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This study introduces an artificial intelligence (AI) framework for autonomous atom manipulation of silver atoms on silicon surfaces at room temperature. The AI system successfully performed complex atomic manipulations for over 25 hours without human intervention.

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Surface Science

Background:

  • Atom manipulation is crucial for atomic-scale fabrication.
  • Current methods often require manual control and specific conditions.
  • Developing autonomous systems can enhance efficiency and precision.

Purpose of the Study:

  • To develop an integrated AI framework for autonomous atom manipulation.
  • To enable room-temperature operation for silver atom manipulation on Si(111)-(7 × 7).
  • To establish a foundation for high-throughput atomic-scale fabrication.

Main Methods:

  • An integrated AI framework combining four machine learning models.
  • Models evaluate tip/surface conditions, detect silver atoms, locate defect-free half-unit cells (HUCs), and assess manipulation parameters.
  • Autonomous scanning tunneling microscopy (STM) operation including thermal drift correction and probe conditioning.

Main Results:

  • Demonstrated robust, long-term autonomous operation for over 25 hours.
  • Successfully executed lateral and vertical silver atom transfers between HUCs.
  • Achieved AI-driven autonomous atomic manipulation at room temperature.

Conclusions:

  • The integrated AI framework shows feasibility for autonomous atomic manipulation at room temperature.
  • Tip stability remains a challenge impacting success rates.
  • This work lays the groundwork for future high-throughput atomic-scale fabrication using AI.