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Brainlike transistor and human intelligence

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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.

Grossly warped ‘nanographene’, a brand new type of carbon

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A new of form carbon sounds exciting although the naming convention escapes me. Why call it ‘nanographene’ (albeit grossly warped) when graphene is already nanoscale? (For anyone who can explain this to me, please do let me know.) A July 15, 2013 news release on EurekAlert (it’s also available as a July 15, 2013 news item on ScienceDaily) describes the new form of carbon,

Bucking planarity, contorted sheets of graphene alter physical, optical and electronic properties of new material

Chemists at Boston College and Nagoya University in Japan have synthesized the first example of a new form of carbon, the team reports in the most recent online edition of the journal Nature Chemistry.

The new material consists of multiple identical pieces of grossly warped graphene, each containing exactly 80 carbon atoms joined together in a network of 26 rings, with 30 hydrogen atoms decorating the rim. Because they measure slightly more than a nanometer across, these individual molecules are referred to generically as “nanocarbons,” or more specifically in this case as “grossly warped nanographenes.”

There’s an explanation of why this discovery is special and how it was made (from,the news release),

Until recently, scientists had identified only two forms of pure carbon: diamond and graphite. Then in 1985, chemists were stunned by the discovery that carbon atoms could also join together to form hollow balls, known as fullerenes. Since then, scientists have also learned how to make long, ultra-thin, hollow tubes of carbon atoms, known as carbon nanotubes, and large flat single sheets of carbon atoms, known as graphene. The discovery of fullerenes was awarded the Nobel Prize in Chemistry in 1996, and the preparation of graphene was awarded the Nobel Prize in Physics in 2010.

Graphene sheets prefer planar, 2-dimensional geometries as a consequence of the hexagonal, chicken wire-like, arrangements of trigonal carbon atoms comprising their two-dimensional networks. The new form of carbon just reported in Nature Chemistry, however, is wildly distorted from planarity as a consequence of the presence of five 7-membered rings and one 5-membered ring embedded in the hexagonal lattice of carbon atoms.

Odd-membered-ring defects such as these not only distort the sheets of atoms away from planarity, they also alter the physical, optical, and electronic properties of the material, according to one of the principle authors, Lawrence T. Scott, the Jim and Louise Vanderslice and Family Professor of Chemistry at Boston College.

“Our new grossly warped nanographene is dramatically more soluble than a planar nanographene of comparable size,” said Scott, “and the two differ significantly in color, as well. Electrochemical measurements revealed that the planar and the warped nanographenes are equally easily oxidized, but the warped nanographene is more difficult to reduce.”

… By introducing multiple odd-membered ring defects into the graphene lattice, Scott and his collaborators have experimentally demonstrated that the electronic properties of graphene can be modified in a predictable manner through precisely controlled chemical synthesis.

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

A grossly warped nanographene and the consequences of multiple odd-membered-ring defects by Katsuaki Kawasumi, Qianyan Zhang, Yasutomo Segawa, Lawrence T. Scott, & Kenichiro Itami. Nature Chemistry (2013) doi:10.1038/nchem.1704  Published online 14 July 2013

This paper is behind a paywall. For those who would like more information but can’t get access to the paper at this time, there’s a brief July 15, 2015 news piece by Caryl Richards on the Chemistry World website.