Metamodeling of high-contrast-index gratings for color reproduction

Meta-model-based multi-objective optimization for robust color reproduction using hybrid diffraction gratings

“…which, in continuity with our previous work [12], was obtained using an artificial neural network (ANN) as intensive learning meta-model [24,25]. In this contribution we consider three hidden layers with 100 neurons each.…”

“…However, since several iterations are needed to search for the optimal solutions, one strategy proposed to avoid time-consuming electromagnetic calculations is to replace them with approximations based on meta-models. Meta-Modeling is increasingly used due to the ongoing improvement of high-performance computer systems and particularly the evolution of artificial intelligence tools, which not only have become a powerful method for the modelling of physical phenomena, but also to understand, simulate and predict the optical or resonant response of the interaction between light and matter at the nanoscale [11][12][13][14][15][16].…”

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

“…In metal‐based metamaterials, for example, DNNs have been used to predict the resonant optical behavior in a number of SRR, [ 111 ] cross‐based, [ 108 ] chiral, [ 111–114 ] and coded metasurfaces. [ 115 ] In ADMs, DL has been applied to problems in color generation, [ 116–118 ] efficient metagrating design, [ 119,120 ] and modeling the complex resonant structure of cylindrical meta‐atoms, [ 109,121,122 ] supercells, [ 14 ] and multilayer nanostructures. [ 123–125 ] In photonic crystals, DNNs have been used to optimize the Q‐factor in nanocavities, [ 99 ] waveguide properties in fibers, [ 126 ] compute the band structure in 1D [ 127 ] and 2D [ 128–130 ] PCs, and predict edge states in topological insulators.…”

“…Instead of using a pure DL model, surrogate DNNs can also be combined with the above mentioned optimization methods to solve AEM inverse design problems, which we categorically label as a hybrid approach. There are many such reports of hybrid optimization models in the DL AEM literature, [ 14,115,116,119,120,132,135,136,140,141,143,175,176,189,197,208–212 ] which we summarize very generally here while noting that the individual models may vary substantially due to the number of optimization techniques available.…”

Bayesian optimization and rigorous modelling of a highly efficient 3D metamaterial mode converter

“…Integrated photonics plays an indispensable role in optical communication, computing, and sensing. Historically, integrated optics uses opto-geometric parameters in order to optimize the performances of the devices, based-on of either analytical [1] or numerical models [2,3]. With the constant growth of data traffic between the consumers and remote data centers, the use optical transceivers is now well established, which will lead in one way or another to an increase of the photonics device density on chip.…”