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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Nature Climate Change, Published online: 31 March 2026; doi:10.1038/s41558-026-02590-4

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Nature Climate Change, Published online: 31 March 2026; doi:10.1038/s41558-026-02598-w

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About EFF

The Electronic Frontier Foundation is the leading nonprofit defending civil liberties in the digital world. EFF’s work to protect your rights on the internet is supported by over 30,000 members who have joined our mission by donating just this year.

For over 35 years, our lawyers, activists, and technologists have been thinking about the next big thing in tech before anyone else—whether that’s age verification, AI, or Palantir. Whatever causes you fight for, you rely on the internet to do so. And EFF protects the infrastructure of rebellion. 

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Privacy's Defender: My Thirty Year Fight Against Digital Surveillance, by Cindy Cohn

In Privacy’s Defender: My Thirty-Year Fight Against Digital Surveillance (MIT Press), EFF Executive Director Cindy Cohn weaves her own personal story with her role as a leading legal voice representing the rights and interests of technology users, innovators, whistleblowers, and researchers during the Crypto Wars of the 1990s, battles over NSA’s dragnet internet spying revealed in the 2000s, and the fight against FBI gag orders.

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Sometimes our supporters call EFF a merch store with a law firm attached because our stickers, hoodies and shirts are so well known. Our "Let's Sue the Government" shirt tells people: When your rights are at risk, you don’t stay quiet.

EFF's History

In early 1990, the U.S. Secret Service conducted raids tracking the distribution of a document illegally copied from a telecom company’s computer; one of those targeted was an Austin, TX publisher named Steve Jackson, whose computers were seized but later returned without any charges filed. Jackson’s business had suffered, and he discovered that the government had read and deleted his customers’ emails. He sought a civil liberties organization to represent him for this violation of his rights, but no existing organization understood the technology well enough to grasp the free speech and privacy issues at hand.

But a few well-informed technologists did understand. Mitch Kapor, former president of Lotus Development Corp.; John Perry Barlow, a Wyoming cattle rancher and lyricist for the Grateful Dead; and John Gilmore, an early employee of Sun Microsystems, with help from Apple co-founder Steve Wozniak, decided to do something about it – and so the Electronic Frontier Foundation was born in July 1990. The Steve Jackson Games case turned out to be an extremely important one for the early internet: For the first time, a court held that electronic mail deserves at least as much protection as telephone calls.

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EFF continued to take on cases that set important precedents for the treatment of rights in cyberspace. In our second big case, Bernstein v. U.S. Department of Justice, the United States government prohibited a University of California mathematics Ph.D. student from publishing online an encryption program he had created. Years earlier, the government had placed encryption on the United States Munitions List, alongside bombs and flamethrowers, as a weapon to be regulated for national security purposes; our lawsuit established that written software code is speech protected by the First Amendment, and the further ruled that the export control laws on encryption violated Bernstein's rights by prohibiting his constitutionally protected speech.  Now everyone has the right to "export" encryption software—by publishing it on the Internet—without prior permission from the U.S. government. 

Since then we’ve fought against government and corporate abuses of our Constitutional rights, on issues including warrantless wiretapping by intelligence agencies, the panopticon of street-level surveillance that seeks to track everything we do, and the corporate surveillance that turns our clicks into their commodity, as well as issues of antitrust and intellectual property, artificial intelligence, cybersecurity, and much more. We are lawyers, technologists, activists, and lobbyists who work every day for the privacy, security and dignity of all who use technology - and if you use technology, this fight is yours, too.

EFF's Greatest Hits

While many early battles over the right to communicate freely and privately stemmed from government censorship, today EFF is fighting for users on many other fronts as well.

Today, certain powerful corporations are attempting to shut down online speech, prevent new innovation from reaching consumers, and facilitating government surveillance. We challenge corporate overreach just as we challenge government abuses of power.

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We also develop technologies that can help individuals protect their privacy and security online, which our technologists build and release freely to the public for anyone to use.

In addition, EFF is engaged in major legislative fights, beating back digital censorship bills disguised as intellectual property proposals, opposing attempts to force companies to spy on users, championing reform bills that rein in government surveillance, documenting police technology and where it's used, helping users protect themselves from surveillance, and much more.

Learn more about some of EFF's most impactful work— Download a PDF of our new catalog, "Now That's What I Call Digital Rights!

EFF Executive Director Cindy Cohn will be on The Daily Show tonight, Monday March 30, at 11 pm ET and PT, speaking with host Jon Stewart. Cindy will discuss her long history of fighting for privacy online and her new book, Privacy’s Defender: My Thirty-Year Fight Against Digital Surveillance (MIT Press). The book details her own personal story alongside her role representing the rights and interests of technology users, innovators, whistleblowers, and researchers during the Crypto Wars of the 1990s, battles over NSA’s dragnet internet spying revealed in the 2000s, and the fight against FBI gag orders. 

You can watch the interview on Comedy Central, and extended episodes are released shortly thereafter on Paramount Plus as well as in segments on YouTube. We will also share the interview when it is uploaded and available online as well. 

About The Daily Show

The Daily Show is a long-running comedy news show that covers the biggest headlines of the day. It has won 26 Primetime Emmy Awards and has introduced the world to now well-known actors and comedians such as Steve Carell, Samantha Bee, Ed Helms, and Trevor Noah, as well as hosts of their own current shows, Stephen Colbert and John Oliver. 

In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during the manufacturing process of products like steel, semiconductors, and solar cells to help improve strength, control electrical conductivity, optimize performance, and more.

But even as defects have become a powerful tool, accurately measuring different types of defects and their concentrations in finished products has been challenging, especially without cutting open or damaging the final material. Without knowing what defects are in their materials, engineers risk making products that perform poorly or have unintended properties.

Now, MIT researchers have built an AI model capable of classifying and quantifying certain defects using data from a noninvasive neutron-scattering technique. The model, which was trained on 2,000 different semiconductor materials, can detect up to six kinds of point defects in a material simultaneously, something that would be impossible using conventional techniques alone.

“Existing techniques can’t accurately characterize defects in a universal and quantitative way without destroying the material,” says lead author Mouyang Cheng, a PhD candidate in the Department of Materials Science and Engineering. “For conventional techniques without machine learning, detecting six different defects is unthinkable. It’s something you can’t do any other way.”

The researchers say the model is a step toward harnessing defects more precisely in products like semiconductors, microelectronics, solar cells, and battery materials.

“Right now, detecting defects is like the saying about seeing an elephant: Each technique can only see part of it,” says senior author and associate professor of nuclear science and engineering Mingda Li. “Some see the nose, others the trunk or ears. But it is extremely hard to see the full elephant. We need better ways of getting the full picture of defects, because we have to understand them to make materials more useful.”

Joining Cheng and Li on the paper are postdoc Chu-Liang Fu, undergraduate researcher Bowen Yu, master’s student Eunbi Rha, PhD student Abhijatmedhi Chotrattanapituk ’21, and Oak Ridge National Laboratory staff members Douglas L Abernathy PhD ’93 and Yongqiang Cheng. The paper appears today in the journal Matter.

Detecting defects

Manufacturers have gotten good at tuning defects in their materials, but measuring precise quantities of defects in finished products is still largely a guessing game.

“Engineers have many ways to introduce defects, like through doping, but they still struggle with basic questions like what kind of defect they’ve created and in what concentration,” Fu says. “Sometimes they also have unwanted defects, like oxidation. They don’t always know if they introduced some unwanted defects or impurity during synthesis. It’s a longstanding challenge.”

The result is that there are often multiple defects in each material. Unfortunately, each method for understanding defects has its limits. Techniques like X-ray diffraction and positron annihilation characterize only some types of defects. Raman spectroscopy can discern the type of defect but can’t directly infer the concentration. Another technique known as transmission electron microscope requires people to cut thin slices of samples for scanning.

In a few previous papers, Li and collaborators applied machine learning to experimental spectroscopy data to characterize crystalline materials. For the new paper, they wanted to apply that technique to defects.

For their experiment, the researchers built a computational database of 2,000 semiconductor materials. They made sample pairs of each material, with one doped for defects and one left without defects, then used a neutron-scattering technique that measures the different vibrational frequencies of atoms in solid materials. They trained a machine-learning model on the results.

“That built a foundational model that covers 56 elements in the periodic table,” Cheng says. “The model leverages the multihead attention mechanism, just like what ChatGPT is using. It similarly extracts the difference in the data between materials with and without defects and outputs a prediction of what dopants were used and in what concentrations.”

The researchers fine-tuned their model, verified it on experimental data, and showed it could measure defect concentrations in an alloy commonly used in electronics and in a separate superconductor material.

The researchers also doped the materials multiple times to introduce multiple point defects and test the limits of the model, ultimately finding it can make predictions about up to six defects in materials simultaneously, with defect concentrations as low as 0.2 percent.

“We were really surprised it worked that well,” Cheng says. “It’s very challenging to decode the mixed signals from two different types of defects — let alone six.”

A model approach

Typically, manufacturers of things like semiconductors run invasive tests on a small percentage of products as they come off the manufacturing line, a slow process that limits their ability to detect every defect.

“Right now, people largely estimate the quantities of defects in their materials,” Yu says. “It is a painstaking experience to check the estimates by using each individual technique, which only offers local information in a single grain anyway. It creates misunderstandings about what defects people think they have in their material.”

The results were exciting for the researchers, but they note their technique measuring the vibrational frequencies with neutrons would be difficult for companies to quickly deploy in their own quality-control processes.

“This method is very powerful, but its availability is limited,” Rha says. “Vibrational spectra is a simple idea, but in certain setups it’s very complicated. There are some simpler experimental setups based on other approaches, like Raman spectroscopy, that could be more quickly adopted.”

Li says companies have already expressed interest in the approach and asked when it will work with Raman spectroscopy, a widely used technique that measures the scattering of light. Li says the researchers’ next step is training a similar model based on Raman spectroscopy data. They also plan to expand their approach to detect features that are larger than point defects, like grains and dislocations.

For now, though, the researchers believe their study demonstrates the inherent advantage of AI techniques for interpreting defect data.

“To the human eye, these defect signals would look essentially the same,” Li says. “But the pattern recognition of AI is good enough to discern different signals and get to the ground truth. Defects are this double-edged sword. There are many good defects, but if there are too many, performance can degrade. This opens up a new paradigm in defect science.”

The work was supported, in part, by the Department of Energy and the National Science Foundation.

A thoughtful review of Apple’s system to alert users that the camera is on. It’s really well-designed, and important in a world where malware could surreptitiously start recording.

The reason it’s tempting to think that a dedicated camera indicator light is more secure than an on-display indicator is the fact that hardware is generally more secure than software, because it’s harder to tamper with. With hardware, a dedicated hardware indicator light can be connected to the camera hardware such that if the camera is accessed, the light must turn on, with no way for software running on the device, no matter its privileges, to change that. With an indicator light that is rendered on the display, it’s not foolish to worry that malicious software, with sufficient privileges, could draw over the pixels on the display where the camera indicator is rendered, disguising that the camera is in use...

The upfront costs of transitioning to clean energy and worries about rising utility bills are forcing tough choices for Democratic leaders.

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