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Indian Researchers Develop Brain-Inspired Image Sensor to Detect Tiny Objects

Indian researchers at the Indian Institute of Science (IISc) have developed a brain-inspired image sensor that can surpass the diffraction limit of light, allowing the detection of minuscule objects such as cellular components or nanoparticles invisible to current microscopes.

Picture: Kumaon Jagran

The technique, which combines optical microscopy with a neuromorphic camera and machine learning algorithms, presents a significant advancement in detecting objects smaller than 50 nanometres in size. The results of the research have been published in Nature Nanotechnology.

The diffraction limit is a barrier that has impeded scientists since the invention of optical microscopes. It means that the microscope cannot distinguish between two objects if they are smaller than a certain size, typically between 200-300 nanometres. Scientists have tried to surpass this barrier by modifying the molecules being imaged or developing better illumination strategies, which led to the 2014 Nobel Prize in Chemistry. However, very few have attempted to use the detector itself to surpass this detection limit, until now.

The neuromorphic camera used in the study measures approximately 40mm in height, 60mm in width, 25mm in diameter, and weighs about 100 grams. It mimics the way the human retina converts light into electrical impulses and has several advantages over conventional cameras. In a typical camera, each pixel captures the intensity of light falling on it for the entire exposure time that the camera focuses on the object, and all these pixels are pooled together to reconstruct an image. In neuromorphic cameras, each pixel operates independently and asynchronously, generating events or spikes only when there is a change in intensity of light falling on that pixel. This generates sparse amounts of data compared to traditional cameras, which capture every pixel value at a fixed rate, regardless of whether there is any change in the scene. This functioning of a neuromorphic camera is similar to how the human retina works, allowing it to sample the environment with higher temporal resolution, because it is not limited by a frame rate like normal cameras, and perform background suppression.

The team used this technique to track the movement of a fluorescent bead moving freely in an aqueous solution. This approach can have widespread applications in tracking and understanding stochastic processes in biology, chemistry, and physics.

Deepak Nair, associate professor at the Centre for Neuroscience (CNS), IISc, and corresponding author of the study, said, "Very few have actually tried to use the detector itself to try and surpass this detection limit." He added, "Our neuromorphic camera combines the strengths of optical microscopy, neuromorphic vision, and machine learning, enabling it to detect sub-diffraction objects and to perform automated tracking in real time."

The development of this technique is a significant step forward in pinpointing objects smaller than 50 nanometres in size, which could have wide-ranging implications in fields such as medicine, materials science, and nanotechnology.

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