Guwahati: Researchers from the Indian Institute of Technology (IIT) Guwahati and IIT Kharagpur have developed a light-sensitive device that can detect, store and process visual information while consuming significantly less energy than conventional devices.
The device consumes 500 times less energy than conventional MXene-based devices in laboratory tests and could have applications in facial recognition, robotic vision and intelligent image processing, according to IIT Guwahati.
The research focuses on developing neuromorphic technology, which mimics the human brain’s ability to process and retain information.
Unlike conventional electronic vision systems, which rely on separate components for image capture, storage and processing, the new device combines these functions in a single platform.
In conventional systems, cameras capture images and convert them into digital information, which is then transferred to processors for analysis. This movement of data between components consumes energy.
The human brain, by contrast, can recognise visual information using interconnected nerve cells, known as neurons, and their connections, called synapses, with relatively low energy consumption.
The researchers developed an ultrathin artificial synapse that responds to light and retains information even after the light source is switched off. Repeated exposure to light can also strengthen its response, mimicking certain aspects of learning in the human brain.
The device uses MXene nanosheets, an electrically conductive two-dimensional material, combined with an organic semiconductor called P3HT. When the materials come into contact, light-induced electrical charges separate at their interface and remain separated for an extended period, producing a memory-like effect.
Explaining the technology, Prof. P.K. Giri, professor in the Department of Physics at IIT Guwahati and lead author of the research, said the device retains an electrical response after the light is switched off.
A second light signal produced a response 180% stronger than the first, indicating that repeated stimulation can strengthen the device’s response, similar to how biological neurons respond to repeated signals, he said.
The researchers tested the device in facial recognition and traffic-scene segmentation applications. It achieved 90% accuracy in facial recognition and 85% accuracy in traffic-scene segmentation, according to the institute.
The device was developed using a low-cost solution-processing technique, which could support the development of energy-efficient components for future computing systems.
The findings have been published in the journal Carbon. The paper was co-authored by Prof. Giri, Sanjoy Sur Roy, Dr Sanju Nandi, Debabrata Sahu, Garima Choudhary and Prof. M. Meyyappan from IIT Guwahati, along with Saswata Goswami and Prof. Chandra Sekhar Tiwary from IIT Kharagpur.
The researchers said the technology could contribute to the development of brain-inspired computing systems that integrate sensing, memory and processing, potentially reducing energy consumption in applications involving visual information.
Also Read: The politics and profits behind Manipur’s Kuki-Naga violence