Dr. Yuri Grinberg Profile

Dr. Yuri Grinberg

at National Research Council Canada

SPIE Involvement:

Author

Publications (17)

Proceedings Article | 13 March 2024 Presentation

Optimization design for distinct types of multimode interferometers on a 220 nm SOI platform

Keru Chen, Manuel Arturo Méndez-Rosales, Andrew Knights, Yuri Grinberg

Proceedings Volume PC12891, PC1289108 (2024) https://doi.org/10.1117/12.3021820

KEYWORDS: Mach Zehnder interferometers, Design, Wavelength division multiplexing, Waveguides, Silicon, Interferometers, Structural design, Silicon nitride, Lutetium, Local area networks

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Proceedings Article | 12 March 2024 Presentation

Experimental demonstration of a SiN integrated polarization beam splitter for the 950 nm wavelength band

Zindine Mokeddem, Laurent Vivien, Delphine Marris-Morini, Eric Cassan, Pavel Cheben, Jens Schmid, Dan-Xia Xu, Yuri Grinberg, Maziyar Milanizadeh, Carlos Ramos, Daniele Melati

Proceedings Volume PC12893, PC1289302 (2024) https://doi.org/10.1117/12.3002840

KEYWORDS: Polarization, Silicon nitride, Beam splitters, Quantum experiments, Polarization control, Photonic integrated circuits, Windows, Wave propagation, Transparency, Quantum technology applications

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Proceedings Article | 9 March 2024 Presentation

Quantifying the luminescent coupling process in C-band multi-junction photonic power converters

Paige Wilson, Gavin Forcade, Robert F. Hunter, Meghan Beattie, Alexandre Walker, Yuri Grinberg, Henning Helmers, Oliver Hohn, David Lackner, Jacob Krich, Karin Hinzer

Proceedings Volume PC12881, PC128810J (2024) https://doi.org/10.1117/12.3000797

KEYWORDS: Photons, TCAD, Solar cells, Reflection, Optical transmission, Matrices, Indium gallium arsenide, Free space optics, Design and modelling, Absorption

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Proceedings Article | 9 March 2024 Presentation

Using machine learning to optimize multi-junction photonic power converters

Robert F. H. Hunter, Gavin P. Forcade, Yuri Grinberg, Meghan Beattie, D. Paige Wilson, Christopher Valdivia, Mathieu de Lafontaine, Louis-Philippe St-Arnaud, Henning Helmers, Oliver Höhn, David Lackner, Carmine Pellegrino, Jacob Krich, Alexandre Walker, Karin Hinzer

Proceedings Volume PC12881, PC1288109 (2024) https://doi.org/10.1117/12.3002658

KEYWORDS: Machine learning, Design and modelling, Pattern recognition, Optoelectronic devices, Telecommunications, Standards development, Receivers, Principal component analysis, Photovoltaics, Performance modeling

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Proceedings Article | 8 March 2024 Presentation + Paper

Fabrication aware design of sensitive photonic devices

Odile Liboiron-Ladouceur, Dusan Gostimirovic, Dan-Xia Xu, Yuri Grinberg, Chenxin Xun, James Darby, Bokun Zhao

Proceedings Volume 12890, 128900A (2024) https://doi.org/10.1117/12.2692673

KEYWORDS: Design, Fabrication, Silicon photonics, Photonic devices, Scanning electron microscopy, Deep learning, Photonic integrated circuits

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Proceedings Article | 17 March 2023 Presentation + Paper

Inverse design of robust photonic components

Yuri Grinberg, Dusan Gostimirovic, Guowu Zhang, Md Mahadi Masnad, Odile Liboiron-Ladouceur, Dan-Xia Xu

Proceedings Volume 12424, 124240C (2023) https://doi.org/10.1117/12.2650529

KEYWORDS: Artificial neural networks, Deep learning, Design and modelling, Fabrication, Etching, Demultiplexers, Silicon, Mathematical optimization, Crosstalk, Silicon photonics, Scanning electron microscopy, Manufacturing

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Proceedings Article | 17 March 2023 Presentation

Subwavelength metamaterial devices with optimization and machine learning

Daniele Melati, Zindine Mokeddem, Paula Nuño-Ruano, Eric Cassan, Delphine Marris-Morini, Laurent Vivien, Carlos Alonso-Ramos, Yuri Grinberg, Muhammad Al-Digeil, Dan-Xia Xu, Maziyar Milanizadeh, Jianhao Zhang, Jens Schmid, Pavel Cheben, Shahrzad Khajavi, Winnie Ye, Abi Waqas, Paolo Manfredi

Proceedings Volume PC12425, PC1242509 (2023) https://doi.org/10.1117/12.2649953

KEYWORDS: Metamaterials, Machine learning, Control systems, Waveguides, Tolerancing, Silicon photonics, Silicon, Photonics systems, Photonics, Photonic devices

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Proceedings Article | 5 March 2022 Presentation

Dimensionality reduction and optimization for the inverse design of photonic integrated devices

Daniele Melati, Mohsen Kamandar Dezfouli, Yuri Grinberg, Muhammad Al-Digeil, Dan-Xia Xu, Jens Schmid, Pavel Cheben, Abi Waqas, Paolo Manfredi, Shahrzad Khajavi, Winnie Ye, Paula Nuño-Ruano, Jianhao Zhang, Eric Cassan, Delphine Marris-Morini, Laurent Vivien, Carlos Alonso-Ramos

Proceedings Volume PC12005, PC120050D (2022) https://doi.org/10.1117/12.2617543

KEYWORDS: Photonic devices, Metamaterials, Machine learning

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Proceedings Article | 5 March 2022 Presentation

Iterative and efficient Bloch mode calculations for integrated photonics applications

Mohsen Kamandar Dezfouli, Daniele Melati, Yuri Grinberg, Muhammad Al-Digeil, Ross Cheriton, Pavel Cheben, Jens Schmid, Siegfried Janz, Dan-Xia Xu

Proceedings Volume PC12004, PC120040F (2022) https://doi.org/10.1117/12.2610477

KEYWORDS: Integrated photonics, Geometrical optics, Signal processing, Reliability, Photonics, Optimization (mathematics), Optical properties, Nanophotonics, Iterative methods, Analytical research

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Proceedings Article | 9 October 2021 Presentation + Paper

Dimensionality reduction in designing advanced silicon photonic components

Yuri Grinberg, Muhammad Al-Digeil, Daniele Melati, Mohsen Kamandar Dezfouli, Jens Schmid, Pavel Cheben, Siegfried Janz, Dan-Xia Xu

Proceedings Volume 11903, 119030H (2021) https://doi.org/10.1117/12.2603513

KEYWORDS: Principal component analysis, Optical design, Neural networks, Silicon photonics, Machine learning, Visualization, Optical simulations, Nanophotonics

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Proceedings Article | 5 March 2021 Presentation

Machine-learning dimensionality reduction for multi-objective design of photonic devices

Daniele Melati, Mohsen Kamandar Dezfouli, Yuri Grinberg, Muhammad Al-Digeil, Siegfried Janz, Ross Cheriton, Jens Schmid, Pavel Cheben, Dan-Xia Xu

Proceedings Volume 11689, 116890H (2021) https://doi.org/10.1117/12.2586717

KEYWORDS: Photonic devices, Machine learning, Metamaterials, Tolerancing, Structural design, Modeling

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Proceedings Article | 5 March 2021 Presentation + Paper

Design of fully apodized and perfectly vertical surface grating couplers using machine learning optimization

Mohsen Kamandar Dezfouli, Yuri Grinberg, Daniele Melati, Jens Schmid, Pavel Cheben, Siegfried Janz, Dan-Xia Xu

Proceedings Volume 11689, 116890J (2021) https://doi.org/10.1117/12.2576945

KEYWORDS: Optical design, Machine learning, Waveguides, Diffraction gratings, Silicon, Optical fibers, Integrated optics, Reflection, Packaging, Interfaces

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Proceedings Article | 2 April 2020 Presentation + Paper

Design of multi-parameter photonic devices using machine learning pattern recognition

Daniele Melati, Yuri Grinberg, Mohsen Kamandar Dezfouli, Jens Schmid, Pavel Cheben, Siegfried Janz, Ross Cheriton, Alejandro Sánchez-Postigo, James Pond, Jens Niegemann, Adam Reid, Dan-Xia Xu

Proceedings Volume 11364, 1136408 (2020) https://doi.org/10.1117/12.2559583

KEYWORDS: Silicon photonics, Photonic devices, Pattern recognition, Machine learning, Optimization (mathematics), Principal component analysis, Waveguides, Integrated photonics, Finite-difference time-domain method

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Enabled by technological improvements, photonic devices and circuits are becoming increasingly more complex. Non-trivial geometries are designed to reduce device footprint, improve performance, and introduce novel functionalities. However, the number of design variables required to properly represent these geometries quickly grows, limiting the effectiveness of classical design approaches. Moreover, parameters are often strongly interdependent, restricting the use of sequential optimizations or independent parameter sweeps. Although several optimization techniques can be effective for multi-parameter design, they commonly allow to optimize for a single or a handful designs and the optimization process needs to be repeated if new performance criteria are introduced. In contrast to classical design approaches, the in uences of the design parameters remain hidden as well as the general behavior of the design space. In this paper we present an extension of our recent work on the application of machine learning pattern recognition to the design of multi-parameter photonic devices. In particular, we propose using a combination of local optimization based on the adjoint method and the use of dimensionality reduction. Adjoint optimization is used multiple times to generate a small set of different designs with high performance. Dimensionality reduction is applied to analyze the relationship between these degenerate designs and identify a lower-dimensional design sub-space that includes all alternative good designs. This sub-space can be mapped for any performance criteria thus enabling informed decisions based on the relative priorities of all relevant performance specifications. As a proof of concept, we demonstrate a ten-parameter design of an integrated photonic power splitter using silicon-on-insulator technology. We identify a region of possible high performance design solutions and select two design candidates either maximizing the splitter efficiency or minimizing back-reflection.

Proceedings Article | 9 March 2020 Presentation

Smart on-chip Fourier-transform spectrometers harnessing machine learning algorithms (Conference Presentation)

Alaine Herrero-Bermello, Jiangfeng Li, Mohammad Khazaei, Yuri Grinberg, Aitor Velasco, Martin Vachon, Pavel Cheben, Lina Stankovic, Vladimir Stankovic, Dan-Xia Xu, Jens Schmid, Carlos Alonso-Ramos

Proceedings Volume 11284, 112841W (2020) https://doi.org/10.1117/12.2546253

KEYWORDS: Spectrometers, Machine learning, Absorption, Silicon photonics, Temperature metrology, Silicon, Detection and tracking algorithms, Spectroscopy, Medicine, Environmental sensing

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Proceedings Article | 26 April 2019 Open Access

Navigating through complex photonic design space using machine learning methods

Dan-Xia Xu, Yuri Grinberg, Daniele Melati, Moshen Kamandar Desfouli, Pavel Cheben, Jens Schmid, Siegfried Janz

Proceedings Volume 11031, 110310S (2019) https://doi.org/10.1117/12.2524909

KEYWORDS: Machine learning, Nanophotonics, Optimization (mathematics), Artificial neural networks, Principal component analysis, Metamaterials, Genetic algorithms, Integrated photonics, Silicon photonics, Pattern recognition

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Proceedings Article | 4 March 2019 Paper

Reaping the benefits of machine learning pattern recognition in nanophotonic component design

Yuri Grinberg, Daniele Melati, Mohsen Kamandar Dezfouli, Siegfried Janz, Jens Schmid, Pavel Cheben, Alejandro Sánchez-Postigo, J. Gonzalo Wangüemert-Pérez, Iñigo Molina-Fernández, Alejandro Ortega-Moñux, Dan-Xia Xu

Proceedings Volume 10921, 109210B (2019) https://doi.org/10.1117/12.2506787

KEYWORDS: Machine learning, Optical design, Silicon, Nanophotonics, Pattern recognition, Principal component analysis, Metamaterials, Device simulation, Optimization (mathematics)

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Proceedings Article | 4 March 2019 Presentation + Paper

Performance robustness analysis in machine-assisted design of photonic devices

Daniele Melati, Yuri Grinberg, Abi Waqas, Paolo Manfredi, Mohsen Kamandar Dezfouli, Pavel Cheben, Jens Schmid, Siegfried Janz, Andrea Melloni, Dan-Xia Xu

Proceedings Volume 10922, 1092203 (2019) https://doi.org/10.1117/12.2508602

KEYWORDS: Monte Carlo methods, Stochastic processes, Photonic devices, Waveguides, Machine learning, Optimization (mathematics), Optical design

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Showing 5 of 17 publications

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