New study reveals an uncanny image-capturing device that mimics the human eye

Would you like to have a bionic eye? You may want to consider this new device that uses perovskite cells and an artificial neural network to function almost like the human eye.
Rupendra Brahambhatt
close up for a 'bionic' eye
The science of eyes


Scientists at the Pennsylvania State University (PSU) have created an image-capturing setup that works just like human eyes. In the future, such a bio-inspired device could give rise to artificial retinas and self-powering cameras.

The new device is made up of an artificial neural network and narrowband perovskite photoreceptors. The former processes visual signals captured by the device and produce good-quality images. 

Whereas the latter works like cone cells, photoreceptors in human eyes that sense red, blue, and green (RBG) light. All kinds of visible light you see around you is the combination of these three colors. 

Kai Wang, one of the study authors and a material science professor at PSU, said, “We borrowed a design from nature — our retinas contain cone cells that are sensitive to red, green, and blue light and a neural network that starts processing what we are seeing even before the information is transmitted to our brain.”   

How does the bio-inspired artificial eye work?

New study reveals an uncanny image-capturing device that mimics the human eye
Diagram depicting the image capturing device.

To understand the working mechanism of an artificial eye, you first need to know how the real human eye enables us to see things. Your eyes have a layer of light-sensitive tissues called a retina. All objects you see emit light that enters your eyes and falls on that retina.

The retina is composed of cone cells that convert the light from an object into electrical signals. The optic nerve behind the retina takes these signals to the brain, a large neural network that processes the electrical signals and finally generates the image that you see.

The device from the PSU team works in almost the same exact fashion but uses artificial components. Light enters the device through a lens, which is then captured by narrowband perovskite photoreceptors. These cells focus on different parts (RBG) of the spectrum that makes up the light and converts it into electrical signals. 

"In this work, we found a novel way to design perovskite material that is sensitive to only one wavelength of light. We created three different perovskite materials, and they are designed in a way that they can only be sensitive to red, green, or blue colors," said Wang.

From perovskite cells, the light reaches the artificial neural network that employs neuromorphic algorithms — special computer programs that are capable of mimicking the human brain’s ability to turn electrical signals into images. Finally, the device produces the image of the object.

The device brings us closer to artificial eye cells

Interestingly, the perovskite photoreceptors used in the device shares functional and structural similarities with the perovskite cells used in solar panels. So when these cells are capturing light, they are also producing power for the device at the same time. 

"The device structure is similar to solar cells that use light to generate electricity. Once you shine a light on it, it will generate a current. So like our eyes, we don't need to apply energy to capture this information from light," said Luyao Zheng, one of the study authors and a researcher at PSU.

This is why the image-capturing system is able to function even without a battery and might also lead to the development of self-powering cameras in the future. 

Moreover, the study authors claim that the artificial components of their device also hint at the possibility of artificial retinas and synthetic eye cells for treating vision-loss-related problems in humans. 

However, further research is required to turn these exciting possibilities into a reality. Hopefully, the bio-inspired image-capturing device will turn out to be a groundbreaking innovation.

The study has been published in the journal Science Advances.

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