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.

404 Media reports (alternate site):

The FBI was able to forensically extract copies of incoming Signal messages from a defendant’s iPhone, even after the app was deleted, because copies of the content were saved in the device’s push notification database….

The news shows how forensic extraction—­when someone has physical access to a device and is able to run specialized software on it—­can yield sensitive data derived from secure messaging apps in unexpected places. Signal already has a setting that blocks message content from displaying in push notifications; the case highlights why such a feature might be important for some users to turn on...

In internal memos earlier this year, a senior official at the agency urged immediate action to alleviate "workforce strain" and other problems.

The chief executive of GE Vernova said AI facilities are driving a sales surge for gas turbines and other components.

A new study warns that efforts to replant trees aren't keeping up with the state's intensifying blazes.

The Republican-drafted legislation doubles down on the Trump administration's attacks on international climate and energy programs.

State lawmakers may fail to pass a budget deal because of a Democratic split on whether to rescind tax breaks for data centers.

Localities impacted by the bills have argued that they are overly broad preemptions.

A late-night deal wins over moderate Democrats on the Judiciary Committee.

"They're mini glaciers, if you will. They just run down everything in their path. Nothing can stop that kind of weight," said one resident.

Heat-related illnesses are increasing as temperatures rise, while drought forces fresh food prices up, making healthy diets out of reach to vulnerable people, says a new report.

“The recent crises have indeed showed us how crucial to have energy diversity," said Turkey's environmental minister. "You should not be dependent on one single energy source.”

HSBC Holdings’ near-term climate risk assessment looks “excessively optimistic,” said investment managers.

As quantum computers advance, they are expected to be able to break tried-and-true security schemes that currently keep most sensitive data secure from attackers. Scientists and policymakers are working to design and implement post-quantum cryptography to defend against these future attacks.

MIT researchers have developed an ultra-efficient microchip that can bring post-quantum cryptography techniques to wireless biomedical devices, like pacemakers and insulin pumps. Such wearable, ingestible, or implantable devices are usually too power-constrained to implement these computationally demanding security protocols.

Their tiny chip, which is about the size of a very fine needle tip, also includes built-in protections against physical hacking attempts that can bypass encryption to steal user data, such as a patient’s social security number or device credentials. Compared to prior designs, the new technology is more than an order of magnitude more energy-efficient.

In the long run, the new chip could enable next-generation wireless medical devices to maintain strong security even as quantum computing becomes more prevalent. In addition, it could be applied to many types of resource-constrained edge devices, like industrial sensors and smart inventory tags.

“Tiny edge devices are everywhere, and biomedical devices are often the most vulnerable attack targets because power constraints prevent them from having the most advanced levels of security. We’ve demonstrated a very practical hardware solution to secure the privacy of patients,” says Seoyoon Jang, an MIT electrical engineering and computer science (EECS) graduate student and lead author of a paper on the chip.

Jang is joined on the paper by Saurav Maji PhD ’23; visiting scholar Rashmi Agrawal; EECS graduate students Hyemin Stella Lee and Eunseok Lee; Giovanni Traverso, an associate professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, and an associate member of the Broad Institute of MIT and Harvard; and senior author Anantha Chandrakasan, MIT provost and the Vannevar Bush Professor of Electrical Engineering and Computer Science. The research was recently presented at the IEEE Custom Integrated Circuits Conference.

Stronger security

A large percentage of wireless biomedical devices, like ingestible biosensors for health monitoring, currently lack strong protection due to the computational demands of existing security protocols, Jang says.

But the complexity of post-quantum cryptography (PQC) can increase power consumption by two or three orders of magnitude.

Implementing PQC is of paramount importance, since regulatory bodies like the National Institute of Standards and Technology (NIST) will soon begin phasing out traditional cryptography protocols in favor of stronger PQC algorithms. In addition, some industry leaders believe rapid advances in quantum hardware make PQC implementation even more urgent.

To bring these power-hungry PQC protocols to wireless biomedical devices, the MIT researchers designed a customized microchip, known as an application-specific integrated circuit (ASIC), that greatly reduces energy overhead while guaranteeing the highest level of security.

“PQC is very secure algorithmically, but making a device resilient against physical attacks usually requires additional countermeasures that pump up the energy consumption at least two or three times. We want our chip to be robust to both security threats in a very lightweight manner,” Jang says.

A multi-pronged approach

To accomplish these goals, the researchers incorporated several design features into the chip.

First, they implemented two different PQC schemes to enhance robustness and “future-proof” their device in case one scheme is later proven to be insecure. To boost energy efficiency, they applied techniques that enable the PQC algorithms to share as much of the chip’s computational resources as possible.

Second, the researchers designed a highly efficient, on-chip true random number generator. This device continually generates random numbers to use for secret keys, which is essential to implement PQC.

Their on-chip design improves energy efficiency and security over standard approaches that usually receive random numbers from an external chip.

Third, they implemented countermeasures that prevent a type of physical hacking attempt, called a power side-channel attack, but only on the most vulnerable parts of the PQC protocols.

In power side-channel attacks, hackers steal secret information by analyzing the power consumption of a device while it processes data. The MIT researchers added just enough redundancy to the PQC operations to ensure the chip is protected from these types of attacks.

Fourth, they designed an early fault-detection mechanism so the chip will abort operations early if it detects a voltage glitch.

Wireless biomedical devices often have erratic power supplies, so they are susceptible to glitches that can cause an entire security procedure to fail. The MIT approach saves energy by stopping the chip from running a doomed procedure to completion.

“At the end of the day, because of the techniques we utilized, we can apply these post-quantum cryptography primitives while adding nothing to the overhead, with the added benefit of robustness to side-channel attacks,” Jang says.

Their device achieved between 20 to 60 times higher energy efficiency than all other PQC security techniques they compared it to, with a more compact area than many existing chips.

“As we transition into post-quantum approaches, providing strong security for even the most resource-limited devices is essential. This work shows that robust cryptographic protection for biomedical and edge devices can be achieved alongside energy efficiency and programmability,” says Chandrakasan.

In the future, the researchers want to apply these techniques to other vulnerable applications and energy-constrained devices.

This research was funded, in part, by the U.S. Advanced Research Projects Agency for Health.

Four MIT faculty members are among the roughly 250 leaders from academia, the arts, industry, public policy, and research elected to the American Academy of Arts and Sciences, the academy announced April 22. Thirteen additional MIT alumni were also honored.

One of the nation’s most prestigious honorary societies, the academy is also a leading center for independent policy research. Members contribute to academy publications, as well as studies of science and technology policy, energy and global security, social policy and American institutions, the humanities and culture, and education.

MIT faculty elected from MIT in 2026 are:

• Isaiah Andrews PhD ’14,  Charles E. and Susan T. Harris Professor of Economics;
• David Atkin, Barton L. Weller (1940) Professor of Economics;
• Pablo Jarillo-Herrero, Cecil and Ida Green Professor of Physics; and
• Benjamin Paul Weiss, Robert R. Shrock Professor of Earth and Planetary Sciences

MIT alumni elected this year include Mark Aguiar PhD ’99 (Economics); Mark G. Allen SM ’86, PhD ’89 (Chemical Engineering); Magdalena Balazinska PhD ’06 (EECS); Keren Bergman SM ’91, PhD ’94 (EECS); Sara Cherry PhD ’00 (Biology); Cynthia J. Ebinger SM ’86, PhD ’88 (EAPS); Charles L. Epstein ’78 (Mathematics); Shanhui Fan PhD ’97 (Physics); Atif Mian ’96, PhD ’01 (Mathematics with Computer Science and Economics); Sarah E. O'Connor PhD ’01 (Chemistry); Darryll J. Pines SM ’88, PhD ’92 (Mechanical Engineering); Phillip (Terry) Ragon ’72 (Physics); and Mansour Shayegan ’79, EE ’81, SM ’81, PhD ’83 (Electrical Engineering).

“We celebrate the achievement of each new member and the collective breadth and depth of their excellence – this is a fitting commemoration of the nation’s 250th anniversary,” said Academy President Laurie Patton.

Since its founding in 1780, the academy has elected leading thinkers from each generation, including George Washington and Benjamin Franklin in the 18th century, Maria Mitchell and Daniel Webster in the 19th century, and Toni Morrison and Albert Einstein in the 20th century. The current membership includes more than 250 Nobel and Pulitzer Prize winners.

Confidence is persuasive. In artificial intelligence systems, it is often misleading.

Today's most capable reasoning models share a trait with the loudest voice in the room: They deliver every answer with the same unshakable certainty, whether they're right or guessing. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have now traced that overconfidence to a specific flaw in how these models are trained, and developed a method that fixes it without giving up any accuracy.

The technique, called RLCR (Reinforcement Learning with Calibration Rewards), trains language models to produce calibrated confidence estimates alongside their answers. In addition to coming up with an answer, the model thinks about its uncertainty in that answer, and outputs a confidence score. In experiments across multiple benchmarks, RLCR reduced calibration error by up to 90 percent while maintaining or improving accuracy, both on the tasks the model was trained on and on entirely new ones it had never seen. The work will be presented at the International Conference on Learning Representations later this month.

The problem traces to a surprisingly simple source. The reinforcement learning (RL) methods behind recent breakthroughs in AI reasoning, including the training approach used in systems like OpenAI's o1, reward models for getting the right answer, and penalize them for getting it wrong. Nothing in between. A model that arrives at the correct answer through careful reasoning receives the same reward as one that guesses correctly by chance. Over time, this trains models to confidently answer every question they are asked, whether they have strong evidence or are effectively flipping a coin.

That overconfidence has consequences. When models are deployed in medicine, law, finance, or any setting where users make decisions based on AI outputs, a system that expresses high confidence regardless of its actual certainty becomes unreliable in ways that are difficult to detect from the outside. A model that says "I'm 95 percent sure" when it is right only half the time is more dangerous than one that simply gets the answer wrong, because users have no signal to seek a second opinion.

"The standard training approach is simple and powerful, but it gives the model no incentive to express uncertainty or say I don’t know," says Mehul Damani, an MIT PhD student and co-lead author on the paper. "So the model naturally learns to guess when it is unsure." 

RLCR addresses this by adding a single term to the reward function: a Brier score, a well-established measure that penalizes the gap between a model's stated confidence and its actual accuracy. During training, models learn to reason about both the problem and their own uncertainty, producing an answer and a confidence estimate together. Confidently wrong answers are penalized. So are unnecessarily uncertain correct ones.

The math backs it up: the team proved formally that this type of reward structure guarantees models that are both accurate and well-calibrated. They then tested the approach on a 7-billion-parameter model across a range of question-answering and math benchmarks, including six datasets the model had never been trained on.

The results showed a consistent pattern. Standard RL training actively degraded calibration compared to the base model, making models worse at estimating their own uncertainty. RLCR reversed that effect, substantially improving calibration with no loss in accuracy. The method also outperformed post-hoc approaches, in which a separate classifier is trained to assign confidence scores after the fact. "What’s striking is that ordinary RL training doesn't just fail to help calibration. It actively hurts it," says Isha Puri, an MIT PhD student and co-lead author. "The models become more capable and more overconfident at the same time."

The team also demonstrated that the confidence estimates produced by RLCR are practically useful at inference time. When models generate multiple candidate answers, selecting the one with the highest self-reported confidence, or weighting votes by confidence in a majority-voting scheme, improves both accuracy and calibration as compute scales.

An additional finding suggests that the act of reasoning about uncertainty itself has value. The researchers trained classifiers on model outputs and found that including the model's explicit uncertainty reasoning in the input improved the classifier's performance, particularly for smaller models. The model's self-reflective reasoning about what it does and doesn’t know contains real information, not just decoration.

In addition to Damani and Puri, other authors on the paper are Stewart Slocum, Idan Shenfeld, Leshem Choshen, and senior authors Jacob Andreas and Yoon Kim.

When we use the internet, we're entrusting tech companies with some of our most private information. These companies have promised they'll keep our data safe. But what happens when the government comes knocking at their doors? In our latest EFFector newsletter, we hear from an EFF client whose data was given to ICE after Google broke its promise to him.

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For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This latest issue covers the ongoing fight to reform NSA surveillance, the many attempts to censor 3D printing, and the cost of Google's broken promise to its users.

Prefer to listen in? EFFector is now available on all major podcast platforms. This time, we're chatting with EFF Senior Staff Attorney F. Mario Trujillo about how state attorneys general can hold Google accountable for failing to protect users targeted by the government. You can find the episode and subscribe on your podcast platform of choice:

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Want to help us hold companies accountable? Sign up for EFF's EFFector newsletter for updates, ways to take action, and new merch drops. You can also fuel the fight for privacy and free speech online when you support EFF today!

Agencies Ignored EFF’s Public-Records Requests Regarding Unlawful Efforts to Locate People Who Criticized the Government or Attended Protests.

SAN FRANCISCO – The Electronic Frontier Foundation (EFF) sued the Department of Homeland Security (DHS) and Immigration and Customs Enforcement (ICE) today demanding public records about their use of administrative subpoenas to try to identify their online critics.

Court records and news reports show that in the past year, DHS has used administrative subpoenas to unmask or locate people who have documented ICE's activities in their community, criticized the government, or attended protests. The subpoenas are sent to technology companies to demand information about internet users who are often engaged in protected First Amendment activity.

These subpoenas are dangerous because they don’t require judges’ approval. But they are also unlawful, and the government knows it. When a few users challenged them in court with the help of American Civil Liberties Union affiliates in Northern California and Pennsylvania, DHS withdrew them rather than waiting for a decision.

DHS and ICE have ignored EFF’s public-records requests for documents about the processes behind these subpoenas, so EFF sued Wednesday in the U.S. District Court for the District of Columbia.

“DHS and ICE should not be able to first claim that they have the legal authority to unmask critics and then run from court when users challenge these administrative subpoenas,” said EFF Deputy Legal Director Aaron Mackey. “The public deserves to know what laws the agencies believe give them the power to issue these speech-chilling subpoenas.”

An administrative subpoena cannot be used to obtain the content of communications, but they have been used to try and obtain some basic subscriber information like name, address, IP address, length of service, and session times. If a technology company refuses to comply, an agency’s only recourse is to drop it or go to court and try to convince a judge that the request is lawful.

EFF and the ACLU of Northern California in February ​wrote to Amazon, Apple, Discord, Google, Meta, Microsoft, Reddit, SNAP, TikTok, and X​ to ask that they insist on court intervention and an order before complying with a DHS subpoena; give users as much notice as possible when they are the target of a subpoena, so the users can seek help; and resist gag orders that would prevent the companies from notifying users who are targets of subpoenas.

And EFF last week ​asked California’s and New York’s attorneys general to investigate Google​ for deceptive trade practices for breaking ​its promise​ to notify users before handing their data to law enforcement, citing the case of a doctoral student who was targeted with an ICE subpoena after briefly attending a pro-Palestine protest.

EFF in early March filed public-records requests with DHS and ICE for their policies, procedures, guidelines, directives, memos, and legal analyses supporting such use of administrative subpoenas. EFF also requested all Inspector General or oversight records, all approval and issuance procedures for the subpoenas, all records reflecting how many such subpoenas have been issued, all communications with technology companies concerning these demands, all communications regarding specific named targets or programs, and all communications with the Department of Justice regarding such subpoenas.

DHS and ICE have not responded, even though EFF requested expedited processing of its requests, which requires agencies to get back to requesters within 10 days.

“The policies, directives, and authorization records governing the program have not been disclosed,” the complaint notes. “The legal basis asserted by DHS and ICE for using a customs statute to compel disclosure of information about persons engaged in constitutionally protected speech and association has not been made public.”

For the complaint: https://www.eff.org/document/eff-v-dhs-ice-administrative-subpoenas-complaint

For EFF’s letter urging tech companies to protect users: ​https://www.eff.org/deeplinks/2026/02/open-letter-tech-companies-protect-your-users-lawless-dhs-subpoenas​

For EFF’s letter urging state probes of Google: ​https://www.eff.org/press/releases/eff-state-ags-investigate-googles-broken-promise-users-targeted-government​

Tags: free speechprivacyanonymityDHSICEContact:  AaronMackey Deputy Legal Director/Free Speech and Transparency Litigation Directoramackey@eff.org

ICE has admitted that it uses spyware from the Israeli company Graphite.

A "carbon-intelligent computing" tool has come in handy as the tech giant negotiates with utilities to connect data centers to the grid.