Diffractive Deep Neural Network (D2NN): UCLA-developed AI device can identify objects at the speed of light

Researchers at the University of California, Los Angeles (UCLA) have developed a 3D-printed all-optical deep learning architecture called Diffractive Deep Neural Network (D2NN).

D2NN is a deep learning neural network physically formed by multiple layers of diffractive surfaces that work in collaboration to optically perform an arbitrary function. While the inference/prediction of the physical network is all-optical, the learning part that leads to its design is done through a computer.

How does D2NN work?

A computer-simulated design was created first, then the researchers with the help of a 3D printer created very thin polymer wafers. The uneven surface of the wafers helped diffract light coming from the object in different directions. The layers are composed of tens of thousands of artificial neurons or tiny pixels from which the light travels through.

These layers together, form an “optical network” that shapes how incoming light travels through them. The network is able to identify an object because the light coming from the object is diffracted mostly toward a single pixel that is assigned to that type of object.

The network was then trained using a computer to identify the objects in front of it by learning the pattern of diffracted light each object produced as the light from that object passes through the device.

What are its advantages?

Scalable: It can easily be scaled up using numerous high-throughput and large-area 3D fabrication methods, such as, soft-lithography, additive manufacturing, and wide-field optical components and detection systems.

Easily reconfigurable: D2NN can be easily improved by additional 3D printed layers or replacing some of the existing layers with newly trained ones.