Post-Doctoral Research Visit F/M Computational design of next-generation optical metasurfaces

The Inria center at Université Côte d'Azur includes 42 research teams and 9 support services. The center’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regional economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur  is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

Atlantis is  a joint project-team  between Inria and  the Jean-Alexandre Dieudonné Mathematics Laboratory at  Université Côte d'Azur. The team  gathers applied mathematicians and  computational scientists who are collaboratively undertaking  research activities aiming at the design, analysis, development and  application of advanced numerical methods for solving systems of  partial differential equations (PDEs) modelling nanoscale light-matter interaction problems. In this context, the team is  developing  the   DIOGENeS  [https://diogenes.inria.fr/]  software suite,  which  implements  several Discontinuous  Galerkin  (DG)  type methods tailored to the systems  of time- and frequency-domain Maxwell equations  possibly coupled  to  differential  equations modeling  the behaviour of propagation  media at optical frequencies. DIOGENeS also includes a component dedicated to the optimization of geometrical characteristics of nanostructures driven by some performance objective in the contex of inverse design strategies of nanophotonic setups.   DIOGENeS is a unique  numerical   framework  leveraging   the  capabilities   of  DG techniques  for  the simulation  of  multiscale  problems relevant  to nanophotonics and nanoplasmonics.

One important line of research of the team during the last years has been dedicated to improve the capabilities of these numerical tools to produce novel inverse design methodologies for optical metasurfcaes. In the last decade metasurfaces, i.e. 2D arrays of optical nanoantennas with subwavelength size and separation [1] have revolutionized the field of linear optics with the promise to replace bulky and difficult-to-align optical components with ultrathin and flat devices like metagratings, metalenses and metaholograms, which can also implement new functionalities in terms of aberrations correction and arbitrary wavefront shaping. In the recent years, by combining a high-fidelity DG-based fullwave solver in the time-domain [2] with a statistical learning-based global optimization method [3], we have introduced innovative inverse design methodologies for mono-objective optimization of metadeflectors [4], multi-objective optimization of RGB metalenses [5] and robust optimization of metadeflectors [6].

[1] W. Chen, A.Y. Zhu and  F. Capasso. Flat optics with dispersion-engineered metasurfaces. Nature Review Material, vol. 5, 604 (2020)

[2] S. Lanteri, C. Scheid and J. Viquerat. Analysis of a generalized dispersive model coupled to a DGTD method with application to nanophotonics. SIAM Journal on Scientific Computing, Vol. 39, No. 3, pp. A831–A859 (2017)

[3] D. Jones. Efficient global optimization of expensive black-box functions.  Journal of Global Optimization, Vol. 13, No. 4, pp. 455-492 (1998)

[4] M. Elsawy, S. Lanteri, R. Duvigneau, G. Brière, M.S. Mohamed and P. Genevet, Global optimization of metasurface designs using statistical learning methods, Scientific Reports, Vol. 9, No. 17918, (2019)

[5] M. Elsawy, A. Gourdin, M. Binois, R. Duvigneau, D. Felbacq, S. Khadir, P. Genevet an S. Lanteri,  Multiobjective statistical learning optimization of RGB metalens, ACS Photonics, Vol. 8, No. 8, pp. 2498–2508 (2021)

[6] M. Elsawy, M. Binois, R. Duvigneau, S. Lanteri, and P. Genevet, Optimization of metasurfaces under geometrical uncertainty using statistical learning, Optics Express 29(19), 29887–29898 (2021)

Our achievements in [4]-[5]-[6] are concerned with linear and passive metasurfaces. A more recent work has been dedicated to active, i.e., tunable, metasurfaces [7]. A first goal of this postodoctoral project will be to delve into novel modeling techniques for the design of next-generation metasurfaces. We will consider two modern topics in metasurface design:

1. Active and dynamically tunable metasurfaces. The objective will be to explore novel active metasurface designs enabled by advanced materials such as liquid crystals and phase-change materials. Special attention will be given to the dynamic modeling of such systems, including thermal effects, liquid crystal dynamical behavior, and their integration into tunable and reconfigurable devices.
2. Nonlinear metasurfaces. The goal will be to  exploit the latest breakthroughs in the modeling and design of nonlinear metasurfaces, focusing on second-harmonic generation, wave mixing, and other nonlinear effects. Different modeling approaches ranging from the state-of-the art linear approximation to advanced nonlinear modelling techniques to achieve superior accuracy and performance.

[7] M.M.R. Elsawy, C. Kyrou, H. Mikheeva, R. Colom, J.Y. Duboz, K.Z. Kamali, S. Lanteri, D. Neshev and P. Genevet.
Universal active metasurfaces for ultimate wavefront molding by manipulating the reflection singularities.
Laser & Photonics Review, Vol. 17, No. 7, pp. 200880 (2023)

Dealing with the above physical contexts regarding dynamicity / tunability and nonlinearity, well require investigating advanced material models and adapt numerical methods currently implemented in teh DIOGENeS software suite.

The second important objective of this postodoctoral project will be to setup inverse design workflows based on the EGO optimization algorithm for unveiling novel metasurface designs with ultimate properties allowing effective operation of active and dynamically tunable metasurfaces on one hand, and nonlinear metasurfaces on the other hand.

The Atlantis team is currently involved in collaborations with several groups of physicisits who are actively working on the fabrication and characterizartion of such metasurfaces. Therefore, the third goal of this project will be to foster these collaborations through experimental demonstrations of the capabilities of the proposed virtual metasurface designs thanks to our adavanced cinoutational modeling, and copublication in high rank journals.

Required knowledge and skills on theory and methodology: computational electromagnetics, finite element methods for PDEs, numerical optimization.

Sound knowledge of nanophotonics, metasurface, metamaterial.

Software development skills : Python and Fortran 2003, parallel programming with MPI and OpenMP.

Relational skills : team worker (verbal communication, active listening, motivation and commitment).

Other valued appreciated : good level of spoken and written english.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Contribution to mutual insurance (subject to conditions)

Gross Salary : 2788 € per month.