A diffractive neural network is first designed in a computer using deep learning techniques and materials modelling techniques. In order to design a broadband diffractive network, the researchers first selected a set of physical parameters to be optimized, such as the thickness of each neuron within the diffractive layers, enabling the control of the phase modulation profile of each diffractive layer in the network.
In addition, to correctly represent the forward model of the broadband light propagation within their optical neural network, the scientists also took into consideration the material dispersion, including the real and imaginary parts of the refractive index of the network material as a function of the wavelength.
In a paper titled "Design of task-specific optical systems using broadband diffractive neural networks" published in the Light: Science & Applications journal, the researchers explain how for each wavelength within the input light spectrum, the all-optical neural network (AONN) yields a unique complex (phase and amplitude) modulation corresponding to the transmission coefficient of each neuron across the different diffractive layers, as determined by its physical thickness obtained during the training of the diffractive optical network.
After training comes the fabrication, the authors used a 3-D printer to create the two diffractive layers corresponding to the trained neurons (at a 0.5mm pitch) and tested the AONN using a pulsed THz source emitting a continuum of wavelengths between 60 and 3,000 micrometers. They reported experimental results in agreement with their corresponding numerical designs. Since the connection between the input and output planes of a diffractive neural network is established via diffraction of light through passive layers, the inference process and the associated optical computation does not consume any power except the light used to illuminate the object of interest.