Introducing a dynamic deep neural network to infer lens design starting points

Abstract

Most lens design problems involve the time-consuming task of finding a proper starting point, that is, a lens design that approximately fulfills the desired first-order specifications while decently correcting aberrations. In recent work, a fully-connected (FC) deep neural network was trained to learn this task by extrapolating from known lens design databases. Here, we introduce a new dynamic neural-network architecture for the starting point problem which is based on a recurrent neural network (RNN) architecture. As we show, the dynamic network can learn to infer good starting points on many lens design structures at once whereas the previous model was limited to a given sequence of glass elements and air gaps. We also show that a pretrained RNN model can generalize its knowledge over new lens design structures for which we have no reference lens design and obtain a significantly better optical performance than a RNN trained from scratch.