  Diffractive deep neural network (DNNet) is a novel machine learning framework
on the modulation of optical transmission. Diffractive network would get
predictions at the speed of light. It's pure passive architecture, no
additional power consumption. We improved the accuracy of diffractive network
with optical waves at different frequency. Each layers have multiple
frequency-channels (optical distributions at different frequency). These
channels are merged at the output plane to get final output. The experiment in
the fasion-MNIST and EMNIST datasets showed multiple frequency-channels would
increase the accuracy a lot. We also give detailed analysis to show the
difference between DNNet and MLP. The modulation process in DNNet is actually
optical activation function. We develop an open source package ONNet. The
source codes are available at https://github.com/closest-git/ONNet.
