Metal Tech News - The Elements of Innovation Discovered

Researchers from the University of Texas at Austin have discovered the potential of using the super material graphene to develop synaptic transistors for brain-like computers.

For most traditional computing devices, silicon remains the gold standard. However, there have long been attempts to use more flexible, more efficient, and more environmentally friendly materials for transistors.

Computers that function like the human brain are inching closer to mainstream adoption, yet many unanswered questions remain. Among the most pressing, what types of materials can serve as the best building blocks to unlock the potential of this new style of computer?

Ironically, although Mankind is a carbon-based lifeform, it appears that carbon itself has been difficult to master despite the significant evidence of its functionality in bioelectronics – i.e., the human body.

That is until the discovery of graphene.

Roughly 200 times stronger than steel, highly conductive to electricity and heat, antibacterial and only one atom thick, graphene is a wonder material that has captured the imaginations of scientists.

In recent years, due to significant improvements in production, graphene has been used to make materials lighter and stronger, batteries charge faster and last longer, and even as a coating to prevent the spread of diseases such as COVID-19.

The wondrous properties of graphene are the product of the carbon atoms it is made of, where each atom is linked to three neighbors, forming hexagons arranged in a two-dimensional honeycomb network.

Due to the stability of its structure, this 2D carbon material has been found to be ideal for semiconductors – enough so that it could very well be the key to unlocking room-temperature superconductors.

Now, researchers have found that not only are they suitable as a future superconductor, but as the transistors that translate your physical actions into digital executions.

Brain-like computers

Synapses are part of the biological circuit that connects sensory organs using the nervous system in the brain. They connect neurons or information messengers to send electrical impulses and chemical signals between different areas of your grey matter, and then back through the nervous system again.

This works the same way as a road system in which vehicles can navigate to and from a desired location.

The basic function of your nervous system is to tell you about the physical world around you, and it has been proven that repeated actions can strengthen your ability to execute them.

As they say, practice makes perfect.

A computer that can function like a brain means that as the roads are traveled more often, those connections grow in strength – like our memory – meaning the computer can grow better at repeated tasks.

"Computers that think like brains can do so much more than today's devices," said Jean Anne Incorvia, an assistant professor in the Cockrell School of Engineering's department of electrical and computer engineer, and the lead author on the paper published in "Nature Communications." "And by mimicking synapses, we can teach these devices to learn on the fly, without requiring huge training methods that take up so much power."

Computers are extremely literal. Coding and programming are such involved jobs because an individual has to precisely create every function a computer must do and not do, and without constructing it painstakingly and exacting, it will result in bugs or exploits that were not originally intended.

It is truly mind-numbing work.

A combination of graphene and nafion, a polymer membrane material, make up the backbone of this new synaptic transistor. Together, these materials demonstrate key synaptic-like behaviors-most importantly, the ability for the pathways to strengthen over time as they are used more often, like a type of neural muscle memory.

In computer, this means that devices will be able to get better at tasks like recognizing and interpreting images over time and do it faster.

While at first glance, this appears to benefit only artificial intelligence systems or supercomputers, a consumer computer could benefit from smart operation through less energy consumed or less power required for repeated tasks, ultimately reducing the wear and tear on your machine.

Researchers, however, expect bioelectronics to be the largest beneficiary of graphene transistors.

Being biocompatible, these transistors will be able to interact with living cells and tissue without the concern for the body rejecting foreign elements.

This is key for potential applications in medical devices that come into contact with the human body.

New computing paradigm

With new high-tech concepts like self-driving cars, drones, and robots, humankind has basically reached the limits of what silicon microchips can efficiently do in terms of data processing and storage. For next-generation technologies, a new computing paradigm is needed.

Neuromorphic devices.

These can mimic processing capabilities similar to the brain, which produces a powerful computer for immersive tasks.

"Biocompatability, flexibility, and softness of our artificial synapses is essential," said Dmitry Kireev, a post-doctoral researcher who co-led the project. "In the future, we envision their direct integration with the human brain, paving the way for futuristic brain prosthesis."

University of Texas at Austin

A thin membranous transistor made to function like a brain could be the beginning of advanced bioelectronics.

Whether or not this technology makes it past the possible ethical ramifications of implants in the body, this discovery certainly opens the door for the possibility of the technology coming to fruition.

Nevertheless, neuropathic platforms are becoming more common as leading chipmakers such as Intel and Samsung have either produced neuromorphic chips already or are in the process of developing them.

However, because current chip materials place limitations on what these devices can do, academic researchers have been searching for the perfect materials for soft brain-like computers.

"It's still a big open space when it comes to materials; it hasn't been narrowed down to the next big solution to try," said Incorvia. "And it might not be narrowed down to just one solution, with different materials making more sense for different applications."