Tag Archives: Su-Yang Xu

Brainlike transistor and human intelligence

This brainlike transistor (not a memristor) is important because it functions at room temperature as opposed to others, which require cryogenic temperatures.

A December 20, 2023 Northwestern University news release (received via email; also on EurekAlert) fills in the details,

  • Researchers develop transistor that simultaneously processes and stores information like the human brain
  • Transistor goes beyond categorization tasks to perform associative learning
  • Transistor identified similar patterns, even when given imperfect input
  • Previous similar devices could only operate at cryogenic temperatures; new transistor operates at room temperature, making it more practical

EVANSTON, Ill. — 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 was published today (Dec. 20 [2023]) 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 current era of big data where digital computing is on track to overwhelm the grid. We have to rethink computing hardware, especially for AI and machine-learning tasks.”

To rethink this paradigm, Hersam and his team explored new advances in the physics of moiré patterns, a type of geometrical design that arises when two patterns are layered on top of one another. When two-dimensional materials are stacked, new properties emerge that do not exist in one layer alone. And when those layers are twisted to form a moiré pattern, unprecedented tunability of electronic properties becomes possible.

For the new device, the researchers combined two different types of atomically thin materials: bilayer graphene and hexagonal boron nitride. When stacked and purposefully twisted, the materials formed a moiré pattern. By rotating one layer relative to the other, the researchers could achieve different electronic properties in each graphene layer even though they are separated by only atomic-scale dimensions. With the right choice of twist, researchers harnessed moiré physics for neuromorphic functionality at room temperature.

“With twist as a new design parameter, the number of permutations is vast,” Hersam said. “Graphene and hexagonal boron nitride are very similar structurally but just different enough that you get exceptionally strong moiré effects.”

To test the transistor, Hersam and his team trained it to recognize similar — but not identical — patterns. Just earlier this month, Hersam introduced a new nanoelectronic device capable of analyzing and categorizing data in an energy-efficient manner, but his new synaptic transistor takes machine learning and AI one leap further.

“If AI is meant to mimic human thought, one of the lowest-level tasks would be to classify data, which is simply sorting into bins,” Hersam said. “Our goal is to advance AI technology in the direction of higher-level thinking. Real-world conditions are often more complicated than current AI algorithms can handle, so we tested our new devices under more complicated conditions to verify their advanced capabilities.”

First the researchers showed the device one pattern: 000 (three zeros in a row). Then, they asked the AI to identify similar patterns, such as 111 or 101. “If we trained it to detect 000 and then gave it 111 and 101, it knows 111 is more similar to 000 than 101,” Hersam explained. “000 and 111 are not exactly the same, but both are three digits in a row. Recognizing that similarity is a higher-level form of cognition known as associative learning.”

In experiments, the new synaptic transistor successfully recognized similar patterns, displaying its associative memory. Even when the researchers threw curveballs — like giving it incomplete patterns — it still successfully demonstrated associative learning.

“Current AI can be easy to confuse, which can cause major problems in certain contexts,” Hersam said. “Imagine if you are using a self-driving vehicle, and the weather conditions deteriorate. The vehicle might not be able to interpret the more complicated sensor data as well as a human driver could. But even when we gave our transistor imperfect input, it could still identify the correct response.”

The study, “Moiré synaptic transistor with room-temperature neuromorphic functionality,” was primarily supported by the National Science Foundation.

Here’s a link to and a citation for the paper,

Moiré synaptic transistor with room-temperature neuromorphic functionality by Xiaodong Yan, Zhiren Zheng, Vinod K. Sangwan, Justin H. Qian, Xueqiao Wang, Stephanie E. Liu, Kenji Watanabe, Takashi Taniguchi, Su-Yang Xu, Pablo Jarillo-Herrero, Qiong Ma & Mark C. Hersam. Nature volume 624, pages 551–556 (2023) DOI: https://doi.org/10.1038/s41586-023-06791-1 Published online: 20 December 2023 Issue Date: 21 December 2023

This paper is behind a paywall.

Better performing solar cells with newly discovered property of pristine graphene

Light-harvesting devices—I like that better than solar cells or the like but I think that the term serves as a category rather than a name/label for a specific device. Enough musing. A December 17, 2018 news item on Nanowerk describes the latest about graphene and light-harvesting devices (Note: A link has been removed,

An international research team, co-led by a physicist at the University of California, Riverside, has discovered a new mechanism for ultra-efficient charge and energy flow in graphene, opening up opportunities for developing new types of light-harvesting devices.

The researchers fabricated pristine graphene — graphene with no impurities — into different geometric shapes, connecting narrow ribbons and crosses to wide open rectangular regions. They found that when light illuminated constricted areas, such as the region where a narrow ribbon connected two wide regions, they detected a large light-induced current, or photocurrent.

The finding that pristine graphene can very efficiently convert light into electricity could lead to the development of efficient and ultrafast photodetectors — and potentially more efficient solar panels.

A December 14, 2018 University of California at Riverside (UCR) news release by Iqbal Pittalwala (also on EurekAlert but published Dec. 17, 2018), which originated the news item,gives a brief description of graphene while adding context for this research,

Graphene, a 1-atom thick sheet of carbon atoms arranged in a hexagonal lattice, has many desirable material properties, such as high current-carrying capacity and thermal conductivity. In principle, graphene can absorb light at any frequency, making it ideal material for infrared and other types of photodetection, with wide applications in bio-sensing, imaging, and night vision.

In most solar energy harvesting devices, a photocurrent arises only in the presence of a junction between two dissimilar materials, such as “p-n” junctions, the boundary between two types of semiconductor materials. The electrical current is generated in the junction region and moves through the distinct regions of the two materials.

“But in graphene, everything changes,” said Nathaniel Gabor, an associate professor of physics at UCR, who co-led the research project. “We found that photocurrents may arise in pristine graphene under a special condition in which the entire sheet of graphene is completely free of excess electronic charge. Generating the photocurrent requires no special junctions and can instead be controlled, surprisingly, by simply cutting and shaping the graphene sheet into unusual configurations, from ladder-like linear arrays of contacts, to narrowly constricted rectangles, to tapered and terraced edges.”

Pristine graphene is completely charge neutral, meaning there is no excess electronic charge in the material. When wired into a device, however, an electronic charge can be introduced by applying a voltage to a nearby metal. This voltage can induce positive charge, negative charge, or perfectly balance negative and positive charges so the graphene sheet is perfectly charge neutral.

“The light-harvesting device we fabricated is only as thick as a single atom,” Gabor said. “We could use it to engineer devices that are semi-transparent. These could be embedded in unusual environments, such as windows, or they could be combined with other more conventional light-harvesting devices to harvest excess energy that is usually not absorbed. Depending on how the edges are cut to shape, the device can give extraordinarily different signals.”

The research team reports this first observation of an entirely new physical mechanism — a photocurrent generated in charge-neutral graphene with no need for p-n junctions — in Nature Nanotechnology today [Dec. 17, 2018].

Previous work by the Gabor lab showed a photocurrent in graphene results from highly excited “hot” charge carriers. When light hits graphene, high-energy electrons relax to form a population of many relatively cooler electrons, Gabor explained, which are subsequently collected as current. Even though graphene is not a semiconductor, this light-induced hot electron population can be used to generate very large currents.

“All of this behavior is due to graphene’s unique electronic structure,” he said. “In this ‘wonder material,’ light energy is efficiently converted into electronic energy, which can subsequently be transported within the material over remarkably long distances.”

He explained that, about a decade ago, pristine graphene was predicted to exhibit very unusual electronic behavior: electrons should behave like a liquid, allowing energy to be transferred through the electronic medium rather than by moving charges around physically.
“But despite this prediction, no photocurrent measurements had been done on pristine graphene devices — until now,” he said.

The new work on pristine graphene shows electronic energy travels great distances in the absence of excess electronic charge.

The research team has found evidence that the new mechanism results in a greatly enhanced photoresponse in the infrared regime with an ultrafast operation speed.
“We plan to further study this effect in a broad range of infrared and other frequencies, and measure its response speed,” said first author Qiong Ma, a postdoctoral associate in physics at the Massachusetts Institute of Technology, or MIT.

The researchers have provided an image illustrating their work,

Caption: Shining light on graphene: Although graphene has been studied vigorously for more than a decade, new measurements on high-performance graphene devices have revealed yet another unusual property. In ultra-clean graphene sheets, energy can flow over great distances, giving rise to an unprecedented response to light. Credit: Max Grossnickle and QMO Labs, UC Riverside.

Here’s a link to and a citation for the paper,

Giant intrinsic photoresponse in pristine graphene by Qiong Ma, Chun Hung Lui, Justin C. W. Song, Yuxuan Lin, Jian Feng Kong, Yuan Cao, Thao H. Dinh, Nityan L. Nair, Wenjing Fang, Kenji Watanabe, Takashi Taniguchi, Su-Yang Xu, Jing Kong, Tomás Palacios, Nuh Gedik, Nathaniel M. Gabor, & Pablo Jarillo-Herrero. Nature Nanotechnology (2018) Published 17 December 2018 DOI: https://doi.org/10.1038/s41565-018-0323-8

This paper is behind a paywall.