CN-122023133-A - CDSEM image processing method, device, medium, terminal and program product

CN122023133ACN 122023133 ACN122023133 ACN 122023133ACN-122023133-A

Abstract

The application provides a CDSEM image processing method, a device, a medium, a terminal and a program product, wherein the method comprises the steps of obtaining a target image generated by measuring a CDSEM machine, and selecting the target image as a guide image of a guide filter; according to the guiding image, guiding filter with preset local window size is adopted to conduct guiding filtering processing on the target image to obtain a first image, image enhancement processing is conducted on the first image based on contrast limited self-adaptive histogram equalization technology to obtain a second image, structural similarity index of the second image and the target image is calculated, if the structural similarity index of the second image and the target image is larger than or equal to a preset threshold value, the second image is output to a display window of the CDSEM machine, and if the structural similarity index of the second image and the target image is smaller than the preset threshold value, the image enhancement processing step is returned. The method can efficiently and rapidly remove noise on the CDSEM image and enhance the definition of the edge profile.

Inventors

LI XIANG
ZHANG HUIMIN
ZHANG JIAJIAN
LIU CHANG

Assignees

芯恩（青岛）集成电路有限公司

Dates

Publication Date: 20260512
Application Date: 20260212

Claims (10)

1. A CDSEM image processing method, comprising: obtaining a target image generated by CDSEM machine measurement, and selecting the target image as a guide image of a guide filter; According to the guide image, a guide filter with a preset local window size is adopted to conduct guide filtering processing on the target image so as to obtain a first image; performing image enhancement processing on the first image based on a contrast limited adaptive histogram equalization technology to obtain a second image; Calculating the structural similarity index of the second image and the target image, outputting the second image to a display window of a CDSEM machine if the structural similarity index of the second image and the target image is larger than or equal to a preset threshold value, and returning to the image enhancement processing step if the structural similarity index of the second image and the target image is smaller than the preset threshold value.
2. The CDSEM image processing method according to claim 1, wherein the performing guided filtering processing on the target image according to the guided image using a guided filter with a preset local window size to obtain a first image includes: calculating the mean value and variance of the gray values corresponding to each pixel in the local window of the guide image; Performing fast Fourier transform on the image in the local window based on the constructed local spectrum parameter calculation formula to obtain local spectrum parameters; according to the calculated variance, the local spectrum parameter and the local contrast parameter, calculating to obtain a weighting coefficient based on a constructed weighting coefficient calculation formula; according to the calculated mean value, variance and weighting coefficient, respectively calculating to obtain a first linear coefficient and a second linear coefficient based on a constructed first linear coefficient calculation formula and a second linear coefficient calculation formula; and calculating and outputting the first image according to the calculated first linear coefficient, the second linear coefficient and the guiding image.
3. The CDSEM image processing method according to claim 2, wherein the first and second linear coefficient calculation formulas are respectively: ; ; Wherein, the Is a first linear coefficient; is a weighting coefficient; is the first The variance of the corresponding gray value of each pixel in the local window with each pixel as the center; Is a smoothing coefficient; Is a second linear coefficient; is the first Each pixel in the local window with the pixel as the center corresponds to the average value of the gray value.
4. The CDSEM image processing method according to claim 3, wherein the weight coefficient calculation formula is: ); Wherein, the Is a weighting coefficient; is the first The variance of the corresponding gray value of each pixel in the local window with each pixel as the center; is the first A total number of pixels within the local window centered at the individual pixel; to guide pixels on the image; For adjusting the coefficients; Is a local contrast parameter; is a local spectral parameter.
5. The CDSEM image processing method according to claim 4, wherein the local spectral parameter calculation formula and the local contrast parameter calculation formula are respectively: ; ; Wherein, the Is a local spectral parameter; is the first Each pixel is as a center and the size is as a size Is a partial window of (2); Performing fast fourier transform on the image in the local window; Is a local contrast parameter; to guide the first in the image Gray values of the individual pixels; is a positive integer and is 1 or more.
6. The method according to claim 1, further comprising calculating a structural similarity index of the first image and the target image, performing an image enhancement processing step on the first image if the structural similarity index of the first image and the target image is equal to or greater than a preset value, and returning to the guide filtering processing step if the structural similarity index of the first image and the target image is less than the preset value.
7. A CDSEM image processing apparatus, comprising: the acquisition module is used for acquiring a target image generated by CDSEM machine measurement and selecting the target image as a guide image of the guide filter; the filtering processing module is used for conducting guide filtering processing on the target image by adopting a guide filter with a preset local window size according to the guide image so as to obtain a first image; the image enhancement module is used for carrying out image enhancement processing on the first image based on a contrast limited self-adaptive histogram equalization technology so as to obtain a second image; The output judging module is used for calculating the structural similarity index of the second image and the target image, outputting the second image to a display window of the CDSEM machine if the structural similarity index of the second image and the target image is larger than or equal to a preset threshold value, and returning to the image enhancement processing step if the structural similarity index of the second image and the target image is smaller than the preset threshold value.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method according to any one of claims 1 to 6.
9. A computer program product comprising computer program code means for causing a computer to carry out the method as claimed in any one of claims 1 to 6 when said computer program code means are run on the computer.
10. An electronic terminal comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to implement the method according to any of claims 1 to 6.

Description

CDSEM image processing method, device, medium, terminal and program product Technical Field The present application relates to the field of semiconductor technologies, and in particular, to a CDSEM image processing method, apparatus, medium, terminal, and program product. Background In the production of semiconductor manufacturing facilities, critical dimensions (Critical Dimension, CD) are critical to the quality of the product. CD is measured mainly by a CD scanning electron microscope (Critical Dimension Scanning Electron Microscope, CDSEM) machine, which becomes the main machine for CD measurement with high accuracy and rapidity of measurement. The measurement principle of CDSEM is to scan the sample surface with a focused high-energy primary electron beam, excite the sample to generate secondary electrons (SE, secondary Electrons) and backscattered electrons (BSE, backscattered Electrons), and capture these electron signals, which are converted into a gray scale image reflecting the morphology and composition information of the sample surface. The secondary electrons mainly come from the ultra-shallow layer (usually less than 10 nanometers) of the sample surface, and the yield efficiency of the secondary electrons is related to the angle of the sample surface. However, in the process of long-term measurement using the CDSEM machine, interference measurement is caused by various reasons such as electron gun discharge, machine charging, wafer (Wafer) charging in the back-end process, etc., which results in blurred edge profile and more noise in the final image. Because these charged effects cannot be easily and fundamentally eliminated at low cost, the Current industry mainly adopts the following solutions that (1) periodically check and monitor whether the machine deceleration (RETARDING) voltage and the built-in probe (i-probe) signal are stable, whether the vacuum level of the primary vacuum (IP 1 vacuum) of the electron gun is fluctuated, and whether the Current of the extraction electrode (TIPS Current) is stable, so as to monitor and maintain the hardware state to maintain the best working state of the machine, and (2) reset the reasonable Amplifier (AMP) parameters in the measurement program (Recipe) according to the standard working program (Standard Operating Procedure, SOP) and the machine measurement characteristics, so as to perform secondary or even multiple measurements. However, whether monitoring and maintaining the hardware state or resetting parameters for multiple measurements, noise and blurred edge contours on the image cannot be efficiently and quickly processed. Accordingly, there is a need to provide a CDSEM image processing method, apparatus, medium, terminal, and program product, which solve the above-mentioned problems in the prior art. Disclosure of Invention In view of the above-mentioned drawbacks of the prior art, an object of the present application is to provide a CDSEM image processing method, apparatus, medium, terminal and program product, which are used for solving the technical problem that the prior art cannot efficiently and rapidly process noise and blurred edge contours on CDSEM images. In order to achieve the above and other related objects, a first aspect of the present application provides a CDSEM image processing method, which includes obtaining a target image generated by measurement of a CDSEM machine, selecting the target image as a guide image of a guide filter, performing guide filtering processing on the target image by using the guide filter with a preset local window size according to the guide image to obtain a first image, performing image enhancement processing on the first image based on a contrast-limited adaptive histogram equalization technology to obtain a second image, calculating a structural similarity index of the second image and the target image, outputting the second image to a display window of the CDSEM machine if the structural similarity index of the second image and the target image is greater than or equal to a preset threshold, and returning to an image enhancement processing step if the structural similarity index of the second image and the target image is less than the preset threshold. In some embodiments of the first aspect of the present application, a pilot filter with a preset local window size is used to perform pilot filtering processing on the target image to obtain a first image, and the specific process includes calculating a mean value and a variance of gray values corresponding to each pixel in a local window of the pilot image, performing fast fourier transform on the image in the local window based on a constructed local spectral parameter calculation formula to obtain local spectral parameters, calculating a local contrast parameter in the local window based on a constructed local contrast parameter calculation formula according to the calculated mean value, calculating a weighting coefficient based on a constru