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Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
The transmission layers of this polarization multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to different input/output polarization combinations.
All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces
In addition to this data-free design approach, we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation.
All-Optical Information Processing Capacity of Diffractive Surfaces
Precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics.
