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Observer design for topography estimation in atomic force microscopy using neural and fuzzy networks

Rafiee Javazm, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1016/j.ultramic.2020.113008
  3. Publisher: Elsevier B.V , 2020
  4. Abstract:
  5. In this study, a novel artificial intelligence-based approach is presented to directly estimate the surface topography. To this aim, performance of different artificial intelligence-based techniques, including the multi-layer perceptron neural, radial basis function neural, and adaptive neural fuzzy inference system networks, in estimation of the sample topography is investigated. The results demonstrate that among the designed observers, the multi-layer perceptron method can estimate surface characteristics with higher accuracy than the other methods. In the classical imaging techniques, the scanning speed of atomic force microscope is restricted due to the time required by the oscillating tip to reach the steady state motion while the closed-loop controller tries to maintain the tip vibration amplitude at a set-point value. To address this issue, we have proposed an innovative imaging technique that not only eliminates the need to a closed-loop controller but also estimates the surface topography very quick and accurate compared to the conventional imaging method. Also, the proposed technique is capable of simultaneous estimation of the topography, Hamaker parameter, and the tip-sample interaction force. © 2020 Elsevier B.V
  6. Keywords:
  7. Adaptive neural fuzzy inference system ; Atomic Force Microscopy ; Fuzzy network ; Multi-layer perceptron ; Neural network ; Fuzzy inference ; Fuzzy logic ; Fuzzy neural networks ; Imaging techniques ; Radial basis function networks ; Surface topography ; Topography ; Adaptive neural fuzzy inference system (ANFIS) ; Closed loop controllers ; Multi layer perceptron ; Radial basis functions ; Simultaneous estimation ; Surface characteristics ; Tip-sample interaction ; Topography estimation ; Multilayer neural networks ; Accuracy ; Adaptive neural fuzzy inference system network ; Amplitude modulation ; Artificial intelligence ; Artificial neural network ; Closed loop controller ; Fuzzy system ; Hamaker parameter ; Image analysis ; Mathematical analysis ; Motion ; Multi layer perception neural network ; Noise ; Oscillation ; Radial basis function neural network ; Regression analysis ; Simulation ; Steady state ; Time ; Vibration
  8. Source: Ultramicroscopy ; Volume 214 , 2020
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0304399119303341