According to the university, the advance marks a key step towards using thin-film transistors as artificial intelligence hardware and moves edge computing forward, with the prospect of reducing power needs and improving efficiency, rather than relying solely on computer chips.
The MMT, first reported by Surrey researchers in 2020, is said to overcome long-standing challenges associated with transistors and can perform the same operations as more complex circuits. This latest research, published in Scientific Reports, uses mathematical modelling to prove the concept of using MMTs in artificial intelligence systems.
Using measured and simulated transistor data, the researchers show that well-designed multimodal transistors could operate robustly as rectified linear unit-type (ReLU) activations in artificial neural networks, achieving practically identical classification accuracy as pure ReLU implementations.
They used measured and simulated MMT data to train an artificial neural network to identify handwritten numbers and compared the results with the built-in ReLU of the software. The results confirmed the potential of MMT devices for thin-film decision and classification circuits. The same approach could be used in more complex AI systems.
The research was led by Surrey undergraduate Isin Pesch, who worked on the project during the final year research module of her BEng (Hons) in Electronic Engineering with Nanotechnology.
“There is a great need for technological improvements to support the growth of low cost, large area electronics which were shown to be used in artificial intelligence applications,” Pesch said in a statement. “Thin-film transistors have a role to play in enabling high processing power with low resource use. We can now see that MMTs, a unique type of thin-film transistor, invented at [Surrey University], have the reliability and uniformity needed to fulfil this role.”