Global design optimization in photonics: from high performance to fabrication robustness

Modern photonic devices are characterized by a large number of parameters and the need for an “holistic” optimization of their behavior taking into account multiple figures of merit, noteworthy tolerance to fabrication uncertainty. We present here a set of tools based on dimensionality reduction capable of handling such multi-parameter, multi-objectives design problems

Dimensionality reduction and optimization for the inverse design of photonic integrated 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 device

Subwavelength metamaterial devices with optimization and machine learning

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

High-performance photonic integrated devices with machine learning and optimization

High performance and large-scale integration are driving the design of innovative photonic devices based on non-trivial shapes and metamaterials. As a consequence, the number of parameters that must be handled vastly increases and often a strong dependence between them is introduced. Moreover, multiple figures of merit must be considered simultaneously to measure the quality of the selected devices, e.g., losses, bandwidth, or tolerance to fabrication uncertainty. In this invited talk we will present our recent work on the use of machine learning and optimization tools for the design of high-performance photonic components

Dimensionality reduction for the on-chip integration of advanced photonic devices and functionalities

Design of modern photonic devices requires to handle a large number of parameters and figures of merit. By scaling down the complexity of the problem, machine learning dimensionality reduction enables the discovery of better performing devices, higher integration scale, and efficient evaluation of fabrication tolerances