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RMIT PhD scholar and study first author Thiha Aung inspects the team’s neuromorphic vision device. Credit: Will Wright, RMIT University
Scientists have built a device that sees and thinks like the brain, opening doors to lightning-fast robotics and safer autonomous vehicles.
Engineers at RMIT University have created a tiny, brain-like device that can detect hand movements, store visual memories, and process information—all without relying on an external computer.
This innovation, known as a neuromorphic device, mimics the way the human brain works. According to Professor Sumeet Walia, the project’s lead researcher, it could pave the way for ultra-fast visual processing in self-driving cars, intelligent robots, and other smart technologies designed to interact more naturally with people.
Energy Efficiency With Analog Power
Unlike traditional digital systems that consume large amounts of energy, neuromorphic vision technology uses analog-style processing similar to how our brains function. This approach allows it to handle complex visual tasks with far greater energy efficiency.
“Neuromorphic vision systems are designed to use similar analogue processing to our brains, which can greatly reduce the amount of energy needed to perform complex visual tasks compared with digital technologies used today,” said Walia, Director of the RMIT Centre for Opto-electronic Materials and Sensors (COMAS).
A Material With a Memory
The work brings together neuromorphic materials and advanced signal processing led by Professor Akram Al-Hourani, who is Deputy Director of COMAS. At the heart of the device is molybdenum disulfide, or MoS2—a metal compound just a few atoms thick.
In their latest research, the scientists showed how tiny defects at the atomic level in MoS2 can be used to detect light and convert it into electrical signals. This is similar to how neurons in the brain fire and communicate, allowing the device to capture and process visual information in real time.
Mimicking the Human Eye and Brain
“This proof-of-concept device mimics the human eye’s ability to capture light and the brain’s ability to process that visual information, enabling it to sense a change in the environment instantly and make memories without the need for using huge amounts of data and energy,” Walia said.
“Current digital systems, by contrast, are very power hungry and unable to keep up as data volume and complexity increases, which limits their ability to make ‘true’ real-time decisions.”
The research is published in Advanced Materials Technologies. Walia and Al-Hourani are corresponding authors and Mr. Thiha Aung, a PhD scholar at RMIT, is first author.
RMIT has filed a provisional patent for the work.
Seeing the Motion, Making the Memory
During experiments, the device detected changes in a waving hand’s movement, without the need to capture the events frame by frame – this is known as edge detection, which requires significantly less data processing and power.
Once the changes were detected, the device stored these events as memories like a brain.
The researchers conducted experiments in the spectrum visible to the human eye, which built upon the team’s previous neuromorphic research in the ultraviolet domain.
“We demonstrated that atomically thin molybdenum disulfide can accurately replicate the leaky integrate-and-fire (LIF) neuron behaviour, a fundamental building block of spiking neural networks,” Thiha said.
The past UV work only involved the detection, memory making and processing of still images. In both the visible-spectrum and UV devices, memories could be reset so that devices were ready to perform the next task.
Life-Saving Speed and Real-World Uses
The team’s innovation could one day improve response times of automated vehicles and advanced robotic systems to visual information, which could be crucial, particularly in dangerous and unpredictable environments.
“Neuromorphic vision in these applications, which is still many years away, could detect changes in a scene almost instantly, without the need to process lots of data, enabling a much faster response that could save lives,” Walia said.
“For robots working closely with humans in manufacturing or as a personal assistant, neuromorphic technology could enable more natural interactions by recognizing and reacting to human behavior with minimal delay,” Al-Hourani said.
Scaling Up and Next Horizons
The team is now scaling up the proof-of-concept single-pixel device to a larger pixel array of MoS2-based devices.
The Australian Research Council has recently funded the team with a Linkage Infrastructure, Equipment and Facilities (LIEF) grant to enable this scaling up of their neuromorphic devices.
“While our system mimics certain aspects of the brain’s neural processing, particularly in vision, it’s still a simplified model,” Walia said.
“We will optimize the devices to perform specific real-world applications with more complex vision tasks, and further reduce power consumption.”
The team plans to develop hybrid systems that integrate their analogue technology with conventional digital electronics.
Hybrid Future: Analog Meets Digital
“We see our work as complementary to traditional computing, rather than a replacement,” Walia said.
“Conventional systems excel at many tasks, while our neuromorphic technology offers advantages for visual processing where energy efficiency and real-time operation are critical.”
The team is also investigating materials other than MoS2 that might extend capabilities into infrared, which could enable real-time tracking of global emissions and intelligent sensing of contaminants such as toxic gases, pathogens and chemicals.
Reference: “Photoactive Monolayer MoS2 for Spiking Neural Networks Enabled Machine Vision Applications” by Thiha Aung, Sindhu Priya Giridhar, Irfan H. Abidi, Taimur Ahmed, Akram AI-Hourani and Sumeet Walia, 23 April 2025, Advanced Materials Technologies.
DOI: 10.1002/admt.202401677
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