CN105787634A - Method and system for detecting abnormities of tool parts - Google Patents
Method and system for detecting abnormities of tool parts Download PDFInfo
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- CN105787634A CN105787634A CN201410837979.8A CN201410837979A CN105787634A CN 105787634 A CN105787634 A CN 105787634A CN 201410837979 A CN201410837979 A CN 201410837979A CN 105787634 A CN105787634 A CN 105787634A
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Abstract
The invention provides a method and system for detecting abnormities of tool parts. The method comprises the steps of: based on the quality information data associated with all the tool parts of a tool, analyzing and determining an abnormity frequency level of each tool part; and selecting a risk part based on the abnormity frequency level of each tool part. According to the invention, the method and system provided can shorten detection time in detecting abnormities of the tool parts, and efficiently detect abnormal tool part of the tool.
Description
Technical field
The present invention relates to technical field of semiconductors, in particular to the method and system of a kind of abnormality detection for tool component.
Background technology
Being likely to use various instrument/hardware in semiconductor technology processing procedure, these instrument/hardware are usually likely to occur exception in the course of the work, thus affecting whole processing procedure.Accordingly, it would be desirable to carry out abnormality detection and solve this abnormal problem.But, these instruments generally include multiple different parts, and any parts are all likely to occur exception.Existing method for detecting abnormality is based on engineering experience and the parts that these are different is checked one by one.Such detection method is very consuming time, and workload is big and inefficiency.
Summary of the invention
For the deficiencies in the prior art, on the one hand, the present invention provides a kind of method of abnormality detection for tool component.Described method includes: based on the abnormal frequency rank (occurrencelevel) of the quality information data analysis relevant to all parts of described instrument all parts determining described instrument；And the abnormal frequency rank based on described all parts filters out risk parts.
In one embodiment of the invention, described method farther includes: based on the described respective life time of risk parts and/or used the time to filter out excessive risk parts.
In one embodiment of the invention, the screening of described excessive risk parts is based further on the life time of described risk parts and the time difference using the time.
In one embodiment of the invention, described method farther includes: based on described risk parts life time with use the time difference of time that described risk parts are ranked up, to determine abnormality detection priority.
In one embodiment of the invention, whether the screening of described risk parts is based further on the abnormal frequency rank of described all parts higher than predetermined threshold.
In one embodiment of the invention, described predetermined threshold is different based on different parts.
In one embodiment of the invention, described quality information data includes warning message data, and described abnormal frequency rank is warning frequency rank.
In one embodiment of the invention, described quality information data also includes scrapping (scrap) information data and/or tool parametric information data.
On the other hand, the present invention provides the system of a kind of abnormality detection for tool component.Described system includes: data analysis module, for the abnormal frequency rank based on the quality information data analysis relevant to all parts of described instrument all parts determining described instrument；And risk parts screening module, filter out risk parts for the abnormal frequency rank based on described all parts.
In one embodiment of the invention, described risk parts screening module is further used for based on the described respective life time of risk parts and/or has used the time to filter out excessive risk parts.
The method and system of the abnormality detection for tool component provided by the present invention can shorten the detection time in the abnormality detection of tool component, detects the exceptional part of instrument efficiently.
Accompanying drawing explanation
The drawings below of the present invention is used for understanding the present invention in this as the part of the present invention.Shown in the drawings of embodiments of the invention and description thereof, it is used for explaining principles of the invention.
In accompanying drawing:
Fig. 1 illustrates the flow chart of the method for abnormality detection for tool component according to an embodiment of the invention；And
Fig. 2 illustrates the flow chart of the method for abnormality detection for tool component in accordance with another embodiment of the present invention.
Detailed description of the invention
In the following description, a large amount of concrete details is given to provide more thorough understanding of the invention.It is, however, obvious to a person skilled in the art that the present invention can be carried out without these details one or more.In other example, in order to avoid obscuring with the present invention, technical characteristics more well known in the art are not described.
It should be appreciated that the present invention can implement in different forms, and should not be construed as being limited to embodiments presented herein.On the contrary, provide these embodiments will make openly thoroughly with complete, and will fully convey the scope of the invention to those skilled in the art.
As used herein term only for purpose of describing specific embodiment and the restriction not as the present invention.When using at this, " one ", " one " and " described/to be somebody's turn to do " of singulative is also intended to include plural form, unless context is expressly noted that other mode.It is also to be understood that term " composition " and/or " including ", when using in this specification, determine the existence of described feature, integer, step, operation, element and/or parts, but be not excluded for one or more other feature, integer, step, operation, element, the existence of parts and/or group or interpolation.When using at this, term "and/or" includes any of relevant Listed Items and all combinations.
In order to thoroughly understand the present invention, detailed step and detailed structure will be proposed in following description, in order to the technical scheme that the explaination present invention proposes.Presently preferred embodiments of the present invention is described in detail as follows, but except these detailed descriptions, the present invention can also have other embodiments.
On the one hand, a kind of method that the present invention provides abnormality detection for tool component.Fig. 1 illustrates the method 100 of abnormality detection for tool component according to an embodiment of the invention.As it is shown in figure 1, method 100 comprises the following steps:
Step 101: based on the abnormal frequency rank of the quality information data analysis relevant to all parts of instrument all parts determining instrument.
Wherein, described instrument is abnormal instrument to be measured occur.Exemplarily, instrument to be measured can be A, and wherein instrument A can include multiple parts, for instance instrument A can include parts a, b, c and d.The quality information data relevant to parts a, b, c and d such as can include warning message data, scrap information data and/or tool parametric information data.These data such as can be passed through warning system, rejection system and/or the intelligence bench monitoring system data gathering system such as (intelligentEquipmentMonitorSystem, iEMS) and collect from the original output data of processing procedure and form.
Alternatively, collected quality information data can be incorporated in data base by data gathering system.For example, it is possible to the quality information data relevant to all parts a, b, c and d of instrument A is incorporated in the data base of periodic maintenance (PeriodicalMaintain, PM) system.PM system can based on the quality information data analysis that the parts a of instrument A, b, c and d are correlated with the abnormal frequency rank determining all parts a of instrument A, b, c and d.
The abnormal frequency rank of all parts of instrument can be understood as all parts and abnormal frequency occur, and unit is such as ppm.Such as, abnormal frequency rank can be the warning frequency rank of the warning message of all parts.Warning frequency rank can be such as the ratio of alarm count and processing piece counting.Wherein, processing piece is counted as the number of the total workpiece of processing procedure.Workpiece can be such as wafer (wafer).
Step 102: the abnormal frequency rank based on all parts filters out risk parts.
Abnormal frequency rank based on all parts of instrument, it can be determined that go out parts and the height of abnormal frequency occurs, thus filtering out the parts occurring that abnormal frequency is higher, it is possible to the parts these filtered out are called risk parts.Preferably, whether the screening of risk parts is based further on the abnormal frequency rank of all parts higher than predetermined threshold.Wherein, predetermined threshold can be different based on different parts.In other words, each parts can have the predetermined threshold of its corresponding abnormal frequency.
Such as, all parts a of instrument A, b, the abnormal frequency rank respectively Oa of c and d, Ob, Oc and Od are determined through step 101.Parts a, the predetermined threshold respectively Oa ' of each self-corresponding abnormal frequency of b, c and d, Ob ', Oc ' and Od '.So, predetermined threshold Oa ' respectively corresponding to all parts a, b, the abnormal frequency rank Oa of c and d, Ob, Oc and Od, Ob ', Oc ' and Od ' are compared.If the abnormal frequency of parts is superior to the predetermined threshold of abnormal frequency corresponding to these parts, then these parts can be considered as risk parts.
Alternatively, step 102 can be completed by failure mode and impact analysis (FailureModeandEffectAnalysis, FMEA) system.FMEA system can all issuable fault modes of each parts and likely affecting that instrument is caused thereof in analytical tool, and can be classified by the order of severity of each fault mode, detection difficulty or ease program and Frequency, it is a kind of very handy reductive analysis system.In an embodiment of the present invention, use FMEA system can by judging whether the abnormal frequency rank of all parts is higher than the screening that the defined corresponding predetermined threshold of this system carrys out the risk parts of implementation tool preferably.
It should be noted that as previously described, the abnormal frequency rank of all parts of instrument can be the warning frequency rank of the warning message of all parts.Wherein, each parts can include multiple warning message.In this case, the screening of risk parts can based on the warning frequency rank of the various warning messages of all parts.
Specifically, the screening of risk parts can based on the relatedness of the warning message of all parts.Such as, if different parts include identical warning message simultaneously, and the warning frequency rank of this warning message is above the predetermined threshold of its correspondence, then can be filtered out by the parts simultaneously including this warning message and as risk parts.Or, if the kind that parts include the warning message that warning frequency is superior to predetermined threshold is more relative to miscellaneous part, then these parts can be filtered out and as risk parts.
Then above example, such as parts a, b, c and d respective warning message in, parts b, c and d have identical warning message m simultaneously, and the warning frequency rank of this warning message m has been above the predetermined threshold of its correspondence simultaneously, then parts b, c and d can be filtered out and as risk parts.Or, in parts a, b, c and d, wherein parts a includes a kind of frequency of reporting to the police and is superior to the warning message of predetermined threshold, and parts b, c and d each all include two kinds of warning frequency and be superior to the warning message of predetermined threshold, then can parts b, c and d be filtered out and as risk parts.Hereinafter will present example more specifically, to promote to understand.
Based on said method, it is possible to filter out the risk parts occurring that abnormal probability is higher from parts exception occur, engineers and technicians first can start detection from the risk parts filtered out, or even only need to detect the risk parts filtered out.So, compared with carrying out detection one by one with to all parts, the detection time is shortened, it is possible to detect that instrument occurs abnormal parts efficiently.
Additionally, said method can also carry out in the middle of processing procedure carries out.Wherein, if FMEA system does not filter out risk parts in a step 102, processing procedure can proceed.Otherwise, then can stop processing procedure, by the risk component feedback that filters out to engineers and technicians, after engineers and technicians detect exceptional part and solve abnormal problem, restart processing procedure again.
Fig. 2 illustrates the method 200 of abnormality detection for tool component in accordance with another embodiment of the present invention.As in figure 2 it is shown, method 200 comprises the following steps:
Step 201: based on the abnormal frequency rank of the quality information data analysis relevant to all parts of instrument all parts determining instrument；
Step 202: the abnormal frequency rank based on all parts filters out risk parts；And
Step 203: based on the respective life time of risk parts and/or used the time to filter out excessive risk parts.
Wherein, step 201 is similar with the step 101 of method 100 and step 102 respectively with step 202, therefore repeats no more herein.
In method 200, after filtering out risk parts in step 202., carry out the screening of a new round more in step 203, thus filtering out the excessive risk parts occurring that abnormal probability is higher, therefore can shorten engineers and technicians further and the time of exceptional part being detected.
Such as, parts b, c and the d of instrument A is gone out in step 202. as risk parts.So, in step 203, it is possible to based on the respective life time of parts b, c and d and/or used the time to filter out excessive risk parts further.Such as, in parts b, c and d, the longest-lived of parts b, the life-span of parts d is the shortest, then parts d can be considered as excessive risk parts.And for example, in parts b, c and d, the use shortest time of parts d, the time of use of parts b is the longest, then parts b can be considered as excessive risk parts.
Preferably, the screening of excessive risk parts is based further on the respective life time of risk parts and uses the time difference of time.Then above example, for instance the life time of parts c and its used the difference of time minimum, then parts c can be considered as excessive risk parts.
Preferably, the screening of excessive risk parts farther includes: based on the respective life time of risk parts with use the time difference of time that risk parts are ranked up, to determine abnormality detection priority.Then above example, such as the life time of parts c and its used the difference of time minimum, the life time of parts b and its secondly used the difference of time, the life time of parts d and its used the difference of time maximum, for d > b > c after then having used the time difference of time to be ranked up based on life time and its, namely the abnormality detection priority of parts c is 1, and namely parts c is the first priority parts of abnormality detection.The abnormality detection priority of parts b and d then respectively 2 and 3.In the parts a excluded in step 202 can also being also included within, then it is apparent that the abnormality detection priority of parts a is 4.The instrument A parts comprised can be carried out efficient abnormality detection according to the order of this priority by engineers and technicians.
In an embodiment of the present invention, the screening of excessive risk parts can be completed by PM system.Based on the risk parts that FMEA screening system feedback come, PM system in step 203 based on the life time of these risk parts with used the time difference of time to determine excessive risk parts and/or its abnormality detection priority, and can feed back to engineers and technicians.Wherein, the life time of risk parts and used the time can be stored in the data base of PM system.
Table 1 below is shown with the example of the abnormality detection result chart of the tool component that the method for abnormality detection for tool component according to an embodiment of the invention draws.It will appreciated by the skilled person that the abnormality detection result chart shown in table 1 is only an example, the method for detecting abnormality according to previous embodiment, it is also possible to draw other abnormality detection result charts.
In Table 1, instrument to be detected is mass flow controller (MassFlowController, MFC), it includes 4 parts, respectively effusion meter (Flowmeter), gas tube (gastube), valve (valve) and o-ring (O-ring), as shown in table 1.
Table 1 abnormality detection result chart
In Table 1, exemplarily, the quality information data of each parts is shown as warning message data, and the abnormal frequency rank of parts is shown as warning frequency rank.Wherein, warning message includes " gas flowing fault (gasflowfault) ", " gas conversion (gasshift) ", " leakage (leakage) " and " particle (particle) " etc..It should be noted that each parts include multiple warning message respectively, the predetermined threshold of every kind of corresponding warning frequency rank of warning message and a warning frequency, these predetermined thresholds can be that FEMA system is defined.Additionally, different parts include identical warning message.
According to embodiments of the invention, in 4 parts of mass flow controller, parts " gas tube ", " valve " and " o-ring " occur that leakage and particle are abnormal simultaneously, and namely parts " gas tube ", " valve " and " o-ring " occur that warning frequency is superior to warning message " leakage " and " particle " of predetermined threshold simultaneously.Therefore, parts " gas tube ", " valve " and " o-ring " are screened for risk parts.
Further, table 1 also illustrating that, each parts are respective and has used time and life time (unit is year).Wherein, the life time of parts " gas tube " is with to have used the time difference of time be 10-8.4=1.6；The life time of parts " valve " and the time difference having used the time are 8-6.5=1.5；The life time of parts " o-ring " and the time difference having used the time are 5-2.9=2.1.Due to 2.1 > 1.6 > 1.5, therefore filter out the parts " valve " with abnormality detection limit priority.The abnormality detection priority of parts " gas tube " and " o-ring " then respectively 2 and 3, the abnormality detection priority being excluded the parts " effusion meter " outside risk parts can be 4, as shown in Table 1.
According to this prioritization, engineers and technicians can shorten the anomaly detection time of mass flow controller parts, is quickly detected from exceptional part.
On the other hand, the present invention provides the system of a kind of abnormality detection for tool component.System includes: data analysis module, for the abnormal frequency rank based on the quality information data analysis relevant to all parts of instrument all parts determining instrument；And risk parts screening module, filter out risk parts for the abnormal frequency rank based on all parts.Further, risk parts screening module is additionally operable to based on the respective life time of risk parts and/or has used the time to filter out excessive risk parts.
The method and system of the abnormality detection for tool component provided by the present invention can shorten the detection time in the abnormality detection of tool component, detects the exceptional part of instrument efficiently.
The present invention is illustrated already by above-described embodiment, but it is to be understood that, above-described embodiment is only intended to citing and descriptive purpose, and is not intended to limit the invention in described scope of embodiments.In addition it will be appreciated by persons skilled in the art that and the invention is not limited in above-described embodiment, more kinds of variants and modifications can also be made according to the teachings of the present invention, within these variants and modifications all fall within present invention scope required for protection.Protection scope of the present invention is defined by the appended claims and equivalent scope thereof.
Claims (10)
1. the method for the abnormality detection of tool component, it is characterised in that described method includes:
Abnormal frequency rank based on the quality information data analysis relevant to all parts of described instrument all parts determining described instrument；And
Abnormal frequency rank based on described all parts filters out risk parts.
2. the method for claim 1, it is characterised in that described method farther includes: based on the described respective life time of risk parts and/or used the time to filter out excessive risk parts.
3. method as claimed in claim 2, it is characterised in that the screening of described excessive risk parts is based further on the life time of described risk parts and the time difference using the time.
4. method as claimed in claim 3, it is characterised in that described method farther includes: based on described risk parts life time with use the time difference of time that described risk parts are ranked up, to determine abnormality detection priority.
5. the method for claim 1, it is characterised in that whether the screening of described risk parts is based further on the abnormal frequency rank of described all parts higher than predetermined threshold.
6. method as claimed in claim 5, it is characterised in that described predetermined threshold is different based on different parts.
7. the method as described in any one in claim 1-6, it is characterised in that described quality information data includes warning message data, and described abnormal frequency rank is warning frequency rank.
8. method as claimed in claim 7, it is characterised in that described quality information data also includes scrapping information data and/or tool parametric information data.
9. the system for the abnormality detection of tool component, it is characterised in that described system includes:
Data analysis module, for the abnormal frequency rank based on the quality information data analysis relevant to all parts of described instrument all parts determining described instrument；And
Risk parts screening module, filters out risk parts for the abnormal frequency rank based on described all parts.
10. system as claimed in claim 9, it is characterised in that described risk parts screening module is further used for based on the described respective life time of risk parts and/or has used the time to filter out excessive risk parts.
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| CN101038639A (en) * | 2007-04-25 | 2007-09-19 | 上海发电设备成套设计研究院 | Service Life predicting method and system for machine and vulnerable component of generating set |
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