Multi-Parameter Optimization of mm-Wave Antenna Layout Using Hybrid Modeling and Incremental Model Learning

Recently, many applications have been developed using mm-Wave devices with different specifications and requirements. The semiconductor manufacturing technology has improved the bandwidth and power delivery of integrated circuits. For mm-wave devices, on-board or on-chip antennas are often the performance limiting factors. With wide-ranging specifications, automation of antenna designs has become necessary to find the optimal sizing and materials. Typically, design optimization relies on being able to conduct many simulations to evaluate the performance of design instances. However, electromagnetic (EM) simulations are very time-consuming. Surrogate model-based techniques have been developed to minimize the number of simulations. For large sets of design parameters and large input space, relying on a single model may not provide the best solution. In this paper, we propose an evolutionary search-based algorithm for the multi-objective antenna design optimization problem using multiple surrogate models to determine candidates for EM simulations. The proposed method models the reflection coefficient as a function of frequency and antenna gain as a function of angle and tries to concurrently maximize bandwidth and field of view. Two architectures have been designed for evaluation with 5 and 14 design parameters. The proposed technique can provide a large bandwidth and field of view with only several hundred samples.