AI Study Explores Transistor Behavior in Extreme Cold
- Fermilab researcher Olivia Seidel is leading a groundbreaking study funded by the U.S.
- The research, conducted at Fermilab’s Microelectronics group, leverages the lab’s deep expertise in both microelectronics and cryogenic devices.
- “Transistors are the computational building blocks of every electronic device—your phone, your laptop, anything that manipulates bits and bytes,” Seidel explained in a recent interview at Fermilab’s Wilson...
Fermilab researcher Olivia Seidel is leading a groundbreaking study funded by the U.S. Department of Energy’s Genesis Mission, using artificial intelligence to model how transistors behave at extreme cryogenic temperatures—work that could revolutionize quantum computing, satellite electronics and other cutting-edge technologies.
The research, conducted at Fermilab’s Microelectronics group, leverages the lab’s deep expertise in both microelectronics and cryogenic devices. Transistors, the fundamental building blocks of modern electronics, have historically been optimized for room-temperature operation. However, emerging fields like quantum computing and space-based systems require electronics that function reliably at temperatures just above absolute zero. Seidel’s AI-driven approach aims to accelerate the discovery of materials and designs that perform optimally in these extreme conditions.
“Transistors are the computational building blocks of every electronic device—your phone, your laptop, anything that manipulates bits and bytes,” Seidel explained in a recent interview at Fermilab’s Wilson Hall. “For most of their history, room temperature was the only environment that mattered. But quantum computing and other emerging technologies require electronics that function at cryogenic temperatures—just a few degrees above absolute zero.”
The Genesis Mission, a national AI initiative spearheaded by the Department of Energy, brings together 17 national laboratories, research universities, and industry partners to supercharge American innovation. Fermilab’s role in the initiative is critical, given its strengths in high-energy physics, advanced computing, accelerator science, and microelectronics. The lab’s contributions will help advance AI-driven accelerator research while securing U.S. Leadership in next-generation electronics—key for applications in medicine, materials science, energy, and national security.

“The Genesis Mission represents a once-in-a-generation opportunity to transform how America does science,” said Fermilab Director Norbert Holtkamp. “By combining AI, advanced computing, and the capabilities of the national laboratories, One can accelerate discovery while strengthening the scientific infrastructure that underpins U.S. Leadership in particle physics and beyond.”
Seidel’s work is part of a broader effort to make particle accelerators more adaptive and autonomous through AI. This research could also have implications for satellite electronics, where components must endure harsh thermal environments. The findings may lead to transistors that are not only more efficient but also capable of operating in conditions previously deemed too extreme for conventional electronics.

While the study is still in progress, its potential impact is significant. If successful, the research could pave the way for more powerful quantum computers, more reliable satellite systems, and advancements in fields where cryogenic electronics are essential. The Genesis Mission’s broader goals include doubling the productivity and impact of American science within the next decade by integrating AI across the national research landscape.
Fermilab’s involvement underscores the lab’s expanding role beyond particle physics, positioning it as a leader in AI-driven microelectronics research. As the initiative progresses, the lab’s findings could influence not only scientific research but also commercial applications in industries where extreme-environment electronics are critical.
For now, Seidel and her team continue their work, using AI to model transistor behavior under cryogenic conditions—a task that would be far more time-consuming and computationally intensive without machine learning. The results of this research could redefine the boundaries of what electronics can achieve in the coldest environments on Earth and beyond.
