Fig. 5: Fast simulation of optical metasurfaces assisted by neural networks.
a Deep neural network considers meta-atom characteristics, such as the index, gap, thickness, and radius as inputs, predicting the real and imaginary parts of the transmission coefficient. Amplitude and phase are then calculated separately based on these predictions104. b Convolutional neural network captures spatial data from a 100 × 100 grid and uses a recurrent neural network to map these data to absorption spectra105. c A three dimensional (3D) convolutional neural network learns effectively from the input data of arbitrary 3D shapes, generating outputs represented by a 3D electric field33. d Deep neural network trained with nine radius inputs implies an increase in the unit cell size and a reduction in the number of incorrect predictions of coupling with the surrounding meta-atoms106.
