Can someone help me Grok how the 3D printed Neural Networks back propagate? As I am trying to go through the paper they describe it as a pure optical approach, but what adjusts the refraction elements during the learning process?
"[...] learnable network parameter that is iteratively adjusted during the training process of the diffractive network, using an error back-propagation method. After this numerical training phase implemented in a computer, the D^2NN design is fixed and the transmission/reflection coefficients of the neurons of all the layers are determined. This D^2NN design, once physically fabricated using e.g., 3D-printing, 3lithography, etc., can then perform, at the speed of light propagation, the specific task that it is trained for, using only optical diffraction and passive optical components/layers, creating an efficient and fast way of implementing machine learning tasks."
https://www.osa.org/en-us/about_osa/newsroom/news_releases/2...
They implemented a back propagation algorithm using just optical.