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Metamaterial absorbers have become a popular research direction due to their broad application prospects, such as in radar, infrared imaging, and solar cell fields. Usually, nanostructured metamaterials are associated with a large number of geometric parameters, and traditional simulation designs are time consuming. In this paper, we propose a framework for designing plasma metamaterial absorbers in both a forward prediction and inverse design composed of a primary prediction network (PPN) and an auxiliary prediction network (APN). The framework can build the relationship between the geometric parameters of metamaterials and their optical response (reflection spectra, absorption spectra) from a large number of training samples, thus solving the problem of time-consuming and case-by-case numerical simulations in traditional metamaterial design. This framework can not only improve forward prediction more accurately and efficiently but also inverse design metamaterial absorbers from a given required optical response. It was verified that it is also applicable to absorbers of different structures and materials. Our results show that it can be used in metamaterial absorbers, chiral metamaterials, metamaterial filters, and other fields. [ABSTRACT FROM AUTHOR]

The process of machine learning-assisted nanophotonicsinverse design has been plagued by the problem of non-uniqueness for a long time, which is a problem worth studying. In this paper, we present a novel methodology for the design of bowtie optical nanoantennas (BONAs) by employing a Bootstrap Sampling Style Ensemble Neural Network (BSENN) model. Our approach combines a bagging algorithm with a tandem neural network to address the non-uniqueness challenge inherent in the inverse design process of BONAs. By splitting the data, training in batches, and integrating the results, our BSENN model is able to provide reliable predictions and offer a solution to the non-uniqueness problem. The main objective of our work is to explore diverse BONAs design structures that yield identical spectral responses, thereby providing a broader range of alternatives for the design of optical nanoantennas. Through the utilization of the BSENN model, we aim to enhance the design process and offer increased flexibility and versatility in the field of optical nanoantenna design. • Using the bagging algorithm and Ensemble learning model, it can provide more possible solutions for the inversedesign of optical nano antennas. • On-demand inverse design can provide more design options. • Combines a tandem neural network and K-means algorithm to ensure a simple and reliable output. [ABSTRACT FROM AUTHOR]

Metasurfaces is one of the research hotspots in the field of micro/nanometer technologies during the last decade. Whereas, it remains challenging to inverse design optics devices according to desired physical response. Here we demonstrate an attempt to design a metasurface meta-atoms with machine learning approach. We use the metasurface structure parameters, nanofin material, phase, and transmission as model data of the deep neural networks (DNN) and light gradient boosting machine (LighGBM), and the mapping relationship between geometric parameters and phase and transmission is established. The two models are highly accurate in forward design, achieving the best regression coefficient of 0.969, while for the inverse design the best regression coefficient is 0.918. Using the trained inverse design network, the structural parameters and nanofin composition of the metasurface meta-atoms can be obtained for a given phase and transmittance, and the two models show strong generalization ability. This method may facilitate the viability of complex metasurface design and it can be also potentially applied in optical zoom imaging, perfect absorbers, metasurface filters, and other nanophysics fields. • The highlights of this paper are mainly the following three points. • We demonstrate the application of LightGBM and DNN algorithms in the forward and inverse design of metasurface meta-atoms. • The transmittance and phase are predicted in the forward design. • The nanofins and structural parameter of the metasurface meta-atoms is design by inverse design. [ABSTRACT FROM AUTHOR]

Slow light devices have important applications in many fields such as optical communication, optical storage, optical signal shaping and synchronization. Although a variety of high-performance slow light devices can be purchased in the market, it is still a worthwhile research topic to design slow light devices according to specific wavelengths and requirements. Here we employ machine learning method to inverse design electromagnetically induced transparency (EIT) based slow light devices in communication band using metal–dielectric hybrid metamaterials. Three characteristic points of transmittance as well as the group refractive index and bandwidth are chosen as input parameters. By replacing the complex fully connected layer with a one-dimensional convolutional neural network (1DCNN) layer to optimize the fully connected network, the proposed model can break the limitation of passive modulation and find the best slow light structure parameters. The slow light parameters could reach a bandwidth of 38.71 nm and average group refractive index of 10.51. In addition, the model can predict hybrid metamaterial structure parameters of slow light devices in the communication band from 1400 nm to 1600 nm. By combining active and passive modulation technologies, our proposed method improves the adjustment range of design parameters of slow light devices. The procedure can be potentially applied in the design of other nano optical devices. • The proposed model can find the optimal slow light structure parameters by utilizing a 1D convolutional neural network (1DCNN) layer to optimize the fully connected network. • By the prediction of the model, the slow light parameters could reach a bandwidth of 38.71 nm and average group refractive index of 10.51 in our metamaterial. • The model can predict hybrid metamaterial structure parameters of slow light devices in the communication band from 1400 nm to 1600 nm. [ABSTRACT FROM AUTHOR]