1        *Mathematics at the speed of light*

TECHNOLOGY GENERATING SPEED EQUAL TO LIGHT SPEED SOLUTION

Researchers at AMOLF, University of Pennsylvania, and City University of
New York (CUNY) created a nanostructured surface capable of solving
equations using light. This discovery opens exciting new opportunities in
the field of analog processing based on optical meta surfaces. AMOLF Ph.D.
student Andrea Cordero and his co-authors published their findings in *Nature
Nanotechnology* on January 12, 2023.

The world's ever-growing needs for efficient computing have been driving
researchers from diverse research fields to explore alternatives to the
current digital computing paradigm. "The processing speed and energy
efficiency of standard electronics have become limiting factors for novel
disruptive applications entering our everyday life
<https://phys.org/tags/everyday+life/>, such as artificial intelligence
<https://phys.org/tags/artificial+intelligence/>, machine learning
<https://phys.org/tags/machine+learning/>, computer vision, and many more,"
says Andrea Cordaro. "In this context, analog computing has resurfaced and
regained significant attention as a complementary route to traditional
architectures."

*Computing at the speed of light*

Optical analog processing refers to the use of light to perform analog
computations, as opposed to traditional electronic methods which use
electricity. One major benefit of using light to perform specific computing
tasks is that it can operate at much higher speeds than electronic methods,
as the computation is performed at the speed of light traveling through
very thin nanostructured surfaces called metasurfaces.

In addition, optical analog processing can be more energy efficient than
electronic methods, since it does not generate heat in the same way that
electronic circuits do. This makes it well-suited for use in
high-performance computing applications where speed and energy efficiency
<https://phys.org/tags/energy+efficiency/> are important.

"In self-driving cars <https://phys.org/tags/self-driving+cars/>, for
example, image detection and processing take up a lot of computing time,"
Cordaro says.

"In an earlier paper, we have shown that it is possible to use an optical
metasurface for very fast edge detection in an input image. Detecting the
edges of objects—like cars, people etc.—is the first step in image
processing in many applications. Performing this step optically can save
processing time and energy."

*Matrix inversions in a jiffy*

Cordaro and his colleagues realized that they could also use metasurfaces
to perform other mathematical operations. "One of the most frequent class
of problems popping up in many fields, including engineering, science, and
economics are so-called 'linear inverse problems.' These typically involve
matrix inversions, which are rather slow mathematical operations," he says.

The team of researchers developed a thin dielectric nanostructure, called a
metagrating, and incorporated a semi-transparent mirror into the sample to
continuously send back the signal to the nanostructures, each time
multiplied by the metagrating scattering matrix.

"We use a special optimization technique to design the unit cell of the
nanostructured array, or meta grating, that can perform the desired matrix
multiplication," says Cordaro. "Each mathematical problem requires a
specific design for the meta grating, but in theory one could engineer a
surface with multiple parallel gratings to solve several integral equations
in parallel."

These results demonstrate the possibility of solving complex mathematical
problems and a generic matrix inversion at speeds that are far beyond those
of the typical digital computing methods. Indeed, the solution converges in
about 349 fs (i.e., less than one thousand-millionth of a second), orders
of magnitude faster than the clock speed of a conventional processor.

Cordaro says, "We have shown a powerful new alliance between nanotechnology
and analog <https://phys.org/tags/analog/> computing that could pave the
way for hybrid optical and electronic computing circuitry. Developing our
ideas further, will result in solving problems of enhanced complexity at
speed and efficiencies that were previously unthinkable."  KR IRS 19123

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