Photonic device sensitivity analysis methods: towards process variation-aware silicon photonics design
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111030P.pdf
Description
Published version
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941.14 KB
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Author(s)
El-Henawy, Sally I.
Zhang, Zhengxing
Miller, Ryan
Boning, Duane S
Date Issued
2019
Journal
Proceedings of SPIE - The International Society for Optical Engineering
Publisher
SPIE-Intl Soc Optical Eng
Version
Final published version
Abstract
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Silicon photonics offers the ability to fabricate and integrate photonic and electronic components using existing integrated circuit fabrication infrastructure. Recent work seeks to understand the impact of IC process variations on performance of photonic components. In particular, methods for analysis that identify sensitivity of photonic components to process variations are crucial to enable viable design and manufacturing of silicon photonic systems. We present two different and complementary methods for understanding the impact of geometric process variations on photonics components: ensemble statistical virtual fabrication simulations, and adjoint methods. These are utilized to identify the most sensitive regions of a Y-splitter photonic component to line edge roughness (LER) due to inherent lithography and etch process variations. In the ensemble approach, we simulate multiple instantiations with random LER applied to specific sections of the Y-splitter. This enables localization and quantification of LER impact on transmission, phase imbalance, and excess losses. These evaluations, however, come at the cost of many simulations. In adjoint sensitivity evaluation, only one or two simulations can identify regions most sensitive to LER. While first-order linear sensitivity is extracted, the adjoint has challenges in quantifying mean variation impacts. Both methods reveal that the Y-splitter is most sensitive to LER in the input taper, accounting for over 95% of the imbalance transmission. These two methods can be combined to quantify mean, variance, and sensitivity of photonic device components in the face of statistical variations. Incorporated into future photonic process design kits (PDKs), these analysis methods will help designers predict and optimize photonic component performance and yield.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1117/12.2529198
