Device and method for judging abnormal jig by counting measurement results of target element

Abstract

The invention relates to a device and a method for judging an abnormal jig by counting measurement results of target elements, which comprises the following steps of: generating statistical data of each jig in each time interval for the target elements of the same element type according to production test data of a circuit board; and whether the jig is abnormal or not is judged according to the standard deviation and the process capability index in the statistical data of the element type of the same target element of each jig in each time interval, so that the abnormality of the jig can be found in real time, and the technical effect of improving the stability and the reliability of the jig during testing is achieved.

Description

Technical Field

[0001] A fixture abnormality judgment system and method thereof, in particular, a device and method for statistically analyzing the measurement results of target components to judge abnormal fixtures. Background Art

[0002] In-Circuit Test (ICT) can verify the electrical connections and functions of individual electronic components on a Printed Circuit Board (PCB). Currently, ICT can effectively detect manufacturing defects.

[0003] ICT uses specialized fixtures to test PCBs. A fixture includes one or more test jigs, and each test jig has multiple probes. Each probe can connect the test points on the PCB to an Automatic Test Equipment (ATE). The ATE sends electrical signals to the electronic components on the PCB through the probes and measures the electrical signals generated by the electronic components to generate measurement results. The ATE can generate test results of the electronic components based on whether the measurement results exceed the upper and lower limits of the measured electronic components.

[0004] In practice, during a large number of circuit tests on the production line, abnormal or incorrect measurement results of electronic components often occur, that is, the measurement results of electronic components exceed the upper and lower limits of the electronic components. This will result in misjudged test results, but in fact, the electronic components are fine, and it is the problem of the measurement results. Therefore, before the test, management and maintenance personnel (engineers) need to calibrate the test fixture to ensure the stability and reliability of the test results.

[0005] However, with the increase in the types of products produced on the production line, the expansion of the mass production scale, and the change of the environment, the workload of calibrating the fixture has become extremely large. Especially, there are a large number of probes used in ICT. After being used for a period of time, measurement result deviations may occur. At the same time, the measurement results of components often have a certain deviation from the standard value, and the deviations generated by measuring the same electronic component using different fixtures are also different. In this way, it is very difficult for management and maintenance personnel to timely detect the measurement result deviations generated by the fixture. Only when the fixture continuously generates problematic measurement results, leading to continuously unpassed test results, will management and maintenance personnel find that the fixture is abnormal. However, it is not easy to determine the location of the abnormal probe only based on the continuously generated unpassed test results.

[0006] In summary, it can be seen that there has long been a problem in the prior art that the statistical and control methods of circuit tests cannot timely detect fixture abnormalities. Therefore, it is necessary to propose improved technical means to solve this problem. Summary of the Invention

[0007] In view of the problem in the prior art that the statistical and control methods for circuit testing cannot detect fixture abnormalities in real time, the present invention discloses a device and method for statistically analyzing the measurement results of target components to determine abnormal fixtures, wherein:

[0008] The device for statistically analyzing the measurement results of target components to determine abnormal fixtures disclosed by the present invention at least includes: a data acquisition module for obtaining production test data including multiple circuit boards and obtaining type information of each target component according to a bill of materials, wherein the production test data is generated by a production process control system and includes measurement results of multiple fixtures on multiple target components on the circuit boards, and the type information represents the component type of the target component; a data statistics module for statistically analyzing the measurement results of multiple fixtures on multiple target components in multiple different time intervals to generate statistical data of each fixture for each component type in each time interval, and the statistical data includes the standard deviation and process capability index of each fixture for each component type in each time interval; an abnormality determination module for determining whether each fixture is abnormal according to the standard deviation and process capability index of each fixture for the component type of the same target component in each time interval.

[0009] The method for statistically analyzing the measurement results of target components to determine abnormal fixtures disclosed by the present invention at least includes the steps of: obtaining production test data including multiple circuit boards, the production test data is generated by a production process control system and includes measurement results of multiple fixtures on multiple target components on the circuit boards; obtaining type information of each target component according to a bill of materials, and the type information represents the component type of the target component; statistically analyzing the measurement results of multiple fixtures on multiple target components in multiple different time intervals to generate statistical data of each fixture for each component type in each time interval, and the statistical data includes the standard deviation and process capability index of each fixture for each component type in each time interval; determining whether each fixture is abnormal according to the standard deviation and process capability index of each fixture for the component type of the same target component in each time interval.

[0010] The device and method disclosed by the present invention are as above. The difference from the prior art is that the present invention generates statistical data of each fixture for each time interval for target components of the same component type based on the production test data of the circuit board, and determines whether the fixture is abnormal according to the standard deviation and process capability index in the statistical data of each fixture for the component type of the same target component in each time interval, so as to solve the problems existing in the prior art and achieve the technical effect of increasing the stability and reliability of the test results of the fixture. Brief Description of the Drawings

[0011] Figure 1 It is a schematic diagram of the components of the device for statistically analyzing the measurement results of target components to determine abnormal fixtures mentioned in the present invention.

[0012] Figure 2It is a module architecture diagram for statistically analyzing the measurement results of target components in the present invention to determine abnormal fixtures.

[0013] Figure 3A It is a method flowchart for statistically analyzing the measurement results of target components in the present invention to determine abnormal fixtures.

[0014] Figure 3B It is a method flowchart for determining fixture abnormalities based on statistical data in the present invention.

[0015] Figure 3C It is a method flowchart for generating type information of target components in the present invention.

[0016] Figure 4 It is a schematic diagram of the coordinate system of the standard deviation and the process capability index in the embodiment of the present invention.

[0017] The description of the reference numerals is as follows:

[0018] 100: Device

[0019] 110: Memory module

[0020] 120: Input module

[0021] 130: Communication interface

[0022] 140: Storage medium

[0023] 150: Display module

[0024] 170: Processing module

[0025] 190: Bus

[0026] 210: Data acquisition module

[0027] 220: Data statistics module

[0028] 230: Result setting module

[0029] 250: Abnormality judgment module

[0030] 411~413: Coordinate center points

[0031] Step 310: Obtain production test data including multiple circuit boards. The production test data is generated by the production process control system and includes the test results and measurement results of the target components on each circuit board.

[0032] Step 320: Obtain the type information of each target component according to the bill of materials. The type information represents the component type of the target component.

[0033] Step 321: Obtain the component material data of each target component from the bill of materials.

[0034] Step 325: Generate type information of each target component according to the component material data

[0035] Step 350: Statistically analyze the measurement results of each target component by each jig in different time intervals to generate statistical data of each jig for each component type in each time interval. The statistical data includes the standard deviation and the process capability index

[0036] Step 370: Determine whether each jig is abnormal according to the statistical data of the same component type by each jig in each time interval

[0037] Step 375: Correlate the standard deviation and the process capability index of each jig for the same component type in each time interval to a coordinate system with the standard deviation and the process capability index as the abscissa and ordinate respectively to generate coordinate points in the coordinate system

[0038] Step 376: Calculate the coordinate center points of each jig in each time interval on the coordinate system according to the coordinate points

[0039] Step 378: Calculate the relative distances of the coordinate center points in the same time interval

[0040] Step 379: Determine whether each jig is abnormal according to whether the maximum of the relative distances of the coordinate center points exceeds a predetermined value Detailed implementation mode

[0041] The features and implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments. The content is sufficient to enable any person skilled in the art to easily and fully understand the technical means applied by the present invention to solve technical problems and implement them accordingly, thereby achieving the effects that the present invention can achieve

[0042] The present invention can determine whether a jig for measuring an electronic component is abnormal based on the deviation and trend generated by statistically analyzing the measurement results obtained by different jigs when measuring the same type of electronic component during testing

[0043] The device for implementing the present invention can be a computing device. The computing device mentioned in the present invention includes, but is not limited to, one or more processing modules, one or more memory modules, and a bus connecting different hardware components (including the memory module and the processing module), etc. Through the multiple hardware components included, the computing device can load and execute an operating system to make the operating system run on the computing device, and can also execute software or programs. The computing device also includes a housing, and the above-mentioned various hardware components are arranged inside the housing

[0044] The bus of the computing device mentioned in the present invention may include one or more types, such as data bus, address bus, control bus, expansion bus, and / or local bus, etc. The bus of the computing device includes, but is not limited to, Industry Standard Architecture (ISA) bus, Peripheral Component Interconnect (PCI) bus, Video Electronics Standards Association (VESA) local bus, and serial Universal Serial Bus (USB), Peripheral Component Interconnect Express (PCI Express, PCI-E / PCIe) bus, etc.

[0045] The processing module of the computing device mentioned in the present invention is coupled to the bus. The processing module includes a register group or register space, which can be completely set on the processing chip of the processing module, or all or part of it can be set outside the processing chip and coupled to the processing chip via dedicated electrical connections and / or via the bus. The processing module can be a central processing unit, a microprocessor, or any suitable processing element. If the computing device is a multi-processor device, that is, the computing device includes multiple processing modules, then the processing modules included in the computing device are all the same or similar, and are coupled and communicate through the bus. In some embodiments, the processing module can interpret a computer instruction or a series of multiple computer instructions to perform specific operations or operations, such as mathematical operations, logical operations, data comparison, copying / moving data, etc., so as to drive other hardware components in the computing device or run the operating system or execute various programs and / or modules. The computer instruction can be an assembly language instruction, an instruction set architecture instruction, a machine instruction, a machine-related instruction, a micro-instruction, a firmware instruction, or source code or object code written in any combination of one or more programming languages, and the computer instruction can be completely executed on a single computing device, partially executed on a single computing device, partially executed on one computing device and partially executed on another connected computing device. Among them, the above-mentioned programming languages include object-oriented programming languages, such as Common Lisp, Python, C++, Objective-C, Smalltalk, Delphi, Java, Swift, C#, Perl, Ruby, etc., and conventional procedural programming languages, such as the C language or other similar programming languages.

[0046] The computing device usually also includes one or more chipsets. The processing module of the computing device can be coupled to the chipset or electrically connected to the chipset through the bus. The chipset is composed of one or more integrated circuits (ICs), including a memory controller and a peripheral input / output (I / O) controller, etc. That is to say, the memory controller and the peripheral input / output controller can be included in one integrated circuit, or can be implemented using two or more integrated circuits. The chipset usually provides input / output and memory management functions, as well as provides multiple general-purpose and / or special-purpose registers, timers, etc. Among them, the above-mentioned general-purpose and / or special-purpose registers and timers can be accessed or used by one or more processing modules coupled or electrically connected to the chipset. In some embodiments, the chipset may also be part of the processing module.

[0047] The processing module of the computing device can also access data stored in the memory module and the mass storage area installed on the computing device through the memory controller. The above-mentioned memory module includes any type of volatile memory and / or non-volatile memory (NVRAM), such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Read-Only Memory (ROM), Flash memory, etc. The above-mentioned mass storage area can include any type of storage device or storage medium, such as a hard disk, an optical disc, a flash drive, a memory card, a Solid State Disk (SSD), or any other storage device, etc. That is to say, the memory controller can access data in the static random access memory, dynamic random access memory, flash memory, hard disk, and solid state disk.

[0048] The processing module of the computing device can also be connected and communicate with peripheral devices or interfaces such as peripheral output devices, peripheral input devices, communication interfaces, various data or signal receiving devices, etc. via the peripheral input / output bus through the peripheral input / output controller. The peripheral input device can be any type of input device, such as a keyboard, mouse, trackball, touchpad, joystick, etc. The peripheral output device can be any type of output device, such as a display, printer, etc. The peripheral input device and the peripheral output device can also be the same device, such as a touch screen, etc. The communication interface can include a wireless communication interface and / or a wired communication interface. The wireless communication interface can include interfaces that support wireless local area networks (such as Wi-Fi, Zigbee, etc.), Bluetooth, infrared, near-field communication (NFC), mobile communication networks (cellular networks) such as 3G / 4G / 5G / 6G, or other wireless data transmission protocols. The wired communication interface can be an Ethernet device, a Digital Subscriber Line (DSL) modem, a Cable modem, an Asynchronous Transfer Mode (ATM) device, or a fiber optic communication interface and / or component, etc. The data or signal receiving device can include a GPS receiver or a physiological signal receiver. The physiological signals received by the physiological signal receiver include but are not limited to heartbeat, blood oxygen, etc. The processing module can periodically poll various peripheral devices and interfaces, enabling the computing device to perform data input and output through various peripheral devices and interfaces, and also communicate with another computing device having the hardware components described above.

[0049] First, the following uses Figure 1 the component schematic diagram of the device for statistically analyzing the measurement results of target components to determine an abnormal jig mentioned in the present invention to illustrate the device implementing the present invention. As Figure 1 shown, the device 100 of the present invention includes a memory module 110, an input module 120, a communication interface 130, a storage medium 140, a display module 150, a processing module 170, and a bus 190. Among them, the memory module 110, the input module 120, the communication interface 130, the storage medium 140, and the display module 150 can be connected to the processing module 170 through the bus 190.

[0050] The memory module 110 can store one or more sets of computer instructions.

[0051] The input module 120 can provide input data. In the present invention, the input data is, for example, whether the test result of an electronic component is misjudged, but the present invention is not limited thereto.

[0052] The communication interface 130 can be connected to a network device such as an external network storage device or server, and request and download data from the connected network device.

[0053] The storage medium 140 can store type information of various electronic components, fixture data of each fixture, etc., but the present invention is not limited thereto. The type information mentioned in the present invention can represent the component type of the corresponding electronic component. Among them, each electronic component can have one or more component types, and the present invention has no special limitation; the fixture data mentioned in the present invention can be data that enables maintenance personnel to distinguish different fixtures, different jigs and probes on the fixture, including but not limited to fixture identification data of the fixture, fixture identification data (or fixture number) of the jig provided on the fixture, probe identification data (or probe number) of the probe provided on the fixture, etc.

[0054] The display module 150 can display the data or signals generated by the processing module 170. For example, it can display information on whether the fixture is abnormal generated by the processing module 170, but the present invention is not limited thereto.

[0055] The processing module 170 can be as shown in the module architecture diagram of the module for statistically analyzing the measurement results of target components to judge abnormal fixtures mentioned in the present invention Figure 2 and includes modules such as a data acquisition module 210, a data statistics module 220, an abnormality judgment module 250, and an optional result setting module 230. In some embodiments, the processing module 170 can execute the computer instructions stored in the memory module 110, and can generate Figure 2 each module in Figure 2 after executing the computer instructions; in another part of the embodiments, Figure 2 each module in

[0056] The data acquisition module 210 is responsible for obtaining production test data. Generally speaking, the production test data obtained by the data acquisition module 210 includes the test records of one or more fixtures on one or more circuit boards. The test record of each circuit board includes the component identification data of multiple electronic components on the same circuit board, the test results and measurement results of each fixture on each electronic component. In the present invention, the electronic components on the circuit board are also referred to as target components. That is to say, the production test data mentioned in the present invention includes the test results and measurement results of multiple target components. Among them, the test results of the target components are usually the test determination data of the target components by the on-site monitoring system (SFC) in the production process control system (MES), but the present invention is not limited thereto; the measurement results of the target components include but are not limited to the signals or data generated by the target components obtained by the fixture performing the measurement, the voltage value or current value output by the target components, etc.

[0057] The data acquisition module 210 can connect to the production process control system through the communication interface 130 to obtain production test data, or can read out the production test data pre-downloaded from the production process control system from the storage medium 140, but the way for the data acquisition module 210 to obtain production test data is not limited to the above. It should be noted that the data acquisition module 210 can include a streaming calculation engine, and when the test device in the production process control system generates production test data, the streaming calculation engine can be used to download the production test data from the data bus of the production process control system through the communication interface 130, so as to shorten the time from the generation of the production test data to its being counted, and then to grasp in real time whether the fixture is abnormal.

[0058] The data acquisition module 210 is also responsible for obtaining the type information of the target components through the bill of materials (BOM). The type information obtained by the data acquisition module 210 corresponds to the target components represented by the component identification data in the production test data. That is to say, the data acquisition module 210 can obtain the type information of the target components whose test results and / or measurement results are included in the production test data. Among them, the type information obtained by the data acquisition module 210 can include one or more component types. For example, the data acquisition module 210 can obtain type information such as resistors, capacitors, and inductors, but the present invention is not limited thereto. It should be noted that each component type can include only one electronic component or multiple electronic components, and the present invention has no special limitation.

[0059] The data acquisition module 210 can also obtain the measurement standards of the target components. The measurement standards obtained by the data acquisition module 210 include the expected range and expected value of the measurement results of the target components, but the present invention is not limited thereto. The data acquisition module 210 can be connected to the production process control system through the communication interface 130 to obtain the type information and / or measurement standards, or can read out the pre-stored type information and / or measurement standards from the storage medium 140, but the manner in which the data acquisition module 210 obtains the type information and / or measurement standards is not limited to the above.

[0060] In some embodiments, the data acquisition module 210 can obtain the component material data of each target component. For example, the component material data of the target component can be obtained from the bill of materials (BOM) according to the component identification data of the target component; the data acquisition module 210 can also generate the type information of each target component according to the obtained component material data. For example, the data acquisition module 210 can further divide the target components with the type information of resistor into two component types according to the resistance value, i.e., the resistance value is less than or equal to 1000 ohms and greater than 1000 ohms, but the manner of using the resistance value for dividing the component types in the present invention is not limited to the above. Among them, the data acquisition module 210 can be connected to the production process control system through the communication interface 130 to obtain the bill of materials, or can read out the pre-stored bill of materials from the storage medium 140, but the manner in which the data acquisition module 210 obtains the bill of materials is not limited to the above.

[0061] The data acquisition module 210 can also obtain the fixture data of the fixture for measuring the target component during the test. More specifically, the fixture corresponding to the fixture data obtained by the data acquisition module 210 is the fixture for measuring the target component whose test results and / or measurement results are included in the production test data. The data acquisition module 210 can be connected to the production process control system through the communication interface 130 and obtain the fixture data with the fixture identification data recorded in the obtained production test data, or can read out the fixture data with the fixture identification data recorded in the obtained production test data from the storage medium 140, but the manner in which the data acquisition module 210 obtains the fixture data is not limited to the above.

[0062] The data statistics module 220 is responsible for statistically analyzing the measurement results of each fixture for each target component to generate the statistical data of each fixture for each component type. The statistical data generated by the data statistics module 220 includes but is not limited to the average value (x), variance, standard deviation (σ), and process capability index (C pk) and so on. That is to say, the data statistics module 220 can statistically analyze the measurement results of each target component generated by each jig for target components of the same type of measurement component, so as to generate statistical data such as the average value, variance, standard deviation, and process capability index of the measurement results of each jig for target components of each component type. For example, the process capability index is calculated based on the accuracy index (C p ) and the accuracy index (C k ). For example, the process capability index is calculated by the formula "accuracy index * (1 - accuracy index)", that is, C pk = C p * (1 - C k ), where the above accuracy index is the ratio of all measurement results (R) falling within the expected range (T), that is, C p = T / R; the above accuracy index is the ratio of the difference between the average value (x) of all measurement results and the expected value (M) falling within half of the expected range (T / 2), that is, C k = (M - x) / (T / 2); the above expected range and expected value can be obtained by the data acquisition module 210. However, the method for the data statistics module 220 to generate the process capability index is not limited to the above.

[0063] In some embodiments, the data statistics module 220 can also select a predetermined number of target components with the highest usage frequency within the same time interval from all target components according to the time interval, and perform statistics based on the selected target components to generate statistical data. For example, the data statistics module 220 can select 30 or 50 target components with the highest usage frequency within the same time interval, and calculate the average measurement value of the selected target components based on the measurement results of the selected target components, and further calculate the standard deviation and process capability index of each jig within the same time interval based on the calculated average measurement value and the measurement results of each jig for the selected target components.

[0064] The abnormality determination module 250 is responsible for determining whether each jig is abnormal based on the statistical data generated by the data statistics module 220. Among them, the above statistical data is the standard deviation and process capability index generated by the data statistics module 220 through statistical analysis of the measurement results generated by each jig for measuring target components for all target components with the same component type as the target component in different time intervals.

[0065] The abnormality determination module 250 can map the standard deviations and process capability indices statistically generated by the data statistics module 220 for the measurement results of target components of the same component type by each fixture within the same time interval to a coordinate system with the standard deviation and the process capability index as the abscissa and ordinate, thereby generating corresponding coordinate points on the coordinate system, calculating the coordinate center points corresponding to each fixture based on the coordinate points on the coordinate system, and determining whether the corresponding fixtures are abnormal based on the calculated coordinate center points. For example, the abnormality determination module 250 can calculate the relative distances between the respective coordinate center points and determine whether the fixture corresponding to the coordinate center point is abnormal based on whether the two coordinate center points that generate the maximum of the calculated relative distances fall within the normal range, that is, when the relative distance between the coordinate center points of two fixtures is the maximum and reaches a predetermined value, it is determined whether the coordinate center points of the two fixtures fall within the normal range. If the coordinate center point of any fixture does not fall within the normal range of that fixture, it is determined that the fixture whose coordinate center point does not fall within the normal range is abnormal. Among them, the normal range of a certain fixture can be a circle with the coordinate point corresponding to the expected value or predetermined value of the standard deviation and the process capability index of the measurement result of the target component by that fixture as the center and a certain value as the radius.

[0066] In some embodiments, the abnormality determination module 250 can also calculate the dispersion degree of the measurement results of the target components of the same component type by each fixture on the coordinate system, and determine whether the fixture for measuring the target component is abnormal based on the change in the calculated dispersion degree. For example, when the dispersion degree of a certain fixture continuously increases, it is determined that the fixture is abnormal.

[0067] The abnormality determination module 250 can also obtain the average value, standard deviation (or variance), and / or process capability index statistically generated by the data statistics module 220 for the measurement results of the target components of the same component type by each fixture in different time intervals, and determine whether each fixture is abnormal by calculating the change in the ratio of the obtained standard deviation (or variance) and / or process capability index to the obtained average value. For example, when the ratio of the standard deviation (or variance) and / or process capability index to the average value in the statistical data of the measurement results of the target components of a certain component type by a certain fixture continuously increases, it is determined that the fixture is abnormal.

[0068] The abnormality determination module 250 can also provide the generated coordinate system, coordinate points, and the statistical data generated by the data statistics module 220 to the display module 150 for display, and can also provide the measurement results of each fixture for each target component obtained by the data acquisition module 210 to the display module 150, so that the display module 150 displays the measurement results of each fixture for each target component within a certain range around the corresponding coordinate points.

[0069] The result setting module 230 can mark the measurement results of the target components that are misjudged. For example, on the coordinate system generated by the anomaly determination module 250, the coordinate points corresponding to the measurement results of the target components are marked as misjudged. Usually, whether the measurement result of the target component is misjudged is input by the input module 120.

[0070] Next, an embodiment is used to illustrate the operation system and method of the present invention. Please refer to the Figure 3A method flowchart for statistically analyzing the measurement results of target components to determine an abnormal jig mentioned in the present invention. In this embodiment, it is assumed that the device 100 is a monitoring host on the production line, but the present invention is not limited thereto.

[0071] When the management and maintenance personnel of the production line use the present invention, after the device 100 is started, the data acquisition module 210 of the device 100 can obtain production test data (step 310). In this embodiment, it is assumed that the data acquisition module 210 can be connected to the production process control system through the communication interface 130 of the device 100, so as to obtain the production test data of in-circuit test (ICT) from the production process control system. The production test data includes the circuit test records of multiple circuit boards, and the circuit test record of each circuit board includes the component identification data and measurement results of multiple target components.

[0072] After the device 100 is started, the data acquisition module 210 of the device 100 can obtain the type information of the target components (step 320). In this embodiment, it is assumed that the data acquisition module 210 can read out the bill of materials from the storage medium 140 of the device 100, and as Figure 3C shown in the process, the data acquisition module 210 can obtain the component material data of each target component from the bill of materials (step 321), and generate the type information of the target components based on the obtained component material data (step 325).

[0073] It should be noted that in the present invention, the data acquisition module 210 of the device 100 usually obtains the production test data first (step 310), and then obtains the type information of the target components (step 320). However, in fact, the present invention does not limit the order of the above two steps. In some embodiments, the data acquisition module 210 can also obtain the type information of the target components (step 320) first, and then obtain the production test data (step 310).

[0074] In addition, also after the device 100 is started, the data acquisition module 210 of the device 100 can also obtain the fixture data of all the fixtures for measuring each target component. In this embodiment, it is assumed that the data acquisition module 210 can screen out the fixture identification data from the fixture data stored in the storage medium 140 of the device 100 and record it in the fixture data of the production test data, or can also obtain the fixture identification data from the production process control system through the communication interface 130 of the device 100 and record it in the fixture data of the fixture in the production test data.

[0075] After the data acquisition module 210 of the device 100 obtains the production test data and the type information of the target components (steps 310 - 320), the data statistics module 220 of the device 100 can statistically analyze the measurement results of each fixture on various target components in different time intervals, so as to generate statistical data of each fixture on each component type in each time interval after the statistics (step 350). In this embodiment, it is assumed that three different fixtures are used to measure the target components, and the fixture identification data of the above three fixtures are T3380601A, T3380601B, and T3380601C respectively. The data statistics module 220 can first select the target components with the component type of resistor from the target components measured by the above three fixtures, and then select the top 50 component numbers with the highest usage frequency (the most measured times) from the selected resistors (target components), and statistically analyze the average resistance value (average measurement value) of the target components (resistors) with the component numbers of the selected 50 component numbers. Then, the data statistics module 220 can calculate the standard deviation and the process capability index of each fixture (i.e., T3380601A, T3380601B, T3380601C) in each time interval based on the calculated average resistance value and the measurement results of the three fixtures on the target components with the component numbers of the selected 50 component numbers. Among them, the measurement standard required for the data statistics module 220 to generate the process capability index can be obtained by the data acquisition module 210 when obtaining the type information of the target components.

[0076] After the data statistics module 220 of the device 100 statistically analyzes the measurement results of each fixture on various target components in different time intervals to generate statistical data (step 350), the abnormality judgment module 250 of the device 100 can judge whether each fixture is abnormal based on the statistical data of each fixture in each time interval (step 370). In this embodiment, it is assumed that as Figure 3BAs shown in the process of [description], the data acquisition module 210 of the device 100 can first connect to the on-site monitoring system (SFC) through the communication interface 130 of the device 100 to obtain the misjudgment status information of each fixture in each time interval. The anomaly judgment module 250 can judge whether the misjudgment rate of each fixture in each time interval is normal according to the misjudgment status information obtained by the data acquisition module 210. When the misjudgment rate of each fixture in each time interval is normal, the anomaly judgment module 250 can map the standard deviation and the process capability index in the statistical data generated by the data statistics module 220, which contains the measurement results of the target components of the same component type by each fixture in each time interval, to a coordinate system with the standard deviation and the process capability index as the abscissa and ordinate, so as to generate coordinate points corresponding to the statistical data of the measurement results of the target components of the same component type by each fixture in each time interval in the coordinate system (step 375), and can calculate the coordinate center points of the statistical data of the measurement results of the target components of the same component type by each fixture in each time interval on the coordinate system according to the coordinate points in the coordinate system (step 376). After that, the anomaly judgment module 250 can calculate the relative distances of the coordinate center points in the same time interval for each time interval (step 378), and can judge whether the fixtures for measuring the same target component are abnormal according to whether the maximum value among the calculated relative distances of the coordinate center points exceeds a predetermined value (step 379). As Figure 4As shown, different shapes and shading, such as a circle with a shaded dot (hereinafter referred to as a dot circle), a triangle with a shaded white dot (hereinafter referred to as a white dot triangle), and a rectangle with a shaded diagonal line (hereinafter referred to as a diagonal rectangle), respectively represent the standard deviation and process capability index of the measurement results of different fixtures (assuming the same manufacturer and model for the three fixtures) for target components of the same component type within the same time interval. The abnormality determination module 250 can calculate the coordinate center points (411 - 413) for the coordinate points (coordinate points of different shapes / shadings) corresponding to the standard deviation and process capability index of each fixture in different time intervals, and determine that the relative distance between the coordinate center point 413 of the diagonal rectangle and the coordinate center point 411 of the dot circle is greater than the relative distances between the coordinate center point 413 of the diagonal rectangle and the coordinate center point 412 of the white dot triangle, and between the coordinate center point 411 of the dot circle and the coordinate center point 412 of the white dot triangle, that is, the relative distance between the coordinate center point 413 of the diagonal rectangle and the coordinate center point 411 of the dot circle is the largest among the calculated relative distances. If the abnormality determination module 250 determines that the relative distance between the coordinate center point 413 of the diagonal rectangle and the coordinate center point 411 of the dot circle exceeds a predetermined value, and the coordinate center point 413 of the diagonal rectangle does not fall within the normal range 420 while the coordinate center point 411 of the dot circle falls within the normal range 420, then the abnormality determination module 250 can determine that the fixture (T3380601C) that produces the measurement result represented by the coordinate point of the diagonal rectangle is abnormal.

[0077] In summary, it can be seen that the difference between the present invention and the prior art lies in the technical means of generating statistical data of each fixture for target components of the same component type based on the production test data of the circuit board, and determining whether the fixture is abnormal based on the standard deviation and process capability index in the statistical data of each fixture for the component type of the same target component in each time interval. By means of this technical means, the problem in the prior art that the statistical and control methods of circuit testing cannot detect fixture abnormalities in real time can be solved, and thus the technical effects of increasing the stability and reliability of the test results of the fixture can be achieved.

[0078] Furthermore, the method of the present invention for statistically analyzing the measurement results of target components to determine abnormal fixtures can be implemented in hardware, software, or a combination of hardware and software, and can also be implemented in a computer system in a centralized manner or in a distributed manner with different components scattered in several interconnected computer systems.

[0079] Although the embodiments disclosed in the present invention are as above, the content described above does not directly define the scope of patent protection of the present invention. Any person of ordinary skill in the art, without departing from the spirit and scope disclosed in the present invention, makes some changes and modifications in the form and details of the implementation of the present invention, which all belong to the scope of patent protection of the present invention. The scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A method for statistically analyzing the measurement results of target components to determine abnormal fixtures, characterized in that, It is applied to a device, and the method at least includes the following steps: Obtain production test data including multiple circuit boards, where the production test data is generated by a production process control system and includes measurement results of multiple fixtures on multiple target components on each of the circuit boards; Obtain the type information of each of the target components according to a bill of materials, where the type information represents the component type of each of the target components; Statistically analyze the measurement results of each of the fixtures on the multiple target components in multiple different time intervals to generate statistical data of each of the fixtures on each of the component types in each of the time intervals, where the statistical data includes the standard deviation and process capability index of each of the fixtures on each of the component types in each of the time intervals; and Judge whether each of the fixtures is abnormal according to the standard deviation and process capability index of each of the fixtures on the same component type in each of the time intervals.

2. The method for statistically analyzing the measurement results of target components to determine an abnormal jig according to claim 1, wherein The step of statistically analyzing the measurement results of each of the fixtures on the multiple target components in multiple different time intervals to generate the statistical data of each of the fixtures on each of the component types in each of the time intervals is to select a predetermined number of target components with the highest usage frequency in the time interval from the multiple target components, statistically analyze the average measurement values of the selected multiple target components, and calculate the standard deviation and process capability index of each of the fixtures in the same time interval according to the average measurement values.

3. The method for statistically analyzing the measurement results of target components to determine an abnormal jig according to claim 1, characterized in that, The step of judging whether each of the fixtures is abnormal according to the standard deviation and process capability index of each of the fixtures on the same component type in each of the time intervals is to obtain the misjudgment status information of each of the fixtures in each of the time intervals generated by a on-site monitoring system, judge whether the misjudgment rate of each of the fixtures in the time interval is normal according to the misjudgment status information, and correspond the standard deviation and process capability index of the multiple fixtures with normal misjudgment rate on the same component type in the same time interval to a coordinate system with the standard deviation and process capability index as the abscissa and ordinate to generate multiple coordinate points in the coordinate system, and calculate the coordinate center point of each of the fixtures on the coordinate system according to the multiple coordinate points, and judge whether each of the fixtures is abnormal according to whether the maximum value among the relative distances of the multiple coordinate center points exceeds a predetermined value.

4. The method for statistically analyzing the measurement results of target components to determine an abnormal jig as claimed in claim 3, wherein The method further includes the step of marking the test results as misjudged on the coordinate system.

5. The method for statistically analyzing the measurement results of target components to determine an abnormal jig according to claim 1, characterized in that The step of obtaining the type information of each of the target components further includes obtaining the component material data of each of the target components from the bill of materials and generating the type information of each of the target components according to the component material data.

6. A device for statistically analyzing the measurement results of target components to determine abnormal fixtures, characterized in that, The device at least includes: A data acquisition module for obtaining production test data including multiple circuit boards and for obtaining the type information of each of the target components according to a bill of materials, where the production test data is generated by a production process control system and includes measurement results of multiple fixtures on multiple target components on each of the circuit boards, and the type information represents the component type of each of the target components; A data statistics module is used to statistically analyze the measurement results of each of the jigs on the multiple target components in multiple different time intervals to generate statistical data of each jig on each component type in each of the time intervals. The statistical data includes the standard deviation and the process capability index of each jig on each component type in each of the time intervals; and An abnormality judgment module is used to judge whether each jig is abnormal based on the standard deviation and the process capability index of each jig on the same component type in each of the time intervals.

7. The apparatus for statistically analyzing the measurement results of target components to determine an abnormal jig according to claim 6, wherein The step in which the data statistics module statistically analyzes the measurement results of each of the jigs on the multiple target components in multiple different time intervals to generate the statistical data of each jig on each component type in each of the time intervals is to select a predetermined number of target components with the highest usage frequency in the time interval from the multiple target components, statistically analyze the average measurement values of the selected multiple target components, and calculate the standard deviation and the process capability index of each jig in the same time interval based on the average measurement values.

8. The device for statistically analyzing the measurement results of target components to determine an abnormal jig according to claim 6, wherein, The data acquisition module is further used to obtain the misjudgment status information of each jig in each of the time intervals generated by connecting to the on-site monitoring system. The abnormality judgment module is used to judge whether the misjudgment rate of each jig in the time interval is normal based on the misjudgment status information, and to correspond the standard deviation and the process capability index of the multiple jigs with normal misjudgment rates on the same component type in the same time interval to a coordinate system with the standard deviation and the process capability index as the abscissa and the ordinate respectively to generate multiple coordinate points in the coordinate system, and to calculate the coordinate center point of each jig on the coordinate system based on the multiple coordinate points, and to judge whether each jig is abnormal based on whether the maximum value among the relative distances of the multiple coordinate center points exceeds a predetermined value.

9. The apparatus for statistically analyzing the measurement results of target components to determine an abnormal jig according to claim 8, wherein The system further includes a result setting module, which is used to mark the test result as misjudged on the coordinate system.

10. The device for statistically analyzing the measurement results of target components to determine an abnormal jig according to claim 6, wherein, The step in which the data acquisition module obtains the type information of each of the target components further includes obtaining the component material data of each of the target components from the bill of materials and generating the type information of each of the target components based on the component material data.

Images