WO2022105318A1 - 监测机台运行状况的方法及装置、存储介质及电子设备 - Google Patents

监测机台运行状况的方法及装置、存储介质及电子设备 Download PDF

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WO2022105318A1
WO2022105318A1 PCT/CN2021/112341 CN2021112341W WO2022105318A1 WO 2022105318 A1 WO2022105318 A1 WO 2022105318A1 CN 2021112341 W CN2021112341 W CN 2021112341W WO 2022105318 A1 WO2022105318 A1 WO 2022105318A1
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Prior art keywords
data points
abnormal data
monitoring
machine
target abnormal
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PCT/CN2021/112341
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English (en)
French (fr)
Inventor
吴雨祥
王莉莉
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长鑫存储技术有限公司
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Priority to US17/647,184 priority Critical patent/US20220157670A1/en
Publication of WO2022105318A1 publication Critical patent/WO2022105318A1/zh

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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
    • H01L21/67—Apparatus specially adapted for handling semiconductor or electric solid state devices during manufacture or treatment thereof; Apparatus specially adapted for handling wafers during manufacture or treatment of semiconductor or electric solid state devices or components ; Apparatus not specifically provided for elsewhere
    • H01L21/67005—Apparatus not specifically provided for elsewhere
    • H01L21/67242—Apparatus for monitoring, sorting or marking
    • H01L21/67253—Process monitoring, e.g. flow or thickness monitoring
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
    • H01L21/67—Apparatus specially adapted for handling semiconductor or electric solid state devices during manufacture or treatment thereof; Apparatus specially adapted for handling wafers during manufacture or treatment of semiconductor or electric solid state devices or components ; Apparatus not specifically provided for elsewhere
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
    • H01L21/67—Apparatus specially adapted for handling semiconductor or electric solid state devices during manufacture or treatment thereof; Apparatus specially adapted for handling wafers during manufacture or treatment of semiconductor or electric solid state devices or components ; Apparatus not specifically provided for elsewhere
    • H01L21/67005—Apparatus not specifically provided for elsewhere
    • H01L21/67242—Apparatus for monitoring, sorting or marking
    • H01L21/67276—Production flow monitoring, e.g. for increasing throughput
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
    • H01L21/67—Apparatus specially adapted for handling semiconductor or electric solid state devices during manufacture or treatment thereof; Apparatus specially adapted for handling wafers during manufacture or treatment of semiconductor or electric solid state devices or components ; Apparatus not specifically provided for elsewhere
    • H01L21/67005—Apparatus not specifically provided for elsewhere
    • H01L21/67242—Apparatus for monitoring, sorting or marking
    • H01L21/67288—Monitoring of warpage, curvature, damage, defects or the like

Definitions

  • the present disclosure relates to the field of semiconductor technology, and in particular, to a method and device for monitoring the running status of a machine, a storage medium and an electronic device.
  • the size, structural features and other rules of the product will fluctuate due to some reasons, and this fluctuation will affect the quality of the semiconductor product.
  • the entire processing process there are many reasons for the fluctuation. Therefore, monitoring the entire processing process, collecting and analyzing various data is essential to ensure product quality.
  • specialized data acquisition systems are generally used to collect various data in the process performed by the semiconductor process equipment.
  • the Statistical Process Control (SPC) system is used to track and analyze the production process of products with the help of mathematical statistics methods, so as to find and solve problems in time to ensure product quality.
  • the SPC system is used to monitor the semiconductor manufacturing process, which needs to be improved in determining the operating status of the machine and the efficiency of failure analysis.
  • the purpose of the present disclosure is to overcome the above-mentioned deficiencies of the prior art, and to provide a method for monitoring the running status of the machine, which can quickly determine the running status of the machine, which is helpful for engineers to quickly find out the cause of the abnormality, thereby improving the efficiency of eliminating the abnormality. Make the whole process reach a controllable state in time.
  • an apparatus for monitoring the running condition of a machine including:
  • the first acquisition module is used for real-time monitoring of the product preparation process and acquisition of monitoring data sets;
  • the second acquisition module is used for extracting the abnormal data points of the machine according to the monitoring data set
  • an extraction module used for screening the abnormal data points of the machine to obtain target abnormal data points
  • a preset module configured to preset a quantity threshold corresponding to the target abnormal data point
  • the judgment module is used for judging whether to generate an alarm signal according to the quantity of the target abnormal data points and the quantity threshold.
  • a computer-readable storage medium having a computer program stored thereon, the computer program implementing the method of the first aspect when executed by a processor.
  • an electronic device comprising:
  • a memory for storing executable instructions for the processor
  • the processor is configured to perform the method of the first aspect by executing the executable instructions.
  • the method for monitoring the running state of a machine provided by the present disclosure is used for product preparation process monitoring.
  • the method provided by the present disclosure is beneficial to quickly determine whether the abnormality is related to a specific machine.
  • the method for monitoring the running status of a machine provided by the present disclosure includes: monitoring a product preparation process in real time, acquiring a monitoring data set, and extracting abnormal data points of the machine according to the monitoring data set; screening the abnormal data points of the machine to obtain a target Abnormal data points; preset the number threshold corresponding to the target abnormal data points, and determine whether to generate an alarm signal according to the number of extracted target abnormal data points and the number threshold.
  • the abnormal data points of the machine corresponding to the machine are extracted from the monitoring data set, and some data points in the monitoring data set, such as abnormal points exceeding the control limit, are displayed in units of machines, so as to facilitate the subsequent analysis of the data points.
  • the operating status of the machine is judged accordingly.
  • Screen from the abnormal data points of the machine to obtain the target abnormal data points compare the number of target abnormal data points with the preset number threshold, and display the judgment results in the form of alarm signals.
  • the alarm standard of the machine is determined, the operation status of the machine is reflected by the alarm signal, and the information of the operation of the machine is intuitively given.
  • the method for monitoring the running status of the machine provided by the present disclosure can quickly determine the running status of the machine through the alarm signal.
  • the key dimensions of the product or important parameters of the process are abnormal, it is helpful for engineers to quickly find out Abnormal causes, thereby improving the efficiency of eliminating abnormalities, so that the entire process can reach a controllable state in time.
  • FIG. 1 is a schematic flowchart of a method for monitoring the running status of a machine in an exemplary embodiment of the present disclosure
  • FIG. 2 is a schematic flowchart of a method for monitoring the running status of a machine in another exemplary embodiment of the present disclosure
  • FIG. 3 is a step-by-step flowchart of a method for monitoring the running status of a machine in an exemplary embodiment of the present disclosure
  • FIG. 4 is a step-by-step flowchart of a method for monitoring the running status of a machine in another exemplary embodiment of the present disclosure
  • FIG. 5 is a step-by-step flowchart of a method for monitoring the running status of a machine in another exemplary embodiment of the present disclosure
  • FIG. 6 is a schematic diagram of abnormal data point results in an exemplary embodiment of the present disclosure.
  • FIG. 7 is a schematic diagram of abnormal data point results in another exemplary embodiment of the present disclosure.
  • FIG. 8 is a schematic diagram of a preset number threshold in an exemplary embodiment of the present disclosure.
  • FIG. 9 is a schematic diagram showing the result of checking the number threshold setting standard corresponding to the machine in an exemplary embodiment of the present disclosure.
  • Figure 10 is a schematic diagram of the number of target abnormal data points corresponding to a machine in an exemplary embodiment of the present disclosure
  • FIG. 11 is a schematic structural diagram of an apparatus for monitoring the running status of a machine in an exemplary embodiment of the present disclosure
  • FIG. 12 is a schematic structural diagram of a computer storage medium in an exemplary embodiment of the present disclosure.
  • FIG. 13 is a schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure.
  • Example embodiments will now be described more fully with reference to the accompanying drawings.
  • Example embodiments can be embodied in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
  • the same reference numerals in the drawings denote the same or similar structures, and thus their detailed descriptions will be omitted.
  • the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale.
  • Example embodiments will now be described more fully with reference to the accompanying drawings.
  • Example embodiments can be embodied in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
  • the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of the embodiments of the present disclosure.
  • a certain structure When a certain structure is "on” other structures, it may mean that a certain structure is integrally formed on other structures, or that a certain structure is “directly” arranged on other structures, or that a certain structure is “indirectly” arranged on another structure through another structure. other structures.
  • SPC is a common technique used to track and analyze changes in semiconductor manufacturing processes.
  • data information based on products or processes is collected, and various charts are generated according to the data information, such as control charts with control limits.
  • the data information obtained by relying on the SPC system is mostly displayed in the unit of product or process, and when an abnormal point occurs in the preparation process, it is impossible to effectively screen and analyze the abnormal point in time, so it is impossible to judge whether the abnormal point is related to the abnormal point in time. It is related to the operating status of the machine, which affects the efficiency of fault analysis and troubleshooting.
  • the present disclosure provides a method for monitoring the operating status of a machine, which is used for product preparation process monitoring, including:
  • Step S100 monitor the product preparation process in real time, and obtain a monitoring data set
  • Step S200 extracting machine abnormal data points according to the monitoring data set
  • Step S300 screening the abnormal data points of the machine to obtain target abnormal data points
  • Step S400 preset a quantity threshold corresponding to the target abnormal data point
  • Step S500 according to the quantity of the target abnormal data points and the quantity threshold, determine whether to generate an alarm signal.
  • the method for monitoring the running state of a machine provided by the present disclosure is used for product preparation process monitoring.
  • the method provided by the present disclosure is beneficial to quickly determine whether the abnormality is related to a specific machine.
  • the method for monitoring the running status of a machine provided by the present disclosure includes: monitoring a product preparation process in real time, acquiring a monitoring data set, and extracting abnormal data points of the machine according to the monitoring data set; screening the abnormal data points of the machine to obtain a target Abnormal data points; preset the number threshold corresponding to the target abnormal data points, and determine whether to generate an alarm signal according to the number of extracted target abnormal data points and the number threshold.
  • the abnormal data points of the machine corresponding to the machine are extracted from the monitoring data set, and some data points in the monitoring data set, such as abnormal points exceeding the control limit, are displayed in units of machines, so as to facilitate the subsequent analysis of the data points.
  • the operating status of the machine is judged accordingly.
  • Screen the abnormal data points of the machine to obtain the target abnormal data points compare the number of target abnormal data points with the preset number threshold, and display the judgment results in the form of alarm signals.
  • the alarm standard of the machine is determined, the operation status of the machine is reflected by the alarm signal, and the information of the operation of the machine is intuitively given.
  • the method for monitoring the running status of the machine provided by the present disclosure can quickly determine the running status of the machine through the alarm signal.
  • the key dimensions of the product or the important parameters of the process are abnormal, it is helpful for engineers to quickly find out the abnormality Therefore, the efficiency of eliminating abnormality is improved, so that the whole process can reach a controllable state in time.
  • step S100 the product preparation process is monitored in real time, and a monitoring data set is obtained.
  • the monitoring data set is obtained by monitoring the product preparation process in real time based on the statistical process control system.
  • Statistical process control systems are used to monitor the product preparation process in real time.
  • the statistical process control system also known as the SPC (Statistical Process Control) system, can be specifically used for real-time monitoring of the semiconductor manufacturing process.
  • SPC Statistical Process Control
  • the statistical process control system is not limited to the SPC system currently used by everyone, and can also be other new systems developed by other developers who can complete statistical process control.
  • the statistical process monitoring system collects relevant data in the product preparation process, such as product critical dimensions, overlay errors, and relevant technical parameters in the process, to obtain a monitoring data set.
  • the key dimensions of the product may be the thickness, width, length or weight of the product, etc.
  • the relevant technical parameters in the process may be temperature, time, speed, and the like.
  • the monitoring data set disclosed in this disclosure may also contain other data information that needs to be monitored, such as process nodes with many unqualified products based on past data statistics, etc. .
  • the data information in the specific monitoring product preparation process can be selected according to actual needs.
  • the SPC system can be used for process monitoring, in which the size of the semiconductor, the overlay error and the related technical parameters in the process can be measured by a measuring machine, etc., and the measured data can be transmitted in real time.
  • the SPC system obtains the monitoring data set in the semiconductor manufacturing process accordingly, and stores the monitoring data set.
  • step S200 according to the monitoring data set, the abnormal data points of the machine are extracted.
  • an anomalous data point refers to a data point that exceeds a control limit or specification limit.
  • LCL lower control limit
  • UCL upper control limit
  • USL upper specification limit
  • LSL lower specification limit
  • Control limits are determined based on the distribution of sample data monitored during the product preparation process, including upper and lower control limits.
  • the data information of the key dimensions of the product that affects the quality of the product or the key process data information of the preparation process that affects the quality of the process will generally be analyzed according to the actual needs.
  • the main data information of product quality or process quality is used as the control object, and the corresponding control chart to be generated is selected according to the control object and control requirements, and the product preparation process is analyzed by analyzing the control chart. Analyze and judge changing trends.
  • a control chart is a graph with control limits, which can be obtained by computational analysis based on sample data points. In the present disclosure, points in the monitored data set that exceed control limits are classified as abnormal data points.
  • the specification limit is the set limit value, including the upper specification limit and the lower specification limit, which are generally artificially set by technicians based on the test results of key data information in the batch wafer process, or can also be set according to customer requirements. Determine the upper and lower specification limits of the product on critical dimensions. In some embodiments of the present disclosure, points in the monitoring data set that exceed specification limits are also classified as abnormal data points.
  • step S200 includes:
  • Step S210 extracting abnormal data points in the product preparation process according to the monitoring data set
  • Step S220 according to the abnormal data points in the product preparation process, extraction is performed in units of machines to obtain the abnormal data points of the machine.
  • step S210 according to the monitoring data set, abnormal data points in the product preparation process are extracted.
  • the critical dimension of the semiconductor product is monitored, a monitoring data set about the critical dimension of the semiconductor product is obtained, and abnormal data points are extracted from the monitoring data set.
  • an abnormal data point during product preparation refers to a data point that exceeds a control limit or specification limit.
  • the abnormal data points can include one or more of the data points exceeding the upper control limit, the data points exceeding the lower control limit, the data points exceeding the upper specification limit and the data points exceeding the lower specification limit, which can be specified by engineers, system administrators, etc. Staff make settings as needed.
  • step 220 according to the abnormal data points in the product preparation process, extraction is performed in units of machines to obtain abnormal data points of the machine.
  • the abnormal data points are extracted in units of machines to obtain abnormal data points corresponding to the machines.
  • the critical dimensions of semiconductor products are monitored, and a monitoring data set about the critical dimensions of semiconductor products is obtained. Model, product batch, product number and other information.
  • the abnormal data points are extracted from the monitoring data set, and the abnormal data points are extracted in units of machines.
  • the extraction results can be displayed in the form of graphs, and then the abnormal data points corresponding to the machines are obtained.
  • abnormal data points corresponding to the machine can also be obtained.
  • the specific steps are similar to the above, and are not repeated here.
  • obtaining the abnormal data points of the machine according to the monitoring data set may also include: extracting the machine as a unit according to the monitoring data set to obtain the monitoring data set of the machine; and extracting the abnormal data points of the machine according to the monitoring data set of the machine.
  • step S300 the abnormal data points of the machine are screened to obtain target abnormal data points.
  • the target abnormal data point is the abnormal data point to be analyzed.
  • Abnormal data points can include data points that exceed the upper control limit, data points that exceed the lower control limit, data points that exceed the upper specification limit and data points that exceed the lower specification limit, and the target abnormal data points can be selected according to actual needs. Data points outside the upper control limits were analyzed. At this point, the filtered data points that exceed the upper control limit are the target abnormal data points.
  • data points exceeding the control limit are recorded as OOC (Out of control), and data points exceeding the specification limit are recorded as OOS (Out of specification).
  • data points exceeding the upper control limit or exceeding the upper specification limit are used as target abnormal data points.
  • the data points exceeding the upper control limit are the target abnormal data points, that is, the points marked by ⁇ .
  • the points exceeding the upper control limit and the upper specification limit are the target abnormal data points, that is, the points marked by ⁇ .
  • the abscissa is the time, and the ordinate is the collected data value.
  • OOC and OOS are only specific marks in specific embodiments, and can be set by a system administrator or the like according to actual conditions during actual use.
  • OOC can include data points that exceed the upper control limit, and can include data points that exceed the lower control limit
  • OOS can include data points that exceed the upper specification limit and can include data points that exceed the lower specification limit.
  • the target abnormal data point may also be a data point that exceeds the lower control limit or the lower specification limit.
  • the abnormal data points of the machine are screened every preset time to obtain the target abnormal data points.
  • the preset time is set according to actual requirements. For example, it is set to refresh the abnormal data points of the machine every 10 minutes, and extract the abnormal data points of the machine to obtain the target abnormal data points.
  • the preset time can be set according to the product preparation process and the like. In the present disclosure, different preset times can be set for different machines and different target abnormal data points.
  • step S300 includes:
  • Step S310 marking the machine abnormal data points with different labels by category
  • Step S320 filter according to the label to obtain the target abnormal data point.
  • step S310 different labels are marked by category for the abnormal data points of the machine.
  • Anomalous data points can generally contain multiple categories, such as data points above upper control limits, data points above lower control limits, data points above upper specification limits, and data points above lower specification limits, as described above. Label different types of abnormal data points, for example, label data points that exceed the upper control limit and data points that exceed the lower control limit with different labels.
  • the target abnormal data points are obtained by filtering according to the tags. For example, the data points that exceed the upper control limit, the data points that exceed the lower control limit, the data points that exceed the upper specification limit and the data points that exceed the lower specification limit are marked with different labels. For example, label 1, label 2, label 3 and Label 4. According to the analysis requirements, select the target abnormal data points to be analyzed. If, according to the actual situation, the data points exceeding the upper control limit are selected as the target abnormal data points, the abnormal data points marked with label 1 are filtered, and the filtered abnormal data points are recorded as the target abnormal data points.
  • step S400 a number threshold corresponding to the target abnormal data point is preset.
  • the number threshold is a predetermined threshold for the number of target data points.
  • the specific value of the quantity threshold is not limited. Different machines and different target abnormal data points in the present disclosure may have different quantity thresholds.
  • the preset number threshold (SpecCount) corresponding to the target abnormal data points is 12.
  • the data is refreshed every preset time to extract the target abnormal data point, wherein the target abnormal data point is OOS.
  • OOS refers to Abnormal data points beyond the upper specification limit based on product critical dimension (CD), the preset time (TimePeriod) is 12hrs.
  • product information can also be further included, so that in the subsequent operation process, engineers can check the corresponding quantity threshold setting standards for machines or products according to actual needs.
  • FIG. 9 the figure shows the quantity threshold setting standards corresponding to different machines.
  • step S500 it is determined whether an alarm signal is generated according to the number of the target abnormal data points and the number threshold.
  • the number of target abnormal data points and the preset number threshold are compared and judged, and the judgment result is displayed in the form of an alarm signal.
  • the operation status of the machine is reflected by the alarm signal, and the engineer can quickly judge the machine according to the alarm signal. operating status.
  • step S500 includes:
  • Step S510 count the number of the target abnormal data points
  • Step S520 comparing the number of the target abnormal data points with the size of the number threshold
  • Step S530 if the quantity of the target abnormal data points exceeds the quantity threshold, an alarm signal is generated, and if the quantity of the target abnormal data points does not exceed the quantity threshold, no alarm signal is generated.
  • step S510 the number of target abnormal data points is counted.
  • the number of target outlier data points specifically refers to the number of times the target outliers appear.
  • the statistical results can be displayed in graphical form.
  • the number of target abnormal data points is 3.
  • the target abnormal data point is an out of control data point (OOC) based on the critical dimension (CD) of the product, and the machine ID is WAAS101.
  • the number of the target abnormal data points is compared with the size of the number threshold. By comparing the number of target abnormal data points with the number threshold, the operating status of the machine is judged. If the number of target abnormal data points exceeds the number threshold, an alarm signal is generated, and if the number of target abnormal data points does not exceed the number threshold, no alarm signal is generated.
  • the number of target abnormal data points is 3, the set number threshold (SpecCount) is 12, and the number of target abnormal data points does not exceed the number threshold, so no alarm signal is generated. It should be noted here that, in some embodiments of the present disclosure, the specific data information in the chart shown in FIG. 10 can be linked to view the corresponding control chart.
  • the engineer when the number of target abnormal data points exceeds the set number threshold, an alarm signal is generated, and the engineer can quickly find out the machine that generated the alarm signal according to the alarm signal, and check the corresponding control chart to control the control Figure analysis, quickly find out the cause of the abnormality, eliminate the abnormality, so that the product preparation process is in a controllable state.
  • the method for monitoring the running status of the machine further includes:
  • Step S600 select whether to execute the reservation stop operation.
  • the scheduled stop operation can be selected whether or not to be executed when the quantity threshold is set. As shown in Figure 8, after setting the number threshold, you can choose whether to execute the inhibitTool (optional), that is, to reserve the stop operation.
  • the scheduled stop operation means that the machine stops running according to a preset scheduled stop operation rule.
  • the scheduled stop operation rule means that the machine stops running when preparing the next product or the next batch of products.
  • the scheduled stop operation rule may also mean that the machine stops running after a period of time. Scheduled stop operation rules can be specifically set according to the requirements of the product preparation process.
  • the scheduled stop operation when the number of target abnormal data points exceeds the number threshold, an alarm signal is generated and the machine stops running according to the preset scheduled stop operation rules. If you do not choose to execute the scheduled stop operation, the machine can continue to run. Even if the number of target abnormal data points exceeds the number threshold, an alarm signal is generated, and the machine will not stop according to the preset scheduled stop operation rules.
  • the present disclosure also provides a device for monitoring the running condition of a machine.
  • the apparatus 100 for monitoring the operating status of a machine includes a first acquisition module 110 , a second acquisition module 120 , an extraction module 130 , a preset module 140 and a judgment module 150 .
  • the first acquisition module 110 is used for real-time monitoring of the product preparation process and acquisition of monitoring data sets;
  • the second acquisition module 120 is configured to extract the abnormal data points of the machine according to the monitoring data set;
  • an extraction module 130 configured to screen the abnormal data points of the machine to obtain target abnormal data points
  • a preset module 140 configured to preset a quantity threshold corresponding to the target abnormal data point
  • the judgment module 150 is configured to judge whether to generate an alarm signal according to the quantity of the target abnormal data points and the quantity threshold.
  • modules or units of the apparatus for action performance are mentioned in the above detailed description, this division is not mandatory. Indeed, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided into multiple modules or units to be embodied.
  • the exemplary embodiments described herein may be implemented by software, or may be implemented by software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of software products, and the software products may be stored in a non-volatile storage medium (which may be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to cause a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to an embodiment of the present disclosure.
  • a computing device which may be a personal computer, a server, a mobile terminal, or a network device, etc.
  • a computer storage medium capable of implementing the above method is also provided.
  • a program product capable of implementing the method described above in this specification is stored thereon.
  • various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code, when the program product runs on a terminal device, the program code is used to cause the terminal device to execute the above-mentioned procedures in this specification. Steps of the described example embodiments.
  • a program product 200 for implementing the above method is described according to an embodiment of the present disclosure, which may adopt a portable compact disk read only memory (CD-ROM) and include program codes, and may be stored in a terminal device, such as run on a personal computer.
  • CD-ROM portable compact disk read only memory
  • the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • the program product may employ any combination of one or more readable media.
  • the readable medium may be a readable signal medium or a readable storage medium.
  • the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
  • a computer readable signal medium may include a propagated data signal in baseband or as part of a carrier wave with readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a readable signal medium can also be any readable medium, other than a readable storage medium, that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • Program code embodied on a readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural programming Language - such as the "C" language or similar programming language.
  • the program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on.
  • the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (eg, using an Internet service provider business via an Internet connection).
  • LAN local area network
  • WAN wide area network
  • an external computing device eg, using an Internet service provider business via an Internet connection
  • an electronic device capable of implementing the above method is also provided.
  • aspects of the present disclosure may be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be embodied in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, which may be collectively referred to herein as implementations "circuit", “module” or "system”.
  • the electronic device 300 according to this embodiment of the present disclosure is described below with reference to FIG. 13 .
  • the electronic device 300 shown in FIG. 13 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.
  • electronic device 300 takes the form of a general-purpose computing device.
  • Components of the electronic device 300 may include, but are not limited to, the above-mentioned at least one processing unit 310 , the above-mentioned at least one storage unit 320 , and a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310 ).
  • the storage unit 320 stores program codes, which can be executed by the processing unit 310, so that the processing unit 310 performs the steps of various exemplary embodiments described in this specification.
  • the processing unit 310 may perform as shown in FIG. 1: Step S100, monitor the product preparation process in real time, and obtain a monitoring data set; Step S200, extract machine abnormal data points according to the monitoring data set; Step S300, Screen the abnormal data points of the machine to obtain target abnormal data points; step S400, preset a quantity threshold corresponding to the target abnormal data points; step S500, according to the number of the target abnormal data points and the quantity threshold , to determine whether an alarm signal is generated.
  • the storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 3201 and/or a cache storage unit 3202 , and may further include a read only storage unit (ROM) 3203 .
  • RAM random access storage unit
  • ROM read only storage unit
  • the storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, An implementation of a network environment may be included in each or some combination of these examples.
  • the bus 330 may be representative of one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any of a variety of bus structures. bus.
  • the electronic device 300 may also communicate with one or more external devices 400 (eg, keyboards, pointing devices, Bluetooth devices, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with Any device (eg, router, modem, etc.) that enables the electronic device 300 to communicate with one or more other computing devices. Such communication may take place through input/output (I/O) interface 350 .
  • An input/output (I/O) interface 350 may be connected to the display unit 340 .
  • the electronic device 300 may communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet) through a network adapter 360 .
  • networks eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet
  • network adapter 360 communicates with other modules of electronic device 300 via bus 330 .
  • other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives and data backup storage systems.
  • the exemplary embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of software products, and the software products may be stored in a non-volatile storage medium (which may be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to cause a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to an embodiment of the present disclosure.
  • a computing device which may be a personal computer, a server, a terminal device, or a network device, etc.

Abstract

本公开提供了一种监测机台运行状况的方法及装置、存储介质及电子设备，属于半导体技术领域。该方法包括：实时监测产品制备工艺过程，获取监控数据集；根据所述监控数据集，提取机台异常数据点；对所述机台异常数据点进行筛选，获得目标异常数据点；预设所述目标异常数据点对应的数量阈值；根据所述目标异常数据点的数量及所述数量阈值，判断是否产生报警信号。

Description

监测机台运行状况的方法及装置、存储介质及电子设备
交叉引用
本公开要求于2020年11月18日提交的申请号为202011294106.9名称为“监测机台运行状况的方法及装置、存储介质及电子设备”的中国专利申请的优先权，该中国专利申请的全部内容通过引用全部并入本文。
技术领域
本公开涉及半导体技术领域，尤其涉及一种监测机台运行状况的方法及装置、存储介质及电子设备。
背景技术
在半导体生产加工过程中，产品的尺寸、结构特征等规则会由于某些原因发生一定的波动，这种波动会影响半导体产品的质量。在实际加工过程中，造成波动的原因有很多，因此，对整个加工过程进行监控，收集和分析各种数据对于保证产品质量至关重要。目前通常采用专门的数据采集系统收集由半导体工艺设备执行的工艺过程中的各种数据。如采用统计过程控制(Statistical Process Control，SPC)系统，借助数理统计方法，跟踪分析产品生产过程，及时发现问题并解决问题，以保证产品质量。现有技术中，采用SPC系统监控半导体制备过程工艺，其在确定机台运行状况及故障分析效率上还有待提高。
需要说明的是，在上述背景技术部分公开的信息仅用于加强对本公开的背景的理解，因此可以包括不构成对本领域普通技术人员已知的现有技术的信息。
公开内容
本公开的目的在于克服上述现有技术的不足，提供一种监测机台运行状况的方法，该方法可以快速的确定机台运行状况，有利于工程师快速找出异常原因，从而提高排除异常效率，使整个过程及时达到可控状 态。
根据本公开的第二个方面，提供一种监测机台运行状况的装置，包括：
第一获取模块，用于实时监测产品制备工艺过程，获取监控数据集；
第二获取模块，用于根据所述监控数据集，提取机台异常数据点；
提取模块，用于对所述机台异常数据点进行筛选，获得目标异常数据点；
预设模块，用于预设所述目标异常数据点对应的数量阈值；
判断模块，用于根据所述目标异常数据点的数量及所述数量阈值，判断是否产生报警信号。
根据本公开的第三个方面，提供一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现第一方面所述的方法。
根据本公开的第四个方面，提供一种电子设备，包括：
处理器；
存储器，用于存储所述处理器的可执行指令；
其中，所述处理器配置为经由执行所述可执行指令来执行第一方面所述的方法。
本公开提供的监测机台运行状况的方法，用于产品制备过程监控。在产品制备过程中，当控制变量，如产品关键尺寸或工艺过程重要参数等出现异常时，本公开提供的方法有利于快速确定该异常是否与具体的机台有关。本公开提供的监测机台运行状况的方法，包括：实时监测产品制备工艺过程，获取监控数据集，并根据监控数据集，提取机台异常数据点；对机台异常数据点进行筛选，获得目标异常数据点；预设目标异常数据点对应的数量阈值，并根据提取的目标异常数据点的数量和数量阈值，判断是否产生报警信号。其中，从监控数据集中提取出机台对应的机台异常数据点，在直观上将监控数据集中的某些数据点，如超出控制界限的异常点，以机台为单位显示，以便于后续对机台的运行状况进行对应判断。从机台异常数据点中进行筛选，获得目标异常数据点，并将目标异常数据点的数量与预设数量阈值进行比较判断，并将判断结 果以报警信号形式展现。该步骤中，以机台目标异常数据点为基础，确定机台报警标准，通过报警信号体现机台的运行状况，直观地给出机台运行状况信息。在产品制备过程中，本公开提供的监测机台运行状况的方法，通过报警信号可以快速的确定机台运行状况，当产品关键尺寸或工艺过程重要参数等出现异常时，有利于工程师快速找出异常原因，从而提高排除异常效率，使整个工艺过程及时达到可控状态。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本公开示例性实施例中监测机台运行状况的方法流程示意图；
图2是本公开另一示例性实施例中监测机台运行状况的方法流程示意图；
图3是本公开一示例性实施例中监测机台运行状况的方法分步流程示意图；
图4是本公开另一示例性实施例中监测机台运行状况的方法分步流程示意图；
图5是本公开又一示例性实施例中监测机台运行状况的方法分步流程示意图；
图6是本公开示例性实施例中异常数据点结果示意图；
图7是本公开另一示例性实施例中异常数据点结果示意图；
图8是本公开示例性实施例中预设数量阈值示意图；
图9是本公开示例性实施例中查看机台对应的数量阈值设定标准结果示意图；
图10是本公开示例性实施例中机台对应的目标异常数据点的数量 结果示意图；
图11是本公开示例性实施例中监测机台运行状况的装置结构示意图；
图12是本公开示例性实施例中计算机存储介质的结构示意图；
图13是本公开示例性实施例中电子设备的结构示意图。
具体实施方式
现在将参考附图更全面地描述示例实施方式。然而，示例实施方式能够以多种形式实施，且不应被理解为限于在此阐述的实施方式；相反，提供这些实施方式使得本公开将全面和完整，并将示例实施方式的构思全面地传达给本领域的技术人员。图中相同的附图标记表示相同或类似的结构，因而将省略它们的详细描述。此外，附图仅为本公开的示意性图解，并非一定是按比例绘制。
现在将参考附图更全面地描述示例实施例。然而，示例实施例能够以多种形式实施，且不应被理解为限于在此阐述的范例；相反，提供这些实施例使得本公开将更加全面和完整，并将示例实施例的构思全面地传达给本领域的技术人员。所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施例中。在下面的描述中，提供许多具体细节从而给出对本公开的实施例的充分理解。
在图中，为了清晰，可能夸大了区域和层的厚度。在图中相同的附图标记表示相同或类似的结构，因而将省略它们的详细描述。
所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施例中。在下面的描述中，提供许多具体细节从而给出对本公开的实施例的充分理解。然而，本领域技术人员将意识到，可以实践本公开的技术方案而没有所述特定细节中的一个或更多，或者可以采用其它的方法、组元、材料等。在其它情况下，不详细示出或描述公知结构、材料或者操作以避免模糊本公开的主要技术创意。
当某结构在其它结构“上”时，有可能是指某结构一体形成于其它结构上，或指某结构“直接”设置在其它结构上，或指某结构通过另一结构“间接”设置在其它结构上。
用语“一个”、“一”、“所述”用以表示存在一个或多个要素/组成部分/等；用语“包括”和“具有”用以表示开放式的包括在内的意思并且是指除了列出的要素/组成部分/等之外还可存在另外的要素/组成部分/等。用语“第一”和“第二”等仅作为标记使用，不是对其对象的数量限制。
相关技术中，SPC用于跟踪和分析半导体制造工艺过程变化的常用技术。通常会采集众多以产品或过程等为基础的数据信息，根据该数据信息生成多种图表，如带有控制界限的控制图等。目前，依赖SPC系统获得的数据信息多以产品或过程为单位予以显示，且当在制备过程中出现异常点时，无法及时对异常点进行有效筛选分析，导致无法及时判断出该异常点是否与机台的运行状况有关，从而影响故障分析及排除异常的效率。
如图1所示，本公开提供一种监测机台运行状况的方法，用于产品制备过程监控，包括：
步骤S100，实时监测产品制备工艺过程，获取监控数据集；
步骤S200，根据所述监控数据集，提取机台异常数据点；
步骤S300，对所述机台异常数据点进行筛选，获得目标异常数据点；
步骤S400，预设所述目标异常数据点对应的数量阈值；
步骤S500，根据所述目标异常数据点的数量及所述数量阈值，判断是否产生报警信号。
本公开提供的监测机台运行状况的方法，用于产品制备过程监控。在产品制备过程中，当控制变量，如产品关键尺寸或过程重要参数等出现异常时，本公开提供的方法有利于快速确定该异常是否与具体的机台有关。本公开提供的监测机台运行状况的方法，包括：实时监测产品制备工艺过程，获取监控数据集，并根据监控数据集，提取机台异常数据点；对机台异常数据点进行筛选，获得目标异常数据点；预设目标异常数据点对应的数量阈值，并根据提取的目标异常数据点的数量和数量阈值，判断是否产生报警信号。其中，从监控数据集中提取出机台对应的机台异常数据点，在直观上将监控数据集中的某些数据点，如超出控制界限的异常点，以机台为单位显示，以便于后续对机台的运行状况进行 对应判断。从机台异常数据点中进行筛选，获得目标异常数据点，并将目标异常数据点的数量与预设数量阈值进行比较判断，并将判断结果以报警信号形式展现。该步骤中，以机台目标异常数据点为基础，确定机台报警标准，通过报警信号体现机台的运行状况，直观地给出机台运行状况信息。在产品制备过程中，本公开提供的监测机台运行状况的方法，通过报警信号可以快速的确定机台运行状况，当产品关键尺寸或过程重要参数等出现异常时，有利于工程师快速找出异常原因，从而提高排除异常效率，使整个过程及时达到可控状态。
下面结合附图对本公开实施方式提供的监测机台运行状况的各步骤进行详细说明：
在步骤S100中，实时监测产品制备工艺过程，获取监控数据集。
在本公开示例性实施例中，基于统计过程控制系统实时监测产品制备工艺过程，获取监控数据集。统计过程控制系统用于实时监控产品制备工艺过程。在本公开中，统计过程控制系统也即SPC(Statistical Process Control)系统，具体可用于半导体制备工艺过程的实时监控。当然，统计过程控制系统也不仅仅限于目前大家所用的SPC系统，也可以是其他一些能够完成统计过程控制的开发人员所开发的其他新系统。
在该步骤中，统计过程监控系统采集产品制备工艺过程中的相关数据，如产品的关键尺寸、套刻误差以及工艺过程中的相关技术参数等，以获取监控数据集。其中，产品的关键尺寸可以是产品的厚度、宽度、长度或重量等，工艺过程中的相关技术参数可以是温度、时间、速度等。当然，本公开监控数据集除产品关键尺寸、套刻误差以及工艺过程中的相关技术参数外，也可以包含其他需要监控的数据信息，如根据以往数据统计出现不合格产品较多的工艺节点等。具体监测产品制备工艺过程中的数据信息，可根据实际需求进行选择。
在半导体制备工艺过程中，可采用SPC系统进行过程监控，其中，半导体的尺寸、套刻误差及工艺过程中的相关技术参数等可由量测机台等进行测量，并将测量后的数据实时传输至SPC系统，SPC系统据此获取半导体制备工艺过程中的监控数据集，并将该监控数据集进行存储。
在步骤S200中，根据所述监控数据集，提取机台异常数据点。
在本公开一些示例性实施例中，异常数据点是指超出控制界限或规格界限的数据点。如超出下控制限(Lower control limit，LCL)或上控制限(Upper control limit，UCL)的数据点，或者是超出上规格限(Upper specification limit，USL)或下规格限(Lower specification limit，LSL)的数据点。
控制界限是根据产品制备工艺过程中监测的样本数据分布来确定的，包括上控制限和下控制限。在产品制备工艺过程中，当采用统计过程监控系统进行实时监控时，一般会根据实际需求对影响产品质量的产品关键尺寸数据信息或影响过程质量的制备工艺关键过程数据信息进行分析，选择能代表产品质量或过程质量的主要数据信息作为控制对象，根据控制对象及控制需求选择对应需生成的控制图，并通过分析控制图来分析产品制备工艺过程，具体可根据样本数据形成的样本点位置以及变化趋势进行分析和判断。其中，控制图是带有控制界限的图，控制界限可根据样本数据点通过计算分析获得。在本公开中，将监控数据集中超过控制界限的点归为异常数据点。
规格界限是设定的界限值，包括上规格限和下规格限，一般为技术人员根据批次晶圆工艺过程中的关键数据信息的测试结果人为设定得来，或者也可以根据客户需求设定产品在关键尺寸上的上规格限和下规格限。在本公开一些实施例中，将监控数据集中超过规格界限的点也归为异常数据点。
如图3所示，在本公开一些实施例中，步骤S200包括：
步骤S210，根据所述监控数据集，提取产品制备工艺过程中的异常数据点；
步骤S220，根据所述产品制备工艺过程中的异常数据点，以机台为单位进行提取，获得所述机台异常数据点。
在步骤S210中，根据监控数据集，提取产品制备工艺过程中的异常数据点。如在半导体制备工艺过程中，对半导体产品的关键尺寸进行监测，获得关于半导体产品关键尺寸的监控数据集，从该监控数据集中提取异常数据点。
在本公开示例性实施例中，产品制备过程中的异常数据点是指超出 控制界限或规格界限的数据点。异常数据点具体可以包括超出上控制限数据点、超出下控制限数据点、超出上规格限数据点和超出下规格限数据点中的一种或多种，具体可以由工程师、系统管理员等工作人员按需求进行设定。
在步骤220中，根据产品制备工艺过程中的异常数据点，以机台为单位进行提取，获得机台异常数据点。在该步骤中，将异常数据点以机台为单位进行提取，以获得对应于机台的异常数据点。举例而言，在半导体制备工艺过程中，对半导体产品的关键尺寸进行监测，获得关于半导体产品关键尺寸的监控数据集，该监控数据集一般可包括半导体产品关键尺寸对应的机台名称、机台型号、产品批次、产品编号等信息。从监控数据集中提取出异常数据点，将该异常数据点以机台为单位进行提取，提取结果可以图表形式予以显示，进而获得机台对应的异常数据点。此外，若对半导体产品过程中的关键技术信息进行监控，同样也可以获得机台对应的异常数据点，具体步骤同上述类似，在此不做赘述。
在此需说明的是，步骤S210及步骤S220中的各个步骤可相互组合且顺序上也可进行调整。如根据监控数据集获得机台异常数据点，也可以包括：根据监控数据集，以机台为单位进行提取，获得机台监控数据集；根据机台监控数据集，提取机台异常数据点。
在步骤S300中，对所述机台异常数据点进行筛选，获得目标异常数据点。
在本公开一些实施例中，目标异常数据点是待分析的异常数据点。异常数据点可以包括超出上控制限数据点、超出下控制限数据点、超出上规格限数据点和超出下规格限数据点，而目标异常数据点则可以根据实际需求，筛选其中的一种，如超出上控制限的数据点进行分析。此时，筛选出的超出上控制限的数据点即为目标异常数据点。
在本公开一些实施例中，将超出控制限的数据点记为OOC(Out of control)，将超出规格限的数据点记为OOS(Out of specification)。在具体一实施例中，将超出上控制限或超出上规格限的数据点作为目标异常数据点。如图6所示，在本公开一具体实施例中，超过上控制限的数据点为目标异常数据点，即☉所标记的点。在本公开又一具体实施例中， 如图7所示，超出上控制限和超出上规格限的点为目标异常数据点，即☉所标记的点。图6及图7中横坐标为时间，纵坐标为采集的数据值，此图仅示例性说明目标异常数据点所指的具体情况，图中数据点的具体数值对本公开不构成限定。在此需说明的是，OOC、OOS仅为在具体实施例中的具体标记，在实际使用过程中，可由系统管理员等按实际情况进行设定。OOC可以包括超出上控制限的数据点，与可以包括超出下控制限的数据点，OOS可以包括超出上规格限的数据点，也可以包括超出下规格限的数据点。此外，在本公开另一些实施例中，目标异常数据点也可以是超出下控制限或下规格限的数据点。
在本公开一些实施例中，每隔预设时间对机台异常数据点进行筛选，获得目标异常数据点。预设时间根据实际要求进行设定，如设定每隔10min刷新机台异常数据点，对机台异常数据点进行提取，获得目标异常数据点。预设时间可根据产品制备工艺进程等进行设定。在本公开中，不同机台、不同目标异常数据点可以设定不同的预设时间。
如图4所示，在本公开一些实施例中，步骤S300包括：
步骤S310，对所述机台异常数据点按类别标记不同标签；
步骤S320，根据所述标签进行筛选，获得所述目标异常数据点。
在步骤S310中，对机台异常数据点按类别标记不同标签。异常数据点一般可包含多个类别，如上述所述的超出上控制限数据点、超出下控制限数据点、超出上规格限数据点和超出下规格限数据点。对不同类别的异常数据点进行标记，如将超过上控制限的数据点和超出下控制限的数据点分别对应标记不同的标签。
在步骤S320中，根据标签进行筛选，获得目标异常数据点。如将超出上控制限数据点、超出下控制限数据点、超出上规格限数据点以及超出下规格限数据点分别标记不同标签，举例而言，对应分别标记标签1、标签2、标签3和标签4。根据分析要求，选择待分析的目标异常数据点。如根据实际情况，选择超出上控制限的数据点为目标异常数据点，则筛选标记有标签1的异常数据点，筛选出的异常数据点记为目标异常数据点。
在步骤S400中，预设所述目标异常数据点对应的数量阈值。
数量阈值是预先设定的目标数据点的数量的界限值。该数量阈值的具体数值不做限定。本公开中不同的机台、不同的目标异常数据点可以有不同的数量阈值。如图8所示，预设目标异常数据点对应的数量阈值(SpecCount)为12。在图8中，针对机台ID(ToolID)为DAAS101的机台，每隔预设时间对数据进行刷新以提取目标异常数据点，其中，目标异常数据点为OOS，该实施例中OOS是指以产品关键尺寸(CD)为基础的超出上规格限的异常数据点，预设时间(TimePeriod)为12hrs。在此需说明的是，在设定数量阈值时，也可进一步包含产品信息，以便在后续操作过程中，工程师可根据实际需求，查看机台或产品等对应的数量阈值设定标准，具体如图9所示，该图中显示出不同机台对应的数量阈值设定标准。
在步骤S500中，根据所述目标异常数据点的数量及所述数量阈值，判断是否产生报警信号。
该步骤中，将目标异常数据点的数量与预设数量阈值进行比较判断，并将判断结果以报警信号形式展现，通过报警信号体现机台的运行状况，工程师可根据报警信号快速判断出机台的运行状况。
如图5所示，在本公开一些实施例中，步骤S500包括：
步骤S510，统计所述目标异常数据点的数量；
步骤S520，比较所述目标异常数据点的数量和所述数量阈值的大小；
步骤S530，若所述目标异常数据点的数量超过所述数量阈值，则产生报警信号，若所述目标异常数据点的数量不超过所述数量阈值，则不产生报警信号。
在步骤S510中，统计目标异常数据点的数量。目标异常数据点的数量具体是指出现目标异常点的次数。该统计结果可以图表形式予以显示。举例而言，在本公开一具体实施例中，如图10所示，目标异常数据点数量(HappenCount)为3。在图10所示的实施例中，目标异常数据点为基于产品关键尺寸(CD)的超出控制界限的数据点(OOC)，机台ID为WAAS101。
在步骤S520和步骤S530中，比较所述目标异常数据点的数量和所述数量阈值的大小。通过比较目标异常数据点的数量与数量阈值的大小， 判断机台的运行状况。若目标异常数据点的数量超过数量阈值，则产生报警信号，若目标异常数据点的数量不超过数量阈值，则不产生报警信号。在图10所示实施例中，目标异常数据点的数量为3，设定的数量阈值(SpecCount)为12，目标异常数据点的数量未超出数量阈值，因此，不产生报警信号。在此需说明的是，在本公开一些实施例中，图10所示图表中具体的数据信息可链接查看对应的控制图。在一些实施例中，当目标异常数据点的数量超过设定的数量阈值，则产生报警信号，工程师可根据报警信号，迅速找出产生报警信号的机台，并查看对应的控制图，对控制图进行分析，快速找出出现异常的原因，排除异常，从而使产品制备工艺过程处于可控状态。
如图2所示，在本公开一些实施例中，监测机台运行状况的方法还包括：
步骤S600，选择是否执行预约停止操作。
若选择执行预约停止操作，则当所述目标异常数据点的数量超过所述数量阈值时，产生报警信号且机台按照预设的预约停止操作规则停止运行。
在该步骤中，预约停止操作可在设定数量阈值时，选择是否执行。如图8所示，在设定好数量阈值后，可选择是否执行inhibitTool(optional)，即预约停止操作。在本公开中，预约停止操作是指机台按照预设的预约停止操作规则停止运行。在本公开一些实施例中，预约停止操作规则是指机台在制备下一个或下一批次产品时停止运行。当然，预约停止操作规则也可以是指机台在一段时间后停止运行。预约停止操作规则具体可根据产品制备工艺过程需求进行设定。
在本公开一些实施例中，若选择执行预约停止操作，则当目标异常数据点的数量超过数量阈值时，产生报警信号且机台按照预设的预约停止操作规则停止运行。若不选择执行预约停止操作，机台则可以继续运行，即使目标异常数据点的数量超过数量阈值时，产生报警信号，机台也不会按照预设的预约停止操作规则停止运行。
本公开还提供一种监测机台运行状况的装置。如图11所示，在本公开一实施例中，监测机台运行状况的装置100，包括第一获取模块110、 第二获取模块120、提取模块130、预设模块140和判断模块150。
第一获取模块110，用于实时监测产品制备工艺过程，获取监控数据集；
第二获取模块120，用于根据所述监控数据集，提取机台异常数据点；
提取模块130，用于对所述机台异常数据点进行筛选，获得目标异常数据点；
预设模块140，用于预设所述目标异常数据点对应的数量阈值；
判断模块150，用于根据所述目标异常数据点的数量及所述数量阈值，判断是否产生报警信号。
上述监测机台运行状况的装置中各模块的具体细节已经在对应的监测机台运行状况的方法中进行了详细的描述，因此此处不再赘述。
应当注意，尽管在上文详细描述中提及了用于动作执行的设备的若干模块或者单元，但是这种划分并非强制性的。实际上，根据本公开的实施方式，上文描述的两个或更多模块或者单元的特征和功能可以在一个模块或者单元中具体化。反之，上文描述的一个模块或者单元的特征和功能可以进一步划分为由多个模块或者单元来具体化。
通过以上的实施方式的描述，本领域的技术人员易于理解，这里描述的示例实施方式可以通过软件实现，也可以通过软件结合必要的硬件的方式来实现。因此，根据本公开实施方式的技术方案可以以软件产品的形式体现出来，该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM，U盘，移动硬盘等)中或网络上，包括若干指令以使得一台计算设备(可以是个人计算机、服务器、移动终端、或者网络设备等)执行根据本公开实施方式的方法。
在本公开实施例中，还提供了一种能够实现上述方法的计算机存储介质。其上存储有能够实现本说明书上述方法的程序产品。在一些可能的实施方式中，本公开的各个方面还可以实现为一种程序产品的形式，其包括程序代码，当程序产品在终端设备上运行时，程序代码用于使终端设备执行本说明书上述描述的示例性实施方式的步骤。
请参见图12，描述了根据本公开的实施方式的用于实现上述方法的 程序产品200，其可以采用便携式紧凑盘只读存储器(CD-ROM)并包括程序代码，并可以在终端设备，例如个人电脑上运行。然而，本公开的程序产品不限于此，在本文件中，可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
程序产品可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以为但不限于电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。
计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了可读程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。可读信号介质还可以是可读存储介质以外的任何可读介质，该可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于无线、有线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言的任意组合来编写用于执行本公开操作的程序代码，程序设计语言包括面向对象的程序设计语言—诸如Java、C++等，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户计算设备上部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。在涉及远程计算设备的情形中，远程计算设备可以通过任意种类的网络，包括局域网(LAN)或广域网(WAN)，连接到用户计算设备，或者，可以连接到外部计算设备(例如利用因特网服务提供商来通过因 特网连接)。
此外，在本公开的示例性实施例中，还提供了一种能够实现上述方法的电子设备。
所属技术领域的技术人员能够理解，本公开的各个方面可以实现为系统、方法或程序产品。因此，本公开的各个方面可以具体实现为以下形式，即：完全的硬件实施方式、完全的软件实施方式(包括固件、微代码等)，或硬件和软件方面结合的实施方式，这里可以统称为“电路”、“模块”或“系统”。
下面参照图13来描述根据本公开的这种实施方式的电子设备300。图13显示的电子设备300仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图13所示，电子设备300以通用计算设备的形式表现。电子设备300的组件可以包括但不限于：上述至少一个处理单元310、上述至少一个存储单元320、连接不同系统组件(包括存储单元320和处理单元310)的总线330。
其中，存储单元320存储有程序代码，程序代码可以被处理单元310执行，使得处理单元310执行本说明书中描述的各种示例性实施例的步骤。例如，处理单元310可以执行如图1中所示的：步骤S100，实时监测产品制备工艺过程，获取监控数据集；步骤S200，根据所述监控数据集，提取机台异常数据点；步骤S300，对所述机台异常数据点进行筛选，获得目标异常数据点；步骤S400，预设所述目标异常数据点对应的数量阈值；步骤S500，根据所述目标异常数据点的数量及所述数量阈值，判断是否产生报警信号。
存储单元320可以包括易失性存储单元形式的可读介质，例如随机存取存储单元(RAM)3201和/或高速缓存存储单元3202，还可以进一步包括只读存储单元(ROM)3203。
存储单元320还可以包括具有一组(至少一个)程序模块3205的程序/实用工具3204，这样的程序模块3205包括但不限于：操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。
总线330可以为表示几类总线结构中的一种或多种，包括存储单元总线或者存储单元控制器、外围总线、图形加速端口、处理单元或者使用多种总线结构中的任意总线结构的局域总线。
电子设备300也可以与一个或多个外部设备400(例如键盘、指向设备、蓝牙设备等)通信，还可与一个或者多个使得用户能与该电子设备300交互的设备通信，和/或与使得该电子设备300能与一个或多个其它计算设备进行通信的任何设备(例如路由器、调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口350进行。输入/输出(I/O)接口350可连接显示单元340。并且，电子设备300还可以通过网络适配器360与一个或者多个网络(例如局域网(LAN)，广域网(WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器360通过总线330与电子设备300的其它模块通信。应当明白，尽管图中未示出，可以结合电子设备300使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。
通过以上的实施方式的描述，本领域的技术人员易于理解，这里描述的示例性实施例可以通过软件实现，也可以通过软件结合必要的硬件的方式来实现。因此，根据本公开实施方式的技术方案可以以软件产品的形式体现出来，该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM，U盘，移动硬盘等)中或网络上，包括若干指令以使得一台计算设备(可以是个人计算机、服务器、终端装置、或者网络设备等)执行根据本公开实施方式的方法。
需要说明的是，尽管在附图中以特定顺序描述了本公开中方法的各个步骤，但是，这并非要求或者暗示必须按照该特定顺序来执行这些步骤，或是必须执行全部所示的步骤才能实现期望的结果。附加的或备选的，可以省略某些步骤，将多个步骤合并为一个步骤执行，以及/或者将一个步骤分解为多个步骤执行等，均应视为本公开的一部分。
应可理解的是，本公开不将其应用限制到本说明书提出的部件的详细结构和布置方式。本公开能够具有其他实施方式，并且能够以多种方式实现并且执行。前述变形形式和修改形式落在本公开的范围内。应可 理解的是，本说明书公开和限定的本公开延伸到文中和/或附图中提到或明显的两个或两个以上单独特征的所有可替代组合。所有这些不同的组合构成本公开的多个可替代方面。本说明书的实施方式说明了已知用于实现本公开的最佳方式，并且将使本领域技术人员能够利用本公开。

Claims (10)

  1. 一种监测机台运行状况的方法，用于产品制备工艺过程监控，包括：
    实时监测产品制备工艺过程，获取监控数据集；
    根据所述监控数据集，提取机台异常数据点；
    对所述机台异常数据点进行筛选，获得目标异常数据点；
    预设所述目标异常数据点对应的数量阈值；
    根据所述目标异常数据点的数量及所述数量阈值，判断是否产生报警信号。
  2. 根据权利要求1所述的监测机台运行状况的方法，其中，根据所述监控数据集，提取机台异常数据点包括：
    根据所述监控数据集，提取产品制备工艺过程中的异常数据点；
    根据所述产品制备工艺过程中的异常数据点，以机台为单位进行提取，获得所述机台异常数据点。
  3. 根据权利要求1所述的监测机台运行状况的方法，其中，对所述机台异常数据点进行筛选，获得目标异常数据点包括：
    对所述机台异常数据点按类别标记不同标签；
    根据所述标签进行筛选，获得所述目标异常数据点。
  4. 根据权利要求1所述的监测机台运行状况的方法，其中，对所述机台异常数据点进行筛选，获得目标异常数据点包括：
    每隔预设时间对所述机台异常数据点进行筛选，获得所述目标异常数据点。
  5. 根据权利要求1所述的监测机台运行状况的方法，其中，根据所述目标异常数据点的数量及所述数量阈值，判断是否产生报警信号包括：
    统计所述目标异常数据点的数量；
    比较所述目标异常数据点的数量和所述数量阈值的大小；
    若所述目标异常数据点的数量超过所述数量阈值，则产生报警信号，若所述目标异常数据点的数量不超过所述数量阈值，则不产生报警信号。
  6. 根据权利要求5所述的监测机台运行状况的方法，其中，所述监测机台运行状况方法还包括：
    选择是否执行预约停止操作；
    若选择执行预约停止操作，则当所述目标异常数据点的数量超过所述数量阈值时，产生报警信号且机台按照预设的预约停止操作规则停止运行。
  7. 根据权利要求1所述的监测机台运行状况的方法，其中，实时监测产品制备工艺过程，获取监控数据集包括：
    基于统计过程控制系统实时监测产品制备工艺过程，获取监控数据集。
  8. 一种监测机台运行状况的装置，其中，包括：
    第一获取模块，用于实时监测产品制备工艺过程，获取监控数据集；
    第二获取模块，用于根据所述监控数据集，提取机台异常数据点；
    提取模块，用于对所述机台异常数据点进行筛选，获得目标异常数据点；
    预设模块，用于预设所述目标异常数据点对应的数量阈值；
    判断模块，用于根据所述目标异常数据点的数量及所述数量阈值，判断是否产生报警信号。
  9. 一种计算机可读存储介质，其上存储有计算机程序，其中，所述计算机程序被处理器执行时实现权利要求1-7任一项所述的方法。
  10. 一种电子设备，其中，包括：
    处理器；
    存储器，用于存储所述处理器的可执行指令；
    其中，所述处理器配置为经由执行所述可执行指令来执行权利要求1-7任一项所述的方法。
PCT/CN2021/112341 2020-11-18 2021-08-12 监测机台运行状况的方法及装置、存储介质及电子设备 WO2022105318A1 (zh)

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