A Chip Off the Old Eye: Device Mimics Human Vision and Memory

Summary: Researchers developed a single-chip device that mimics the human eye’s capacity to capture, process, and store visual data.

This groundbreaking innovation, fueled by a thin layer of doped indium oxide, could be a significant leap towards applications like self-driving cars that require quick, complex decision-making abilities. Unlike traditional systems that need external, energy-intensive computation, this device encapsulates sensing, information processing, and memory retention in one compact unit.

As a result, it enables real-time decision-making without being hampered by processing extraneous data or being delayed by transferring information to separate processors.

Key Facts:

  1. The neuromorphic device uses a single layer of doped indium oxide, which is thousands of times thinner than a human hair, to mimic the human eye’s ability to capture, process, and store visual information.
  2. The device can retain information for more extended periods without the need for frequent electric signals to refresh the memory, significantly reducing energy consumption and improving performance.
  3. The scientists behind the device envisage a wide range of applications, from self-driving cars to bionic vision and advanced forensics. Future work includes extending the technology for visible and infrared light detection.

Source: RMIT University

Researchers have created a small device that ‘sees’ and creates memories in a similar way to humans, in a promising step towards one day having applications that can make rapid, complex decisions such as in self-driving cars.

The neuromorphic invention is a single chip enabled by a sensing element, doped indium oxide, that’s thousands of times thinner than a human hair and requires no external parts to operate.

RMIT University engineers in Australia led the work, with contributions from researchers at Deakin University and the University of Melbourne.

This shows an eye
The team’s research demonstrates a working device that captures, processes and stores visual information. Credit: Neuroscience News

The team’s research demonstrates a working device that captures, processes and stores visual information. With precise engineering of the doped indium oxide, the device mimics a human eye’s ability to capture light, pre-packages and transmits information like an optical nerve, and stores and classifies it in a memory system like the way our brains can.

Collectively, these functions could enable ultra-fast decision making, the team says.

Team leader Professor Sumeet Walia said the new device can perform all necessary functions – sensing, creating and processing information, and retaining memories – rather than relying on external energy-intensive computation, which prevents real-time 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 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.”

What did the team achieve and how does the technology work?

The new device was able to demonstrate an ability to retain information for longer periods of time, compared to previously reported devices, without the need for frequent electrical signals to refresh the memory. This ability significantly reduces energy consumption and enhances the device’s performance.

Their findings and analysis are published in Advanced Functional Materials.

First author and RMIT PhD researcher Aishani Mazumder said the human brain used analog processing, which allowed 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

What are the potential applications?

The team used ultraviolet light as part of their experiments, and are working to expand this technology even further for visible and infrared light – with many possible applications such as bionic vision, autonomous operations in dangerous environments, shelf-life assessments of food and advanced forensics.

“Imagine a self-driving car that can see and recognise objects on the road in the same way that a human driver can or being able to able to rapidly detect and track space junk. This would be possible with neuromorphic vision technology.”

Walia said neuromorphic systems could adapt to new situations over time, becoming more efficient with more experience.

“Traditional computer vision systems – which cannot be miniaturised 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.”

The human eye has a single retina that captures an entire image, which is then processed by the brain to identify objects, colours and other visual features.

The team’s device mimicked the retina’s capabilities by using single-element image sensors that capture, store and process visual information on one platform, Walia said.

“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 the process of neuromorphic engineering.” 

Support for the research

The team used the Micro Nano Research Facility and the Microscopy and Microanalysis Research Facility at RMIT.

Funding: The work was also supported by the Australian Research Council and the National Computational Infrastructure.

About this neurotech research news

Author: Will Wright
Source: RMIT University
Contact: Will Wright – RMIT University
Image: The image is credited to Neuroscience News

Original Research: Closed access.
Long duration persistent photocurrent in 3 nm thin doped indium oxide for integrated light sensing and in-sensor neuromorphic computation” by Sumeet Walia et al. Advanced Functional Materials


Long duration persistent photocurrent in 3 nm thin doped indium oxide for integrated light sensing and in-sensor neuromorphic computation

Miniaturization and energy consumption by computational systems remain major challenges to address.

Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for 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 endows 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 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.

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