Subwavelength metamaterials allow to synthesize tailored optical properties which enabled the demonstration of photonic devices with unprecedented performance and scale of integration. Yet, the development of metamaterial-based devices often involves a large number of interrelated parameters and figures of merit whose manual design can be impractical or lead to suboptimal solutions. In this invited talk, we will discuss the potentiality offered by multi-objective optimization and machine learning for the design of high-performance photonic devices based on metamaterials. We will present both integrated devices for on-chip photonic systems as well as recent advances in the development of devices for free-space applications and optical beam control.
Design of novel integrated photonic components often benefits from periodic geometries (either fully periodic or apodized) along the direction of light propagation, offering a wide range of capabilities including mode matching and optical rerouting. Here, we show how existing iterative methods that were originally developed for resonant nanophotonic systems in the frequency domain can be reliably used for calculation of optical Bloch modes in periodic systems in the complex wavevector domain. This method can be used for arbitrary shaped geometries and even when open boundary conditions are applied, therefore heavily impacting the fast-paced design of integrated photonic devices.
The widespread use of metamaterials and non-trivial geometries has radically changed the way photonic integrated devices are developed, opening new design possibility and allowing for unprecedented performance. Yet, these devices are often described by a large number of interrelated parameters which cannot be handled manually, requiring innovative design approaches for their effective optimization. In this invited talk, we will discuss the potentiality offered by the combination of machine learning dimensionality reduction and multi-objective optimization for the design of high performance photonic integrated devices.
Design of modern integrated nanophotonic components requires increasingly sophisticated optical simulation and optimization tools. Modeling and computational challenges arise with the increase in the number of design parameters and the introduction of multiple and often competing performance criteria. In such high dimensional design parameter spaces, it becomes difficult to navigate, explore, and visualize the best candidate designs that satisfy all the requirements. We present our recently developed approach that leverages dimensionality reduction - an area of machine learning – to identify and efficiently investigate only the most relevant portion of the design space. Once this reduced space is found, mapping and optimization can often be achieved several orders of magnitude faster than in the original design space. We showcase our approach on several design scenarios focusing on components such as optical grating couplers and power splitters. We employ principal component analysis for linear dimensionality reduction, achieving impressive performance despite its simplicity. We also demonstrate the use of a non-linear technique, i.e. neural-network based autoencoders, which can improve the effectiveness of dimensionality reduction even further. All components have nontrivial regions of interest in their design space that are identified and explored through the evaluation of various performance metrics. Visualizations of these regions offer a global picture of device behavior. Different component geometries can then be chosen depending on specific performance requirements or fabrication constraints. The proposed framework can be easily integrated into various design toolkits.
Modern design of photonic devices is quickly and steadily departing from classical geometries to focus more and more on non-trivial structures and metamaterials. These devices are governed by a multitude of parameters and the optimal design requires to simultaneously consider different figure of merits. In this invited talk we will present our recent work on the application of machine learning tools to the multi-objective optimization of multi-parameter photonic devices. In particular, we will demonstrate the potentiality of dimensionality reduction for the analysis of the complex design space of subwavelength metamaterials devices.
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