How a microelectronic device performs depends on what it is made of, how well its materials and interfaces are built and how electricity, heat and tiny defects change over time. These devices power much of modern life - from smartphones and laptops to secure communications and artificial intelligence (AI) hardware.
As next-generation devices become smaller, faster and more tightly packed, their performance is affected more and more by tiny flaws in materials and interfaces. These defects can lead to overheating, electrical leakage, unreliable switching and, in the end, shorter device lifetimes. But defects are not always harmful. They can also influence electrical and thermal behavior in useful ways, depending on how they are distributed and how they evolve over time. Understanding both the harmful and beneficial effects of defects is essential for designing better microelectronics.
To address the challenges defects pose, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory, Lawrence Berkeley National Laboratory (Berkeley Lab), Oak Ridge National Laboratory (ORNL) and Northwestern University plan to develop the Materials Discovery Cloud. The project will create a physics-informed AI framework that learns how material composition, structure and operating conditions influence defect evolution and key functional properties such as electrostatic potential, current density and temperature.
The Materials Discovery Cloud is part of DOE's Genesis Mission, a bold national initiative to double America's research and development productivity within a decade while strengthening U.S. technological leadership and global competitiveness.
From Protein Folding to Device Function
AlphaFold is an AI system developed by Google DeepMind that predicts a protein's 3D structure based on its amino acid sequence. It transformed biology by making structure prediction much faster and more accurate, helping scientists better understand how proteins work.
The new microelectronics effort follows a similar idea but applies it to a very different problem. Instead of predicting protein structure from sequence, the team aims to predict how networks of defects form, change and affect how a device works.
"In biology, AlphaFold learned to connect sequence to structure," said Subramanian Sankaranarayanan, Argonne scientist and lead principal investigator on the project, as well as a professor at the University of Illinois Chicago. "We want to connect defect distributions in materials and interfaces to the electrical and thermal properties that matter for microelectronics. Such a framework remains elusive."
Why Defects Are So Hard to Understand
Defects are tiny irregularities in a material's structure. They can include missing atoms, dislocations, voids or chemical disorder. Some defects hurt performance. Others can help enable useful behavior. The challenge is knowing which defects matter, when they matter and how they change under real operating conditions.
These attributes are difficult to determine because no single instrument can capture the whole picture.
Some tools, such as electron microscopes, can directly image features at very small scales. X-ray methods can reveal strain, buried structures and defect motion. Other techniques measure chemistry, electrical behavior and heat flow. Each method shows one part of the story, but not the whole system.
A useful way to think about it is like trying to understand the day's weather from the temperature alone. Temperature tells you something important, but you also need to factor in wind, clouds, precipitation and humidity to see the full picture. The same is true for microelectronics.
Bringing Many Tools Into One Framework
To close that gap, the team is combining many types of data from DOE Office of Science user facilities and advanced computing systems, drawing information from different tools and scales into one unified platform.
At Argonne, those include the Advanced Photon Source and the Center for Nanoscale Materials for X-ray and microscopy measurements, as well as the Argonne Leadership Computing Facility for large-scale computing. Partner capabilities include the Advanced Light Source, the Molecular Foundry and the National Energy Research Scientific Computing Center at Berkeley Lab, and the Center for Nanophase Materials Sciences at ORNL.
The Materials Discovery Cloud will help researchers gather experimental data, run simulations and generate synthetic data that mimic experiments. The synthetic data is especially important because complete experimental data sets for material samples are still complicated and time-consuming to collect. Simulations can fill in missing pieces and help train the AI framework.
Another part of the effort focuses on autonomous discovery, which uses AI, machine learning and robotics to help researchers decide which measurements to run next and collect new data more quickly. That can help address one of the project's key challenges: generating enough high-quality experimental data to build and refine AI models.
In this work, researchers are developing an AI-guided platform for microelectronics materials that can synthesize samples and carry out multiple kinds of characterization in a more integrated, high-throughput workflow. By reducing the need to move samples among facilities for separate measurements, the approach could speed data collection and help fill important gaps in the multimodal datasets used to train the framework.
"What makes the Materials Discovery Cloud powerful is that we can bring together experiments, simulations and AI in one workflow," Sankaranarayanan explained. "That gives us a way to learn from limited data today while building a framework that can grow more capable as new data comes in."
AI Guided By Physics
This system is not designed to be a black box that gives answers without explanation. Instead, it will be built around well-established laws of physics. This helps ensure the AI's predictions are grounded in how materials and devices actually behave.
That certainty is important because the researchers want the system to do more than spot patterns in data. They want it to help reveal why certain defects lead to specific changes in performance, reliability or lifetime.
To do that, the framework will combine several kinds of AI tools that can bring together many types of data, learn from both experiments and simulations and identify which new measurements would be most useful next. In the end, the goal is to connect tiny, atomic-scale features in a material to the larger electrical, thermal and mechanical behavior of a real device.
What Success Could Look Like
If successful, the Materials Discovery Cloud could change how scientists design and test new materials and devices.
Instead of waiting through long rounds of experiments, researchers may be able to get useful answers from a smaller set of early measurements. That could help them spot problems sooner, avoid spending time on weak candidates and focus more quickly on the most promising designs.
Over time, the framework could also support inverse design. Rather than starting with a material and seeing how it performs, they could start with a goal - such as better heat management or a longer-lasting device - and ask what kind of material structure or defect pattern would be needed to achieve it.
By bringing many different measurements into one predictive system, the effort aims to help scientists better understand, control and design the materials behind the next generation of microelectronics.
Other contributors to this work include Pierre Darancet, Henry Chan, Maria Chan, Daniel Durham, Tom Gage, Dina Sheyfer, Hua Zhou, Ross Harder, Jeffrey Klug, Mathew Cherukara, Supratik Guha and Aditya Koneru from Argonne; Mary Scott, Archana Raja, Ramamoorthy Ramesh, Ed Barnard and Sujoy Roy from Berkeley Lab; Panchapakesan Ganesh and Rama Vasudevan from ORNL; and Mark Hersam from Northwestern University.
This research was supported by the DOE's Basic Energy Sciences (BES) Office under the Genesis Mission's BES AI Pathfinder Program, Materials Discovery Cloud.