Monthly Archives: November 2016

Simple method for making smaller microchip patterns

For the last few decades, microchip manufacturers have been on a quest to find ways to make the patterns of wires and components in their microchips ever smaller, in order to fit more of them onto a single chip and thus continue the relentless progress toward faster and more powerful computers. That progress has become more difficult recently, as manufacturing processes bump up against fundamental limits involving, for example, the wavelengths of the light used to create the patterns.

Now, a team of researchers at MIT and in Chicago has found an approach that could break through some of those limits and make it possible to produce some of the narrowest wires yet, using a process with the potential to be economically viable for mass manufacturing with standard types of equipment.

The new findings are reported this week in the journal Nature Nanotechnology, in a paper by postdoc Do Han Kim, graduate student Priya Moni, and Professor Karen Gleason, all at MIT, and by postdoc Hyo Seon Suh, Professor Paul Nealey, and three others at the University of Chicago and Argonne National Laboratory. While there are other methods that can achieve such fine lines, the team says, none of them are cost-effective for large-scale manufacturing.

The new approach includes a technique in which polymer thin films are formed on a surface, first by heating precursurs so they vaporize, and then by allowing them to condense and polymerize on a cooler surface, much as water condenses on the outside of a cold drinking glass on a hot day.

“People always want smaller and smaller patterns, but achieving that has been getting more and more expensive,” says Gleason, who is MIT’s associate provost as well as the Alexander and I. Michael Kasser (1960) Professor of Chemical Engineering. Today’s methods for producing features smaller than about 22 nanometers (billionths of a meter) across generally require either extreme ultraviolet light with very expensive optics or building up an image line by line, by scanning a beam of electrons or ions across the chip surface — a very slow process and therefore expensive to implement at large scale.

The new process uses a novel integration of three existing methods. First, a pattern of lines is produced on the chip surface using well-established lithographic techniques, in which an electron beam is used to “write” the pattern on the chip.

Integrates with email

Hyper-connectivity has changed the way we communicate, wait, and productively use our time. Even in a world of 5G wireless and “instant” messaging, there are countless moments throughout the day when we’re waiting for messages, texts, and Snapchats to refresh. But our frustrations with waiting a few extra seconds for our emails to push through doesn’t mean we have to simply stand by.

To help us make the most of these “micro-moments,” researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a series of apps called “WaitSuite” that test you on vocabulary words during idle moments, like when you’re waiting for an instant message or for your phone to connect to WiFi.

Building on micro-learning apps like Duolingo, WaitSuite aims to leverage moments when a person wouldn’t otherwise be doing anything — a practice that its developers call “wait-learning.”

“With stand-alone apps, it can be inconvenient to have to separately open them up to do a learning task,” says MIT PhD student Carrie Cai, who leads the project. “WaitSuite is embedded directly into your existing tasks, so that you can easily learn without leaving what you were already doing.”

WaitSuite covers five common daily tasks: waiting for WiFi to connect, emails to push through, instant messages to be received, an elevator to come, or content on your phone to load. When using the system’s instant messaging app “WaitChatter,” users learned about four new words per day, or 57 words over just two weeks.

Ironically, Cai found that the system actually enabled users to better focus on their primary tasks, since they were less likely to check social media or otherwise leave their app.

WaitSuite was developed in collaboration with MIT Professor Rob Miller and former MIT student Anji Ren. A paper on the system will be presented at ACM’s CHI Conference on Human Factors in Computing Systems next month in Colorado.

Among WaitSuite’s apps include “WiFiLearner,” which gives users a learning prompt when it detects that their computer is seeking a WiFi connection. Meanwhile, “ElevatorLearner” automatically detects when a person is near an elevator by sensing Bluetooth iBeacons, and then sends users a vocabulary word to translate.

Though the team used WaitSuite to teach vocabulary, Cai says that it could also be used for learning things like math, medical terms, or legal jargon.

“The vast majority of people made use of multiple kinds of waiting within WaitSuite,” says Cai. “By enabling wait-learning during diverse waiting scenarios, WaitSuite gave people more opportunities to learn and practice vocabulary words.”

Still, some types of waiting were more effective than others, making the “switch time” a key factor. For example, users liked that with “ElevatorLearner,” wait time was typically 50 seconds and opening the flashcard app took 10 seconds, leaving free leftover time. For others, doing a flashcard while waiting for WiFi didn’t seem worth it if the WiFi connected quickly, but those with slow WiFi felt that doing a flashcard made waiting less frustrating.

In the future, the team hopes to test other formats for micro-learning, like audio for on-the-go users. They even picture having the app remind users to practice mindfulness to avoid reaching for our phones in moments of impatience, boredom, or frustration.