Lens design optimization by back-propagation

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.