Brain-like computers think about 2D material

Researchers from KTH Royal Institute of Technology in Sweden and Stanford University have fabricated a two-dimensional (2D) material for use in components that could help make computers able to mimic the human brain. The researchers report their work in a paper in Advanced Functional Materials.

Electrochemical random-access memory (ECRAM) components made with 2D titanium carbide showed outstanding potential for complementing classical transistor technology, and contributing toward the commercialization of powerful computers modeled after the brain’s neural network. Such neuromorphic computers could be thousands of times more energy efficient than today’s computers.

According to Max Hamedi, an associate professor at KTH, these advances in computing are possible because of some fundamental differences between ECRAM, a component that acts as a sort of synaptic cell in an artificial neural network, and the classic computing architecture in use today.

“Instead of transistors that are either on or off, and the need for information to be carried back and forth between the processor and memory – these new computers rely on components that can have multiple states, and perform in-memory computation,” Hamedi says.

The scientists at KTH and Stanford focused on testing better materials for building an ECRAM, where switching occurs by inserting ions into an oxidation channel, making an ECRAM similar to our brain, which also works with ions. What has been needed to make these chips commercially viable are materials that overcome the slow kinetics of metal oxides and the poor temperature stability of plastics.

The key material in the novel ECRAM units is MXene – a 2D compound, barely a few atoms thick, consisting of titanium carbide (Ti₃C₂T_x). This MXene combines the high speed of organic chemistry with the integration compatibility of inorganic materials in a single device operating at the nexus of electrochemistry and electronics, Hamedi says.

According to co-author Alberto Salleo at Stanford University, MXene ECRAMs combine the speed, linearity, write noise, switching energy and endurance metrics essential for parallel acceleration of artificial neural networks. “MXenes are an exciting materials family for this particular application as they combine the temperature stability needed for integration with conventional electronics with the availability of a vast composition space to optimize performance,” he says.

While there are many other barriers to overcome before consumers can buy their own neuromorphic computers, the new 2D ECRAMs represent a breakthrough in the area of neuromorphic materials They could potentially lead to artificial intelligence that can adapt to confusing input and nuance, the way the brain does with much less energy consumption, and to portable devices capable of heavy computing tasks without having to rely on the cloud.

This story is adapted from material from KTH Royal Institute of Technology, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.