Imagine a camera which is mounted on your car, being able to identify black ice on the road by warning you before passing through it. Or a cell phone camera that can determine if your skin lesion is probably cancerous. Or the ability for Face ID to work even when you have a face mask. These are all features that Metalenz is promoting with its new PolarEyes polarization technology.
Last year, the company introduced a flat-lens system called optical meta-surfaces for mobile devices that take up less space, while claiming to produce images with similar, if not better, quality than a traditional smartphone camera. Instead of using multiple lens elements stacked on top of each other – the design used in most phone cameras that requires a voluminous “camera shot” – Metalenz’s solution relies on a single lens equipped with nanostructures that bend light rays and deliver them to the camera is a sensor that produces an image with levels of brightness and clarity on par with photos taken by traditional systems. Rob Devlin, CEO of Metalenz, says we will see this technology in a product in the second quarter of 2022.
However, consider Metalenz’s latest announcement of a second-generation version, which may appear in devices in 2023. It is built on the same technology, but nanostructures can now support information about the polarization in light. Normal cameras, such as those in our phones, do not capture this data, but simply focus on light intensity and color. But with the added flow of data, our phones may soon learn some new tricks.
Wait, what is polarization?
Light is a type of electromagnetic radiation and travels in waves. When light interacts with certain objects, such as crystals, its waveform changes and begins to oscillate with a unique signature.
“Polarization information really tells you the direction of light,” says Devlin. “When light enters the chamber after bouncing off something smooth against something rough, or after hitting the edge or interacting with certain molecules, it will have a very different direction depending on what material, what molecules, what actually has bounced. With this information, you can get that contrast and find out what things are made of. ”
Think of it this way: light waves reflected from ordinary ice on the side of the road will oscillate differently from light reflected from black ice. If the camera can capture this information, you can submit it to a computer-assisted machine learning algorithm and train it to learn the difference between black ice and normal ice. The car can now inform you of the impending danger.