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MIT Department of Materials Science and Engineering
MIT Department of Materials Science and Engineering

Computing and Data Science

Device Fabrication

New camera and sensor technologies that can focus on object at multiple depths, without changes to its position or shape

Computation and Design

AI-based techniques help find new materials for faster, more efficient electronics

Semiconductors

Self-assembling materials that form tiny wires and junctions could make microchip manufacturing cheaper and faster

New camera and sensor technologies that can focus on object at multiple depths, without changes to its position or shape

AI-based techniques help find new materials for faster, more efficient electronics

Self-assembling materials that form tiny wires and junctions could make microchip manufacturing cheaper and faster

Revolutionizing Technology

Materials science and engineering underpins every aspect of our modern computing and telecommunications infrastructure—and will enable those of tomorrow. Some examples: DMSE researchers have used artificial intelligence techniques to build free and easy-to-use tools that bypass the traditional trial-and-error approach of materials discovery, allowing scientists to identify new materials at a much faster rate. A new “metalens” can change focus without tilting or shifting, potentially enabling tiny zoom lenses for smartphones or night-vision goggles. And new self-assembling three-dimensional structures could lead the way to microchip production that is faster and cheaper than ever before.

Using self-assembling polymers, DMSE researchers have produced 3-D configurations that could lead to new microchips.

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size, in nanometers, of posts that guided the self-assembly

Advancing Computing Materials

DMSE researchers do extensive work in this diverse field. Some focus on device fabrication, designing next-generation hardware components and electronic devices. Others are experts in computation and design, doing atomistic simulation to model materials at the level of atoms.

Materials used in computing and data science include semiconductors, a necessity for microchips, and metals, for wires and magnets and coatings. Soft matter, too, finds application here: DMSE researchers are experimenting with new polymers that can efficiently convert signals from biological tissue into electronic signals used in transistors, potentially leading to better wearable devices.

Related Materials and Research Types

Research Types

Characterization
Computation and Design
Synthesis and Processing
Device Fabrication

Materials

Soft Matter
Metals
Semiconductors
Ceramics

Related Faculty and Researchers

Key Publications

Reconfigurable all-dielectric metalens with diffraction-limited performance

Proved that you don’t need mechanical movement to change the focus of a lens. Instead, a transparent “metalens” changes the way it interacts with infrared light when it undergoes heat-based phase transformation. To see objects far and near, one would simply heat the material using microheaters.

Traditional glass-based optical lenses need mechanical motion to focus on objects. The knobs or other components used for this purpose add unwanted bulk to imaging instruments and are prone to wear and tear.

Because it doesn’t require bulky mechanical elements, the metalens may enable miniature and lighter imaging systems in a variety of devices—from drones to night-vision goggles.

Molecularly hybridized conduction in DPP-based donor–acceptor copolymers toward high-performance iono-electronics

Synthesized a new category of polymers that can be used to produce more long-lasting and intelligent wearable devices. The materials efficiently convert ion-based signals from hydrated environments—for example, biological tissue—to electron-based signals that can easily be read through devices.

Today’s wearables are somewhat limited in their electronic performance because they waste a lot of energy sampling biological signals—like insulin from sweat, for example. We need to optimize wearables’ sampling efficiency without compromising electronic performance.

Wearables are becoming crucial in long-term health monitoring, so they need to be long-lasting, easy to manufacture at scale, and more seamlessly integrate with body functions.

Nanosecond protonic programmable resistors for analog deep learning

Developed programmable resistors, or artificial synapses—devices that can be used to build analog deep learning processors. Compatible with silicon fabrication techniques, these artificial synapses increase the speed and reduce the energy needed to train neural network models.

Deep learning, a subset of artificial intelligence (AI), is key for successful automation, facilitating many analytical and computational tasks without human intervention. But training these models using current computers is associated with unsustainably high energy demand. Low-energy alternatives need to be found.

Deep learning processors that can execute computations fast while using much less energy can satisfy the growing need for AI while still meeting sustainability goals. Faster training of neural networks means faster deployment of deep learning use cases like fraud detection and medical imaging analysis.