamaterials for field enhancement and presented a quad-beam focusing metamaterial lens with MFEF < 5 over the entire operating band for this metasurface. This approach was applied to negative-index metamaterial (NIM), zero-index metamaterial (ZIM), and low-index metamaterial (LIM) structures [68].…”

Section: A Modeling Contributionsmentioning

confidence: 99%

A Review of Nonlinear Transmission Line System Design

2020

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Nonlinear transmission lines (NLTLs) have been increasingly studied to produce high power microwaves (HPM) at higher repetition rates than conventional HPM devices without requiring the same auxiliary systems. The ability to array NLTLs to produce higher power and achieve beam steering provides an additional capability. This review summarizes the various NLTL topologies designed to achieve these design objectives. Specifically, we summarize modeling and experimental studies for three primary topologies: the lumped element NLTL, the split ring resonator, and the traditional transmission line geometry using nonlinear materials. The lumped element NLTL and the traditional transmission line constructed with nonlinear materials lend themselves to higher power applications, while the split ring resonator is better suited for applications involving antennas. We provide a detailed summary of past studies for these topologies and conclude by exploring ongoing work and future opportunities for technological development.

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Section: A Modeling Contributionsmentioning

confidence: 99%

A Review of Nonlinear Transmission Line System Design

2020

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“…In [27], the maximum field enhancement factor (MFEF) is introduced to straightly represent the enhancement degrees of the electric field, and is determined by the ratio of the maximum electric field (E max ) to the electric field of the incident electromagnetic wave (E 0 ). The detailed expression is as follows:…”

Section: Theory Of Cavity Mode a Power Capacity Of Radiation Unitsmentioning

confidence: 99%

Design of a Novel Compact Slotted Cavity With Shaped Beam for High Power Microwave Feed Antenna Using Asymmetric High Order Mode

Yang

et al. 2019

IEEE Access

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In order to achieve a shaped beam on a compact aperture and reduce the cost and the volume, a conformal slotted cavity as a feed antenna for high-power microwave using the asymmetric high-order mode is investigated. The proposed antenna working in a high-order mode has merits on reducing the complexity of a power network and the volume of a mode converter. The alternate arrangement of the slot array is taken into account, and the asymmetric high-order mode TE 430 is selected to improve the symmetry of far-field pattern at the E-plane. A novel design of wide slot with the horn-cavity etched on the top surface of the upper cavity achieves the uniform aperture field and mitigates the maximum electric field enhancement, and calculated results verify that the proposed antenna is capable of high-power handling capability (above 1.29 GW in vacuum). The conformal slot array is introduced to obtain a shaped beam, which helps to illuminate reflect array with shaped aperture efficiently. The simulation and measured results show that the feed antenna achieves a low-profile structure of 2.4λ 0 × 2.8λ 0 × 0.8λ 0 , the 10-dB gain beamwidth of E-plane is 56.6 • at the operating frequency of 9.1 GHz, the maximum difference of the gain, and the 10-dB beamwidth angle between the two sides of main beam is no more than 1 dB and 1 • , respectively. INDEX TERMS Asymmetric high order mode, shaped beam, compact feed antenna, conformal array, high power microwave, horn-cavity, power handling capability, slotted cavity.

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“…Optical nanoantenna array configurations [5] and shapes [6] have been optimized to maximum field enhancement at the selected location. In [7], the GA has a decreased field enhancement, which is a prohibitive factor for metamaterial usage in high power microwave implementations and thus, has a reflectarray unit cell design based on pixels. In recent applications, coding metasurfaces based on radar cross-section (RCS) reduction and best suitable metamaterials developed for polarization conversion, has been included by the GA in [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Metamaterial Design with Nested-CNN and Prediction Improvement with Imputation

Kıymık

Erçelebi

2022

Applied Sciences

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Metamaterials, which are not found in nature, are used to increase the performance of antennas with their extraordinary electromagnetic properties. Since metamaterials provide unique advantages, performance improvements have been made with many optimization algorithms. Objective: The article aimed to develop a deep learning model that, unlike traditional optimization algorithms, takes the desired reflection coefficients’ parameter as an input and gives the image of the corresponding metamaterial. Method: An amount of 29,722 metamaterial images and reflection coefficients corresponding to the metamaterials were collected. Nested-CNN, designed for this task, consisted of Model-1 and Model-2. Model-1 was designed to generate the shape of metamaterial with a reflection coefficient as the input. Model-2 was designed to detect the reflection coefficient of a given image of metamaterial input. Created by using Model-2 in Model-1’s loss function, the nested-CNN was updated by comparing the reflection coefficient of the produced image with the desired reflection coefficient. Secondly, imputation, which is usually the complete missing data before the process of training in machine learning algorithms, was proposed to use in the prediction side to improve the performance of the nested-CNN. The imputation for prediction was used for the non-interested part of the reflection coefficient to decrease the error of the interested region of the reflection coefficient. In the experiment, 27,222 data were used for the KNN-imputer, half of the reflection coefficient was considered as the non-interested region. Additionally, 40 neighbors and 50 neighbors were given the best mean absolute errors (MAE) for specified conditions. Result: The given results are based on test data. For Model-2, the MAE was 0.27, the R2 score was 0.96, and the mean correlation coefficient was 0.93. The R2 score for the nested-CNN was 0.9., the MAE of nested-CNN was 0.42, and the MAE of nested-CNN with 50 neighbors was 0.17.

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Mitigating Field Enhancement in Metasurfaces and Metamaterials for High-Power Microwave Applications

Cited by 16 publications

References 27 publications

A Review of Nonlinear Transmission Line System Design

A Review of Nonlinear Transmission Line System Design

Design of a Novel Compact Slotted Cavity With Shaped Beam for High Power Microwave Feed Antenna Using Asymmetric High Order Mode

Metamaterial Design with Nested-CNN and Prediction Improvement with Imputation

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