Surface-Periodicity-Guided Structure Mapping for Automated Scanning Tunneling Microscopy

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Abstract
Automating scanning tunneling microscopy (STM) is essential for scaling atomically precise characterization and, ultimately, fabrication of next-generation materials and devices. A key challenge is to maintain atomic precision across extended surface regions, where local structures must be aligned, identified, and revisited in a common lattice frame. Here we use surface periodicity as an intrinsic reference frame to transform lattice-resolved STM images into lattice-registered unit-cell maps. Using Ag/Si(111)-(7×7) as a model system, the framework preserves lattice indices and neighborhood relationships while standardizing local structural units. This representation enables data-efficient supervised recognition of Ag adsorption configurations from one or a few reference images and supports environment-aware target-site selection. We further implement the framework in a continuous automated STM workflow, demonstrating autonomous scanning, target identification, and structure-specific characterization. Our results show that crystalline surface periodicity can provide a transferable basis for automated STM operations on lattice-resolved surfaces.
Funding Information
  • National Natural Science Foundation of China (12474183)
  • Guangzhou Municipal Science and Technology Project (2024A04J6304)
  • National Key Research and Development Program of China (2022YFA1203503)
  • Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515013040)
  • Basic and Applied Basic Research Foundation of Guangdong Province (2025A1515011503)

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