All-Optical diffractive deep neural network is 3D-printed

Using a 3D printer, a research team at the UCLA Samueli School of Engineering has created an artificial neural network that can analyze large volumes of data and identify objects at the speed of light. Called a diffractive deep neural network (D2NN), the technology uses the light scattering from an object to identify it. The technology is based on a deep learning-based design of passive diffractive layers that work collectively.

The team created a computer-simulated design, then used a 3D printer to create thin, 8 cm-sq polymer wafers. Each wafer was created with uneven surfaces to help diffract light coming from an object.

Source: https://www.photonics.com/