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Revolutionary In-Memory Light Sensor Creates Artificial Visual Perception Nervous System

Artificial Intelligence (AI) and the Internet of Things (IoT) have caused a big increase in the number of sensory nodes, which gather a ton of raw data and turn it into digital information to be used for computations. However, this process can be slow and power-consuming when using the conventional von Neumann architecture, which uses separate devices for each function. This can be a problem for new technologies like autonomous cars and robotics that require speedy and low-power computations.

But now, scientists from the Smart Advanced Memory Devices and Applications (SAMA) laboratory at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, in partnership with colleagues from Khalifa University in the UAE, have developed a new device that can do sensing, storage, and processing all in one. The device uses a two-terminal solution-processable MoS2-based metal-oxide-semiconductor (MOS) device that has a 2D material-based charge-trapping layer that can sense light, mimicking the human visual system.



Schematic of the human visual-perception process: visual perception is one of the vital human senses where the brain decodes what the eyes see or sense. The human eye receives more than 80% of information through light. The brain visual-perception process is represented wherein the human eye receives light from an external source. This light is focused on the retina of the eye, which captures an image of the visual stimuli. Nerve cells present in the retina function as photoreceptors that convert light into electrical impulses. These impulses move from the optic nerve to the visual cortex at the back of the human brain. b, A small convolutional neural network (CNN) was designed to demonstrate the device’s optical sensing and electrical programming abilities. For that, we extracted images from the Canadian Institute for Advanced Research (CIFAR)-10 dataset to make a simple binary image recognition, wherein the object “dog” and “automobile” were chosen as the classification tasks. The original images consist of three RGB channels of size 32×32×3, wherein in each channel, discrete pixels are stored with three light intensities (Red, Green, and Blue). Our device showed the capability to sense blue light. Thus, we only extracted pixels of the blue channel for the recognition task. (c), The confusion matrix of the test results for 764 images in the CIFAR-10 dataset. The yellow-colored diagonal elements in the matrix represent the correctly identified cases.

This new device can sense, store, and process data all in one go. This is a big improvement from traditional devices that have separate photosensors that detect light and then convert that information into digital data, which is then stored elsewhere.

Scientists have shown that their device is very reliable and can work at high temperatures for a long time without any issues. It can also sense light and store it directly within the same device. They used a convolutional neural network (CNN) to measure the device’s optical sensing, storing, and processing capabilities. They tested it by transmitting optical images over the blue light wavelength and the device was able to recognize objects in the images with an accuracy of 91%.



This new technology can be used to develop artificial retina networks for artificial visual perception and in-memory light sensing applications. This study is also a significant step towards the development of smart cameras with artificial visual perception capabilities, using a similar structure as charge-coupled devices (CCD) in CCD cameras.

Research paper for more info.

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