All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces

by   Onur Kulce, et al.

We report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (N_i) and output (N_o), where N_i and N_o represent the number of pixels at the input and output fields-of-view (FOVs), respectively. First, we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation. 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. We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven (deep learning-based) diffractive designs to all-optically perform (i) arbitrarily-chosen complex-valued transformations including unitary, nonunitary and noninvertible transforms, (ii) 2D discrete Fourier transformation, (iii) arbitrary 2D permutation operations, and (iv) high-pass filtered coherent imaging. Our analyses reveal that if the total number (N) of spatially-engineered diffractive features/neurons is N_i x N_o or larger, both design methods succeed in all-optical implementation of the target transformation, achieving negligible error. However, compared to data-free designs, deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N < N_i x N_o. These conclusions are generally applicable to various optical processors that employ spatially-engineered diffractive surfaces.



There are no comments yet.


page 35

page 36

page 37

page 38

page 39

page 40

page 41

page 42


All-Optical Information Processing Capacity of Diffractive Surfaces

Precise engineering of materials and surfaces has been at the heart of s...

Cascadable all-optical NAND gates using diffractive networks

Owing to its potential advantages such as scalability, low latency and p...

Simultaneous Multiple Surface Segmentation Using Deep Learning

The task of automatically segmenting 3-D surfaces representing boundarie...

Scale-, shift- and rotation-invariant diffractive optical networks

Recent research efforts in optical computing have gravitated towards dev...

A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks

Metasurfaces have become a promising means for manipulating optical wave...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.