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WO2008066055A1 - Dispositif de détection de défaut linéaire, dispositif de fabrication de substrat semi-conducteur, procédé de détection de défaut linéaire, procédé de fabrication de substrat semi-conducteur, programme pour amener un ordinateur à fonctionner en tant que d - Google Patents

Dispositif de détection de défaut linéaire, dispositif de fabrication de substrat semi-conducteur, procédé de détection de défaut linéaire, procédé de fabrication de substrat semi-conducteur, programme pour amener un ordinateur à fonctionner en tant que d Download PDF

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Publication number
WO2008066055A1
WO2008066055A1 PCT/JP2007/072888 JP2007072888W WO2008066055A1 WO 2008066055 A1 WO2008066055 A1 WO 2008066055A1 JP 2007072888 W JP2007072888 W JP 2007072888W WO 2008066055 A1 WO2008066055 A1 WO 2008066055A1
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WO
WIPO (PCT)
Prior art keywords
data
defect
defect candidate
range
linear
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2007/072888
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English (en)
Japanese (ja)
Inventor
Masakazu Yanase
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Sharp Corp
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Sharp Corp
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Publication date
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Publication of WO2008066055A1 publication Critical patent/WO2008066055A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N2021/9513—Liquid crystal panels
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/9501—Semiconductor wafers
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30164—Workpiece; Machine component

Definitions

  • Linear defect detection apparatus and semiconductor substrate manufacturing apparatus linear defect detection method and semiconductor substrate manufacturing method
  • program for causing a computer to function as the detection apparatus or the manufacturing apparatus program for causing a computer to function as the detection apparatus or the manufacturing apparatus, and the program Recording medium
  • the present invention relates to a technique for detecting a linear defect. More specifically, the present invention relates to a technique for detecting the presence or absence of a linear defect whose density changes from image data acquired by photographing a subject.
  • an inspection process in the manufacturing process plays an important role as a quality control method.
  • a wide variety of defect inspection devices using image processing technology have been developed and used in the inspection process in order to stabilize costs and reduce costs by reducing the number of visual inspectors.
  • One of the defects to be detected by such a defect inspection apparatus is a linear defect.
  • An example of the cause of the occurrence of the linear defect is uneven coating by a coater apparatus.
  • a coater apparatus is used to form a film by coating a resist or the like on a glass substrate with a uniform thickness.
  • a glass substrate with a non-uniform region in resist film thickness is a defect.
  • the above-mentioned linear defect is that the shape of the non-uniform film thickness is linear.
  • Patent Document 1 discloses a technique for detecting a line defect in a panel with high accuracy. According to this technique, first, an input image is pre-processed by background image difference processing and flattening processing, and then edge detection processing is performed by edge enhancement filter processing. Next, the image after edge detection processing is divided into a direction perpendicular to the direction in which the linear defect occurs, and the luminance value of each pixel of each divided image is divided into a direction horizontal to the direction in which the linear defect occurs. To obtain 1D projection data.
  • the moving average, standard deviation, and maximum value of the one-dimensional projection data Then, the minimum value is obtained, and a threshold value for line defect detection is calculated. Finally, if the maximum value of the one-dimensional projection data is equal to or greater than the threshold value, it is determined that there is a line defect.
  • Patent Document 1 JP-A-2005-172559
  • a glass substrate is illuminated and the reflected light is imaged by a camera or other imaging apparatus.
  • the interference light of the light on the lower surface and the upper surface of the film is imaged by an imaging device.
  • an image captured in this way is referred to as an interference image.
  • the luminance value varies along a line from a large value to a small value, and further from a small value to a large value. Repeated periodically.
  • the luminance value of the linear defect area in the interference image is constant due to the influence of the deflection of the glass substrate.
  • Such a linear defect whose luminance value changes along the defect is referred to as a linear defect having a shading change.
  • the present invention has been made to solve the above-described problems, and an object of the present invention is to provide a detection device that can detect a linear defect whose density changes.
  • Another object is to provide a semiconductor substrate manufacturing apparatus using the above-described detection apparatus. Another object is to provide a detection method for detecting a linear defect whose density changes.
  • Another object is to provide a method for manufacturing a semiconductor substrate using the above detection method.
  • Another object is to provide a program for causing a computer to function as the detection device.
  • Still another object is to provide a computer-readable recording medium storing the above program.
  • a linear defect detection device is provided.
  • This detection apparatus is an image representing an image acquired by imaging a subject.
  • a storage unit for storing image data; a generation unit for generating a plurality of divided images by dividing the image along a first direction; and luminance values of a plurality of regions constituting each of the plurality of divided images.
  • a first calculation unit that calculates first data based on the luminance value of each region along the second direction orthogonal to the first direction, and each divided image based on each first data. 2nd data in which each defect candidate range is emphasized is calculated based on a range specifying unit for specifying each defect candidate range including linear defect candidates and image data corresponding to each defect candidate range.
  • the integration unit that integrates each second data along the second direction, and the data obtained by integrating each second data,
  • a defect identification unit that identifies a linear defect.
  • the first direction is a direction orthogonal to a direction in which a linear defect occurs.
  • the second direction is a direction in which linear defects are generated.
  • the first calculation unit calculates the first data by calculating an average value of the luminance values of the respective regions.
  • the second calculation unit calculates the second data by aligning the sign of the image data corresponding to each defect candidate range to either positive or negative.
  • the second calculation unit is configured to display each image data when the defect candidate range is brighter than the range other than the defect candidate range, or when the defect candidate is darker than the range other than the defect candidate range.
  • the second data is calculated by adding the image data after the sign is inverted and adding the image data after the sign is inverted.
  • the second calculation unit calculates the intensity of the linear defect candidate, and if the intensity is lower than a preset reference value, the defect candidate range having an intensity lower than the reference value is selected. Image data is excluded from the calculation of the second data.
  • the range specifying unit calculates a defect candidate range by using a morphology process.
  • a linear defect detection apparatus includes a storage unit that stores image data representing an image acquired by imaging a subject, and a plurality of images obtained by dividing the image along a first direction. Based on the brightness value of each of the regions along the second direction orthogonal to the first direction, the brightness values of the plurality of regions constituting each of the plurality of split images, and the generation unit that generates the split images, A first calculation unit for calculating each of the first data and each first data; Therefore, based on the range specifying unit for specifying the first defect candidate range including the linear defect candidate in each divided image, and the image data corresponding to each first defect candidate range, An intensity calculator that calculates the intensity of each linear defect candidate; a second calculator that calculates second data in which each first defect candidate range is emphasized based on each intensity; and Based on the data obtained by integrating each second data along with the integration unit that integrates each second data along the direction of 2, at least 2 of the plurality of divided images A determination unit that determines a second defect candidate range that includes
  • the determining unit specifies and specifies each of the first defect candidate ranges having a luminance value that exceeds a predetermined value for each divided image with a predetermined threshold value.
  • a range on the coordinate axis along the first direction is specified, and for each first defect candidate range, each of the specified range on the coordinate axis overlaps! /
  • the first defect candidate range is identified as a second defect candidate range by overlapping each of the specified ranges on the coordinate axis! /.
  • the confirmation unit calculates the first median of the range on the coordinate axis according to the first direction for the first defect candidate range, and follows the first direction for the second defect candidate range.
  • Calculate the second median of the range on the coordinate axis calculate the difference between the first median and the second median, and whether the difference is within the preset tolerance range If the difference is within the allowable error range, it is determined that a linear defect exists in the second defect candidate range.
  • the range specifying unit calculates the first defect candidate range using morphology processing.
  • a semiconductor substrate manufacturing apparatus includes a first forming portion that forms a thin film on a semiconductor substrate, a second forming portion that forms a resist film on the thin film, and an exposure that transfers the pattern to the resist film by exposing the pattern.
  • the optical part, the first removal part for removing the resist film other than the transferred pattern, and the substrate are photographed.
  • An imaging unit that captures an image and obtains image data, and the detection device described above are provided.
  • the semiconductor substrate manufacturing apparatus includes a determination unit that determines whether or not the substrate satisfies a predetermined condition based on a result of the substrate inspection, and a case where the substrate satisfies the condition.
  • a second removal unit that removes an unnecessary thin film from the substrate and a rework unit that returns the substrate to the first removal unit when the substrate does not satisfy the conditions are further provided.
  • the second forming portion applies a resist in one direction to the substrate.
  • the second forming unit includes a supply unit that applies a resist to the substrate.
  • the semiconductor substrate manufacturing apparatus further includes an adjustment unit that adjusts the supply unit when the substrate does not satisfy the conditions.
  • the adjustment unit changes a resist application condition.
  • the adjustment unit includes a cleaning unit that cleans the supply unit.
  • a linear defect detection method includes a step of reading image data representing an image acquired by imaging a subject, a step of dividing the image along a first direction to generate a plurality of divided images, and a plurality of divided images.
  • a first calculation step for calculating the first data based on the luminance values of the regions along the second direction orthogonal to the first direction, the luminance values of the plurality of regions constituting Based on each first data, a range specifying step for specifying each defect candidate range including the linear defect candidates in each divided image, and each defect based on the image data corresponding to each defect candidate range.
  • a second calculation step for calculating each second data in which the candidate range is emphasized, a step for multiplying each second data along the second direction, and integrating each second data Obtained by Based on over data, and a step of identifying a linear defect.
  • the first direction is a direction orthogonal to a direction in which a linear defect occurs.
  • the second direction is a direction in which linear defects are generated.
  • the first data is calculated by calculating an average value of the luminance values of the respective regions.
  • the second calculation step calculates the second data by aligning the sign of the image data corresponding to each defect candidate range to either positive or negative.
  • each image data is calculated by inverting the sign and adding the image data after the sign is inverted.
  • the second calculation step calculates an intensity of the linear defect candidate, and if the intensity is lower than a preset reference value, an image of the defect candidate range having an intensity lower than the reference value. Exclude the data from the calculation of the second data.
  • the range specifying step calculates a defect candidate range using morphology processing.
  • the linear defect detection method is an image representing an image acquired by imaging a subject. A step of reading data, a step of dividing the image along the first direction to generate a plurality of divided images, and a luminance value of a plurality of regions constituting each of the plurality of divided images, A first calculation step for calculating the first data based on the luminance value of each region along the second direction orthogonal to the direction of 1, and each divided image based on the first data Based on the range identification step for identifying the first defect candidate range including the linear defect candidates in each and the image data corresponding to each first defect candidate range, the intensity of each linear defect candidate is determined.
  • a calculating step; A step of calculating second data in which each first defect candidate range is emphasized based on each intensity, a step of integrating each second data along the second direction, and a second data Determining a second defect candidate range that includes linear defect candidates that span at least two of the plurality of divided images based on the data obtained by integrating the data of A step of confirming whether or not there is a linear defect that satisfies a predetermined condition in the second defect candidate range, and a divided image that includes a linear defect that satisfies the condition. Determining whether or not there is a linear defect based on a ratio existing in the plurality of divided images.
  • the step of determining the second defect candidate range includes, for each divided image, a first defect candidate having a luminance value whose difference from a predetermined threshold exceeds a predetermined value. For each step of identifying the range and for each identified first defect candidate range, Identifying each range on the coordinate axis along the first direction, determining whether each identified range on the coordinate axis overlaps for each first defect candidate range, Identifying a first defect candidate range that overlaps each of the ranges on the coordinate axes as a second defect candidate range.
  • a step of calculating a first median of a range on the coordinate axis according to the first direction and a second defect candidate range A step of calculating a second median of the range on the coordinate axis following the direction of 1, a step of calculating a difference between the first median and the second median, and a tolerance range in which the difference is set in advance.
  • the range specifying step includes a step of calculating a first defect candidate range using morphology processing.
  • a method for manufacturing a semiconductor substrate was transferred to a step of forming a thin film on a semiconductor substrate, a step of forming a resist film on the thin film, and a step of transferring the pattern to the resist film by exposing the pattern.
  • the method for manufacturing a semiconductor substrate includes a step of determining whether or not the substrate satisfies a predetermined condition based on a result of inspection of the substrate, and a case where the substrate satisfies the condition.
  • Substrate force The method further includes the step of removing the unnecessary thin film and the step of returning the substrate to the step of removing the resist film when the substrate does not satisfy the conditions.
  • the resist is applied in one direction to the substrate.
  • the step of forming the resist film includes a step of applying a resist to the substrate by the resist film forming apparatus.
  • the method for manufacturing a semiconductor substrate further includes a step of adjusting the resist film forming apparatus when the substrate does not satisfy the conditions.
  • the adjusting step includes a step of changing a resist coating condition.
  • the adjusting step is performed by a resist film forming apparatus for applying the resist.
  • a program for causing a computer to function as a detection device that detects a linear defect reads to the computer image data representing an image acquired by capturing an image of the subject, generates a plurality of divided images by dividing the image along the first direction, and each of the plurality of divided images. Calculating each of the first data based on the luminance values of the plurality of regions constituting the plurality of regions, and the luminance values of the respective regions along the second direction orthogonal to the first direction; Based on the data of 1, each defect candidate range is emphasized based on the step of identifying the defect candidate range including the linear defect candidate in each divided image and the image data corresponding to each defect candidate range. Based on the data obtained by calculating each of the second data, integrating each second data along the second direction, and integrating each second data. , And a step of identifying a linear defect.
  • a program includes a step of reading image data representing an image acquired by imaging a subject to a computer, and dividing the image along a first direction into a plurality of divided images.
  • a first step based on the luminance value of the plurality of regions constituting each of the plurality of divided images and the luminance value of each region along the second direction orthogonal to the first direction.
  • a computer-readable recording medium storing the above program is provided.
  • FIG. 1 is a flowchart showing a main process among processes for manufacturing a thin panel.
  • FIG. 2 is a diagram showing a configuration of manufacturing system 1 according to the embodiment of the present invention.
  • FIG. 3 is a diagram showing a part of resist coating apparatus 12.
  • FIG. 4 is a diagram showing a configuration of an inspection system 14 including an inspection apparatus 40 according to an embodiment of the present invention.
  • FIG. 5 is a diagram showing an image acquired by photographing substrate 30.
  • FIG. 6 shows a procedure of a defect detection method according to the present embodiment.
  • FIG. 7 is a diagram conceptually showing one mode of data storage in storage unit 42.
  • FIG. 8 is a block diagram showing a configuration of functions realized by a calculation unit 43 that realizes the inspection apparatus 40.
  • FIG. 9 is a flowchart showing a part of a series of processes executed by calculation unit 43.
  • FIG. 10 is a diagram showing a defect state in image data 1000 obtained by photographing substrate 30.
  • FIG. 11 is a diagram showing a light and shade profile of a linear defect.
  • FIG. 12 is a diagram illustrating an image dividing method and an image dividing direction in image data 1000.
  • FIG. 13 is a diagram showing one-dimensional projection data of each divided image.
  • FIG. 14 is a diagram showing a process of extracting linear defect candidates from one-dimensional data.
  • FIG. 15 is a diagram showing the relationship between the average luminance value and the X coordinate value for each one-dimensional projection data.
  • FIG. 16 is a diagram showing the relationship between the luminance value difference and the X coordinate value for data obtained by emphasizing defect candidates.
  • FIG. 17 is a diagram showing the relationship between the luminance value difference and the X coordinate value for each one-dimensional projection data.
  • FIG. 18 is a diagram showing a relationship between an average value of luminance value differences and an X coordinate value.
  • FIG. 19 is a flowchart showing a manufacturing method according to this modification.
  • FIG. 20 is a block diagram showing a configuration of functions realized by a calculation unit 43 included in the inspection apparatus according to the present embodiment.
  • FIG. 21 is a diagram conceptually showing one mode of data storage in storage unit 42 included in the inspection apparatus according to the second embodiment of the present invention.
  • FIG. 22 is a flowchart showing a series of operations executed by the inspection apparatus according to the second embodiment of the present invention.
  • FIG. 23 is a block diagram showing a configuration of functions realized by a calculation unit 43 included in the inspection apparatus 40 according to the third embodiment of the present invention.
  • FIG. 24 is a diagram conceptually showing one mode of data storage in storage unit 42 of inspection apparatus 40 according to the third embodiment of the present invention.
  • FIG. 25 is a flowchart showing a series of operations executed by a calculation unit 43 that implements an inspection apparatus 40 according to a third embodiment of the present invention (part 1).
  • FIG. 26 is a flowchart showing a series of operations executed by a calculation unit 43 that implements an inspection apparatus 40 according to the third embodiment of the present invention (part 2).
  • FIG. 27 is a diagram showing a change in one-dimensional data by morphology processing.
  • FIG. 28 is a diagram showing defect candidate enhancement one-dimensional data.
  • FIG. 29 is a block diagram showing a hardware configuration of a computer system 2800 functioning as the inspection apparatus 40. Explanation of symbols
  • FIG. 1 is a flowchart showing the main steps among the steps for manufacturing a thin panel.
  • step S110 as a thin film forming process, a thin film is formed on the substrate.
  • the resist film forming apparatus forms a resist film on the thin film.
  • the exposure apparatus transfers the pattern to the resist film.
  • step S140 as a development process, the resist film other than the pattern portion is removed.
  • step S150 the state of the substrate after development is inspected. This inspection is performed by an image processing technique described later.
  • step S160 an unnecessary thin film is removed from the developed substrate.
  • step S 170 the resist remaining on the substrate is removed as a resist removal process.
  • FIG. 2 is a diagram showing the configuration of the manufacturing system 1.
  • the manufacturing system 1 includes a pre-process 10, a resist coating device 12, an inspection system 14, a next process 16, a control device 18, a cleaning device 20, and transfer devices 22, 24, and 26.
  • Pre-process 10 includes an apparatus for forming a thin film in step S110. Previous process 1
  • the glass substrate on which the thin film is formed at 0 is transferred to the resist coating device 1 by the transport device 22.
  • the resist coating apparatus 12 applies a resist on the substrate on which the thin film is formed.
  • the resist coating device 12 may be a device using a force spin method, which is a device using a scanning method, for example.
  • an exposure process (Step S 130) and a development process (Step S 140) are performed, and then the substrate is carried into the inspection system 14 by the transport device 24.
  • the inspection system 14 includes an imaging device and an image processing inspection device.
  • white light is irradiated onto the glass substrate, and the imaging device captures the reflected light, and Get image data.
  • the image processing inspection apparatus performs image processing using the image data, and determines whether there is a defect that may exist on the glass substrate.
  • the inspection result obtained by the inspection system 14 is sent to the control device 18.
  • the substrate that has been inspected is carried into the next process 16 by the transfer device 26.
  • the next step 16 includes, for example, an etching step (step S160) and a resist removal step (step S170).
  • the control device 18 controls the operating conditions of the resist coating device 12 and the cleaning device 20 based on the inspection result given from the inspection system 14. For example, when it is detected as a result of inspection by the inspection system 14 that a defect exists in the substrate, the control device 18 sends a command to the resist coating device 12 to interrupt the resist coating. If it is determined as a result of the inspection that the nozzle (not shown) in the resist coating device 12 needs to be cleaned, the control device 18 instructs the cleaning device 20 to clean the nozzles constituting the resist coating device 12. . In this case, the nozzle moves from the resist coating device 12 to the cleaning device 20.
  • FIG. 3 shows a part of the resist coating apparatus 12.
  • the resist coating device 12 applies a resist to the substrate by, for example, a scanning method.
  • the resist coating apparatus 12 includes a nozzle 32 for supplying a resist to the substrate 30.
  • the resist coating apparatus 12 moves the nozzle 32 in a predetermined direction with respect to the substrate 30 to apply the resist 34 to the surface of the substrate 30.
  • the form of the resist coating apparatus 12 is not limited to a so-called spinless coater using a scanning method, and may be a so-called spin coater using a spin method.
  • FIG. 4 is a diagram showing the system configuration of the inspection system 14.
  • the inspection system 14 includes a light 46, a camera 48, an inspection device 40, and a display device 50.
  • the inspection device 40 includes an image input unit 41, a storage unit 42, a calculation unit 43, an output unit 44, and an input unit 45.
  • the substrate 30 as an inspection object is carried into the inspection system 14 from the resist coating apparatus 12, and is arranged at a predetermined position on a stage (not shown). Inspection
  • the object is not limited to the substrate 30 and may be, for example, a liquid crystal panel, a semiconductor, an electronic component, a plastic, metal, wood, paper, cloth, or the like.
  • the substrate 30 is coated with a resist.
  • the substrate 30 is photographed by the camera 48, and is taken out of the stage after photographing. Thereafter, another glass substrate is carried onto the stage, and the above-described processing is repeated.
  • the light 46 is a substrate disposed at the position based on a light emission command from the inspection device 40.
  • the irradiated light is, for example, general white light.
  • the camera 48 receives and reflects the reflected light from the substrate 30 and outputs it as image data to the inspection apparatus 40.
  • the camera 48 captures the reflected light from the substrate 30 in response to the signal output from the inspection device 40.
  • the camera 48 is realized by, for example, a CCD (Charge Coupled Device) method, a CMOS (Complementary Mental Oxide Semiconductor) method, or other methods.
  • the camera 48 sends the data acquired by photographing the board 30 to the detection device 40 as image data.
  • the inspection device 40 receives the image data input via the image input unit 41 and stores it in the storage unit 42.
  • Camera 48 can be an area sensor camera or line sensor camera!
  • the storage unit 42 stores data given from the outside of the inspection apparatus 40 and data generated in the inspection apparatus 40.
  • the data given from the outside includes, for example, the image data, a set value that defines the operation of the inspection apparatus 40, and the like.
  • the generated data includes data for image processing calculated by the calculation unit 43, inspection results, and the like.
  • the storage unit 42 stores data in a nonvolatile and volatile manner.
  • the storage unit 42 that stores data in a nonvolatile manner is realized by, for example, a hard disk that can store a large amount of data.
  • the storage unit 42 may be a non-volatile storage device (for example, flash memory) that can hold data even if power is not supplied to the hard disk.
  • the storage unit 42 is an EPROM (Erasable Programmable Read Only Memory) that can erase and write data any number of times, and an EEPROM (Electronically Erasable and Programmable ROM) that can electrically rewrite the contents. ), With UV rays Any of UV (Ultra-Violet) EPROM capable of erasing and rewriting data any number of times, and other circuits having a configuration capable of storing and storing data in a nonvolatile manner may be used.
  • EPROM Erasable Programmable Read Only Memory
  • EEPROM Electrically Erasable and Programmable ROM
  • the storage unit 42 that stores data in a volatile manner functions as a work memory that temporarily stores data used by the calculation unit 43.
  • the storage unit 42 is, for example, a high-speed RAM (Random Access Memory), SRAM (Static RAM), DRAM (Dynamic RAM), SDRAM (Synchronous DRAM), or double data rate mode capable of temporarily storing data.
  • the calculation unit 43 executes a predetermined image process using the image data stored in the storage unit 42 and data prepared in advance.
  • the computing unit 43 outputs an instruction to generate an image and an instruction to display the image on the display device 50 (hereinafter referred to as “drawing instruction”) according to the program stored in the storage unit 42.
  • operation unit 43 controls communication with an external device (for example, control device 18) of inspection device 40 via a communication interface.
  • the calculation unit 43 is specifically a microprocessor, an LSI capable of programming S
  • the main function of the inspection apparatus 40 is realized by cooperation of hardware and software. Specifically, this function is realized by the arithmetic unit 43 executing a program prepared in advance.
  • the arithmetic unit 43 can be realized by a circuit configured to execute processing realized by the program, for example, an FPGA (Fiend Programmable Gate Array).
  • the output unit 44 outputs the data generated by the calculation unit 43 to the display device 50.
  • the display device 50 displays an image based on the data.
  • the display device 50 displays, for example, an original image taken by the camera 48 and an image of a defect including resist coating unevenness detected based on image processing. Further, the display device 50 may display the result of the inspection of the substrate 30.
  • the input unit 45 receives input of data or instructions from the outside.
  • the input unit 45 is realized by, for example, a touch panel, a touch pad, a keyboard, a pen tablet, a mouse, or other pointing devices mounted on the surface of the display device 50.
  • the display device 50 displays various types of information, such as characters and images, to the user (operator) of the inspection device 40.
  • the display device 50 displays an image based on the image data output from the inspection device 40.
  • the display device 50 refers to the data stored in the image display area of the storage unit 42 and displays an image corresponding to the data.
  • the display device 50 is, for example, a liquid crystal display device, a CRT (Cathode Ray Tube), an FED (Field Emission Display), a PDP (Plasma Display Panel), a 3 ⁇ 4 EL (Electro Luminescence) display, a dot matrix, or other image display. Display of the system!
  • FIG. 5 (A) is a diagram showing an image acquired by photographing substrate 30.
  • FIG. 5 (B) is a diagram showing a state in which a part of the image is divided.
  • an image 52 is acquired.
  • An area 54 to be inspected is set in advance for the substrate 30.
  • the area 54 is specified by giving data defining the area to the input unit 45, for example.
  • each image has a luminance value smaller than the surrounding luminance value (that is, one having a darker area than the surrounding area) and an area having a luminance value larger than the surrounding luminance value (the surrounding area than the surrounding area). Also bright parts). For example, areas 56-1, 56-2, 56-3, 56-4 (which are detected as brighter areas (hereinafter referred to as “white stripes”) than the surrounding areas. , 58—2 is the brightness value than the surrounding area Is detected as an area with a small amount of black (hereinafter referred to as “black stripe”). If such white stripes or black stripes are lined up in the negative direction, these areas are detected as linear defects.
  • FIG. 6 is a diagram showing the flow of procedures performed to detect a linear defect as shown in FIG.
  • the image 52 acquired by photographing the substrate 30 is divided into a plurality of rectangular images (hereinafter referred to as “divided images”).
  • an area (streak candidate A) having a luminance value different from the surrounding luminance value is recognized. In some cases, this area may not be recognized.
  • areas are recognized, the number of areas varies depending on the object to be inspected.
  • the brightness value of the white stripe is different from the brightness value of the black stripe, one of the signs (positive or negative) of the brightness value is inverted and unified to one of the signs.
  • the luminance value of each area is integrated along a predetermined direction 610. Furthermore, an average value is calculated based on the integrated value.
  • each candidate is defective based on the threshold value and the average value prepared in advance.
  • streak candidate B exceeds the threshold value (weak muscle unevenness position determination threshold value 620), and is thus identified as a defect candidate (streaks candidate B). Others are treated as noise because they are below the threshold.
  • the contrast of the streak candidate A exceeds a preset contrast threshold and whether or not the position is within a preset range with respect to the center position.
  • the Line candidate C corresponds to line candidate A that satisfies the above two conditions.
  • FIG. 7 is a diagram conceptually showing one mode of data storage in storage unit 42.
  • the storage unit 42 includes a plurality of areas for storing data.
  • the number of pixels (DPIX) of the divided image is stored in area 710.
  • the number of pixels of the divided image defines the number of pixels for dividing the acquired image data. For example, when image data having 500 pixels in the vertical direction is used, the image data is divided into five because the value of the pixel number DPIX is 100.
  • the difference (BDA) of the luminance values of the linear defect candidates included in the divided image is stored in area 720.
  • a threshold (THA) for extracting a linear defect across a plurality of divided images is stored in area 730.
  • a second threshold value (THB) for extracting a linear defect across a plurality of divided images is stored in area 740.
  • the image data acquired by the camera 48 is stored in the area 750.
  • the image data constitutes a database composed of different types of image data.
  • the data stored in the area 740 as well as the area 710 force is input through the input unit 45, for example. In another aspect, these data may be transmitted from the control device 18.
  • FIG. 8 is a block diagram showing a configuration of functions realized by the calculation unit 43.
  • Each function is realized by the CPU or other processor functioning as the arithmetic unit 43 executing a program for realizing each function.
  • the calculation unit 43 includes a divided image generation unit 810, a first calculation unit 820, a defect candidate identification unit 830, a second calculation unit 840, an integration unit 850, and a defect range determination unit 860. Including.
  • the calculation unit 43 reads image data acquired by imaging the substrate 30 from the storage unit 42.
  • the divided image generation unit 810 divides the image data along the first direction and generates a plurality of divided images. Specifically, the divided image generation unit 810 has the number of pixels DPIX (region The image is divided into a plurality of images according to the value of 710).
  • the first calculation unit 820 is a luminance value of a plurality of regions constituting each of the luminance values of a region along a second direction orthogonal to the first direction. Calculate the first data based on the value. Specifically, the first calculation unit 820 calculates the average value of the luminance values of the divided images in a direction parallel to the direction in which the linear defect occurs.
  • the defect candidate specifying unit 830 specifies a range including the linear defect candidate in each divided image based on the first data calculated by the first calculating unit 820. Specifically, the defect candidate specifying unit 830 specifies each of the above ranges using the difference (BDA) of the brightness values stored in the storage unit 42.
  • the second calculation unit 840 calculates data (second data) in which each range is emphasized using image data corresponding to the range specified by the defect candidate specifying unit 830. Specifically, in one aspect, the second calculation unit 840 calculates the second data by aligning the sign of the image data corresponding to each range to either positive or negative.
  • the second calculation unit 840 determines whether each of the candidates is darker than the range other than the range or when the defect candidate is darker than the range other than the range.
  • the second data is calculated by inverting the sign of the image data and adding the image data after the sign is inverted.
  • second calculation unit 840 calculates the intensity of the linear defect candidate. When the intensity falls below a preset reference value, the second calculation unit 840 extracts the image data of the defect candidate range having an intensity below the reference value from the calculation target of the second data. exclude.
  • Accumulation unit 850 accumulates the second data calculated by second calculation unit 840 along the second direction (for example, the direction in which linear defects occur).
  • the defect range determination unit 860 determines the range of the linear defect based on the data calculated by the integration unit 850. Specifically, the defect range determination unit 860 uses the linear defect extraction threshold (THA) to determine whether or not the region corresponding to the calculated data is a linear defect.
  • TAA linear defect extraction threshold
  • FIG. 9 is a flowchart showing a part of a series of processes executed by the calculation unit 43.
  • FIG. 10 is a diagram showing a state of a defect in the image data 1000 obtained by photographing the substrate 30.
  • FIG. 11 is a diagram showing the density profile of the linear defect shown in FIG. FIG.
  • FIG. 12 is a diagram showing an image dividing method in image data 1000 and a direction in which the image is divided.
  • FIG. 13 shows one-dimensional projection data of each divided image.
  • FIG. 14 is a diagram showing a process for extracting linear defect candidates from one-dimensional data.
  • FIG. 15 is a diagram showing the relationship between the average luminance value and the X coordinate value for each one-dimensional projection data.
  • FIG. 16 is a diagram showing the relationship between the difference in luminance value and the X coordinate value for data obtained by emphasizing defect candidates.
  • FIG. 17 shows the relationship between the luminance value difference and the X coordinate value for each one-dimensional projection data.
  • FIG. 18 is a diagram showing the relationship between the average value of the luminance value differences and the X coordinate value.
  • the X coordinate of the image data 1000 is defined along the direction 1030, for example.
  • the Y coordinate is defined along direction 1040.
  • the brightness value Bgood in the captured image other than the area of the linear defect 1020 is “128”.
  • the shading profile on line A-B of the linear defect 1020 changes as shown in FIG.
  • inspection apparatus 40 receives an initial setting. Specifically, when the operator of the inspection apparatus 40 inputs the number of divided image pixels (DPIX), the luminance difference (BDA), and the threshold values (THA, THB) via the input unit 45, the calculation unit 43 Is These data are stored in the storage unit 42 (areas 710 to 740).
  • DPIX number of divided image pixels
  • BDA luminance difference
  • THB threshold values
  • step S920 operation unit 43 receives the input of the original image data output from camera 48, and stores the data as image data 1000 in storage unit 42 (area 750).
  • step S930 operation unit 43 reads image data 1000 from area 750 into the work area.
  • the work area is secured in the storage unit 42 when the calculation unit 43 executes processing.
  • the calculation unit 43 generates divided image data by dividing the image data in a direction perpendicular to the direction in which the linear defect occurs using the number of pixels (DPIX).
  • the last divided image is The number of pixels DPIX of the divided image is used from the other image edge different from the one image edge where the image division is started. In this case, a partially overlapping region exists between the last generated divided image and the divided image generated immediately before that.
  • step S940 the computing unit 43 projects the shade (one-dimensionalization) for each divided image. Specifically, for each divided image, the calculation unit 43 accumulates the image data in a direction parallel to the direction in which the linear defect occurs. Specifically, as shown in FIG. 10, the calculation unit 43 integrates the luminance values of the pixels having the same X coordinate value along the positive direction of the Y coordinate axis defined as the integration direction 1040. . Further, the calculation unit 43 obtains the average value of the luminance values by executing the division process using the number of pixels in the Y coordinate axis direction for the integrated value (first data). For example, in FIG. 13, the data calculated in step S940 is represented as one-dimensional projection data IDPA, IDPB, IDPC, IDPD, IDPE (1310, 1320, 1330, 1340, 1350) of each divided image.
  • step S950 the arithmetic unit 43 extracts, for each divided image, a linear defect candidate A that is a candidate for a linear defect in the divided image using the one-dimensional projection data. .
  • this processing will be specifically described using the one-dimensional projection data IDPA (1310).
  • the line defect candidate A SA_IDPA1
  • the calculation unit 43 executes the same processing for all the one-dimensional projection data IPD A (1310) to IPDE (1350). The result is shown in Figure 15.
  • step S960 operation unit 43 generates one-dimensional data in which defect candidates are emphasized (defect candidate reinforcement tone one-dimensional data). Specifically, the computing unit 43 calculates the difference from the brightness value Bgood using the one-dimensional projection data IPDA (1310) of the range of the linear defect candidate A extracted in step S950, and Find the absolute value. The calculation unit 43 further sets the values in other ranges to 0 and generates defect candidate-emphasized one-dimensional data. For example, when the one-dimensional projection data IPDA (1310) shown in FIG. 15 is used, defect candidate-emphasized one-dimensional data based on the data is obtained as shown in FIG. The calculation unit 43 calculates the data for all the data shown in FIG. The result is shown in FIG.
  • step S970 operation unit 43 integrates the defect candidate-enhanced one-dimensional data generated in step S960 for each of all the divided images. More specifically, the calculation unit 43 integrates each defect candidate emphasizing one-dimensional data having the same X coordinate value along the integration direction 1710 shown in FIG. Further, the calculation unit 43 calculates the average value of the one-dimensional data in which the defect candidates are emphasized by dividing the value obtained by the integration by the number of divided images. As an example, the average value obtained using the data shown in FIG. 17 is shown in FIG.
  • step S980 the calculation unit 43 extracts a linear defect across a plurality of divided images using the average value generated in step S970. Specifically, the calculation unit 43 calculates an X coordinate that exceeds the threshold (THB) value for extracting linear defects from the average value. Specify a range of For example, in the example shown in FIG. 18, since the interval between the X coordinate value (X ⁇ ) and the X coordinate value (X_R) exceeds the threshold value THB, the calculation unit 43 has a linear shape across the plurality of divided images. Judge that the defect exists in this range. The calculation unit 43 associates the result of the determination with the image data 1000 and stores it in the area secured in the storage unit 42 as the inspection result.
  • THB threshold
  • the linear defect 1020 included in the substrate 1010 is detected using the image data 1000.
  • the calculation unit 43 sets a threshold THA for extracting a linear defect candidate with a divided image force.
  • the threshold THA 10.
  • step S960 the calculation unit 43 calculates the strength of the linear defect candidate A.
  • the calculation unit 43 sets, for each linear defect candidate A, the one having the largest value of the defect candidate reinforcing tone one-dimensional data (first data) as the intensity. For example, referring to SA_IDPA1 in FIG. 16, the calculation unit 43 calculates the maximum value in the defect candidate emphasizing one-dimensional data value between the X process and X_R as the intensity. Further, the calculation unit 43 classifies the linear defect candidate A whose intensity is equal to or higher than the threshold THA as the “true” linear defect candidate A, and excludes the other linear defect candidates A. Therefore, all the values of the defect candidate emphasizing one-dimensional data in the range of the linear defect candidate A having an intensity less than the threshold THA are set to “0”.
  • step S960 With the above processing added in step S960, noise components that are considered to be components other than true linear defects can be eliminated at the stage of linear defect candidate A.
  • the inspection apparatus 40 according to this embodiment can detect a linear defect with higher accuracy.
  • the method for calculating the intensity of the linear defect candidate A according to the present embodiment is not limited to the aspect using the maximum value in the defect candidate-enhanced one-dimensional data as described above.
  • an average value of defect candidate-emphasized one-dimensional data values in a range between X_L and X_R may be calculated, and the average value may be used as the linear defect candidate A intensity. Even if there is random noise in the brightness value in the direction perpendicular to the direction of the linear defect, The device 40 can extract the linear defect candidate A more accurately.
  • the calculation unit 43 calculates the defect candidate emphasis of the width WX / 2 in the center between X_L and X_R.
  • the average value is calculated, and the average value is used as the intensity of the linear defect candidate A.
  • the calculation unit 43 calculates the defect candidate emphasis one-dimensional data value for all sections between XL and X_R as described above.
  • the decrease in the intensity of the linear defect candidate A due to the influence of both ends of the range, which is a problem when calculating the average value, can be eliminated, and as a result, the inspection apparatus 40 can more accurately detect the linear defect candidate A. Can be extracted.
  • the defect value is calculated as the absolute value of the difference between the one-dimensional projection data in the range of the linear defect candidate A and the luminance value Bgood. Reinforcement tone 1D data is generated. Therefore, it is possible to treat a line defect that is brighter than the surrounding area and a line defect that is darker than the surrounding area without distinction. Value cancellation due to simple integration of positive and negative data is prevented. As a result, the inspection apparatus 40 can accurately extract even a linear defect with a change in shading.
  • the calculation unit 43 integrates the defect candidate-emphasized one-dimensional data for all the divided images, and calculates an average value of the data obtained by the integration. For this reason, the inspection apparatus 40 uses a force S to extract a linear defect split into a plurality of line segments with high accuracy.
  • the calculation unit 43 sets the defect candidate emphasis one-dimensional data other than the range of the linear defect candidate A to 0, thereby providing data having a noise component. Has been removed in the previous stage of processing used. Therefore, after the above-mentioned defect candidate coordination 1D data is integrated and the integrated value force average value is calculated, the occurrence of false detection due to the increase in the intensity of the noise component due to the integration of only the noise component is prevented. obtain. As a result, the inspection apparatus 40 can extract linear defects with high accuracy without erroneous detection.
  • the manufacturing method according to this modification outputs an instruction to change the manufacturing process of the glass substrate according to the result of the inspection by the inspection device 40. It differs from the manufacturing method described above in that it can be performed.
  • FIG. 19 is a flowchart showing a manufacturing method according to this modification. The same steps as those described above are given the same step numbers. Therefore, the description thereof will not be repeated here.
  • step S1910 it is determined whether or not the result of the inspection by inspection apparatus 40 is satisfactory. Specifically, the control device 18 determines whether or not the inspection result sent from the inspection system 14 satisfies a preset reference value. If the inspection result exceeds the reference value, the controller 18 determines that the glass substrate has been processed as specified (YES in step S 1910), and carries the glass substrate into the next step 16. The instruction to do is output. Specifically, after carrying in, the substrate is etched in step S160.
  • control device 18 determines that the processing was not performed as specified, and outputs an instruction to reprocess the glass substrate. .
  • the glass substrate is transferred to the rework process. Specifically, in step S1920, the applied resist film is once removed from the glass substrate, and after being cleaned, it is carried into the resist film forming process again.
  • FIG. 20 is a block diagram showing a configuration of functions realized by arithmetic unit 43 provided in the inspection apparatus according to the present embodiment.
  • the calculation unit 43 further includes a confirmation unit 870 and a determination unit 880 in addition to the configuration shown in FIG.
  • the defect candidate specifying unit 830 identifies each of the ranges (first defect candidate ranges) including the linear defect candidates in the divided image based on the divided image data generated by the division. Determine.
  • the second calculation unit 840 calculates the intensity of each linear defect candidate based on the image data corresponding to the first defect candidate range. Furthermore, the second calculation unit 840 calculates data (defect candidate emphasis one-dimensional data) in which the first defect candidate range is emphasized based on the intensity as the second data.
  • the accumulating unit 850 accumulates the second data along the above-described second direction (the direction in which the linear defect occurs).
  • the defect range determination unit 860 generates a linear defect candidate spanning at least two divided images out of a plurality of divided images based on the data obtained by integrating the second data.
  • a second defect candidate range to be included is determined.
  • the defect range determination unit 860 specifies, for each divided image, the first defect candidate range having a luminance value that is greater than a predetermined value with respect to a predetermined threshold value.
  • the defect range determination unit 860 specifies a range on the coordinate axis along the first direction for the specified first defect candidate range.
  • the defect range determination unit 860 determines, for each first defect candidate range, whether or not each of the specified ranges on the coordinate axis overlaps.
  • the defect range determination unit 860 determines the first defect candidate range in which each of the specified ranges on the coordinate axis overlaps as the second defect candidate range.
  • the confirmation unit 870 confirms whether or not a linear defect that satisfies a predetermined condition exists in the second defect candidate range. Specifically, the confirmation unit 870 calculates the first median of the range on the coordinate axis according to the first direction for the first defect candidate range. The confirmation unit 870 calculates the second median of the range on the coordinate axis along the first direction for the second defect candidate range. The confirmation unit 870 calculates a difference between the first median and the second median, and confirms whether or not the difference is within a preset allowable error range. When the difference is within the allowable error range, the confirmation unit 870 determines that the linear defect exists in the second defect candidate range.
  • Determination unit 880 determines the presence / absence of a linear defect based on the ratio at which a divided image including a linear defect that satisfies the above condition exists in a plurality of divided images.
  • FIG. 21 is a diagram conceptually showing one mode of data storage in the storage unit 42.
  • a threshold value (THB2) for extracting a linear defect across a plurality of divided images is stored in area 2040.
  • the threshold value (THC) for reconfirming the linear defect candidate in the divided image is stored in the area 2050.
  • the number of pixels (SCDPIX) that defines the tolerance of positional deviation of linear defects is stored in area 2060.
  • a ratio (SCR) for determining whether or not a linear defect exists in the divided image is stored in an area 2070.
  • FIG. 22 is a flowchart showing a series of operations executed by the inspection apparatus according to the second embodiment of the present invention.
  • description will be made using the configuration shown in FIG.
  • the same step numbers are assigned to the same operations as those in the first embodiment. Therefore, description thereof will not be repeated here.
  • step S2110 operation unit 43 of inspection device 40 receives input of data for initial setting. Specifically, the calculation unit 43 calculates the number of divided image pixels (DPIX), the difference in luminance value (BDA), the threshold (THA, THB2, THC), and the allowable number of misalignment pixels (SCD PIX). Then, each input of the linear defect candidate existence rate (SCR) is received, and the data is written in an area secured in advance in the storage unit 42. Further, the calculation unit 43 reads the original image data 1000 that has already been input from the camera 48 into the inspection apparatus 40 into the work area, and executes the same process as the process described in the first embodiment (step S1). S 920 to S950).
  • DPIX the number of divided image pixels
  • BDA difference in luminance value
  • THB2 the threshold
  • SCD PIX allowable number of misalignment pixels
  • step S2120 operation unit 43 calculates the difference between the one-dimensional data in the range of linear defect candidate A determined in step S950 and luminance value Bgood.
  • the calculation unit 43 calculates the defect candidate enhancement data (second data) by obtaining the absolute value of the difference and setting the values in other ranges to 0.
  • defect candidate enhancement data second data
  • the calculation unit 43 calculates the intensity of the linear defect candidate A.
  • the calculation unit 43 is connected to each line.
  • Candidate defect enhancement for line defect candidate A The one with the maximum value of the one-dimensional data is the intensity of the line defect candidate A.
  • the maximum value of the defect candidate emphasizing one-dimensional data between X ⁇ and X_R shown in FIG. 16 is obtained as the intensity.
  • the calculation unit 43 sets the linear defect candidate A whose intensity is equal to or greater than the threshold (THA) as a “true” linear defect candidate A, and excludes the other linear defect candidates A.
  • the defect candidate emphasis that specifies the range of the linear defect candidate A having an intensity less than the threshold THA in the divided image is changed to “0” for all defect candidate emphasis 1D data.
  • step S970 operation unit 43 integrates the defect candidate one-dimensional data, and further calculates an average value of the data after the integration.
  • step S2140 operation unit 43 satisfies the predetermined condition for the range of linear defect candidate B specified in step S2130 for the one-dimensional data for each divided image.
  • the condition satisfies the overlap degree between the range of the linear defect candidate B and the range of the linear defect candidate A specified in step S2130 and the condition of the overlap degree. This is the strength of the candidate linear defect A.
  • the calculation unit 43 calculates the central coordinate value BCX of the linear defect candidate B.
  • the median value of X ⁇ and X_R is calculated as the median coordinate value BCX.
  • the computing unit 43 similarly calculates the central coordinate value ACX for each linear defect candidate A.
  • the linear defect candidate A SA_IDPA1
  • the median value of X ⁇ and X_R is calculated as the median coordinate value ACX.
  • the calculation unit 43 calculates the difference between the central coordinate value BCX and the central coordinate value ACX.
  • the condition for the degree of overlap is a misalignment of linear defects. This means that the allowable number of pixels is below SCDPIX.
  • the intensity of the linear defect candidate A needs to exceed the threshold value (THC) set in step S2110.
  • THC threshold value
  • step S2150 the calculation unit 43 calculates, for each linear defect candidate B, how much of the divided image has the linear defect candidate C, and the ratio is equal to or greater than the existence ratio SCR. It is determined that a linear defect candidate D such as is a “true linear defect”.
  • the inspection apparatus 40 determines whether or not the linear defect candidate A really exists in the range of the linear defect candidate B as described in step S2140 again. By checking, it is possible to more accurately determine whether the linear defect candidate B is a true defect or a noise. As a result, the inspection apparatus 40 can detect even a linear defect having a change in shading with high accuracy.
  • step S2150 by calculating the ratio of the divided image in which the linear defect candidate C exists, the ratio is compared with a preset reference value, thereby inspecting the inspection apparatus. 40 can more accurately determine whether the linear defect candidate B is a true defect or noise. As a result, the inspection apparatus 40 can accurately extract even a linear defect with a change in shading.
  • the inspection apparatus 40 according to the present embodiment has a function of determining the range using data subjected to morphology processing when determining the range of linear defect candidates. And different. Note that the inspection apparatus according to the present embodiment is realized by causing the plug processor to execute processing having the above-described functions unique to the apparatus. Other functions are the same as those of the inspection apparatus 40 shown in FIG. Therefore, the inspection apparatus according to this embodiment will be described based on the configuration of the inspection apparatus 40 shown in FIG.
  • FIG. 23 is a block diagram showing a configuration of functions realized by the arithmetic unit 43 provided in the inspection apparatus 40.
  • the calculation unit 43 further includes a morphology processing unit 2210 in addition to the configuration shown in FIG. Prepare for.
  • the morphology processing unit 2210 is functionally connected to the first calculation unit 820 so as to operate based on the output from the first calculation unit 820.
  • the output from the morphology processing unit 2210 is input to the defect candidate specifying unit 830.
  • FIG. 24 is a diagram conceptually showing one mode of data storage in the storage unit 42 of the inspection apparatus 40.
  • the morphology processing filter size (MFS) is stored in the area 2310.
  • the intensity (BDTHA) for extracting the linear defect candidate in the divided image is stored in area 2320.
  • FIG. 25 and FIG. 26 are flowcharts showing a series of operations executed by the calculation unit 43 that implements the inspection apparatus 40 according to the present embodiment. The same steps as those described above are given the same step numbers. Therefore, description thereof will not be repeated here.
  • Fig. 27 shows the change in one-dimensional data due to morphology processing.
  • FIG. 28 shows one-dimensional data for defect candidate emphasis.
  • step S2410 operation unit 43 accepts an input of an initial value for executing a process defined in advance in inspection apparatus 40.
  • the calculation unit 43 receives input of the number of pixels of the divided image (DPIX), the morphology processing filter size (MFS), and the intensity (BDTHA) for extracting a linear defect candidate.
  • DPIX the number of pixels of the divided image
  • MFS morphology processing filter size
  • BDTHA the intensity
  • operation unit 43 executes the above-described processing (FIG. 9).
  • step S2500 operation unit 43 executes a morphology process to be described later (FIG. 26).
  • step S2420 the calculation unit 43 generates one-dimensional data in which defect candidates are emphasized based on the linear defect candidate A determined in the morphology process (defect candidate enhanced one-dimensional data). Is generated.
  • the computing unit 43 calculates the average value of the one-dimensional data of defect candidate emphasis (step S970), calculates data representing the area of the linear defect based on the average value, and specifies the area. (Step S980).
  • step S2510 operation unit 43 extracts candidate A (1) for a linear defect brighter than the surroundings.
  • step S2510 the computing unit 43 performs minimum value filtering on the data as shown in FIG. 27A (extraction of a linear defect candidate A (l) brighter than the surroundings).
  • the filter size of the minimum value filter is the morphology processing filter size (MFS)
  • MFS morphology processing filter size
  • FIG. 27 (B) is a diagram showing a result obtained by performing the above-described minimum value filtering process on the one-dimensional data shown in FIG. 27 (A).
  • the computing unit 43 performs maximum value filtering on the one-dimensional data shown in FIG.
  • the filter size in the maximum value filter processing is the morphology processing filter size (MFS), and this value is “11” in the present embodiment. Therefore, the calculation unit 43 calculates the largest value among all 11 pixels in consideration of up to 5 pixels adjacent to the left and right of the target pixel, and changes the value of the target pixel to the largest value. .
  • FIG. 27C is a diagram showing a result obtained by performing the maximum value filtering process on the one-dimensional data shown in FIG. 27B.
  • the calculation unit 43 uses the one-dimensional data shown in Fig. 27 (A) and the one-dimensional data shown in Fig. 27 (C) to calculate the difference between the values of the positions of the respective pixels.
  • the range of the peak that appears by calculation is specified as the linear defect candidate A (l).
  • the calculation unit 43 specifies the maximum value of the peak range as the intensity of the linear defect candidate A (l) and stores it in the storage unit 42.
  • FIG. 27 (D) is a diagram showing the one-dimensional data of the results obtained in this way.
  • step S 2520 operation unit 43 extracts dark line defect candidate A (2) from the surroundings. Specifically, the calculation unit 43 determines the maximum value for one-dimensional data. Perform filter processing and obtain the processed data. Next, the calculation unit 43 performs minimum value filtering on the acquired data, and acquires the processed one-dimensional data. The calculation unit 43 calculates a difference between the one-dimensional data before the first filter processing is executed and the one-dimensional data after the minimum value filter processing is performed, and stores the difference in the storage unit 42.
  • the data obtained by the difference has a value of 0 or less due to its nature, it is converted into one-dimensional data having only a value of 0 or more by obtaining the absolute value of the data. .
  • the range of the peak is specified as the linear defect candidate A (2).
  • the calculation unit 43 further specifies the maximum value of the peak range as the intensity of the linear defect candidate A (2).
  • step S2530 operation unit 43 determines linear defect candidate A. More specifically, the calculation unit 43 calculates the intensity of the linear defect candidate A (1) extracted in step S2510 and the linear defect candidate A (2) extracted in step S2520. Those with an extraction strength (BDTHA) or higher are identified as linear defect candidate A.
  • BDTHA extraction strength
  • step S2420 operation unit 43 calculates the one-dimensional data after calculating the difference of the range of linear defect candidate A identified in the morphology process (step S2500). It is stored in the storage unit 42, and one-dimensional data for defect candidate emphasis is generated with values in other ranges set to zero. Specifically, when the above-described process for emphasizing defect candidates is performed on the one-dimensional data after the difference calculation shown in FIG. 27 (D), only defect candidates are displayed as shown in FIG. One-dimensional data DSA with emphasis on is calculated.
  • the computing unit 43 executes the processes in steps S970 and S980 to identify a linear defect.
  • the inspection apparatus 40 performs filtering using the parameters.
  • a plurality of morphology processing filter sizes MFS may be set in the inspection apparatus 40. In this way, processing similar to the above can be executed for each filter size.
  • the calculation of the intensity of the linear defect candidate A (l) and the linear defect candidate A (2) is not limited to the aspect in which the maximum value of each peak range is the intensity as described above. Average peak range A value or the like may be used as the intensity.
  • the inspection apparatus 40 determines a linear defect candidate A using a morphology process. As a result, as shown in FIG. 27 (A), even when there is a change in the brightness value of the range other than the defect such as the one-dimensional projection data, the inspection apparatus 40 accurately identifies the linear defect range. It can be detected.
  • FIG. 29 is a block diagram illustrating a hardware configuration of a computer system 2800 that functions as the inspection apparatus 40.
  • the computer system 2800 mainly includes a CPU 2810 for executing a program, a mouse 2820 and a keyboard 2830 for receiving an instruction input by a user of the computer system 2800, and data generated by the execution of the program by the CPU 2810.
  • RAM 2840 that stores data input via mouse 2820 or keyboard 2830 in a volatile manner
  • hard disk 2850 that stores data in a nonvolatile manner
  • optical disk drive 2860 monitor 2880
  • communication I / O F (Interface) 2890 Each hardware is connected to each other by a data bus.
  • a CD-ROM 2862 is attached to the optical disk drive 2860.
  • the processing in the computer system 2800 is realized by each hardware and software executed by the CPU 2810.
  • Such software may be stored in the hard disk 2850 in advance.
  • the software may be stored in a CD-ROM2862 or other recording medium and distributed as a program product.
  • the software may be provided as a program product that can be downloaded by an information provider connected to the Internet.
  • Such software is read from the recording medium by the optical disk drive 2860 or another reading device, or downloaded via the communication I / F 2890, and then temporarily stored in the hard disk 2850.
  • the software is read from the hard disk 2850 by the CPU 2810 and stored in the RAM 2840 in the form of an executable program.
  • the CPU 2810 executes the program.
  • DVD-ROM Digital Versatile Disk ROM
  • CD Compact Disk
  • MO Magnetic Optical Disk
  • floppy registered trademark
  • CF Compact Flash
  • SM Smart Media (registered trademark)
  • MMC Multi Media Card
  • SD Secure Digital
  • Memory Stick registered trademark
  • xD picture card xD picture card
  • USB Universal Serial Bus
  • the program includes a source program format program, a compressed program, an encrypted program, and the like that can be executed only by a program that can be directly executed by the CPU 2810.
  • the communication I / F 2890 is connected to the computer system 2800 based on the control signal output by the CPU 2810 and communicates with other communication devices (not shown! /).
  • Communication I / F 2890 exchanges data with CPU2810.
  • the communication I / F 2890 receives image data from the camera 48 and other image data acquisition devices via wired or wireless communication.
  • CPU 2810 transmits data for controlling the image data acquisition apparatus via communication I / F 2890. Further, the CPU 2810 receives data returned from the image data acquisition apparatus according to the transmitted data via the communication I / F2 890.
  • Communication I / F2890 is a USB (Universal Serial Bus) 1 • 1, USB2.0, other communication interface for serial transfer, an interface using Ethernet (registered trademark), a Centronics specification, IEEE (Institute of Electrical and Electronic Engineers) 1284, other communication interfaces that perform parallel transfer, IEEE1394, other interfaces using the SCSI (Small Computer System Interface) standard, IEEE802.11a, IEEE802. l ib, IEEE802.l lg, or any other interface for data communication using wireless technology [0181]
  • the embodiments disclosed this time should be considered as illustrative in all points and not restrictive.
  • the scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.
  • the present invention can be used for, for example, an image inspection device, an image recognition device, a positioning device using an image, an autonomous robot with an image recognition function, an industrial robot with an image recognition function, and the like.

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Abstract

L'invention concerne un procédé d'inspection capable de détecter un défaut linéaire avec une précision élevée. Le procédé d'inspection comprend une étape de lecture de données d'image (S920), une étape de division de l'image (S930), une étape de projection d'une densité pour chaque image divisée pour générer des données unidimensionnelles (S940), une étape de détermination d'un candidat de défaut linéaire (A) pour chaque image divisée (S950), une étape de génération de données unidimensionnelles mises en évidence par un candidat de défaut (S960), une étape d'intégration des données unidimensionnelles mises en évidence par un candidat de défaut dans la zone du candidat de défaut linéaire (A) pour toutes les images divisées afin de calculer la valeur moyenne (S970), et une étape de calcul de la zone du candidat de défaut linéaire s'étendant à travers les multiples images divisées à partir de la valeur moyenne (S980).
PCT/JP2007/072888 2006-11-29 2007-11-28 Dispositif de détection de défaut linéaire, dispositif de fabrication de substrat semi-conducteur, procédé de détection de défaut linéaire, procédé de fabrication de substrat semi-conducteur, programme pour amener un ordinateur à fonctionner en tant que d Ceased WO2008066055A1 (fr)

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JP2006322082A JP4160991B2 (ja) 2006-11-29 2006-11-29 線状の欠陥の検出装置および半導体基板の製造装置、線状の欠陥の検出方法および半導体基板の製造方法、コンピュータを当該検出装置または…
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WO2014034526A1 (fr) * 2012-08-28 2014-03-06 住友化学株式会社 Appareil et procédé de contrôle de défauts
WO2014032312A1 (fr) * 2012-08-31 2014-03-06 深圳市华星光电技术有限公司 Procédé et dispositif de réparation de motifs d'un substrat matriciel
CN103822924A (zh) * 2014-03-04 2014-05-28 周俊雄 电磁炉面板组件检测设备
US9098897B2 (en) 2011-03-29 2015-08-04 Hewlett-Packard Development Company, L.P. Detection of scratches on an image
CN112598689A (zh) * 2020-12-29 2021-04-02 凌云光技术股份有限公司 一种纹理背景下弱线的提取方法
CN115808382A (zh) * 2023-02-02 2023-03-17 深圳裕典通微电子科技有限公司 一种应用于压力传感器的压电薄膜在线检测方法及系统
CN117745724A (zh) * 2024-02-20 2024-03-22 高唐县瑞景精密机械有限公司 基于视觉分析的石材打磨加工缺陷区域分割方法
CN119044222A (zh) * 2024-09-29 2024-11-29 中国科学院苏州纳米技术与纳米仿生研究所 基于球差电镜的样品位错快速定位方法

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JP7185839B2 (ja) * 2018-06-15 2022-12-08 オムロン株式会社 画像処理装置、画像処理方法及び画像処理プログラム
CN109934802B (zh) * 2019-02-02 2021-06-22 浙江工业大学 一种基于傅里叶变换和图像形态学的布匹疵点检测方法
US11430235B2 (en) * 2019-09-06 2022-08-30 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and storage medium
JP7367462B2 (ja) * 2019-10-29 2023-10-24 コニカミノルタ株式会社 画像検査装置及び画像検査システム

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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9098897B2 (en) 2011-03-29 2015-08-04 Hewlett-Packard Development Company, L.P. Detection of scratches on an image
WO2014034526A1 (fr) * 2012-08-28 2014-03-06 住友化学株式会社 Appareil et procédé de contrôle de défauts
JP5643918B2 (ja) * 2012-08-28 2014-12-17 住友化学株式会社 欠陥検査装置および欠陥検査方法
WO2014032312A1 (fr) * 2012-08-31 2014-03-06 深圳市华星光电技术有限公司 Procédé et dispositif de réparation de motifs d'un substrat matriciel
CN103822924A (zh) * 2014-03-04 2014-05-28 周俊雄 电磁炉面板组件检测设备
CN103822924B (zh) * 2014-03-04 2016-04-06 周俊雄 电磁炉面板组件检测设备
CN112598689A (zh) * 2020-12-29 2021-04-02 凌云光技术股份有限公司 一种纹理背景下弱线的提取方法
CN112598689B (zh) * 2020-12-29 2024-05-17 凌云光技术股份有限公司 一种纹理背景下弱线的提取方法
CN115808382A (zh) * 2023-02-02 2023-03-17 深圳裕典通微电子科技有限公司 一种应用于压力传感器的压电薄膜在线检测方法及系统
CN117745724A (zh) * 2024-02-20 2024-03-22 高唐县瑞景精密机械有限公司 基于视觉分析的石材打磨加工缺陷区域分割方法
CN117745724B (zh) * 2024-02-20 2024-04-26 高唐县瑞景精密机械有限公司 基于视觉分析的石材打磨加工缺陷区域分割方法
CN119044222A (zh) * 2024-09-29 2024-11-29 中国科学院苏州纳米技术与纳米仿生研究所 基于球差电镜的样品位错快速定位方法

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