WO2019037490A1 - 彩膜基板缺陷的分析方法、检测修复方法及装置 - Google Patents

彩膜基板缺陷的分析方法、检测修复方法及装置 Download PDF

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Publication number
WO2019037490A1
WO2019037490A1 PCT/CN2018/087864 CN2018087864W WO2019037490A1 WO 2019037490 A1 WO2019037490 A1 WO 2019037490A1 CN 2018087864 W CN2018087864 W CN 2018087864W WO 2019037490 A1 WO2019037490 A1 WO 2019037490A1
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Prior art keywords
defect
detected
defects
repair
size
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English (en)
French (fr)
Inventor
李鹏
李坚
李志宾
冯孟
孙瑞雪
丁秀娟
赵帅
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BOE Technology Group Co Ltd
Ordos Yuansheng Optoelectronics Co Ltd
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BOE Technology Group Co Ltd
Ordos Yuansheng Optoelectronics Co Ltd
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    • G—PHYSICS
    • G02—OPTICS
    • G02F—OPTICAL DEVICES OR ARRANGEMENTS FOR THE CONTROL OF LIGHT BY MODIFICATION OF THE OPTICAL PROPERTIES OF THE MEDIA OF THE ELEMENTS INVOLVED THEREIN; NON-LINEAR OPTICS; FREQUENCY-CHANGING OF LIGHT; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS
    • G02F1/00—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics
    • G02F1/01—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour 
    • G02F1/13—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour  based on liquid crystals, e.g. single liquid crystal display cells
    • G02F1/1306—Details
    • G02F1/1309—Repairing; Testing
    • G—PHYSICS
    • G02—OPTICS
    • G02F—OPTICAL DEVICES OR ARRANGEMENTS FOR THE CONTROL OF LIGHT BY MODIFICATION OF THE OPTICAL PROPERTIES OF THE MEDIA OF THE ELEMENTS INVOLVED THEREIN; NON-LINEAR OPTICS; FREQUENCY-CHANGING OF LIGHT; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS
    • G02F1/00—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics
    • G02F1/01—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour 
    • G02F1/13—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour  based on liquid crystals, e.g. single liquid crystal display cells

Definitions

  • the present disclosure relates to the field of color film substrate defect detection technology, and in particular, to a color film substrate defect analysis method, a detection and repair method and device.
  • AOI automatic optical detection device
  • the micro-defective color film substrate also needs to be sent to a repair device for defect repair. Whether all the microscopic defects detected by AOI directly affect the quality of the color film substrate. In actual production, only about 30% of the microscopic defects detected by AOI need to be repaired, and the remaining 70% of defects are not visible in the repair process or need not be repaired. However, in order to ensure the quality of the color film substrate, these 70% micro defects need to be confirmed in the repair equipment, resulting in wasted time and seriously affecting the production capacity.
  • the AOI defect detection standard may be relaxed, or some defects may be directly filtered in the repairing device, but the two filtering methods completely rely on the engineer experience, and the filtering effect is not quantifiable.
  • the risk of AOI missed inspection and the risk of repairing equipment repair are uncontrollable, and the quality of the color film substrate cannot be guaranteed.
  • the present disclosure provides a method for analyzing defects of a color film substrate, including:
  • the difference data of the data is determined according to the difference data, and the filtering range corresponding to the filtering parameter is determined.
  • the filtering parameter includes at least one of a defect size, a defect grayscale value, and a grayscale difference value of the defect grayscale value and the corresponding normal grayscale value.
  • the filtering parameter includes a defect size
  • the data of the filtering parameter of the first quantity of detected defects and the filtering parameter of the second quantity of repairing defects are analyzed, and the obtained
  • the step of detecting the difference between the data of the filtering parameter of the defect and the data of the filtering parameter of the defect, and determining the filtering range corresponding to the filtering parameter according to the difference data includes:
  • the step of determining the quantity distribution of the different defect sizes of the detected defect and the repair defect includes:
  • the difference data of the defect size of the detected defect and the data of the defect size of the repair defect are obtained according to the quantity distribution of the different defect sizes of the detected defect and the repair defect, and the difference data is determined according to the difference data.
  • the step of filtering the range corresponding to the defect size includes:
  • the filtering parameter includes a grayscale value of the defect grayscale value and the defect grayscale value and the corresponding normal grayscale value
  • the data and the filtering parameter of the first number of detected defects are The data of the filter parameter of the second number of repair defects is analyzed, and the difference data between the data of the filter parameter of the detected defect and the data of the filter parameter of the repair defect is obtained, and the filter parameter is determined according to the difference data.
  • the steps of filtering range include:
  • the quantity distribution of the grayscale value of the detected defect and the grayscale value of the corresponding normal grayscale value on different grayscale partitions and the grayscale value of the repairing defect and the gray of the corresponding normal grayscale value a quantity distribution of the step difference values on different gray-scale partitions, obtaining a gray-scale difference between the gray-scale value of the detected defect and the corresponding normal gray-scale value, and a gray-scale value of the repaired defect and a corresponding normal gray-scale value
  • the present disclosure also provides a method for detecting and repairing defects of a color film substrate, including:
  • the defect of the color filter substrate is detected or repaired according to the filtering range of the filter parameter.
  • the method for detecting or repairing defects of the color filter substrate according to the filtering range of the filtering parameter includes:
  • the color filter substrate is optically detected as a defect determination condition on the automatic optical detecting device, and defects not included in the filtering range are detected to obtain a detected defect.
  • the method for detecting or repairing defects of the color filter substrate according to the filtering range of the filtering parameter includes:
  • the repairing device determines whether the detected defect obtained by the automatic optical detecting device belongs to the filtering range, obtains the detected defect that does not belong to the filtering range as the detected detecting defect, and performs the filtered detected defect. repair.
  • the present disclosure also provides an analysis device for a defect of a color film substrate, comprising:
  • a first acquiring module configured to acquire parameter information of a first quantity of detected defects detected by the automatic optical detecting device from the plurality of color film substrate samples
  • a second acquiring module configured to acquire parameter information of a second number of repair defects repaired by the repairing device from the first number of detected defects
  • a determining module configured to analyze data of the first number of filtering parameters of the detected defects and the filtering parameters of the second number of repairing defects, to obtain data and repair of the filtering parameters of the detected defects The difference data of the data of the filter parameter of the defect, and determining the filter range corresponding to the filter parameter according to the difference data.
  • the filtering parameter includes at least one of a defect size, a defect grayscale value, and a grayscale difference value of the defect grayscale value and the corresponding normal grayscale value.
  • the determining module includes:
  • a first determining unit configured to determine different defect sizes of the detected defect and the repair defect according to the data of the defect size of the first number of detected defects and the data of the defect size of the second number of repair defects Quantity distribution;
  • a second determining unit configured to obtain, according to the quantity distribution of the different defect sizes of the detected defect and the repair defect, difference data of the data of the defect size of the detected defect and the data of the defect size of the repair defect, according to the The difference data determines a filtering range corresponding to the defect size.
  • the first determining unit is configured to determine a first value range of all the defect sizes of the detected defects, and divide the first value range into a plurality of first defect size partitions, where each Determining the length of the first defect size partition; determining the number of detected defects in each of the first defect size partitions; obtaining the detection defect according to the first defect size partition and the corresponding number of detected defects a columnar profile of the defect size; performing a Lorentz curve fitting on the columnar profile of the defect size of the detected defect to obtain a Lorentz curve of the defect size of the detected defect; determining defects of all repair defects a second value range of the size, the second value range is divided into a plurality of second defect size partitions, wherein each second defect size partition has the same length; determining each of the second defect size partitions The number of repair defects; according to the second defect size partition and the number of corresponding repair defects, a columnar map of the defect size of the repair defect is obtained; Size distribution cylindrical trap for Lorenz curve fitting, the Lorenz curve to obtain the
  • the second determining unit is configured to integrate the Lorentz curve of the defect size of the detected defect and the Lorentz curve of the defect size of the repair defect into the same coordinate system for comparison, according to the comparison result
  • the difference data of the data of the defect size of the defect and the data of the defect size of the repair defect are described, and the filtering range corresponding to the defect size is determined according to the difference data.
  • the determining module includes:
  • a third determining unit configured to determine a quantity distribution of the detected defect and the repair defect on different gray levels
  • a dividing unit configured to divide all gray levels into a plurality of gray level partitions according to the quantity distribution of the detected defects and the repair defects on different gray levels
  • a fourth determining unit configured to determine a quantity distribution of a grayscale value of the detected defect and a grayscale difference of the corresponding normal grayscale value on different grayscale partitions, and determine a grayscale value of the repaired defect The number distribution of grayscale differences with corresponding normal grayscale values on different grayscale partitions;
  • a fifth determining unit configured to: according to the quantity distribution of the grayscale value of the detected defect and the grayscale difference of the corresponding normal grayscale value on different grayscale partitions, and the grayscale value of the repairing defect and corresponding The number distribution of the grayscale difference values of the normal grayscale values on different grayscale partitions, and the grayscale difference between the grayscale value of the detected defect and the corresponding normal grayscale value and the grayscale of the repairing defect are obtained The difference data of the grayscale difference between the value and the corresponding normal grayscale value, and determining the filtering range corresponding to the grayscale difference of the different grayscale partitions according to the difference data.
  • the present disclosure also provides a detection and repair device for a defect of a color film substrate, comprising:
  • An obtaining module configured to obtain a filtering range of a filtering parameter for detecting a defect of the color filter substrate, wherein a filtering range of the filtering parameter is obtained by the foregoing analyzing method;
  • a filtering module configured to detect or repair defects of the color filter substrate according to the filtering range of the filtering parameter.
  • the detecting and repairing device for the defect of the color filter substrate comprises an automatic optical detecting device, and the filtering module optically detects the color filter substrate by using the filtering range of the filtering parameter as a defect determining condition, Defects within the filtering range are detected and defects are detected.
  • the detecting and repairing device of the color film substrate defect comprises a repairing device, and the filtering module determines whether the detected defect obtained by the automatic optical detecting device belongs to a filtering range, and obtains a detecting defect that does not belong to the filtering range. As a detected defect after filtration, the detected defect detected after the filtration is repaired.
  • the present disclosure also provides an analysis device for a defect of a color film substrate, comprising: a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor implements the computer program when the computer program is executed The steps in the analysis method of the color film substrate defect.
  • the present disclosure also provides a detection and repair device for a defect of a color film substrate, comprising: a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor executes the computer program The steps in the method for detecting and repairing defects of the color film substrate.
  • FIG. 1 is a schematic flow chart of a method for analyzing defects of a color filter substrate according to some embodiments of the present disclosure
  • FIG. 2 is a schematic flow chart of a method for analyzing defects of a color filter substrate according to some embodiments of the present disclosure
  • FIG. 3 is a schematic flow chart of a method for analyzing defects of a color filter substrate according to some embodiments of the present disclosure
  • FIG. 4 is a distribution diagram of defect sizes of detected defects detected by an automatic optical detecting device according to some embodiments of the present disclosure
  • FIG. 5 is a distribution diagram of defect sizes of repair defects repaired by a repair device according to some embodiments of the present disclosure
  • FIG. 6 is a schematic diagram showing the integration of the Lorentz curve of the defect size of the detected defect in FIG. 4 and the Lorentz curve of the defect size of the repair defect in FIG. 5;
  • FIG. 7 is a schematic diagram of the Lorentz curve of the defect size of the detected defect in FIG. 6 and the Lorentz curve of the defect size of the repair defect;
  • FIG. 8 is a schematic flow chart of a method for analyzing defects of a color filter substrate according to some embodiments of the present disclosure
  • FIG. 9 is a quantity distribution diagram of detection defects detected by an automatic optical detecting device and repair defects of a repairing device repaired on different gray levels according to an embodiment of the present disclosure
  • FIG. 10 is a schematic diagram of gray-scale difference values between detected defects and repair defects and corresponding normal gray-scale values on different gray-scale partitions
  • 11 is a quantity distribution diagram of actual detection defects and repair defects on different gray scales after detecting or repairing defects of the color filter substrate by using the filter range of the gray scale difference;
  • FIG. 12 is a schematic flow chart of a method for detecting and repairing defects of a color filter substrate according to some embodiments of the present disclosure
  • FIG. 13 is a structural block diagram of an analysis apparatus for a defect of a color filter substrate according to some embodiments of the present disclosure.
  • FIG. 14 is a structural block diagram of a detecting and repairing device for a defect of a color filter substrate according to an embodiment of the present disclosure.
  • FIG. 1 is a schematic flow chart of a method for analyzing defects of a color filter substrate according to some embodiments of the present disclosure, where the analysis method includes:
  • Step 11 Acquire data of filtering parameters of the first number of detected defects detected by the automatic optical detecting device from the plurality of color film substrate samples;
  • a batch of color film substrates that have been subjected to defect detection and repaired by the repair device may be used as samples by an automatic optical detecting device.
  • the filtering parameter may include at least one of a defect size, a defect grayscale value, and a grayscale difference value of the defect grayscale value and the corresponding normal grayscale value.
  • the normal gray scale value refers to a gray scale value of the same region of the color filter substrate having no defect corresponding to the gray scale value of the defect.
  • Step 12 Obtain data of a filtering parameter of a second number of repair defects repaired by the repair device from the first number of detected defects;
  • Step 13 analyzing data of the first number of detection parameter of the detected defect and the data of the filtering parameter of the second quantity of the repair defect, and obtaining data of the filter parameter of the detected defect and repairing the defect Filtering the difference data of the data of the parameter, and determining, according to the difference data, a filtering range corresponding to the filtering parameter.
  • the data of the filtering parameter of the detection defect of the automatic optical detecting device and the filtering parameter of the repairing defect of the repairing device are integrated, the data of the filtering parameter for detecting the defect and the filtering parameter of the repairing defect are The data is compared to obtain the difference data of the two, and the filtering range corresponding to the filtering parameter is determined according to the difference data, so that the determined filtering range is more accurate, and the filtering range can be used to filter the defects that need not be repaired, thereby saving the repairing device confirmation check.
  • the time of the defect has increased the production capacity.
  • FIG. 2 is a schematic flowchart diagram of a method for analyzing defects of a color filter substrate according to some embodiments of the present disclosure, where the analysis method includes:
  • Step 21 Obtain data of a defect size of the first number of detected defects detected by the automatic optical detecting device from the plurality of color film substrate samples;
  • the defect size may refer to an area of a defect or other size information of the defect.
  • Step 22 Obtain data of a defect size of the second number of repair defects repaired by the repair device from the first number of detected defects;
  • Step 23 determining, according to the data of the defect size of the first number of detected defects and the defect size of the second number of repair defects, a quantity distribution of different defect sizes of the detected defect and the repair defect;
  • Step 24 Obtain difference data of the data of the defect size of the detected defect and the data of the defect size of the repair defect according to the quantity distribution of the different defect sizes of the detected defect and the repair defect, and determine the data according to the difference data.
  • the number of different defect sizes of the detected defect and the repaired defect is counted, and the defect size with less quantity distribution may be determined according to the quantity distribution of different defect sizes, because the defect size indicating that the quantity distribution is less has It may not be necessary to make a repair so that the filtering range corresponding to the defect size can be determined.
  • FIG. 3 is a schematic flowchart diagram of a method for analyzing defects of a color filter substrate according to some embodiments of the present disclosure, where the analysis method includes:
  • Step 31 Obtain data of a defect size of the first number of detected defects detected by the automatic optical detecting device from the plurality of color film substrate samples;
  • Step 32 Obtain data of a defect size of the second number of repair defects repaired by the repair device from the first number of detected defects;
  • Step 33 Determine a first value range of the defect size of all detected defects, and divide the first value range into a plurality of first defect size partitions, wherein each first defect size partition has the same length;
  • the first value range may be A ⁇ Size ⁇ B, and the first value range is divided into “B+1-A” partitions, and each partition is: A ⁇ Size ⁇ A+1, A +1 ⁇ Size ⁇ A+2,..., B-1 ⁇ Size ⁇ B, where Size is the defect size.
  • the minimum value of the defect size in the first number of detected defects is 15, the maximum value of the defect size is 140, and the first value range of the defect size of the detected defect may be 15 ⁇ Size ⁇ 140, which will be 15 ⁇ Size ⁇ 140 is divided into “140+1-15” partitions, and each partition is: 15 ⁇ Size ⁇ 16, 16 ⁇ Size ⁇ 17, ..., 139 ⁇ Size ⁇ 140.
  • the first range of values may not be determined based on the minimum and minimum values of the defect sizes in the first number of detected defects, for example, may be determined based on empirical values.
  • the size of the defect is partitioned, which can effectively reduce the amount of calculation.
  • the finer the partition the more accurate the result is.
  • Step 34 Determine the number of detected defects in each of the first defect size partitions
  • Step 35 Obtain a columnar distribution map of the defect size of the detected defect according to the first defect size partition and the corresponding number of detected defects;
  • FIG. 4 is a distribution diagram of defect sizes detected by the automatic optical detecting device according to the embodiment of the present disclosure.
  • the X axis represents the defect size (Size)
  • the Y axis represents the number of detected defects on the different defect size partitions.
  • the first value range of the defect size of detecting the defect may be 15 ⁇ Size ⁇ 140, and 15 ⁇ Size ⁇ 140 is divided into 25 partitions, each partition is: 15 ⁇ Size ⁇ 20, 20 ⁇ Size ⁇ 25, ..., 135 ⁇ Size ⁇ 140.
  • the columnar profile in Figure 4 can reflect the size distribution of the actual detected defects of the automated optical detection device.
  • Step 36 Perform a Lorentz curve fitting on the columnar profile of the defect size of the detected defect to obtain a Lorentz curve of the defect size of the detected defect;
  • Lorentz fitting is a fitting closest to the actual production data, converting the columnar distribution into a Lorenz curve. The purpose is to find the distribution peak of the defect size of the automatic optical detection device actually detecting the defect, so as to find the optimal parameter value between the two peaks.
  • Step 37 Determine a second range of values of defect sizes of all repair defects, and divide the second value range into a plurality of second defect size partitions, wherein each second defect size partition has the same length;
  • the second value range may be, for example, C ⁇ Size ⁇ D, and the first value range is divided into “D+1-C” partitions, and each partition is: C ⁇ Size ⁇ C+1, C+ 1 ⁇ Size ⁇ C+2, . . . , D-1 ⁇ Size ⁇ D, where Size is the defect size.
  • the second value range is the same as the first value range, that is, C is equal to A, and D is equal to B.
  • the minimum size of the defect in the second number of repair defects is 15, the maximum value of the defect size is 140, and the second value range of the defect size of the repair defect may be 15 ⁇ Size ⁇ 140, and 15 ⁇ Size ⁇ 140 is divided into "140+1-15" partitions, each partition is: 15 ⁇ Size ⁇ 16, 16 ⁇ Size ⁇ 17, ..., 139 ⁇ Size ⁇ 140.
  • the second value range may not be determined according to the minimum value and the minimum value of the defect size in the second number of repair defects, for example, may be determined according to empirical values, or directly The first range of values takes the same range.
  • Step 38 Determine the number of repair defects in each of the second defect size partitions
  • Step 39 Obtain a columnar distribution map of the defect size of the repair defect according to the second defect size partition and the number of corresponding repair defects;
  • FIG. 5 is a distribution diagram of defect sizes of repair defects repaired by the repair device according to an embodiment of the present disclosure.
  • the X axis represents the defect size (Size)
  • the Y axis represents the number of repair defects on the different defect size partitions.
  • the second value range of the defect size of the repair defect may be 15 ⁇ Size ⁇ 140, and 15 ⁇ Size ⁇ 140 is divided into 25 partitions, each partition is: 15 ⁇ Size ⁇ 20,20 ⁇ Size ⁇ 25, ..., 135 ⁇ Size ⁇ 140.
  • the columnar profile in Figure 5 can reflect the size distribution of the actual repair defects of the repair equipment.
  • Step 310 Perform a Lorentz curve fitting on the columnar profile of the defect size of the repair defect to obtain a Lorentz curve of the defect size of the repair defect;
  • the purpose of converting the columnar map into the Lorentz curve is to find the distribution peak of the defect size of the repairing device actually repairing the defect.
  • Step 311 Integrate the Lorentz curve of the defect size of the detected defect and the Lorentz curve of the defect size of the repair defect into the same coordinate system for comparison, and obtain the defect of the detected defect according to the comparison result.
  • the difference data of the size data and the defect size data of the repair defect, and the filtering range corresponding to the defect size is determined according to the difference data.
  • FIG. 6 is a schematic diagram of the integration of the Lorentz curve of the defect size of the detected defect in FIG. 4 and the Lorentz curve of the defect size of the repair defect in FIG.
  • the X axis represents the defect size (Size)
  • the Y axis represents the number of detected defects and repair defects on the partitions of different defect sizes.
  • the size of the defect ranges from 15 ⁇ Size ⁇ 140, and 15 ⁇ Size ⁇ 140 is divided into 25 partitions, each partition is: 15 ⁇ Size ⁇ 20, 20 ⁇ Size ⁇ 25,... ..., 135 ⁇ Size ⁇ 140.
  • the Lorentz curve of the defect size of the detected defect in FIG. 4 and the Lorentz curve of the defect size of the repair defect in FIG. 5 are integrated into the same coordinate system for comparison, and the defect size is determined according to the comparison result.
  • the filtering range is shown in Fig. 6. As can be seen from Fig. 6, the number of detected defects and the number of repaired defects after the defect size tend to be 0, so the filtering range of the defect size can be determined as Greater than 110.
  • FIG. 7 is a schematic diagram of the Lorentz curve of the defect size of the defect detected in FIG. 6 and the Lorentz curve of the defect size of the repair defect, which may be normalized.
  • the filtering range of the defect size is set, and the small number of defects that do not need to be repaired are precisely filtered. .
  • the analysis method of the color film substrate defect of the embodiment of the present disclosure is described by taking the filter parameter including the defect size as an example.
  • the following filter parameters include the defect gray scale value and the defect gray scale value and the corresponding normal gray scale value.
  • the gray scale difference is taken as an example to describe the analysis method of the color film substrate defect of the embodiment of the present disclosure.
  • FIG. 8 is a schematic flowchart diagram of a method for analyzing defects of a color filter substrate according to some embodiments of the present disclosure, where the analysis method includes:
  • Step 81 Acquire data of a defect grayscale value of the first number of detected defects detected by the automatic optical detecting device from the plurality of color film substrate samples, and a normal grayscale value of each detected defect;
  • Step 82 Obtain data of a defect grayscale value of a second number of repair defects repaired by the repair device from the first number of detected defects, and a normal grayscale value of each repair defect;
  • Step 83 determining a quantity distribution of the detected defect and the repair defect on different gray levels
  • FIG. 9 is a diagram showing the quantity distribution of the detection defects detected by the automatic optical detecting device and the repairing defects of the repairing device repaired on different gray levels according to the embodiment of the present disclosure.
  • the optical definition gray scale is divided into 0 to 255 steps, and the defects detected by the color film substrate are distributed on the 256 gray scales.
  • the X axis represents the standard Gray scale value
  • the Y axis represents the number of defects detected and repair defects on different gray scales. It can be seen from Fig. 9 that the number of detected defects and repair defects on each gray-scale partition is distributed, thereby analyzing which gray-level partitions need to be filtered and dividing the gray-scale partition.
  • Step 84 Divide all gray levels into a plurality of gray level partitions according to the quantity distribution of the detected defects and the repair defects on different gray levels;
  • gray levels (0 to 255) can be divided into four partitions.
  • the gray scale range of partition one is: 0 ⁇ Gray1 ⁇ 60
  • the gray scale range of partition two is: 60 ⁇ Gray2 ⁇ 110
  • partition three is:
  • the grayscale range is: 110 ⁇ Gray3 ⁇ 140
  • the grayscale range of the partition four is: 140 ⁇ Gray4 ⁇ 255.
  • Step 85 Determine a quantity distribution of the grayscale value of the detected defect and a grayscale value of the corresponding normal grayscale value on different grayscale partitions, and determine a grayscale value of the repairing defect and a corresponding normal The number distribution of grayscale values of grayscale values on different grayscale partitions;
  • FIG. 10 is a schematic diagram of gray-scale difference between detected defects and repair defects and corresponding normal gray-scale values on different gray-scale partitions.
  • the X-axis represents the standard gray scale
  • the Y-axis represents the gray scale.
  • the gray scale value of the defect and the repair defect is detected on the step partition and the gray scale difference ⁇ Gray of the corresponding normal gray scale value.
  • Step 86 According to the quantity distribution of the grayscale value of the detected defect and the grayscale value of the corresponding normal grayscale value on different grayscale partitions, and the grayscale value of the repairing defect and the corresponding normal grayscale a quantity distribution of grayscale differences of values on different grayscale partitions, obtaining grayscale values of the detected defects and grayscale values of corresponding normal grayscale values and corresponding grayscale values of the repaired defects The difference data of the grayscale difference values of the normal grayscale values, and determining the filtering range corresponding to the grayscale difference values of the different grayscale partitions according to the difference data.
  • the filtering range of the gray-scale difference ⁇ Gray on each divided gray-scale partition is set to prevent defects of each partition from being repaired. Filter out separately.
  • Table 1 is the result of setting the filtering range of the grayscale difference ⁇ Gray on each divided grayscale partition according to the change of ⁇ Gray corresponding to the detected defect and the repaired defect in FIG. :
  • the filtering range is that the grayscale difference is less than 30, that is, only the defect with the grayscale difference greater than or equal to 30 is detected or repaired, and the defect with the grayscale difference of less than 30 is not Test or repair.
  • the number of detected defects of the automatic optical detecting device can be reduced by 3098.
  • the number of repair defects in the repair equipment can be reduced by seven, that is to say, the defect detection rate of 0.03% and the defect repair rate of 0.114% reduce the total defect amount by 13.76%.
  • FIG. 11 is a diagram showing the quantity distribution of actual detected defects and repair defects on different gray scales after detecting or repairing the defects of the color filter substrate by using the filter range of the gray scale difference.
  • the X axis represents the standard gray scale value
  • the Y axis represents the number of defects detected and repair defects on different gray scales. Comparing Fig. 11 and Fig. 9, it can be seen that the number of detection defects and repair defects of some gray-scale partitions is significantly reduced, that is, the defects that are not required to be repaired by each partition are separately filtered, thereby reducing the repair equipment to confirm the detection of defects. Time to effectively increase production capacity.
  • the filtering parameter includes a grayscale value of the defect and a grayscale difference between the grayscale value of the defect and the corresponding normal grayscale value.
  • the filtering parameter may also include only the gray of the defect. Order value.
  • the filtering parameter may also include the defect size, the defect grayscale value, and the grayscale difference value of the defect grayscale value and the corresponding normal grayscale value, so that the filtering range of the obtained filtering parameter is obtained. more acurrate.
  • the defect filtering rate and the defect detection rate may be calculated after the color filter substrate is subjected to defect filtering by using the filtering range of the filtering parameter. And/or defect miss rate, etc. to assess whether the filtering range is reasonable and to obtain an optimal filtering range.
  • the defect filtration rate, the defect miss detection rate and the defect miss repair rate can be calculated by the following formula:
  • the above embodiments of the present disclosure have the advantages of simple operation and easy implementation, and the filtering method of the filtering parameters can be quickly obtained by using the analysis method.
  • the statistics show that the filter range of the filter parameters obtained by the analysis method in the embodiment of the present disclosure can detect the defects of the color filter substrate or repair the defects of the color filter substrate without affecting the quality of the color filter substrate.
  • the rate has been raised from about 30% to over 60%, and the production capacity of color film substrate defects has been improved by more than 20%.
  • FIG. 12 is a schematic flowchart of a method for detecting and repairing defects of a color filter substrate according to an embodiment of the present disclosure, and the method for detecting and repairing includes:
  • Step 121 Obtain a filtering range of a filtering parameter for detecting a defect of a color filter substrate, where a filtering range of the filtering parameter is obtained by an analysis method of a defect of a color filter substrate according to any one of the above embodiments;
  • Step 122 Detect or repair the defect of the color filter substrate according to the filtering range of the filtering parameter.
  • the filtering range of the filtering parameter can filter out defects that do not need to be repaired, thereby reducing the time for the repairing device to confirm the detection of defects, and effectively increasing the productivity.
  • the filtering range of the filtering parameter may be set on the automatic optical detecting device.
  • the method for detecting or repairing the defect of the color filter substrate according to the filtering range of the filtering parameter comprises: performing optical detection on the color filter substrate on the automatic optical detecting device by using the filtering range of the filtering parameter as a defect determining condition, and detecting a defect that does not belong to the filtering range, thereby obtaining a detected defect.
  • the automatic optical detecting device uses the filtering range of the filtering parameter as the defect determining condition to optically detect the color filter substrate, and when the defect is detected, the number of detected defects detected by the automatic optical detecting device can be reduced, thereby reducing the number of detected defects
  • the repair equipment confirms the time of detecting the defect and effectively increases the production capacity.
  • a filtering range of the filtering parameter may be set on the repairing device.
  • the method for detecting or repairing the defect of the color filter substrate according to the filtering range of the filtering parameter includes: The repairing device determines whether the detected defect obtained by the automatic optical detecting device belongs to the filtering range, obtains the detected defect that does not belong to the filtering range as the detected detecting defect, and performs the filtered detected defect. repair.
  • the repairing device can repair the detected defects detected after filtering, and does not need to confirm the repaired defects, and the filtering on the side of the repairing device is better than filtering on the side of the automatic optical detecting device, because the filtering is performed in the repairing device. It does not affect the actual detection of defects in the automatic optical inspection equipment, enabling the automatic optical inspection equipment to better monitor the changes in defects in actual production.
  • the automatic optical detecting device may first perform optical detection on the color filter substrate according to the defect determination condition in the related art to obtain a detected defect, and then adopt the filtering parameter.
  • the filtering range filters the detected defects, obtains the detected defects after filtering, and repairs the detected defects after the filtering.
  • the repairing device can repair the detected defect detected by the filter, thereby reducing the time for the repairing device to confirm the detected defect and effectively increasing the productivity.
  • automated optical inspection equipment is also able to better monitor changes in defects in actual production.
  • some embodiments of the present disclosure further provide an analysis device for a defect of a color filter substrate, including:
  • a first acquiring module configured to acquire parameter information of a first quantity of detected defects detected by the automatic optical detecting device from the plurality of color film substrate samples
  • a second acquiring module configured to acquire parameter information of a second number of repair defects repaired by the repairing device from the first number of detected defects
  • a determining module configured to analyze data of the first number of filtering parameters of the detected defects and the filtering parameters of the second number of repairing defects, to obtain data and repair of the filtering parameters of the detected defects The difference data of the data of the filter parameter of the defect, and determining the filter range corresponding to the filter parameter according to the difference data.
  • the data of the filtering parameter of the detection defect of the automatic optical detecting device and the filtering parameter of the repairing defect of the repairing device are integrated, the data of the filtering parameter for detecting the defect and the filtering parameter of the repairing defect are The data is compared to obtain the difference data of the two, and the filtering range corresponding to the filtering parameter is determined according to the difference data, so that the determined filtering range is more accurate, and the filtering range can be used to filter the defects that need not be repaired, thereby saving the repairing device confirmation check.
  • the time of the defect has increased the production capacity.
  • the filtering parameter includes at least one of a defect size, a defect grayscale value, and a grayscale difference value of the defect grayscale value and the corresponding normal grayscale value.
  • the determining module when the filtering parameter includes a defect size, includes:
  • a first determining unit configured to determine different defect sizes of the detected defect and the repair defect according to the data of the defect size of the first number of detected defects and the data of the defect size of the second number of repair defects Quantity distribution;
  • a second determining unit configured to obtain, according to the quantity distribution of the different defect sizes of the detected defect and the repair defect, difference data of the data of the defect size of the detected defect and the data of the defect size of the repair defect, according to the The difference data determines a filtering range corresponding to the defect size.
  • the first determining unit is configured to determine a first value range of all the defect sizes of the detected defects, and divide the first value range into a plurality of first defect size partitions, where each Determining the length of the first defect size partition; determining the number of detected defects in each of the first defect size partitions; obtaining the detection defect according to the first defect size partition and the corresponding number of detected defects a columnar profile of the defect size; performing a Lorentz curve fitting on the columnar profile of the defect size of the detected defect to obtain a Lorentz curve of the defect size of the detected defect; determining defects of all repair defects a second value range of the size, the second value range is divided into a plurality of second defect size partitions, wherein each second defect size partition has the same length; determining each of the second defect size partitions The number of repair defects; according to the second defect size partition and the number of corresponding repair defects, a columnar map of the defect size of the repair defect is obtained; Size distribution cylindrical trap for Lorenz curve fitting, the Lorenz curve to obtain the
  • the second determining unit is configured to integrate the Lorentz curve of the defect size of the detected defect and the Lorentz curve of the defect size of the repair defect into the same coordinate system for comparison, according to the comparison result
  • the difference data of the data of the defect size of the defect and the data of the defect size of the repair defect are described, and the filtering range corresponding to the defect size is determined according to the difference data.
  • the determining module when the filtering parameter includes a grayscale value of a defect grayscale value and a defect grayscale value and a corresponding normal grayscale value, the determining module includes:
  • a third determining unit configured to determine a quantity distribution of the detected defect and the repair defect on different gray levels
  • a dividing unit configured to divide all gray levels into a plurality of gray level partitions according to the quantity distribution of the detected defects and the repair defects on different gray levels
  • a fourth determining unit configured to determine a quantity distribution of a grayscale value of the detected defect and a grayscale difference of the corresponding normal grayscale value on different grayscale partitions, and determine a grayscale value of the repaired defect The number distribution of grayscale differences with corresponding normal grayscale values on different grayscale partitions;
  • a fifth determining unit configured to: according to the quantity distribution of the grayscale value of the detected defect and the grayscale difference of the corresponding normal grayscale value on different grayscale partitions, and the grayscale value of the repairing defect and corresponding The number distribution of the grayscale difference values of the normal grayscale values on different grayscale partitions, and the grayscale difference between the grayscale value of the detected defect and the corresponding normal grayscale value and the grayscale of the repairing defect are obtained The difference data of the grayscale difference between the value and the corresponding normal grayscale value, and determining the filtering range corresponding to the grayscale difference of the different grayscale partitions according to the difference data.
  • some embodiments of the present disclosure further provide a detection and repair device for a defect of a color film substrate, including:
  • An obtaining module configured to obtain a filtering range of a filtering parameter for detecting a defect of the color filter substrate, wherein a filtering range of the filtering parameter is obtained by an analysis method of a defect of the color filter substrate in any of the above embodiments;
  • a filtering module configured to detect or repair defects of the color filter substrate according to the filtering range of the filtering parameter.
  • the filtering range of the filtering parameter can filter out defects that do not need to be repaired, thereby reducing the time for the repairing device to confirm the detection of defects, and effectively increasing the productivity.
  • the color film substrate defect detecting and repairing device includes an automatic optical detecting device, and the filtering module optically detects the color filter substrate by using the filtering range of the filtering parameter as a defect determining condition. Detection of defects that are not within the filtering range is detected, and defects are detected.
  • the detecting and repairing device for the defect of the color filter substrate comprises a repairing device, and the filtering module determines whether the detected defect obtained by the automatic optical detecting device belongs to the filtering range, and the filter does not belong to the filtering.
  • the detected defect in the range is detected as a detected defect after the filtration, and the detected defect detected after the filtration is repaired.

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Abstract

一种彩膜基板缺陷的分析方法、检测修复方法及装置，彩膜基板缺陷的分析方法，包括：获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的过滤参数的数据；获取修复设备从第一数量个检出缺陷中修复的第二数量个修复缺陷的过滤参数的数据；对第一数量个检出缺陷的过滤参数的数据和第二数量个修复缺陷的过滤参数的数据进行分析，得到检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据差异数据确定过滤参数对应的过滤范围。

Description

彩膜基板缺陷的分析方法、检测修复方法及装置
相关申请的交叉引用
本申请主张在2017年8月23日在中国提交的中国专利申请号No.201710730999.9的优先权，其全部内容通过引用包含于此。
技术领域
本公开涉及彩膜基板缺陷检测技术领域，尤其涉及一种彩膜基板缺陷的分析方法、检测修复方法及装置。
背景技术
目前，在完成薄膜晶体管液晶显示器(TFT-LCD)彩膜基板的每一道工序后，通常都需要将彩膜基板送入自动光学检出设备(AOI)进行微观缺陷检测，经AOI检测出的具有微观缺陷的彩膜基板还需要送入修复(Repair)设备进行缺陷修复。AOI检出的微观缺陷是否全部修复直接影响彩膜基板的品质。在实际生产中，AOI检出的微观缺陷仅有30％左右需要修复，其余70％缺陷在修复工序中，要么不可见，要么不需要修复。但为了保证彩膜基板的品质，这70％的微观缺陷都需要在修复设备进行确认，造成时间浪费，严重影响产能。
为了降低确认不需要修复的缺陷的时间浪费，相关技术中，可以采用放宽AOI缺陷检出标准，或者在修复设备直接过滤部分缺陷，但是这两种过滤方式完全依靠工程师经验，过滤效果不可量化，AOI漏检风险及修复设备漏修风险均不可控，无法保证彩膜基板的品质。
发明内容
本公开提供一种彩膜基板缺陷的分析方法，包括：
获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的过滤参数的数据；
获取修复设备从所述第一数量个检出缺陷中修复的第二数量个修复缺陷 的过滤参数的数据；
对所述第一数量个检出缺陷的过滤参数的数据和所述第二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围。
可选地，所述过滤参数包括缺陷大小、缺陷灰阶值以及缺陷灰阶值与对应的正常灰阶值的灰阶差值中的至少一个。
可选地，所述过滤参数包括缺陷大小时，所述对所述第一数量个检出缺陷的过滤参数的数据和所述第二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围的步骤包括：
根据所述第一数量个检出缺陷的缺陷大小的数据和所述第二数量个修复缺陷的缺陷大小的数据，确定所述检出缺陷和修复缺陷的不同缺陷大小的数量分布；
根据所述检出缺陷和修复缺陷的不同缺陷大小的数量分布，得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，根据所述差异数据确定所述缺陷大小对应的过滤范围。
可选地，所述确定所述检出缺陷和修复缺陷的不同缺陷大小的数量分布的步骤包括：
确定所有检出缺陷的缺陷大小的第一取值范围，将所述第一取值范围划分成多个第一缺陷大小分区，其中，每一第一缺陷大小分区的长度相同；
确定每一所述第一缺陷大小分区中的检出缺陷的数量；
根据所述第一缺陷大小分区和对应的检出缺陷的数量，得到所述检出缺陷的缺陷大小的柱状分布图；
对所述检出缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述检出缺陷的缺陷大小的洛伦兹曲线；
确定所有修复缺陷的缺陷大小的第二取值范围，将所述第二取值范围划分成多个第二缺陷大小分区，其中，每一第二缺陷大小分区的长度相同；
确定每一所述第二缺陷大小分区中的修复缺陷的数量；
根据所述第二缺陷大小分区和对应的修复缺陷的数量，得到所述修复缺陷的缺陷大小的柱状分布图；
对所述修复缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述修复缺陷的缺陷大小的洛伦兹曲线；
其中，所述根据所述检出缺陷和修复缺陷的不同缺陷大小的数量分布，得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，根据所述差异数据确定所述缺陷大小对应的过滤范围的步骤包括：
将所述检出缺陷的缺陷大小的洛伦兹曲线和所述修复缺陷的缺陷大小的洛伦兹曲线整合至同一坐标系中进行比较，根据比较结果得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，并根据所述差异数据确定所述缺陷大小对应的过滤范围。
可选地，所述过滤参数包括缺陷灰阶值和缺陷灰阶值与对应的正常灰阶值的灰阶差值时，所述对所述第一数量个检出缺陷的过滤参数的数据和所述第二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围的步骤包括：
确定所述检出缺陷和修复缺陷在不同灰阶上的数量分布；
根据所述检出缺陷和修复缺陷在不同灰阶上的数量分布，将所有灰阶划分为多个灰阶分区；
确定所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及，确定所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布；
根据所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，得到所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值与所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值的差异数据，并根据所述差异数据确定不同灰阶分区的灰阶差值对应的过滤范围。
本公开还提供一种彩膜基板缺陷的检测修复方法，包括：
获取用于检测彩膜基板缺陷的过滤参数的过滤范围，所述过滤参数的过滤范围由上述彩膜基板缺陷的分析方法得到；
根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复。
可选地，所述根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复的方法包括：
在自动光学检出设备上将所述过滤参数的过滤范围作为缺陷判定条件对彩膜基板进行光学检测，对不属于所述过滤范围内的缺陷进行检测，得到检出缺陷。
可选地，所述根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复的方法包括：
在修复设备判断自动光学检出设备得到的检出缺陷是否属于过滤范围，得到不属于所述过滤范围内的检出缺陷作为过滤后的检出缺陷，并对所述过滤后的检出缺陷进行修复。
本公开还提供一种彩膜基板缺陷的分析装置，包括：
第一获取模块，用于获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的参数信息；
第二获取模块，用于获取修复设备从所述第一数量个检出缺陷中修复的第二数量个修复缺陷的参数信息；
确定模块，用于对所述第一数量个检出缺陷的过滤参数的数据和所述第二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围。
可选地，所述过滤参数包括缺陷大小、缺陷灰阶值以及缺陷灰阶值与对应的正常灰阶值的灰阶差值中的至少一个。
可选地，所述过滤参数包括缺陷大小时，所述确定模块包括：
第一确定单元，用于根据所述第一数量个检出缺陷的缺陷大小的数据和所述第二数量个修复缺陷的缺陷大小的数据，确定所述检出缺陷和修复缺陷的不同缺陷大小的数量分布；
第二确定单元，用于根据所述检出缺陷和修复缺陷的不同缺陷大小的数 量分布，得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，根据所述差异数据确定所述缺陷大小对应的过滤范围。
可选地，所述第一确定单元，用于确定所有检出缺陷的缺陷大小的第一取值范围，将所述第一取值范围划分成多个第一缺陷大小分区，其中，每一第一缺陷大小分区的长度相同；确定每一所述第一缺陷大小分区中的检出缺陷的数量；根据所述第一缺陷大小分区和对应的检出缺陷的数量，得到所述检出缺陷的缺陷大小的柱状分布图；对所述检出缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述检出缺陷的缺陷大小的洛伦兹曲线；确定所有修复缺陷的缺陷大小的第二取值范围，将所述第二取值范围划分成多个第二缺陷大小分区，其中，每一第二缺陷大小分区的长度相同；确定每一所述第二缺陷大小分区中的修复缺陷的数量；根据所述第二缺陷大小分区和对应的修复缺陷的数量，得到所述修复缺陷的缺陷大小的柱状分布图；对所述修复缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述修复缺陷的缺陷大小的洛伦兹曲线；
所述第二确定单元，用于将所述检出缺陷的缺陷大小的洛伦兹曲线和所述修复缺陷的缺陷大小的洛伦兹曲线整合至同一坐标系中进行比较，根据比较结果得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，并根据所述差异数据确定所述缺陷大小对应的过滤范围。
可选地，所述过滤参数包括缺陷灰阶值和缺陷灰阶值与对应的正常灰阶值的灰阶差值时，所述确定模块包括：
第三确定单元，用于确定所述检出缺陷和修复缺陷在不同灰阶上的数量分布；
划分单元，用于根据所述检出缺陷和修复缺陷在不同灰阶上的数量分布，将所有灰阶划分为多个灰阶分区；
第四确定单元，用于确定所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及，确定所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布；
第五确定单元，用于根据所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及所述修复缺陷的灰阶值与对应 的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，得到所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值与所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值的差异数据，并根据所述差异数据确定不同灰阶分区的灰阶差值对应的过滤范围。
本公开还提供一种彩膜基板缺陷的检测修复装置，包括：
获取模块，用于获取用于检测彩膜基板缺陷的过滤参数的过滤范围，所述过滤参数的过滤范围由上述分析方法得到；
过滤模块，用于根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复。
可选地，所述彩膜基板缺陷的检测修复装置包括自动光学检出设备，所述过滤模块将所述过滤参数的过滤范围作为缺陷判定条件对彩膜基板进行光学检测，对不属于所述过滤范围内的缺陷进行检测，得到检出缺陷。
可选地，所述彩膜基板缺陷的检测修复装置包括修复设备，所述过滤模块判断自动光学检出设备得到的检出缺陷是否属于过滤范围，得到不属于所述过滤范围内的检出缺陷作为过滤后的检出缺陷，并对所述过滤后的检出缺陷进行修复。
本公开还提供一种彩膜基板缺陷的分析装置，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现上述彩膜基板缺陷的分析方法中的步骤。
本公开还提供一种彩膜基板缺陷的检测修复装置，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现上述彩膜基板缺陷的检测修复方法中的步骤。
附图说明
为了更清楚地说明本公开实施例的技术方案，下面将对本公开实施例的描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本公开一些实施例的彩膜基板缺陷的分析方法的流程示意图；
图2为本公开一些实施例的彩膜基板缺陷的分析方法的流程示意图；
图3为本公开一些实施例的彩膜基板缺陷的分析方法的流程示意图；
图4为本公开一些实施例的自动光学检出设备检出的检出缺陷的缺陷大小的分布图；
图5为本公开一些实施例的修复设备修复的修复缺陷的缺陷大小的分布图；
图6是图4中的检出缺陷的缺陷大小的洛伦兹曲线和图5中的修复缺陷的缺陷大小的洛伦兹曲线的整合示意图；
图7为图6中的检出缺陷的缺陷大小的洛伦兹曲线和修复缺陷的缺陷大小的洛伦兹曲线做归一化处理之后的示意图；
图8为本公开一些实施例的彩膜基板缺陷的分析方法的流程示意图；
图9为本公开实施例的自动光学检出设备检出的检出缺陷和修复设备修复的修复缺陷在不同灰阶上的数量分布图；
图10为不同灰阶分区上检出缺陷和修复缺陷与对应的正常灰阶值的灰阶差值的示意图；
图11为采用灰阶差值的过滤范围对彩膜基板的缺陷进行检测或修复后实际检出缺陷和修复缺陷在不同灰阶上的数量分布图；
图12是本公开一些实施例的彩膜基板缺陷的检测修复方法的流程示意图；
图13为本公开一些实施例的彩膜基板缺陷的分析装置的结构框图；及
图14为本公开实施例的彩膜基板缺陷的检测修复装置的结构框图。
具体实施方式
为使本公开实施例的目的、技术方案和优点更加清楚，下面将结合本公开实施例的附图，对本公开实施例的技术方案进行清楚、完整地描述。显然，所描述的实施例是本公开的一部分实施例，而不是全部的实施例。基于所描述的本公开的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本公开保护的范围。
请参考图1，图1为本公开一些实施例的彩膜基板缺陷的分析方法的流程 示意图，该分析方法包括：
步骤11：获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的过滤参数的数据；
例如，可以采用自动光学检出设备进行过缺陷检测并经修复设备修复过的一批彩膜基板作为样本。
所述过滤参数可以包括缺陷大小、缺陷灰阶值以及缺陷灰阶值与对应的正常灰阶值的灰阶差值等中的至少一个。所谓正常灰阶值是指与缺陷灰阶值对应的不存在缺陷的彩膜基板的相同区域的灰阶值。
步骤12：获取修复设备从所述第一数量个检出缺陷中修复的第二数量个修复缺陷的过滤参数的数据；
步骤13：对所述第一数量个检出缺陷的过滤参数的数据和所述第二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围。
本公开实施例中，综合了自动光学检出设备的检出缺陷的过滤参数的数据和修复设备的修复缺陷的过滤参数的数据，对检出缺陷的过滤参数的数据和修复缺陷的过滤参数的数据进行比较，得到两者的差异数据，根据差异数据确定过滤参数对应的过滤范围，使得确定的过滤范围更加准确，使用该过滤范围可以对不需要修复的缺陷进行过滤，节省了修复设备确认检出缺陷的时间，提高了产能。
下面对于不同类型的过滤参数，对本公开实施例中的彩膜基板缺陷的分析方法进行详细说明。
请参考图2，图2为本公开一些实施例的彩膜基板缺陷的分析方法的流程示意图，该分析方法包括：
步骤21：获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的缺陷大小的数据；
本公开实施例中，所述缺陷大小可以是指缺陷的面积，或者缺陷的其他尺寸信息。
步骤22：获取修复设备从所述第一数量个检出缺陷中修复的第二数量个 修复缺陷的缺陷大小的数据；
步骤23：根据所述第一数量个检出缺陷的缺陷大小的数据和所述第二数量个修复缺陷的缺陷大小的数据，确定所述检出缺陷和修复缺陷的不同缺陷大小的数量分布；
步骤24：根据所述检出缺陷和修复缺陷的不同缺陷大小的数量分布，得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，根据所述差异数据确定所述缺陷大小对应的过滤范围。
本公开实施例中，对检出缺陷和修复缺陷的不同缺陷大小的数量进行统计，可以根据不同缺陷大小的数量分布，确定数量分布较少的缺陷大小，因为数量分布较少的缺陷大小说明有可能不需要进行修复，从而可以确定缺陷大小对应的过滤范围。
请参考图3，图3为本公开一些实施例的彩膜基板缺陷的分析方法的流程示意图，该分析方法包括：
步骤31：获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的缺陷大小的数据；
步骤32：获取修复设备从所述第一数量个检出缺陷中修复的第二数量个修复缺陷的缺陷大小的数据；
步骤33：确定所有检出缺陷的缺陷大小的第一取值范围，将所述第一取值范围划分成多个第一缺陷大小分区，其中，每一第一缺陷大小分区的长度相同；
例如，所述第一取值范围可以是A<Size≤B，将第一取值范围划分为“B+1-A”个分区，每一分区分别为：A<Size≤A+1，A+1<Size≤A+2，……，B-1<Size≤B，其中Size为缺陷大小。
举例来说，第一数量个检出缺陷中缺陷大小的最小值为15，缺陷大小的最大值为140，检出缺陷的缺陷大小的第一取值范围可以为15≤Size≤140，将15≤Size≤140划分为“140+1-15”个分区，每一分区分别为：15≤Size≤16，16<Size≤17，……，139<Size≤140。
当然，在本公开的其他一些实施例中，第一取值范围也可以不根据第一数量个检出缺陷中缺陷大小的最小值和最小值确定，例如，可以根据经验值 确定。
本公开实施例中，将缺陷大小进行分区，可以有效减小计算量，当然，分区越细，得到的结果越准确。
步骤34：确定每一所述第一缺陷大小分区中的检出缺陷的数量；
步骤35：根据所述第一缺陷大小分区和对应的检出缺陷的数量，得到所述检出缺陷的缺陷大小的柱状分布图；
请参考图4，图4为本公开实施例的自动光学检出设备检出的检出缺陷的缺陷大小的分布图。图4中，X轴表示缺陷大小(Size)，Y轴表示不同缺陷大小分区上的检出缺陷的数量。本公开实施例中，检出缺陷的缺陷大小的第一取值范围可以为15≤Size≤140，将15≤Size≤140划分为25个分区，每一分区分别为：15≤Size≤20，20<Size≤25，……，135<Size≤140。图4中的柱状分布图能够体现自动光学检出设备实际的检出缺陷的大小分布。
步骤36：对所述检出缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述检出缺陷的缺陷大小的洛伦兹曲线；
请参考图4中的黑色曲线，则为检出缺陷的缺陷大小的洛伦兹曲线，洛伦兹拟合是一种最接近实际生产数据的拟合，将柱状分布图转换成洛伦兹曲线的目的是，寻找自动光学检出设备实际检出缺陷的缺陷大小的分布峰值，便于在两个峰值之间寻找最优化参数值。
当然，在本公开的其他一些实施例中，也可以采用其他拟合方法，例如高斯拟合等。
步骤37：确定所有修复缺陷的缺陷大小的第二取值范围，将所述第二取值范围划分成多个第二缺陷大小分区，其中，每一第二缺陷大小分区的长度相同；
所述第二取值范围例如可以是C<Size≤D，将第一取值范围划分为“D+1-C”个分区，每一分区分别为：C<Size≤C+1，C+1<Size≤C+2，……，D-1<Size≤D，其中Size为缺陷大小。可选地，所述第二取值范围与上述第一取值范围相同，即C等于A，D等于B。
举例来说，第二数量个修复缺陷中缺陷大小的最小值为15，缺陷大小的最大值为140，修复缺陷的缺陷大小的第二取值范围可以为15≤Size≤140，将 15≤Size≤140划分为“140+1-15”个分区，每一分区分别为：15≤Size≤16，16<Size≤17，……，139<Size≤140。
当然，在本公开的其他一些实施例中，第二取值范围也可以不根据第二数量个修复缺陷中缺陷大小的最小值和最小值确定，例如，可以根据经验值确定，或者，直接与第一取值范围取相同的范围。
步骤38：确定每一所述第二缺陷大小分区中的修复缺陷的数量；
步骤39：根据所述第二缺陷大小分区和对应的修复缺陷的数量，得到所述修复缺陷的缺陷大小的柱状分布图；
请参考图5，图5为本公开实施例的修复设备修复的修复缺陷的缺陷大小的分布图。图5中，X轴表示缺陷大小(Size)，Y轴表示不同缺陷大小分区上的修复缺陷的数量。本公开实施例中，修复缺陷的缺陷大小的第二取值范围可以为15≤Size≤140，将15≤Size≤140划分为25个分区，每一分区分别为：15≤Size≤20，20<Size≤25，……，135<Size≤140。图5中的柱状分布图能够体现修复设备实际的修复缺陷的大小分布。
步骤310：对所述修复缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述修复缺陷的缺陷大小的洛伦兹曲线；
请参考图5中的黑色曲线，则为修复缺陷的缺陷大小的洛伦兹曲线，将柱状分布图转换成洛伦兹曲线的目的是，寻找修复设备实际修复缺陷的缺陷大小的分布峰值。
步骤311：将所述检出缺陷的缺陷大小的洛伦兹曲线和所述修复缺陷的缺陷大小的洛伦兹曲线整合至同一坐标系中进行比较，根据比较结果得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，并根据所述差异数据确定所述缺陷大小对应的过滤范围。
请参考图6，图6是图4中的检出缺陷的缺陷大小的洛伦兹曲线和图5中的修复缺陷的缺陷大小的洛伦兹曲线的整合示意图。图6中，X轴表示缺陷大小(Size)，Y轴表示不同缺陷大小分区上的检出缺陷和修复缺陷的数量。本公开实施例中，缺陷大小的取值范围为15≤Size≤140，将15≤Size≤140划分为25个分区，每一分区分别为：15≤Size≤20，20<Size≤25，……，135<Size≤140。
即，将图4中的检出缺陷的缺陷大小的洛伦兹曲线和图5中的修复缺陷 的缺陷大小的洛伦兹曲线整合至同一坐标系中进行比较，并根据比较结果确定缺陷大小对应的过滤范围，以图6为例，从图6中可以看出，缺陷大小在110之后的检出缺陷的数量和修复缺陷的数量均趋于0，因此，可以将缺陷大小的过滤范围定为大于110。
为了更形象的示出检出缺陷的缺陷大小的洛伦兹曲线和修复缺陷的缺陷大小的洛伦兹曲线的分布情况，本公开实施例中，还可以将检出缺陷和修复缺陷的总数量做归一化处理，请参考图7，图7为图6中的检出缺陷的缺陷大小的洛伦兹曲线和修复缺陷的缺陷大小的洛伦兹曲线做归一化处理之后的示意图，可以依据归一化处理后的检出缺陷的缺陷大小的洛伦兹曲线和修复缺陷的缺陷大小的洛伦兹曲线，设置缺陷大小的过滤范围，将数量较小的不需要修复的缺陷进行精确过滤。
上述实施例中，是以过滤参数包括缺陷大小为例对本公开实施例的彩膜基板缺陷的分析方法进行说明，下面以过滤参数包括缺陷灰阶值以及缺陷灰阶值与对应的正常灰阶值的灰阶差值为例，对本公开实施例的彩膜基板缺陷的分析方法进行说明。
请参考图8，图8为本公开一些实施例的彩膜基板缺陷的分析方法的流程示意图，该分析方法包括：
步骤81：获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的缺陷灰阶值的数据，以及每一检出缺陷的正常灰阶值；
步骤82：获取修复设备从所述第一数量个检出缺陷中修复的第二数量个修复缺陷的缺陷灰阶值的数据，以及每一修复缺陷的正常灰阶值；
步骤83：确定所述检出缺陷和修复缺陷在不同灰阶上的数量分布；
请参考图9，图9为本公开实施例的自动光学检出设备检出的检出缺陷和修复设备修复的修复缺陷在不同灰阶上的数量分布图。光学定义灰阶分为0～255阶，彩膜基板检出的缺陷都分布在这256个灰阶上。图9中X轴表示标准灰阶(Gray)值，Y轴表示在不同灰阶上检出缺陷和修复缺陷的数量。从图9中可以看出，检出缺陷和修复缺陷在各个灰阶分区上的数量分布，从而分析出哪个灰阶分区上缺陷需要过滤，对灰阶分区进行划分。
步骤84：根据所述检出缺陷和修复缺陷在不同灰阶上的数量分布，将所 有灰阶划分为多个灰阶分区；
举例来说，可以将所有灰阶(0～255)划分为四个分区，分区一的灰阶范围是：0＜Gray1≤60，分区二的灰阶范围是：60＜Gray2≤110，分区三的灰阶范围是：110＜Gray3≤140，分区四的灰阶范围是：140＜Gray4≤255。
当然，可以理解的是，设置的分区越多，可以实现越准确的过滤。
步骤85：确定所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及，确定所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布；
请参考图10，图10为不同灰阶分区上检出缺陷和修复缺陷与对应的正常灰阶值的灰阶差值的示意图，图10中，X轴表示标准灰阶，Y轴表示各灰阶分区上检出缺陷和修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值ΔGray。
步骤86：根据所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，得到所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值与所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值的差异数据，并根据所述差异数据确定不同灰阶分区的灰阶差值对应的过滤范围。
例如，根据图10中的检出缺陷和修复缺陷对应的ΔGray的变化，对每一划分的灰阶分区上的灰阶差值ΔGray的过滤范围进行设定，以将各分区不需要修复的缺陷分别过滤掉。
请参考下面的表1，表1是按照图10中的检出缺陷和修复缺陷对应的ΔGray的变化，对每一划分的灰阶分区上的灰阶差值ΔGray的过滤范围进行设定的结果：
表1
Figure PCTCN2018087864-appb-000001
也就是说，对于分区0＜Gray1≤60，过滤范围是灰阶差值小于30，即仅对灰阶差值大于或等于30的缺陷进行检测或修复，对于灰阶差值小于30的缺陷不进行检测或修复。
采用本公开实施例中设置的每一灰阶分区上的灰阶差值ΔGray的过滤范围，对彩膜基板的缺陷进行检测或修复时，自动光学检出设备的检出缺陷的数量可以减少3098个，修复设备的修复缺陷的数量可以减少7个，也就是说以0.03％的缺陷漏检率，0.114％的缺陷漏修率，将缺陷总量降低了13.76％。
请参考图11，图11为采用灰阶差值的过滤范围对彩膜基板的缺陷进行检测或修复后实际检出缺陷和修复缺陷在不同灰阶上的数量分布图。图11中X轴表示标准灰阶值，Y轴表示在不同灰阶上检出缺陷和修复缺陷的数量。比较图11和图9，可以看出，部分灰阶分区的检出缺陷和修复缺陷的数量明显降低，即将各分区不需要修复的缺陷被分别过滤掉，从而可以减少修复设备确认检出缺陷的时间，有效提高产能。
上述实施例中，过滤参数包括缺陷灰阶值以及缺陷灰阶值与对应的正常灰阶值的灰阶差值，当然，在本公开的其他一些实施例中，过滤参数也可以仅包括缺陷灰阶值。
在本公开的其他一些实施例中，过滤参数也可以同时包括缺陷大小、缺陷灰阶值以及缺陷灰阶值与对应的正常灰阶值的灰阶差值，以使得得到的过滤参数的过滤范围更准确。
可选地，本公开实施例中，为了进一步提高过滤参数的过滤范围的准确度，还可以在采用该过滤参数的过滤范围对彩膜基板进行缺陷过滤后，计算缺陷过滤率、缺陷漏检率和/或缺陷漏修率等来来评估过滤范围是否合理，并获取最优过滤范围。
其中，缺陷过滤率、缺陷漏检率和缺陷漏修率可以采用下述公式计算得到：
Figure PCTCN2018087864-appb-000002
Figure PCTCN2018087864-appb-000003
Figure PCTCN2018087864-appb-000004
本公开的上述实施例具有作业简单，容易实施的优点，采用该分析方法，可快速获取过滤参数的过滤范围。统计表明，在不影响彩膜基板品质的前提下，采用本公开实施例中的分析方法得到的过滤参数的过滤范围对彩膜基板的缺陷进行检测或修复后，可将彩膜基板的缺陷修复率由30％左右提升到60％以上，实现彩膜基板缺陷修复产能提升20％以上。
请参考图12，图12是本公开实施例的彩膜基板缺陷的检测修复方法的流程示意图，该检测修复方法包括：
步骤121：获取用于检测彩膜基板缺陷的过滤参数的过滤范围，所述过滤参数的过滤范围由上述任一实施例所述的彩膜基板缺陷的分析方法得到；
步骤122：根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复。
本公开实施例中，采用过滤参数的过滤范围可以将不需要修复的缺陷过滤掉，从而可以减少修复设备确认检出缺陷的时间，有效提高产能。
在本公开的一些实施例中，可以在自动光学检出设备上设置过滤参数的过滤范围，此时，所述根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复的方法包括：在自动光学检出设备上将所述过滤参数的过滤范围作为缺陷判定条件对彩膜基板进行光学检测，对不属于所述过滤范围内的缺陷进行检测，得到检出缺陷。
自动光学检出设备采用所述过滤参数的过滤范围作为缺陷判定条件对彩膜基板进行光学检测，得到检出缺陷时，可以减少自动光学检出设备检测出的检出缺陷的数量，从而可以减少修复设备确认检出缺陷的时间，有效提高产能。
在本公开的另外一些实施例中，可以在修复设备上设置过滤参数的过滤范围，此时，所述根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检 测或修复的方法包括：在修复设备判断自动光学检出设备得到的检出缺陷是否属于过滤范围，得到不属于所述过滤范围内的检出缺陷作为过滤后的检出缺陷，并对所述过滤后的检出缺陷进行修复。
修复设备可以对过滤后的检出缺陷进行修复，不需要在对检出缺陷再进行修复确认，在修复设备一侧进行过滤优于在自动光学检出设备一侧进行过滤，因为在修复设备过滤不影响自动光学检出设备实际检出缺陷，能让自动光学检出设备更好地监控实际生产中缺陷的变化情况。
当然，在本公开的另外一些实施例中，也可以是自动光学检出设备首先按照相关技术中的缺陷判定条件对彩膜基板进行光学检测，得到检出缺陷，然后，再采用所述过滤参数的过滤范围对检出缺陷进行过滤，得到过滤后的检出缺陷，并对所述过滤后的检出缺陷进行修复。
修复设备可以对过滤后的检出缺陷进行修复，从而可以减少修复设备确认检出缺陷的时间，有效提高产能。同时，自动光学检出设备也能够更好地监控实际生产中缺陷的变化情况。
请参考图13，本公开一些实施例还提供一种彩膜基板缺陷的分析装置，包括：
第一获取模块，用于获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的参数信息；
第二获取模块，用于获取修复设备从所述第一数量个检出缺陷中修复的第二数量个修复缺陷的参数信息；
确定模块，用于对所述第一数量个检出缺陷的过滤参数的数据和所述第二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围。
本公开实施例中，综合了自动光学检出设备的检出缺陷的过滤参数的数据和修复设备的修复缺陷的过滤参数的数据，对检出缺陷的过滤参数的数据和修复缺陷的过滤参数的数据进行比较，得到两者的差异数据，根据差异数据确定过滤参数对应的过滤范围，使得确定的过滤范围更加准确，使用该过滤范围可以对不需要修复的缺陷进行过滤，节省了修复设备确认检出缺陷的 时间，提高了产能。
本公开实施例中，可选地，所述过滤参数包括缺陷大小、缺陷灰阶值以及缺陷灰阶值与对应的正常灰阶值的灰阶差值中的至少一个。
在本公开的一些实施例中，所述过滤参数包括缺陷大小时，所述确定模块包括：
第一确定单元，用于根据所述第一数量个检出缺陷的缺陷大小的数据和所述第二数量个修复缺陷的缺陷大小的数据，确定所述检出缺陷和修复缺陷的不同缺陷大小的数量分布；
第二确定单元，用于根据所述检出缺陷和修复缺陷的不同缺陷大小的数量分布，得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，根据所述差异数据确定所述缺陷大小对应的过滤范围。
可选地，所述第一确定单元，用于确定所有检出缺陷的缺陷大小的第一取值范围，将所述第一取值范围划分成多个第一缺陷大小分区，其中，每一第一缺陷大小分区的长度相同；确定每一所述第一缺陷大小分区中的检出缺陷的数量；根据所述第一缺陷大小分区和对应的检出缺陷的数量，得到所述检出缺陷的缺陷大小的柱状分布图；对所述检出缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述检出缺陷的缺陷大小的洛伦兹曲线；确定所有修复缺陷的缺陷大小的第二取值范围，将所述第二取值范围划分成多个第二缺陷大小分区，其中，每一第二缺陷大小分区的长度相同；确定每一所述第二缺陷大小分区中的修复缺陷的数量；根据所述第二缺陷大小分区和对应的修复缺陷的数量，得到所述修复缺陷的缺陷大小的柱状分布图；对所述修复缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述修复缺陷的缺陷大小的洛伦兹曲线；
所述第二确定单元，用于将所述检出缺陷的缺陷大小的洛伦兹曲线和所述修复缺陷的缺陷大小的洛伦兹曲线整合至同一坐标系中进行比较，根据比较结果得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，并根据所述差异数据确定所述缺陷大小对应的过滤范围。
在本公开的另外一些实施例中，所述过滤参数包括缺陷灰阶值和缺陷灰阶值与对应的正常灰阶值的灰阶差值时，所述确定模块包括：
第三确定单元，用于确定所述检出缺陷和修复缺陷在不同灰阶上的数量分布；
划分单元，用于根据所述检出缺陷和修复缺陷在不同灰阶上的数量分布，将所有灰阶划分为多个灰阶分区；
第四确定单元，用于确定所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及，确定所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布；
第五确定单元，用于根据所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，得到所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值与所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值的差异数据，并根据所述差异数据确定不同灰阶分区的灰阶差值对应的过滤范围。
请参考图14，本公开一些实施例还提供一种彩膜基板缺陷的检测修复装置，包括：
获取模块，用于获取用于检测彩膜基板缺陷的过滤参数的过滤范围，所述过滤参数的过滤范围由上述任一实施例中的彩膜基板缺陷的分析方法得到；
过滤模块，用于根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复。
本公开实施例中，采用过滤参数的过滤范围可以将不需要修复的缺陷过滤掉，从而可以减少修复设备确认检出缺陷的时间，有效提高产能。
在本公开的一些实施例中，所述彩膜基板缺陷的检测修复装置包括自动光学检出设备，所述过滤模块将所述过滤参数的过滤范围作为缺陷判定条件对彩膜基板进行光学检测，对不属于所述过滤范围内的缺陷进行检测，得到检出缺陷。
在本公开的另外一些实施例中，所述彩膜基板缺陷的检测修复装置包括修复设备，所述过滤模块判断自动光学检出设备得到的检出缺陷是否属于过滤范围，得到不属于所述过滤范围内的检出缺陷作为过滤后的检出缺陷，并对所述过滤后的检出缺陷进行修复。
除非另作定义，本公开中使用的技术术语或者科学术语应当为本公开所属领域内具有一般技能的人士所理解的通常意义。本公开中使用的“第一”、“第二”以及类似的词语并不表示任何顺序、数量或者重要性，而只是用来区分不同的组成部分。同样，“一个”或者“一”等类似词语也不表示数量限制，而是表示存在至少一个。“连接”或者“相连”等类似的词语并非限定于物理的或者机械的连接，而是可以包括电性的连接，不管是直接的还是间接的。“上”、“下”、“左”、“右”等仅用于表示相对位置关系，当被描述对象的绝对位置改变后，则该相对位置关系也相应地改变。
以上所述是本公开的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本公开所述原理的前提下，还可以作出若干改进和润饰，这些改进和润饰也应视为本公开的保护范围。

Claims (18)

  1. 一种彩膜基板缺陷的分析方法，包括：
    获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的过滤参数的数据；
    获取修复设备从所述第一数量个检出缺陷中修复的第二数量个修复缺陷的过滤参数的数据；
    对所述第一数量个检出缺陷的过滤参数的数据和所述第二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围。
  2. 根据权利要求1所述的彩膜基板缺陷的分析方法，其中，所述过滤参数包括缺陷大小、缺陷灰阶值以及缺陷灰阶值与对应的正常灰阶值的灰阶差值中的至少一个。
  3. 根据权利要求2所述的彩膜基板缺陷的分析方法，其中，所述过滤参数包括缺陷大小时，所述对所述第一数量个检出缺陷的过滤参数的数据和所述第二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围的步骤包括：
    根据所述第一数量个检出缺陷的缺陷大小的数据和所述第二数量个修复缺陷的缺陷大小的数据，确定所述检出缺陷和修复缺陷的不同缺陷大小的数量分布；
    根据所述检出缺陷和修复缺陷的不同缺陷大小的数量分布，得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，根据所述差异数据确定所述缺陷大小对应的过滤范围。
  4. 根据权利要求3所述的彩膜基板缺陷的分析方法，其中，
    所述确定所述检出缺陷和修复缺陷的不同缺陷大小的数量分布的步骤包括：
    确定所有检出缺陷的缺陷大小的第一取值范围，将所述第一取值范围划 分成多个第一缺陷大小分区，其中，每一第一缺陷大小分区的长度相同；
    确定每一所述第一缺陷大小分区中的检出缺陷的数量；
    根据所述第一缺陷大小分区和对应的检出缺陷的数量，得到所述检出缺陷的缺陷大小的柱状分布图；
    对所述检出缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述检出缺陷的缺陷大小的洛伦兹曲线；
    确定所有修复缺陷的缺陷大小的第二取值范围，将所述第二取值范围划分成多个第二缺陷大小分区，其中，每一第二缺陷大小分区的长度相同；
    确定每一所述第二缺陷大小分区中的修复缺陷的数量；
    根据所述第二缺陷大小分区和对应的修复缺陷的数量，得到所述修复缺陷的缺陷大小的柱状分布图；
    对所述修复缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述修复缺陷的缺陷大小的洛伦兹曲线；
    其中，所述根据所述检出缺陷和修复缺陷的不同缺陷大小的数量分布，得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，根据所述差异数据确定所述缺陷大小对应的过滤范围的步骤包括：
    将所述检出缺陷的缺陷大小的洛伦兹曲线和所述修复缺陷的缺陷大小的洛伦兹曲线整合至同一坐标系中进行比较，根据比较结果得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，并根据所述差异数据确定所述缺陷大小对应的过滤范围。
  5. 据权利要求2-4任一项所述的彩膜基板缺陷的分析方法，其中，所述过滤参数包括缺陷灰阶值和缺陷灰阶值与对应的正常灰阶值的灰阶差值时，所述对所述第一数量个检出缺陷的过滤参数的数据和所述第二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围的步骤包括：
    确定所述检出缺陷和修复缺陷在不同灰阶上的数量分布；
    根据所述检出缺陷和修复缺陷在不同灰阶上的数量分布，将所有灰阶划分为多个灰阶分区；
    确定所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及，确定所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布；
    根据所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，得到所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值与所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值的差异数据，并根据所述差异数据确定不同灰阶分区的灰阶差值对应的过滤范围。
  6. 一种彩膜基板缺陷的检测修复方法，包括：
    采用权利要求1-5任一项所述的彩膜基板缺陷的分析方法获取用于检测彩膜基板缺陷的过滤参数的过滤范围；
    根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复。
  7. 根据权利要求6所述的彩膜基板缺陷的检测修复方法，其中，所述根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复的方法包括：
    在自动光学检出设备上将所述过滤参数的过滤范围作为缺陷判定条件对彩膜基板进行光学检测，对不属于所述过滤范围内的缺陷进行检测，得到检出缺陷。
  8. 根据权利要求6所述的彩膜基板缺陷的检测修复方法，其中，所述根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复的方法包括：
    在修复设备判断自动光学检出设备得到的检出缺陷是否属于过滤范围，得到不属于所述过滤范围内的检出缺陷作为过滤后的检出缺陷，并对所述过滤后的检出缺陷进行修复。
  9. 一种彩膜基板缺陷的分析装置，包括：
    第一获取模块，用于获取自动光学检出设备从多个彩膜基板样本中检出的第一数量个检出缺陷的参数信息；
    第二获取模块，用于获取修复设备从所述第一数量个检出缺陷中修复的第二数量个修复缺陷的参数信息；
    确定模块，用于对所述第一数量个检出缺陷的过滤参数的数据和所述第 二数量个修复缺陷的过滤参数的数据进行分析，得到所述检出缺陷的过滤参数的数据与修复缺陷的过滤参数的数据的差异数据，根据所述差异数据确定所述过滤参数对应的过滤范围。
  10. 根据权利要求9所述的彩膜基板缺陷的分析装置，其中，所述过滤参数包括缺陷大小、缺陷灰阶值以及缺陷灰阶值与对应的正常灰阶值的灰阶差值中的至少一个。
  11. 根据权利要求10所述的彩膜基板缺陷的分析装置，其中，所述过滤参数包括缺陷大小时，所述确定模块包括：
    第一确定单元，用于根据所述第一数量个检出缺陷的缺陷大小的数据和所述第二数量个修复缺陷的缺陷大小的数据，确定所述检出缺陷和修复缺陷的不同缺陷大小的数量分布；
    第二确定单元，用于根据所述检出缺陷和修复缺陷的不同缺陷大小的数量分布，得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，根据所述差异数据确定所述缺陷大小对应的过滤范围。
  12. 根据权利要求11所述的彩膜基板缺陷的分析装置，其中，
    所述第一确定单元，用于确定所有检出缺陷的缺陷大小的第一取值范围，将所述第一取值范围划分成多个第一缺陷大小分区，其中，每一第一缺陷大小分区的长度相同；确定每一所述第一缺陷大小分区中的检出缺陷的数量；根据所述第一缺陷大小分区和对应的检出缺陷的数量，得到所述检出缺陷的缺陷大小的柱状分布图；对所述检出缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述检出缺陷的缺陷大小的洛伦兹曲线；确定所有修复缺陷的缺陷大小的第二取值范围，将所述第二取值范围划分成多个第二缺陷大小分区，其中，每一第二缺陷大小分区的长度相同；确定每一所述第二缺陷大小分区中的修复缺陷的数量；根据所述第二缺陷大小分区和对应的修复缺陷的数量，得到所述修复缺陷的缺陷大小的柱状分布图；对所述修复缺陷的缺陷大小的柱状分布图进行洛伦兹曲线拟合，得到所述修复缺陷的缺陷大小的洛伦兹曲线；
    所述第二确定单元，用于将所述检出缺陷的缺陷大小的洛伦兹曲线和所述修复缺陷的缺陷大小的洛伦兹曲线整合至同一坐标系中进行比较，根据比 较结果得到所述检出缺陷的缺陷大小的数据与修复缺陷的缺陷大小的数据的差异数据，并根据所述差异数据确定所述缺陷大小对应的过滤范围。
  13. 据权利要求10-12任一项所述的彩膜基板缺陷的分析装置，其中，所述过滤参数包括缺陷灰阶值和缺陷灰阶值与对应的正常灰阶值的灰阶差值时，所述确定模块包括：
    第三确定单元，用于确定所述检出缺陷和修复缺陷在不同灰阶上的数量分布；
    划分单元，用于根据所述检出缺陷和修复缺陷在不同灰阶上的数量分布，将所有灰阶划分为多个灰阶分区；
    第四确定单元，用于确定所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及，确定所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布；
    第五确定单元，用于根据所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，以及所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值在不同灰阶分区上的数量分布，得到所述检出缺陷的灰阶值与对应的正常灰阶值的灰阶差值与所述修复缺陷的灰阶值与对应的正常灰阶值的灰阶差值的差异数据，并根据所述差异数据确定不同灰阶分区的灰阶差值对应的过滤范围。
  14. 一种彩膜基板缺陷的检测修复装置，包括：
    获取模块，用于采用权利要求1-5任一项所述的彩膜基板缺陷的分析方法获取用于检测彩膜基板缺陷的过滤参数的过滤范围；
    过滤模块，用于根据所述过滤参数的过滤范围，对彩膜基板的缺陷进行检测或修复。
  15. 根据权利要求14所述的彩膜基板缺陷的检测修复装置，其中，所述彩膜基板缺陷的检测修复装置包括自动光学检出设备，所述过滤模块将所述过滤参数的过滤范围作为缺陷判定条件对彩膜基板进行光学检测，对不属于所述过滤范围内的缺陷进行检测，得到检出缺陷。
  16. 根据权利要求14所述的彩膜基板缺陷的检测修复装置，其中，所述彩膜基板缺陷的检测修复装置包括修复设备，所述过滤模块判断自动光学检 出设备得到的检出缺陷是否属于过滤范围，得到不属于所述过滤范围内的检出缺陷作为过滤后的检出缺陷，并对所述过滤后的检出缺陷进行修复。
  17. 一种彩膜基板缺陷的分析装置，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现如权利要求1-5任一项所述彩膜基板缺陷的分析方法中的步骤。
  18. 一种彩膜基板缺陷的检测修复装置，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现如权利要求6-8任一项所述彩膜基板缺陷的检测修复方法中的步骤。
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