Graphene synapses advance brain-like computers

Computers that think more like human brains are slowly approaching mainstream adoption. But many questions remain unanswered. Among the most pressing are what kinds of materials can serve as the best building blocks to unlock the potential of this new style of computing.

For most traditional computing devices, silicon remains the gold standard. However, there is a movement to use more flexible, efficient and environmentally friendly materials for these brain-like devices.

In a new paper, researchers at the University of Texas at Austin have developed synaptic transistors for brain-like computers using graphene, a thin and flexible material. These transistors are similar to synapses in the brain, which connect neurons to each other.

“Computers that think like brains can do so much more than today’s devices,” said Jean Anne Incorvia, assistant professor in the Cockrell School of Engineering’s Department of Electrical and Computer Engineering and lead author of the article published today in Nature Communication. “And by mimicking synapses, we can teach these devices to learn on the fly, without requiring huge training methods that consume so much power.”

A combination of graphene and nafion, a polymeric membrane material, forms the backbone of the synaptic transistor. Together, these materials demonstrate key synaptic-like behaviors — most importantly, the ability of pathways to strengthen over time as they are used more often, a type of neural muscle memory. In computing, this means that devices will be able to get better at tasks like recognizing and interpreting images over time and do it faster.

Another important discovery is that these transistors are biocompatible, meaning they can interact with living cells and tissues. This is the key to potential applications in medical devices that come into contact with the human body. Most of the materials used for these early brain-like devices are toxic, so they could never come into contact with living cells.

With new high-tech concepts like self-driving cars, drones and robots, we are reaching the limits of what silicon chips can do efficiently in terms of data processing and storage. For these next-generation technologies, a new computing paradigm is needed. Neuromorphic devices mimic the processing capabilities of the brain, a powerful computer for immersive tasks.

“The biocompatibility, flexibility and smoothness of our artificial synapses are essential,” said Dmitry Kireev, postdoctoral researcher who co-led the project. “In the future, we envision their direct integration with the human brain, paving the way for a futuristic brain prosthesis.”

Neuromorphic platforms are starting to become more common. Major chipmakers such as Intel and Samsung have already produced neuromorphic chips or are in the process of developing them. However, current chip materials limit what neuromorphic devices can do, so academic researchers are working hard to find the perfect materials for soft-brain-like computers.

“It’s still a big open space as far as materials go; it hasn’t been narrowed down to the next big thing to try,” Incorvia said. “And it may not be just one solution, with different materials making more sense for different applications.”

The research was led by Incorvia and Deji Akinwande, a professor in the Department of Electrical and Computer Engineering. The two have collaborated on several occasions in the past, and Akinwande is a leading expert in graphene, using it in multiple research breakthroughs, most recently as part of a wearable electronic tattoo for pressure monitoring. arterial.

The idea for the project was conceived by Samuel Liu, a Ph.D. student and first author on the paper, in a class taught by Akinwande. Kireev then suggested the specific project. Harrison Jin, an electrical and computer engineering undergraduate student, measured the devices and analyzed the data.

The team collaborated with T. Patrick Xiao and Christopher Bennett of Sandia National Laboratories, who ran neural network simulations and analyzed the resulting data.