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Fabrication of atomic junctions with experimental parameters optimized using ground-state searches of Ising spin
Shotaro Sakai1, Yosuke Hirata1, Mitsuki Ito1
1Department of Electrical and Electronic Engineering, Tokyo University of Agriculture & Technology, Koganei, Tokyo, 184-8588, Japan.
Scientific Reports
|November 9, 2019
Summary
Feedback-controlled electromigration (FCE) uses an Ising spin model to optimize parameters for creating atomic junctions. This AI-driven approach enhances control over quantized conductance in metal nanowires.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Computational Physics
Background:
- Feedback-controlled electromigration (FCE) is a key technique for fabricating atomic junctions and controlling quantized conductance in metal nanowires.
- Current FCE parameter selection relies heavily on empirical methods, limiting systematic optimization and exploration of the parameter space.
- The complexity of FCE parameter tuning makes it challenging to achieve consistent and reliable results.
Purpose of the Study:
- To introduce a novel computational approach for optimizing Feedback-controlled electromigration (FCE) parameters.
- To enhance the controllability and reproducibility of atomic junction fabrication using FCE.
- To leverage artificial intelligence for accelerating materials research and discovery in nanoscale fabrication.
Main Methods:
- Utilized the Ising spin model to systematically search for optimal FCE parameters.
- Employed the energy convergence properties of the Ising spin model for parameter determination.
- Validated the optimized parameters through experimental Feedback-controlled electromigration (FCE) on metal nanowires.
Main Results:
- Demonstrated improved controllability of quantized conductance in atomic junctions fabricated with optimized FCE parameters.
- Showcased the Ising spin model's capability to efficiently explore multidimensional parameter spaces and identify global optima.
- Confirmed that the proposed computational method significantly enhances the precision of FCE processes.
Conclusions:
- The Ising spin model offers an effective and computationally efficient strategy for optimizing Feedback-controlled electromigration (FCE) parameters.
- This AI-driven approach facilitates the development of intelligent machines capable of conducting advanced materials research autonomously.
- The findings pave the way for more reliable and precise fabrication of atomic-scale electronic devices.
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