title: Uncertainty quantification of silicon photonic devices with correlated and non-Gaussian random parameters.
abstract: Process variations can significantly degrade device performance and chip yield in silicon photonics. In order to reduce the design and production costs, it is highly desirable to predict the statistical behavior of a device before the final fabrication. Monte Carlo is the mainstream computational technique used to estimate the uncertainties caused by process variations. However, it is very often too expensive due to its slow convergence rate. Recently, stochastic spectral methods based on polynomial chaos expansions have emerged as a promising alternative, and they have shown significant speedup over Monte Carlo in many engineering problems. The existing literature mostly assumes that the random parameters are mutually independent. However, in practical applications such assumption may not be necessarily accurate. In this paper, we develop an efficient numerical technique based on stochastic collocation to simulate silicon photonics with correlated and non-Gaussian random parameters. The effectiveness of our proposed technique is demonstrated by the simulation results of a silicon-on-insulator based directional coupler example. Since the mathematic formulation in this paper is very generic, our proposed algorithm can be applied to a large class of photonic design cases as well as to many other engineering problems.
authors.forename: Tsui-Wei
authors.initials: TW
authors.lastname: Weng
authors.forename: Zheng
authors.initials: Z
authors.lastname: Zhang
authors.forename: Zhan
authors.initials: Z
authors.lastname: Su
authors.forename: Youssef
authors.initials: Y
authors.lastname: Marzouk
authors.forename: Andrea
authors.initials: A
authors.lastname: Melloni
authors.forename: Luca
authors.initials: L
authors.lastname: Daniel