4 ms·
If I understand correctly that's sort of exactly what this is. The geometry of the diffraction gratings encodes a forward propagation model trained as classifi
by Enginerrrd 4y ago
If I understand correctly that's sort of exactly what this is. The geometry of the diffraction gratings encodes a forward propagation model trained as classifier of the number "2".
I don't quite understand the mathematics of how it was trained, but they were able to discretize the geometry of those layers somehow into little 0.4mm pixels of "trainable diffractive neurons" and they simulated light transmission through the layers to compute a loss function.
I'm really surprised that this was computationally feasible. Simulation of light through the gratings must have been cheap enough as a function evaluation to train the network.
- Lramseyer 4y agoI would imagine that you generate the desired transform function of a diffractive structure rather than the structure itself, because the structure is ultimately derived from the transform function. Since the transform function is basically a 2D fourier transform and a spatial frequency/phase plot, it's not _that_ computationally costly. Once you settle on functions you like, you then generate and or simulate a diffractive structure and see if it behaves how you expect.