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

TL;DR

This paper presents methods for designing diffractive surfaces to perform arbitrary complex-valued linear transformations optically, using both data-free and deep learning-based approaches, with applications in various optical processing tasks.

Contribution

It introduces a novel combination of matrix pseudoinverse and deep learning methods for designing diffractive surfaces for arbitrary linear transformations.

Findings

Deep learning designs achieve higher diffraction efficiency than data-free methods.

Both methods succeed when the number of features N ≥ N_i x N_o.

Transformations include unitary, nonunitary, Fourier, permutation, and filtering.

Abstract

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.