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This study introduces a new simulation method, Reduced Spatial Correlation Matrix-based Monte-Carlo (RSCM-MC), to efficiently analyze fabrication variations in photonic integrated circuits (PICs). The method helps optimize designs for manufacturing robustness and yield.

Area of Science:

Photonics and Integrated Optics
Semiconductor Device Fabrication
Computational Modeling

Background:

Fabrication variability critically affects photonic integrated circuit (PIC) performance.
Quantifying these variations pre-fabrication is essential for design optimization and yield maximization.
Existing methods may lack efficiency in handling spatially correlated variations.

Purpose of the Study:

To present an efficient simulation methodology, Reduced Spatial Correlation Matrix-based Monte-Carlo (RSCM-MC), for studying spatially correlated fabrication variations in PICs.
To enable robust design and manufacturing of PICs by accurately predicting performance impacts.
To provide a computationally efficient approach compared to existing methods.

Main Methods:

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Extraction of physical correlation lengths defining spatial frequencies of width and height variations.

Generation of correlated variations for Monte Carlo (MC) simulations using RSCM-MC.

Utilizing Cholesky decomposition on a reduced correlation matrix for generating correlated variations across circuit components.

Main Results:

Demonstrated accuracy of the RSCM-MC methodology using a Mach-Zehnder lattice filter.
Evaluated computational performance against Virtual wafer-based Monte-Carlo (VW-MC) using a second-order Mach-Zehnder filter and a 16x16 optical switch matrix.
The RSCM-MC method shows comparable accuracy and improved computational efficiency.

Conclusions:

The RSCM-MC methodology provides an efficient and accurate approach to simulate spatially correlated fabrication variations in PICs.
This simulation technique aids designers in creating more robust PICs and achieving higher manufacturing yields.
The method offers a valuable tool for the design-for-manufacturing process in integrated photonics.