Study And Intelligent Design Schemes Of The Optical Devices Based On Metamaterials

Metamaterials are artificial materials with unit sizes smaller than the working wavelength,which can exhibit unique physical properties that can not be found in natural materials.By employing custom-designed metamaterial structures,optical devices can be significantly enhanced in performance or expanded in functionality.Under the growing requirements of miniaturization,integration and functional complexity for optical devices,metamaterial optical devices have broad application prospects.However,due to the complex structures of metamaterial optical devices,traditional formula-based design methods cannot fully meet their requirements in design.Thanks to the rapid development of intelligent algorithms,many new design methods for metamaterial optical devices based on intelligent algorithms have been proposed.These methods are data-driven and can be used to design complex metamaterial optical devices without the need for designers to deduce basic principles and repeat device adjustments.However,there are still many sufferings that are brought to the design of metamaterial optical devices by applying intelligent algorithms,such as insufficient scale of data sets,complexity of optimization goals,and adaptability of the algorithms themselves.Based on the basic principles related to metamaterials,this article introduces adaptive and improved intelligent algorithms,proposes intelligent design schemes for several new optical devices,and resolves several difficulties in developing metamaterial optical devices based on intelligent algorithms.Details are as follows:(1)To overcome the problem of the difficulty in obtaining datasets of some metamaterials,a small-database device design scheme based on small population evolution algorithms is proposed.Since the design schemes based on intelligent algorithms are data-driven,the scale and quality of the dataset greatly affect the performance of the design schemes.For those metamaterials with unique structures or materials,such as graphene metamaterials which are approximately two-dimensional,a large scale of database can not be obtained due to electromagnetic simulation conditions.For these optical devices,the maximization of the value of the available samples is critical.To deal with this problem,this article proposes an adaptive device design scheme based on a small population evolutionary algorithm.With the help of this scheme,graphene metamaterials assisted metallectric gratings have been designed.Based on them,plasmon-induced reflection phenomena can be dynamically controlled,thereby enhancing the intensity of the interaction between light of specific frequency in the infrared band and graphene metamaterials.Based on this basic structure,a perfect absorber with an absorption peak exceeding 0.995 and a highperformance third harmonic converter with a third harmonic conversion efficiency of-59.2 dB are designed.Different from the method of enhancing the conversion efficiency based on the phase matching of the fundamental frequency and the triple frequency,the device designed in this chapter utilizes the interaction enhancement between the nonlinear metamaterials and the light at a specific wavelength,which can be achieved without strict phase matching conditions and the same level of triple frequency generation effect can be obtained.(2)For the design of metamaterial optical devices with multiple design targets,a hybrid multi-objective device optimization scheme based on different evolutionary optimization algorithms is propoesd.In addition to the optimizations of the devices for single physical properties,devices such as optical beam splitters need to optimize different properties at the same time.For the design work of metamaterial optical devices with multiple optimization objectives,the fast non-dominated multi-objective optimization algorithm(NSGA-Ⅱ)with an elite retention strategy is introduced,and integrate the direct binary search algorithm.In detail,the designs of grating filters based on one-dimensional coding metamaterials are demonstrated by experiments.Then based on two-dimensional coding metamaterials with unified input and output sizes,this paper applies a multi-objective optimization scheme to design several plasmonic couplersplitters with power splitting of different splitting ratios,frequency splitting and frequency extraction functions.All the power coupler-splitter designs can realize a total coupling efficiency of more than 92%and a splitting ratio error of less than 1%.Compared with previous optical mode conversion devices,our designs not only maintain high coupling efficiency over a wider operating frequency band(1.45-1.65 μm),but also integrate more additional functions and have more universal structure and integration compatibility.(3)To solve the problems that the intelligent algorithms introduce when machine learning is employed in the design of metamaterial optical devices such as over-fitting problem,an inverse design scheme based on clustering algorithms,encoder models and artificial neural networks is proposed.After intelligent algorithms are introduced into the design of metamaterial optical devices,some defects of the intelligent algorithms will also affect the performance of the schemes.Among them,the most common problems encountered by machine learning algorithms is overfitting.This article proposes a new hybrid metamaterial optical device design scheme that combines supervised learning and unsupervised learning,and designs a set of planar electromagnetic-induced transparent metamaterials composed of multiple metal strips.Compared with traditional inverse design schemes based on artificial neural networks,thanks to the introduction of clustering algorithms and characteristic encoding tools,under the same training settings,the loss functions(mean square error)of both the training data set and the test data set can be reduced by more than 51%.This scheme provides an effective optimization solution for metamaterial optical device design works that encounter overfitting problems.