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Related Experiment Video

Updated: Dec 21, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy
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Random residual neural network-based nanoscale positioning measurement.

Chenyang Zhao, Yang Li, Yingxue Yao

    Optics Express
    |May 15, 2020
    PubMed
    Summary

    This study introduces a deep learning positioning method using nanomanufactured patterns for unlimited measurement range. The novel approach achieves 97.6% accuracy and near real-time speeds, outperforming laser interferometry.

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    Area of Science:

    • Nanotechnology
    • Deep Learning
    • Precision Measurement

    Background:

    • Traditional positioning measurement faces limitations due to complex components, stringent environments, and time-consuming calibration.
    • Existing methods struggle with measurement range and efficiency.

    Purpose of the Study:

    • To develop a deep learning-based positioning methodology integrating image processing and nanomanufacturing.
    • To overcome the limitations of conventional positioning systems by introducing an unlimited measurement range.
    • To enhance the accuracy and speed of precision positioning measurements.

    Main Methods:

    • Fabrication of non-periodic microstructures with nanoscale resolution for surface patterning.
    • Utilizing a residual neural network (ResNet) for efficient surface pattern recognition.
    • Implementing a survival probability mechanism to improve network layer transmission efficiency.
    • Combining template matching and sub-pixel interpolation algorithms for precise pattern matching.

    Main Results:

    • Achieved a high pattern recognition accuracy of 97.6%.
    • Demonstrated a measurement speed close to real-time.
    • The proposed methodology offers an unlimited measurement range.
    • Experimental results show superior advantages and competitiveness compared to the laser interferometer method.

    Conclusions:

    • The developed deep learning-based positioning methodology provides a comprehensive framework for precision measurement.
    • The integration of nanomanufacturing and deep learning significantly enhances positioning accuracy and efficiency.
    • The approach offers a competitive alternative to traditional methods like laser interferometry for high-precision positioning tasks.