Researchers shrink camera to the size of a salt grain

Date:
November 29, 2021
Source:
Princeton University, Engineering School
Summary:
Researchers have developed an ultracompact camera the size of a
coarse grain of salt. The new system can produce crisp, full-color
images on par with a conventional compound camera lens 500,000
times larger in volume.



FULL STORY ========================================================================== Micro-sized cameras have great potential to spot problems in the human
body and enable sensing for super-small robots, but past approaches
captured fuzzy, distorted images with limited fields of view.


==========================================================================
Now, researchers at Princeton University and the University of Washington
have overcome these obstacles with an ultracompact camera the size of
a coarse grain of salt. The new system can produce crisp, full-color
images on par with a conventional compound camera lens 500,000 times
larger in volume, the researchers reported in a paper published Nov. 29 inNature Communications.

Enabled by a joint design of the camera's hardware and computational processing, the system could enable minimally invasive endoscopy with
medical robots to diagnose and treat diseases, and improve imaging for
other robots with size and weight constraints. Arrays of thousands of
such cameras could be used for full-scene sensing, turning surfaces
into cameras.

While a traditional camera uses a series of curved glass or plastic
lenses to bend light rays into focus, the new optical system relies on
a technology called a metasurface, which can be produced much like a
computer chip. Just half a millimeter wide, the metasurface is studded
with 1.6 million cylindrical posts, each roughly the size of the human immunodeficiency virus (HIV).

Each post has a unique geometry, and functions like an optical
antenna. Varying the design of each post is necessary to correctly shape
the entire optical wavefront. With the help of machine learning-based algorithms, the posts' interactions with light combine to produce
the highest-quality images and widest field of view for a full-color metasurface camera developed to date.

A key innovation in the camera's creation was the integrated design of
the optical surface and the signal processing algorithms that produce
the image.

This boosted the camera's performance in natural light conditions, in
contrast to previous metasurface cameras that required the pure laser
light of a laboratory or other ideal conditions to produce high-quality
images, said Felix Heide, the study's senior author and an assistant
professor of computer science at Princeton.



==========================================================================
The researchers compared images produced with their system to the
results of previous metasurface cameras, as well as images captured
by a conventional compound optic that uses a series of six refractive
lenses. Aside from a bit of blurring at the edges of the frame, the
nano-sized camera's images were comparable to those of the traditional
lens setup, which is more than 500,000 times larger in volume.

Other ultracompact metasurface lenses have suffered from major image distortions, small fields of view, and limited ability to capture the
full spectrum of visible light -- referred to as RGB imaging because it combines red, green and blue to produce different hues.

"It's been a challenge to design and configure these little
microstructures to do what you want," said Ethan Tseng, a computer science Ph.D. student at Princeton who co-led the study. "For this specific task
of capturing large field of view RGB images, it's challenging because
there are millions of these little microstructures, and it's not clear
how to design them in an optimal way." Co-lead author Shane Colburn
tackled this challenge by creating a computational simulator to automate testing of different nano-antenna configurations. Because of the number
of antennas and the complexity of their interactions with light, this
type of simulation can use "massive amounts of memory and time," said
Colburn. He developed a model to efficiently approximate the metasurfaces' image production capabilities with sufficient accuracy.

Colburn, who conducted the work as a Ph.D. student at the University of Washington Department of Electrical & Computer Engineering (UW ECE), where
he is now an affiliate assistant professor. He also directs system design
at Tunoptix, a Seattle-based company that is commercializing metasurface imaging technologies. Tunoptix was cofounded by Colburn's graduate adviser
Arka Majumdar, an associate professor at the University of Washington
in the ECE and physics departments and a coauthor of the study.



========================================================================== Coauthor James Whitehead, a Ph.D. student at UW ECE, fabricated the metasurfaces, which are based on silicon nitride, a glass-like material
that is compatible with standard semiconductor manufacturing methods
used for computer chips -- meaning that a given metasurface design could
be easily mass-produced at lower cost than the lenses in conventional
cameras.

"Although the approach to optical design is not new, this is the first
system that uses a surface optical technology in the front end and
neural-based processing in the back," said Joseph Mait, a consultant
at Mait-Optik and a former senior researcher and chief scientist at the
U.S. Army Research Laboratory.

"The significance of the published work is completing the Herculean task
to jointly design the size, shape and location of the metasurface's
million features and the parameters of the post-detection processing
to achieve the desired imaging performance," added Mait, who was not
involved in the study.

Heide and his colleagues are now working to add more computational
abilities to the camera itself. Beyond optimizing image quality, they
would like to add capabilities for object detection and other sensing modalities relevant for medicine and robotics.

Heide also envisions using ultracompact imagers to create "surfaces
as sensors." "We could turn individual surfaces into cameras that have ultra-high resolution, so you wouldn't need three cameras on the back
of your phone anymore, but the whole back of your phone would become
one giant camera. We can think of completely different ways to build
devices in the future," he said.

Besides Tseng, Colburn, Whitehead, Majumdar and Heide, the study's authors include Luocheng Huang, a Ph.D. student at the University of Washington;
and Seung-Hwan Baek, a postdoctoral research associate at Princeton.

The work was supported in part by the National Science Foundation,
the U.S.

Department of Defense, the UW Reality Lab, Facebook, Google, Futurewei Technologies, and Amazon.

========================================================================== Story Source: Materials provided by
Princeton_University,_Engineering_School. Original written by Molly
Sharlach. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Ethan Tseng, Shane Colburn, James Whitehead, Luocheng Huang,
Seung-Hwan
Baek, Arka Majumdar, Felix Heide. Neural nano-optics for
high-quality thin lens imaging. Nature Communications, 2021; 12
(1) DOI: 10.1038/ s41467-021-26443-0 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211129122716.htm

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