NSF CAREER awardee creates brain-like synaptic transistor
The University of Arizona’s Xiaodong Yan is developing two-dimensional moiré quantum materials for computer chips that take on AI tasks at much greater speeds using far less energy.
Xiaodong Yan, assistant professor of materials science and engineering and electrical and computer engineering, develops energy-efficient computer chips that mimic the human brain.
With support from a National Science Foundation CAREER Award, Xiaodong Yan, Frank L. and Daphna Lederman Professor, is creating a neuromorphic, or brain-inspired, computer chip that eliminates energy-intensive data transfers and advances AI toward higher-level thinking.
Conventional computer chips consume enormous amounts of energy constantly transferring data between different areas devoted to either processing or memory storage. Yan is leveraging quantum mechanics to develop materials that simultaneously process and store information in the same location, just like a human brain. The chips can handle AI tasks, including associative learning, with much greater speed and energy efficiency.
“This breakthrough is the result of years of teamwork involving advanced materials preparation, nanofabrication and innovative testing methods,” Yan said.
With the $599,980 award, Yan – who joined the departments of materials science and engineering and electrical and computer engineering as an assistant professor in 2023 – is creating moiré synaptic transistors, which mimic the region of the human brain that combines memory, logic and parallel processing.
Moiré patterns are geometrical designs that arise when two patterns are layered on top of each other. The moiré synaptic transistors involve stacking two-dimensional materials just a few atoms thick – graphene and hexagonal boron nitride – then twisting the layers, resulting in unprecedented tunability of electronic properties.
Boosting AI devices
Laying the groundwork for neuromorphic computers, these brain-inspired chips are stable at room temperature, retain data without power, and consume just 20 picowatts – billions of times less power than existing neuromorphic devices.
“By greatly reducing energy consumption, our devices will make it possible to bring AI and advanced computing into settings where current chips cannot operate effectively,” Yan said, adding that devices such as autonomous drones, intelligent robots and wearable health monitors stand to benefit.
Sammy Tin, MSE head and the Patrick R. Taylor Endowed Department Leadership Chair, noted that the award is a tremendous recognition of Yan’s expertise.
“Professor Yan is an incredibly innovative faculty member who has very quickly become recognized as one of the world's experts in two-dimensional materials and neuromorphic computing,” he said.
From theory to fabrication
Next steps for the project include refining chip designs, scaling up fabrication methods, and integrating them into systems such as robots and wearable devices.
“It is deeply rewarding to see years of persistence and exploration acknowledged on such a prestigious platform,” Yan said. “I am grateful for the strong support from the University of Arizona College of Engineering, which has created an environment that made it possible for our work to be recognized by the NSF.”
The project also involves K-12 and undergraduate students in hands-on research, “giving young scientists the chance to contribute directly to cutting-edge discoveries.”
