Think of them as master Lego builders, only at an atomic scale. Engineers at 抖阴旅行射 Boulder have taken a major step forward in combing advanced computer simulations with artificial intelligence to try to predict how electronics, like the transistors in your cell phone, will fail.
The new research was led by physicist and aerospace engineer Sanghamitra Neogi听and appears this week in the journal .
Sanghamitra Neogi
In their latest study, Neogi and her colleagues mapped out the physics of small building blocks made up of atoms, then used machine learning techniques to estimate how larger structures created from those same building blocks might behave. It鈥檚 a bit like looking at a single Lego brick to try to predict the strength of a much larger castle.
鈥淲e鈥檙e trying to understand the physics of devices with billions of atoms,鈥� said Neogi, assistant professor in the Ann and H.J. Smead Department of Aerospace Engineering Sciences.
It鈥檚 a pursuit that could be a boon for the electronics that underpin our daily lives, from smartphones and electric cars to emerging quantum computers. One day, Neogi said, engineers could use the team鈥檚 methods to pinpoint in advance weak points in the design of electronic components.
The project is part of Neogi鈥檚 larger focus on how the world of very small things, such as the wiggling of atoms, can help people build new and more efficient computers鈥攅ven ones that take their inspiration from human brains. Artem Pimachev, a research associate in aerospace engineering at 抖阴旅行射 Boulder, is a coauthor of the new study.
鈥淩ather than wait for years to figure out why devices fail, our methods can give us a priori knowledge on how a device is going to work before we even build it,鈥� Neogi said.
Heating up
Her latest research focuses on a big sticking point in the electronics industry: Hotspots.
And, no, that doesn鈥檛 mean the mobile WiFi hookups. Neogi explained that most modern computing tools carry a large number of imperfections鈥撯�搒mall defects in electronic components that cause heat to build up at certain sites, a bit like how a bicycle slows down when you ride over rough terrain. Such 鈥渉otspots鈥� also make your smartphone a lot less efficient.
The problem, Neogi said, is that engineers drawing on computer simulations, or models, struggle to predict ahead of time where those weak points are likely to turn up.
鈥淲e can use physics models to understand systems with approximately 100 atoms in them,鈥� Neogi said. 鈥淏ut that doesn鈥檛 compare to the billions of atoms in these devices.鈥�
She thinks that machine intelligence can help engineers to design better electronics.
From atoms to devices
Think back to those individual Lego bricks, which, in this case, are clumps of 16 silicon and germanium atoms, the main ingredients in many computer components.
In the new study, Neogi and her colleagues developed a computer model that uses artificial intelligence to learn the physical properties within those building blocks鈥攐r how atoms and electrons come together to determine the energy landscape within a material. The model can then extrapolate from those basic blocks to estimate the distribution of energy in a much larger chunk of atoms.
鈥淚t collects information from each individual unit and combines them to predict the final properties of the collective system, which can be made up of two, three or more units,鈥� Neogi said.
Her team still has a long way to go before it can pinpoint all of the potential weak points in a device the size of your phone. But, so far, the group鈥檚 model has proved effective. Neogi and her colleagues have used the tool to accurately predict the properties of several real-world materials made from silicon and germanium.
The researcher is also drawing on her understanding of how heat and energy flow at very small scales to not just improve existing devices, but also help create the devices of future. In 2019, Neogi joined a $1.7 million national effort to explore the potential for 鈥渘euromorphic鈥� computers鈥撯�搊r devices that store and analyze information by mimicking the activity of neurons in the brain.
鈥淲hat I want to do is poke at this world of atoms in your handheld device and understand how materials and electronics come together to make a device work,鈥� she said.
