Lens design optimization by back-propagation

Congli Wang; Ni Chen; Wolfgang Heidrich

doi:10.1117/12.2603675

19 November 2021 Lens design optimization by back-propagation

Congli Wang, Ni Chen, Wolfgang Heidrich

Proceedings Volume 12078, International Optical Design Conference 2021; 120781O (2021) https://doi.org/10.1117/12.2603675
Event: International Optical Design Conference - IODC 2021, 2021, Online Only

Abstract

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 optics-network optimization for extended-depth-of-field applications.

Citation Download Citation

Congli Wang, Ni Chen, and Wolfgang Heidrich "Lens design optimization by back-propagation", Proc. SPIE 12078, International Optical Design Conference 2021, 120781O (19 November 2021); https://doi.org/10.1117/12.2603675

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