Smart Antenna Engineering: Revolutionizing Design with VLSI Modelling
Keywords:
Antenna design, VLSI modeling, machine learning, micro machined antennas, optimization techniques, convolutional neural networks (CNNs), variablefidelity simulation, genetic algorithms, RF antennas, millimeter-wave antennasAbstract
The evolution of antenna design integrated with VLSI modeling and machine learning is driving innovations in the development of compact, high-performance antennas for diverse applications, particularly in the RF and millimeter-wave domains. This paper highlights the transformation impact of micro machined antennas, machine learning-driven designs, and advanced optimization techniques. Micro machined antennas, employing substrate removal and wet etching, demonstrate superior performance for miniaturized devices, achieving exceptional gain and return loss metrics. Machine learning, leveraging convolutional neural networks,
facilitates rapid and accurate inverse design of planar antennas, significantly reducing simulation overheads and broadening design possibilities. Additionally, optimization techniques such as variable-fidelity simulations and genetic algorithms enable cost-effective and efficient design of high-gain antennas across wide frequency ranges. Despite these advancements, challenges persist in ensuring manufacturability and reliability in realworld scenarios. This study provides a comprehensive exploration of cutting-edge methodologies, setting the stage for future breakthroughs in antenna engineering
