Researchers in Australia have developed a neuromorphic vision chip that can see, process, and store visual information in a single device, mimicking how the human eye and brain work together. The small chip, built using doped indium oxide, is designed to reduce reliance on external processors and enable faster decision-making in applications such as autonomous systems.

The work was led by engineers at RMIT University, with contributions from Deakin University and the University of Melbourne. The team says the device combines sensing, processing, and memory functions in one platform, removing the need for separate hardware components that typically slow down machine vision systems.

Unlike conventional imaging systems that capture data and send it to external processors, the new chip performs computation directly where the light is detected. The sensing layer, which is thousands of times thinner than a human hair, is engineered to respond to light and retain information over time, allowing it to function more like a biological visual system.

The researchers say this integrated approach could reduce energy consumption and improve response speed in real-time environments. The device was tested using ultraviolet light, and the team is now working to extend its capabilities to visible and infrared light for broader applications.

Brain-like vision system

The chip is designed to mimic the way the human eye captures light and how the brain processes and stores visual input. It performs multiple tasks on a single platform, including sensing incoming light, processing signals, and storing visual information for later use.

Team leader Professor Sumeet Walia said the goal was to remove the delay and energy cost of transferring data between separate systems. “We’ve made real-time decision making a possibility with our invention, because it doesn’t need to process large amounts of irrelevant data and it’s not being slowed down by data transfer to separate processors.”

The device also showed the ability to retain visual information for longer periods without frequent electrical refresh signals, which reduces energy use and improves efficiency.

First author and RMIT PhD researcher Aishani Mazumder said the system draws inspiration from how the brain processes information. “Neuromorphic vision systems are designed to use similar analog processing to the human brain, which can greatly reduce the amount of energy needed to perform complex visual tasks compared with today’s technologies.”

Toward autonomous machines

The researchers say the technology could be used in self-driving cars, autonomous robots, and monitoring systems that operate in dangerous environments. Possible applications include object recognition in vehicles, detection systems in remote or hazardous areas, and advanced imaging for forensics and industrial inspection.

Because the chip integrates multiple functions into a single element, it could also support longer-term autonomous operation without heavy computational infrastructure. The team says this makes it suitable for systems that need to adapt quickly to changing environments.

The device mimics the retina’s ability to capture an entire image and the brain’s ability to interpret and store it, enabling a more compact and efficient approach to machine vision. The researchers believe this could eventually lead to vision systems that improve with experience, similar to biological systems.

The team used specialized nanofabrication and microscopy facilities at RMIT University, with support from the Australian Research Council and the National Computational Infrastructure.

The study appears in the journal Advanced Functional Materials.

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