Different forms of linear transformations, such as the Fourier transform,
are widely employed in processing of information in various applications.
These transformations are generally implemented in the digital domain using
electronic processors, and their computation speed is limited with the
capacity of the electronic chip being used, which sets a bottleneck as the
data and image size get large. A remedy of this problem might be to replace
digital processors with optical counterparts and use light to process
information.

In a new paper published in Light: Science & Applications, a team of
optical engineers, led by Professor Aydogan Ozcan from the Electrical and
Computer Engineering Department at the University of California, Los Angeles
(UCLA), U.S., and co-workers have developed a deep learning-based design
method for all-optical computation of an arbitrary linear transform. This
all-optical processor uses spatially-engineered diffractive surfaces in
manipulating optical waves and computes any desired linear transform as the
light passes through a series of diffractive surfaces. This way, the
computation of the desired linear transform is completed at the speed of
light propagation, with the transmission of the input light through these
diffractive surfaces. In addition to its computational speed, these
all-optical processors also do not consume any power to compute, except for
the illumination light, making it a passive and high-throughput computing
system.

The analyses performed by the UCLA team indicate that deep learning-based
design of these all-optical diffractive processors can accurately synthesize
any arbitrary linear transformation between an input and output plane, and
the accuracy as well as the diffraction efficiency of the resulting optical
transforms significantly improve as the number of diffractive surfaces
increases, revealing that deeper diffractive processors are more powerful in
their computing capabilities.

The success of this method has been demonstrated by performing a wide range
of linear transformations including for example randomly generated phase and
amplitude transformations, the Fourier transform, image permutation and
filtering operations. This computing framework can be broadly applied to any
part of the electromagnetic spectrum to design all-optical processors using
spatially-engineered diffractive surfaces to universally perform an
arbitrary complex-valued linear transform. It can also be used to form
all-optical information processing networks to execute a desired
computational task between an input and output plane, providing a passive,
power-free alternative to digital processors.

### Reference:

Onur Kulce et al, All-optical synthesis of an arbitrary linear
transformation using diffractive surfaces, Light: Science & Applications
(2021).
DOI: 10.1038/s41377-021-00623-5

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