Efficient layout-aware statistical analysis for photonic integrated circuits.

Fabrication variability significantly impacts the performance of photonic integrated circuits (PICs), which makes it crucial to quantify the impact of fabrication variations before the final fabrication. Such analysis enables circuit and system designers to optimize their designs to be more robust and obtain maximum yield when designing for manufacturing. This work presents a simulation methodology, Reduced Spatial Correlation Matrix-based Monte-Carlo (RSCM-MC), to efficiently study the impact of spatially correlated fabrication variations on the performance of PICs. First, a simple and reliable method to extract physical correlation lengths, variability parameters that define the inverse of the spatial frequencies of width and height variations over a wafer, is presented. Then, the process of generating correlated variations for MC simulations using RSCM-MC methodology is presented. The methodology generates correlated variations by first creating a reduced correlation matrix containing spatial correlations between all the circuit components, and then processing it using Cholesky decomposition to obtain correlated variations for all circuit components. These variations are then used to conduct MC simulations. The accuracy and the computation performance of the proposed methodology are compared with other layout-dependent Monte-Carlo simulation methodologies, such as Virtual wafer-based Monte-Carlo (VW-MC). A Mach-Zehnder lattice filter is used to study the accuracy, and a second-order Mach-Zehnder filter and a 16x16 optical switch matrix system are used to compare the computational performance.

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

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.