  Optical diffractive neural networks have triggered extensive research with
their low power consumption and high speed in image processing. In this work,
we propose a reconfigurable digital all-optical diffractive neural network
(R-ODNN) structure. The optical neurons are built with Sb2Se3 phase-change
material, making our network reconfigurable, digital, and non-volatile. Using
three digital diffractive layers with 14,400 neurons on each and 10
photodetectors connected to a resistor network, our model achieves 94.46%
accuracy for handwritten digit recognition. We also performed full-vector
simulations and discussed the impact of errors to demonstrate the feasibility
and robustness of the R-ODNN.
