CN101178941B - Method for dynamically estimating memory body characteristic ineffective cause of defect - Google Patents
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- CN101178941B CN101178941B CN2007101570017A CN200710157001A CN101178941B CN 101178941 B CN101178941 B CN 101178941B CN 2007101570017 A CN2007101570017 A CN 2007101570017A CN 200710157001 A CN200710157001 A CN 200710157001A CN 101178941 B CN101178941 B CN 101178941B
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Abstract
The invention discloses a method of the invalidation of memory characteristics due to dynamic estimation defects, which includes the following steps: (1) creating a database in which the invalidation of design layout geometry and the invalidation of memory characteristics are corresponding; (2) judging the invalidation of layout geometry due to the defects in production line; (3) judging the invalidation of memory characteristics corresponding to the characteristics of geometry invalidation; (4) judging whether a whole memory chip is a finished product. The invention preliminarily judges whether the memory is a finished product or not by utilizing the method in which the invalidation of characteristics due to the production defects in memory is dynamically preestimated by the combination of memory design layout and the detection result of the defects in production line and by combining the available restoration resources. The method of the invention can analyze defects dynamically and instantly, and estimate whether the memory is a finished product or not before being performed electrical measurement, which can apply to dynamically preestimating the invalidation of memory characteristics due to the defects detected in the memory production line in manufacturing of integrated circuit.
Description
Technical field
The present invention relates to integrated circuit and make the field, especially relate to a kind of method of dynamically estimating the memory body feature inefficacy that detected defective caused on the memory body production line.
Background technology
Traditionally the dynamic instant analysis of the defective on the semiconductor production line is not comprised the relation between defective and the design layout is analyzed mutually.Usually when obtaining defective data after from checkout equipment, will to some defective make scanning type electron microscope (SEM) thus further analysis attempt definite its composition and source.The defect part of doing scanning type electron microscope sem analysis is seldom, because scanning type electron microscope sem analysis is bothersome time-consuming.
For memory circuit, finish that feature lost efficacy (Bit map) and all electricity performance measurement after, did the position between sometimes defective data and feature being lost efficacy and overlap and analyze, attempt to determine which kind of defective or reason cause this feature inefficacy.But these analyses all are the analyses of doing after production is finished, and are not dynamically the defective on the line to be made instant analysis.
If can do corresponding analysis to the defective in the memory body inside and its position on design layout, just can judge this defective and may cause which kind of domain defective, and then can know the importance of this defective, thereby be SEM can for which defective guidance is provided. and can estimate from graphic defects whether the feature that this defective causes lost efficacy, so can just can estimate the memory body finished product before the measurement of carrying out electricity.
So such method can be made instant analysis to the defective of memory circuit production, thereby find the reason of defective generation more effectively and reduce defective apace and the raising yield rate.
Summary of the invention
The invention provides a kind of method that detected defective caused on the memory body production line memory body feature lost efficacy of dynamically estimating, is to realize by detected defective on the circuit layout of memory body and the production line is done dynamically to analyze.
A kind of method of dynamically estimating memory body characteristic ineffective cause of defect in turn includes the following steps:
1. occur in the kind that the various domain figures that may the described defective of failure predictions may cause at various possible positions place in the memory circuit lost efficacy by simulation, the corresponding memory body feature of definite and described domain figure inefficacy lost efficacy, and set up the database of corresponding relation between described domain figure inefficacy of storage and the inefficacy of memory body feature;
At first the domain element in the memory circuit is identified with layer and contact hole type, the defective of simulating the different size size with the method for Monte Carlo every layer contingent various may situations, thereby obtain all kinds that various possible figures outages and electric leakage were lost efficacy.
Memory circuit is dimeric; Address decoding circuitry and the amplifying circuit of a part around being, i.e. X address decoder and Y address demoder, they are used for giving an address to memory cell, and write and read individually; Another part is a memory cell, is repeated to form by single unit, and the minimum unit of each memory body is identical. in memory body inside, some is used for repairing the inefficacy circuit.They are the same with other memory body, just are not connected into circuit.Behind the element failure of inside, this part just can be used for repairing.
This structures shape of memory body the determinacy of its failure characteristics, and its failure characteristics and its internal circuit domain figure failure characteristics have correspondence. can occur in the different of the position of circuit and design level according to defective, predict the feature of the circuit layout figure inefficacy that this defective may cause, and then predict the kind of the feature inefficacy that this defective may cause.
Any figure failure conditions will cause the feature of certain memory body to lose efficacy, so just can set up a figure inefficacy defective and the memory body feature corresponding database that lost efficacy.
2. determine the position of described defective on the design layout of correspondence according to the precision information of the position of size, position and the defective of defective, the domain figure that is caused according to the described defective of position judgment on the described design layout lost efficacy;
According to the size of defective, the trueness error that the situation of position binding deficient on design layout obtains the position of defective, and hypothesis certain position distribution function, trueness error and the certain position distribution function according to the position calculates the type of possible domain figure inefficacy and the probability that described domain figure lost efficacy and takes place then.The described type that calculates possible domain figure inefficacy according to the trueness error and the certain position distribution function of position is meant according to the trueness error and the certain position distribution function of position judges type and the probability that certain domain figure lost efficacy and takes place.
It it should be noted that because defective locations has certain precision, that is to say that its position is a scope, so when analyzing, may not be unique that the figure that defective caused lost efficacy.If suppose certain position distribution function, not only can know the figure failure type that this defective may cause, also can know the probability that this kind figure lost efficacy and takes place.
3. use the described database of setting up in the step (1) judge the domain figure lost efficacy corresponding memory body feature lost efficacy with and whether can be repaired;
After making comparisons with database, just can infer the kind that feature that this defective may cause lost efficacy.Resource is certain or suppose under the situation of certain reparation resource repairing, and can determine to repair resource and whether can repair every kind of feature and lost efficacy; Can be repaired the resource reparation if this feature loses efficacy, it is recoverable that this feature inefficacy then is listed in; If the existing resource of repairing of the inefficacy of a certain feature can not be repaired, this feature lost efficacy and then is listed in and can not repairs.4. lost efficacy whether can be repaired according to the memory body feature described in the step (3) and judged whether finished product of whole memory body chip.
Judge whether finished product is meant after the defective of all technological processs is made analysis memory body, set up the chart that domain figure lost efficacy,, estimate whether finished product of this memory body chip according to what and the kind of repairing resource.
After the feature failure analysis that all defectives are caused is finished, can not repair if there is any feature to lose efficacy, then this memory body may not can finished product; If it all is recoverable that all features lost efficacy, to see then whether repair resource can repair all features and lost efficacy, if can repair, this memory body may finished product; Repair all feature inefficacies inadequately if repair resource, then this memory body may lose efficacy.
The feature that the present invention utilizes defects detection result on memory body design layout and the production line to combine dynamically to estimate the production defective that drops on memory body inside to be caused lost efficacy, and did whether tentatively judge this memory body finished product in conjunction with available reparation resource.The kind that memory body domain figure lost efficacy and the feature fail category of memory body are corresponding, therefore can by the analogy method of Monte Carlo (MONTOCARLO) set up that a domain figure lost efficacy and the inefficacy of memory body feature between correspondence database.By dynamically to the size of defective, size with and the position analysis that drops on memory body inside, just can infer the domain figure failure type that this defective may cause, and then and the graphic data base set up relatively predict the classification of the feature inefficacy that this defective institute may cause.
After all defects detection and analysis are finished, just can with repair resource and judge relatively whether this memory circuit can be finished product.The inventive method has the following advantages:
(1) can carry out dynamically instant analysis to defective;
(2) can before electrical measurement, whether estimate finished product.
Description of drawings
Fig. 1 is the process flow diagram of the inventive method;
Fig. 2 is a process flow diagram of setting up the database of graphic defects and feature inefficacy;
Fig. 4 is the example of the inner M1 pattern identification of memory body;
Fig. 5 is the example that M1 outage defective takes place;
Fig. 6 is POLY to the contact hole of the M1 domain in memory body inside;
Fig. 7 is the example 1 (element failure) of POLY to the contact hole type identification of M1;
Fig. 8 is that POLY is to the contact hole type of M1 and the corresponding example (Unit 2 of going together lost efficacy simultaneously) of feature inefficacy;
Fig. 9 is the synoptic diagram (the M1 line is at Y direction endless) of one dimension (1D) simulation M1 outage defective;
Figure 10 is under the defect situation of 0.2 micron of hypothesis, causes M1 outage inefficacy odds distribution curve;
Figure 11 is under the defect situation of 0.4 micron of hypothesis, causes M1 outage inefficacy odds distribution curve;
Figure 12 is the simulated experiment figure (2D) that defective causes contact hole to lose efficacy;
Figure 13 is the analog result figure that contact hole lost efficacy under the hypothesis diverse location error condition.
Embodiment
Adopt the program circuit explanation the present invention of typical data now in conjunction with Fig. 1:
1. setting up the design layout figure lost efficacy and memory body feature fail data storehouse
Memory circuit is dimeric; Address decoder and the amplifying circuit of a part around being, i.e. X address decoder and Y address demoder, they are used for giving an address to memory cell, and write and read individually; Another part is a memory cell, is repeated to form by single unit, and the minimum unit of each memory body is identical.In memory body inside, some is used for repairing the inefficacy circuit.They are the same with other memory body, just are not connected into circuit.Behind the element failure of inside, this part just can be used for repairing.
This structures shape of memory body the determinacy of its failure characteristics, and its failure characteristics has correspondence with the failure characteristics of its internal circuit domain figure. can occur in the different of the position of circuit and design level according to defective, predict the feature of the circuit layout figure inefficacy that this defective may cause, and then predict the kind of the feature inefficacy that this defective may cause.For instance, because the uniqueness of memory cell, the contact hole of certain position lost efficacy and may cause the inefficacy of individual unit, and the contact hole of another position lost efficacy and may cause two unit that close on to lose efficacy simultaneously; Similarly, the inefficacy that the outage defective can cause a unit takes place in the M1 of certain position, and the inefficacy that the outage defective may will cause a plurality of unit takes place the M1 figure of another position.
Set up that the design layout figure lost efficacy and memory body feature fail data storehouse is exactly the feature of the memory body inefficacy of the situation correspondence that will know that any figure lost efficacy.
Be that example illustrates this process with the SRAM memory cell below.Generally speaking, the SRAM memory cell includes AA, POLY, CONTACT, M1, VIA1, M2, VIA2 and M3 layer.Its peripheral circuits may include other high-rise metal.In memory cell internal circuit part, its defective should comprise following kind:
The AA defective of cutting off the power supply;
The POLY defective of cutting off the power supply;
The M1 defective of cutting off the power supply;
The M2 defective of cutting off the power supply;
The M3 defective of cutting off the power supply;
The AA defective of leaking electricity;
The POLY defective of leaking electricity;
The M1 defective of leaking electricity;
The M2 defective of leaking electricity;
The M3 defective of leaking electricity;
The Poly CONTACT defective of cutting off the power supply;
The NAA CONTACT defective of cutting off the power supply;
The PAA CONTACT defective of cutting off the power supply;
The VIA1 defective of cutting off the power supply;
The VIA2 defective of cutting off the power supply.
For the ground corresponding relation of setting up that figure lost efficacy and the memory body feature lost efficacy, must know the figure fail category that any layer defects institute may cause, and then judge the kind of its memory body feature that causes inefficacy according to the classification of figure inefficacy.The kind that this process can lose efficacy with the figure that the analogy method of Monte Carlo (MONTOCARLO) comes simulated defect to cause.Concrete implementation procedure as shown in Figure 2.
Occur as the process that example explanation is set up M1 figure outage defect database with M1 outage defective below.Domain from Fig. 3 as can be seen, the M1 domain is repeated to form by multiple identical shape, this is all to be repeated to form by identical unit because of whole memory body.In order to simulate the situation that different features lost efficacy, must identify each different domain shape earlier.As can be seen from Figure 4, the domain of this shape is designated M1Group4.The shape of these domains is classified as together, mainly is because one be that their shape is the same, the 2nd, and their take place that they cause after the defective that outage lost efficacy feature lost efficacy is the same.For instance, if M1Group4 produces the outage defective, will cause the inefficacy of a memory cell.Repeat same process, just can sorting out to same shape in the memory body domain.
Because defective is unordered event in the production run.Can come the process that simulated defect takes place and write down the kind of the domain defective that this defective may cause with the method for Monte Carlo (MONTO CARLO).At first, select the defective of a series of sizes, according to this each defective is done the simulation of Monte Carlo (MONTO CARLO) then, and note the kind of all incidents.In the process that the various defectives of simulation take place, also can calculate the useful area that variety of event takes place simultaneously. the useful area that this incident takes place is to cause the probability of inefficacy to multiply by area of chip by this incident to obtain.For instance, take place 10000 times with the method simulated defect of Monte Carlo, wherein cause the generation of this kind incident 100 times, the probability of this incident is 100/10000 so, and useful area is exactly that the area of entire chip multiply by 100/10000.The size of useful area reflects the significance level of this kind inefficacy---useful area is big more, and this kind inefficacy odds is just big more.
Shown in the table 1 be the kind that lost efficacy of the possible feature of M1 outage defective with and useful area.
The situation that any domain lost efficacy all and can corresponding a kind of feature to lose efficacy.With M1 outage defective among Fig. 5 is example, and M1Group 4 (1) outage defectives can cause BIT1 (single memory body inefficacy).The rest may be inferred, to the situation that any domain lost efficacy, all can have a kind of feature to lose efficacy and its correspondence.
Repeat above process, just can obtain AA, POLY, M1, the situation that the caused domain of the useful area of the defective incident of the outage of M2 and M3 and electric leakage and this incident lost efficacy.
For various contact holes, also to carry out same analysis.For instance, to the VIA1 contact hole, the VIA1 that has lost efficacy back and caused the inefficacy of a unit; The position VIA1 inefficacy that has can cause two adjacent element failures-mainly be because the residing position of VIA1 is fixed.Utilize same reason, just can classify VIA1.
Be that example illustrates this process with poly to the M1 contact hole below.What Fig. 6 showed is that all poly arrive
The kind of M1 contact hole.Fig. 7 returns the poly that can cause an element failure and is CellPolyCNT group (group) together to the M1 contact hole; Fig. 8 is categorized in the poly that can cause two unit of colleague to lose efficacy simultaneously together to the M1 contact hole, is the colleague adjacent two element failures (Row2Bit).
Repeat above process and just can classify, and can know the feature inefficacy that any contact hole lost efficacy and causes which kind of memory body all contact holes.
Every layer and the various domains of every kind of contact hole were lost efficacy with and feature lost efficacy make calculate and classification after, just set up the correspondence database between figure failure class and the memory body feature fail category.
2. judge that the domain figure that the production line defective is caused lost efficacy
Defective on the production line can be obtained by different checkout equipments, and what adopt in this example is that KLARF data .KLARF data are standard data formats of KLA-Tencor defective that checkout equipment is reported.In the KLARF data, it contains the source of defectiveness, information such as the size of defective and position thereof. according to the position of defective, the situation of information binding deficient on design layout of size and positional precision just can analyze the failure conditions of the domain figure that this defective may cause.
It it is worthy of note,, that is to say that its position is a scope, so when analyzing, may not be unique that the figure that defective caused lost efficacy because defective locations has certain trueness error.If suppose certain position distribution function, not only can know the figure failure type that this defective may cause, also can know the probability that this kind figure lost efficacy and takes place.
Illustrate that with the example of M1 line outage defective the positional precision sum of errors estimates the relation that figure lost efficacy below. what Fig. 9 showed is the situation of one dimension outage defective. suppose that live width and line-spacing are 0.1 micron, the defective locations error is normal probability distribution, shown in meter (1).
Wherein, P is the distribution function value, the 6th, and error, x is the position, and μ is a mean value. and here μ is exactly the positional value of this defective of defect level measurement equipment report.
The relation that the probability of line outage and defective locations error are caused in position shown in a defective drops on as shown in figure 10, wherein the 6=0.001 micron is that to be used for assumed position accurate, so the defective odds of its estimation is 100%.
From above legend as can be seen, the site error of defective exceedes greatly, the probability of the graphic defects of estimating is just more little, that is to say that error can be big more. in the circuit of reality,, depend on the size of site error more so estimate probability accurately because the distribution curve of position is a 2D ground distribution function.
In addition, prediction accuracy also depends on the size of flaw size, and flaw size size relative and figure is big more, and its prediction accuracy is just high more. and this can find out from the result of Figure 10 and Figure 11.For 0.2 micron defective, if its error=0.05 micron, its accuracy has only 70%; For 0.4 micron defective, if its error=0.05 micron, its accuracy can reach 100%.
What Figure 12 showed is the simple analog that a defective causes contact hole to lose efficacy, and its defective locations is the 2D normal distribution, and its probability that causes defective as shown in figure 13.
In example of the present invention, suppose that the defective locations trueness error is 0.001 micron---assumed position does not almost have the situation of error.
3. judge that the figure failure characteristics lost efficacy corresponding with the memory body feature
After making comparisons with database, just can infer the kind that feature that this defective may cause lost efficacy.Resource is certain or suppose under the situation of certain reparation resource repairing, and can determine to repair resource and whether can repair every kind of feature and lost efficacy; Can be if this feature lost efficacy by all resource reparations, it is recoverable that this feature inefficacy then is listed in; If the existing resource of repairing of the inefficacy of a certain feature can not be repaired, this feature lost efficacy and then is listed in and can not repairs.
Table 2 shows is the result that lost efficacy of the figure of M1 M1 that defective causes outage and electric leakage and the situation of characteristic of correspondence inefficacy.It is worthy of note that if consider the situation of site error and its distribution, a defective may cause a plurality of figures to lose efficacy, every kind of inefficacy odds value of all ining succession.It calculates the many of the corresponding complexity of meeting.
4. judge whether finished product of memory body
After the feature failure analysis that all defectives cause is finished, can repair all features and lost efficacy if repair resource, this memory body may finished product, otherwise this memory body may lose efficacy.
| The M1 outage defective incident of Monte Carlo simulation | The useful area (cm^2) that incident takes place | The pairing feature failure type of incident | Judge whether and to be repaired |
| M1Group4(1) | 1.89E-07 | A unit | Can repair |
| M1Group3(1) | 1.75E-07 | A unit | Can repair |
| M1Group5(1) | 1.74E-07 | A unit | Can repair |
| M1Group2(1) | 1.73E-07 | A unit | Can repair |
| M1Group1(1) | 1.59E-07 | Two the adjacent unit of going together | Can repair |
| M1Group4(1)_M1Group6(1) | 5.14E-08 | Square adjacent Unit four | Can repair |
| M1Group2(1)_M1Group6(1) | 4.70E-09 | Two the adjacent unit of going together | Can repair |
| Or the like | |||
Table 1
| The defective numbering | Defect layer | X | Y | Size X | Size Y | |
|
Occurrence probability _ 1 |
| 1 | M1 | 1401 | 2400 | 2.05 | 2.08 | The M1Group4 outage | An element failure | 100％ |
| 2 | M1 | 1804 | 2402 | 1.78 | 2.05 | The M1Group3 outage | An element failure | 100％ |
| 3 | M1 | 2401 | 2410 | 5.07 | 3.08 | The M1Group4_M1Group2 outage | Adjacent two element failures of going together | 100％ |
| ..... |
Table 2
Claims (3)
1. the method for a dynamically estimating memory body characteristic ineffective cause of defect comprises:
(1) occurs in the kind that the various domain figures that may the described defective of failure predictions may cause at various possible positions place in the memory circuit lost efficacy by simulation, the corresponding memory body feature of definite and described domain figure inefficacy lost efficacy, and set up the database of corresponding relation between described domain figure inefficacy of storage and the inefficacy of memory body feature;
(2) according to the size of defective, the trueness error that the situation of position binding deficient on design layout obtains the position of defective, and hypothesis certain position distribution function, trueness error and the certain position distribution function according to the position calculates the type of possible domain figure inefficacy and the probability that described domain figure lost efficacy and takes place then;
(3) use the described database of setting up in the step (1) judge the domain figure lost efficacy corresponding memory body feature lost efficacy with and whether can be repaired;
(4) lost efficacy whether can be repaired according to the memory body feature described in the step (3) and judged whether finished product of whole memory body chip.
2. according to the method described in the claim 1, it is characterized in that: the kind that the domain figure that described prediction defective may cause lost efficacy comprises: at first the domain element in the memory circuit is identified with layer and contact hole type, the defective of simulating the different size size with the method for Monte Carlo every layer contingent various may situations, thereby obtain all kinds that various possible figures outages and electric leakage were lost efficacy.
3. method according to claim 1, it is characterized in that: describedly judge whether finished product is meant after the defective of all technological processs is made analysis memory body, set up the chart that domain figure lost efficacy,, estimate whether finished product of this memory body chip according to what and the kind of repairing resource.
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