Inferring the solution space of microscope objective lenses using deep learning

Author:

Côté Geoffroi1,Zhang Yueqian2,Menke Christoph2,Lalonde Jean-François1,Thibault Simon1

Affiliation:

1. Université Laval

2. Carl Zeiss AG

Abstract

Lens 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.

Funder

Natural Sciences and Engineering Research Council of Canada

Canada First Research Excellence Fund

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics

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