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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title AFM-net: From Scarce Data to Fast Scans. Machine Learning Acceleration of AFM Nanometrology
Author(s) Eva Natinsky, Ryan Khan, Qianshu Wang, David Morris, Remi Dingreville, Michael Cullinan
On-Site Speaker (Planned) Eva Natinsky
Abstract Scope The need for accelerated nanometrology becomes more pressing with the rise of advanced manufacturing technologies, and bottlenecks exist in both data acquisition and processing. Machine learning (ML)-driven image reconstruction offers a solution to accelerate data processing and eliminate imaging artifacts. Here, a novel training workflow called AFM-net is presented for an image reconstruction ML model, applied to atomic force microscopy (AFM), that enables data augmentation far exceeding experimental capabilities. The results show AFM-net produces high quality reconstructions and achieves accurate height predictions from AFM measurements. Critically, AFM-net accelerates data processing by 75x to 500x over traditional methods. The modularity of AFM-net also makes it adaptable to new data types with minimal tuning.
Proceedings Inclusion? Undecided

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