JP2007157781A - Apparatus, method, and program of judging part failure and recording medium storing the program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a part failure judging apparatus which can specify a cause of failure in a part to be processed in a mounting step without newly providing a sensor for inspection. <P>SOLUTION: It is judged whether or not monitoring fraction defective increases for each of manufacturing lots of parts (S13). If the monitoring fraction defective increases (YES in S13), the stability is judged in the respective apparatuses constituting a mounting step (S14-S16). When the respective apparatuses constituting the mounting step is judged stable (YES in S14-S16), a warning is given that the cause of failure is a defective part (S18). <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a component failure determination device, a component failure determination method, a component failure determination program, and a recording medium on which a component failure determination program is recorded. In particular, the cause of component failure indirectly without directly measuring the component. The component defect determination apparatus, the component defect determination method, the component defect determination program, and the recording medium on which the component defect determination program is recorded.

For example, Japanese Patent Application Laid-Open No. 2004-214322 (Patent Document 1), Japanese Patent Application Laid-Open No. 2000-258458 (Patent Document 2), and the like are known. JP-A-2003-188596 (Patent Document 3), JP-A-2003-8300 (Patent Document 4) and the like. According to Japanese Patent Application Laid-Open No. 2004-133620, component defect detection is performed by appearance inspection. According to Japanese Patent Application Laid-Open No. 2004-228561, component defect detection is performed using an electrical signal. According to Japanese Patent Application Laid-Open No. 2003-228561, component defect detection is performed by a vision device, a laser sensor, or the like. According to Japanese Patent Application Laid-Open No. 2004-228561, the defect detection of a component is performed using an imaging unit.
JP-A-2004-214322 (paragraph number 0012 and the like) JP 2000-258458 A (paragraph number 0010, etc.) JP 2003-188596 A (paragraph number 0013, etc.) JP 2003-8300 A (paragraph number 0009, etc.)

    Defect determination of components processed in a conventional electronic component mounting apparatus has been performed as described above. In any patent document, the external shape and electrical failure of a part are directly measured, and specifying the cause of the failure takes time to measure individual parts and requires various devices for measurement. There was a problem of being.

  The present invention has been made paying attention to the above-mentioned problems, and it is possible to identify the cause of failure of a component processed in the mounting process without providing a new sensor for inspection. It is an object of the present invention to provide an apparatus, a component failure determination method, a component failure determination program, and a recording medium on which the component failure determination program is recorded.

  A component defect determination device for an electronic component mounting apparatus that mounts a component on a substrate through a plurality of steps according to the present invention includes an operation status input means for inputting data indicating the operation status of each device constituting the plurality of steps. A stability determination unit that determines the stability of each device based on the operation status data input to the operation status input unit; and a failure rate detection unit that detects a component defect rate in the last step of the plurality of steps. And a failure cause specifying means for specifying the cause of the component failure based on the stability of each device determined by the stability determination means and the failure rate detected by the failure rate detection means.

  In the electronic component mounting process, in order to identify the cause of component failure from the operation status of the devices that make up each step and the component defect rate in the last step, the cause of the mounting process failure without providing a new sensor, etc. Can be provided.

  Preferably, it includes a production lot data input means for inputting production lot data for specifying a production lot of the part, and the defect cause identification means manufactures a cause of the defect of the part based on the production lot data input by the production lot data input means. Specify for each lot.

  More preferably, the part includes a plurality of parts different from each other, each of the parts different from each other is managed as a different production lot, and the production lot data input means is configured to manufacture each of a plurality of different parts. Enter lot data.

  More preferably, the defect rate detection means detects an increase in the defect rate related to the component by comparing with a predetermined value. The predetermined value may be an average defect rate detected by the defect rate detection means + (2 × standard deviation).

  According to another aspect of the present invention, a component defect determination device for an electronic component mounting apparatus that mounts a component on a substrate through a plurality of steps is provided in each of the plurality of steps and detects data relating to the appearance of the component. An appearance inspection apparatus, a defect rate detection means for detecting a defect rate of parts in the last step of the plurality of processes, an appearance inspection data detected by the appearance inspection apparatus, and a defect rate of parts detected by the defect rate detection means And a cause-of-failure identifying means for identifying the cause of failure of a part that cannot be captured by appearance.

  In still another aspect of the present invention, a component failure determination method for an electronic component mounting apparatus that mounts a component on a substrate through a plurality of processes inputs data indicating the operation status of each device constituting the plurality of processes. A step of determining the stability of each device based on the input operation status data, a step of detecting a defective rate of parts in the last step of the plurality of steps, and the stability of each device, Identifying the cause of the component failure from the detected defect rate.

  In still another aspect of the present invention, there is provided a component failure determination program for operating a computer as a component failure determination device of an electronic component mounting apparatus that mounts a component on a substrate through a plurality of steps. Operation status input means for inputting data indicating the operation status of each device constituting the process, and stability determination means for determining the stability of each device based on the operation status data input to the operation status input means; Defect rate detection means for detecting the defect rate of parts in the last of a plurality of processes, the stability of each device determined by the stability determination means, and the defect rate detected by the defect rate detection means Operates as a cause of failure identification to identify the cause.

  Note that the component defect determination program may be stored in a computer-readable recording medium.

An embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram showing a configuration around an electronic component mounting apparatus to which a component defect determination device according to the present invention is applied. Referring to FIG. 1, electronic component mounting apparatus 10 includes a printing process, a mounting process, and a reflow process arranged from the upstream side to the downstream side of the substrate on which the electronic component is mounted. Each process is connected by a conveyor, a robot, and other transfer devices. Each process is provided with an apparatus for performing the process.
In the printing process, a printing machine 11 for printing lands on a substrate and a post-printing inspection machine 12 for performing an inspection after printing are provided. In the mounting process, a mounter 13 for mounting a component on a substrate and a post-mount inspection machine 14 for performing an inspection after mounting are provided. In the reflow process, a reflow furnace 15 for soldering the terminal of the component to the land and an after-reflow inspection machine 16 for performing an inspection after soldering are provided.

  The printing machine 11, the post-printing inspection machine 12, the mounter 13, the post-mounting inspection machine 14, the reflow furnace 15, and the post-reflow inspection machine 16 are connected to the component defect determination device 20 via the network 17.

  The setting value a of the printing machine 11 from the printing machine 11, the printing stability b as data indicating the operation status of the printing machine 11 from the post-printing inspection machine 12, and the component mounted on the board from the mounter 13 The manufacturing lot information for each and the information c regarding the used head, the used nozzle and the used feeder of the mounter 13, and the mounting stability information d as data indicating the operation status of the mounter 13 from the post-mount inspection machine 14 , The reflow furnace set value and the reflow furnace actual temperature e are input to the component defect determination device 20 from the post-reflow inspection machine 16, and the post-reflow inspection result f and the data indicating the operation status of the reflow furnace 15 are input to the component defect determination device 20. .

  The set value a of the printing machine 11 is, for example, a land size to be printed. The printing stability information b refers to a print pressing setting value or printing pressure by a squeegee and a squeegee speed setting value or a squeegee speed. The information c regarding the used head, the used nozzle and the used feeder in the mounter 13 is the specification data of these devices, and the mounting stability information d refers to the correction amount at the time of component mounting, the suction vacuum degree, and the like. The post-reflow inspection result information f is a set temperature profile, an external temperature, or the like.

  Here, as a result of the inspection after reflow, generally, a bridge (solder adheres so as to short-circuit between component electrodes), a wetting defect (solder and land, or a bond between solder and component electrodes) ), Fillet abnormality (the amount of solder is too much or too little, and the contour when the solder is viewed from the cross section is not a clean mountain shape), and there is no part (there is no part) However, in the present invention, since the failure of the component itself is determined, no consideration is given to a bridge or no component that is not directly related to the failure of the component itself. That is, a component directly related to a defect of the component itself, for example, wet defect data or the like is used for determining a component defect as a monitoring defect rate described later.

  Next, the configuration of the component defect determination device 20 will be described. FIG. 2 is a block diagram illustrating a configuration of the component defect determination device 20. Referring to FIG. 2, the component defect discrimination device 20 is basically the same as a personal computer (hereinafter referred to as “personal computer”). The component failure determination device 20 includes a CPU 21 that controls the entire device, and a ROM, RAM (not shown), a display device 23, a hard disk 24, a keyboard 25, a mouse 26, and a network connected to the CPU 21 via an interface 22. Including the connecting device 27 and the like. By this network connection device 27, the component defect determination device 20 is connected to each of the printing machines 11, the post-printing inspection machine 12, the mounter 13, the post-mounting inspection machine 14, the reflow furnace 15, and the post-reflow inspection machine 16. .

  FIG. 3 is a flowchart showing an operation performed by the CPU 21 of the component defect discriminating apparatus 20 shown in FIG. CPU21 judges the presence or absence of a defect of components, and the stability of each apparatus based on the information af from the apparatus which comprises each process mentioned above.

  Referring to FIG. 3, the CPU 21 recalculates the monitoring failure rate every time the production lot of each part is changed. The CPU 21 determines whether or not there is an input for changing a part manufacturing lot by the user via the keyboard 25 or the mouse 26 (manufacturing lot data input means) (S11, the following steps are omitted). If there is (YES in S11), the defect rate for the part so far is stored in the hard disk 24, and calculation of the defect rate is started for the parts of the new production lot (S11, S12).

  Note that this part manufacturing lot change input is not limited to a user input, but may be input via communication from another apparatus (for example, a parts inventory management apparatus not shown) via the network 17. In this case, the network connection device 27 and the like function as manufacturing lot data input means.

  First, it is determined whether or not the component monitoring failure rate has increased (S13). Therefore, the CPU 21 operates as a defect rate detection unit. Here, as described above, for example, it is determined whether or not the wet defect rate has increased.

  Here, a specific method for determining the change of the wet defect rate will be described. FIG. 4 is a diagram illustrating a method of determining a change in the component monitoring failure rate (wetting failure rate). The CPU 21 constantly detects the failure rate for each component, and determines that the monitoring failure rate has increased when the value falls outside a predetermined range. Specifically, a graph as shown in FIG. 4 is created to constantly monitor the change in the defect rate. In FIG. 4, the vertical axis represents the defect rate, and the horizontal axis represents time. FIG. 4A is a diagram illustrating an example in which an abnormality is determined when the average defect rate exceeds a predetermined value. Here, the average defect rate is T, variance and standard deviation are calculated, and when the defect rate exceeds T + 2σ, it is determined as abnormal. Here, σ is a standard deviation calculated from the variance.

  On the other hand, FIG. 4B is an example of a case where it is determined that an abnormality occurs when the average defect rate falls below a certain value (here, average defect rate T−2σ). Originally, if the average defect rate T decreases, it cannot be said that there is an abnormality. Here, however, such a case is detected and the reason why such a phenomenon occurs is investigated and used for the future.

  Here, the average defect rate T may be an average value of the defect rate until a certain number of parts are processed, an average value of the defect rate of the previous production lot, or allowed from past examples. It may be a defective rate.

  Next, it is determined whether or not the devices constituting the mounting process are operating stably (S14 to S16). This is because when the component monitoring failure rate increases, it is determined whether the component is due to the component itself or the device. First, it is determined whether or not the operation of the printing press 11 is stable (S14). FIG. 5 is a diagram for explaining an example of a method for determining the stability of the printing press 11, and shows a frequency distribution when the printing area with respect to the opening area in the printing press 11 is expressed in% (horizontal axis). Yes. The CPU 21 takes out one substrate as a sample at random, and sets the average value ± 3σ as a threshold value based on the print area data detected so far, and when it deviates from it, the stability of the printing press 11 is stabilized. Judge that the degree is worse.

  In addition to the above, collected data may be determined. That is, the distribution of the determination learning data and the distribution of the survey data may be compared, and the evaluation may be performed by a test reliability survey such as a t test.

  Next, it is determined whether the operation of the mounter 13 is stable. FIG. 6 is a diagram for explaining an example of a method for determining the stability of the mounter 13. For example, when the amount of deviation in the X direction of the mounted component in the mounter 13 is expressed in mm (horizontal axis). The frequency distribution is shown. The CPU 21 takes out one board as a sample, and on the basis of the component deviation amount data detected so far, the CPU 21 sets the average value ± 3σ as a threshold value, and when it deviates from that, the stability of the mounter 13 is increased. Judge that it has gotten worse.

  In addition to the above, collected data may be determined. That is, the distribution of the determination learning data and the distribution of the survey data may be compared, and the evaluation may be performed by a test reliability survey such as a t test.

  Note that this method may be applied to determine the stability of the printing press 11 based on the printing misalignment in the printing press 11 described above.

  Similarly, it is determined whether or not the operation of the reflow furnace 15 is operating stably by determining the poor wetting (S16). As described above, the CPU 21 operates as an operation status input unit that inputs data indicating the operation status of each device and a stability determination unit that determines the stability of each device based on the input operation status data.

  Returning to FIG. 3, if the monitoring failure rate increases (YES in S13) and any of the printing machine 11, the mounter 13 and the reflow furnace 15 is not stable (NO in any of S14 to S16), these The alarm device 30 warns the user that the failure is caused by an unstable device among the devices (S17). The monitoring device 30 may have an output device such as a buzzer or sound, or a display screen, and display a warning there.

  On the other hand, if all of the printing machine 11, the mounter 13 and the reflow furnace 15 are stable (YES in S14 to S16), the parts themselves, even though the monitoring failure rate has increased (YES in S13). It is determined that the failure is the cause of the failure, and the alarm device 30 warns the user (S18).

  Therefore, the CPU 21 functions as a failure cause identifying unit that identifies the cause of the component failure from the stability of each device and the detected failure rate.

  When the monitoring failure rate does not increase (NO in S13) and falls below a predetermined value (YES in S19), it is in the state shown in FIG. 4B, so why such a phenomenon has occurred. In order to investigate the cause, current operating conditions of the equipment and manufacturing lot information of the board are collected.

  Next, a specific determination method will be described. FIG. 7 is a graph generated by the CPU 21 in S13 shown in FIG. 3 and created based on the calculation. The printing area variation in the printing machine 11, the variation in the printing deviation amount, the variation in the mounting deviation amount in the mounter 13, and the reflow. The set value of the furnace 15 and the variation of the wetting defect rate are plotted for each production lot of parts. Such a graph may be constantly displayed on the display device 23 or the display of the warning device 30.

  Referring to FIG. 7, from production lot A to production lot D, all data vary within a certain range and are stable, but the wetting defect rate after the lot switches to production lot E It can be seen that only exceeds a threshold value (for example, the above average value + 2σ).

  Such a change is caused by a change in the production lot of the part, and it can be determined that the part of the production lot E has a cause.

  As described above, according to this embodiment, in the electronic component mounting process, the cause of component failure can be identified from the operation status of the devices constituting each step and the component defect rate in the last step. The cause of the defective mounting process can be specified without providing a sensor or the like.

  Although the case where the CPU 21 automatically determines a defect has been described here, the present invention is not limited to this, and a graph as shown in FIG. 7 is continuously printed by the display device 23 or a printer (not shown). The user may see it and determine the cause of the failure.

  In addition, here, wetting failure is taken as an example of failure in the final process, but this is not a limitation, and a fillet abnormality or the like may be detected.

  In S14 to S16 of FIG. 3, data indicating the stability of the device such as the printing press 11 is input. For example, the data such as the printing area and the shift amount in the printing process is changed. It is also data on appearance. Therefore, in this embodiment, the post-printing inspection device 12, the post-mounting inspection device 14, the post-reflow inspection device 16 and the like function as an appearance inspection device that detects data relating to the appearance of the parts. From the defect rate of the parts in this process, the CPU 21 specifies the cause of the component failure that cannot be seen in the appearance, for example, the cause of the defect is the production lot of the component. That is, the CPU 21 functions as a failure cause identifying unit that identifies the cause of the failure of a component that cannot be captured by the appearance from the appearance inspection data detected by the appearance inspection device and the defect rate.

  Next, the monitoring failure rate and the threshold value for determining the increase will be described. In the above embodiment, the average failure rate is taken as an example of the monitoring failure rate. Here, what kind of data should be used as the monitoring failure rate and the threshold value will be specifically described.

  FIG. 8 is a flowchart showing the operation performed by the CPU 21 when determining an increase in the monitoring failure rate shown in S13 in FIG.

  Referring to FIG. 8, the CPU 21 first grasps the past state of the component to be inspected (S21). Then, the state of the current production lot is calculated (S22). When both are compared and it is determined that the predetermined threshold value is exceeded (YES in S23), it is determined that the monitoring failure rate has increased (S24).

  This will be specifically described below. First, as the grasp of the past state shown in S21, an average value of defects for each production lot in the past is adopted. Since the average value of defects for each past production lot is stored in the hard disk 24, the CPU 21 refers to this and adopts the average value of the immediately preceding production lot, for example. For example, a value such as 200 ppm is an average value of defects, and this value is a past grasped state.

  As an example other than the above, it may be determined based on the defective rate of all the production lots so far. If the parameter is small, the defect rate for each production lot is difficult to use because the defect rate fluctuates. However, if the defect rate for all the production lots is used, the value becomes almost constant and easy to use. Note that this method cannot be used when the defect rate has improved due to improvement activities.

  Furthermore, in the most recent production lot, for example, the defect rate may be calculated using data for a population of 1000 pieces.

  Next, the determination of whether or not the threshold value has been exceeded from the state calculation and comparison of the current production lot shown in S22 to S23 will be described. The defective rate from the start of the production lot is calculated and compared with the average defective rate e determined as described above. If the current failure rate is within a range smaller than the average failure rate e × 2.0 (current failure rate <e × 2.0), the increase in the monitoring failure rate is not determined within the threshold (S23). NO).

  If the parameter is small, the fluctuation will increase if a defect occurs once. In this case, the average defect rate is doubled to give a margin so that the change can be detected. .

  Next, another example will be described. The following calculation is performed on the average defect rate e.

From the defect rate, the standard deviation σ of the defect occurrence interval (= 1 / defective rate) is calculated. When a defect occurs, the probability of occurrence at the occurrence interval (χ) is calculated from the normal distribution formula (1)
Calculate the percentage of the probability of failure that has occurred with respect to the probability of failure at the average interval (maximum value in the normal distribution diagram shown in FIG. 9 when χ = μ (average)). 2)
Calculate the above (1) and (2) from the average interval for the most recent past (2 or 3) failures, and if the interval is below the set value and there is a tendency to have many failures It is determined that the monitoring failure rate increases. In this case, even if the interval falls below a predetermined value, if the number of defects is small, it is not determined that the monitoring failure rate increases. The determination may be made using any of the methods described above.

  As described above, since a defect due to a part can be determined for each predetermined unit, such as for each part production lot, a defect that cannot be recognized only by viewing the part or a difference in part characteristics can be determined. For example, in the case where a part of a part is plated, it is assumed that the plated part of the part of a certain production lot is oxidized. In this case, it cannot be determined by visual observation or the like whether or not it is oxidized. However, according to the embodiment of the present invention, since a defect can be determined in units of production lots, it is possible to cope with such a case.

  Further, in the above-described embodiment, the case where the defect rate of a part is determined in units of production lots has been described. However, the present invention is not limited to this, and the present invention can be applied to a group unit that can distinguish parts.

  In the above embodiment, the case where the component defect determination device is the dedicated device has been described. However, the present invention is not limited to this, and the component failure determination device is a general-purpose personal computer. May be used as an inspection machine by operating with the program. In this case, the program may be provided on a recording medium such as an optical disk or a hard disk, or may be downloaded from a server on the network via a network.

  As mentioned above, although embodiment of this invention was described with reference to drawings, this invention is not limited to the thing of embodiment shown in figure. Various modifications and variations can be made to the illustrated embodiment within the same range or equivalent range as the present invention.

It is a block diagram which shows the whole structure of an electronic component mounting apparatus. It is a block diagram which shows the structure of a component defect discrimination device. It is a flowchart which shows the operation | movement which CPU of a component defect discrimination device performs. It is a figure for demonstrating the judgment method of the change of a wetting defect rate. It is a figure which shows the method of judging the stability of a printing press. It is a figure which shows the method of judging the stability of a mounter. It is a figure which shows the method of judging the defect of components for every manufacturing lot. It is a figure which shows other embodiment of this invention. It is a figure which shows normal distribution.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Electronic component mounting apparatus, 11 Printing machine, 12 Post printing inspection machine, 13 Mounter, 14 Post mounting inspection machine, 15 Reflow furnace, 16 Post reflow inspection machine, 17 Network, 18 CAD apparatus, 20 Component defect discrimination apparatus, 21 CPU , 22 interface, 23 display device, 24 hard disk, 25 keyboard, 26 mouse, 27 communication device, 30 alarm device.

Claims (9)

A component defect determination device for an electronic component mounting apparatus that mounts a component on a substrate through a plurality of processes,
Operation status input means for inputting data indicating the operation status of each device constituting the plurality of steps;
Stability determination means for determining the stability of each device based on the operation status data input to the operation status input means;
A failure rate detection means for detecting a failure rate of the component in the last step of the plurality of steps;
A component defect determination device, comprising: a failure cause identification unit that identifies a cause of a defect of the component from the stability of each device determined by the stability determination unit and the defect rate detected by the defect rate detection unit. The part defect discrimination device includes a production lot data input means for inputting production lot data for specifying a production lot of parts,
2. The component defect determination device according to claim 1, wherein the defect cause identifying unit identifies a cause of defect of the component for each of the production lots based on the production lot data input by the production lot data input unit. The part includes a plurality of parts different from each other,
Each of the plurality of different parts is managed as a different production lot,
The part defect discrimination device according to claim 2, wherein the production lot data input means inputs manufacturing lot data for each of the plurality of different parts. The component defect determination device according to claim 1, wherein the defect rate detection unit detects an increase in a defect rate related to a component by comparing with a predetermined value. 5. The component defect determination device according to claim 4, wherein the predetermined value is an average defect rate detected by the defect rate detection unit + (2 × standard deviation). A component defect determination device for an electronic component mounting apparatus that mounts a component on a substrate through a plurality of processes,
An appearance inspection device that is provided in each of the plurality of steps and detects data relating to the appearance of the component;
A failure rate detection means for detecting a failure rate of the component in the last step of the plurality of steps;
Component defect determination, including defect cause identification means for identifying a cause of defect of the component that cannot be captured by appearance from the appearance inspection data detected by the appearance inspection device and the defect rate of the component from the defect rate detection unit apparatus. A component defect determination method for an electronic component mounting apparatus that mounts a component on a substrate through a plurality of processes,
Inputting data indicating the operating status of each device constituting the plurality of steps;
Determining the stability of each device based on the input operation status data;
Detecting a defect rate of the part in the last step of the plurality of steps;
A component defect determination method including a step of identifying a cause of a defect of the component from the stability of each device and the detected defect rate. A component failure determination program for operating a computer as a component failure determination device of an electronic component mounting apparatus that mounts a component on a substrate through a plurality of processes,
Computer
Operation status input means for inputting data indicating the operation status of each device constituting a plurality of processes;
Stability determination means for determining the stability of each device based on the operation status data input to the operation status input means;
A failure rate detection means for detecting a failure rate of the component in the last step of the plurality of steps;
A component failure determination program that operates as a failure cause identification unit that identifies a cause of failure of the component from the stability of each device determined by the stability determination unit and the failure rate detected by the failure rate detection unit . A computer-readable recording medium on which the part defect determination program according to claim 8 is recorded.

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