Performance robustness analysis in machine-assisted design of photonic devices

摘要

Machine-assisted design of integrated photonic devices (e.g. through optimization and inverse design methods) isopening the possibility of exploring very large design spaces, novel functionalities and non-intuitive geometries. Thesemethods are generally used to optimize performance figures-of-merit. On the other hand, the effect of manufacturingvariability remains a fundamental challenge since small fabrication errors can have a significant impact on lightpropagation, especially in high-index-contrast platforms. Brute-force analysis of these variabilities during the mainoptimization 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 theexpected fabrication yield of each tentative device generated by the optimization. In this invited talk we present anoverview of the recent advances in the implementation of stochastic techniques in photonics, focusing in particular onstochastic 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 ofpolynomials to efficiently represent the dependence of a function to statistical variabilities. They achieve a considerablereduction of the simulation time compared to Monte Carlo, at least for mid-scale problems, making feasible theincorporation of tolerance analysis and yield optimization within the photonic design flow.