Learnable freeform optical system design with differentiable ray tracing

Yunfeng Nie; Jingang Zhang; Runmu Su; Runmu Su; Heidi Ottevaere

doi:10.1117/12.2692532

Optica Design and Fabrication Congress 2023 (IODC, OFT)
Technical Digest Series (Optica Publishing Group, 2023),
paper 127982D
•https://doi.org/10.1117/12.2692532

Learnable freeform optical system design with differentiable ray tracing

Yunfeng Nie, Jingang Zhang, Runmu Su, and Heidi Ottevaere

International Optical Design Conference 2023

Québec City, Québec Canada
4–8 June 2023
ISBN: 978-1-957171-22-7

From the session
Freeform Emerging Design Methods (FEMD)

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Y. Nie, J. Zhang, R. Su, and H. Ottevaere, "Learnable freeform optical system design with differentiable ray tracing," in Optica Design and Fabrication Congress 2023 (IODC, OFT), Technical Digest Series (Optica Publishing Group, 2023), paper 127982D.

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Abstract

Unsupervised deep learning has only been used in rotationally symmetric optical design. This work presents a differentiable three-dimensional ray tracing module and related loss functions, enabling unsupervised learning of non-rationally symmetric freeform optical systems.

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