# 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, et al.

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## To cite this version:

Daniele Melati, Zindine Mokeddem, Paula Nuño Ruano, Eric Cassan, Delphine Marris-Morini, et al. Subwavelength metamaterial devices with optimization and machine learning. Smart Photonic and Optoelectronic Integrated Circuits 2023, SPIE, Jan 2023, San Francisco, CA, United States. pp.11, 10.1117/12.2649953. hal-04740320

HAL Id: hal-04740320

https://hal.science/hal-04740320v1

Submitted on 17 Apr 2025

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# Subwavelength metamaterial devices with optimization and machine learning

Daniele Melati¹, Zindine Mokeddem¹, Paula Nuño-Ruano¹, Eric Cassan¹, Delphine Marris-Morini¹, Laurent Vivien¹, Carlos Alonso-Ramos¹, Yuri Grinberg², Muhammad Al-Digeil², Dan-Xia Xu², Maziyar Milanizadeh², Jianhao Zhang², Jens H. Schmid², Pavel Cheben², Shahrzad Khajavi³, Winnie N. Ye³, Abi Waqas⁴, and Paolo Manfredi⁵

1 Centre de Nanosciences et de Nanotechnologies, Université Paris-Saclay, CNRS, 91120 Palaiseau, France

2 National Research Council Canada, 1200 Montreal Rd., Ottawa, ON K1A 0R6, Canada

3 Department of Electronics, Carleton University, Ottawa, Canada

4 Department of Telecommunication, Mehran University of Engineering and Technology, Jamshoro
76062, Pakistan

5 Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy

**ABSTRACT (250 words):**

Subwavelength metamaterials are realized by structuring a dielectric material with a period much smaller than the wavelength. In this way, it is possible to frustrate diffraction and obtain an artificial material with synthesized optical properties tuned for each device of interest. The extensive use of this class of metamaterials especially in (but not limited to) silicon-based photonics allowed to demonstrate a broad range of devices including waveguide-to-fiber couplers, optical antennas, wavelength demultiplexers, mode controllers, and filters with ultra-low losses, large operational bandwidth, complex spectral responses, and compact footprint. While enabling high performance and enhanced scale of integration, the development of metamaterial-based devices with complex topologies often requires to control a large number of interrelated parameters whose manual design can be impractical or lead to suboptimal solutions. Likewise, multiple figures of merit are often involved in the evaluation of the performance of a device, e.g., losses, bandwidth, footprint, or tolerance to fabrication uncertainty, complicating the selection of optimal device design. In this invited talk, we will discuss the potentiality offered by multi-objective optimization and machine learning for the design of high-performance photonic devices based on non-trivial shapes and metamaterials. We will present both integrated devices for on-chip photonic systems as well as recent advances in the development of devices for free-space applications and optical beam control.

**ABSTRACT (100 words):**

Subwavelength metamaterials allow to synthesize tailored optical properties which enabled the demonstration of photonic devices with unprecedented performance and scale of integration. Yet, the development of metamaterial-based devices often involves a large number of interrelated parameters and figures of merit whose manual design can be impractical or lead to suboptimal solutions. In this invited talk, we will discuss the potentiality offered by multi-objective optimization and machine learning for the design of high-performance photonic devices based on metamaterials. We will present
both integrated devices for on-chip photonic systems as well as recent advances in the development of devices for free-space applications and optical beam control.