A potentially revolutionary new technology that could greatly advance optical communications, surveillance, and photonic device isolation has something in common with the most captivating construction design of the ancient world: the pyramid.
Researchers at UCLA have produced a revolutionary new design for diffractive deep neural networks, or D2NNs, that they say significantly enhances unidirectional image magnification and demagnification. Dubbed Pyramid D2NNs, the new design architecture lives up to its name by introducing a pyramid-structured network that offers high-fidelity image formation while reducing refractive features, all by aligning its layers in the same direction of image magnification and demagnification.
What Are Diffractive Deep Neural Networks?
D2NNs are constructed from individual transmissive layers that are optimized through deep learning, allowing them to perform computation almost entirely through the use of optics.
In their recent research, the UCLA team, led by Professor Aydogan Ozcan, worked with a pyramid-shaped diffractive optical network, a design that allowed the team to achieve unidirectional imaging with fewer diffractive degrees of freedom.
The result is a design that helps to ensure high-fidelity image formation, but only in one direction. By contrast, significant image inhibition occurs in the opposite direction, conditions that are key for use with applications where imaging in one direction (i.e., unidirectional imaging) is required. Such fields include defense and security technologies, telecommunications applications, and systems used for privacy protection.
Hellbound and Down 