  In their Comment, Wei et al. (arXiv:1809.08360v1 [cs.LG]) claim that our
original interpretation of Diffractive Deep Neural Networks (D2NN) represent a
mischaracterization of the system due to linearity and passivity. In this
Response, we detail how this mischaracterization claim is unwarranted and
oblivious to several sections detailed in our original manuscript (Science,
DOI: 10.1126/science.aat8084) that specifically introduced and discussed
optical nonlinearities and reconfigurability of D2NNs, as part of our proposed
framework to enhance its performance. To further refute the mischaracterization
claim of Wei et al., we, once again, demonstrate the depth feature of optical
D2NNs by showing that multiple diffractive layers operating collectively within
a D2NN present additional degrees-of-freedom compared to a single diffractive
layer to achieve better classification accuracy, as well as improved output
signal contrast and diffraction efficiency as the number of diffractive layers
increase, showing the deepness of a D2NN, and its inherent depth advantage for
improved performance. In summary, the Comment by Wei et al. does not provide an
amendment to the original teachings of our original manuscript, and all of our
results, core conclusions and methodology of research reported in Science (DOI:
10.1126/science.aat8084) remain entirely valid.
