Efficient Optical Spatial First-Order Differentiator Based on Graphene-Based Metalines and Evolutionary Algorithms

Abstract

We propose a novel optical spatial differentiator to perform the differentiation computation in terahertz region based on the graphene metalines, which consist of graphene layers with different widths and chemical potentials. The numerical simulation results show that when beam waist size w>1.9λ, the metalines perform the first-order differentiation in the reflection spectrum with efficiency>97%, which can be theoretically demonstrated by using transfer matrix method. In order to further improve the performance of the differentiator, evolutionary algorithm, such as genetic algorithm, is used to inversely design the structure parameters and chemical potentials of graphene metalines. The optimization results show that some performance metrics of the differentiator, for example normalized root-mean-square deviation, are better than the previous structures. Obviously, the proposed graphene metalines combined with inverse design technology can achieve a high-performance optical spatial differentiator in terahertz region and provide a new way to design the photonics devices.

Programmable Multifunctional Plasmonic Waveguide System based on Coding Metamaterials and Inverse Design

“…Genetic algorithm (GA) and particle swarm optimization (PSO) are two representative evolution algorithms, which are inspired by the genetic inheritance and group cooperation. 47,48 Although GA and PSO have been widely applied in the optimization and design of photonic devices, [49][50][51][52][53][54][55][56] they are easy to converge too early and fall into local optimal solution in sometimes. Moreover, simulated annealing (SA) and direct-binary search (DBS) are traditional search algorithms which require less time to converge.…”