Reference number : 172-2024-2025-Q2-1014845, amendment number : , agreement type : G, recipient legal name : National Center for Scientific Research, recipient country : FR, recipient province : nan, recipient city : Paris|Paris, recipient postal code : nan, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : 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., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Bridging Simulation and Measurement Data through Machine Learning for Enhanced Silicon Photonics Device Models, agreement title french : Rapprocher les données de simulation et de mesure grâce à l'apprentissage automatique pour améliorer les modèles de dispositifs photoniques en silicium, agreement number : 1014845, agreement value : 345400.0, agreement start date : 2024-04-01, agreement end date : 2026-03-31, description english : 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., description french : Rapprocher les données de simulation et de mesure grâce à l'apprentissage automatique pour améliorer les modèles de dispositifs photoniques en silicium, expected results english : 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., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : nan, additional information french : nan, owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada