Tech Briefs Magazine - May 2023 - PIT-17

scope and captured clear images of the
lunar surface - achieving greater resolution
of objects and much farther imaging
distance than previous metalenses. Before
the technology can be applied to modern
cameras, however, researchers must address
the issue of chromatic aberration,
which causes image distortion and blurriness
when different colors of light, which
bend in different directions, enter a lens.
" We are currently exploring smaller and
more sophisticated designs in the visible
range, and will compensate for various
optical aberrations, including chromatic
aberration, " Ni said.
For more information, contact College
of Engineering Media Relations at
communications@engr.psu.edu
Wavelength-Multiplexed Diffractive Optical Processor Computes
Hundreds of Linear Transformations in Parallel
Computing using light can potentially provide lower latency and reduced power consumption, benefiting
from the parallelism of optical systems.
University of California, Los Angeles, Samueli School of Engineering, Los Angeles, CA
C
omputing using light can potentially
provide lower latency and
reduced power consumption, benefiting
from the parallelism of optical
systems. A single optical processor can
execute many distinct computational
tasks simultaneously, such as the parallel
computing of different transformations,
which could be valuable
for numerous applications, including
the acceleration of machine learning-based
inference.
A new research paper, published in
Advanced Photonics, demonstrated the
feasibility of massively parallel optical
computing by employing a wavelength
multiplexing scheme. In their publication,
researchers from the University
of California, Los Angeles (UCLA) reported
a wavelength-multiplexed diffractive
optical processor that enables
the simultaneous computation of hundreds
of different complex-valued linear
transformations through different
wavelength channels.
Designed using deep learning tools,
this diffractive optical processor consists
of structured diffractive surfaces, made
of passive transmissive materials. In this
optical processor, a pre-determined
group of discrete wavelengths encodes
the input and output information. Each
wavelength is dedicated to a unique target
function or complex-valued linear
transformation.
Following the deep learning-based design
phase, this processor can be fabricated
using 3D printing or photolithography
and then assembled to physically
form an optical processor, which can simultaneously
perform a large group of
target transformations between its input
and output. These target transforms can
be specifically assigned for distinct functions,
including, for example, image
Photonics & Imaging Technology, May 2023
Inputs
Wavelength channel 1
(Transformation 1)
Outputs
Wavelength channel 2
(Transformation 2)
Wavelength channel 3
(Transformation 3)
Multiplex
Demultiplex
Wavelength channel W
(Transformation W)
Figure Wavelength-multiplexed Diffractive Optical Network Computes
Hundreds of Linear Tranferormations in parallel.
I
UCLA researchers present a diffractive optical processor that can all-optically compute a large group of W
(e.g., W > 180) different linear transformations or functions in parallel by modulating broadband input light
propagating through structured passive diffractive surfaces. (Image: Ozcan Lab @ UCLA)
classification, segmentation, encryption,
and reconstruction. Or they can be dedicated
to computing different convolutional
filter operations or fully connected
layers in a neural network.
By virtue of this unique wavelength
multiplexed design, all these linear
transforms or desired functions are executed
simultaneously at the speed of
light, where each desired function is assigned
to a unique wavelength, allowing
the broadband optical processor to compute
with extreme throughput.
This broadband diffractive processor
design does not require any wavelength-selective
elements such as spectral
or color filters and is compatible
with a wide range of materials with different
dispersion properties. The UCLA
researchers believe that this platform
and the underlying concepts can be
used to develop high-performance optical
processors that operate at different
parts of the electromagnetic spectrum,
including the visible and infrared wavelengths.
In
addition, due to its ability to directly
process the input spectral information,
the reported framework will also inspire
the development of multicolor and hyperspectral
machine vision systems that
can perform statistical inference based
on the spatial and spectral information
of the input objects. This might find
applications in biomedical imaging and
sensing for the detection and specific
imaging of substances with unique spectral
characteristics.
For more information, contact Aydogan
Ozcan at ozcan@ee.ucla.edu.
17
Broadband Diffractive Optical
Tech Briefs Magazine - May 2023

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