Random residual neural network-based nanoscale positioning measurement.

Abstract

In the field of positioning measurement, a combination of complex components, a stringent environment, and time-consuming calibration are the main limitations. To address these issues, this paper presents a deep learning-based positioning methodology, which integrates image processing with nanomanufacturing technology. Non-periodic microstructure with nanoscale resolution is fabricated to provide the surface pattern. The main advantage of the proposed microstructure is its unlimited measurement range. A residual neural network is used for surface pattern recognition to reduce the search area, a survival probability mechanism is proposed to improve the transmission efficiency of the network layers, and template matching and sub-pixel interpolation algorithms are combined for pattern matching. The proposed methodology defines a comprehensive framework for the development of precision positioning measurement, the effectiveness of which was collectively validated by pattern recognition accuracy and positioning measurement performance. The trained network exhibits a recognition accuracy of 97.6%, and the measurement speed is close to real time. Experimental results also demonstrate the advantages and competitiveness of the proposed approach compared to the laser interferometer method.