Artificial intelligence is currently attracting unprecedented attention for its ability to tackle hard problems with huge datasets, which have been rendered tractable by the giant computational power and amount of training data available today. Photonic inverse design, in which one seeks to find objects of desired electromagnetic response, belongs to this class of complex problems that can greatly benefit from these ideas. In this work, artificial intelligence concepts are applied to advance the quest for invisible particles that do not perturb the background field; in particular, a fully connected neural network is proposed to address such a problem by learning the dynamics of visible-light interaction with low-scattering multilayered nanospheres. By swapping the roles between inputs and outputs, the same network can then be used for the inverse design of invisible nanoparticles, obtaining superior performance with respect to the best elements of the training set. The proposed approach can be generalized to approximate Maxwell interactions by simulating the electromagnetic response of more complicated optical configurations, and accomplish their inverse design directly, without successive iterations.
ASJC Scopus subject areas
- Physics and Astronomy(all)