Performance robustness analysis in machine-assisted design of photonic devices

Abstract

Machine-assisted design of integrated photonic devices (e.g. through optimization and inverse design methods) is opening the possibility of exploring very large design spaces, novel functionalities and non-intuitive geometries. These methods are generally used to optimize performance figures-of-merit. On the other hand, the effect of manufacturing variability remains a fundamental challenge since small fabrication errors can have a significant impact on light propagation, especially in high-index-contrast platforms. Brute-force analysis of these variabilities during the main optimization process can become prohibitive, since a large number of simulations would be required. To this purpose, efficient stochastic techniques integrated in the design cycle allow to quickly assess the performance robustness and the expected fabrication yield of each tentative device generated by the optimization. In this invited talk we present an overview of the recent advances in the implementation of stochastic techniques in photonics, focusing in particular on stochastic spectral methods that have been regarded as a promising alternative to the classical Monte Carlo method. Polynomial chaos expansion techniques generate so called surrogate models by means of an orthogonal set of polynomials to efficiently represent the dependence of a function to statistical variabilities. They achieve a considerable reduction of the simulation time compared to Monte Carlo, at least for mid-scale problems, making feasible the incorporation of tolerance analysis and yield optimization within the photonic design flow.

Navigating through complex photonic design space using machine learning methods

“…Recently, we have taken the first steps to incorporate machine learning (ML) methods including supervised learning, dimensionality reduction techniques, and global optimization, into the photonic component design process [17][18][19]. The ultimate objective is to create a methodology for identifying the subspaces in parameter space that encompass all good designs (with respect to a performance objective), and then building a complete and validated global map of subspaces, using readily available resources and within a reasonable amount of time.…”

“…2. Details of the strategy, implementation and validation have been reported elsewhere [17][18][19]. Here we only describe the main steps of the process, and then focus on highlighting the impact such mapping strategy brings to the fore.…”

“…On the other hand, fully evaluating the statistical behavior of a device requires a large number of computations, with Monte Carlo simulation as the standard 'brute-force' method. Stochastic techniques such as polynomial chaos expansion have emerged as efficient alternatives to assess the performance robustness and expected fabrication yield [19,21,22]. Even with these advances, carrying out robustness assessment during the device optimization process requires prohibitive computation resources as well as being wasteful, since most evaluations would be carried out on structures that do not meet the primary objective.…”