Guwahati, Oct 12: Researchers from IIT Guwahati and IIT Kharagpur have developed an ultrathin, brain-inspired device that can sense light, retain visual information and process images using significantly less energy than conventional approaches.
The device combines two-dimensional MXene nanosheets with an organic semiconductor to mimic the memory functions of biological synapses. According to the research findings, it consumes 500 times less energy than conventional MXene-based visual processing devices.
The technology could have applications in autonomous robots, drones, smart security systems and traffic monitoring, where rapid visual processing and low power consumption are essential.
Conventional digital cameras rely on separate components to capture, process and store images. The transfer of data between sensors, processors and memory units consumes energy and can increase processing delays.
The human visual system, however, integrates sensory signals with memory and learning. Inspired by this mechanism, the researchers developed a single device that combines light detection with memory-like behaviour, reducing the need for repeated data transfers between separate components.
The device uses MXene nanosheets, an ultrathin conductive material, alongside P3HT, an organic semiconductor. While MXene provides a pathway for electrical charge, P3HT helps convert incoming light into electrical signals.
When light strikes the interface between the two materials, electrical charges separate and remain trapped even after the light source is switched off. This creates a persistent memory effect, allowing the device to retain information about previous light exposure.
The researchers also observed that a second flash of light produced an electrical response 180% stronger than the first. This behaviour indicates that the device can strengthen its response through repeated stimulation, resembling the way biological synapses change with experience.
The technology was tested for visual recognition tasks, achieving 90% accuracy in facial recognition and 85% accuracy in real-time traffic scene segmentation, according to the reported findings.
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