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Stochastic process design kits for photonic circuits based on polynomial chaos augmented macro-modelling
Optics Express
|March 14, 2018
Summary
This study introduces stochastic-based building blocks for designing photonic circuits, enabling accurate performance prediction by accounting for fabrication uncertainties. This method significantly enhances simulation efficiency for complex photonic integrated circuits.
Area of Science:
- Photonics and Optical Engineering
- Computational Electromagnetics
- Integrated Circuit Design
Background:
- Fabrication tolerances in photonic circuits lead to performance degradation and reduced process yield.
- Predicting statistical device behavior requires incorporating stochastic uncertainties during the design phase.
Purpose of the Study:
- To develop a novel method for creating stochastic-based building blocks for Process Design Kits (PDKs).
- To enable efficient analysis and design of photonic circuits by integrating fabrication uncertainties.
Main Methods:
- Utilized generalized polynomial chaos (gPC) based augmented macro-models.
- Employed stochastic collocation and Galerkin methods to capture stochastic behavior.
- Integrated these models into PDKs for photonic circuit analysis.
Main Results:
- A single deterministic simulation can compute stochastic moments for arbitrary photonic circuits.
- Achieved dramatic improvements in simulation efficiency compared to traditional methods.
- Effectiveness validated on photonic circuit examples with multiple uncertain variables.
Conclusions:
- The proposed stochastic-based building blocks and PDKs offer an efficient approach for designing robust photonic circuits.
- This method accurately predicts statistical performance, mitigating fabrication tolerance issues.
- Facilitates reliable design and fabrication of complex photonic integrated circuits.
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