Three Lincoln Laboratory inventions named IEEE Milestones
Achievements in air traffic control, microelectronics, and lasers are recognized for their lasting benefit to humanity.
Achievements in air traffic control, microelectronics, and lasers are recognized for their lasting benefit to humanity.
Lightmatter, founded by three MIT alumni, is using photonic computing to reinvent how chips communicate and calculate.
The work will help researchers tune surface properties of perovskites, a promising alternative and supplement to silicon, for more efficient photovoltaics.
The printed solenoids could enable electronics that cost less and are easier to manufacture — on Earth or in space.
An MIT team precisely controlled an ultrathin magnet at room temperature, which could enable faster, more efficient processors and computer memories.
MIT engineers developed a tag that can reveal with near-perfect accuracy whether an item is real or fake. The key is in the glue on the back of the tag.
The advanced fabrication tools will enable the next generation of microelectronics and microsystems while bridging the gap from the lab to commercialization.
State-of-the-art toolset will bridge academic innovations and industry pathways to scale for semiconductors, microelectronics, and other critical technologies.
A system designed at MIT could allow sensors to operate in remote settings, without batteries.
Lightweight and inexpensive, miniaturized mass filters are a key step toward portable mass spectrometers that could identify unknown chemicals in remote settings.
Swallowing the device before a meal could create a sense of fullness, tricking the brain into thinking it’s time to stop eating.
A new method enables optical devices that more closely match their design specifications, boosting accuracy and efficiency.
The advance opens a path to next-generation devices with unique optical and electronic properties.
The results open the door to exploring superconductivity and other exotic electronic states in three-dimensional materials.
Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.