Inspired by mantis shrimp, researchers have developed a new type of optical sensor.

“Many artificial intelligence (AI) programs can take advantage of data-rich hyperspectral and polarimetric images, but the equipment required to capture these images is currently quite bulky,” said Michael Kudenov, associate professor of electrical and computer engineering at North Carolina State University and co-author of the paper. “Our work here makes it possible to use smaller, more user-friendly devices. This will allow us to better apply these AI capabilities to a wide range of fields, from astronomy to biomedicine.”
 
In the context of this study, hyperspectral imaging refers to the technique of breaking down visible light wavelengths into narrower bands. The human eye cannot distinguish these subtle variations in color, but computers can—making hyperspectral imaging valuable for tasks such as determining the chemical composition of objects in an image.
 
Polarization methods refer to the measurement of the polarization of light, and this data can be used to determine the surface geometry of objects in an image. For example, is the surface rough or smooth? What is the angle of the surface relative to the light source?
 
Light is difficult to describe because it is both a particle and a wave. If a beam of light moves from point A to point B, the path between these two points is the direction of the light. If you think of light as a particle, it moves in a straight line from point A to point B. However, light is also an electromagnetic field, and its fluctuations are like waves. If you imagine this wave as oscillating up and down or left and right as it propagates from point A to point B, then polarizability is a measure of the direction of the wave along its path.
 
Despite the availability of larger devices capable of capturing hyperspectral and polarization images, smartphone-sized imaging technology faces significant challenges.
 
For example, the design of mobile phone camera technology can lead to very small errors in the alignment of different wavelengths of light in the final image. This isn't a major problem for taking family photos, but it is problematic for scientific image analysis. The problem worsens when cameras can capture more colors (similar to hyperspectral technology).
 
The creators of the new light sensor were inspired by the eyes of mantis shrimp, which are adept at accurately capturing subtle color gradations. Therefore, researchers created an organic electronic sensor that mimics the mantis shrimp's eye. It's called the stomatal foot-inspired multispectral and polarimetric analysis (SIMPOL) sensor. And yes, stomatopod is a fancy name for the mantis shrimp.
 
Researchers have developed a prototype SIMPOL sensor that can simultaneously record four spectral channels and three polarization channels. In contrast, charge-coupled devices (CCDs) used in smartphones have only three spectral imaging sensors, detecting red, green, and blue separately, and only two polarization channels. Furthermore, the SIMPOL prototype can measure four color channels and three polarization channels at a single point, while CCDs rely on imaging sensors distributed across multiple points.
 
Although it is only a proof of concept, the researchers used modeling simulations to determine that the design could be used to create a detector capable of sensing up to 15 spatially registered spectral channels.
 
Kudenov said, "SIMPOL's color channels can distinguish spectral features that are 10 times narrower than those of typical imaging sensors; in other words, it is 10 times more accurate."
 
"Our work shows that it is possible to create small, efficient sensors that can capture both hyperspectral and polarization images simultaneously," said Brendan O'Connor, associate professor of mechanical and aerospace engineering at North Carolina State University and co-corresponding author of the paper. "I think this opens the door to new organic electronic sensing technologies."