Neural networks for calculation of complex fields from far-field intensities in mesoscopic photonic systems

Abstract

Mesoscopic photonics is built on the foundations of theories from mesoscopic physics of electron propagation, although optical techniques have enabled major strides in experimental research on the topic. Theoretical techniques calculate relevant parameters using wave functions or electric fields inside a sample, while experiments mostly measure intensities in the far field. Ideally, the theoretically calculated and experimentally measured parameters must be on equal footing. Here, we employ deep neural networks that calculate near-field intensities and, subsequently, real and complex fields, from simulated far-field measurements. A fully connected deep neural network is built for one-dimensional systems, while a convolutional neural network is developed for two-dimensional systems. The accuracy of these networks is consistently above 95%. We reveal the improvement in estimation of transport parameters by the predicted near-field data from raw measurement data.