A method for inspecting particles inside an apparatus based on a violet light

Abstract

The present application relates to the field of particulate matter detection, and more particularly to a method for checking particles inside an equipment based on a violet light lamp, comprising: dividing a sub-target area; performing fixed irradiation of the violet light lamp and obtaining a first image of the sub-target area; obtaining a second image of a second sub-target area and a third image of a third sub-target area; marking a particle contour area after image preprocessing and obtaining a particle contour area ratio; obtaining a particle contour pixel fluctuation characteristic value to determine the eligibility of violet light detection; in response to a determination that the violet light detection is unqualified, checking the eligibility of the violet light detection, or, correcting the preset intensity; when the determination is qualified, performing a blowing test on a fourth sub-target area and obtaining a fourth image of the sub-target area; determining a corresponding cleaning strategy, including airflow dust suction, cleaning liquid flushing, and physical wiping. The present application improves the accuracy of particulate matter detection inside a silicon polishing wafer equipment based on a violet light lamp and image recognition.

Description

Technical Field

[0001] This invention relates to the field of particulate matter detection, and more particularly to a method for inspecting particles inside an equipment using ultraviolet light. Background Technology

[0002] For 8-inch and 12-inch silicon wafers, the cleanliness of the equipment interior is a crucial indicator. The presence of particles inside the equipment can lead to poor wafer flatness. Currently, particle detectors and monitoring chips are primarily used for particle detection and monitoring within semiconductor processing equipment.

[0003] Existing technologies for particle detection inside equipment have several problems and limitations. First, some tiny particles are difficult to identify and remove under fluorescent or daylight lamp illumination, making it impossible to determine their location and quantity, thus hindering effective improvement in the cleanliness of the equipment. Second, existing technologies have limited adaptability to particle types and sizes, failing to comprehensively meet the detection needs of particles of different sizes. Particle analyzers are primarily used to detect the quantity of particles of a certain size, especially those suspended in the air. For example, tiny particles attached to specific locations on the equipment, or crystals that may fall off, cannot be detected. Furthermore, even if a specific quantity is detected, its exact location cannot be determined, making cleaning work difficult. Monitoring sheet testing can only detect suspended particles; particles attached to the equipment cannot be detected or accurately removed, and its accuracy in detecting mold is low. Summary of the Invention

[0004] Therefore, the present invention provides a method for inspecting particles inside a device using ultraviolet light, in order to overcome the problem of low accuracy in detecting organic or inorganic microparticles inside silicon polishing wafer equipment in the prior art.

[0005] To achieve the above objectives, the present invention provides a method for inspecting particles inside an equipment using a UV lamp, comprising:

[0006] Step S1: Select any continuous planar region within the device and divide the region into several sub-target regions;

[0007] Step S2: Irradiate the first sub-target area with a fixed ultraviolet light using preset ultraviolet light detection parameters and acquire the first image of the sub-target area. The preset detection parameters include preset intensity, preset wavelength and preset angle.

[0008] Step S3: After increasing only the preset intensity and the preset wavelength, the second image of the second sub-target region and the third image of the third sub-target region are obtained sequentially.

[0009] Step S4: After performing image preprocessing on the first image, the second image, and the third image, mark the particle contour region and calculate the proportion of the particle contour region. The image preprocessing includes grayscale processing, contrast unification, and brightness unification.

[0010] Step S5: Obtain the pixel fluctuation feature value of the particle outline to determine the pass rate of the violet light detection;

[0011] Step S6: In response to the determination that the violet light detection is unqualified, the qualification of the violet light detection is verified based on the average brightness difference between the third image and the first image before processing, or the preset intensity is corrected based on the fluctuation difference.

[0012] Step S7: In response to the determination that the ultraviolet light detection is qualified, a blowing test is performed on the fourth sub-target area to obtain the fourth image of the sub-target area and the image preprocessing of the image is completed.

[0013] Step S8: Based on the preprocessed first image and the fourth image, obtain particle adhesion feature values ​​and determine the corresponding cleaning strategy according to the particle adhesion feature values ​​and the average brightness difference between the third image and the first image before processing, including airflow dust suction, cleaning fluid rinsing and physical wiping.

[0014] Further, in step S4, marking the particle contour region includes:

[0015] Step S401: Obtain the average brightness value of the first image pixels;

[0016] Step S402: Mark the pixels with a brightness deviation exceeding 5%;

[0017] Step S403: The image constructed from all the marked pixels is recorded as the particle outline region;

[0018] Step S404: Marking of the particle contour regions of the second image and the third image is completed sequentially according to steps S401 to S403.

[0019] Further, in step S4, the particle contour region proportion is the ratio of the area of ​​the particle contour region to the area of ​​a single sub-target region, and the particle contour pixel fluctuation feature value is the variance of the particle contour region proportion from the first image to the third image.

[0020] Furthermore, in step S6, if the particle outline pixel fluctuation feature value is greater than the first preset fluctuation feature value, the violet light detection is determined to be unqualified.

[0021] Further, in step S6, the passability of the violet light detection is verified based on the average brightness difference between the third image and the first image before processing, provided that the particle outline pixel fluctuation feature value is greater than the first preset fluctuation feature value and less than or equal to the second preset fluctuation feature value; and the preset intensity is corrected based on the fluctuation difference, provided that the particle outline pixel fluctuation feature value is greater than the second preset fluctuation feature value.

[0022] Furthermore, the increase in the preset intensity correction in the preset violet light detection parameters is positively correlated with the fluctuation difference, which is the difference between the particle outline pixel fluctuation feature value and the second preset fluctuation feature value.

[0023] Furthermore, the passability of the violet light detection is verified based on the average brightness difference between the first image and the third image before processing, wherein,

[0024] If the average brightness difference is less than the verification threshold, then the violet light detection is qualified.

[0025] If the average brightness difference is greater than or equal to the verification threshold, the violet light detection is deemed unqualified, and the preset angle is increased according to the ratio between the average brightness difference and the verification threshold, with the increase in the preset angle being proportional to the ratio between the average brightness difference and the verification threshold.

[0026] Furthermore, in step S7, the violet light detection is deemed qualified if the particle contour pixel fluctuation feature value is less than or equal to the first preset fluctuation feature value.

[0027] Furthermore, in step S7, the blowing test involves blowing air at a preset wind speed along a preset blowing angle onto the fourth sub-target area for a preset duration.

[0028] Furthermore, the determination corresponding to the cleaning strategy includes:

[0029] Under the condition that the particle adhesion characteristic value is greater than a preset adhesion threshold and the average brightness difference is less than a cleaning threshold, the corresponding cleaning strategy is determined to be airflow dust suction.

[0030] Under the condition that the particle adhesion characteristic value is less than a preset adhesion threshold and the average brightness difference is less than a cleaning threshold, the corresponding cleaning strategy is determined to be cleaning solution rinsing.

[0031] If the average brightness difference is greater than or equal to the cleaning threshold, the corresponding cleaning strategy is directly determined to be physical wiping.

[0032] The particle adhesion feature value is the difference in the proportion of the particle outline region between the first image and the fourth image.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention uses ultraviolet light irradiation, which can more accurately identify the location of tiny particles, thereby initially improving the accuracy of particle detection.

[0034] Furthermore, this invention uses image recognition and self-learning to more accurately determine the detection parameters and conditions of ultraviolet lamp irradiation, thereby accurately assessing the particulate matter pollution status inside the equipment.

[0035] Furthermore, this invention obtains the pixel fluctuation feature value of the particle outline to determine the pass rate of the ultraviolet light detection, thereby accurately characterizing the stability of the particles detected by the ultraviolet light lamp. At the same time, it determines the corresponding optimized intensity and irradiation angle when the ultraviolet light detection fails.

[0036] Furthermore, the present invention also accurately determines the type of particles inside the silicon polishing equipment, including organic or inorganic particles, through ultraviolet light detection and image recognition, thereby automatically matching a precise cleaning strategy accordingly. Attached Figure Description

[0037] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0038] Figure 1 This invention provides a method for inspecting internal particles of an equipment using a UV lamp.

[0039] Figure 2 This is a flowchart illustrating how to obtain pixel fluctuation feature values ​​of particle outlines to determine the passability of violet light detection in an embodiment of the present invention.

[0040] Figure 3 This is a schematic diagram illustrating the acquisition of a first image of a sub-target region according to an embodiment of the present invention;

[0041] In the image: 1. Ultraviolet light; 2. Sub-target area; 3. Image acquisition device. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] Please see Figures 1 to 3These are, respectively, a method for inspecting internal particles of an equipment based on ultraviolet light according to an embodiment of the present invention; a flowchart for obtaining particle contour pixel fluctuation feature values ​​to determine the passability of ultraviolet light detection according to an embodiment of the present invention; and a schematic diagram for obtaining a first image of a sub-target region according to an embodiment of the present invention.

[0044] An embodiment of the present invention provides a method for inspecting internal particles of an instrument using a UV lamp, comprising:

[0045] Step S1: Select any continuous planar region within the device and divide the region into several sub-target regions;

[0046] Step S2: Irradiate the first sub-target area with a fixed ultraviolet light using preset ultraviolet light detection parameters and acquire the first image of the sub-target area. The preset detection parameters include preset intensity, preset wavelength and preset angle.

[0047] Step S3: After increasing only the preset intensity and the preset wavelength, the second image of the second sub-target region and the third image of the third sub-target region are obtained sequentially.

[0048] Step S4: After performing image preprocessing on the first image, the second image, and the third image, mark the particle contour region and calculate the proportion of the particle contour region. The image preprocessing includes grayscale processing, contrast unification, and brightness unification.

[0049] Step S5: Obtain the pixel fluctuation feature value of the particle outline to determine the pass rate of the violet light detection;

[0050] Step S6: In response to the determination that the violet light detection is unqualified, the qualification of the violet light detection is verified based on the average brightness difference between the third image and the first image before processing, or the preset intensity is corrected based on the fluctuation difference.

[0051] Step S7: In response to the determination that the ultraviolet light detection is qualified, a blowing test is performed on the fourth sub-target area to obtain the fourth image of the sub-target area and the image preprocessing of the image is completed.

[0052] Step S8: Based on the preprocessed first image and the fourth image, obtain particle adhesion feature values ​​and determine the corresponding cleaning strategy according to the particle adhesion feature values ​​and the average brightness difference between the third image and the first image before processing, including airflow dust suction, cleaning fluid rinsing and physical wiping.

[0053] Specifically, the preset detection parameters are set as follows: including a preset intensity of 850 μW / cm. 2 The preset wavelength is 254nm and the preset angle θ is 45°. For the arrangement of the UV lamp 1, sub-target area 2, and image acquisition device 3, please refer to [reference needed]. Figure 3 Image acquisition devices, such as industrial cameras, are not specifically limited.

[0054] Specifically, in step S3, only the preset intensity and the preset wavelength are increased, and the magnitude of the increase is not limited. In this embodiment, the increased preset intensity is 920 μW / cm. 2 The preset wavelength after the increase is 300nm.

[0055] Specifically, in step S4, marking the particle contour region includes:

[0056] Step S401: Obtain the average brightness value of the first image pixels;

[0057] Step S402: Mark the pixels with a brightness deviation exceeding 5%;

[0058] Step S403: The image constructed from all the marked pixels is recorded as the particle outline region;

[0059] Step S404: Marking of the particle contour regions of the second image and the third image is completed sequentially according to steps S401 to S403.

[0060] Specifically, in step S4, the particle contour region proportion is the ratio of the area of ​​the particle contour region to the area of ​​a single sub-target region, and the particle contour pixel fluctuation feature value is the variance of the particle contour region proportion from the first image to the third image.

[0061] Specifically, in step S5, the particle outline pixel fluctuation feature value is obtained to determine the qualification of the violet light detection. The violet light detection is determined to be unqualified if the particle outline pixel fluctuation feature value is greater than a first preset fluctuation feature value, and the violet light detection is determined to be qualified if the particle outline pixel fluctuation feature value is less than or equal to the first preset fluctuation feature value. The first preset fluctuation feature value is set to 1.235×10-7.

[0062] Specifically, in step S6, the passability of the violet light detection is verified based on the average brightness difference between the third image and the first image before processing, under the condition that the particle outline pixel fluctuation feature value is greater than the first preset fluctuation feature value and less than or equal to the second preset fluctuation feature value, and the preset intensity is corrected based on the fluctuation difference under the condition that the particle outline pixel fluctuation feature value is greater than the second preset fluctuation feature value, wherein the second preset fluctuation feature value is set to 5.538×10-7.

[0063] Specifically, the increase in the correction of the preset intensity in the preset violet light detection parameters is positively correlated with the fluctuation difference. The fluctuation difference is the difference between the fluctuation feature value of the particle outline pixel and the second preset fluctuation feature value. It can be understood that the positive correlation can be linear or nonlinear. There is no specific limitation. It is only necessary to satisfy that the larger the fluctuation difference, the larger the increase in the correction of the preset intensity.

[0064] Specifically, the pass / fail status of the violet light detection is verified based on the average brightness difference between the first image and the third image before processing.

[0065] If the average brightness difference is less than the verification threshold of 2.52, then the violet light detection is qualified.

[0066] If the average brightness difference is greater than or equal to the verification threshold, the ultraviolet light detection is deemed unqualified. The preset angle is then increased according to the ratio between the average brightness difference and the verification threshold. The increase in the preset angle is proportional to the ratio between the average brightness difference and the verification threshold. It can be understood that the larger the ratio between the average brightness difference and the verification threshold, the larger the increase in the preset angle. The specific percentage increase is not limited and can be set according to the actual usage.

[0067] Specifically, in step S7, the blowing test involves blowing air onto the fourth sub-target area for a preset duration at a preset wind speed and a preset blowing angle. In this embodiment, the preset wind speed is set to 6.5 m / s, the preset blowing angle is set to 10°, and the preset duration is set to 5 s.

[0068] Specifically, the determination corresponding to the cleaning strategy includes:

[0069] Under the condition that the particle adhesion characteristic value is greater than a preset adhesion threshold and the average brightness difference is less than a cleaning threshold, the corresponding cleaning strategy is determined to be airflow dust suction.

[0070] Under the condition that the particle adhesion characteristic value is less than a preset adhesion threshold and the average brightness difference is less than a cleaning threshold, the corresponding cleaning strategy is determined to be cleaning solution rinsing.

[0071] If the average brightness difference is greater than or equal to the cleaning threshold, the corresponding cleaning strategy is directly determined to be physical wiping.

[0072] The particle adhesion feature value is the difference between the proportion of the particle outline region in the first image and the fourth image.

[0073] In this embodiment, the preset adhesion threshold is set to 75.00% and the cleaning threshold is set to 5.52. This setting can effectively define the trend of inorganic particles and organic particles. When inorganic particles are dominant, airflow dust suction is used; when organic and inorganic particles are mixed, cleaning solution is used for rinsing; and when organic particles are dominant, physical wiping is used. The above-mentioned preset adhesion threshold and cleaning threshold can also be set according to the actual usage.

[0074] The scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to encompass all variations falling within the meaning and scope of equivalents of the claims. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device in software or hardware. Terms such as "first," "second," etc., are used only for distinguishing descriptions and do not indicate any particular order, nor should they be construed as indicating or implying relative importance.

[0075] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A method for inspecting particles inside an equipment using ultraviolet light, characterized in that, include: Step S1: Select any continuous planar region within the device and divide the region into several sub-target regions; Step S2: Irradiate the first sub-target area with a fixed ultraviolet light using preset ultraviolet light detection parameters and acquire the first image of the sub-target area. The preset detection parameters include preset intensity, preset wavelength and preset angle. Step S3: After increasing only the preset intensity and the preset wavelength, the second image of the second sub-target region and the third image of the third sub-target region are obtained sequentially. Step S4: After performing image preprocessing on the first image, the second image, and the third image, mark the particle contour region and calculate the proportion of the particle contour region. The image preprocessing includes grayscale processing, contrast unification, and brightness unification. In step S4, marking the particle contour region includes: Step S401: Obtain the average brightness value of the first image pixels; Step S402: Mark the pixels with a brightness deviation exceeding 5%; Step S403: The image constructed from all the marked pixels is recorded as the particle contour region; Step S404: Mark the particle outline regions of the second image and the third image sequentially according to steps S401 to S403; The particle outline region proportion is the ratio of the area of ​​the particle outline region to the area of ​​a single sub-target region, and the particle outline pixel fluctuation feature value is the variance of the particle outline region proportion from the first image to the third image. Step S5: Obtain the pixel fluctuation feature value of the particle outline to determine the pass rate of the violet light detection; Step S6: In response to the determination that the violet light detection is unqualified, the qualification of the violet light detection is verified based on the average brightness difference between the third image and the first image before processing, or the preset intensity is corrected based on the fluctuation difference. In step S6, the passability of the violet light detection is verified based on the average brightness difference between the third image and the first image before processing, under the condition that the particle outline pixel fluctuation feature value is greater than the first preset fluctuation feature value and less than or equal to the second preset fluctuation feature value; and the preset intensity is corrected based on the fluctuation difference, under the condition that the particle outline pixel fluctuation feature value is greater than the second preset feature value. Step S7: In response to the determination that the ultraviolet light detection is qualified, a blowing test is performed on the fourth sub-target area to obtain the fourth image of the sub-target area and the image preprocessing of the image is completed. Step S8: Based on the preprocessed first image and the fourth image, obtain particle adhesion feature values ​​and determine the corresponding cleaning strategy according to the particle adhesion feature values ​​and the average brightness difference between the third image and the first image before processing, including airflow dust suction, cleaning fluid rinsing and physical wiping.

2. The method for inspecting internal particles of an equipment using a UV lamp according to claim 1, characterized in that, In step S6, if the particle outline pixel fluctuation feature value is greater than the first preset fluctuation feature value, the violet light detection is determined to be unqualified.

3. The method for inspecting internal particles of an equipment using a UV lamp according to claim 1, characterized in that, The increase in the preset intensity correction in the preset violet light detection parameters is positively correlated with the fluctuation difference, which is the difference between the particle outline pixel fluctuation feature value and the second preset fluctuation feature value.

4. The method for inspecting internal particles of an equipment using a UV lamp according to claim 3, characterized in that, The pass rate of the violet light detection is verified based on the average brightness difference between the first image and the third image before processing. If the average brightness difference is less than the verification threshold, then the violet light detection is qualified. If the average brightness difference is greater than or equal to the verification threshold, the violet light detection is deemed unqualified, and the preset angle is increased according to the ratio between the average brightness difference and the verification threshold, with the increase in the preset angle being proportional to the ratio between the average brightness difference and the verification threshold.

5. The method for inspecting internal particles of an equipment using a UV lamp according to claim 1, characterized in that, In step S7, the violet light detection is deemed qualified if the particle contour pixel fluctuation feature value is less than or equal to the first preset fluctuation feature value.

6. The method for inspecting internal particles of an equipment using a UV lamp according to claim 5, characterized in that, In step S7, the blowing test involves blowing air at a preset wind speed and along a preset blowing angle onto the fourth sub-target area for a preset duration.

7. The method for inspecting internal particles of an equipment using a UV lamp according to claim 1, characterized in that, The determination corresponding to the cleaning strategy includes: Under the condition that the particle adhesion characteristic value is greater than a preset adhesion threshold and the average brightness difference is less than a cleaning threshold, the corresponding cleaning strategy is determined to be airflow dust suction. Under the condition that the particle adhesion characteristic value is less than a preset adhesion threshold and the average brightness difference is less than a cleaning threshold, the corresponding cleaning strategy is determined to be cleaning solution rinsing. If the average brightness difference is greater than or equal to the cleaning threshold, the corresponding cleaning strategy is directly determined to be physical wiping. The particle adhesion feature value is the difference in the proportion of the particle outline region between the first image and the fourth image.

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