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Observer Design for Topography Estimation in Atomic Force Microscopy Using Neural and Fuzzy Networks.
Mohammad Rafiee Javazm1, Hossein Nejat Pishkenari1
1Nano Robotics Laboratory, Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran.
Ultramicroscopy
|May 17, 2020
Summary
A new artificial intelligence (AI) method accurately estimates surface topography, improving on traditional atomic force microscopy. This AI approach enhances imaging speed and detail, also measuring interaction forces.
Area of Science:
- Materials Science
- Artificial Intelligence
- Surface Metrology
Background:
- Traditional atomic force microscopy (AFM) faces limitations in scanning speed due to tip-sample interaction dynamics and closed-loop controller requirements.
- Accurate surface topography estimation is crucial for understanding material properties and nanoscale phenomena.
Purpose of the Study:
- To develop and evaluate a novel artificial intelligence-based approach for direct surface topography estimation.
- To compare the performance of various AI techniques, including multi-layer perceptron (MLP), radial basis function (RBF), and adaptive neural fuzzy inference system (ANFIS) networks.
- To introduce an innovative imaging technique that bypasses the need for a closed-loop controller, enhancing speed and accuracy.
Main Methods:
- Investigated the performance of MLP, RBF, and ANFIS neural networks for surface topography estimation.
- Developed a novel imaging technique that eliminates the requirement for a closed-loop controller.
- Simultaneously estimated surface topography, Hamaker parameter, and tip-sample interaction force.
Main Results:
- The multi-layer perceptron (MLP) network demonstrated superior accuracy in estimating surface characteristics compared to RBF and ANFIS methods.
- The proposed innovative imaging technique significantly increased scanning speed and accuracy over conventional methods.
- The technique successfully enabled simultaneous estimation of topography, Hamaker parameter, and tip-sample interaction force.
Conclusions:
- Artificial intelligence, particularly the multi-layer perceptron network, offers a highly accurate method for direct surface topography estimation.
- The novel AI-driven imaging technique overcomes the speed limitations of traditional atomic force microscopy.
- This approach provides a faster, more accurate, and versatile tool for nanoscale surface analysis and material characterization.
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