Journal
JOURNAL OF ELECTRONIC MATERIALS
Volume 46, Issue 8, Pages 4963-4975Publisher
SPRINGER
DOI: 10.1007/s11664-017-5487-8
Keywords
Antenna; machine learning; magneto-dielectric nanomaterial
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We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.
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