  Antennas are more prevalent than ever enabling 5G connectivity for wide
ranging applications like cellular communication, IoT, autonomous vehicles,
etc. Optimizing an antenna design can be challenging and employing traditional
optimization techniques oftentimes require evaluating ~100s of design
variations, which is time prohibitive to tight design deadlines. Instead, the
common optimization approach relies on engineering expertise and solving only a
handful design variations to manually determine a design with good-enough
performance. Here, we present a novel approach for antenna optimization, which
utilizes analytical derivatives calculated by Ansys HFSS to dramatically reduce
the number of necessary simulation runs. In the presented approach a local
updating scheme, which converges quickly to the next local optimum, is extended
to a global search strategy intended to detect several local and global optima.
This global search procedure uses a consistent interpolation model to
approximate the simulation model outputs and its derivatives depending on the
design parameters values. The presented approach is implemented in an
optimization workflow, which automatically interacts with the HFSS simulation
model. Since this optimizer relies on the analytical derivatives calculated by
HFSS, it is applicable to any design optimization where geometry or material
properties are varied, and the device response is described by SYZ-parameters
or far-field quantities. The usability of this new optimizer is therefore wide
ranging from antenna design to signal integrity applications.
