Personne : Thibault, Simon
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Université Laval. Département de physique, de génie physique et d'optique
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- PublicationAccès libreInferring the solution space of microscope objective lenses using deep learning(Optical Society of America, 2022-02-14) Thibault, Simon; Zhang, Yueqian; Lalonde, Jean-François; Menke, Christoph; Côté, GeoffroiLens design extrapolation (LDE) is a data-driven approach to optical design that aims to generate new optical systems inspired by reference designs. Here, we build on a deep learning-enabled LDE framework with the aim of generating a significant variety of microscope objective lenses (MOLs) that are similar in structure to the reference MOLs, but with varied sequences—defined as a particular arrangement of glass elements, air gaps, and aperture stop placement. We first formulate LDE as a one-to-many problem—specifically, generating varied lenses for any set of specifications and lens sequence. Next, by quantifying the structure of a MOL from the slopes of its marginal ray, we improve the training objective to capture the structures of the reference MOLs (e.g., Double-Gauss, Lister, retrofocus, etc.). From only 34 reference MOLs, we generate designs across 7432 lens sequences and show that the inferred designs accurately capture the structural diversity and performance of the dataset. Our contribution answers two current challenges of the LDE framework: incorporating a meaningful one-to-many mapping, and successfully extrapolating to lens sequences unseen in the dataset—a problem much harder than the one of extrapolating to new specifications.
- PublicationAccès libreDeep learning-enabled framework for automatic lens design starting point generation(Optical Society of America, 2021-01-25) Côté, Geoffroi; Thibault, Simon; Lalonde, Jean-FrançoisWe present a simple, highly modular deep neural network (DNN) framework to address the problem of automatically inferring lens design starting points tailored to the desired specifications. In contrast to previous work, our model can handle various and complex lens structures suitable for real-world problems such as Cooke Triplets or Double Gauss lenses. Our successfully trained dynamic model can infer lens designs with realistic glass materials whose optical performance compares favorably to reference designs from the literature on 80 different lens structures. Using our trained model as a backbone, we make available to the community a web application that outputs a selection of varied, high-quality starting points directly from the desired specifications, which we believe will complement any lens designer's toolbox.
- PublicationAccès libreOptical porosimetry of weakly absorbing porous materials(Optical Society of America, 2019-07-29) Libois, Quentin; Thibault, Simon; Lévesque-Desrosiers, Félix; Dominé, Florent; Lambert Girard, SimonThe physical porosity Φ of a porous material determines most of its properties. Although the optical porosity Φopt can be measured, relating this quantity to Φ remains a challenge. Here we derive relationships between the optical porosity, the effective refractive index 𝑛eff and the physical porosity of weakly absorbing porous media. It introduces the absorption enhancement parameter 𝐵, which quantifies the asymmetry of photon path lengths between the solid material and the pores and can be derived from the absorption coefficient 𝜇𝑎 of the material. Hence Φ can be derived from combined measurements of 𝑛eff and 𝜇𝑎. The theory is validated against laboratory measurements and numerical experiments, thus solving a long-standing issue in optical porosimetry. This suggests that optical measurements can be used to estimate physical porosity with an accuracy better than 10%.