Neuromorphic Chip Mimics Human Vision and Memory

Author: RMIT University
Published: 2023/06/14 - Updated: 2026/05/28
Publication Details: Peer-Reviewed, Scientific Discovery

Contents: Synopsis - Definition - Introduction - Main - Insights, Updates - Related Publications

Synopsis: This peer-reviewed research, published in the journal Advanced Functional Materials by engineers at RMIT University in Australia, details the development of a neuromorphic chip that replicates how the human eye captures light and how the brain stores and classifies information - all within a single, ultra-thin device. The work is authoritative, drawing on contributions from three Australian universities and support from the Australian Research Council. For people with disabilities, seniors, and the wider assistive technology community, the implications are significant - potential applications include bionic vision systems, autonomous decision-making in hazardous environments, and advances in self-driving vehicle technology that could one day improve accessibility and independence for those who cannot drive.

At a Glance

• 1 - The sensing element, doped indium oxide, is thousands of times thinner than a human hair and requires no external parts to operate, making the device exceptionally compact and self-contained.
• 2 - The chip retains visual information for longer periods than previously reported devices without needing frequent electrical signals to refresh its memory, which significantly reduces energy consumption.
• 3 - Potential real-world applications include bionic vision, tracking space debris, autonomous robots operating in dangerous environments such as mines, food shelf-life assessment, and advanced forensics.

Topic Definition: Neuromorphic Computing

Neuromorphic computing is an approach to computer engineering in which the architecture of hardware and software is modeled on the structure and function of the human brain and nervous system. Unlike traditional von Neumann computers, which separate memory and processing into distinct components, neuromorphic systems use artificial neurons and synapses to process information in a highly parallel, energy-efficient manner - much the way biological brains do. The term, coined in the 1980s by scientist Carver Mead, covers both the physical chip design and the algorithms that run on them. Key advantages over conventional computing include dramatically lower energy consumption, faster execution, resilience against localized failures, and the capacity to adapt and improve through experience. Neuromorphic chips are increasingly seen as a foundation for next-generation artificial intelligence, autonomous systems, and sensory prosthetics.

Introduction

Long Duration Persistent Photocurrent in 3 NM Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

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.

Main Content

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

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.

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

The team's research, 'Long duration persistent photocurrent in 3 nm thin doped indium oxide for integrated light sensing and in-sensor neuromorphic computation', is published in Advanced Functional Materials.

Insights, Analysis, and Developments

Attribution/Source(s): This peer reviewed publication was selected for publishing by the editors of Disabled World (DW) due to its relevance to the disability community. Originally authored by RMIT University and published on 2023/06/14, this content may have been edited for style, clarity, or brevity.