WO2018088760A2 - 검사체에 대한 양부 판정 조건을 조정하는 방법 및 장치 - Google Patents

검사체에 대한 양부 판정 조건을 조정하는 방법 및 장치 Download PDF

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Publication number
WO2018088760A2
WO2018088760A2 PCT/KR2017/012408 KR2017012408W WO2018088760A2 WO 2018088760 A2 WO2018088760 A2 WO 2018088760A2 KR 2017012408 W KR2017012408 W KR 2017012408W WO 2018088760 A2 WO2018088760 A2 WO 2018088760A2
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WO
WIPO (PCT)
Prior art keywords
error
determination
test
value
reference value
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
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PCT/KR2017/012408
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English (en)
French (fr)
Korean (ko)
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WO2018088760A3 (ko
Inventor
구대성
김용
박기원
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Koh Young Technology Inc
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Koh Young Technology Inc
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Filing date
Publication date
Application filed by Koh Young Technology Inc filed Critical Koh Young Technology Inc
Priority to JP2019525739A priority Critical patent/JP2020500308A/ja
Priority to CN201780070386.5A priority patent/CN109997028B/zh
Priority to US16/349,802 priority patent/US11199503B2/en
Priority to EP17869347.9A priority patent/EP3540412B1/en
Priority to CN202210685954.5A priority patent/CN115112663A/zh
Priority to EP21196822.7A priority patent/EP3961332B1/en
Publication of WO2018088760A2 publication Critical patent/WO2018088760A2/ko
Publication of WO2018088760A3 publication Critical patent/WO2018088760A3/ko
Anticipated expiration legal-status Critical
Priority to US16/713,886 priority patent/US11366068B2/en
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
    • 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
    • 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
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/1717—Systems in which incident light is modified in accordance with the properties of the material investigated with a modulation of one or more physical properties of the sample during the optical investigation, e.g. electro-reflectance
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N2021/1765—Method using an image detector and processing of image signal
    • G01N2021/177—Detector of the video camera type
    • 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
    • G01N2021/8854—Grading and classifying of flaws
    • 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
    • G01N2021/8854—Grading and classifying of flaws
    • G01N2021/8858—Flaw counting
    • 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
    • G01N2021/8887—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 based on image processing techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N35/00—Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/00 - G01N33/00; Handling materials therefor
    • G01N35/00584—Control arrangements for automatic analysers
    • G01N35/00722—Communications; Identification
    • G01N2035/00891—Displaying information to the operator
    • G01N2035/0091—GUI [graphical user interfaces]

Definitions

  • the present disclosure relates to a method and an apparatus for adjusting the acceptance judgment condition for a test object.
  • the inspection system can determine the quality of the inspection object by measuring the structure of the inspection object and determining whether the measured value is within a preset range. For example, the inspection system irradiates light on the specimen and receives light reflected from the specimen to obtain image data of the specimen. In addition, the inspection system obtains a measurement value of the inspection object based on the acquired image data, and derives an inspection result for determining whether the inspection object is Good or NG based on the measured value and a preset reference value. do.
  • the inspection result derived by the inspection system may include a determination error that determines whether the inspection body that is actually good (False Call) or escapes the inspection object that is actually bad (Escape).
  • the reference value used for the acceptance determination can be changed.
  • the measured value of the test object is simply displayed as a number on the display unit of the test system, and the reference value is often entered by the user directly.
  • the user wants to change the reference value there is a problem in that the measured value displayed on the display unit must be input to the new value.
  • the present disclosure provides a method and apparatus for graphically displaying a test result for a test object and more conveniently adjusting a reference value used for the quality judgment.
  • the present disclosure provides a method and apparatus capable of visually displaying the inspection result of the inspection object, the acceptance determination result, and the examination result for the acceptance determination.
  • the present disclosure provides a method and apparatus that can visually indicate the change of the acceptance determination result and the examination result for the acceptance determination according to the adjustment of the reference value.
  • the present disclosure provides a method and apparatus for making the user input for reducing the quality of an inspection object's acceptance determination error more intuitively and conveniently.
  • One aspect of the present disclosure provides a method for adjusting a condition for determining an object for a test object in a condition determining device including a database, a processor, a user input unit, and an output unit.
  • a method according to an exemplary embodiment includes the steps of obtaining, by a processing unit, measured values of structures of a plurality of test objects, and by the processing unit, comparing an error value of a measured value with respect to a design value of a structure and a predetermined reference value.
  • Determining good or bad for each test object of the plurality of test objects identifying, by the processing unit, one or more test objects in which a determination error has occurred among the plurality of test objects, and by the processing unit, a plurality according to the error value Generating a test result graph including the number of test subjects, a reference value, and the number of one or more test subjects in which a determination error occurred, and outputting the test result graph through an output unit, by the processing unit, one or more inspections in which a determination error occurred; Updating the reference value according to the graphical input through the user input on the test result graph, so as to reduce the number of sieves, and the processing unit
  • the determination error includes a first error in which the test object determined to be good is identified as bad and a second error in which the test object determined to be bad is identified as good.
  • the test result and the reference value for the test object are graphically displayed, and the displayed reference value may be adjusted by the user's graphical input.
  • the test result for the test object may be updated based on the reset reference value, and the updated test result may be displayed graphically.
  • the user can adjust the reference value used for determining the quality of the test object more efficiently and simply.
  • a graph showing the inspection result of the inspection object, the acceptance determination result, and the examination result for the acceptance determination is output, and an input for the user to reduce the acceptance determination error on the output graph.
  • the change in the result of the review on the acceptance judgment according to the user's input is visually shown on the graph. As a result, it is possible for the user to more conveniently and intuitively check whether or not the acceptance judgment error is reduced.
  • the updated reference value is not required to be re-measured. It is possible to carry out the trial of the inspector. As a result, the transfer judgment according to the update of the reference value can be executed quickly.
  • FIG. 1 is a view schematically showing an inspection system for determining an inspection object as good or defective according to an embodiment of the present disclosure.
  • FIG. 2 is a view schematically showing the configuration of a measuring device for measuring the structure of the test body according to an embodiment of the present disclosure.
  • FIG. 3 is a block diagram showing a detailed configuration of a good quality determination device for determining good quality of a test object according to an embodiment of the present disclosure.
  • FIG. 4 is a diagram illustrating a test result list including a determination error according to an embodiment of the present disclosure.
  • FIG. 5 is a diagram illustrating a graph of test results showing a result of acceptance judgment and a result of a decision review according to an exemplary embodiment of the present disclosure.
  • FIG. 6 is a diagram illustrating a reference value update on a test result graph according to an embodiment of the present disclosure.
  • FIG. 7 is a diagram illustrating a graph of test results in which a partial region is enlarged according to an exemplary embodiment of the present disclosure.
  • FIG. 8 is a diagram illustrating a graph of test results showing a result of a judgment determination result and a judgment review result according to an exemplary embodiment of the present disclosure.
  • FIG. 9 is a diagram illustrating a test result graph in which a reference value is updated according to an embodiment of the present disclosure.
  • FIG. 10 is a flowchart illustrating a method of adjusting the acceptance judgment condition for a test object according to an embodiment of the present disclosure.
  • Embodiments of the present disclosure are illustrated for the purpose of describing the present disclosure. Embodiments of the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth below or the detailed description of these embodiments.
  • part refers to hardware components such as software, field-programmable gate arrays (FPGAs), and application specific integrated circuits (ASICs). However, “part” is not limited to hardware and software.
  • the “unit” may be configured to be in an addressable storage medium, and may be configured to play one or more processors.
  • parts means components such as software components, object-oriented software components, class components, and task components, and processors, functions, properties, procedures, subroutines, program code. Includes segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Functions provided within a component and “part” may be combined into a smaller number of components and “parts” or further separated into additional components and “parts”.
  • the expression “based on” is used to describe one or more factors that affect the behavior or behavior of a decision or judgment described in the phrase in which the expression is included, which expression is used in the act of decision or judgment or It does not exclude additional factors that affect its behavior.
  • a component When a component is referred to herein as being “connected” or “connected” to another component, the component may be directly connected to or connected to the other component. It is to be understood that there may be new other components between the component and the other components.
  • FIG. 1 is a schematic diagram of an inspection system 10 for determining an inspection object as good or defective according to an embodiment of the present disclosure.
  • the inspection system 10 determines whether each of the plurality of inspection bodies 20 is a good product or a defective product, and classifies the product into the good storage device 30 or the defective product storage device 40 according to the determination result.
  • the test body 20 may be any manufactured article having a three-dimensional structure manufactured according to a predetermined design criterion.
  • the test body 20 may be a printed circuit board (PCB) on which electronic components are mounted.
  • PCB printed circuit board
  • the inspection system 10 may include the measurement apparatus 100, the acceptance determination apparatus 120, the determination review apparatus 140, and the classification apparatus 160.
  • the inspection system 10 may also include a network 180 that communicates with the connection between the measurement device 100, the acceptance determination device 120, the determination review device 140, and the classification device 160. As shown in FIG. 1, the inspection body 20 passes through the measurement device 100, the determination review device 140, and the classification device 160 along the direction of the arrow, and the good storage device 30 or the defective product storage device ( 40).
  • the inspection system 10 may be installed at a rear end of a manufacturing stage for manufacturing the specimen 20 or a processing stage for processing the specimen 20.
  • the inspection system 10 can determine whether the manufactured or processed inspection object 20 was manufactured according to a predetermined design criterion.
  • the inspection system 10 can classify the good inspection body 20 into the goods storage device 30 and classify the defective inspection body 20 into the defective goods storage device 40 according to a determination result. have.
  • the measuring device 100 may generate a measured value measuring the structure (eg, the three-dimensional structure) of the test body 20.
  • the measuring device 100 may measure the structure of the test object 20 using light.
  • the measuring device 100 irradiates structured light to the test body 20, receives light reflected from the test body 20, and measures the test body 20 based on the received light. Can generate image data.
  • the measuring device 100 may generate a measured value measuring the structure of the test object 20 based on the image data.
  • the measurement value generated by the measurement device 100 may be transmitted to the acceptance determination device 120 through the network 180. The configuration and operation of the measuring device 100 will be described in more detail with reference to FIG. 2.
  • the acceptance determination apparatus 120 may determine whether the test object 20 is good or bad.
  • the quality determining device 120 may determine whether the measured value generated by the measuring device 100 is within a predetermined range, and thereby determine whether the test object 20 is good or bad.
  • the acceptance determination device 120 may calculate an error value between the measured value and the design value of the structure of the test body 20.
  • the adequacy determination device 120 determines that the test object 20 whose error value is equal to or less than a predetermined reference value is good, and determines that the test object 20 whose error value exceeds the predetermined reference value is defective (NG). Can be.
  • the acceptance determination apparatus 120 may determine that a part of the test object 20 of the test object 20 whose error value is equal to or less than a predetermined reference value (that is, good) is warned. .
  • a predetermined reference value that is, good
  • the delivery judgment device 120 may determine the test object 20 as a warning.
  • the determination examination apparatus 140 can determine whether there is an error in the acceptance determination of the test object 20 by the acceptance determination apparatus 120. For example, there may be a case where the inspection object 20 determined as good by the delivery judgment device 120 is actually defective (Escape). In addition, the test body 20 judged to be defective by the acceptance determination apparatus 120 may actually be good (False Call). This determination error may occur when the reference value used for the acceptance determination in the acceptance determination apparatus 120 is not set appropriately. For example, when a predetermined reference value to be compared with an error value is set high, the affirmation determination device 120 can determine that the inspection object 20 that is actually defective is good, but the determination review device 140 determines such good judgment. This error can be determined. In addition, when the predetermined reference value compared with the error value is set low, the acceptance determination apparatus 120 can determine that the inspection body 20 which is actually good is defective, and the determination review apparatus 140 determines that such failure determination is an error. Can be judged.
  • the determination review device 140 may be implemented using a device capable of determining whether the test object 20 is actually good or bad.
  • the determination review device 140 may include a device that can more accurately measure the structure of the test object 20.
  • the determination review device 140 may include a device capable of confirming an electrical characteristic of the test object 20.
  • the determination reviewing apparatus 140 may determine whether a determination error occurs for a part of the inspection object 20 of the inspection object 20 in which the acceptance determination is made by the acceptance determination device 120. For example, the determination review apparatus 140 can determine whether there is a determination error with respect to the test object 20 that is determined to be warning or defective by the acceptance determination apparatus 120. In this case, the efficiency can be improved as compared with the case of determining whether a determination error for all the test object.
  • the determination review apparatus 140 may determine whether or not a determination error has been made for all the inspection objects 20 for which the acceptance determination has been made by the acceptance determination apparatus 120. In this case, the accuracy may be improved as compared with the case of determining whether a determination error for some of the inspection object.
  • the examination result by the determination review apparatus 140 may be transmitted to the acceptance determination apparatus 120 through the network 180.
  • the determination review apparatus 140 estimates the good error value range of the good specimens 20 from the distribution of the measured error values, and additionally, the bad specimens 20.
  • the range of defective error values can be estimated.
  • the error value of the physical property of the product produced according to a certain production process can be said to have a certain probability distribution.
  • the error values of the good specimens 20 produced through a given process may have a good error value distribution expressed by, for example, a gamma distribution curve.
  • the error values of the test specimens 20 that are defective due to problems outside the process may have a defective error value distribution expressed by, for example, a normal distribution curve.
  • the decision review apparatus 140 determines at least one probability distribution curve that fits most to the distribution of the measured error values, and selects a probability distribution curve closest to the origin among the determined probability distribution curves, with a good error. Value distributions can be considered and the remaining probability distribution curves (if any) can be regarded as bad error value distributions.
  • the vertical axis is the number of specimens that must be natural numbers. Therefore, one or more specimens exist only within the horizontal axis range where the graph has a vertical axis value of 1 or more. If it is out of the horizontal axis range, it can be considered that the specimen exists in less than one sample, that is, it is absent.
  • the user may regard only a range of error values in which the number of specimen samples is a predetermined number or more as a significant error value range.
  • a user may consider only a range of test sample samples that contain a certain proportion of test sample samples (e.g., 99.5% test sample samples in order of decreasing error value) to be a significant error range. Can be.
  • the good error value range or the bad error value range which the user considers to be significant in the good error value distribution curve or the bad error value distribution curve can be estimated, respectively.
  • a significant distribution of defective error values may not be obtained, and thus, a range of meaningful defective error values may not be estimated.
  • the decision review apparatus 140 may estimate the good error value distribution from a given distribution of sample error values using a predetermined probability distribution function and further estimate the bad error value distribution if necessary.
  • the judgment review device 140 may estimate a good error value range in which the number of samples is 1 or more, for example, in the good error value distribution, and a bad error value range in which the number of samples is 1 or more, for example, in the bad error value distribution (if any). Can be estimated.
  • the determination review apparatus 140 considers a point where the good error value distribution and the bad error value distribution overlap each other, and thus the shortened good error value range. And error ranges can be estimated again.
  • the determination review apparatus 140 may re-establish the integrated defective error value ranges so as to encompass all estimated defective error value ranges from the defective error value distributions. .
  • the determination examination apparatus 140 makes an error in the acceptance determination of the test body 20 by the acceptance determination apparatus 120 based on the estimated good error value range, and further based on the defective error value range (if present). It can be determined whether there is.
  • the judgment review device 140 determines the test object. Although 20 is determined to be bad by the reference value, it can be determined that it is actually a false call of the second type which is good, and it can be determined that the current reference value is too strict.
  • the determination review device 140 It can be determined that the test body 20 is an escape of the first type which is actually bad even though it is determined to be good by the reference value, and it can be determined that the current reference value is too relaxed.
  • the determination review apparatus 140 determines the number of the inspection objects 20 larger than the reference value while the error value is within the good error value range when the reference value of the good judgment is within the good error value range,
  • the number of the inspected objects that are the determination errors may be determined as the number of the inspected objects 20 that are smaller than the reference value while the error value is within the defective error value range.
  • the determination result by the determination review apparatus 140 may be transmitted to the acceptance determination apparatus 120 through the network 180.
  • the good error value distribution and the good error value range estimated by the determination review device 140 may also be transmitted to the positive determination device 120 through the network 180.
  • the defective error value distribution and the defective error value range estimated by the determination review apparatus 140 may also be transmitted to the contradictory determination apparatus 120 via the network 180.
  • the inspection objects which are strongly estimated to have a determination error ( 20) can be identified, and furthermore, the appropriateness of the current reference value can be determined.
  • the acceptance determination apparatus 120 adjusts the acceptance determination condition for the inspection object 20 so that the number of the inspection object 20 determined as the determination error by the determination review device 140 is reduced. Can be.
  • the acceptance determination apparatus 120 is based on the acceptance determination result for the inspection object 20 generated by the acceptance determination device 120 and the determination review result for the inspection object 20 generated by the determination review device 140.
  • the reference value compared with the error value can be updated. For example, when it is determined by the judgment review device 140 that a false call has occurred in at least a part of the test object 20, the fitness judgment device 120 may increase the reference value compared with the error value. have. In addition, when it is determined by the determination review device 140 that a determination error (Escape) has occurred in at least a part of the test object 20, the acceptance determination device 120 can lower the reference value compared with the error value.
  • the acceptance determination apparatus 120 may update the reference value compared with the error value according to the user input.
  • the acceptance determination apparatus 120 can graphically display the acceptance determination result, the determination examination result, and the reference value for the inspection object 20.
  • the user may provide the acceptance determination apparatus 120 with a graphic input for adjusting the reference value so that the number of the inspection objects in which the determination error has occurred is reduced, based on the acceptance determination result and the determination review result displayed graphically.
  • the acceptance determination apparatus 120 may update the reference value in response to the graphical input of the user.
  • the acceptance determination apparatus 120 may judge the good or bad of the test object 20 by comparing the updated reference value with the error value.
  • the pass / fail determination device 120 has an error in the judgment result of the inspection body 20 based on the determination review result generated by the determination review device 140 indicating whether the inspection body 20 is actually good or bad. It can be identified whether or not it has occurred. Accordingly, the acceptance determination apparatus 120 may graphically indicate the result of the acceptance judgment for the inspection object 20, the updated reference value and the number of inspection objects issued by the judgment error.
  • the acceptance determination apparatus 120 may be implemented using a computing device, for example, a server computer, a personal computer, a laptop computer, a smartphone, a tablet. The configuration and operation of the acceptance determination apparatus 120 will be described in more detail with reference to FIGS. 3 to 10.
  • the classification apparatus 160 may classify the test body 20 into the good storage device 30 or the defective product storage device 40.
  • the classification apparatus 160 stores the good inspection body 20 and the bad inspection object 20 based on the judgment result in the acceptance determination device 120, respectively. Can be classified as
  • the network 180 enables the connection and communication between the measurement apparatus 100, the acceptance determination apparatus 120, the determination review apparatus 140, and the classification apparatus 160.
  • the network 180 may be a wired network such as a local area network (LAN), a wide area network (WAN), or a value added network (VAN), a mobile radio communication network, a satellite, or the like. It can be implemented using any kind of wireless network such as a communication network, Bluetooth, Wireless Broadband Internet (Wibro), High Speed Downlink Packet Access (HSDPA), and the like.
  • each apparatus of the inspection system 10 is shown in FIG. 1 as a separate configuration, the present disclosure is not limited thereto, and any of the acceptance determination apparatus 120, the determination review apparatus 140, and the classification apparatus 160 may be used. At least some components of may be integrated into other devices. According to one embodiment, at least some components of the determination review apparatus 140 may be integrated into the acceptance determination apparatus 120. For example, the configuration of the determination review apparatus 140 that determines the acceptance determination error through estimation from the error value distribution may be implemented in the acceptance determination apparatus 120.
  • the measuring device 200 of FIG. 2 may include all technical features of the measuring device 100 of FIG. 1.
  • the measuring device 200 includes an illumination unit 210, an imaging unit 220, and an image processing unit 230.
  • the illumination unit 210 irradiates the inspection object 22 with pattern light to measure the inspection object 22 that is a part of the inspection object 20.
  • the test body 20 is a printed circuit board
  • the test object 22 is a solder formed on the printed circuit board or an electronic component mounted on the printed circuit board.
  • the test body 20 and the test target 22 according to the present disclosure are not limited thereto, and may be any manufactured product having a three-dimensional structure.
  • the lighting unit 210 includes a light source 211 for generating light, a grating element 212 for converting light from the light source 211 into pattern light, and a grating transfer for pitch conveying the grating element 212. And a projection lens 214 for projecting the patterned light converted by the instrument 213 and the grating element 212 onto the inspection object 22.
  • the grating element 212 is a predetermined distance (e.g., 2 ⁇ / N; N is a natural number of two or more) by a grating transfer mechanism 213 such as a PZT actuator for phase shifting of patterned light. Can be transported by number.
  • two lighting units 210 may be provided.
  • the lighting unit 210 according to the present disclosure is not limited thereto and may be provided with one or three or more.
  • the plurality of lighting units 210 may be installed to be spaced at a predetermined angle along a circumferential direction or a virtual polygonal plane, or at regular intervals along a direction perpendicular to the test body 20. It may be installed to be spaced apart.
  • the imaging unit 220 may receive the light reflected by the inspection target 22 to obtain image data of the inspection target 22.
  • the imaging unit 220 may be implemented using a charge coupled device (CCD) camera or a complementary metal oxide semiconductor (CMOS) camera, but is not limited thereto.
  • CMOS complementary metal oxide semiconductor
  • the imaging unit 220 may be installed at an upper position perpendicular to the test body 20.
  • the image processor 230 processes the image data acquired by the imaging unit 220 to generate a measurement value of the structure of the inspection object 22. For example, the image processor 230 measures a horizontal length, a vertical length, a height length, an area, a volume, and the like of the inspection object 22 from the image data of the inspection object 22.
  • the measurement value generated by the image processor 230 may be stored in the storage unit 232 of the image processor 230, or transmitted by the communicator 234 to the acceptance determination apparatus 120.
  • the acceptance determination apparatus 300 of FIG. 3 may include all technical features of the acceptance determination apparatus 160 of FIG. 1.
  • the determination unit 300 according to an embodiment of the present disclosure includes a communication unit 310, an input / output unit 320, a processing unit 330, and a database 340.
  • the communication unit 310 may communicate with other devices, for example, the measurement device 100, the determination review device 140, and the classification device 160 of FIG. 1.
  • the communication unit 310 subcomponents for communicating with these devices may be integrated into one hardware device.
  • the input / output unit 320 is a configuration for interfacing with a user and includes a user input unit 322 and an output unit 324.
  • the user input unit 322 may receive an input relating to the acceptance decision from the user.
  • the user input unit 322 may receive an input for adjusting the reference value used for the acceptance determination, an input for displaying the acceptance determination result, an input for selecting one of the acceptance determination result, and the like.
  • the user input unit 322 may include a keyboard, a mouse, a touch pad, a touch screen, and the like.
  • the output unit 324 provides an output related to the acceptance decision to the user.
  • the output unit 324 may display the result of the acceptance decision of the test object 20, the reference value used for the acceptance determination, and the like.
  • the output unit 324 may include a LCD (liduid crytal display), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, and the like.
  • the processor 330 may process data related to the acceptance decision.
  • the processing unit 330 includes a quality determination unit 332, a determination result generation unit 334, and a determination reference adjustment unit 336.
  • the database 340 is a configuration for storing data related to good or bad judgment, and includes a design value DB 342, a measured value DB 344, an error value DB 346, a reference value DB 348, and a determination result DB ( 350 and the decision review result DB 352.
  • the design value DB 342 stores the design values for all the inspection objects 22 of the test object 20.
  • the design values include the width and length of the pad formed on the PCB, the volume and area of the solder placed on the pad, the height from the electronic component placed on the pad to the pad, and the like.
  • the design value DB 342 may be stored.
  • the measured value DB 344 the measured value about all the test
  • the measurement value stored in the measurement value DB 344 may correspond to the design value stored in the design value DB 342.
  • the measured value for the test body 20 may be generated by the measuring device 200 of FIG. 2.
  • the measurement value generated in the measurement device 200 may be stored in the measurement value DB 344 through the communication unit 234 of the measurement device 200 and the communication unit 310 of the acceptance determination device 300.
  • the acceptance determination unit 332 may calculate an error value of the measured value with respect to the design value of the inspection target 22 of the inspection object 20.
  • the adequacy determination unit 332 determines the design value of the inspection target 22 of the inspected object 20 stored in the design value DB 342 and the inspection object 22 of the inspected object 20 stored in the measured value DB 344. The difference between the measured values is calculated as an error value.
  • the calculated error value may be stored in the error value DB 346.
  • the acceptance determination unit 332 may determine whether the structure of the test object 20 satisfies a predetermined criterion.
  • the acceptance determination unit 332 may compare the error value stored in the error value DB 346 with the reference value stored in the reference value DB 348 to determine whether the specimen 20 is good or bad.
  • the acceptance determination unit 332 determines that the test object 20 is good if the error value of the test object 20 of the test object 20 is less than or equal to the reference value of the test object 20. When the error value exceeds the reference value, the test body 20 may be determined to be defective.
  • the positive determination unit 332 rejects the test object 20 having an error value of 0.6 mm with respect to the vertical length of the pad. It is determined that the test body 20 having an error value of 0.4 mm is good.
  • the acceptance determination result generated by the acceptance determination unit 332 may be stored in the determination result DB 350.
  • a decision examination result indicating whether or not there is an error in the acceptance determination of the acceptance determination unit 332 with respect to the inspection target 22 of the inspection object 20 is stored.
  • a 'decision error' may be displayed as a result of the determination review of the inspection object 22.
  • the determination error is a first type (Escape) in which the inspection target 22 determined as good by the acceptance determination unit 332 is actually defective, and the inspection target 22 determined as defective by the acceptance determination unit 332. ) Contains the second type (False Call) which is actually good.
  • the decision review result stored in the decision review result DB 352 may be generated by the decision review device 140 of FIG. 1.
  • the communication unit 310 may receive the determination review result generated by the determination review device 140 and store the result in the determination review result DB 352.
  • the communication unit 310 of the acceptance judgment device 300 also includes the good error value distribution, the good error value range, the bad error value distribution (if any), and the bad error value range (if any) estimated by the determination review device 140. ) Can be stored in the decision review result DB 352.
  • the determination result generator 334 may generate an inspection result graph indicating the number of inspection objects according to the error value.
  • the test result graph may be a two-dimensional graph, in which the horizontal axis may represent an error value, and the vertical axis may represent the number of test bodies 20 having the corresponding error value.
  • the determination result generation unit 334 may display, as a reference value, a graphical user interface (GUI) object that is movable by a user operation on the inspection result graph.
  • GUI graphical user interface
  • the GUI object representing the reference value may have a shape of a bar, an arrow, a line, a dot, a rectangle, or the like.
  • the determination result generation unit 334 can display the result of the determination of the quality of the inspection body 20 in the inspection result graph.
  • the determination result generator 334 may indicate good, warning, and bad as a result of the positive determination on the test result graph.
  • the determination result generation unit 334 displays a region where the error value is equal to or less than the reference value on the inspection result graph, and displays a region where the error value exceeds the reference value as defective.
  • the determination result generation unit 334 displays a predetermined area on the inspection result graph close to the reference value as a warning. In this case, the boundary of the area corresponding to the warning may be displayed on the test result graph.
  • the determination result generator 334 may indicate the number of the test bodies 20 corresponding to good, warning, and bad on the test result graph.
  • the determination result generation unit 334 may display the result of the examination on the result of the acceptance determination on the inspection result graph.
  • the determination result generator 334 may indicate an error of the first type (Escape) and an error of the second type (False Call) as the determination examination result on the inspection result graph.
  • the determination result generation unit 334 displays, as an error of the first type (Escape), an area to which the test object 20 determined as actually good among the areas where the error value exceeds the reference value on the inspection result graph.
  • the determination result generation unit 334 displays an area of the test object 20 to which the test object 20, which is determined to be actually defective, among the areas where the error value is equal to or less than the reference value, as an error of the second type.
  • determination errors such as an error of the first type and an error of the second type may be determined through detailed inspection by the decision review apparatus 140.
  • the determination examining device 140 may include a device capable of measuring the structure of the test body 20 more precisely, or a device capable of measuring the electrical characteristics of the test body 20.
  • the determination examination device 140 may determine whether the test body 20 is actually good or bad by measuring the structural electrical characteristics of the test body 20 more precisely. As a result, the determination examination apparatus 140 discriminates
  • determination errors may be determined through estimation from an error value distribution by the determination review apparatus 140.
  • the determination review apparatus 140 determines at least one probability distribution curve fitted to the error value distribution of the test object 20 measured by the measuring apparatus 100, and the probability distribution closest to the origin among the determined probability distribution curves. The curve can be considered a good error distribution and the remaining probability distribution curves (if any) can be considered a bad error distribution.
  • the determination review apparatus 140 may estimate the good error value range from the good error value distribution, and estimate the bad error value range from the bad error value distribution (if any).
  • the determination examination apparatus 140 can discriminate
  • the determination review apparatus 140 judges the acceptance determination error through estimation from the error value distribution, and the determination result generation unit 334 receives the acceptance determination error determined by the determination review apparatus 140.
  • produced is identified on the basis, this indication is not limited to this.
  • the determination result generation unit 334 may be implemented to determine whether the determination of the positive and negative determination by the estimation directly from the distribution of the error value, to identify the test object 20 in which the determination error.
  • the determination result generator 334 may determine the candidate reference value so that the reference value can be updated.
  • the determination result generator 334 may determine at least one candidate reference value for reducing or minimizing the number of the inspected objects 20 in which the determination error has occurred.
  • the determination result generation unit 334 is configured such that the area corresponding to the error of the first type (Escape) or the error of the second type (False Call) is reduced or eliminated by updating the reference value with the candidate reference value.
  • the candidate reference value can be determined.
  • the determination result generator 334 may display the determined at least one candidate reference value on the test result graph.
  • the candidate reference value may be represented by a dot, a line, a rectangle, an arrow, or the like.
  • the determination result generation unit 334 determines the candidate reference value based on the good error value range and the bad error value range (if any) of the test object 20 estimated by the determination review device 140. Can be. If there is a bad error value range, the determination result generator 334 may determine a candidate reference value from among values greater than or equal to the maximum value of the good error value range and less than or equal to the minimum value of the bad error value range. If there is no defective error value range, the determination result generator 334 may determine a candidate reference value among certain values greater than or equal to the maximum value of the good error value range.
  • the user may select a predetermined area on the test result graph through the user input unit 322.
  • the determination result generator 334 may enlarge the selected predetermined area and output the enlarged predetermined area through the output unit 324.
  • the enlarged predetermined area may be output to overlap the graph of the test result.
  • the determination result generator 334 may generate a test result list including at least one of a measured value, an error value, a positive judgment result, and a determination error review result for the test object 20.
  • the determination result generator 334 may output the inspection result graph and the inspection result list through the output unit 324.
  • the user may check the test result graph and the test result list through the output unit 324.
  • the user may select one test object 20 from the test result list through the user input unit 322.
  • the determination result generator 334 may display an error value of the selected test object 20 in the test result graph in response to a user input received through the user input unit 322.
  • the error value of the selected specimen 20 may be represented by a dot, a line, a rectangle, an arrow, or the like.
  • the test object 20 which has been most recently determined to be acceptable may be automatically selected. In this case, an error value of the test body 20 which has been judged most recently may appear on the test result graph.
  • the determination criterion adjusting unit 336 may update the reference value according to the input received from the user through the user input unit 322. According to an embodiment of the present disclosure, the determination criterion adjusting unit 336 may receive a graphic input from the user to move the position of the GUI object representing the reference value on the test result graph. For example, a user may use a mouse as the user input unit 322 to click and drag a movable bar-shaped GUI object indicating a reference value on a test result graph to a predetermined position. In this case, the decision criterion adjusting unit 336 may update the reference value to a value corresponding to the dragged predetermined position in response to the graphic input.
  • the determination criterion adjusting unit 336 may receive a graphic input for designating a predetermined position on the test result graph from the user. For example, the user may click a predetermined position on the test result graph by using a mouse as the user input unit 322. In this case, the decision criterion adjusting unit 336 may update the reference value to a value corresponding to the clicked predetermined position in response to the graphic input. The updated reference value may be stored in the reference value DB 348 by the determination reference adjustment unit 336.
  • the acceptance determination unit 332 may re-determine good or bad for the test object 20 based on the reference value updated by the determination reference adjustment unit 336. According to one embodiment, the acceptance determination unit 332 judges the inspection body 20 as good when the error value of the inspection object 20 with respect to the inspection object 22 is less than or equal to the updated reference value, and the error value is updated. In the case where the reference value is exceeded, the test body 20 may be determined to be defective.
  • the acceptor determination unit 332 may identify the inspector 20 in which the judgment error has occurred among the inspector 20. According to one embodiment, the acceptance determination unit 332 may identify the inspection body 20 in which the judgment error has occurred, based on the judgment result of the inspection body 20 and the determination review result stored in the determination review result DB 352. Can be. For example, the adequacy determination unit 332 judges that the inspection object 20 is actually bad but is judged to be a good first type (Escape) error, and judges that the inspection object 20 is actually good but is determined to be bad. It can be determined that the error is of the second type (False Call).
  • the adequacy determination unit 332 judges that the inspection object 20 is actually bad but is judged to be a good first type (Escape) error, and judges that the inspection object 20 is actually good but is determined to be bad. It can be determined that the error is of the second type (False Call).
  • the determination result generation unit 334 may display the reference value updated by the determination criterion adjustment unit 336, the result of the parliamentary judgment using the updated reference value, and the result of the examination review on the result of the parliamentary judgment in the test result graph. By checking the graph of the test result output through the output unit 324, the user may confirm that an error occurred in the pausing judgment using the updated reference value is reduced compared to an error occurred in the acceptance determination using the pre-update reference value.
  • the acceptance determination apparatus 300 graphically displays the acceptance determination result and the reference value for the test object, and may adjust the reference value by the graphical input of the user.
  • the acceptance judgment apparatus 300 may execute the acceptance judgment on the inspected object based on the reset reference value, examine whether the acceptance judgment is free of errors, and graphically display the results of the acceptance judgment and the determination review thereof. .
  • the user can adjust the reference value used for determining the quality of the test object more efficiently and simply.
  • the inspection result list 400 of FIG. 4 may be generated by the determination result generation unit 334 of FIG. 3 and output through the output unit 324.
  • the test result list 400 includes test result data 410, 420, 430, 440, 450, and 460 for each of the plurality of test objects.
  • Each test result data 410, 420, 430, 440, 450, and 460 shows an object ID, an object ID, an object to be tested, a measurement object, a measurement value, an error value, a good judgment result, and a judgment review for the test object. Include the result.
  • the test result data 410, 430, and 450 includes a measured value of a horizontal length of the 'pad 1' formed on the test object, and an error value of calculating a difference between the measured value and the design value 10.0 mm. It is assumed that the reference value used for determining whether the pad 1 has a horizontal length is set to 0.5 mm. Referring to the test result data 410, it is determined that the test object having the test piece ID '1' is good because the error value is 0.5 mm or less. On the other hand, referring to the inspection result data 430 and 450, the specimens having the specimen IDs '2' and '459' are determined to be defective because their error values exceed 0.5 mm.
  • the inspector having the inspector ID '459' has an error of the second type (False Call) as a result of the acceptance decision examination. For example, in a test piece whose test piece ID is "459", the horizontal length of the "pad 1" is poor according to a predetermined determination criterion, but actually has good characteristics.
  • the test result data 420, 440, and 460 include a measured value of measuring the longitudinal length of the 'pad 1' formed on the test object, and an error value of calculating a difference between the measured value and the design value of 10.0 mm. It is assumed that the reference value used for determining whether the pad 1 has a vertical length is set to 0.5 mm. Referring to the test result data 410, it is determined that the test piece having the test piece IDs '1', '2', and '459' is good because the error value is 0.5 mm or less. Among them, the inspector having the inspector ID '459' is judged to have an error of the first type (Escape) as a result of the acceptance judgment examination. For example, in a test piece whose test piece ID is "459", the vertical length of "Pad 1" is good according to a predetermined determination criterion, but actually has a bad characteristic.
  • Escape error of the first type
  • FIG. 5 is a diagram illustrating an inspection result graph 500 showing a result of a dissatisfaction determination and a determination review, according to an exemplary embodiment.
  • the test result graph 500 of FIG. 5 may be generated by the determination result generator 334 of FIG. 3 and output through the output unit 324.
  • the test result graph 500 of FIG. 5 may input an input for selecting any test result data (for example, test result data 450) from the test result list 400 of FIG. 4. It may be generated in response to receiving through the user input unit 322.
  • the horizontal axis of the test result graph 500 represents an error value
  • the vertical axis represents the number of test objects.
  • the test result graph 500 includes a curve 510 indicating the number of test objects having a corresponding error value.
  • the test result graph 500 includes a first reference value GUI 520 indicating the first reference value used for the determination of the quality of the test object, and a test object used to determine a test object corresponding to a warning among the test objects determined to be good.
  • a second reference value GUI 530 may be included that indicates the second reference value. For example, the second reference value may be set to 90% of the first reference value.
  • the test result graph 500 may include a sample error value indicator 540 indicating an error value p of a test sample of a particular test sample, for example, a user of particular interest.
  • the sample error value indicator 540 may indicate an error value of the test object selected from the test result list 400 of FIG. 4.
  • the sample error value indicator 540 may indicate an error value of the most recently inspected specimen.
  • the results of the acceptance determination and the determination review may be displayed.
  • the inspection result graph 500 may display an area corresponding to good, warning, and error as a result of the acceptance decision, and an area corresponding to defective as a result of the decision examination.
  • the test result graph 500 may also display the number of test objects corresponding to good, warning, error, and bad. As shown in FIG. 5, 352 specimens having an error value equal to or less than the first reference value b were judged as good, and 107 specimens having an error value exceeding the first reference value b were once determined to be defective.
  • test specimens between the first reference values b and c may be determined to be false calls of the second type. Indeed, to verify that such a determination is a second type of error, the user can select and examine a particular test object whose error value is p between the first reference value b and c.
  • the first reference value is maintained at b, even if the process problem that causes the abnormal error value between d and e is solved, the error value will naturally be distributed between 0 and c in the normal process. If product production continues, a significant amount of subsequent products will be judged defective, with an error value constantly between the first reference values b and c. That is, in the example of Figure 5, if the product production process itself is not wrong, the first reference value for determining good and bad may not be excessively set without reflecting the natural error distribution characteristics of the product production process.
  • the test result graph 500 may include a candidate reference value indicator 550 indicating a candidate reference value for minimizing the number of test objects in which a determination error has occurred.
  • the candidate reference value is a candidate of a reference value such that the number of specimens determined to be an error is minimum (eg, 0), and may be selected within a range in which an error value is between c and d.
  • the candidate reference value indicator 550 is indicated by a dot in FIG. 5, the present invention is not limited thereto and may be displayed in various forms such as an arrow, a line, and a rectangle.
  • the candidate reference value indicator 550 is shown in the singular in FIG. 5, the present invention is not limited thereto, and a plurality of candidate reference value indicators 550 may be displayed.
  • test result graph 600 is a diagram illustrating a test result graph 600 in which a reference value is updated according to an embodiment of the present disclosure.
  • the test result graph 600 of FIG. 6 may be a reference value updated from the test result graph 500 of FIG. 5.
  • the user may update the reference value through the user input unit 322 on the test result graph 600.
  • the user may drag the first reference value GUI 520 to the position of the candidate reference value indicator 550 on the test result graph 600 using the mouse as the user input unit 322.
  • the user may touch the position of the candidate reference value indicator 550 on the test result graph 600 using the touch pad.
  • the position of the first reference value GUI 520 is moved in the test result graph 600 by the graphic input through the user input unit 322.
  • the first reference value may also be updated. For example, as shown in FIG. 6, the first reference value is updated from b to b '.
  • the position of the second reference value GUI 530 may also be moved without a separate user input.
  • the second reference value GUI 530 may be moved to the right so that the second reference value is 90% of the updated first reference value.
  • the second reference value GUI 530 may be moved such that the second reference value is updated from a to 90% of b ′.
  • the second reference value 530 may be moved by the graphic input through the user input unit 322 on the test result graph 600 to move the position of the second reference value GUI 530.
  • the acceptance determination unit 332 determines the acceptance judgment for each of the specimens based on the updated first and second reference values. You can run The acceptance determination unit 332 may determine that the error value of each of the test objects is less than or equal to b ′, which is the first reference value, and determine that the error value is defective if the error value of each of the test objects is more than b ′. In addition, the acceptance determination unit 332 may determine as a warning when an error value of each of the test objects is greater than a 'and less than b' which is the second reference value. In addition, the transfer decision unit 332 may identify a test object in which a trial error occurs in the test object.
  • the examination result graph 600 may show the results of the paternity trial and the results of the trial review. As shown in FIG. 6, 399 specimens having an error value of b 'or less are' good ', 12 specimens having an error value of greater than a' and 'b' or less are 'warned', and 60 specimens having an error value of d or more and e or less. Each is judged as 'bad'.
  • the inspection result graph 500 of FIG. 5 the number of the test object which the error occurred with respect to the acceptance judgment changes from 47 in FIG. 5 to 0 in FIG. In other words, the first reference value used as the reference for the acceptance judgment of the test object is updated, thereby minimizing the error of the acceptance judgment.
  • the first reference value GUI 520 is moved by the graphical input of the user, so that the good determination device 300 can newly determine whether the inspection object according to the updated reference value. That is, unlike the conventional process in which the user has to visually check the measured value to determine a new reference value and input it as a numerical value, according to the present disclosure, the user can determine the new reference value while viewing the graph of the test result, and the graphic The reference value can be updated by positive input. As a result, since it is possible to change the first reference value by moving the first reference value GUI 520 in a state where the determination error is visually displayed, it is possible to correct the determination error quickly and conveniently. In addition, since the quality of the test object can be newly determined based on the updated reference value, user convenience can be attained.
  • the inspection result graph 700 of FIG. 7 is a graph showing the result of the inspection and the result of the examination of the inspection object, and may be the same as the inspection result graph 500 of FIG. 5.
  • the user may enlarge at least a portion of the test result graph 700 output through the output unit 324 using the user input unit 322.
  • the user may select a predetermined area 710 on the test result graph 700 by using a mouse as the user input unit 322.
  • the determination result generator 334 may generate an enlarged graph 720 in which the predetermined region 710 is enlarged.
  • the generated enlarged graph 720 may be output through the output unit 324.
  • the enlarged graph 720 may be output separately from the test result graph 700 or may be output to overlap on the test result graph 700.
  • test result graph 800 of FIG. 8 is a diagram illustrating a test result graph 800 showing a result of a judgment decision and a decision review result according to an exemplary embodiment of the present disclosure.
  • the test result graph 800 of FIG. 8 may be generated by the determination result generator 334 of FIG. 3 and output through the output unit 324.
  • the test result graph 800 of FIG. 8 may input an input for selecting any one test result data (eg, test result data 460) from the test result list 400 of FIG. 4. It may be generated in response to receiving through the user input unit 322.
  • the test result graph 800 includes a curve 810 indicating the number of test objects having a corresponding error value, and a reference value GUI 820 indicating a reference value used for determining whether the test object is successful.
  • 330 specimens having an error value equal to or less than the first reference value d are determined to be 'good', and 129 specimens having an error value between e and f greater than the first reference value d are determined to be 'bad'. It became.
  • 25 specimens with an error value between b and c were found to be good, but are outside the natural error distribution pattern (from 0 to a) in a given process. Some problems that do not belong to the process may be defective. Accordingly, twenty five specimens between b and c may be determined to be the first type of escape.
  • the first reference value d is maintained, even if an abnormal problem in the process causing an abnormal error value between e and f is solved, the error value will naturally be distributed only between 0 and a. If production continues, a significant amount of subsequent products will be judged to be good, while being actually defective and consistently showing an error value lower than the first reference value d. That is, in the example of Figure 8, if the product production process itself is not wrong, the first reference value for determining good and bad may not be excessively set without reflecting the natural error distribution characteristics of the product production process.
  • the test result graph 800 may include a candidate reference value indicator 830 indicating a candidate reference value for minimizing the number of test objects in which a determination error has occurred.
  • the candidate reference value is a candidate of the reference value such that the number of specimens determined to be an error is minimum (eg, 0), and may be selected within a range in which the error value is between a and b.
  • the candidate reference value indicator 830 is indicated by a dot, but is not limited thereto, and may be displayed in various forms such as an arrow, a line, and a rectangle.
  • the candidate reference value indicator 830 is singularly displayed in FIG. 8, the present invention is not limited thereto, and a plurality of candidate reference value indicators 830 may be displayed.
  • the inspection result graph 800 includes a GUI indicating a reference value used to determine an inspection object corresponding to a warning among inspection objects determined to be good, and a plurality of inspections. It may include an indicator indicating the error value of any one of the sieve.
  • test result graph 900 is a diagram illustrating a test result graph 900 in which a reference value is updated according to an embodiment of the present disclosure.
  • the test result graph 900 of FIG. 9 may be a reference value updated from the test result graph 800 of FIG. 8.
  • the user may update the reference value through the user input unit 322 on the test result graph 900.
  • the user may drag the reference value GUI 820 to the position of the candidate reference value indicator 830 on the test result graph 900 by using a mouse as the user input unit 322.
  • the user may touch the position of the candidate reference value indicator 830 on the test result graph 900 using the touch pad.
  • the position of the reference value GUI 820 is moved in the test result graph 900 by the graphic input through the user input unit 322.
  • the reference value may be updated as the position of the reference value GUI 820 is moved. For example, as shown in Fig. 9, the reference value is updated from d to d '.
  • the acceptance determination unit 332 may execute the acceptance judgment for each of the specimens based on the updated reference value.
  • the acceptance determination unit 332 may determine that the error value of each of the test objects is equal to or less than d ', which is a reference value, and determine that the error value is defective if the error value of each of the test objects is more than d'.
  • the transfer decision unit 332 may identify a test object in which a trial error occurs in the test object.
  • test result graph 900 the results of the paternity trial and the results of the trial review may be displayed.
  • 305 test objects having an error value of d 'or less are determined to be' good '
  • 154 test objects having an error value of more than d' are determined as 'bad'.
  • the number of the test object which the error occurred with respect to the acceptance judgment changes from 25 in FIG. 8 to 0 in FIG.
  • the reference value used as the reference for the acceptance decision of the test object is updated, thereby minimizing the error of the acceptance decision.
  • FIG. 10 is a flowchart illustrating a method of adjusting the acceptance judgment condition for a test object according to an embodiment of the present disclosure. At least some of the steps shown in FIG. 10 may be performed by the configurations shown in FIGS.
  • the acceptance determination apparatus 300 obtains the measured values of the structures of the plurality of test objects.
  • the measuring apparatus 100 may irradiate light onto the test object, receive light reflected from the test object, and generate image data of the test object based on the received light.
  • the measuring apparatus 100 may generate a measured value measuring the structure of the test object based on the image data.
  • the acceptance determination apparatus 300 may obtain the measurement value generated by the measurement apparatus 100 through the communication unit 310.
  • the acceptance determination unit 332 executes acceptance determination for each of the plurality of inspection objects. For example, the quality determining unit 332 may determine whether the measured value obtained in step S1000 is within a predetermined range, to determine whether the test object is good or bad. The acceptance determination unit 332 calculates an error value of the measured value with respect to the design value of the structure of the test object, and compares the calculated error value with a predetermined reference value. The acceptance determination unit 332 may determine that the test object whose error value is less than or equal to the predetermined reference value is good, and determine that the test object whose error value exceeds the predetermined reference value is defective (NG).
  • the quality determining unit 332 may determine whether the measured value obtained in step S1000 is within a predetermined range, to determine whether the test object is good or bad.
  • the acceptance determination unit 332 calculates an error value of the measured value with respect to the design value of the structure of the test object, and compares the calculated error value with a predetermined reference value.
  • the acceptance determination unit 332 may determine that the test
  • the determination result generation unit 334 identifies the inspection object in which a determination error has occurred among the plurality of inspection objects.
  • the determination result generation part 334 is a test
  • the determination error includes a first error in which the test object determined to be good is identified as actually defective, and a second error in which the test object determined to be bad is identified as actually good.
  • the determination review apparatus 140 determines the good error value distribution and the bad error value distribution (if any) based on the error value distribution of the test object 20 measured by the measuring device 100 and From the good error value distribution and the bad error value distribution (if any), the good error value range and the bad error value range (if any) can be estimated, respectively.
  • the determination examination apparatus 140 can discriminate
  • the determination result generation unit 334 can receive the determination examination result from the determination review apparatus 140 to identify the inspection object 20 in which the determination error has occurred.
  • the determination result generation unit 334 outputs the inspection result graph.
  • the determination result generation unit 334 generates a inspection result graph indicating the number of inspection objects according to the error value.
  • the test result graph is a two-dimensional graph, in which the horizontal axis represents an error value, and the vertical axis represents the number of test objects having the corresponding error value among the plurality of test objects.
  • the determination result generation unit 334 may display, as a reference value, a bar-shaped GUI object that is movable by a user operation on the inspection result graph.
  • the determination result generating unit 334 may indicate good, warning, and bad as a result of the positive determination on the inspection result graph.
  • the determination result generation unit 334 may display the result of the examination on the result of the acceptance determination on the inspection result graph. In addition, the determination result generation unit 334 may determine and display at least one candidate reference value for minimizing the number of one or more specimens having a determination error on the examination result graph.
  • the candidate reference value may be determined based on the good error value range and the bad error value range (if present) estimated in step S1020. If there is a range of bad error values, the candidate reference value may be selected from any values that are greater than or equal to the maximum value of the good error value range and less than or equal to the minimum value of the bad error value range. If there is no bad error value range, the candidate reference value may be selected from any values greater than or equal to the maximum value of the good error value range.
  • the determination criterion adjusting unit 336 updates the reference value according to the graphical input on the test result graph.
  • the user may provide a graphical input on the test result graph to reduce the number of one or more test objects for which a decision error occurred. For example, the user may drag a GUI representing a reference value on the test result graph 600 to a predetermined position by using a mouse as the user input unit 322.
  • the decision criterion adjusting unit 336 updates the reference value to the predetermined value in response to the graphical input of the user (that is, the movement of the GUI representing the reference value).
  • step S1050 the payment judgment unit 332 executes the transfer judgment for each of the plurality of test objects based on the updated reference value. For example, the acceptance determination unit 332 compares the reference value updated in step S1040 with the error value to judge good or bad for each test object of the plurality of test objects. In addition, the pass / fail determination unit 332 identifies a test object in which a trial error has occurred among the plurality of test objects. In addition, the determination result generation unit 336 shows the updated reference value and the number of the inspection bodies in which the judgment error occurred on the inspection result graph.
  • Computer-readable recording media include all kinds of recording devices that store data that can be read by a computer system.
  • the computer-readable recording medium may include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
  • the computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.
  • functional programs, codes, and code segments for implementing the above embodiments can be easily inferred by programmers in the art to which the present disclosure belongs.

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PCT/KR2017/012408 2016-11-14 2017-11-03 검사체에 대한 양부 판정 조건을 조정하는 방법 및 장치 Ceased WO2018088760A2 (ko)

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JP2019525739A JP2020500308A (ja) 2016-11-14 2017-11-03 検査体に対する良否判定条件を調整する方法及び装置
CN201780070386.5A CN109997028B (zh) 2016-11-14 2017-11-03 调整对检查体的是否良好判定条件的方法及装置
US16/349,802 US11199503B2 (en) 2016-11-14 2017-11-03 Method and device for adjusting quality determination conditions for test body
EP17869347.9A EP3540412B1 (en) 2016-11-14 2017-11-03 Method and device for adjusting quality determination conditions for test body
CN202210685954.5A CN115112663A (zh) 2016-11-14 2017-11-03 调整对检查体的是否良好判定条件的方法及装置
EP21196822.7A EP3961332B1 (en) 2016-11-14 2017-11-03 Method and device for adjusting quality determination conditions for test body
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EP3961332C0 (en) 2024-03-13
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CN109997028B (zh) 2022-07-05
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