Synaptic Transistor Mimicking Human Brain Developed By Researchers

Thursday - December 21 2023, 21:42 UTC - 11 months ago

Researchers have developed a new synaptic transistor capable of higher-level thinking that operates at room temperatures and consumes very little energy. The device is 100 times more power-efficient than conventional transistors and can store up to 100 times more information. It is ideal for real-world applications and could be used in a wide range of industries, such as AI, machine learning, IoT, robotics and wearables.

Taking inspiration from the human brain, researchers have developed a new synaptic transistor capable of higher-level thinking. Designed by researchers at Northwestern University, Boston College and the Massachusetts Institute of Technology (MIT), the device simultaneously processes and stores information just like the human brain. In new experiments, the researchers demonstrated that the transistor goes beyond simple machine-learning tasks to categorize data and is capable of performing associative learning. Although previous studies have leveraged similar strategies to develop brain-like computing devices, those transistors cannot function outside cryogenic temperatures. The new device, by contrast, is stable at room temperatures. It also operates at fast speeds, consumes very little energy and retains stored information even when power is removed, making it ideal for real-world applications. The study, titled "Moiré synaptic transistor with room-temperature neuromorphic functionality" was published Dec. 20 in the journal Nature.

"The brain has a fundamentally different architecture than a digital computer," said Northwestern's Mark C. Hersam, who co-led the research. "In a digital computer, data move back and forth between a microprocessor and memory, which consumes a lot of energy and creates a bottleneck when attempting to perform multiple tasks at the same time. On the other hand, in the brain, memory and information processing are co-located and fully integrated, resulting in orders of magnitude higher energy efficiency. Our synaptic transistor similarly achieves concurrent memory and information processing functionality to more faithfully mimic the brain." .

Hersam is the Walter P. Murphy Professor of Materials Science and Engineering at Northwestern's McCormick School of Engineering. He also is chair of the department of materials science and engineering, director of the Materials Research Science and Engineering Center and member of the International Institute for Nanotechnology. Hersam co-led the research with Qiong Ma of Boston College and Pablo Jarillo-Herrero of MIT.

Recent advances in artificial intelligence (AI) have motivated researchers to develop computers that operate more like the human brain. Conventional, digital computing systems have separate processing and storage units, causing data-intensive tasks to devour large amounts of energy. With smart devices continuously collecting vast quantities of data, researchers are scrambling to uncover new ways to process it all without consuming an increasing amount of power.

Currently, the memory resistor, or "memristor," is the most well-developed technology that can perform combined processing and memory function. But memristors still suffer from energy costly switching.

"For several decades, the paradigm in electronics has been to build everything out of transistors and use the same silicon architecture," Hersam said. "Significant progress has been made by simply packing more and more transistors into integrated circuits. You cannot deny the success of that strategy, but it comes at the cost of high power consumption, especially in the wake of increased device size and decreased voltages. For many problems, such as image processing, it is much more efficient and desirable to mimic the brain — at the component level — in which processing and memory are interwoven." .