CN1521824A - Online quality detecting parametric analysis method - Google Patents
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Abstract
An online quality detection parametric analysis method comprises the steps of, analyzing the online quality detecting parameter value meets the predetermined specifications, searching the sample testing items or chip testing items relating to online quality detecting items from the data base, searching the testing parameter values items corresponding to each batch of inventory products from the data base, generating the relational expression between the online quality detecting items and the sample testing items, or the relational expression between the online quality detecting items and the chip testing items.
Description
Technical field
The present invention relates to a kind of process parameter analytical method, relate in particular to a kind of analytical method of online Quality Detection parameter.
Background technology
In semiconductor fabrication, finish semiconductor product, to pass through many processing procedures, for example micro-photographing process, etch process, ion implantation manufacture process etc. usually; That is to say in semiconductor fabrication to be applied to a large amount of boards, and many red tapes.Therefore, the people who is familiar with this technology is devoted to guarantee that the board running is normal, keep or improve the product yield, operations such as problem points and board maintenance are confirmed in detecting, so that the speed of production of semiconductor product and quality can conform with customer demand.
Generally speaking, inquire into the problem of manufacture of semiconductor, can comprise process parameter data, online Quality Detection (In-line QC) data, defects detection (defect inspection) data, sample test (sample test) data, wafer sort (wafer test) data and encapsulation back test (final test) data from following several item numbers according to setting about analyzing.Wherein, online Quality Detection data are to carry out the resulting test value of online Quality Detection at wafer (wafer in process), and it is carried out after being arranged in some processing procedure usually.
In the prior art, as shown in Figure 1, at first carry out step 101, know the operator can carry out the test of every online Quality Detection project at each wafer this moment, as the thickness detection etc.
Then, in step 102, know the result that the operator can observe the every online Quality Detection project of each wafer, so that find out online Quality Detection result product devious, the wherein observed not performed project in online Quality Detection project normally last processing procedure station.
At last, in step 104, know processing procedure station that the operator checks that step 103 judges each board in not, so that find out unusual board.For example, know the operator and can detect substandardly, judge that the deposition manufacture process station that problematic processing procedure station Wei silicon nitride layer is other, and check out unusual board, as deposition machine, etching machine etc. according to the thickness of silicon nitride layer.
In addition, as shown in Figure 2, know the yield that result that the operator can also utilize online Quality Detection project predicts successive process, in the hope of can effectively utilizing prepared semi-finished product.
At first, step 201 can carry out the test of every online Quality Detection project at each wafer, as the thickness detection etc.Then in step 202, know the result that the operator can observe the every online Quality Detection project of each wafer, so that find out the product that online Quality Detection result does not meet the specification.
When the online Quality Detection project of finding product had deviation, knowing the operator can have two kinds of processing modes: it is just like shown in the step 203, promptly directly this batch product is considered as defective products and gives up; It is two shown in step 204, promptly rule of thumb whether this batch of prediction product can be by follow-up sample test or wafer sort earlier, when predicting that answer is negative, carry out step 203, and when predicting that answer is sure, carry out step 205 and keep this batch product, so that carry out follow-up processing procedure and test.
Yet, because prior art is to utilize artificial experience to judge to decide analysis result (step 103), or the follow-up test result (step 204) of prediction product, so the result's that ultimate analysis is come out accuracy and confidence level will remain to be discussed; Adding the semiconductor manufacturing industry personage changes frequently, experience before and after causing between the phase engineer is difficult for succession, and each engineer is limited in one's ability, can't take into account the mode of operation of all boards of plant area, so, when the test result of semiconductor product takes place when unusual, the engineer may not be certain to have enough experiences fast and to judge rightly out be which link goes wrong, or correctly predict the yield of product in successive process, thereby may must expend many times and carry out correlative study, even might do the judgement that makes mistake, so, not only reduce the efficient of processing procedure, increase production cost, also can't in time improve the online production situation to improve yield.
Therefore, how providing a kind of can take place in the online Quality Detection data of semiconductor product when unusual, fast and to judge rightly out be which link goes wrong and correctly dopes the analytical method of product at the yield of successive process, be one of important topic of current semiconductor fabrication.
Summary of the invention
In order to overcome the above-mentioned defective of prior art, the object of the present invention is to provide a kind of can the generation in the online Quality Detection data of semiconductor product when unusual, fast and to judge rightly out be the online Quality Detection parameters analysis method which link goes wrong.
Another object of the present invention is to provide a kind of can the generation correctly to dope the online Quality Detection parameters analysis method of semiconductor product when unusual at the yield of successive process in the online Quality Detection data of semiconductor product.
The invention is characterized in, utilize online Quality Detection result and wafer test parameters value and sample test parameter value to set up the processing procedure project of successive process and the relational expression of online Quality Detection project, and analyze the not discrepant board in each processing procedure station with the result of online Quality Detection.
For achieving the above object, online Quality Detection parameters analysis method of the present invention is to analyze the many batches of products that have a lot number respectively, every batch of product is obtained through a plurality of boards, and a slice in the every batch of product or above wafer at least through the test of an online Quality Detection project to produce an online Quality Detection parameter value, online Quality Detection project and the sample test project relevant with online Quality Detection project and wafer sort items storing are in a database, this database also stores the every test event and the test parameter value of the high inventory of online Quality Detection parameter value and the many batches of output yields, and this method may further comprise the steps:
Analyze online Quality Detection parameter value and whether conform with a default specification;
When online Quality Detection parameter value does not conform with default specification, from database, search sample test project or the wafer sort project relevant with online Quality Detection project;
According to online Quality Detection project and sample test project that is searched or wafer sort project, from database, search the test parameter value of corresponding each batch inventory; And
Search result according to inventory produces the relational expression of online Quality Detection project and sample test project or the relational expression of online Quality Detection project and wafer sort project.
From the above, online Quality Detection parameters analysis method according to the present invention is to be that control group is set up the processing procedure project of successive process and the relational expression of online Quality Detection project with the high product of follow-up process rate, so can take place in the online Quality Detection data of semiconductor product correctly to dope the yield of semiconductor product when unusual in successive process; The present invention analyzes the not discrepant board in each processing procedure station with online Quality Detection result in addition, thus can take place in the online Quality Detection data of semiconductor product when unusual, fast and to judge rightly out be which link is out of joint.Therefore can reduce the mistake of artificial judgement effectively, thereby improve efficient, the minimizing production cost of processing procedure and in time improve the online production situation to improve yield.
Description of drawings
Fig. 1 shows the flow chart of existing online Quality Detection parameters analysis method;
Fig. 2 shows the flow chart of another existing online Quality Detection parameters analysis method;
Fig. 3 shows the flow chart of the online Quality Detection parameters analysis method of preferred embodiment according to the present invention;
Fig. 4 shows the flow chart of the online Quality Detection parameters analysis method of another preferred embodiment according to the present invention, causes the not good board of yield which is in order to judgement;
Fig. 5 shows the flow chart of the flow chart that continuity is shown in Figure 4;
Fig. 6 shows the flow process of the online Quality Detection parameters analysis method of another preferred embodiment according to the present invention, uses the relational expression in the hope of various kinds product test event and all online Quality Detection projects;
Fig. 7 shows the flow process of the online Quality Detection parameters analysis method of another preferred embodiment according to the present invention, in order to find out other preferable operating condition of each processing procedure station.
Symbol description among the figure
The flow process of 101～104 existing online Quality Detection parameters analysis methods
The flow process of 201～205 another existing online Quality Detection parameters analysis methods
The online Quality Detection parameter analysis side of 301～307 preferred embodiments of the present invention
The flow process of method
The online Quality Detection parameter branch of 401～406 another preferred embodiments of the present invention
The flow process of analysis method
The flow process of 501～504 continuity steps 409
The online Quality Detection parameter branch of 601～603 another preferred embodiments of the present invention
The flow process of analysis method
The online Quality Detection parameter branch of 701～704 another preferred embodiments of the present invention
The flow process of analysis method
Embodiment
Following conjunction with figs. illustrates the online Quality Detection parameters analysis method of the preferred embodiment according to the present invention, and wherein identical assembly will be represented with identical reference marks.
As shown in Figure 3, at first, in step 301, the online Quality Detection parameters analysis method of preferred embodiment according to the present invention, search the several batches of online Quality Detection of process earlier and do not pass through sample test and the product of wafer sort, to obtain each online Quality Detection parameter value of these products.In the present embodiment, the search result of this step can directly be exported to the engineer, so that the engineer can obtain relevant information, and for example batch number of these products, production time, each online Quality Detection parameter value etc.
Whether the obtained online Quality Detection parameter value of step 302 analysis conforms with default specification then.For example, suppose that online Quality Detection project is the oxide layer thickness, its default specification is 30 to 60 μ m, and the online Quality Detection parameter value of the oxide layer thickness of the product of being analyzed is 50 μ m, then the result who is analyzed is for conforming with default specification, this moment constipation beam analysis program; If the online Quality Detection parameter value of the oxide layer thickness of the product of being analyzed is 90 μ m, then the result who is analyzed is not for conforming with default specification, and at this moment, online Quality Detection parameters analysis method according to the present invention just then carry out step 303.In addition, know this operator and should understand, the online Quality Detection project that step 302 is analyzed can also comprise silicon nitride layer thickness, polysilicon layer thickness etc.
Then, step 303 is searched sample test project or the wafer sort project relevant with online Quality Detection project from an experience cumulative data storehouse, this experience cumulative data storehouse records senior engineer and followed the trail of the experience that problem is accumulated in the past, judge that online Quality Detection project is unusual, may cause which sample test project or which wafer sort project result's deviation.For example, when the online Quality Detection parameter value of the oxide layer thickness of the product of analyzing when step 302 does not conform with default specification, this step can be searched the sample test project that online therewith Quality Detection project (oxide layer thickness) has correlation from database, as the capacitance characteristic project.
Afterwards, step 304 judges whether the sample test project or the wafer sort project that are searched are zero in step 303, if zero, then stop to analyze action.If it is non-vanishing, then carry out step 305, so that according to sample test project or the wafer sort project that online Quality Detection project and the step 303 that do not conform with default specification in the step 302 are searched, corresponding online Quality Detection project and the sample test project or the wafer sort project of the inventory that several batches of output yields of search are high from database.For example, if the sample test project analyzed of step 302 is the oxide layer thickness, be the capacitance characteristic project and step 303 searched, then this step is searched oxide layer thickness, capacitance characteristic project and the parameter value thereof of inventory.In the present embodiment, inventory is meant other by sample test or wafer sort project, and its test result and yield are good quality production.
After the online Quality Detection project that hunts out inventory and sample test project or wafer sort project, step 306 facility is tried to achieve the relational expression of this online Quality Detection project and sample test project or wafer sort project with statistical method.In the present embodiment, this step utilizes the linear regression mode to try to achieve the relational expression of online Quality Detection project and sample test project or wafer sort project; Be shown below:
Sample test parameter value or wafer test parameters value=a * online Quality Detection parameter value+b (1)
For example, if the sample test project analyzed of step 302 is the oxide layer thickness, be the capacitance characteristic project and step 303 searched, then following formula (1) is to be rewritten into as shown in the formula shown in (2):
Capacitance characteristic parameter value=a * oxide layer thickness parameter value+b (2)
Therefore, the relational expression of trying to achieve according to step 306, just the engineer can be easily and utilize the result of online Quality Detection to predict the test result of this batch product exactly in subsequent sample test event or wafer sort project, shown in step 307.For example, when in step 306, trying to achieve formula (2), then in step 307, the engineer just can analyze step 302 the online Quality Detection parameter value that does not conform with default specification, as oxide layer thickness parameter value, in the substitution formula (2), so that try to achieve the predicted value of capacitance characteristic parameter value.Therefore, the engineer just can be according to the predicted value of this capacitance characteristic parameter value, judge that this batch product can be by follow-up sample test, and then whether decision will transfer this batch product and continue next processing procedure, or the calcellation of this batch product will have been wasted manufacturing cost to avoid follow-up yield to cross low.
In addition, the online Quality Detection project of online Quality Detection parameters analysis method system's analysis of another preferred embodiment causes with judgement which the not good board of yield is according to the present invention, as shown in Figure 4.At first step 401 is searched the several batches of online Quality Detection of process earlier and is not passed through sample test and the product of wafer sort, to obtain each online Quality Detection parameter value of these products.In the present embodiment, the search result of this step can directly be exported to the engineer, so that the engineer can obtain relevant information, and for example batch number of these products, production time, each online Quality Detection parameter value etc.
The step 402 several batches of products that will meet the specification of online Quality Detection project are set at the A set product then, for example comprise lot number 1,2,3,4, and 5 (shown in steps 403); And several batches of products that will not meet the specification of online Quality Detection project are set at the B set product, for example comprise lot number 6,7,8,9, and 10 (shown in steps 404).
Wherein, each batch (lot) product has a lot number (lot number), and every batch of product includes 25 wafer, and the every batch of product is through a plurality of boards of multiple tracks processing procedure.With regard to an online Quality Detection project, project A for example is that a certain depositional coating (layer) to a slice in a collection of product or above wafer carries out thickness and detects, and obtains the thickness parameter values for detection of this depositional coating.All be provided with a control test (control spec) for each depositional coating.When the thickness parameter values for detection of this depositional coating conforms with control test, then at last by this online Quality Detection project; And when the thickness parameter values for detection of this depositional coating does not conform with control test, then be to pass through this online Quality Detection project, and in this stage, the online Quality Detection project A of this wafer is defective (fail) at last just.
In step 405, will from an experience cumulative data storehouse, go to search relevant information available.According to experience in the past, when senior engineer follows the trail of problem, can judge according to its experience: " when online Quality Detection project A is defective; may be relevant? " with which kind of reason its answer is normally: " should go to follow the trail of not relevant with which processing procedure station? " so, this experience cumulative data storehouse is imported this system by senior engineer with its experience, judges the direction of following the trail of the path automatically in order to a kind of computer to be provided.Certainly, this experience cumulative data storehouse also can be upgraded by computer self, and the experience that is obtained in the contingency question tracing process is stored so far in the experience cumulative data storehouse voluntarily.
Referring to step 406, behind step 405 search experience cumulative data storehouse, show that when online Quality Detection project A was defective, the project that should follow the trail of was that a certain processing procedure station is other, be connected to the flow process of Fig. 5 to carry out subsequent step this moment.
Please refer to shown in Figure 5ly, in step 501, search earlier tracked processing procedure station and do not comprise which board, for example E1, E2, E3 ...Then, step 502 is calculated the probability of these other these boards of processing procedure station of process in the B set product.In addition, step 503 is calculated the probability of A set product through these other these boards of processing procedure station.Then, in step 504, utilize intercommunity to analyze gimmick, find out the B set product through the higher board of probability.These B set products of being tried to achieve by step 504 pass through the higher board of probability, the possible problematic board that the online Quality Detection parameters analysis method of preferred embodiment is analyzed according to the present invention exactly.
As shown in Figure 6, the online Quality Detection parameters analysis method of another preferred embodiment according to the present invention, analyze online Quality Detection project, relational expression in the hope of each sample test event and all online Quality Detection projects, so that allow the engineer can after production process in, according to this relational expression estimate and the result of the every online Quality Detection of keyholed back plate to the result's of sample test influence.
At first, step 601 is searched the product of several batches of online Quality Detection of process and sample test earlier, with each online Quality Detection parameter value and each the sample test parameter value that obtains these products.Then step 602 utilizes statistical to try to achieve the relational expression of each sample test event and these online Quality Detection projects.In this example, the relational expression of each sample test event and these online Quality Detection projects can be to utilize the multiple regression mode to produce.For example, the electric capacity of wafer is made of an oxide layer, a silicon nitride layer and a polysilicon layer in regular turn, so the characteristic of electric capacity is relevant with the thickness of the oxide layer, silicon nitride layer and the polysilicon layer that constitute this electric capacity, therefore, utilize relational expression that multiple regression method (multiple regression) tried to achieve as the formula (3) in this step:
Capacitance characteristic parameter value=a * oxide layer thickness parameter value+
B * silicon nitride layer thickness parameter value+
C * polysilicon layer thickness parameter value (3)
Should be noted that, each sample test event and these online Quality Detection project relationship formulas that this step can also utilize other multiple regression mode to try to achieve, for example progressively the Return Law (stepwiseregression), the back-and-forth method of advancing (forward), retreat elimination approach (backward) etc.
In addition, present embodiment can also utilize residual plot (residual plot) to inspect the relational expression (step 603) of each sample test event and these online Quality Detection projects, therefore, the engineer can observe this residual plot so that judge the whether realistic use of this relational expression apace.
In addition, in the online Quality Detection parameters analysis method of another preferred embodiment of the present invention (as shown in Figure 7), it analyzes several batches of products through online Quality Detection and sample test, so that utilize to analyze other the preferable operating condition of processing procedure station before gimmick is found out online Quality Detection, and feed back to this processing procedure station not so that as the process conditions of subsequent product.
At first, step 701 is searched the product of several batches of online Quality Detection of process and sample test earlier, with each online Quality Detection parameter value and each the sample test parameter value that obtains these products.Then step 702 is divided into several groups according to the grade of each online Quality Detection project with each sample test event; For example, oxide layer thickness, silicon nitride layer thickness and polysilicon layer thickness are divided into Three Estate (high, medium and low) respectively, then with each product assortment, for example, higher if the mean value of the oxide layer thickness parameter value of a collection of product and control test is in a ratio of, during the mean value of its silicon nitride layer thickness parameter value and control test is in a ratio of partially, the mean value of its polysilicon layer thickness parameter value and control test is in a ratio of on the low side, then this batch product can be classified in (height, in, low) group; If the oxide layer thickness parameter value of a collection of product and the mean value of control test are in a ratio of on the low side, the mean value of its silicon nitride layer thickness parameter value and control test is in a ratio of on the low side, the mean value of its polysilicon layer thickness parameter value and control test is in a ratio of higher, then this batch product can be classified in (low, low, height) group.
Then, whether the sample test parameter value of product was variant between step 703 was analyzed and respectively organized.Whether in the present embodiment, this step utilizes the ANOVA analytic approach to analyze to try to achieve the sample test parameter value of product between each group variant.Do not have difference if find the sample test parameter value of product between each group, then finish analysis process; If find that the sample test parameter value of product between each group is variant, then carry out step 704.
Step 704 utilizes sample test parameter value which group the polynary interval method of testing of Duncan (Duncan ' s multiple rangetest) analyze near the desired value (target) of control test.In the present embodiment, each product group of being distinguished utilizes box figure (box plot) to represent, and this step is analyzed default specification or control test that each online Quality Detection parameter value of the group that obtains can be used as the subsequent production processing procedure.For example, if it is (low, low, height) product the putting up the best performance of group in sample test, then after in the production process, can advise that default value with the oxide layer thickness of product is decided to be the mean value a little less than control test, the default value of its silicon nitride layer thickness is decided to be the mean value a little less than control test, and the default value of its polysilicon layer thickness system is decided to be the mean value a little more than control test.
In sum, because according to online Quality Detection parameters analysis method of the present invention is to be that control group is set up the processing procedure project of successive process and the relational expression of online Quality Detection project with the high product of follow-up process rate, so can take place in the online Quality Detection data of semiconductor product correctly to dope the yield of semiconductor product when unusual in successive process; In addition, the present invention is that control group is to analyze the not discrepant board in each processing procedure station with online Quality Detection product up to specification, so can take place in the online Quality Detection data of semiconductor product when unusual, fast and to judge rightly out be which link is out of joint.The mistake that therefore can reduce artificial judgement effectively improves the efficient of processing procedure, reduces production cost, and in time improves the online production situation to improve yield.
The above only is for example, and is not limitation of the present invention.Anyly do not break away from spirit of the present invention and category, and, all should be contained in claims of the present invention its equivalent modifications of carrying out or change.
Claims (13)
1, a kind of online Quality Detection parameters analysis method, in order to analyze the many batches of products that have a lot number respectively, these many batches of products are obtained through a plurality of boards, and a slice in the every batch of product or above wafer at least through the test of an online Quality Detection project to produce an online Quality Detection parameter value, this online Quality Detection project and a sample test project relevant with this online Quality Detection project and a wafer sort items storing are in a database, this database also stores the every test event and the test parameter value of the high inventory of this online Quality Detection parameter value and the many batches of output yields, and this online Quality Detection parameters analysis method comprises:
Analyze this online Quality Detection parameter value and whether conform with default specification;
When this online Quality Detection parameter value does not conform with this default specification, from this database, search this sample test project and this wafer sort project wherein at least one relevant with this online Quality Detection project;
According to this online Quality Detection project and this sample test project that is searched and wherein at least one of this wafer sort project, from this database, search the test parameter value of corresponding these many batches of inventories; And
The search result of these many batches of inventories of foundation produces the relational expression of this online Quality Detection project and this sample test project, and wherein at least one of relational expression of this online Quality Detection project and this wafer sort project.
2, online Quality Detection parameters analysis method as claimed in claim 1 is characterized in that, these many batches of products do not pass through sample test processing procedure and wafer sort processing procedure.
3, online Quality Detection parameters analysis method as claimed in claim 1, it is characterized in that the relational expression of the relational expression of this online Quality Detection project and this sample test project and this online Quality Detection project and this wafer sort project is to utilize the linear regression mode to produce.
4, online Quality Detection parameters analysis method as claimed in claim 1 is characterized in that this method also comprises:
According to not conforming with this online Quality Detection parameter value of this default specification and the relational expression of this online Quality Detection project and this sample test project, predict the result of the sample test of these many batches of products.
5, online Quality Detection parameters analysis method as claimed in claim 1 is characterized in that this method also comprises:
According to not conforming with this online Quality Detection parameter value of this default specification and the relational expression of this online Quality Detection project and this wafer sort project, predict the result of the wafer sort of these many batches of products.
6, online Quality Detection parameters analysis method as claimed in claim 1, it is other that this database also stores the processing procedure station relevant with this online Quality Detection project, it is characterized in that, more comprises:
According to should default specification being that benchmark should many batches of product differentiations be two product groups, these two product groups comprise one up to specification group and one group that falls short of specifications;
It is other to search this processing procedure station relevant with this online attribute test project from this database;
Search the board of its other process respectively at this processing procedure station according to the batch number of these two product groups; And
Judge that this product of substandard group is higher than this up to specification group board through probability through probability.
7, online Quality Detection parameters analysis method as claimed in claim 6 is characterized in that, it utilizes intercommunity analysis gimmick to judge that the product of residue lot number in this low yield product group is higher than the board of this high yield product group through probability through probability.
8, online Quality Detection parameters analysis method as claimed in claim 1 is characterized in that this method also comprises:
After these many batches of products are more through a sample test processing procedure, search each sample test event and each online Quality Detection project of these many batches of products; And
Produce the relational expression of each sample test event and each online Quality Detection project according to this search result.
9, online Quality Detection parameters analysis method as claimed in claim 8 is characterized in that, the relational expression of each sample test event and each online Quality Detection project is utilized the multiple regression mode or the mode that progressively returns produces or utilize residual plot to represent.
10, online Quality Detection parameters analysis method as claimed in claim 1 is characterized in that this method also comprises:
After these many batches of products are more through a sample test processing procedure, search each sample test event and each online Quality Detection project of these many batches of products;
The parameter value of each online Quality Detection project of these many batches of products of foundation should many batches of product differentiations be many batches of product groups;
Whether the sample test parameter value of analyzing between each product group is variant; And
When variant, analyze, obtain and have the product group of the sample test parameter value of approaching default specification.
11, online Quality Detection parameters analysis method as claimed in claim 10, it is characterized in that, whether its sample test parameter value that utilizes the ANOVA analytic approach to analyze between each product group is variant, or utilizes the polynary interval method of testing of Duncan to analyze, obtain the product group of the sample test parameter value with the most approaching this default specification.
12, online Quality Detection parameters analysis method as claimed in claim 10 is characterized in that, many batches of product group systems of this that distinguished utilize a box figure to represent.
13, online Quality Detection parameters analysis method as claimed in claim 10 is characterized in that, the online Quality Detection parameter value of each of the product group of analyzing, obtaining is the default specification as a subsequent product.
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| CN100394426C (en) * | 2004-12-03 | 2008-06-11 | 台湾积体电路制造股份有限公司 | Event fineness rate associated analysis system and method and computer readable memory medium |
| CN100427963C (en) * | 2005-02-24 | 2008-10-22 | 台湾积体电路制造股份有限公司 | Semiconductor test management system and method |
| CN100440092C (en) * | 2004-08-27 | 2008-12-03 | 株式会社日立制作所 | Quality control system for manufacturing industrial products |
| CN100495678C (en) * | 2004-10-13 | 2009-06-03 | 台湾积体电路制造股份有限公司 | Semiconductor test management system and method |
| CN101112776B (en) * | 2006-07-27 | 2010-12-15 | 中芯国际集成电路制造(上海)有限公司 | Instrument for accurate cutting of crystal round examples and method of use thereof |
| CN101452027B (en) * | 2007-11-30 | 2011-08-03 | 中芯国际集成电路制造(上海)有限公司 | Wafer shipment quality guarantee detecting method |
| CN102117731B (en) * | 2009-12-31 | 2013-01-02 | 中芯国际集成电路制造(上海)有限公司 | Method and device for monitoring measurement data in process production flow of semiconductor |
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2003
- 2003-01-28 CN CN 03102252 patent/CN1279600C/en not_active Expired - Fee Related
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| CN109767996A (en) * | 2018-12-29 | 2019-05-17 | 上海华力微电子有限公司 | Wafer defect analysis system and analysis method |
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| CN111667111B (en) * | 2020-06-02 | 2023-04-07 | 上海哥瑞利软件股份有限公司 | Yield prediction method in integrated circuit wafer manufacturing |
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