Grants and Contributions:

Bridging Simulation and Measurement Data through Machine Learning for Enhanced Silicon Photonics Device Models

1014845

$345,400.00

Apr 1, 2024 - Mar 31, 2026

Integrated photonics, and in particular silicon-based photonics, is a rapidly developing technology showcasing immense potential in fields such as communication, sensing, and ultrafast analog computing. However, the quest for innovative devices that are more compact, efficient, and multifunctional presents new challenges. The high refractive index of silicon facilitates strong light confinement, which leads to compact devices. Yet, this same property means even tiny structural changes, on the order of a few nanometers, can significantly impact device performance. Such nanometer scale variations are strongly influenced by factors such as the feature size, pattern density, and process uniformity across the die or wafer. Controlling the device fabrication to such a degree of precision is pushing the limit of current fabrication technologies. In silicon photonics, this is the dominant cause for device performance degradation. Notably, the performance of fabricated on-chip integrated silicon photonic components and devices deviates from the theory due to imperfect fabrication and other external factors. Using a variety of machine learning approaches that include surrogate models, inverse models and transfer learning, the project will develop methods to correlate the actual shape of the fabricated devices to the optical measurement data. The devices in questions are individual grating couplers and coupler-based optical phased arrays. For the former, the results can be used by various fabrication facilities to perform higher quality process calibrations than what is currently available. For the latter, the resulting models will allow for a significantly faster calibration of a fabricated OPA.

National Research Council Canada

In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.

Paris, FR

172-2024-2025-Q2-1014845

Grant

Grants and Contributions

000000000

Academia

National Center for Scientific Research

Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives

Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.

541710 - R&D in the physical, engineering and life sciences