CN114002233A - Method and system for monitoring automatic optical detection device - Google Patents
Method and system for monitoring automatic optical detection device Download PDFInfo
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- G01N21/84—Systems specially adapted for particular applications
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- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
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- G01N21/956—Inspecting patterns on the surface of objects
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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
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
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Abstract
The present disclosure provides a method and a system for monitoring an automatic optical inspection device, the method for monitoring the automatic optical inspection device comprising: generating error log (log) information by an Automated Optical Inspection (AOI) device; receiving the error log information transmitted by the automatic optical detection device through a computing device, and obtaining a statistical value by performing statistical operation on the error log information; and judging whether the statistic value exceeds a threshold value by using a verification method through a computing device.
Description
Technical Field
The present disclosure relates to a method and system for monitoring an Automated Optical Inspection (AOI) device, and more particularly, to a method and system for monitoring an AOI device using big data analysis.
Background
The Automatic Optical Inspection (AOI) technology can realize fast, high-precision and nondestructive Inspection of wafers, chips or other objects to be inspected. The technology is widely applied to the fields of PCB, IC wafer, LED, TFT, solar panel and the like. Automatic optical inspection technology generally adopts high accuracy optical imaging system to image the article that awaits measuring, and the workstation bears the weight of the article that awaits measuring and carries out high-speed scanning in order to realize high-speed measurement. The system compares the scanned image with an ideal reference image, or identifies the surface defects of the object to be detected through modes such as feature extraction and the like. However, when the automatic optical inspection device fails, it is not discovered in time, and the utilization rate is affected.
Therefore, a method and system for monitoring an automatic optical inspection device is needed to improve the above problems.
Disclosure of Invention
The following disclosure is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features, other aspects, embodiments, and features will be apparent from consideration of the drawings and from the detailed description below. That is, the following disclosure is provided to introduce concepts, points, benefits and novel and non-obvious technical advantages described herein. Selected, but not all, embodiments are described in further detail below. Thus, the following disclosure is not intended to identify essential features of the claimed subject matter, nor is it intended to be used in determining the scope of the claimed subject matter.
It is therefore a primary objective of the present disclosure to provide a method and system for monitoring an automatic optical inspection device to improve the above-mentioned disadvantages.
The present disclosure provides a system for monitoring an automatic optical inspection device, comprising: the method comprises the following steps: an Automatic Optical Inspection (AOI) device that generates error log (log) information; and a calculating device for receiving the error log information transmitted by the automatic optical detection device, obtaining a statistic value by the error log information through a statistic operation, and judging whether the statistic value exceeds a threshold value by using a verification method.
The present disclosure provides a method for monitoring an automatic optical inspection apparatus, comprising: generating error log information by an automatic optical detection device; receiving the error log information transmitted by the automatic optical detection device through a computing device, and obtaining a statistical value by performing statistical operation on the error log information; and judging whether the statistic value exceeds a threshold value by using a verification method through a computing device.
Drawings
FIG. 1 is an exemplary diagram illustrating a system for monitoring an automated optical inspection device in accordance with one embodiment of the present invention;
FIG. 2 is a flow chart illustrating a method for monitoring an automated optical inspection device according to an embodiment of the present disclosure;
FIG. 3 is a diagram illustrating a distribution function according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating an error log generated by an automatic optical inspection device according to an embodiment of the present disclosure;
FIG. 5 is a diagram illustrating a database CSV file established by a server according to error log information according to an embodiment of the disclosure;
FIG. 6A is a scatter chart showing the number of days and the number of occurrences of an error of the automatic optical inspection device with a severity level of 3 in month 6 counted by the server according to an embodiment of the disclosure;
FIG. 6B illustrates a computing device according to a distribution function according to an embodiment of the present disclosure.
[ notation ] to show
100 system
110A-110D automatic optical detection device
120 server
130 computing device
140A-140D receiving end computer
200 method
S205, S210, S215, S220, S225 step
300 distribution function
400 error Log information
500 database CSV File
510 error information
530 IP Address
540 time (time)
Grade 550: grade
610 scatter diagram
Detailed Description
Aspects of the present disclosure are described more fully hereinafter with reference to the accompanying drawings. This disclosure may, however, be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Based on the teachings herein one skilled in the art should appreciate that the scope of the present disclosure is intended to cover any aspect disclosed herein, whether alone or in combination with any other aspect of the present disclosure to achieve any aspect disclosed herein. For example, it may be implemented using any number of the apparatus or performing methods set forth herein. In addition, the scope of the present disclosure is intended to cover apparatuses or methods implemented using other structure, functionality, or structure and functionality in addition to or other than the various aspects of the present disclosure set forth herein. It should be understood that any aspect disclosed herein may be embodied by one or more elements of a claim.
The word "exemplary" is used herein to mean "serving as an example, instance, or illustration. Any aspect of the present disclosure or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects of the present disclosure or design. Moreover, like numerals refer to like elements throughout the several views, and the articles "a" and "the" include plural references unless otherwise specified in the description.
It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a similar manner (e.g., "between …" versus "directly between …," "adjacent" versus "directly adjacent," etc.).
The embodiment of the disclosure provides a method and a system for monitoring an automatic optical detection device, which further solve the problem of operation errors of the automatic optical detection device by using statistics and big data analysis technologies.
FIG. 1 is an exemplary diagram illustrating a system 100 for monitoring an automated optical inspection device in accordance with one embodiment of the present invention. The system 100 may comprise a plurality of automatic optical inspection devices 110A-110D, a server 120, a computing device 130, and a plurality of receiving computers 140A-140D.
The automatic optical inspection devices 110A to 110D may respectively generate respective error log information and transmit the error log information to the server 120, where the error log information includes: line class, AOI optical system type, AOI device name, error severity, time stamp, and error type.
The server 120 is connected to the automatic optical inspection devices 110A to 110D and the computing device 130, collects error log information transmitted by the automatic optical inspection devices 110A to 110D in a verification period, and transmits the error log information to the computing device 130. In one embodiment, the verification period is one month.
The types of computing devices 130 range from small handheld devices (e.g., mobile phones/portable computers) to large mainframe systems (e.g., mainframe computers). The computing device 130 receives the error log information collected by the server 120 in a verification period, and determines whether the automatic optical inspection apparatuses 110A to 110D have an operation error according to the error log information.
When the computing device 130 determines that the automatic optical inspection devices 110A-110D have an operation error, it will transmit a warning signal to the receiving computers 140A-140D corresponding to the automatic optical inspection devices 110A-110D. The receiving computers 140A to 140D receive the warning signal transmitted from the computing device 130, and then notify the user that an operation error occurs in the automatic optical detection devices 110A to 110D.
In this system, the automatic optical inspection devices 110A to 110D, the server 120, the computing device 130, and the plurality of receiving computers 140A to 140D may be connected directly or through a network.
In some embodiments, the number of the automatic optical inspection devices 110A-110D and the receiving computers 140A-140D can be expanded to more than four or less than four, and thus the present invention is not limited to the embodiment shown in fig. 1.
It should be understood that the automated optical inspection device 110A-110D, the server 120, the computing device 130, and the plurality of receiving computers 140A-140D shown in FIG. 1 are examples of a system 100 architecture for monitoring an automated optical inspection device. Each of the elements shown in fig. 1 may be implemented via any type of electronic device.
In one embodiment, the automated optical inspection devices 110A-110D may be used to perform inspection of optical films. In one embodiment, the optical film may include a polyvinyl alcohol (PVA) resin film. In some embodiments, the optical film can include a film that is beneficial for optical gain, alignment, compensation, turning, orthogonality, diffusion, protection, adhesion resistance, scratch resistance, glare resistance, reflection suppression, high refractive index, and the like.
FIG. 2 is a flow chart illustrating a method 200 of monitoring an automated optical inspection device according to an embodiment of the present disclosure. This method may be implemented in the system 100 shown in fig. 1.
In step S205, the automatic optical inspection apparatus generates error log (log) information, wherein the error log information includes: line class, AOI optical system type, AOI device name, error severity, time stamp, and error type.
In one embodiment, the AOI optical system type includes: penetration inspection, reflection inspection, orthogonal inspection.
Next, in step S210, the computing device receives the error log information transmitted by the automatic optical inspection device, and performs a statistical operation on the error log information to obtain a statistical value. In one embodiment, the statistical operation may be a central limit theorem.
Then, in step S215, the computing device determines whether the statistic exceeds a threshold value by using a verification method. In one embodiment, the assay method may be a two-tailed assay.
When the computing means judges that the statistical value exceeds the threshold value (yes in step S215), in step S220, the computing means transmits a warning signal to notify the automatic optical detection device of the occurrence of an operation error. More specifically, the computing device first sends an alert signal to the receiving computer. After the receiving end computer receives the warning signal transmitted by the computing device, a user is informed that the automatic optical detection device has an operation error.
When the calculation means judges that the statistical value does not exceed the threshold value (no in step S215), the calculation means judges that the automatic optical detection device operates normally in step S225.
How the computing device obtains a statistic value by a statistic operation in step S210 and determines whether the statistic value exceeds a threshold value by a verification method in step S215 will be described in detail below.
Since error log information may include different levels of error severity: level 1 is that the automatic optical inspection device will fail immediately; grade 2 is that the automatic optical detection device will not fail immediately, but will fail after a period of time; and level 3 is that the automatic optical inspection device will not fail immediately and the probability of failure of the automatic optical inspection device is not high. The present embodiment is described with a level 3 that the automatic optical inspection device will not fail immediately and the failure probability of the automatic optical inspection device is not high.
It should be noted that the statistical value may be calculated differently according to whether there is a large amount of old error log information, and three examples are described herein.
The first method is as follows: a large amount of old error log information is needed to be certified with the new error log information
In one approach, the computing device has first taken the mean and standard deviation of a large amount (i.e., a multiple over the verification period) of old error log information. In this embodiment, the verification period for old error log information is one year.
The computing device firstly makes a central limit theorem on new error log information in a verification period so as to enable the error log information to present normal distribution, wherein the error log information is the times of a certain error severity degree, namely the times of grade 3, generated by the automatic optical detection device every day in the verification period. According to the normal distribution, a statistical value Z can be calculated. The statistical value Z is a multiple of a standard deviation σ representing that the new error log information is an average of the old error log information, wherein the statistical value Z can be represented as follows:
wherein the content of the first and second substances,
the severity of the error occurred in the automatic optical inspection apparatus per day in the old error log information is an average of class 3, σ is a standard deviation of class 3, and μ0The severity of the error occurring in the new error log information by the automatic optical inspection apparatus per day is an average number of class 3, and n is the number of days of the verification period.
Then, after the computing device obtains the statistical value Z, the two-tailed verification can be used to determine whether the statistical value Z exceeds a threshold. Whether the statistical value Z exceeds the threshold value can be better understood by referring to the distribution function 300 shown in fig. 3. For example, 95% of the number of times of error log information recording level 3 is distributed in the range
The above. Thus, in this embodiment, the threshold is defined as
That is, when the statistical value Z does not exceed the threshold value
It means that the new error log information is not significantly different from the old error log information, i.e. the capabilities of the automatic optical inspection apparatus are not changed.
Conversely, when the statistical value Z exceeds the threshold value
It is shown that the new error log information is significantly different from the old error log information, i.e. the capabilities of the automatic optical detection apparatus are deteriorated. The computing device transmits a warning signal to a receiving end computer corresponding to the automatic optical detection device so as to inform a user that the automatic optical detection device has an operation error and needs to be checked immediately.
The second method comprises the following steps: it is not necessary to have a large amount of old error log information to verify with the new error log information
In approach two, the computing device has first taken the mean and standard deviation of a portion (e.g., about one verification period) of the old error log information. In this embodiment, the verification period for the old error log information is the previous month of the new error log information.
The computing device makes a central limit theorem on new error log information of a latest verification period so as to enable the error log information to be in normal distribution, wherein the error log information is generated by the automatic optical detection device every day in the verification period for the times of the degree of severity of the error being grade 3. According to the normal distribution, a statistical value t can be calculated. The statistical value t is a multiple of the standard deviation σ representing that the new error log information is the average of the old error log information, wherein the statistical value t can be represented as follows:
wherein the content of the first and second substances,
the severity of the error of the automatic optical detection device in the last month is the average number of grade 3, s1The severity of the error of the automatic optical detection device in the last month is grade 3 standard deviation,
The severity of the errors occurring in the month for new error log information by the automatic optical inspection device is an average number of class 3, s2The automatic optical detection device generates standard deviation n with error severity grade 3 for new error log information in the month1Is the number of days of the last month and n2Is the number of days of the month.
In this embodiment, after the calculation device obtains the statistical value t, the degrees of freedom of the old error log information and the new error log information need to be calculated. The computing device then looks up the table according to the degrees of freedom shown in table 1 to obtain the threshold, and can use two-tailed verification to determine whether the statistic t does not exceed the threshold. If the statistical value t does not exceed the threshold, it represents that there is no significant difference between the old error log information and the new error log information. The determination method can be analogized from the method one, and is not described herein again.
Table 1
As an example, assume that a past verification period n1The number of the errors of the automatic optical detection device is 29 days in one month, and the error severity is the average number of 3
Is 5 times, standard deviation s1The number of the reaction was 9. New one verification period n2The average number of errors in the automatic optical detection device is grade 3 in 30 days per month
Is 4 times, standard deviation s2The number of times was 4. The statistical value t can be expressed as follows:
after the statistical value t is calculated, the degree of freedom (df) of the past and new verification periods is obtained by the calculation system through table lookup. The degrees of freedom are found from the df and alpha lookup tables. df can be expressed as follows:
and α ═ 0.05, where α ═ 0.05 refers to a portion where the left and right tail probabilities are 0.05 or less in the distribution function of the statistical value t. As can be seen from the table look-up of the degrees of freedom in table 1, the threshold is 1.684 when α is 0.05 and df is 40. Therefore, the statistical value t of 0.548 does not exceed the threshold value 1.684, which means that the old error log information is not significantly different from the new error log information.
The third method comprises the following steps: having newly picked up old error log information
In mode three, the computing device has first obtained the average and standard deviation of the old error log information for the last year (a whole year).
The computing device performs a central limit theorem on new error log information of a verification period (one month in the last year) so as to enable the error log information to present a normal distribution, wherein the error log information is generated by the automatic optical detection device every day in the verification period for the times of the error severity degree of grade 3. According to the normal distribution, a statistical value x can be calculated2. The statistical value x2Is a value representing a multiple of the standard deviation of the average of the old error log information when the new error log information is the old error log informationσ, wherein the statistical value x2Can be expressed as follows:
wherein s is standard deviation, sigma of grade 3 of error severity of automatic optical detection device in last month of old error log information0The severity of the error of the automatic optical inspection apparatus in the last year is a standard deviation of class 3 for the old error log information, and n is the number of error log information of the last month.
In this embodiment, the statistical value x is obtained at the computing device2Then, the degree of freedom for sampling the old error log information and the new error log information is calculated. The computing device then looks up the table according to the degrees of freedom shown in Table 2 to obtain the threshold, and can use single-tailed verification to determine the statistic x2Whether a threshold is exceeded. The determination method can be analogized from the method one, and is not described herein again.
Table 2
As an example, assume that the automatic optical inspection apparatus has a standard deviation σ of class 3 in the severity of errors every day in the past year0Was 6.25. And the standard deviation of the error severity of the automatic optical detection device in the last year in June (a verification period n is 30 days in a month) is 4, wherein the standard deviation is grade 3. Statistical value x2Can be expressed as follows:
after calculating the statistical value x2Then, the computing system is required to look up the table to obtain the degrees of freedom of the last year and the last june. The degree of freedom is according to df and
the table look-up shows that Df is n-1 ═ 29. From the table lookup with the degrees of freedom in Table 2, when df is equal to 29 and
the threshold is 42.5569. Therefore, the statistical value x211.89 does not exceed threshold 42.5569, the error log information representing the last year is not significantly different from the error log information of the last june.
FIG. 4 is a diagram illustrating an error log message 400 generated by an automatic optical inspection device according to an embodiment of the present disclosure. As shown, the error log information 400 may display at least error severity, time stamp, error type, and Automatic Optical Inspection (AOI) device name.
FIG. 5 is a diagram illustrating an embodiment of a server classifying and creating a database CSV file 500 according to error log information. As shown, database CSV file 500 may display at least error information 510(AOI device IP address, time stamp, error content), setup time 520, AOI device IP address 530, time 540, and error severity 550 (level).
It should be noted that the contents and information of the error log information 400 in fig. 4 and the database CSV file 500 in fig. 5 are not intended to limit the present disclosure, and those skilled in the art can appropriately change or adjust the same according to the present embodiment. For example, information such as the type of line, AOI optics type, etc. may be added to the schematic.
Fig. 6A-6B are experimental data tables showing a verification period of 6 months and one month (30 days) for monitoring an automatic optical inspection device according to an embodiment of the present disclosure. Suppose that the automatic optical inspection apparatus has occurred with an average number μ of level 3 of error severity every day in the past year0Is 4 times, and has a standard deviation σ of 3.
FIG. 6A is a scatter chart 610 showing the number of days and occurrences in 6 months of the auto-optics detection device error severity level 3 counted by the server according to an embodiment of the disclosure. As shown, this month automatic optical detection device takes placeError severity is the average of grade 3
Is 4 times, total days is 30. The calculation means calculates the parameters by equation (1) to obtain σ whose statistical value Z is 1.828 times.
FIG. 6B shows a computing device according to a distribution function 620, according to one embodiment of the present disclosure. As shown, in this embodiment, 95% of the number of times of the error log information recording level 3 is distributed over the range [ -1.96 σ, 1.96 σ ]. Therefore, the threshold is defined as 1.96 σ. In other words, when the computing device determines that the statistical value Z is 1.828 σ and does not exceed the threshold value 1.96 σ, it indicates that the new error log information is not significantly different from the old error log information, i.e., the capability of the automatic optical inspection device is not changed.
In addition, in one embodiment, after the receiving end computer receives the warning signal transmitted by the computing device, the receiving end computer can transmit the warning signal through a related user interface, such as: the automatic optical detection device comprises a Light Emitting Diode (LED), a display, a microphone, a Buzzer (Buzzer) and Bluetooth series flow, and reminds a user of the occurrence of operation errors of the automatic optical detection device. In another embodiment, the receiving computer may also send an email to the user's mailbox to notify the user.
Therefore, the method and the system for monitoring the automatic optical detection device can effectively prevent the automatic optical detection device from happening before the automatic optical detection device fails, so that the appropriateness is continuously stabilized and improved, and the vision that the equipment production is stable for a long time and the appropriateness is improved for a long time is realized. In other words, the method and system for monitoring an automatic optical inspection device of the present disclosure can effectively reduce risk lots, reduce labor consumption, reduce customer complaints, reduce downtime and repair time, and increase productivity.
With respect to the described embodiments of the present invention, an exemplary operating environment in which embodiments of the present invention may be implemented is described below.
The present invention may be implemented in computer program code or machine-useable instructions, such as computer-executable instructions of program modules, executed by a computer or other machine, such as a personal digital assistant or other portable device. Generally, program modules include routines, programs, objects, components, data structures, etc., which refer to program code that performs particular tasks or implements particular abstract data types. The present invention may be implemented in a variety of system configurations, including portable devices, consumer electronics, general purpose computers, more specialized computing devices, and the like. The present invention may also be implemented in a distributed computing environment, processing devices linked by a communications network.
Any particular order or hierarchy of steps for processes disclosed herein is purely exemplary. Based upon design preferences, it should be understood that any specific order or hierarchy of steps in the processes may be rearranged within the scope of the disclosure. The accompanying method claims present elements of the various steps in a sample order, and are therefore not to be limited to the specific order or hierarchy presented.
The use of ordinal terms such as "first," "second," "third," etc., in the claims to modify an element does not by itself connote any priority, precedence, order of various elements, or order of steps performed by the method, but are used merely as labels to distinguish one element from another element having a same name (but for use of a different ordinal term).
Although the present disclosure has been described with reference to exemplary embodiments, it should be understood that various changes and modifications can be made by those skilled in the art without departing from the spirit and scope of the disclosure, and therefore, the scope of the disclosure should be limited only by the appended claims.
Claims (17)
1. A system for monitoring an automated optical inspection device, comprising:
an Automatic Optical Inspection (AOI) device that generates error log (log) information; and
and the calculating device is used for receiving the error log information transmitted by the automatic optical detection device, obtaining a statistical value by performing statistical operation on the error log information, and judging whether the statistical value exceeds a threshold value by using a verification method.
2. The system for monitoring automated optical inspection devices of claim 1, wherein the system further comprises: a server, coupled to the automatic optical inspection device and the computing device, for collecting the error log information of the automatic optical inspection device in a verification period and transmitting the error log information to the computing device; and/or a receiving end computer which receives the warning signal transmitted by the computing device and informs a user that the automatic optical detection device has an operation error.
3. The system for monitoring automated optical inspection devices of claim 1, wherein the error log information includes: line class, AOI optical system type, AOI device name, error severity, time stamp, and error type.
4. The system for monitoring an automated optical inspection device according to claim 3, wherein the automated optical inspection device is capable of being used to perform inspection of optical films, and the AOI optical system types include: penetration inspection, reflection inspection, orthogonal inspection.
5. The system for monitoring automated optical inspection devices of claim 3, wherein the error log information includes: wherein the error severity is selected from a rating of 1 that the automated optical inspection device will immediately fail;
grade 2 is that the automatic optical inspection device will not fail immediately; and
level 3 is a group that the automatic optical inspection device will not fail immediately and the probability of failure of the automatic optical inspection device is not high.
6. The system for monitoring automated optical inspection devices of claim 1, wherein the statistical operation is a central limit theorem.
7. The system for monitoring automated optical inspection devices of claim 1, wherein the assay method is a two-tailed assay.
8. A method of monitoring an automated optical inspection device, comprising:
generating error log (log) information by an Automated Optical Inspection (AOI) device; and
receiving the error log information transmitted by the automatic optical detection device through a computing device, and obtaining a statistical value by performing statistical operation on the error log information; and
determining, by a computing device, whether the statistical value exceeds a threshold using a verification method.
9. The method of monitoring an automated optical inspection device according to claim 8, wherein the method further comprises: when the statistic value exceeds the threshold value, transmitting a warning signal by the computing device to inform the automatic optical detection device of the occurrence of operation errors; and/or receiving the warning signal transmitted by the computing device through a receiving end computer and informing a user that the automatic optical detection device has an operation error; and/or prior to the computing device receiving the error log information, the method further comprising:
and collecting the error log information of the automatic optical detection device in a verification period through a server, and transmitting the error log information to the computing device.
10. The method of monitoring an automated optical inspection device according to claim 8, wherein the error log information includes: line class, AOI optical system type, AOI device name, error severity, time stamp, and error type.
11. The method of monitoring an automated optical inspection device according to claim 10, wherein the automated optical inspection device is capable of being used to perform inspection of optical films, and the AOI optical system types include: penetration inspection, reflection inspection, orthogonal inspection.
12. The method of monitoring an automated optical inspection device according to claim 10, wherein the error log information includes: wherein the error severity is selected from a rating of 1 that the automated optical inspection device will immediately fail;
grade 2 is that the automatic optical inspection device will not fail immediately; and
level 3 is a group that the automatic optical inspection device will not fail immediately and the probability of failure of the automatic optical inspection device is not high.
13. The method of monitoring an automated optical inspection device according to claim 8, wherein the statistical operation is a central limit theorem.
14. The method of monitoring an automated optical inspection device according to claim 8, wherein the assay method is a two-tailed assay.
15. The method of monitoring an automated optical inspection device according to claim 12, wherein the statistical value is represented as follows:
wherein the content of the first and second substances,
an average of a first level of severity of said error for each day of occurrence of said error by said automatic optical inspection device in old error log informationThe number, sigma, is a standard deviation, mu, of the severity of the error occurring in the old error log information by the automatic optical inspection device every day to the first level0An average number of times that the automatic optical inspection apparatus has the first level of severity of the error per day in the error log information, and n is a number of days of a verification period for which the automatic optical inspection apparatus has generated the error log information.
16. The method of monitoring an automated optical inspection device according to claim 12, wherein the statistical value is represented as follows:
wherein the content of the first and second substances,
the severity of the error occurring by the automatic optical inspection device in a second verification period is an average of a first level, s1Generating, for the old error log information, a standard deviation of the error severity of the first level,
The average number s of the error severity of the grade 3 of the automatic optical detection device in the month is taken as the error log information2The severity of the error occurring during a verification period in which the error log information is generated for the automatic optical inspection apparatus is a standard deviation of the first level, n1Is the number of days of the second verification period and n2Is the number of days of the verification cycle.
17. The method of monitoring an automated optical inspection device according to claim 12, wherein the statistical value is represented as follows:
wherein s is a standard deviation, σ, of old error log information, and the severity of the error occurring in the automatic optical inspection device in a first verification period is a first level0The severity of the error occurring to the automated optical inspection device in a second verification period for the old error log information is a standard deviation of the first level, and n is the number of error log information generated by the automated optical inspection device in a verification period.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020147571A1 (en) * | 2001-02-22 | 2002-10-10 | Mitsubishi Denki Kabushiki Kaisha | Measuring method and measuring device |
| US20030065477A1 (en) * | 2001-08-30 | 2003-04-03 | Opdyke John D. | Two-sample permutation tests |
| US20060141489A1 (en) * | 2004-07-13 | 2006-06-29 | Allison David B | Method of statistical genomic analysis |
| US20170044492A1 (en) * | 2013-12-06 | 2017-02-16 | Childern's Medical Center Corporation | Conversion of somatic cells into nociceptors, and methods of use thereof |
| CN108599977A (en) * | 2018-02-13 | 2018-09-28 | 南京途牛科技有限公司 | System and method based on statistical method monitoring system availability |
| CN109101398A (en) * | 2018-08-10 | 2018-12-28 | 武汉精测电子集团股份有限公司 | AOI wire body monitoring method and system |
| CN109115780A (en) * | 2017-06-23 | 2019-01-01 | 大陆泰密克汽车系统（上海）有限公司 | For monitoring the method and device thereof of multiple automated optical detection equipments |
| TW201916640A (en) * | 2017-09-25 | 2019-04-16 | 中華電信股份有限公司 | Abnormity monitoring system and anbormity monitoring method |
| CN112152823A (en) * | 2019-06-26 | 2020-12-29 | 北京易真学思教育科技有限公司 | Website operation error monitoring method and device and computer storage medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5563702A (en) * | 1991-08-22 | 1996-10-08 | Kla Instruments Corporation | Automated photomask inspection apparatus and method |
| US5506793A (en) * | 1994-01-14 | 1996-04-09 | Gerber Systems Corporation | Method and apparatus for distortion compensation in an automatic optical inspection system |
| TWI240346B (en) * | 2004-10-21 | 2005-09-21 | Promos Technologies Inc | Inspecting method for killer defect size |
| CA2849104A1 (en) * | 2011-09-25 | 2013-04-11 | Theranos, Inc. | Systems and methods for multi-analysis |
| TWI585407B (en) * | 2015-09-21 | 2017-06-01 | 群燿科技股份有限公司 | Optical detecting device, an optical detecting method and an optical detecting system |
| CN112541881A (en) * | 2019-09-05 | 2021-03-23 | 鸿富锦精密电子(郑州)有限公司 | Electronic equipment failure analysis method and system |
-
2021
- 2021-04-09 TW TW110112842A patent/TWI787781B/en active
- 2021-09-30 CN CN202111163645.3A patent/CN114002233A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020147571A1 (en) * | 2001-02-22 | 2002-10-10 | Mitsubishi Denki Kabushiki Kaisha | Measuring method and measuring device |
| US20030065477A1 (en) * | 2001-08-30 | 2003-04-03 | Opdyke John D. | Two-sample permutation tests |
| US20060141489A1 (en) * | 2004-07-13 | 2006-06-29 | Allison David B | Method of statistical genomic analysis |
| US20170044492A1 (en) * | 2013-12-06 | 2017-02-16 | Childern's Medical Center Corporation | Conversion of somatic cells into nociceptors, and methods of use thereof |
| CN109115780A (en) * | 2017-06-23 | 2019-01-01 | 大陆泰密克汽车系统（上海）有限公司 | For monitoring the method and device thereof of multiple automated optical detection equipments |
| TW201916640A (en) * | 2017-09-25 | 2019-04-16 | 中華電信股份有限公司 | Abnormity monitoring system and anbormity monitoring method |
| CN108599977A (en) * | 2018-02-13 | 2018-09-28 | 南京途牛科技有限公司 | System and method based on statistical method monitoring system availability |
| CN109101398A (en) * | 2018-08-10 | 2018-12-28 | 武汉精测电子集团股份有限公司 | AOI wire body monitoring method and system |
| CN112152823A (en) * | 2019-06-26 | 2020-12-29 | 北京易真学思教育科技有限公司 | Website operation error monitoring method and device and computer storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 梁晟等: "基于统计的网络流量异常检测模型", 《计算机工程》, vol. 31, no. 24 * |
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