BLOGS

Nanoantennas convert light to electrical power and vice-versa, and are essential in the design of tiny electro-optical devices. They have diverse potential applications in just about anything based on light–matter interaction, including optical sensing and signalling, microscopy, solar-power conversion and quantum cryptography.

Inspired by natural selection, evolutionary optimization algorithms work towards an ideal design rather than evaluating the performance of all possible designs. For the problem tackled by Feichtner's team, the latter would be impossible because more than 10¹³² antenna designs would need to be evaluated using a process that takes 20 minutes per structure. The team's goal was to find a geometry that would enhance the near-field intensity of an illuminating beam of light as much as possible, so they chose this as the "fitness parameter" that they would judge each design against. Just as in nature, the fittest patterns got the chance to pass on their characteristics to the next generation, while the weaker specimens were discarded. The highest-performing five from each batch were used to build a new generation of 20 structures via crossing techniques and mutations. The new structures were in turn pitted against one another, so the overall fitness of the designs improved generation by generation – over 100 generations – until the near-field intensity enhancement registered almost twice that of the reference antenna.