CN119043557B - A PVA film stress detection method and system based on machine vision - Google Patents
A PVA film stress detection method and system based on machine vision Download PDFInfo
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- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
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Abstract
The invention relates to a PVA film stress detection method and system based on machine vision, and belongs to the technical field of film stress detection. The method comprises the steps of building a test platform, measuring the thickness of a sample through the thickness test assembly, testing the curvature of the substrate through the curvature test assembly, obtaining the thickness of the sample and the curvature variation of the substrate, and inputting the curvature variation of the substrate into a corrected Stoney formula to calculate stress based on heat treatment of a PVA film in the test. According to the invention, the testing platform for simultaneously testing the thickness of the film and the curvature of the substrate is built, so that the real-time performance and the high efficiency of detection are improved, the test images are collected and processed through machine vision, the physical damage to the sample possibly caused by traditional contact measurement is avoided, the accurate measurement of the thickness of the sample and the curvature of the substrate is realized, and the accuracy of stress calculation is improved.
Description
Technical Field
The invention belongs to the technical field of film stress detection, and particularly relates to a PVA film stress detection method and system based on machine vision.
Background
Polyvinyl alcohol (PVA) films are widely used in the fields of packaging, medical materials, optical applications, water treatment and the like due to water solubility, excellent mechanical properties and biodegradability. Thin film deposition is an unbalanced process in which the deposited atoms are not completely in equilibrium, meaning that the thin film is in a stressed state. In general, tensile stress may cause cracking of the film or limit the effective thickness of the film, and compressive stress may cause wrinkling, blistering, and peeling of the film. It follows that film stress is a significant cause of film failure.
Currently, in film stress testing, an X-ray method, a Raman spectroscopy method, a substrate curvature method and the like are commonly used. However, in practical measurement, the material characteristics of the film need to be considered to select a test method, damage may be caused to the film in the process of stress detection, the real-time performance of stress detection is poor, meanwhile, since the crystallinity of the PVA film under different heat treatment conditions is different, the crystallinity can directly influence the stress of the film, and influence factors such as the crystallinity cannot be associated in the test process, so that the reliability of the detection result is poor.
Therefore, it is needed to provide a method and a system for detecting the stress of a PVA film based on machine vision, which can realize non-contact, efficient and accurate stress detection of the film.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a PVA film stress detection method and system based on machine vision, which improves the real-time performance and high efficiency of detection by constructing a test platform for simultaneously testing the thickness of a film and the curvature of a substrate, and avoids physical damage to a sample possibly caused by traditional contact measurement by collecting test images and processing the images through the machine vision, thereby realizing accurate measurement of the thickness of the sample and the curvature variation of the substrate and improving the accuracy of stress calculation.
The aim of the invention can be achieved by the following technical scheme:
The first aspect of the present disclosure provides a PVA film stress detection method based on machine vision, comprising the steps of:
setting up a test platform, wherein the test platform comprises a curvature test assembly, a thickness test assembly and an objective table, a test sample is placed on the objective table, and the composition structures of the test assemblies are respectively arranged according to test requirements;
measuring the curvature of the substrate, namely measuring the thickness of the sample through a thickness testing assembly, testing the curvature of the substrate through a curvature testing assembly, and obtaining the thickness of the sample and the curvature variation of the substrate;
Stress calculation, namely correcting a Stoney formula based on heat treatment of the PVA film in the test, and inputting the curvature change of the tested substrate into the corrected Stoney formula to calculate stress;
The substrate curvature measurement comprises the following steps:
Measuring the thickness of a sample, namely measuring the thickness of the sample by a thickness testing component by adopting a transmission optical density method, starting the thickness testing component to respectively obtain incident gray level images and emergent gray level images before and after the sample is placed, and calculating the transmission optical density value of the test sample;
And testing the curvature of the sample, namely testing the curvature of the substrate by a curvature testing assembly by adopting a substrate curvature method, starting the curvature testing assembly to capture reflected light signals, collecting and recording the reflected light signals, obtaining the distance between adjacent light spots, and converting the distance between adjacent light spots into the curvature variation of the substrate.
Further, the curvature testing component comprises a laser, an etalon, a polarizer and a first camera, wherein the first camera is aligned with a reflecting area of the laser, and the focal length and the angle of the first camera are adjusted;
The thickness testing assembly comprises a scattering area light source, a milky glass diffusion plate and a second camera, the film to be tested is arranged between the second camera and the scattering area light source, and the film to be tested is formed on the glass substrate.
Further, the PVA film stress detection method further comprises the following steps:
Preparing a test sample, namely selecting a glass substrate, taking a certain amount of PVA solution on the clean glass substrate, uniformly coating the PVA solution on the surface of the glass substrate by controlling a low-speed spin coater, completely volatilizing water in the solution, and placing the formed film in a silica gel drier;
and selecting temperature and time conditions required by testing the PVA film by using a gradient heating furnace, and performing heat treatment on the PVA film to obtain a final test sample.
Further, the measuring the thickness of the sample comprises the following steps:
Determining extinction coefficients of PVA film by measuring thickness of different areas of film sample by contact measuring instrument, placing film sample with different thickness on test platform, collecting incident and emergent gray values before and after placing sample by CCD camera, calculating optical density value, and drawing curve slope according to measured and calculated thickness-optical density value point as extinction coefficient;
Calculating a transmitted optical density value by preprocessing incident and emergent gray images before and after placing a sample:
under the condition that a sample is not placed, capturing a distribution image of incident light intensity, placing a film sample to be detected between a light source and a camera, and capturing a light intensity distribution image after the film sample is transmitted;
Denoising the acquired image, equalizing the histogram to make the incident gray level image and the emergent gray level image have the same dynamic range, calculating the gray level value of each pixel in the incident image and the emergent image, and normalizing the gray level value;
And obtaining a sample thickness value, namely calculating the sample thickness according to the beer lambert law.
Further, the formula of the calculated optical density value D is as follows:
;
In the formula, The gray value of the film is not placed,Gray values for the placement film;
The calculation formula of the sample thickness d is as follows:
;
Wherein a is an extinction coefficient.
Further, the test sample curvature comprises the steps of:
collecting image data, namely firstly collecting a data image of a reflected light signal before depositing a PVA film, and then collecting the data image of the reflected light signal by using a CCD camera by adopting a prepared test sample;
image processing, namely detecting the light spots through image preprocessing to obtain the distance between adjacent light spots of the reflected light signals before and after the PVA film is deposited on the substrate;
Calculating the curvature change of the substrate, namely representing the distance of adjacent light spots by the distance of adjacent centroid, thereby obtaining the initial value and the change of the distance of the adjacent light spots, and calculating the curvature change of the substrate :
;
In the formula,Is an initial value of the distance between adjacent spots,Is the amount of change in the distance of the adjacent spots,Is the light source incident angle, L is the distance between the substrate and the camera.
Further, the image processing includes the steps of:
converting the image into a gray level image, smoothing the image by using a Gaussian filter, and enhancing the edge of the light spot area through morphological operation;
Selecting a Canny edge detection algorithm, calculating the gradient intensity and direction of each pixel of the image, and inhibiting the non-maximum value in the gradient image;
Traversing the obtained binarized graph to find out all independent white pixel connected areas, representing the areas as outlines, and calculating the space moment of each outline;
The calculated moment is utilized, the coordinate of the mass center is obtained through the ratio of the first moment to the zero moment, specifically, the x coordinate of the mass center is obtained through dividing the first moment of the profile in the x direction by the zero moment of the profile, and the y coordinate of the mass center is obtained through dividing the first moment of the profile in the y direction by the zero moment of the profile.
Further, the stress calculation includes the steps of:
The thermal expansion coefficient of the film is set as that of the substrate, and the thermal expansion coefficient of the substrate is set as that of the temperature change delta T, so that the thermal stress generated by the difference of the thermal expansion coefficients of the film and the substrate Expressed as:
;
In the formula, Is the Young's modulus of the PVA film,AndThe coefficients of thermal expansion of the PVA film and substrate respectively,In order to be the amount of change in temperature,Poisson ratio for PVA film;
inputting the acquired curvature change amount of the substrate into a corrected Stoney formula to calculate stress, wherein the corrected Stoney formula is expressed as follows:
+;
In the formula, As a result of the stress of the film,For the young's modulus of the substrate,For the thickness of the substrate,As the amount of change in the curvature of the substrate,Is the poisson's ratio of the substrate,Is the thickness of the PVA film.
A second aspect of the present disclosure provides a PVA film stress detection system based on machine vision, applied to a PVA film stress detection method based on machine vision as described above, comprising a sample preparation module, a sample testing module, a machine vision module, and a stress calculation module;
The sample preparation module is used for preparing a PVA film, and comprises spin coating, drying and heat treatment, so as to obtain the PVA film with the preset crystallinity;
Obtaining a PVA film for testing the preset crystallinity, measuring an XRD spectrum of the PVA film by adopting an X-ray powder diffractometer, and calculating the corresponding crystallinity by adopting an amorphous standard sample method so as to obtain the heat treatment temperature and time for obtaining the preset crystallinity;
the sample testing module is used for constructing a testing platform, respectively obtaining the transmission optical density value of the PVA film and the change amount of the substrate curvature, and realizing the measurement of the thickness of the PVA film and the change amount of the substrate curvature before and after the spin coating of the sample;
The test platform comprises a curvature test component, a thickness test component and an objective table, wherein the thickness test component calculates a transmission optical density value by acquiring incident and emergent gray level images before and after sample placement, and converts the optical density value into a sample thickness by adopting the Bill law, and the curvature test component calculates a curvature change amount of a substrate by acquiring adjacent light spot distances before and after sample placement.
As a preferable technical scheme of the invention, the machine vision module is used for collecting incident and emergent gray level images before and after PVA film preparation and reflected signal facula images before and after sample placement through a CCD camera and preprocessing the obtained image data;
Preprocessing an incident gray level image and an emergent gray level image, carrying out mean denoising on a plurality of images, and calculating the gray level value of each pixel in the images through histogram equalization;
Preprocessing a reflected signal light spot image, converting the image into a gray level image, smoothing the image by using a Gaussian filter, enhancing the edge of a light spot area through morphological operation, carrying out edge detection, extracting the light spot outline, and obtaining the distance between adjacent light spots;
the machine vision module is also used for calibrating a thickness measurement system before optical density measurement, and comprises the following steps:
The method comprises the steps of dark noise acquisition and analysis, namely testing under the condition of complete darkness, acquiring a plurality of dark noise images under the condition of complete darkness, carrying out average treatment on all acquired dark noise images to obtain a mean value image of dark noise, carrying out variance calculation on the dark noise images to obtain a variance graph of dark noise;
The method comprises the steps of calibrating incident light gray scale, namely preparing a plurality of standard light sources with known optical density, collecting a plurality of images for each standard light source, subtracting an obtained dark noise mean value image from each standard light source image to obtain a net light gray scale image, carrying out average treatment on the plurality of net light gray scale images of each standard light source to obtain an average light gray scale value, and establishing a calibration curve between the optical density and the gray scale value according to the known standard light source optical density value and the measured average light gray scale value;
correcting the nonlinear response of the system by utilizing an optical density-gray scale curve, and evaluating the influence of dark noise on measurement accuracy under the condition of small signals;
The stress calculation module is used for calculating stress by adopting a corrected Stoney formula through testing the thickness of the PVA film and the curvature change of the substrate, and establishing a change curve of the stress thickness product along with the thickness of the film to obtain a stress detection value related to the crystallinity and the thickness of the PVA film.
The beneficial effects of the invention are as follows:
According to the method, firstly, a test sample meeting the stress test requirement is prepared according to the characteristics of the PVA film, then a test platform for simultaneously carrying out film thickness test and substrate curvature test is built, the test image is acquired through machine vision and image processing is carried out, and the thickness of the sample and the curvature variation of the substrate are obtained, so that the film stress is calculated through Stoney formula, and in the test process, the film thickness measurement and the substrate curvature measurement are carried out simultaneously, so that the curvature and thickness measurement efficiency is improved, the data consistency is ensured during measurement under the same environmental condition, and the stress calculation accuracy is improved.
According to the invention, when a test sample is prepared, the PVA film is subjected to heat treatment to control the crystallinity of the film, so that the crystallinity of the PVA film is related to the film stress test, correspondingly, the thermal stress is generated through heat treatment due to the difference of the thermal expansion coefficients of the film and the substrate, the more real stress state is reflected by correcting Stoney formula by considering the difference of the thermal expansion coefficients, the accuracy of stress calculation is improved, meanwhile, a thickness measurement system is calibrated before optical density measurement, and each pixel is uniformly corrected and matched with a calibration value, so that the influence of dark noise on optical density measurement is effectively reduced, and the accuracy and reliability of the thickness measurement are improved.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a schematic diagram of steps of a method for detecting stress of a PVA film based on machine vision according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a test platform according to an embodiment of the present invention;
FIG. 3 is a schematic diagram showing steps for measuring curvature of a substrate according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a PVA film stress detecting system based on machine vision according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention for achieving the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects according to the invention with reference to the attached drawings and the preferred embodiment.
The embodiment provides a PVA film stress detection method based on machine vision, as shown in fig. 1, comprising the following steps:
S1, preparing a test sample, namely selecting a glass substrate, taking a certain amount of PVA solution on the clean glass substrate, uniformly coating the PVA solution on the surface of the glass substrate by controlling a low-speed spin coater, completely volatilizing water in the solution, and placing the formed film in a silica gel drier to prevent moisture absorption.
Since the film thickness measurement is performed later, the glass substrate in this example was selected as the substrate for the curvature test, and the curvature test and the thickness test by the transmission optical density method were satisfied at the same time. The glass substrate is selected with known thickness, young's modulus, poisson's ratio, etc.
It will be appreciated that the glass substrate is selected from optical grade glass, has a smooth surface and is free of impurities, and is cleaned with a suitable cleaning agent and a fiber-free cloth prior to coating, ensuring that the substrate is free of dust and dirt and is baked. In the process of coating the PVA solution on the glass substrate by using the low-speed spin coater, the solution can be uniformly distributed on the surface of the substrate by rotating the substrate, so that the thickness variation caused by uneven coating is reduced, and the drying process can be better controlled by controlling the rotating speed and time.
And selecting temperature and time conditions required by testing the PVA film by using a gradient heating furnace, and performing heat treatment on the PVA film to obtain a final test sample.
It can be understood that the crystallinity of the PVA film under different heat treatment conditions is different, and the higher the heat treatment temperature of the PVA film is, the higher the crystallinity thereof is, and in the test of film stress, in this embodiment, in order to ensure the effectiveness of the test and the practicality of practical application, the corresponding heat treatment conditions are selected according to the predetermined crystallinity of the PVA film, and the test sample is obtained.
S2, building a test platform, wherein the test platform comprises a curvature test assembly, a thickness test assembly and an objective table, as shown in FIG. 2, a test sample is placed on the objective table, and the composition structures of the test assemblies are respectively arranged according to test requirements:
The curvature test assembly comprises a laser 1, an etalon 2, a polarizer 4 and a first camera 3, wherein the first camera 3 is aligned with a reflection area of the laser 1 so as to capture a reflection light spot;
The thickness testing component comprises a scattering surface light source 8, a milky glass diffusion plate 7 and a second camera 9, the film 5 to be tested is arranged between the second camera 9 and the scattering surface light source 8, and the film 5 to be tested is formed on the glass substrate 6.
It can be understood that the first camera 3 and the second camera 9 are CCD (charge coupled device) cameras, in the curvature testing component, the laser 1 is used for emitting monochromatic light beams with strong directivity, the etalon 2 is responsible for dividing the incident light into parallel light beams for projection, the polarizer 4 is used for adjusting the polarization state of the laser light beams, reducing scattering and reflection interference of light and improving the accuracy of measurement, and the first camera 3 is used for capturing a spot image reflected from a substrate, converting an optical signal into an electrical signal and forming a digital image. In the thickness measuring assembly, a diffusion surface light source 8 is used for providing uniform and stable illumination, a milky glass diffusion plate 7 is used for further homogenizing light emitted from the light source, and a second camera 9 is used for capturing transmitted light passing through the film 5 to be measured, converting light signals into electric signals and then into digital images.
It should be noted that, in this embodiment, the built test platform performs measurement of film thickness and measurement of substrate curvature simultaneously, and is used for calculation of film stress detection, so that efficiency of curvature and thickness measurement is improved, consistency of data is ensured by measurement under the same environmental condition, and accuracy of stress calculation is improved.
S3, measuring the curvature of the substrate, namely measuring the thickness of the sample through a thickness testing assembly, and testing the curvature of the substrate through a curvature testing assembly, wherein the curvature testing assembly comprises the following steps of:
S31, measuring the thickness of the sample, namely measuring the thickness of the sample by a transmission optical density method through a thickness testing component, starting the thickness testing component to respectively obtain incident gray level images and emergent gray level images before and after placing the sample, calculating the transmission optical density value of the test sample, and then converting the optical density value into the thickness of the sample by using the Bill' S Law, wherein the method specifically comprises the following steps of:
S311, determining an extinction coefficient of the PVA film, namely measuring thicknesses of different areas of a series of film samples to be measured by adopting a contact type measuring instrument, placing the film samples with different thicknesses on a test platform, collecting incident gray values and emergent gray values before and after placing the samples by a camera, calculating an optical density value, and taking a slope of a curve drawn according to the measured and calculated thickness-optical density value points as the extinction coefficient.
It will be appreciated that the extinction coefficient is constant for a particular film, and therefore the slope of the curve plotted through the measured and calculated thickness-optical density value points may be approximated as the extinction coefficient.
S312, calculating a transmission optical density value by preprocessing incident and emergent gray level images before and after placing a sample, and calculating the optical density value:
under the condition that a sample is not placed, capturing a distribution image of incident light intensity, placing a film sample to be detected between a light source and a camera, and capturing a light intensity distribution image after the film sample is transmitted;
Denoising the acquired image, reducing the influence of random noise in the image on measurement, and enabling the incident gray level image and the emergent gray level image to have the same dynamic range through histogram equalization generally so as to improve the contrast ratio and detail expression of the image;
For each pixel in the incoming and outgoing images, its gray value is calculated. These gray values are typically expressed as integers between [0,255], and are normalized to eliminate the effects of light source non-uniformity and camera response non-uniformity;
The optical density value D is calculated as follows:
;
In the formula, The gray value of the film is not placed,To place the grey value of the film.
S313, obtaining a sample thickness value, wherein the calculation formula of the sample thickness d is as follows according to the Bellambert law:
;
Wherein a is an extinction coefficient.
It is understood that the extinction coefficient is a function of the film material and the illumination wavelength, and is a key parameter in establishing the relationship between optical density D and thickness D, and that different sample extinction coefficients are obtained experimentally.
S32, testing the curvature of the sample, namely testing the curvature of the substrate by a curvature testing assembly through a substrate curvature method, starting the curvature testing assembly to capture reflected light signals, collecting and recording the reflected light signals to obtain the distance between adjacent light spots, and converting the distance between adjacent light spots into the curvature variation of the substrate, wherein the method comprises the following steps:
It will be appreciated that the curvature testing component generates a set of two-dimensional arrays of parallel light beams incident at an angle to the surface of the sample, these beams typically being in the form of a uniformly distributed array ensuring coverage of multiple locations on the surface of the sample for a wide range of stress measurements. When a two-dimensional parallel beam is incident on the sample surface, the beam is reflected from the surface. The shape or curvature of the sample surface can cause the path of the reflected beam to change, and the reflected light signal is captured by the CCD camera, so that the position and intensity of the reflected light spot can be accurately recorded.
S321, collecting image data, namely firstly collecting a data image of a reflected light signal before depositing a PVA film, and then collecting the data image of the reflected light signal by using a CCD camera by adopting a prepared test sample;
S322, image processing, namely detecting the light spots through image preprocessing, and obtaining the distance between adjacent light spots of the reflected light signals before and after the PVA film is deposited on the substrate, wherein the method comprises the following steps:
The image is converted into a gray level image, so that the processing process is simplified, and the gray level image is an image which only retains brightness information after color information is removed, so that the image is suitable for subsequent edge detection work.
The image is smoothed using a gaussian filter to reduce the effect of noise on edge detection, and morphological operations (e.g., dilation, erosion) are used to enhance the edges of the spot area to ensure edge continuity.
And selecting a Canny edge detection algorithm, and calculating the gradient intensity and direction of each pixel of the image, wherein the gradient can reflect the change of the brightness of the pixel. Non-maxima in the gradient image are suppressed to refine the edges. Only the locally largest gradient pixels remain, the rest being suppressed to 0. The edges are separated into strong edges, weak edges and non-edges using dual threshold detection, and then all weak edges are connected to strong edges to form complete edges.
It will be appreciated that a binarized image is ultimately obtained by edge detection, where white pixels represent detected edges and black pixels represent non-edge regions.
The resulting binarized pattern is traversed to find all the individual white pixel connected regions and represent them as contours, for each contour its spatial moment is calculated. The spatial moment contains a series of statistical information about the contour shape, including zero order moment, first order moment, etc.
The calculated moment is utilized, the coordinate of the mass center is obtained through the ratio of the first moment to the zero moment, specifically, the x coordinate of the mass center is obtained through dividing the first moment of the profile in the x direction by the zero moment of the profile, and the y coordinate of the mass center is obtained through dividing the first moment of the profile in the y direction by the zero moment of the profile.
S323, calculating the curvature change of the substrate, namely representing the distance of the adjacent light spots by the distance of the adjacent centroid, thereby obtaining the initial value and the change of the distance of the adjacent light spots, and calculating the curvature change of the substrate:
;
In the formula,Is an initial value of the distance between adjacent spots,Is the amount of change in the distance of the adjacent spots,Is the light source incident angle, L is the distance between the substrate and the camera.
S4, stress calculation, namely correcting a Stoney formula based on heat treatment of the PVA film in the test, and inputting the curvature change quantity of the tested substrate into the corrected Stoney formula to calculate the stress, wherein the stress calculation comprises the following steps of:
s41, setting the thermal expansion coefficient of the film as that of the substrate and the thermal expansion coefficient of the temperature change delta T, and generating thermal stress by the difference of the thermal expansion coefficients of the film and the substrate Expressed as:
;
In the formula, Is the Young's modulus of the PVA film,AndThe coefficients of thermal expansion of the PVA film and substrate respectively,In order to be the amount of change in temperature,Poisson's ratio for PVA film.
S42, inputting the acquired curvature change amount of the substrate into a corrected Stoney formula to calculate stress, wherein the corrected Stoney formula is expressed as:
+;
In the formula, As a result of the stress of the film,For the young's modulus of the substrate,For the thickness of the substrate,As the amount of change in the curvature of the substrate,Is the poisson's ratio of the substrate,Is the thickness of the PVA film.
In the embodiment, the Stoney formula is corrected by calculating the thermal stress, so that the stress calculation is better adapted to the actual requirement of the thermal treatment, and the accuracy of the stress calculation is improved.
S5, establishing a stress change curve, namely establishing a change curve of a stress thickness product along with the thickness of the film according to the calculated stress and the thickness of the corresponding film, wherein the slope of a connecting line between any point and a starting point on the curve represents the average stress of the corresponding thickness of the point.
The embodiment also provides a PVA film stress detection system based on machine vision, which comprises a sample preparation module, a sample testing module, a machine vision module and a stress calculation module as shown in fig. 4;
the sample preparation module is used for preparing the PVA film and comprises spin coating, drying and heat treatment, so as to obtain the PVA film with the preset crystallinity.
And (3) controlling the actual crystallinity, obtaining a PVA film for testing the preset crystallinity, measuring an XRD spectrum of the PVA film by adopting an X-ray powder diffractometer, and calculating the corresponding crystallinity by adopting an amorphous standard sample method, thereby obtaining the heat treatment temperature and time for obtaining the preset crystallinity.
It should be noted that, as the semi-crystalline polymer, the crystallinity of the PVA film increases with increasing heat treatment temperature, and in this embodiment, the naturally dried PVA film is heat treated to test the film stress with different crystallinity, and the actual heat treatment temperature should be lower than 180 ℃ to prevent the PVA film from thermal degradation. By controlling the influence factors such as crystallinity and thickness of the PVA film, the test stress result is related to the influence factors, and the referenceability of the result is enhanced.
The sample testing module is used for building a testing platform, respectively obtaining the transmission optical density value of the PVA film and the change amount of the substrate curvature, and realizing the measurement of the thickness of the PVA film and the change amount of the substrate curvature before and after the spin coating of the sample.
The test platform comprises a curvature test component, a thickness test component and an objective table, wherein the thickness test component calculates a transmission optical density value by acquiring incident and emergent gray level images before and after sample placement, and converts the optical density value into a sample thickness by adopting the Bill law, and the curvature test component calculates a curvature change amount of a substrate by acquiring adjacent light spot distances before and after sample placement.
It should be noted that, the stress of the PVA film is greatly affected by environmental conditions (such as temperature and humidity), and when the film stress test is performed, the temperature and humidity of the test environment should be kept under standardized conditions to ensure the consistency of the environmental conditions.
The machine vision module is used for collecting incident gray level images and emergent gray level images before and after PVA film preparation and reflected signal light spot images before and after sample placement through a CCD camera, and preprocessing the obtained image data.
Preprocessing an incident gray level image and an emergent gray level image, carrying out mean denoising on a plurality of images, and calculating the gray level value of each pixel in the images through histogram equalization;
Preprocessing a reflected signal light spot image, converting the image into a gray level image, smoothing the image by using a Gaussian filter, enhancing the edge of a light spot area through morphological operation, carrying out edge detection, extracting the light spot outline, and obtaining the distance between adjacent light spots.
The machine vision module is also used for calibrating a thickness measurement system before optical density measurement, and comprises the following steps:
The method comprises the steps of testing under the condition of complete darkness, collecting a plurality of (usually tens to hundreds of) dark noise images, recording each image under the identical condition, carrying out average processing on all the collected dark noise images to obtain a mean image representing dark noise, and carrying out variance calculation on all the dark noise images to obtain a variance diagram of dark noise.
It will be appreciated that the variance diagram describes the variation of each pixel over multiple measurements for noise suppression in subsequent signal processing.
The gray scale of incident light is calibrated by preparing a plurality of standard light sources with known optical density, and the optical density of the light sources needs to cover a plurality of points in the measuring range so as to ensure the calibration accuracy. For each standard light source, a plurality of images are acquired, and the stability and uniformity of the light source are ensured during each acquisition. The previously obtained dark noise mean value map is subtracted from each standard light source image to obtain a net light gray scale image. And carrying out average processing on a plurality of net light gray images of each standard light source to obtain an average light gray value. And establishing a calibration curve between the optical density and the gray value according to the known standard light source optical density value and the measured average optical gray value.
And correcting the system response, namely correcting the nonlinear response of the system by utilizing an optical density-gray scale curve, so as to ensure that the gray scale value output during optical density measurement can accurately reflect the actual optical density. The effect of dark noise on the measurement accuracy in the case of small signals (low optical density) was evaluated.
It is understood that the thickness measurement system includes a thickness measurement assembly and a machine vision module for effecting acquisition of the thickness of the PVA film. Wherein the thickness measurement component comprises a process of thickness calculation. Through calibrating the thickness measurement system, the pixels are uniformly corrected and matched with the calibration value, so that the influence of dark noise on optical density measurement is effectively reduced, and the measurement precision and reliability are improved.
The stress calculation module is used for calculating stress by adopting a corrected Stoney formula through testing the thickness of the PVA film and the curvature change of the substrate, and establishing a change curve of the stress thickness product along with the thickness of the film to obtain a stress detection value related to the crystallinity and the thickness of the PVA film.
It should be noted that the influence of the film thickness on the film stress has a general rule, and as the film thickness increases, the film stress also changes from a compressive stress to a tensile stress, and only when the film thickness reaches a certain critical thickness, the film stress is generated, so the film thickness is an essential influencing factor in stress analysis. In contrast, for PVA films, the crystallization characteristics are related to heat treatment, and the crystallinity has a significant effect on the mechanical properties, internal stress and stress distribution of the PVA film, so that in the stress test, the stress test results of films with different degrees of crystallinity are different, and thus, in the process of preparing a sample, the conditions of the heat treatment need to be emphasized.
According to the method, firstly, a test sample meeting the stress test requirement is prepared according to the characteristics of the PVA film, then a test platform for simultaneously carrying out film thickness test and substrate curvature test is built, the test image is acquired through machine vision and image processing is carried out, and the thickness of the sample and the curvature variation of the substrate are obtained, so that the film stress is calculated through Stoney formula, and in the test process, the film thickness measurement and the substrate curvature measurement are carried out simultaneously, so that the curvature and thickness measurement efficiency is improved, the data consistency is ensured during measurement under the same environmental condition, and the stress calculation accuracy is improved.
According to the invention, when a test sample is prepared, the PVA film is subjected to heat treatment to control the crystallinity of the film, so that the crystallinity of the PVA film is related to the film stress test, correspondingly, the thermal stress is generated through heat treatment due to the difference of the thermal expansion coefficients of the film and the substrate, the more real stress state is reflected by correcting Stoney formula by considering the difference of the thermal expansion coefficients, the accuracy of stress calculation is improved, meanwhile, a thickness measurement system is calibrated before optical density measurement, and each pixel is uniformly corrected and matched with a calibration value, so that the influence of dark noise on optical density measurement is effectively reduced, and the accuracy and reliability of the thickness measurement are improved.
The present invention is not limited in any way by the above-described preferred embodiments, but is not limited to the above-described preferred embodiments, and any person skilled in the art will appreciate that the present invention can be embodied in the form of a program for carrying out the method of the present invention, while the above disclosure is directed to equivalent embodiments capable of being modified or altered in some ways, it is apparent that any modifications, equivalent variations and alterations made to the above embodiments according to the technical principles of the present invention fall within the scope of the present invention.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101515558A (en) * | 2006-03-30 | 2009-08-26 | 西安电子科技大学 | Method for on-line detection of film growth rate and stress |
| CN107219030A (en) * | 2016-03-21 | 2017-09-29 | 中国科学院深圳先进技术研究院 | Membrane stress tester and its method of testing |
| CN107687815A (en) * | 2017-07-31 | 2018-02-13 | 深港产学研基地 | Light transmission film method for measuring thickness, system and terminal device |
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| CN1808056B (en) * | 2001-09-21 | 2011-09-14 | Kmac株式会社 | Device and method for measuring film characteristics by using two-dimensional detector |
| CN112986320A (en) * | 2021-02-07 | 2021-06-18 | 复旦大学 | Method for measuring thermal expansion coefficient of film |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101515558A (en) * | 2006-03-30 | 2009-08-26 | 西安电子科技大学 | Method for on-line detection of film growth rate and stress |
| CN107219030A (en) * | 2016-03-21 | 2017-09-29 | 中国科学院深圳先进技术研究院 | Membrane stress tester and its method of testing |
| CN107687815A (en) * | 2017-07-31 | 2018-02-13 | 深港产学研基地 | Light transmission film method for measuring thickness, system and terminal device |
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