4.6 Article

Performance prediction for silicon photonics integrated circuits with layout-dependent correlated manufacturing variability

期刊

OPTICS EXPRESS
卷 25, 期 9, 页码 9712-9733

出版社

Optica Publishing Group
DOI: 10.1364/OE.25.009712

关键词

-

类别

资金

  1. Natural Sciences and Engineering Research Council of Canada (NSERC)
  2. Silicon Electronic-Photonic Integrated Circuits (SiEPIC) Program

向作者/读者索取更多资源

This work develops an enhanced Monte Carlo ( MC) simulation methodology to predict the impacts of layout-dependent correlated manufacturing variations on the performance of photonics integrated circuits (PICs). First, to enable such performance prediction, we demonstrate a simple method with sub-nanometer accuracy to characterize photonics manufacturing variations, where the width and height for a fabricated waveguide can be extracted from the spectral response of a racetrack resonator. By measuring the spectral responses for a large number of identical resonators spread over a wafer, statistical results for the variations of waveguide width and height can be obtained. Second, we develop models for the layout-dependent enhanced MC simulation. Our models use netlist extraction to transfer physical layouts into circuit simulators. Spatially correlated physical variations across the PICs are simulated on a discrete grid and are mapped to each circuit component, so that the performance for each component can be updated according to its obtained variations, and therefore, circuit simulations take the correlated variations between components into account. The simulation flow and theoretical models for our layout-dependent enhanced MC simulation are detailed in this paper. As examples, several ring-resonator filter circuits are studied using the developed enhanced MC simulation, and statistical results from the simulations can predict both common-mode and differential-mode variations of the circuit performance. (C) 2017 Optical Society of America

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据