Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

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At the recent AI and Society Forum at MIT, experts from across the Institute discussed the potential benefits and dangers of technological innovation on labor, the nature of work, civil discourse, election administration, and other topics.

The event featured individual research presentations and panel discussions, as well as a musical performance exploring the use of generative artificial intelligence in the arts.

The forum was co-organized by the School of Humanities, Arts, and Social Sciences (SHASS) and the Social and Ethical Responsibilities of Computing (SERC). It was presented in collaboration with two of MIT’s strategic initiatives: the MIT Generative AI Impact Consortium (MGAIC) and the MIT Human Insight Collaborative (MITHIC).

Agustín Rayo, the Kenan Sahin Dean of SHASS, and Dan Huttenlocher, dean of the MIT Schwarzman College of Computing, provided opening remarks.

Rayo said bringing scholars from across MIT together was intentional because understanding AI’s impact requires expertise from disciplines throughout the Institute.

“Paying attention to the societal consequences of AI is not a departure from MIT’s mission; it’s a way of ensuring that our technical leadership has maximum impact,” Rayo said.

Huttenlocher added that computing and AI’s rapid growth makes it critical to support interdisciplinary conversations and research.

“Understanding where AI excels and where it falls short is essential not only to unlocking its benefits, but also to avoiding critical errors, overreliance, and unintended consequences,” Huttenlocher said.

Jobs and AI 

Held in the Tull Concert Hall in MIT’s Linde Music Building, the May 12 forum opened with a keynote presentation from economist David Autor, the Daniel (1972) and Gail Rubinfeld Professor in the MIT Department of Economics. Autor challenged the common narrative that AI will simply eliminate jobs by proposing instead that technology's impact depends on how it affects the scarcity and value of human expertise. 

“When I think about how technology interacts with the value of labor, I think about it in terms of how it changes the scarcity of expertise, whether it makes it more valuable or whether it makes it more of a commodity,” he said.

Autor said that what matters is whether automation removes routine supporting tasks or removes expert tasks. He argued that AI will likely create new specialized work, requiring proactive policies around worker training, wage insurance, and broader capital ownership.

A panel discussion followed, moderated by Rob Loughlin, a partner at McKinsey & Company, featuring experts from MIT discussing how work is changing and what it means for society. 

Daniela Rus, the MIT Panasonic Professor of Computer Science and director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), described excitement around ways AI could enhance the workplace.

“I’d like to imagine the robot as your friend and assistant, as someone who watches you and figures out how to help you as someone you can task at a high level,” she said. 

Still, Rus said, human judgment remains critical in decision-making.

“We could really think about co-work with the AI tools, but the role of the human as the decider, as the person with good judgment, as the person deciding the next step, whatever that is, remains super important,” she said.

David Mindell, professor of Aeronautics and Astronautics and the Dibner Professor of the History of Engineering and Manufacturing in the Program in Science, Technology, and Society, says the nature of work has constantly changed over the years, but “what matters is the new work.” 

“We need to be supporting individuals, the economy, professions, to constantly be creating the new work,” he said. “It’s absolutely imperative that we give the tools to the young people and let them do what they find creative and show us what the new work is going to be.”

Panelists also talked about the need to maintain safety standards, while also exploring ways to find efficiencies. Mindell used an example of cargo flights that require six pilots due to the length of the flight.

“We don’t know how to take that six number down to five yet, much less two, one, or zero. There's a lot of money behind solving that problem, but there's also a very rich system that has evolved to make those systems safe,” he said.

Sendhil Mullainathan, the Peter de Florez Professor with dual appointments in the MIT departments of Economics and Electrical Engineering and Computer Science (EECS), described a vision of AI’s utility and growth that offers productivity improvements, but also cautioned, “I think it's very much worth differentiating productivity gains from things that actually drive long-term growth.”

Either way, Mullainathan said, it’s clear we’re entering a time of high variance with regard to AI’s impact on the workforce.

“If you said, ‘exactly how will organizations restructure?’ I don’t know. But is there going to be a lot of restructuring? It’s hard to believe there isn’t going to be a lot of restructuring. And in some sense, if we know that what we’re entering is a period of high variance, that itself is incredibly informative,” he said.

Democracy and AI

The day’s second session focused on AI technology and its impact on democracy. 

Chara Podimata, the Class of 1942 Career Development Assistant Professor and assistant professor of operations research and statistics in the MIT Sloan School of Management, presented her research on auditing large language models for bias in election information.

“Algorithms decide a lot of things about our lives right now,” she said. “With regard to chatbots and election information, if I take two people and they interact with the same chatbot … how will the chatbot respond? How will it personalize the information it gives to these people?”

A longitudinal study of 12 major models during the 2024 U.S. presidential election season found responses varied dramatically based on stated demographics and political leanings. Her research team is now working on a new audit of the 2026 U.S. midterm elections, using a redesigned survey with input from political science experts.

During a panel discussion moderated by Songyee Yoon, founder and managing partner at Principal Venture Partners and member of the MIT Corporation, experts raised concern about the potential for AI to erode democratic norms and processes, but also explored potential positive outcomes.

Bailey Flanigan, the Theodore T. Miller (1922) Career Development Professor in the Department of Political Science, who holds an MIT Schwarzman College of Computing shared position with EECS, said she’s skeptical of how some are applying AI as a tool that can get people to reach decisions or consensus more quickly.

“And there is a reason to think that this is nice because it is more efficient. It's easier. But it loses a lot of these procedural elements of democracy that are the rituals of how we come together and make decisions,” she said. “And I think it’s a mistake to forget about that when we start thinking about automation.”

Charles Stewart III, the Kenan Sahin (1963) Distinguished Professor of Political Science and founding director of the MIT Election Data and Science Lab, said one challenge is that governmental structures do not evolve at the same rate as technology.

Stewart said his biggest concern is the potential for AI to lead to chaos during and after elections.

“If and when things go wrong, they can go really bad, and really wrong. If an election is called into question, that can lead to violence,” Stewart said.

“We’ve already seen in the low-tech eras election results being manipulated. What worries me is what I’m going to observe this coming Election Day, and the Wednesday after, and if AI has helped to create irreversible disruptions to the election system,” he added.

Lily Tsai, the Ford Professor of Political Science and director and founder of the MIT Governance Lab (MIT GOV/LAB), said in many ways, AI runs against the democratic norms and commitments necessary for a healthy democracy.

“It is really important not just in terms of design principles, but the commitments of designers to be familiar with the values and principles that characterize what democracy is based on: agency, political equality, mutual respect, inclusion, and autonomy,” Tsai said.

Tsai also noted her research has shown some people are more comfortable interacting with machines. She described a “Socratic dialogue chatbot” her team designed that asks people to articulate the thinking behind their beliefs and positions.

“And that actually, interestingly, seems to moderate their policy position in the process,” Tsai said. “So there are absolutely examples of ways in which AI can have positive impacts on democracy. But it really is about designing with the right principles and evaluating them rigorously.”

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