The future of microelectronics will depend on more than making today’s chips smaller. Researchers are searching for new materials and new ways to combine them — advances to support the faster computing, reduced energy use and secured domestic production that will guarantee continued U.S. technological leadership. But that search is slow and extraordinarily complex, requiring scientists to perform many experiments with multiple competing parameters and laboriously interpret the huge streams of resulting data.

At the Center for Nanophase Materials Sciences, a Department of Energy Office of Science user facility at Oak Ridge National Laboratory, researchers are meeting this challenge with a new generation of scientific technology: autonomous workflows for microelectronics research. By combining advanced instruments, artificial intelligence and theoretical know-how, they are creating systems that can do more than automate routine steps. CNMS systems are increasingly capable of interpreting incoming data and determining next steps in real time, helping researchers quickly navigate vast experimental spaces and achieve results that are beyond reach with conventional methods.

For Rama Vasudevan, who leads CNMS’s data analytics efforts for autonomous science, the value of these systems goes well beyond speed. “It’s not as simple as a performance enhancement from one-fold to ten-fold,” he said. “It’s really a qualitative difference in capability.”

At CNMS, that qualitative shift is beginning on two fronts. First, autonomous systems are helping researchers more intelligently grow and refine materials for microelectronics, using data from each experiment to guide the next one. Second, they are helping scientists connect measurements, simulations and AI into a fuller picture of how those materials behave — and why they succeed or fail under real operating conditions.

Autonomous science in action

Autonomous systems are already changing how researchers create materials for microelectronics. Growing these materials means navigating a maze of interacting variables, from temperature and pressure to substrate conditions and laser settings. Each of those choices can affect the final material, and changing one often alters how the others interact. Instead of relying only on trial and error or a researcher’s intuition to discover the best way to proceed, automated workflows can access a wealth of stored information alongside each experimental run to guide the process in real time.

Adaptive synthesis methods like these are especially valuable for creating the delicate nanomaterials used in next-generation microelectronics. In one ongoing CNMS project led by staff scientist Sumner Harris, researchers are working to grow oxide membranes that can be lifted off one surface and transferred onto another, giving scientists a way to stack and combine materials in new configurations. These configurations offer a lever to alter the electronic properties of the resulting composite, creating opportunities for dramatically improved memory and transistor performance.

That promise comes with a difficult materials challenge. Oxide membranes are often grown on a surface material such as graphene so they can be later separated and transferred to their destination surface, but there is a catch: the oxide material needs oxygen to form, but too much oxygen will degrade the graphene, making it impossible to transfer the target material. Autonomous workflows are especially useful for this challenge because AI systems can digest and apply a vast body of prior research and experimental information to complement human expertise as researchers search for the conditions that preserve both materials.

Vasudevan describes this project and others like it as a kind of human-AI conference, where researchers and autonomous systems each contribute where they are strongest. “Humans do really well in sparse-data regimes,” he said. “But once a problem becomes too large and high-dimensional, AI can find patterns and simplify it in ways people can’t.”

Autonomous workflows are not just iterative loops of synthesis and measurement. By placing theory in the loop and leveraging ORNL’s high-performance computing, these systems can generate and test new hypotheses based on discrepancies between simulation and experiment. This is particularly valuable for complex problems like materials synthesis, where intuition alone may be insufficient. “These autonomous workflows seamlessly integrate knowledge from literature, multi-modal experiments and simulations to accelerate materials discovery and build foundational knowledge,” said Panchapakesan Ganesh, leader of the Theory and Computation section at CNMS.

Teaching instruments to choose

Autonomous experiments at CNMS are built step by step, not switched on all at once. Researchers first need to automate their tasks by teaching an instrument to independently and reliably perform a set of tasks, whether that means running a synthesis recipe or scanning a sample in a repeatable way. Then they begin adding AI: training models on data from prior runs, relevant literature and synthetic datasets so the system can recognize patterns, interpret what it is seeing and choose how to respond.

 “Automated is necessary for autonomous,” explained Marti Checa Nualart, an R&D staff scientist in the CNMS Functional Atomic Force Microscopy Group. “The switch happens when we empower automated instruments with properly trained models to make autonomous decisions, adapting as the experiment unfolds.”

Autonomous workflows are changing more than operations of individual instruments. They are also helping researchers connect what they learn across many tools, datasets and simulations, adding up isolated measurements into a more complete picture of how microelectronic materials behave. In a project led by Argonne National Laboratory, Alphafold for Microelectronics, ORNL researchers are helping develop AI tools that combine experiments and simulations to better understand materials for memory devices and transistors, including investigating why some promising ferroelectric memory materials degrade with repeated use.

“The memory degradation problem is difficult because no single measurement can explain it,” said Vasudevan. Researchers are working to integrate several disparate data sources from multiple facilities and experimental approaches, while simulations add still another layer of information. CNMS is using autonomous workflows to combine those data streams in a process called multimodal fusion — in which AI models are recruited to sort through incoming information and identify patterns that are nearly undetectable by human-level intelligence — guiding experimental approaches to zero in on the mechanisms behind memory fatigue.

Supporting users worldwide

CNMS is structured to support exactly that kind of work. As one of DOE’s five Nanoscale Science Research Centers, it supports users from around the world. The constant flow of outside researchers and variable scientific challenges they present has helped push CNMS toward more streamlined and increasingly autonomous workflows, while Vasudevan’s embedded Data NanoAnalytics Group helps connect those capabilities across synthesis, microscopy and theory.

For microelectronics, that integration could prove just as important as any single breakthrough material. The field is increasingly faced with problems that are too complex, too data-rich and too interconnected for traditional workflows alone. At CNMS, researchers are building systems that can learn from each step quickly enough to guide the next one — not only speeding discovery but also expanding what discovery can look like.

The DOE Office of Science and ORNL Laboratory Directed Research and Development program supported this research.

ORNL is supported by the DOE Office of Science. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.

Source: Oak Ridge National Laboratory

Guest post for Technology.org • by Flamingo

IT teams now field between 500 and 1,200 alerts on an average day, according to AIOps trend data. Most of those alerts are noise. A human still has to look. That math stopped working, and it’s the reason AIOps, the practice of using AI to run and automate IT operations, jumped from a niche idea to a market projected to grow from $2.67 billion in 2026 to $11.8 billion by 2034.

The interesting part isn’t the size of the market. It’s who gets access. The same automation that used to require an enterprise budget is becoming cheap enough for a small IT team or a two-person service provider to run. Here’s what’s changing, and why the open-source angle matters more than the AI hype.

What AIOps Means (And What It Doesn’t)

AIOps is machine learning pointed at the flood of logs, metrics, and alerts that every IT environment produces. Instead of a technician reading each alert, software spots the patterns, groups related incidents into one, and resolves the routine ones on its own.

It is not a robot replacing your IT team, and it is not magic. It’s pattern recognition plus automation. The model learns what normal looks like, flags what isn’t, and runs the fix you’ve already approved for known problems. The human moves up the stack to the work that genuinely needs judgment.

Why IT Operations Hit a Breaking Point

For years the answer to more systems was more people. That stopped scaling. Cloud sprawl, remote endpoints, and security tooling multiplied the number of things that can break, and each one generates its own stream of alerts. Hiring your way out got too slow and too expensive.

So teams turned to automation out of necessity, not novelty. Adoption of AI-powered monitoring climbed from 42% to 54% of enterprises in a single year. The shift isn’t being led by the technology being impressive. It’s being led by the alternative being unsustainable.

What Automation Changes Day to Day

The practical win is boring, and that’s the point. AIOps platforms reduce mean time to resolution by around 60% and cut alert noise by up to 85% within the first year, mostly by handling the repetitive work no one wanted to do anyway.

Underneath an AIOps layer sits ordinary IT automation software that turns recurring tasks into runbooks: patch a fleet of machines, onboard a new user, restart a failed service, respond to a known alert, all without a person clicking through every step. The comparison there walks through the main categories and where newer AI-native platforms like OpenFrame fit against established names. The takeaway for any IT leader is simple: every task you automate is capacity you get back permanently, and one engineer starts covering the load that used to need three.

The Open Shift Nobody Priced In

Here’s the part the enterprise vendors would rather you skip. For most of this technology’s life, the automation and the back-office software to run an IT operation came with enterprise licensing: long contracts, per-seat fees, and a price tag that assumed you had hundreds of seats to spread it across.

That assumption is breaking. A growing set of open-source PSA and IT management tools now cover the core workflow, ticketing, assets, time, and billing, at 30 to 50% less than the commercial licensing, with the trade-off that you host and maintain them yourself. Tools like ITFlow target small providers directly, and directories like OpenMSP map which open options fit a given team size. Pair that with open automation tooling, and a small shop runs a stack that looked like an enterprise budget two years ago.

This is the real democratization story inside the AIOps headlines. The AI gets the press. The falling cost of the tooling around it is what changes who can compete.

Manual IT Operations vs AI-Augmented Operations

The contrast is easiest to see side by side. Same work, two very different cost and effort profiles.

The Caveats

None of this runs itself yet. Even with AIOps platforms deployed, 58% of IT professionals still report struggling to interpret the machine learning output, which means the tools are only as useful as the people reading them. On-premise deployments still account for more than half the market, because plenty of environments can’t or won’t send everything to the cloud.

And automation built on bad data automates bad decisions faster. The teams getting real results are the ones that cleaned up their monitoring and documented their fixes first, then let the software take over the parts that were already well understood. The technology rewards preparation, not shortcuts.

Where This Is Headed

The direction is clear even if the timeline isn’t. IT operations are moving from humans watching dashboards toward software that handles the known problems and escalates only the genuinely new ones. The enterprises that combined AIOps with observability went from under 10% to a projected 40% in three years, and that curve isn’t slowing.

The winners won’t be whoever has the most AI. They’ll be the teams, large or small, that paired it with tooling cheap enough to run lean. For the first time, that group includes the