Lens design optimization by back-propagation

Congli Wang, Ni Chen, Wolfgang Heidrich

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


We propose a lens design ray tracing engine that is derivative-Aware, using automatic differentiation. This derivative-Aware property enables the engine to infer gradients of current design parameters, i.e., how design parameters affect a given error metric (e.g., spot RMS or irradiance values), by back-propagating the derivatives through a computational graph via differentiable ray tracing. Our engine not only enables designers to employ gradient descent and variants for design optimization, but also provides a numerically compatible way to perform back-propagation on both the optical design and the post-processing algorithm (e.g., a neural network), making hardware-software end-To-end designs possible. Examples are demonstrated by freeform designs and joint opticsnetwork optimization for extended-depth-of-field applications.

Original languageEnglish (US)
Title of host publicationInternational Optical Design Conference, IODC 2021
PublisherOptica Publishing Group (formerly OSA)
ISBN (Electronic)9781943580880
StatePublished - 2021
EventInternational Optical Design Conference, IODC 2021 - Virtual, Online, United States
Duration: Jun 27 2021Jul 1 2021

Publication series

NameOptics InfoBase Conference Papers
ISSN (Electronic)2162-2701


ConferenceInternational Optical Design Conference, IODC 2021
Country/TerritoryUnited States
CityVirtual, Online


  • Automatic differentiation
  • End-To-end learning
  • Freeform engineering
  • Lens design

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials


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