An AFM rapid detection method for micro-nano level defects on surface of optical elements based on convolutional neural network

By employing a fast AFM detection method based on convolutional neural networks, and utilizing optical microscope image stitching and AFM undersampled data to reconstruct the model, the problem of low detection efficiency of micro-nano defects on the surface of optical components is solved, achieving efficient and accurate detection of micro-nano defects.

CN117452025BActive Publication Date: 2026-07-21HARBIN INST OF TECH
View PDF 1 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HARBIN INST OF TECH
Filing Date
2023-10-25
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing methods for detecting micro- and nano-defects on the surface of optical components are inefficient, especially for defects smaller than 0.5 to 20 μm, where the identification accuracy is low. Furthermore, long-term AFM scanning can lead to mechanical drift, affecting the detection accuracy.

Method used

A fast AFM detection method based on convolutional neural networks is adopted. Sub-aperture images are obtained by optical microscope and stitched together. An image reconstruction model is constructed by combining undersampled and fully sampled data from AFM. End-to-end mapping is performed using convolutional neural networks to achieve rapid detection of defects.

Benefits of technology

It improves AFM detection efficiency and reduces the impact of mechanical drift on defect morphology information, making it suitable for efficient and rapid detection of a large number of micro- and nano-defects on the surface of optical components.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN117452025B_ABST
    Figure CN117452025B_ABST
Patent Text Reader

Abstract

The application provides a kind of optical element surface micro-nano level defect AFM rapid detection method based on convolutional neural network, it is related to micro-nano manufacturing technical field, to solve the problem of low detection efficiency of the existing detection method for the defect point with random profile distribution on the surface of optical element. Including the following steps: step one, obtain the full aperture image of optical element surface by optical microscope, extract the minimum circumscribed rectangle of target point according to image gradient, obtain the position, size and morphology information of surface defect point, and divide the size of target defect point;Step two, combined with the position information of defect point, different sampling ratios are used for different size target defect points by AFM, to obtain undersampling data and full sampling data;Step three, construct an image reconstruction model based on convolutional neural network;Step four, using the constructed image reconstruction model, reconstruct the undersampling data of target defect point collected by AFM, realize the AFM rapid detection of micro-nano level defect on the surface of optical element.
Need to check novelty before this filing date? Find Prior Art