Controlling light: Methods for inverse design of nonlinear nanophotonic waveguides on chip
Doktorsavhandling, 2025
Modern advances in manufacturing and nanophotonics have made possible a high degree of tailoring of waveguide geometries for near-field light enhancement to meet these requirements. However, simulating and designing these nonlinear integrated optical devices is challenging.
In this thesis, I will present methods for simulating periodic optical waveguide structures with nontrivial unit cells, and how we can use knowledge of the physics to tailor the mesh adaptation in finite-element simulations to electrodynamic problems. I will also present how we can combine machine learning and physics for scattering problems and how we can use inverse design to suggest waveguide cross sections that fulfil multiple design requirements on dispersion characteristics.
FEM
machine learning
FWM
Inverse design
metamaterials
OPA
four wave mixing
Nanophotonics
waveguide
nonlinear optics
parametric amplifiers
PSA
AI
Författare
Albin Jonasson Svärdsby
Chalmers, Fysik, Kondenserad materie- och materialteori
Adaptive meshing strategies for nanophotonics using a posteriori error estimation
Optics Express,;Vol. 32(2024)p. 24592-24602
Artikel i vetenskaplig tidskrift
Viktor A. Lilja, Albin J. Svärdsby, Timo Gahlmann, and Philippe Tassin. A general framework for knowledge integration in machine learning for electromagnetic scattering using quasinormal modes
Albin J. Svärdsby and Philippe Tassin. Determining the dispersion and nonlinear characteristics of 3D periodic waveguides using finite-element eigenmode simulations
Albin J. Svärdsby et. al . Inverse design of optical waveguides for phase-sensitive amplifiers using machine learning
Fascinating physics can occur in materials. If you shine rays of light with different colours into a
material and the conditions are just right, quantum effects can produce another ray of light with a
different colour. If the conditions are just right...
In order for these conditions to be just right, we need to manufacture our devices with nanometre-
level precision so that the features of the structure are smaller than the wavelength of the light. This
poses considerable manufacturing challenges, but modern advances in manufacturing have now en-
abled us to be this precise. However, before we build it we need to know what is just right...
To achieve this, we use simulations to predict the behaviour of our devices. These simulations can
take a long time and be computationally expensive and this poses a challenge for us when we want to
create good designs that are just right...
In this thesis I will present work that decreases the computational cost associated with the simulation
of devices that guide light and how we can use AI to assist us in predicting device designs that control
light in a desired way, so that the conditions become just right.
Utveckling av nya fotoniska metaytor med hjälp av artificiell intelligens
Vetenskapsrådet (VR) (2020-05284), 2020-12-01 -- 2024-11-30.
Ämneskategorier (SSIF 2025)
Atom- och molekylfysik och optik
Annan fysik
Infrastruktur
Chalmers e-Commons (inkl. C3SE, 2020-)
DOI
10.63959/chalmers.dt/5813
ISBN
978-91-8103-356-4
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5813
Utgivare
Chalmers
PJ-salen, byggnad Fysik Origo, Fysikgården 1, Göteborg
Opponent: Prof. Peter Wiecha, National Centre for Scientific Research (CNRS), Frankrike