Brochure | Nanomaterials | Nanotechnology| Nanoengineering | Nanomanufacturing

Moving to autonomous experimentation: Growing thin films with machine learning

2025-04-02 - 2025-04

From cell phones to solar panels to quantum computers, thin films are essential to current and emerging technologies. But making functional thin films requires control. During hours-long processes, thin films form atom by atom. Small changes in data readouts can tell researchers when something is going wrong. Detecting defects as soon as possible may help scientists fix films while they're growing, saving time and money.

Researchers at Pacific Northwest National Laboratory (PNNL) are using machine learning (ML) to identify subtle changes in growing films that are imperceptible to humans, detailed in a paper published in the Journal of Vacuum Science & Technology A. Their ML program can pinpoint emerging differences in film growth data faster than human experts, an important initial step toward developing autonomous experimentation with instruments that can make data-based decisions without additional human input.

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Moving to autonomous experimentation: Growing thin films with machine learning

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