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Nanowire Single-photon Detector Lets Machines See Through Walls

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Using a superconducting nanowire single-photon detector (SNSPD) as the light-sensing element, researchers from Tianjin University demonstrated non-line-of-sight (NLOS) imaging at two infrared (IR) wavelengths, 1560 nm and 1997 nm.

NLOS systems provide the capability to see around corners and even through walls, making them valuable tools for applications ranging from endoscopy to autonomous vehicles and robotic vision. Most NLOS imaging is performed in the visible bands, due to the limited spectral sensitivity of the light sensors used. Extending NLOS imaging capabilities to the near- and mid-infrared (NIR and MIR) wavelengths could expand the application spaces for this emerging technology.
Non-line-of-sight imaging can detect objects even if they are behind a wall. Researchers have now extended this method from visible wavelengths into the near and mid-infrared region. Courtesy of Xiaolong Hu, Tianjin University.
Non-line-of-sight imaging can detect objects even if they are behind a wall. Researchers have now extended this method from visible wavelengths into the near- and mid-infrared region. Courtesy of Xiaolong Hu/Tianjin University.

To develop a sensitive, efficient photodetector for NIR and MIR, the researchers created a single-photon detector with 40-nm-wide nanowires arranged in a fractal pattern. They cooled the detector to about 2 Kelvin — just above absolute zero — to achieve superconductivity.

The team based its approach on the knowledge that a single photon will disrupt superconductivity, creating a measurable change in electrical resistance that will allow individual photons to be detected with high efficiency. The fractal pattern, which exhibited similar shapes at various magnifications, detected all photons regardless of their polarization state. The fractal SNSPD demonstrated single-photon sensitivity over an ultrabroad spectral range, extending the spectral range of the NLOS system into the NIR and MIR wavelengths.

“We designed and made a superconducting nanowire single-photon detector that acts as a very sensitive eye for seeing an object hidden around a corner,” professor Xiaolong Hu said.

The researchers also developed a de-noising algorithm and combined it with the light-cone-transform (LCT) algorithm to reconstruct the shape of a hidden object with enhanced signal-to-noise ratios (SNRs). In addition to improving the SNR, the new algorithm can reduce the artifacts generated by the standard LCT methods.

The researchers used the photodetector to acquire NLOS imaging at 1560 and 1997 nm — two wavelengths that are technologically important for specific applications. The researchers achieved a spatial resolution of less than 2 cm for both wavelengths. They also showed that images reconstructed using the new algorithm had a significantly lower root mean square error (i.e., a measure of the deviation from the ideal image) than those reconstructed using other methods. The SNSPD demonstrated better timing resolution and lower noise than an InGaAs/InP single-photon avalanche diode (SPAD) and outperformed other single-photon detectors in terms of detection efficiency in the NIR and MIR spectral ranges.

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The researchers created a superconducting nanowire single-photon detector with nanowires arranged in a fractal pattern, which extended the imaging technique’s spectral range into near and mid-infrared wavelengths. Courtesy of Xiaolong Hu, Tianjin University.
The researchers created a superconducting nanowire single-photon detector with nanowires arranged in a fractal pattern, which extended the imaging technique’s spectral range into near- and mid-infrared wavelengths. Courtesy of Xiaolong Hu/Tianjin University.

“This detector outperforms other single-photon detectors in terms of detection efficiency in the near- and mid-infrared spectral ranges, which made it possible to perform non-line-of-sight imaging at longer wavelengths,” Hu said. “This proof-of-principle demonstration opens doors for more research opportunities and potential applications.”

The NIR NLOS imaging approach is ocularly safe, exhibits low atmospheric losses, and works with many of the optoelectronic components that are available. Hu said that moving NLOS imaging toward the MIR wavelengths also provides advantages for many applications. “In addition to improving navigation for robots and vehicles, it could also enhance the signal-to-noise ratio for biological imaging,” he said.

“Infrared non-line-of-sight imaging can improve the safety and efficiency of unmanned vehicles by helping them detect and navigate around obstacles that are not directly visible,” Hu said. “Using near-infrared wavelengths could also help reduce eye safety concerns and lower the background noise, which could potentially allow imaging over longer distances during daytime.”

The researchers are currently exploring other wavelengths of interest and investigating the possibility of arranging multiple SNSPDs into arrays to provide additional capabilities. They also want to experiment with using the new system to achieve NLOS imaging over longer distances during the day.

The research was published in Optics Express (www.doi.org/10.1364/OE.497802).

Published: December 2023
Glossary
optoelectronics
Optoelectronics is a branch of electronics that focuses on the study and application of devices and systems that use light and its interactions with different materials. The term "optoelectronics" is a combination of "optics" and "electronics," reflecting the interdisciplinary nature of this field. Optoelectronic devices convert electrical signals into optical signals or vice versa, making them crucial in various technologies. Some key components and applications of optoelectronics include: ...
machine vision
Machine vision, also known as computer vision or computer sight, refers to the technology that enables machines, typically computers, to interpret and understand visual information from the world, much like the human visual system. It involves the development and application of algorithms and systems that allow machines to acquire, process, analyze, and make decisions based on visual data. Key aspects of machine vision include: Image acquisition: Machine vision systems use various...
near-infrared
The shortest wavelengths of the infrared region, nominally 0.75 to 3 µm.
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