CN102467089A

CN102467089A - Process control method of semiconductor technology and system thereof

Info

Publication number: CN102467089A
Application number: CN2010105401043A
Authority: CN
Inventors: 谢凯
Original assignee: Beijing North Microelectronics Co Ltd
Current assignee: Beijing North Microelectronics Co Ltd
Priority date: 2010-11-10
Filing date: 2010-11-10
Publication date: 2012-05-23
Anticipated expiration: 2030-11-10
Also published as: CN102467089B

Abstract

The invention provides a process control method of a semiconductor technology and a system thereof as well as a semiconductor control system containing the process control system, so that a problem that current univariate process control can not meet demands can be solved. The method comprises: a multivariate statistical process control model corresponded to a technological process that needs to be monitored is started; a monitoring sample is received, wherein the monitoring sample contains multiple technological data of all variables in the technological process that needs to be monitored; according to the multivariate statistical process control model and the multiple technological data of all the variables in the technological process that needs to be monitored, a fault value corresponded to the technological process that needs to be monitored is obtained; and it is determined whether the fault value exceeds the control limit of the multivariate statistical process control model; if so, a quantitative analysis is carried out on the fault for finding the fault cause. According to the invention, a fault cause can be obtained by a quantitative analysis, so that technological personnel can get rational understanding on a fault alarm.

Description

Course control method for use in the semiconductor technology and system

Technical field

The present invention relates to process control technology, particularly relate to course control method for use and system in a kind of semiconductor technology, and the semiconductor control system that comprises this Process Control System.

Background technology

In the process industrial (like oil refining, petrochemical complex, metallurgy etc.) that with continuous production is characteristic; Process control has entered into the control of production overall process from separate device control, be a kind of comprehensive automation control model that integrates optimization, dispatches, manages.

Univariate statistics process control technology (Univariate Statistical Process Control; Be called for short USPC) be a kind of process control technology commonly used; Main X-control chart (Xbar-X), average-standard deviation control chart (Xbar-S), mean-range chart (Xbar-R) and the monodrome-moving range figure control charts such as (X-MR) of adopting monitored variable; When having process data, monitored variable surpasses control in limited time; Control chart can capture this unusual (or claiming fault), and sends warning message; Simultaneously, use indexs such as historical sample data computation Process Capability Index Cp, Cpk, weigh also consistance, the extent of stability of monitoring industrial processes.

In the semiconductor production manufacture process; Because complex procedures, the characteristics that the cost of raw material is high; Require same batch product quality of each production link to reach consistance highly; Therefore also use univariate statistics process control technology (USPC) that the variable in producing is carried out complete monitoring, thereby instruct the slip-stick artist to find and handle the unusual variable fluctuation situation of appearance, guarantee the stable of production run.

But; The all types of control charts of USPC can only be monitored a variable at every turn, and along with the raising of semiconductor manufacturing facility complexity, controllable parameter also constantly increases; Need the variable of monitoring also just more and more; If monitor a plurality of variablees simultaneously, not only calculated amount is huge, and the switching displayed between control chart is also very loaded down with trivial details.

In addition,, can not reflect the incidence relation that exists between variable, therefore can cause the erroneous judgement of fault and fail to judge because each variable is separately statistics monitoring.For example, with reference to Fig. 1, be that two variablees sample in the USPC control chart shows normal synoptic diagram.Two variablees are presented in the control chart separately, although show two process datas in each control chart near the last lower control limit shown in the dotted line among the figure (being the process data in the circle among Fig. 1), do not surpass the control limit and trigger fault alarm.But, be the synoptic diagram of two variablees sample display abnormality in the associated diagram of variable with reference to Fig. 2.From the associated diagram of these two variablees, can see two unusual process datas being arranged away from other process data straight lines and trigger fault alarm (being the process data in the circle among Fig. 2).Though the associated diagram of variable can be judged abnormal conditions, when monitored variable quantity more for a long time, the variables corresponding associated diagram is also more, calculates and checks that the variable associated diagram is loaded down with trivial details equally.And in the prior art, can only the qualitatively analyze fault alarm based on the associated diagram of two variablees of USPC technology, and can not make the technologist not have rational understanding to its quantitative test to fault alarm, can not fundamentally solve the relevant issues of fault alarm.

In sum, along with improving constantly of semiconductor manufacturing facility complexity, failure monitoring is proposed higher requirement, the USPC technology can not satisfy this demand.

Summary of the invention

The invention provides course control method for use and system in a kind of semiconductor technology, and the semiconductor control system that comprises this Process Control System, to solve the problem that existing single argument process control can't satisfy the demands.

In order to address the above problem, the invention discloses the course control method for use in a kind of semiconductor technology, comprising:

Start the Multivariable Statistical Process Control model that needs the monitoring process step corresponding;

Receive the monitoring sample, said monitoring sample comprises the said a plurality of process datas that need each variable in the monitoring process step;

According to said Multivariable Statistical Process Control model and the said a plurality of process datas that need each variable in the monitoring process step, obtain the corresponding said fault score value that needs the monitoring process step;

Judge whether said fault score value exceeds the control limit of said Multivariable Statistical Process Control model, if, then quantitative test failure cause.

Preferably, said quantitative test failure cause comprises: according to each statistic of a plurality of process datas of fault signature database and said each variable, obtain the fault signature data statistic; Said fault signature database is to set up based on the univariate statistics process control technology; According to said fault signature data statistic with preset the fault alarm rule, obtain fault score contribution plot, said fault score contribution plot has write down the fault score and the corresponding fault alarm type of each variable; In conjunction with said fault score contribution plot and said fault signature data statistic, quantitative test failure cause.

Preferably, under off-line state, said method comprises: reception comprises a plurality of monitoring samples that need the monitoring process step; Each needs the fault score value of monitoring process step to obtain correspondence respectively; Judge respectively whether each fault score value exceeds the pairing control limit that needs the Multivariable Statistical Process Control model of monitoring process step, if, then quantitative test failure cause.

Preferably, under presence, said reception monitoring sample is specially: reception in real time comprises a certain monitoring sample that needs the monitoring process step.

Preferably, through adding up the same fault score value that needs the monitoring process step in each silicon chip etching technology process, set up the multivariate statistics control chart.

Preferably; Said startup needs before the corresponding Multivariable Statistical Process Control model of monitoring process step; Also comprise: set up the Multivariable Statistical Process Control model; Specifically comprise: set the configuration information of modeling, said configuration information comprises processing step, data screening parameter, modeling variable, degree of confidence, the pivot computing method of appointment; Screen according to data screening parameter and modeling variable a plurality of process datas each variable in the processing step specified in each training sample; To the training sample compute statistics after the screening; Utilize statistic, degree of confidence and the pivot computing method of training sample to set up the Multivariable Statistical Process Control model, and the control of definite model limit; Judge whether there is the training sample that surpasses the control limit in institute's established model,, then preserve the model of being set up if do not exist; If exist, then remove out-of-limit sample, and utilize remaining training sample circulation to carry out said statistic calculating, modelling and determining step, in institute's established model, there is not the training sample that surpasses the control limit.

Preferably, said screening comprises: utilize the data screening device automatically in the processing step of appointment, training sample is carried out the screening pre-service of named variable; Wherein, said data screening device with the processing step be unit pre-configured the screening parameter value of all optional variablees under each processing step.

The present invention also provides the Process Control System in a kind of semiconductor technology, comprising:

Model imports module, is used to start the Multivariable Statistical Process Control model that needs the monitoring process step corresponding;

Data reception module is used for receiving the monitoring sample, and said monitoring sample comprises the said a plurality of process datas that need each variable in the monitoring process step;

Model computation module is used for obtaining the corresponding said fault score value that needs the monitoring process step according to said Multivariable Statistical Process Control model and the said a plurality of process datas that need each variable of monitoring process step;

Failure analysis module is used to judge whether said fault score value exceeds the control limit of said Multivariable Statistical Process Control model, if, then quantitative test failure cause.

Preferably, said failure analysis module comprises: first analytic unit, be used for each statistic according to a plurality of process datas of fault signature database and said each variable, and obtain the fault signature data statistic; Said fault signature database is to set up based on the univariate statistics process control technology; Second analytic unit, be used for according to said fault signature data statistic with preset the fault alarm rule, obtain fault score contribution plot, said fault score contribution plot has write down the fault score and the corresponding fault alarm type of each variable; Integerated analytic unit is used to combine said fault score contribution plot and said fault signature data statistic, the quantitative test failure cause.

Preferably, said system also comprises: model building module is used to set up the Multivariable Statistical Process Control model.

Preferably, said model building module comprises: the model dispensing unit, be used to set the configuration information of modeling, and said configuration information comprises processing step, data screening parameter, modeling variable, degree of confidence, the pivot computing method of appointment; The data screening unit is used for screening according to data screening parameter and the modeling variable a plurality of process datas to each variable in the specified processing step of each training sample; The statistical computation unit is used for the training sample compute statistics after the screening; The pre-established unit of model is used to utilize statistic, degree of confidence and the pivot computing method of training sample to set up the Multivariable Statistical Process Control model, and the control of definite model limit; The iteration modeling unit is used for judging whether institute's established model exists the training sample that surpasses the control limit, if do not exist, then preserves the model of being set up; If exist, then remove out-of-limit sample, and utilize remaining training sample to combine statistical computation unit and model pre-established unit circulation to carry out said statistic calculating, modelling and judgement, in institute's established model, there is not the training sample that surpasses the control limit.

Preferably, said system also comprises: the data screening device, and being used for the processing step is the screening parameter value of all optional variablees under pre-configured each processing step of unit; The data screening module; Be used for according to model specified processing step and named variable; Utilize the data screening device automatically in the processing step of model appointment; The monitoring sample is carried out the screening pre-service of named variable, and the monitoring sample after will screen passes to said model computation module, said model computation module utilizes model that the monitoring sample after screening is calculated.

The present invention also provides a kind of semiconductor control system, comprises apparatus control system and Process Control System, it is characterized in that: said Process Control System comprises the arbitrary described Process Control System of claim 8 to 12.

Preferably, said semiconductor control system also comprises: data acquisition system (DAS) is used for the monitoring sample of collecting device control system institute control equipment at art production process, and stores in the database; Database is used for storage monitoring sample.

It is data source with said database that semiconductor is controlled said Process Control System, directly receives the monitoring sample from this database.

Compared with prior art, the present invention has the following advantages:

First; The present invention proposes a kind of Multivariable Statistical Process Control (Multivariate Statistical Process Control that is applicable to semiconductor technology; Be called for short MSPC) method; This method can be utilized the incidence relation between a plurality of variablees, with unusual the reflecting of monitoring sample through the fault score value that obtains by a plurality of aggregation of variable, and according to said fault score value the quantitative test reason that is out of order; Thereby make the technologist to the rational understanding of fault alarm, and then fundamentally solve the relevant issues of fault alarm.Based on said fault score value; The present invention can just can accurately analyze abnormal conditions through a multivariate statistics control chart of having drawn the fault score value to the monitoring of sample, the fault erroneous judgement the problem includes: of having avoided univariate statistics process control (USPC) problem and the problem of failing to judge; And the present invention just can judge guilty culprit easily and quickly through multivariate statistics control chart intuitively; Need not to switch loaded down with trivial detailsly and check a large amount of univariate statistics process control charts; Alleviated operational load greatly; Improve controlling level, further improved the production efficiency and the product yield of semiconductor equipment.

Second; During quantitative test failure cause of the present invention; On the basis that the multivariate statistics control chart is provided; Also combine the USPC technology that fault score contribution plot and the fault signature data statistic of reflection univariate statistics situation are provided, the solid dot that the user is transfinited in the multivariate statistics control chart double-clicks and can open simultaneously, through this figure and table can be intuitively, the clearly variable that breaks down of analysis and the basic reason of fault.

The 3rd, during quantitative test failure cause of the present invention, also combine the USPC technology that fault alarm information has been carried out grade classification; The mode that this classification is reported to the police quantizes fault type; Can reflect the situation of change of fault variable, both realize capturing major failure fast, realize again can doing further quantitative test major failure with qualitative mode by quantitative change to qualitative change; Make fault analysis more comprehensive, the result has more actual directive significance.Simultaneously, the alert levels of this quantification can provide more horn of plenty, warning accurately for machine control system, and the fault handling function of further enhancing and the current control system of refinement is for process control provides advanced decision-making and scheduling feature.

The 4th; The present invention has also designed the data screening device and has come monitoring sample or model training sample are carried out pre-service; This data screening device with the processing step be unit pre-configured the screening parameter value of all optional variablees under each processing step; Therefore in pre-treatment step, can directly import, carry out automatically thereby the troublesome operation that manual work is provided with the data screening parameter is optimized for by system.

The 5th, the present invention is to provide at the MSPC of tool level (directly towards board equipment) and use based on the real-time process data, directly the database with data acquisition system (DAS) is a data source, to satisfy the requirement of control in real time.

The 6th, the present invention can also realize failure monitoring and the diagnosis under off-line and the online two states.

Description of drawings

Fig. 1 is that two variablees sample in the USPC control chart shows normal synoptic diagram in the prior art;

Fig. 2 is the synoptic diagram of two variablees sample display abnormality in the associated diagram of variable in the prior art;

Fig. 3 is the synoptic diagram of two variablees sample display abnormality in the MSPC control chart among the present invention;

Fig. 4 is a process flow diagram of setting up the MSPC model in the embodiment of the invention;

Fig. 5 is the Multivariable Statistical Process Control figure of institute's reference in the MSPC modelling process in the embodiment of the invention;

Fig. 6 is the MSPC off-line procedure control method process flow diagram in the said a kind of semiconductor technology of the embodiment of the invention;

Fig. 7 is the multivariate statistics control chart of a certain processing step in the embodiment of the invention;

Fig. 8 is a MSPC fault score contribution plot in the embodiment of the invention;

Fig. 9 is the online course control method for use process flow diagram of MSPC in the said a kind of semiconductor technology of the embodiment of the invention;

Figure 10 is the Process Control System structural drawing in the said a kind of semiconductor technology of the embodiment of the invention;

Figure 11 is the Organization Chart of the said a kind of semiconductor control system of the embodiment of the invention.

Embodiment

For make above-mentioned purpose of the present invention, feature and advantage can be more obviously understandable, below in conjunction with accompanying drawing and embodiment the present invention done further detailed explanation.

The present invention proposes a kind of Multivariable Statistical Process Control (MultivariateStatistical Process Control that is applicable to semiconductor technology; Be called for short MSPC) method; MSPC is also referred to as fault diagnosis and classification (Fault Detection and Classification; FDC) technology; Unusual (or fault) situation that can utilize the incidence relation between a plurality of variablees to come variable in the monitoring industrial processes, with the USPC compared with techniques, the fault variable that can occur in the identification equipment production run easily and quickly through succinct control chart.For example; To example shown in Figure 1, the sample of two variablees in the USPC control chart shows normal, but with reference to Fig. 3; Be the synoptic diagram of these two variablees sample display abnormality in the multivariate statistics control chart, from the control chart of Fig. 3, can directly find out this two unusual process datas.

The present invention is applicable to the failure monitoring of complicated procedures of forming in the semiconductor fabrication, mainly comprise Multivariable Statistical Process Control (MSPC) model foundation, utilize the MSPC model to monitor and fault analysis two parts.To be that example is elaborated below with the semiconductor etching process.

1, MSPC modelling

Characteristics according to the conductor etching process; The etching production overall process of each wafer (silicon chip) all need be passed through some processing steps; And the recipe of each step (technical recipe) is provided with difference; Being that the setting value (or claim desired value) of each variable in each step is not necessarily identical, therefore, need be that unit sets up the model that adapts respectively with the step.

With reference to Fig. 4, be the process flow diagram of setting up the MSPC model in the embodiment of the invention.

Step 401 is that unit imports a plurality of training samples with wafer, and each training sample comprises a plurality of process datas of each variable in each processing step;

Said training sample is used for model training, and in conductor etching technology, the sample data of a training sample comprises a plurality of process datas of each variable in each processing step of etching wafer (silicon chip).In this step, generally be that unit imports training sample with wafer, the modeling here is specially adopts a plurality of training samples that specified processing step is carried out modeling.

Step 402, the given process step; Need the monitoring process step in the corresponding practical application of the processing step of appointment here;

Step 403 is provided with the data screening parameter and chooses the variable of participating in modeling;

The processing step of above-mentioned appointment, data screening parameter, modeling variable and subsequent step 406 preset confidence, pivot computing method all belong to the main configuration information of modeling, in addition also comprise other configuration informations.These configuration informations are initial in case set generally no longer change in step 405 to 408 the iteration modeling process in the back in modeling.Certainly, according to the characteristics of processing step, the different process step need be set up different model, and is therefore generally also inequality to the corresponding configuration information of different models.

Step 404 is screened according to data screening parameter and the modeling variable a plurality of process datas to each variable in the processing step specified in each training sample;

Said screening is preferred pre-treatment step, is used for filtering out the training sample data that meet modeling demand from the training sample that imports, and promptly a plurality of process datas to each variable in the processing step specified in each training sample screen.Wherein, said modeling variable is meant the variable of participating in modeling, each variable in the training sample that promptly imports in the specified processing step, but possibly only use the center variation per minute during modeling, therefore need be configured into row filter according to model.In addition, said data screening parameter comprises a plurality of parameters, is used for the training sample that initially imports is carried out processing such as denoising, to guarantee modeling precision.For example, can whether eliminate the labile factor in this stage with decision to different variablees by the number that the user sets the process data of removing beginning and finishing; Simultaneously, can also set maximal value, the minimum value scope of process data to different variable permission users, to remove noise data.

In practical operation, generally the data screening parameter is set and chooses the modeling variable, and then screen importing training sample through manual type.Since comprise a lot of variablees in each processing step, and the different process step has nothing in common with each other to the data screening parameter of same variable and the requirement of modeling variable, if it is each modeling all is provided with through manually-operated one by one, very loaded down with trivial details.Based on this, present embodiment proposes a kind of data preprocessing method of optimization, and the design data screening washer is provided with the reduced data screening.Said data screening device with the processing step be unit pre-configured the screening parameter value of all optional variablees under each processing step.The data screening device can be constructed through file layouts such as Excel, and the recipe with the typical etch processes of wafer shown in the table 1 is an example below:

Step?ID	1	2	3	4	5	6	7	8	9	10
											Step?Name	BT_sta	BT	ME1_sta	ME1	ME2_sta	ME2	OE_sta	OE	purge	dechuck
PM.S1.Prc	10	10	10	15	10	75	10	26	5	3
											Pressure	7	7	10	10	30	30	30	30	0	15
SRFpower	0	350	0	500	0	250	350	350	0	300
											BRFpower	0	40	0	80	0	75	40	40	0	0
GasCl2	0	0	60	60	50	50	15	15	0	0
											GasHBr	0	0	0	0	150	150	170	170	0	0
GasCF4	50	50	0	0	0	0	0	0	0	0
											GasCHF3	0	0	0	0	0	0	0	0	0	0
GasCH2F2	0	0	0	0	0	0	0	0	0	0
											GasSF6	0	0	30	30	0	0	0	0	0	0
GasAr	0	0	0	0	0	0	0	0	0	0
											...

Table 1

In the table 1, the Step ID in first row representes the processing step sequence number, and the Step Name in second row representes the processing step title, and what except that Step ID and Step Name, represent in first example is the title of each variable.Can find out that by table 1 same variable is different in the recipe of different process step value in the etching process.

The data screening device of correspondence table 1 structure is as shown in table 2:

Table 2

In the Excel file shown in the table 2, a certain processing step of the corresponding etching wafer of each worksheet (being each sheet), the ParameterName field is to all optional variablees in should step; Step-start representes that this processing step begins the process data of part; Step-Stop representes the process data of latter end, and Max-limit representes the maximum control limit, and Min-limit representes minimum control limit; Include is used to set the variable of participating in modeling, and " 1 " representative is selected.After data screening device structure finishes, can directly import the MSPC system and use, the troublesome operation that manual work is provided with the data screening parameter is optimized for by system carries out automatically.

Step 405 is to the training sample compute statistics after the screening;

Promptly the training sample that meets modeling demand after the screening is carried out statistical computation, the statistic that obtains comprises Min (minimum value), Max (maximal value), Mean (mean value), Stdev (standard deviation) etc.These statistics will be used for Modeling Calculation.Particularly, carry out statistical computation based on a plurality of process datas of each variable in the specified processing step in each training sample after the screening, to obtain each statistic.

Step 406 is selected degree of confidence and pivot computing method;

Said degree of confidence is used for confirming the control limit of model, and optional degree of confidence is as 95%, 99%, 99.9% etc.Said pivot computing method are used to calculate the pivot number, promptly from a plurality of variablees of participating in modeling, select representative variable again and are used for modeling, thereby reduce calculated amount and computation complexity.Optional pivot computing method such as 90%CPV (cumulative percent variance, accumulative total of variance and number percent), AE (Average Eigenvalue, mean eigenvalue) etc.

In addition, except can using the pivot computing method, also can use computing method such as neural network or decision tree.

Step 407 utilizes statistic, degree of confidence and the pivot computing method of training sample to set up the Multivariable Statistical Process Control model, and the control of definite model limit;

Modeling process is calculated the pivot number for utilizing the pivot computing method, and the utilization of preference pattern algorithm utilizes statistic, degree of confidence and the pivot number etc. of training sample to calculate, and then can obtain the control limit of model.Said control limits the use of in judging whether processing step has fault generating, and model is in case confirm, control limit that should model is also promptly confirmed.In addition; In modeling process; Also need the Multivariable Statistical Process Control figure of preference pattern use and the Multivariable Statistical Process Control figure of screening model data, the Multivariable Statistical Process Control figure of said screening model data is used for step 408 and judges whether to exist the training sample that surpasses the control limit.Wherein, optional model algorithm such as SPE, Hotelling T ², φ etc., corresponding, control chart also can be from SPE (Squared prediction error, square prediction error), Hotelling T ², φ (a kind of combination SPE and Hotelling T ²Algorithm) in select.

The MSPC model of setting up can be with reference to shown in Figure 5.Fig. 5 is the Multivariable Statistical Process Control figure of institute's reference in the MSPC modelling process in the embodiment of the invention; What this figure showed is after model tentatively builds up; The failure condition of corresponding etching the 6th processing step of a plurality of training samples that is imported, and this model has been selected SPE, T simultaneously ²With the calculating of φ and the Multivariable Statistical Process Control figure of screening model data.At SPE, T ²In the φ control chart, the 6th processing step of the corresponding wafer of each solid dot, and each solid dot produces by a monitoring sample; Solid line representative control limit among the figure, the solid dot that exceeds number line is specially the solid dot that exceeds the control limit, and this solid dot shows that this wafer fault occurred in this 6th processing step etching process.

Step 408 judges whether there is the training sample that surpasses the control limit in institute's established model;

Particularly, with reference to Fig. 5,, then accept the model set up, and continue step 409 if promptly there is not the solid dot that exceeds the control limit in the above-mentioned training sample that surpasses the control limit that do not exist; If exist; Then remove the training sample that produces the solid dot that surpasses the control limit; Promptly remove the training sample that produces the solid dot that surpasses the control limit in a plurality of training samples that import in the step 401, and return step 405, and utilize remaining training sample to recomputate statistic; And the continuation subsequent step, up to meeting the requirements.

In the modelling process, the user can remove the training sample that surpasses the control limit, in the figure of similar Fig. 5, does not have solid dot to exceed control and only is limited to.Preferably, the model that obtains through the iteration modeling is optimum model, so present embodiment adopts the mode of iteration modeling.The iteration modeling process is: the described statistic calculating of step 405, the modelling of step 407 and the determining step of step 408 are carried out in circulation, in institute's established model, do not have the training sample that surpasses the control limit.Because the number of training of each iteration is reducing, the control extreme position that therefore at every turn calculates can be different, when all sample points in the model are all within the control limit, promptly confirmed final control extreme position.In this iterative process, select unified degree of confidence and pivot computing method.

Step 409 is preserved the model of being set up.

After the iteration modeling finishes, then fixed model is preserved, in the production monitoring process for use in reality.

In above-mentioned modeling process, preferred, can also be to the compute statistics of a plurality of training samples of original importing, and preserve, for the slip-stick artist with reference to use.

Above-mentioned flow process is the flow process of the processing step of some appointments being carried out modeling, for the modeling of other processing steps that need monitor, also can repeat no more at this with reference to above-mentioned flow process.

2, MSPC monitoring and fault analysis

After the MSPC modelling, just can utilize the MSPC model that the etching production run of wafer is monitored.Said monitoring comprises monitored off-line and two kinds of patterns of on-line monitoring, describes through Fig. 6 and flow process shown in Figure 9 respectively below.

With reference to Fig. 6, be the MSPC off-line procedure control method process flow diagram in the said a kind of semiconductor technology of the embodiment of the invention.

Under the monitored off-line pattern, the MSPC system can calculate and analyze all data that import, and catches the wafer sample that surpasses the control limit.Concrete steps are following:

Step 601, starting each needs the corresponding Multivariable Statistical Process Control model of monitoring process step;

Under the monitored off-line pattern, need define which processing step in advance and need carry out off-line analysis, start the MSPC model of corresponding these steps then.

Step 602, reception comprise a plurality of monitoring samples that need the monitoring process step, and each monitoring sample comprises a plurality of process datas that need each variable in the monitoring process step;

Under the monitored off-line pattern; Also need all receive the monitoring sample in these processing steps that need monitor; For example receive the wafer monitoring sample that the 6th step of etching technics and the 9th goes on foot in the table 1, the monitoring sample in each step has comprised a plurality of process datas of each variable.

Step 603 is screened pre-service to the monitoring sample that receives;

This step is a preferred process, and above-mentioned data screening device capable of using screens, and the content of screening comprises Step-start, Step-Stop, Max-limit, Min-limit, Include etc.Certainly, the employed MSPC model of different process step is different, and parameter of screening and relevant parameter value also can be different.

Step 604, according to the said a plurality of process datas that need each variable in the monitoring process step of each said Multivariable Statistical Process Control model and each, each needs the fault score value of monitoring process step to obtain correspondence respectively;

Be unit promptly, utilize the MSPC model that the monitoring sample after screening is calculated, comprehensively obtain the fault score value that each needs the monitoring process step by the monitoring a plurality of process datas that respectively need each variable in the monitoring process step that sample comprised with the processing step;

To the different process step, with using different MSPC models to carry out statistical computation.Utilizing each MSPC model to carry out in the calculation process; The statistic of a plurality of process datas after the screening of a plurality of process datas of each variable, calculating named variable in all first loading need monitoring process step; Degree of confidence and the pivot computing method of utilizing statistic, model to confirm then; Last calculating again needs the fault score value of monitoring process step, and the control chart that uses through model shows.

In the MSPC model, finally represent the normal or abnormal conditions of a monitoring sample in technological process through the fault score value.Said fault score value is the result of calculation that a plurality of process datas according to each variable in the need monitoring process step comprehensively obtain, and has taken into full account the incidence relation between the variable.

Step 605 judges respectively whether each fault score value exceeds the pairing control limit that needs the Multivariable Statistical Process Control model of monitoring process step, if, then quantitative test failure cause.

In practical application, a kind of adoptable analytical approach is to utilize the various types of control charts of Multivariable Statistical Process Control modeling rendering, through the control chart intuitive analysis reason that is out of order.For example, utilize the MSPC model that the fault score value of the corresponding processing step of each monitoring sample is drawn the multivariate statistics control chart, and corresponding given process step switching displayed.The fault score value of same processing step in each wafer etching process has been shown in the said multivariate statistics control chart, has also marked the position of control limit, the solid dot that surpasses this control limit is the trouble spot.

Exhibition Fig. 7 is the MSPC off-line analysis figure of a certain processing step in the embodiment of the invention.MSPC model to should processing step has used SPE, T simultaneously ²With φ multivariate statistics control chart, because fault score value and the normal range value of trouble spot differ too much, so the control of each multivariate statistics control chart is limit all near 0 position that is worth.But from any one control chart, can judge the trouble spot of breaking down, promptly surpass the solid dot of control limit, and judged result be consistent.

In addition; Because off-line mode can calculate and analyze down all data that receive; Therefore for the multivariate statistics control chart of other processing steps; Off-line mode provides similar operation interface shown in Figure 5, can in Step ID, import the sequence number of processing step, the below at interface will switching displayed to multivariate statistics control chart that should given step.

Preferably, to each solid dot (can be called for short out-of-limit solid dot) that exceeds model control limit in the said multivariate statistics control chart, can also draw the fault score contribution plot and the fault signature data statistic of the monitoring sample that produces this out-of-limit solid dot respectively.At which processing step fault has taken place though can qualitatively analyze have which wafer from above-mentioned multivariate statistics control chart, can't further analyze quantitatively is that which variable of this processing step has caused this fault actually.Therefore, this step has further been drawn fault score contribution plot to the pairing abnormal monitoring sample of out-of-limit solid dot.Said fault score contribution plot provides univariate fault score contribution margin and the corresponding univariate fault alarm type that the monitoring sample that produces this out-of-limit solid dot is comprised, and therefore can analyze out of order basic reason from this figure.Simultaneously; In order to be illustrated more clearly in failure cause; The single argument compute statistics that can also combine univariate statistics process control (USPC) model that the monitoring sample that produces this out-of-limit solid dot is comprised, and utilize statistic to draw the fault signature data statistic of abnormal monitoring sample.Said fault score contribution plot and fault signature data statistic can be opened in the out-of-limit solid dot in double-clicking the multivariate statistics control chart and show.

Based on above analysis, said quantitative test failure cause specifically can comprise following processing:

At first, according to each statistic of a plurality of process datas of fault signature database and said each variable, obtain the fault signature data statistic; Said fault signature database is to set up based on USPC;

Wherein, Preserved the tables of data of record product yield detailed technology index in the said fault signature database; These technical indicators are on the basis of summing up the low reason of yield, to draw; Said technical indicator comprises the Max_Limit described in the following table 3 (maximum control limit), Min_Limit (minimum control limit) or the like, but does not comprise Mean (mean value), Max (maximal value), Min (minimum value), Stdev (standard deviation) and Range (scope).Each statistic of a plurality of process datas of said each variable is meant the result who each variable is carried out statistical computation, as calculating Min (minimum value), Max (maximal value), Mean (mean value), Stdev (standard deviation) or the like.These statistics are combined each item technical indicator in the fault signature database, just can obtain the fault signature data statistic shown in the table 3.

Secondly, according to said fault signature data statistic and the fault alarm rule that presets, obtain fault score contribution plot, said fault score contribution plot has write down the fault score and the corresponding fault alarm type of each variable;

Said fault alarm rule is meant carries out carrying out the rule that the fault alarm type is divided according to the order of severity to failure cause, can confirm the division rule shown in the for example following table 4 according to practical application.

Once more, in conjunction with said fault score contribution plot and said fault signature data statistic, quantitative test failure cause.

With reference to Fig. 8, be MSPC fault score contribution plot in the embodiment of the invention; With reference to table 3, be the USPC analytical table of MSPC fault.For certain the out-of-limit solid dot among Fig. 7, the position of this out-of-limit solid dot double-click can open Fig. 8 and below table 3.Wherein, fault score contribution plot shown in Figure 8 comprises two parts, shows SPE, T in the left side of figure ²With the single argument fault score value figure of φ, then show each univariate fault score contribution margin (Score) and fault and warning descriptor (Alarm) accordingly according to from high to low order on the right side of figure.In table 3, analyze the statistics that has provided each variable in conjunction with USPC.

Table 3

Table 4

Based on above-mentioned Fig. 8 and table 3, table 4, the fault alarm analysis process is following:

Analysis with certain out-of-limit solid dot among Fig. 7 is an example; In the fault score contribution plot of Fig. 8; The fault score of HeliumLeakage variable is the highest and make number one; Show the possibility maximum that this variable breaks down, its fault and warning are described as " MeanAboveMaxLimit&MeanAboveUpper3Std ".Further question blank 3; The mean value (Mean) that can find out this variable is 5.8299; The Maximum tolerance (Max_Limit) 4 that has surpassed model specification, and the UCL of USPC system X-control chart controls limit

i.e. 1.3258 (MeanPlus3Std).So correspondence table 4, these data have shown this fault triggering 3 grades and 4 grades of warnings.In addition, the analytic process of other out-of-limit samples is the same.

Similar with top monitored off-line; Multivariate multivariate statistics control chart, fault score contribution plot that also can be through the MSPC model under the on-line monitoring pattern and the fault signature data statistic that combines USPC, process data and analyze out of order basic reason notes abnormalities.But line model and off-line mode are different again, and line model need receive the wafer sample data in the etching production run down in real time, carries out statistical computation and analysis, and in time sends fault alarm information to control system, and idiographic flow is as shown in Figure 9.

With reference to Fig. 9, be the online course control method for use process flow diagram of MSPC in the said a kind of semiconductor technology of the embodiment of the invention.

Step 901 starts the Multivariable Statistical Process Control model that needs the monitoring process step corresponding;

Under the on-line monitoring pattern, equally also need define which processing step in advance and need carry out on-line analysis, be that unit starts corresponding M SPC model then with the processing step.

Step 902 starts monitoring in real time;

Step 903 receives certain in real time and comprises a certain monitoring sample that needs the monitoring process step, and each monitoring sample comprises the said a plurality of process datas that need each variable in the monitoring process step;

Because the execution of processing step is an order, so the reception of data also is the monitoring sample that receives only current processing step at every turn; When implementing next processing step, if this processing step need be analyzed, then receive a plurality of process datas of each variable in this processing step again, and by MSPC model that should processing step is carried out statistical study.

Step 904 is screened pre-service to the monitoring sample that receives;

The processing of said screening pre-service and off-line model is similar, and above-mentioned data screening device capable of using screens, but only is that the data of the current processing step that receives are screened.

Step 905 according to said Multivariable Statistical Process Control model and the said a plurality of process datas that need each variable in the monitoring process step, is obtained the corresponding said fault score value that needs the monitoring process step;

After this processing step finishes, utilize the monitoring sample after corresponding MSPC model screens down this processing step to calculate, comprehensively obtain the fault score value of this monitoring process step by a plurality of process datas of each variable in the need monitoring process step;

Said online Model Calculation is identical with the Model Calculation of off-line, at this slightly.

Step 906 judges whether said fault score value exceeds the control limit of said Multivariable Statistical Process Control model, if, then quantitative test failure cause;

Promptly can utilize corresponding MSPC model that the fault score value of need monitoring process step is drawn multivariate statistics control chart and demonstration.Multivariate statistics control chart and Fig. 7 of under line model, obtaining are similar, but owing to online a plurality of processing steps of carrying out are in order monitored, and are the figure of a dynamic change therefore.

Preferably; The solid dot that exceeds model control limit to fault score value in the said multivariate statistics control chart; Can also be according to the failure cause quantitative test mode under the off-line mode; Draw the fault score contribution plot and the fault signature data statistic of monitoring sample respectively, the concurrent warning message that is out of order.

Fault score contribution plot that under line model, obtains and fault signature data statistic also can be referring to Fig. 8 and table 3, at this slightly.

In addition, different with off-line mode is, also need in time report to the police to the fault of finding during on-line monitoring, so that the slip-stick artist in time makes corresponding measure, reduces the loss that fault is brought as far as possible.

Step 907 is waited for the monitoring sample of next processing step that receives the model appointment, and is returned step 903.

After this processing step finishes, continue to wait for the execution of the processing step that next one needs to monitor, and utilize corresponding model to carry out the fault analysis of step 903 to 906.For example, suppose that the etching process of wafer relates to 10 processing steps, but only step 3, step 5 and step 9 are carried out on-line monitoring, then when implementing step 3, utilize the MSPC model of corresponding step 3 that the sample data of step 3 is carried out fault analysis; When waiting until execution in step 5 then, utilize the MSPC model of corresponding step 5 that the sample data of step 5 is carried out fault analysis again; At last, wait for again step 9 is carried out failure monitoring.

In sum, the MSPC method for supervising that provides of the embodiment of the invention has the following advantages:

Need to prove that above content only is that example describes with the semiconductor etching process, certainly, the present invention also is applicable to other semiconductor processes except that etching technics.

Based on above content, the present invention also provides corresponding system embodiment.

With reference to Figure 10, be the Process Control System structural drawing in the said a kind of semiconductor technology of the embodiment of the invention.

Said Process Control System comprises that mainly model imports module 1, data reception module 2, model computation module 3 and failure analysis module 4, and wherein, said model imports module 1 and is used to start the Multivariable Statistical Process Control model that needs the monitoring process step corresponding; Data reception module 2 is used for receiving the monitoring sample, and said monitoring sample comprises the said a plurality of process datas that need each variable in the monitoring process step; Model computation module 3 is used for obtaining the corresponding said fault score value that needs the monitoring process step according to said Multivariable Statistical Process Control model and the said a plurality of process datas that need each variable of monitoring process step; Failure analysis module 4 is used to judge whether said fault score value exceeds the control limit of said Multivariable Statistical Process Control model, if, then quantitative test failure cause.

Wherein, said Process Control System provides two kinds of monitoring modes, is respectively monitored off-line pattern and on-line monitoring pattern.Under off-line mode, to a plurality of processing steps that need monitoring in the technological process, it will be that unit imports a plurality of MSPC models with the step that model imports module 1; Data reception module 2 comprises a plurality of monitoring samples that need the monitoring process step with reception; To each processing step, failure analysis module 4 judges respectively whether each fault score value exceeds the pairing control limit that needs the Multivariable Statistical Process Control model of monitoring process step, if, then quantitative test failure cause.

Under the line model, to a plurality of processing steps that need monitoring in the technological process, it also will be that unit imports a plurality of MSPC models with the step that model imports module 1; Data reception module 2 is then according to the execution sequence of processing step; Receive the monitoring sample of certain monitoring step in real time; Model computation module 3 and failure analysis module 4 are utilized corresponding model to calculate and draw out multivariate statistics control chart that should processing step is shown that data reception module 2 is waited for the monitoring sample of (promptly need monitor) next processing step that receives the model appointments then.In addition, said Process Control System can also comprise under the presence: the fault alarm module is used for fault score value when multivariate statistics control chart monitoring sample and exceeds model control and send warning message in limited time.

Above-mentioned semiconductor processes control system can be utilized the incidence relation between a plurality of variablees; The unusual of monitoring sample reflected through the fault score value that is obtained by a plurality of aggregation of variable; Therefore the monitoring to sample just can accurately analyze abnormal conditions through a multivariate statistics control chart of having drawn the fault score value, the fault erroneous judgement the problem includes: of having avoided univariate statistics process control (USPC) problem and the problem of failing to judge; And just can judge guilty culprit easily and quickly through multivariate statistics control chart intuitively; Need not to switch loaded down with trivial detailsly and check a large amount of univariate statistics process control charts; Alleviated operational load greatly; Improve controlling level, further improved the production efficiency and the product yield of semiconductor equipment.

Based on above-mentioned semiconductor processes control system; In order further to analyze out of order basic reason; Preferably; Said failure analysis module 4 further can comprise: first analytic unit, be used for each statistic according to a plurality of process datas of fault signature database and said each variable, and obtain the fault signature data statistic; Said fault signature database is to set up based on the univariate statistics process control technology; Second analytic unit, be used for according to said fault signature data statistic with preset the fault alarm rule, obtain fault score contribution plot, said fault score contribution plot has write down the fault score and the corresponding fault alarm type of each variable; Integerated analytic unit is used to combine said fault score contribution plot and said fault signature data statistic, the quantitative test failure cause.

That is: said Process Control System can also exceed each abnormal monitoring sample of model control limit to fault score value in the said multivariate statistics control chart, draws the fault score contribution plot of abnormal monitoring sample respectively; Wherein, said fault score contribution plot provides univariate fault score value and corresponding univariate fault and the warning message that the abnormal monitoring sample is comprised, specifically can be referring to Fig. 8.

Simultaneously; Said Process Control System can also combine the USPC technology; Each the abnormal monitoring sample that exceeds model control limit to fault score value in the said multivariate statistics control chart; The single argument compute statistics that the abnormal monitoring sample is comprised in conjunction with the univariate statistics process control model, and utilize said statistic to draw the fault signature data statistic of abnormal monitoring sample, specifically can be referring to table 3.The user double-clicks unusual process data position in the multivariate statistics control chart and can open fault score contribution plot and fault signature data statistic simultaneously, intuitively, clearly analyzes the variable break down and the basic reason of fault through this figure and table.

Preferably, for the quantitative analysis failure cause, said Process Control System can also combine said fault score contribution plot with the fault signature data statistic, according to failure cause said fault alarm information is carried out type and divides.This classification can reflect the situation of change of fault variable by quantitative change to qualitative change; Both realized capturing major failure fast with qualitative mode; Realized again can doing further quantitative test to major failure, made fault analysis more comprehensive, the result has more actual directive significance.

Preferably, in order to remove the noise data in the monitoring sample, said Process Control System can also comprise data screening module 5, for the operation of reduced data screening module 5, can also comprise data screening device 6 simultaneously.It is the screening parameter value of all optional variablees under pre-configured each processing step of unit that said data screening device 6 is used for the processing step; Said data screening module 5 is used for according to model specified processing step and named variable; Utilize the data screening device automatically in the processing step of model appointment; The monitoring sample is carried out the screening pre-service of named variable; And the monitoring sample after will screening passes to said model computation module, and the monitoring sample after said model computation module utilizes model to screening calculates.

Preferably, said Process Control System can also comprise: model building module 7 is used to set up the Multivariable Statistical Process Control model.Said model building module 7 further can comprise:

The model dispensing unit is used to set the configuration information of modeling, and said configuration information comprises processing step, data screening parameter, modeling variable, degree of confidence, the pivot computing method of appointment;

The data screening unit is used for according to data screening parameter and modeling variable the training sample under the given process step being screened; Preferably, said data screening unit can utilize data screening device 6 automatic in the processing step of appointment, training sample is carried out the screening pre-service of named variable;

The statistical computation unit is used for the training sample compute statistics after the screening;

The pre-established unit of model is used to utilize statistic, degree of confidence and the pivot computing method of training sample to set up the Multivariable Statistical Process Control model, and the control of definite model limit;

The iteration modeling unit is used for judging whether institute's established model exists the training sample that surpasses the control limit, if do not exist, then preserves the model of being set up; If exist, then remove out-of-limit sample, and utilize remaining training sample to combine statistical computation unit and model pre-established unit circulation to carry out said statistic calculating, modelling and judgement, in institute's established model, there is not the training sample that surpasses the control limit.

In sum; The above-mentioned semiconductor processes control system that has comprised a plurality of preferred module can be based on the Multivariable Statistical Process Control model; And combination univariate statistics process control model; Directly perceived, succinct fault analysis and diagnosis is provided, further improved the control ability of semiconductor equipment.

Based on the said process control system, the embodiment of the invention also provides a kind of semiconductor control system that comprises this Process Control System.

With reference to Figure 11, be the Organization Chart of the said a kind of semiconductor control system of the embodiment of the invention.

Said semiconductor control system comprises apparatus control system and Process Control System; Said apparatus control system is used for the various sensor devices on the CONTROL PROCESS production line; And for Process Control System provides Data Source, said Process Control System promptly refers to system shown in Figure 10.

Figure 11 shows a kind of system diagram based on the distributed design framework, specifically comprises with lower module:

Clustering equipment control system (Cluster Tool Controller, CTC);

The transport module control system (Transfer Module Controller, TMC);

Technological process module controls system (Process Module Controller, PMC);

The factory automation module (Factory Automation, FA);

Data acquisition system (DAS) (Data Acquisition, DA);

The univariate statistics Process Control System (Univariate Statistical Process Control, USPC);

The Multivariable Statistical Process Control system (Multivariate Statistical Process Control, MSPC);

Real-time feedback control system (Run-to-Run Control, R2R);

Database (Data Base, DB).

Wherein, the upper level of control system be factory automation module FA, communicate by letter with factory Manufacturing Executive System MES (Manufacturing Execution System) through plant network; Next is clustering equipment control system CTC, embedded real-time feedback control system R2R among the CTC, same level also have data acquisition system (DAS) DA, USPC and MSPC, database D B; CTC is connecting the technological process module controls PMC of system of subordinate, and PMC is directly controlling the various sensor devices on the production line, like EndPoint Detector, V/I Probe, Other Hardware or the like; PMC and TMC carry out direct communication through LAN and CTC, R2R, DA, USPC and MSPC, database D B.

Based on above-mentioned distributed design framework; Because DA and PMC store all the monitoring samples in the wafer production run among the database D B; The MSPC system can directly be data source with DB, realizes data monitoring and fault analysis based on DB, to satisfy the requirement of control in real time.Therefore, said MSPC system is that a kind of multivariable process control based on the tool level is used, and said tool level promptly refers to MSPC directly towards board control, can obtain real-time process data based on DB and realize data monitoring and fault analysis.

Each embodiment in this instructions all adopts the mode of going forward one by one to describe, and what each embodiment stressed all is and the difference of other embodiment that identical similar part is mutually referring to getting final product between each embodiment.For system embodiment, because it is similar basically with method embodiment, so description is fairly simple, relevant part gets final product referring to the part explanation of method embodiment.

More than to course control method for use and system in a kind of semiconductor technology provided by the present invention; And the semiconductor control system that comprises this Process Control System; Carried out detailed introduction; Used concrete example among this paper principle of the present invention and embodiment are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, the part that on embodiment and range of application, all can change, in sum, this description should not be construed as limitation of the present invention.

Claims

1. the course control method for use in the semiconductor technology is characterized in that, comprising:

2. method according to claim 1 is characterized in that, said quantitative test failure cause comprises:

According to each statistic of a plurality of process datas of fault signature database and said each variable, obtain the fault signature data statistic; Said fault signature database is to set up based on the univariate statistics process control technology;

According to said fault signature data statistic with preset the fault alarm rule, obtain fault score contribution plot, said fault score contribution plot has write down the fault score and the corresponding fault alarm type of each variable;

In conjunction with said fault score contribution plot and said fault signature data statistic, quantitative test failure cause.

3. method according to claim 1 is characterized in that, under off-line state, comprising:

Reception comprises a plurality of monitoring samples that need the monitoring process step;

Each needs the fault score value of monitoring process step to obtain correspondence respectively;

Judge respectively whether each fault score value exceeds the pairing control limit that needs the Multivariable Statistical Process Control model of monitoring process step, if, then quantitative test failure cause.

4. method according to claim 1 is characterized in that, under presence, said reception monitoring sample is specially: reception in real time comprises a certain monitoring sample that needs the monitoring process step.

5. according to claim 3 or 4 described methods, it is characterized in that,, set up the multivariate statistics control chart through adding up the same fault score value that needs the monitoring process step in each silicon chip etching technology process.

6. according to the arbitrary described method of claim 1 to 4, it is characterized in that said startup needs also to comprise: set up the Multivariable Statistical Process Control model, specifically comprise before the corresponding Multivariable Statistical Process Control model of monitoring process step:

Set the configuration information of modeling, said configuration information comprises processing step, data screening parameter, modeling variable, degree of confidence, the pivot computing method of appointment;

Screen according to data screening parameter and modeling variable a plurality of process datas each variable in the processing step specified in each training sample;

To the training sample compute statistics after the screening;

Utilize statistic, degree of confidence and the pivot computing method of training sample to set up the Multivariable Statistical Process Control model, and the control of definite model limit;

Judge whether there is the training sample that surpasses the control limit in institute's established model,, then preserve the model of being set up if do not exist; If exist, then remove out-of-limit sample, and utilize remaining training sample circulation to carry out said statistic calculating, modelling and determining step, in institute's established model, there is not the training sample that surpasses the control limit.

7. method according to claim 6 is characterized in that, said screening comprises:

Utilize the data screening device automatically in the processing step of appointment, training sample is carried out the screening pre-service of named variable; Wherein, said data screening device with the processing step be unit pre-configured the screening parameter value of all optional variablees under each processing step.

8. the Process Control System in the semiconductor technology is characterized in that, comprising:

9. system according to claim 8 is characterized in that, said failure analysis module comprises:

First analytic unit is used for each statistic according to a plurality of process datas of fault signature database and said each variable, obtains the fault signature data statistic; Said fault signature database is to set up based on the univariate statistics process control technology;

Second analytic unit, be used for according to said fault signature data statistic with preset the fault alarm rule, obtain fault score contribution plot, said fault score contribution plot has write down the fault score and the corresponding fault alarm type of each variable;

Integerated analytic unit is used to combine said fault score contribution plot and said fault signature data statistic, the quantitative test failure cause.

10. system according to claim 9 is characterized in that, also comprises:

Model building module is used to set up the Multivariable Statistical Process Control model.

11. system according to claim 10 is characterized in that, said model building module comprises:

The data screening unit is used for screening according to data screening parameter and the modeling variable a plurality of process datas to each variable in the specified processing step of each training sample;

12. according to Claim 8 or 11 described systems, it is characterized in that, also comprise:

The data screening device, being used for the processing step is the screening parameter value of all optional variablees under pre-configured each processing step of unit;

The data screening module; Be used for according to model specified processing step and named variable; Utilize the data screening device automatically in the processing step of model appointment; The monitoring sample is carried out the screening pre-service of named variable, and the monitoring sample after will screen passes to said model computation module, said model computation module utilizes model that the monitoring sample after screening is calculated.

13. a semiconductor control system comprises apparatus control system and Process Control System, it is characterized in that: said Process Control System comprises the arbitrary described Process Control System of claim 8 to 12.

14. system according to claim 13 is characterized in that, also comprises:

Data acquisition system (DAS) is used for the monitoring sample of collecting device control system institute control equipment at art production process, and stores in the database;

Database is used for storage monitoring sample.

15. system according to claim 14 is characterized in that:

Said Process Control System is a data source with said database, directly receives the monitoring sample from this database.