Innovative human eye-mimicking chip has been developed at RMIT

Professor Sumeet Walia, the team leader, stated that the new device could perform all essential tasks – such as sensing, creating and processing information, and retaining memories – without relying on external energy-intensive computation, which hinders rapid decision-making. “Performing all of these functions on one small device had proven to be a big challenge until now,” said Walia from RMIT’s School of Engineering.

“We’ve made real-time decision-making possible 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,” he added.

According to the study, the new device has shown the ability to retain information for longer periods without needing frequent electrical signals to refresh memory. This feature significantly reduces energy consumption while enhancing the device's performance. The study's first author and RMIT Ph.D. researcher, Aishani Mazumder, explained that the human brain utilizes analog processing, which enables it to process information quickly and efficiently using minimal energy.

“By contrast, digital processing is energy and carbon-intensive and inhibits rapid information gathering and processing,” she said. “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," she added. “Imagine a self-driving car that can see and recognize objects on the road in the same way that a human driver can or can detect and track space junk rapidly. This would be possible with neuromorphic vision technology," explained Walia.

According to Walia, neuromorphic systems can adapt to new situations and improve efficiency with increased experience. “Traditional computer vision systems – which cannot be miniaturized like neuromorphic technology – are typically programmed with specific rules and can't adapt as easily,” he said. “Neuromorphic robots have the potential to run autonomously for long periods in dangerous situations where workers are exposed to possible cave-ins, explosions, and toxic air," he added.

The human eye can remarkably capture an entire image using a single retina. The brain then processes this image to identify objects, colors, and other visual features. A team of researchers has successfully developed a device that mimics the capabilities of the retina. They achieved this by using single-element image sensors that capture, store, and process visual information on a single platform.

“The human eye is exceptionally adept at responding to changes in the surrounding environment in a faster and much more efficient way than cameras and computers currently can,” he said. “Taking inspiration from the eye, we have been working for several years on creating a camera that possesses similar abilities through neuromorphic engineering," he added.

You can view the study for yourself on the Wiley Online Library.

Study abstract:

"Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics-based synaptic and light sensing provides an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow the reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In2O3) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endow features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for a UV spectrum-based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand."