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.
Highlights
As a high-performance solution to implement the fundamental mathematical operation, optical computing, which includes optical spatial computing [1]–[12] and optical time computing [13], [14], has attracted a great deal of attention
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 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
Summary
As a high-performance solution to implement the fundamental mathematical operation, optical computing, which includes optical spatial computing [1]–[12] and optical time computing [13], [14], has attracted a great deal of attention. Graphene based metamaterials or metasurfaces have been applied in many photonics devices because they show great potential to manipulate electromagnetic waves [17]–[20]. GA falls into local optimum and demands tremendous computational time, it has been applied in the inverse design for many photonics devices, such as polarization beam splitters [33], polarization rotators [35], diodes [34], waveguide crossings [36], optical neural networks [37] and so on. It should be noted that previous researches pay little attention to the inverse design and performance optimization of OSD and graphene-based photonics devices, especially for the optimization of the physical parameters for graphene metasurfaces, such as chemical potential, relaxation time and so on. The combination of the graphene-based OSD and inverse design technology promotes the development of OSD
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