Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofin And Annular Groove Phase Masks

Metasurfaces offer a flexible framework for the manipulation of light properties in the realm of thin film optics. Specifically, the polarization of light can be effectively controlled through the use of thin phase plates. This study aims to introduce a surrogate optimization framework for these devices. The framework is applied to develop two kinds of vortex phase masks (VPMs) tailored for application in astronomical high-contrast imaging. Computational intelligence techniques are exploited to optimize the geometric features of these devices. The large design space and computational limitations necessitate the use of surrogate models like partial least squares Kriging, radial basis functions, or neural networks. However, we demonstrate the inadequacy of these methods in modeling the performance of VPMs. To address the shortcomings of these methods, a data-efficient evolutionary optimization setup using a deep neural network as a highly accurate and efficient surrogate model is proposed. The optimization process in this study employs a robust particle swarm evolutionary optimization scheme, which operates on explicit geometric parameters of the photonic device. Through this approach, optimal designs are developed for two design candidates. In the most complex case, evolutionary optimization enables optimization of the design that would otherwise be impractical (requiring too much simulations). In both cases, the surrogate model improves the reliability and efficiency of the procedure, effectively reducing the required number of simulations by up to 75% compared to conventional optimization techniques

Design and Optimisation of Optical Metasurfaces Using Deep Learning

This thesis centres on the design, processing, and fabrication of tunable optical metamaterials. It incorporates physics-based simulation, deep learning (DL), and thin film fabrication techniques to offer a comprehensive exploration of the field of optical metamaterials. Placing stiff resonators on a flexible substrate is a common type of mechanically tunable metasurface, whose optical responses are tuned by dynamically adjusting the spacing between resonators by applying mechanical force. However, the significant modulus mismatch between materials causes stress concentration at the interface, leading to crack propagation and delamination at lower strain levels (20-50%), and limiting the optical tunability of the structure. To address this challenge, we propose two designs to manipulate stress distribution. Under mechanical force, the structure enables localised deformation, redirecting stress from critical areas. This mechanism minimises the accumulation of stress in the interface, thereby diminishing the risk of material failure and improving stretchability up to 120% compared to traditional designs. This extreme stretchability leads to a 143 nm resonance shift, which is almost twice as large as that of conventional geometry. A universal machine learning (ML)-based approach was developed to optimise the metasurface design across three key aspects: geometric parameters, material development, and free-form shape configuration. In design parameters optimisation, a fully connected neural network (FCNN) was developed with a mean absolute error (MAE) of 0.0051, recommending a single geometry with a 104 order of magnitude decrease in computational time when compared to finite element method (FEM) simulations used for data generation. The suggested structure provides extensive coverage of the colour space, encompassing 27.65% of the standard RGB (sRGB) space. For the materials development part, an inverse design (ID) network was combined with effective medium approximation (EMA), navigating infinite materials composition space to identify new compositions for custom applications. The last network was tasked to explore boundless free-form shape space to propose the one for the on-demand optical properties with MAE of 0.21. The accuracy of all networks was experimentally validated