CN100375258C - Method for detecting again fault - Google Patents
Method for detecting again fault Download PDFInfo
- Publication number
- CN100375258C CN100375258C CNB2004100332677A CN200410033267A CN100375258C CN 100375258 C CN100375258 C CN 100375258C CN B2004100332677 A CNB2004100332677 A CN B2004100332677A CN 200410033267 A CN200410033267 A CN 200410033267A CN 100375258 C CN100375258 C CN 100375258C
- Authority
- CN
- China
- Prior art keywords
- defective
- defect
- defects
- type
- destructive
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 230000007547 defect Effects 0.000 claims abstract description 128
- 238000001514 detection method Methods 0.000 claims abstract description 36
- 238000004519 manufacturing process Methods 0.000 claims abstract description 32
- 238000005070 sampling Methods 0.000 claims abstract description 17
- 230000002950 deficient Effects 0.000 claims description 146
- 230000001066 destructive effect Effects 0.000 claims description 23
- 238000012797 qualification Methods 0.000 claims description 12
- 238000009825 accumulation Methods 0.000 claims description 5
- 239000004065 semiconductor Substances 0.000 description 19
- 238000004458 analytical method Methods 0.000 description 12
- 235000012431 wafers Nutrition 0.000 description 12
- 238000012360 testing method Methods 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 9
- 239000008187 granular material Substances 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000007689 inspection Methods 0.000 description 2
- HCOLPNRPCMFHOH-UHFFFAOYSA-N Prodigiosin Natural products CCCCCC1C=C(C=C/2N=C(C=C2OC)c3ccc[nH]3)N=C1C HCOLPNRPCMFHOH-UHFFFAOYSA-N 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- TWFGRJUTAULJPZ-USZBIXTISA-N prodigiosin Chemical compound N1=C(C)C(CCCCC)=C\C1=C/C1=NC(C=2[N]C=CC=2)=C[C]1OC TWFGRJUTAULJPZ-USZBIXTISA-N 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
Images
Abstract
The present invention relates to a method for defecting defects again, wherein the present invention provides a wafer. The wafer is provided with a plurality of defects, and the wafer carries out the defect detection to detect the defects. The present invention carries out the automatic defect classification on the defects on the basis of a preset data base. The defects are divided into a plurality of defect types. The present invention finally detects the defect, wherein the defect is detected again on the basis of the influence degree of each defect type on the qualified rate in the manufacture process. The present invention utilizes different ratios to carry out the sampling detection.
Description
Technical field
The invention provides a kind of defective and detect (defect review) method again, be meant especially and a kind ofly carry out the method that defective detects again with different ratios according to defect type.
Background technology
In each semiconductor fabrication; tend to because some are unavoidable former thereby generate tiny particulate or defective; and along with constantly dwindling of component size and improving constantly of circuit level in each semiconductor manufacturing; these atomic little defectives or particulate also are on the rise to the influence of integrated circuit quality; therefore for keeping the stable of yield and quality amount; usually when carrying out each semiconductor fabrication; also must carry out defects detection at the semiconductor element of being produced; to analyze the basic reason that causes these defectives according to the result who detects; could further avoid or reduce generation of defects afterwards, to reach the purpose that promotes each semiconductor fabrication qualification rate and reliability via the adjustment of procedure parameter.
Please refer to Fig. 1, Fig. 1 is the schematic diagram of a defect inspection method 10 in the known technology.As shown in Figure 1, at first take a sample 12, selected semiconductor wafer is that sample carries out follow-up defects detection and analytical work, then carry out a defect detection procedure 14, generally speaking, utilize the mode of suitable defect checking machine platform mostly with large area scanning, detect all defect on the described semiconductor wafer, because the defective number on the semiconductor wafer is quite big mostly, therefore can not carry out sweep electron microscope (SEM) in artificial mode one by one on practice detects again, therefore, for convenience's sake, carry out artificial defect classification 16 most likely earlier, in detected all defect, some more representative defect types are taken out in sampling, allow the engineer in artificial mode selected sample be carried out defective again and detect (defectreview) 18 again, further described defective is carried out defect cause analysis (defect root causeanalysis) 20, enterprise finds out the method that suppresses or reduce these defectives.
In known technology, the maximum problem that suffers from is exactly often can find a large amount of defective numbers in defects detection 14, for example possibility is 1,000, but the engineer often also can only therefrom pick out a part of defective as sample in the mode of sampling, for example 100, carry out defective and detect 18 and follow-up defect analysis work again.Generally speaking, choosing of these samples almost is to lean on engineer's personal experience to judge fully, in other words, except some senior engineers can rule of thumb carry out the check and analysis to find out some more representative defect types, most of people also only therefrom some defectives of picked at random detect 18 sample again as defective, yet in the middle of all defectives, most defective mostly for irrelevant following layer defects (underlayer defect) of manufacture process at that time or the non-destructive defective (non-killer defect) that some do not influence qualification rate, the really destructive defective (killer defect) that qualification rate is had considerable influence negligible amounts often.Generally speaking, the engineer comes to detect 18 again to carrying out defective in the mode of random sampling, under this situation, carry out defective and detect again in 18 the sample, often having only a few is to belong to effective sample, this falls and causes expending of time and manpower, the accuracy of the follow-up defect analysis of related influence.
In known technology, if wish to improve the accuracy of defect analysis, only can may observe quantity via what increase significantly that defective detects that sample size in 18 increases destructive defective wherein again, in other words, this not only need expend great amount of manpower, more can significantly increase defective and detect 18 observation time again, cause the significantly prolongation of testing time or when volume production, output caused seriously influencing.
Progress along with each semiconductor fabrication, the diameter of wafer is to march toward 12 inches by 8 inches of past, the live width size also enters 0.13 micron even 0.1 micron by 0.18 micron of the past, at this from the process of testing volume production, the often suitable height of generation probability of defective, in other words, the detection of defective will be day by day important also with analyzing, therefore, we press for a kind of quick and efficient defect inspection method, to address the above problem.
Summary of the invention
Main purpose of the present invention is to provide a kind of high efficiency defective detection method again, to address the above problem.
Most preferred embodiment of the present invention discloses a kind of defective detection method again, one wafer at first is provided, have a plurality of defectives on the described wafer, and carry out a defects detection, to detect described defective, and according to a preset database described defective is carried out an automatic defect and classify, after will descending layer defects to filter out, the newly-increased defective that further will be left is divided into the number of drawbacks type again, carrying out a defective at last detects again, wherein said defective detects according to the influence degree of each defect type to the manufacture process qualification rate again, carries out sampling Detection with different ratios, to promote the efficient that defective detects again.
Because defective of the present invention detection method again utilizes a preset database to carry out the automatic defect classification, therefore but active zone is every destructive defective, non-destructive defective and following layer defects, and then carry out sampling Detection with a higher ratio at destructive defective, so can finish defects detection and analytical work more accurately with the short time, reach the purpose that promotes product percent of pass and reliability.
Description of drawings
Fig. 1 is a known defective testing process schematic diagram again.
Fig. 2 is a defective of the present invention testing process schematic diagram again.
Fig. 3 is a classification of defects schematic diagram.
The reference numeral explanation
The classification of 14 defects detection, 16 artificial defects
18 defectives detect 20 defect cause analyses again
The classification of 114 defects detection, 116 automatic defects
118 defectives detect 120 database updates again
122 times layer defects 124 newly-increased defectives
126 defect type A, 128 defect type B
130 defect causes are analyzed 132 defect type C
134 destructive defective 136 non-destructive defectives
Embodiment
Please refer to Fig. 2, Fig. 2 is the schematic diagram of testing process 110 again of a defective among the present invention.As shown in Figure 2, at first, selected one each semiconductor fabrication of being scheduled to is as the subject matter of defects detection and analysis, take a sample 112 then, in a plurality of semiconductor wafers of finishing described each semiconductor fabrication, select semiconductor wafer and carry out a defects detection 114, mode with large area scanning, detect size, shape and the position of each defective on the described semiconductor wafer, and utilize a preset database that each defective is carried out automatic defect classification (automatic defect classification, ADC) 116.Wherein said databases has the number of drawbacks type and corresponding to the defect information of each defect type, utilizing an automatic defect classification tool that each defective is divided into different defect types according to parameter such as its size and shape, and each defect type is carried out the number statistics.
Please refer to Fig. 3, Fig. 3 is the schematic diagram of automatic defect classification 116.As shown in Figure 3, after in defect detection procedure 114, detecting defective, these defectives will be earlier according to the data in the described database, these defectives are divided into layer defects (underlayer defect) 122 and newly-increased defective (adderdefect) 124 2 classes down, owing to the manufacture process before layer defects 122 all comes from down causes, having only newly-increased defective 124 to be only by our manufacture process of institute's desire monitoring is caused, therefore, described automatic defect classification tool will filter out following layer defects 122 wherein, and at the work of further classifying of newly-increased defective 124.
In preferred embodiment of the present invention, the defect information of being deposited in the described database includes a classified information, make described automatic defect sorter prodigiosin according to described classified information, according to parameters such as the size of defective, shapes each newly-increased defective 124 is divided into different defect types, defect type A 126 as shown in Figure 3, defect type B 128 and defect type C 132, and the number of each defect type added up, understand defect condition on the described semiconductor wafer to assist the operator.It should be noted that described database is interior except the classified information that contains each defect type, also include the influence degree of these defect types, and the kill rank of manufacture process qualification rate further is divided into each defective 136 liang of classes of destructive defective 134 and non-destructive defective according to each defect type to the manufacture process qualification rate.
Then can be in the mode of sampling, respectively choosing several defect sample in each defect kind carries out defective and detects 118 again, and detect again at 118 o'clock carrying out defective, the defect sample number of choosing in each defect kind decides according to the influence degree of described defect kind to the manufacture process qualification rate.Example with Fig. 3, because defect type A 126 belongs to destructive defective 134, and defect type B 128 belongs to non-destructive defective 136 with defect type C 132, therefore, detect again at 118 o'clock carrying out defective, will be based on defect type A 126, for example can in defect type A 126, get 80 defect sample, and respectively get 10 defect sample from defect type B 128 and defect type C 132, to detect 118 efficient again and promote follow-up defect cause and analyze 130 accuracy via improving defective.
For further specifying embodiments of the present invention, below defective of the present invention detection method again is described especially exemplified by an example.Suppose to include on the semiconductor wafer 2000 defectives, and include 1000 following layer defects, 900 granule defectives and 100 big grain defectives in these defectives, if according to known technology, take out 100 samples in the mode of random sampling and carry out defective and detect, the sample that so obviously can get can roughly include 50 following layer defects, 45 granule defectives and 5 big grain defectives.
If the method according to this invention, then can classify to the qualification rate kill rank of various different manufacture processes according to each defect type in the described database earlier, each semiconductor fabrication of supposing to desire to carry out fault detection analysis is a leading portion manufacture process (front end process), because in the leading portion manufacture process, the defective of various sizes size all can be subjected to the influence of subsequent deposition manufacture process and constantly grow up, therefore except with the irrelevant following layer defects of this manufacture process, no matter be big grain defective or granule defective, this manufacture process is all destructive defective, in other words, all must carry out defective and detect 118 again with a higher ratio, therefore, we can mode at random carry out sampling Detection, for example get the big grain of 90 granule defectives and 10 defectives according to its quantity ratios and carry out defective and detect 118 again.Yet if we desire to carry out each semiconductor fabrication of fault detection analysis is a back segment manufacture process (back end process), so just have only big grain defective to be only destructive defective, the granule defective then almost can be ignored, under this situation, though the number of granule defective is much larger than the number of big grain defective, yet owing to have only big grain defective to be only destructive defective, therefore we can add the sampling ratio of great grain defective, for example get 10 granule defectives and carry out defective with 90 big defectives and detect 118 again.Carry out defective with known technology in the mode of random sampling and detect and compare, it may get 50 following layer defects, 45 granule defectives and 5 big grain defectives.Compare with the defect sample of obtaining with random fashion in the known technology, defect sample of the present invention obviously can obtain preferable defective testing result again, carries out follow-up defective in order to the engineer and generates the analysis of causes.
In addition, though defective of the present invention detection method is again carried out automatic defect classification 116 and is determined to carry out the sampling ratio that defective detects 118 o'clock each defect types again according to a preset database, yet the data in this database are not changeless, upgrade described database and can detect 118 result again according to defective, for example can detect 118 results again according to the defective that reality obtains, change defect type B 128 into destructive defective 134 or at defect type A 126, outside defect type B 128 and the defect type C 132, other increases a defect type D, so that being detected 118 steps again, automatic defect classification 116 and defective revise, obtaining an optimized result, and promote defect cause and analyze 130 sensitivity and accuracy.
Except aforementioned according to the influence degree of manufacture process qualification rate with carry out defective with different ratios and detect 118 again; the engineer also can adjust defective according to any specific characteristic that is observed in the defects detection 114 or anomaly and detect sampling distribution situation in 118 again; for instance; sometimes regular meeting finds that some are distributed in the accumulation type defective (clusterdefect) in semiconductor wafer corner in defects detection 114; this moment, the engineer also can detect 118 again at the defective that these accumulation type defectives are carried out a higher sampling ratio, so that these are further analyzed mutually mutually unusually.
With defective in the known technology again detection method compare, therefore defective of the present invention detection method again can obtain preferable defective detection efficiency again via the detected ratios that increases destructive defective or special abnormality situation.In other words, the present invention obtains out a preferable testing result again under the identical number of times of detection again, and can have more fully data carry out the defect cause analysis, the easier more accurate defect cause analysis result that obtains out.Relatively, if wish with the defective of known technology again detection method obtain the defective identical testing result again with the present invention, the time and the manpower that then often need to spend several times carry out, in other words, defective of the present invention detection method again can be so can significantly promote the speed and the accuracy of defects detection, therefore can satisfy the demand when test is with a large amount of production on the line, finish the adjustment of each procedure parameter with less time cost, to promote output and production reliability.
The above only is preferred embodiment of the present invention, and all equivalences of carrying out according to claim of the present invention change and revise, and all should belong to covering scope of the present invention.
Claims (9)
1. method that defective detects again, it includes the following step:
One wafer is provided, has a plurality of defectives on the described wafer;
Carry out a defects detection, to detect described defective;
According to a preset database described defective is carried out automatic defect classification, described defective is divided into the number of drawbacks type; And
Carrying out a defective detects again;
Wherein,, carry out sampling Detection with different ratios according to the influence degree of each defect type to the manufacture process qualification rate when carrying out described defective when detecting again.
2. method as claimed in claim 1, wherein said database include the number of drawbacks type and corresponding to the defective data of each described defect type.
3. method as claimed in claim 2, wherein the defective data of each described defect type includes the influence degree of each described defect type to the manufacture process qualification rate.
4. method as claimed in claim 3, wherein said database be according to the influence degree of each described defect type to the manufacture process qualification rate, and the defective of each described defect type is divided into destructive defective and non-destructive defective.
5. method as claimed in claim 4 is wherein being carried out described defective when detecting again, and the shared ratio of destructive defective is greater than the ratio of non-destructive defective.
6. method as claimed in claim 3, wherein said database is divided into layer defects and newly-increased defective down with the defective of each described defect type, and further will to increase classification of defects newly be destructive defective and non-destructive defective.
7. method as claimed in claim 6, wherein said method are carried out defective at described newly-increased defective and are detected.
8. method as claimed in claim 1, wherein said method will be carried out an accumulation type defective and differentiate after being carried out described defects detection, wherein when having described accumulation type defective on the described wafer, will carry out defective to described accumulation type defective with higher ratio and detect.
9. method as claimed in claim 1, wherein said method is in carrying out the result who detects again according to described defective again being upgraded described database after described defective detects again.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2004100332677A CN100375258C (en) | 2004-03-29 | 2004-03-29 | Method for detecting again fault |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2004100332677A CN100375258C (en) | 2004-03-29 | 2004-03-29 | Method for detecting again fault |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1677637A CN1677637A (en) | 2005-10-05 |
| CN100375258C true CN100375258C (en) | 2008-03-12 |
Family
ID=35050050
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2004100332677A Expired - Fee Related CN100375258C (en) | 2004-03-29 | 2004-03-29 | Method for detecting again fault |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100375258C (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101295659B (en) * | 2007-04-29 | 2010-06-09 | 中芯国际集成电路制造(上海)有限公司 | Method for detecting defect of semiconductor device |
| CN102142355B (en) * | 2010-02-02 | 2013-07-17 | 吕一云 | Application method of object manufacture defect |
| CN102445934A (en) * | 2011-10-17 | 2012-05-09 | 上海华力微电子有限公司 | Defect monitoring method based on connection with internal process information of machine |
| CN103513854B (en) * | 2012-06-19 | 2017-03-01 | 旺矽科技股份有限公司 | The display packing of the user interface of semiconductor detection system |
| CN103943523B (en) * | 2013-01-21 | 2016-08-31 | 中芯国际集成电路制造(上海)有限公司 | Sampling method for measurement in semiconductor production process |
| CN104465434B (en) * | 2013-09-23 | 2017-07-11 | 中芯国际集成电路制造(上海)有限公司 | Defect analysis method |
| CN105204377A (en) * | 2014-06-18 | 2015-12-30 | 上海华力微电子有限公司 | Method for improving product standard |
| CN108122801B (en) * | 2017-12-12 | 2021-07-09 | 武汉新芯集成电路制造有限公司 | Wafer marking method and wafer marking system |
| CN111403309A (en) * | 2020-03-30 | 2020-07-10 | 上海华力集成电路制造有限公司 | Wafer aggregation-like defect detection method and detection system thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6466895B1 (en) * | 1999-07-16 | 2002-10-15 | Applied Materials, Inc. | Defect reference system automatic pattern classification |
| CN1392954A (en) * | 2000-10-02 | 2003-01-22 | 应用材料有限公司 | Defect knowledge library |
| TW535207B (en) * | 2002-05-08 | 2003-06-01 | Powerchip Semiconductor Corp | Method for automatically controlling defect-specification in manufacturing process of semiconductors |
-
2004
- 2004-03-29 CN CNB2004100332677A patent/CN100375258C/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6466895B1 (en) * | 1999-07-16 | 2002-10-15 | Applied Materials, Inc. | Defect reference system automatic pattern classification |
| CN1392954A (en) * | 2000-10-02 | 2003-01-22 | 应用材料有限公司 | Defect knowledge library |
| TW535207B (en) * | 2002-05-08 | 2003-06-01 | Powerchip Semiconductor Corp | Method for automatically controlling defect-specification in manufacturing process of semiconductors |
Also Published As
| Publication number | Publication date |
|---|---|
| CN1677637A (en) | 2005-10-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US6265232B1 (en) | Yield based, in-line defect sampling method | |
| US7332359B2 (en) | Semiconductor device inspection method | |
| JP4077951B2 (en) | Defect analysis method, recording medium, and process management method | |
| KR101600209B1 (en) | Region setting device, inspection device, region setting method, and inspection method using region setting method | |
| US7352890B2 (en) | Method for analyzing circuit pattern defects and a system thereof | |
| US20020145112A1 (en) | Defect inspection efficiency improvement with in-situ statistical analysis of defect data during inspection | |
| CN104062305B (en) | A kind of analysis method of integrated circuit defect | |
| KR20070045332A (en) | Methods and apparatus for local outlier detection | |
| JP2002100660A (en) | Defect detecting method, defect observing method and defect detecting apparatus | |
| US10719655B2 (en) | Method and system for quickly diagnosing, classifying, and sampling in-line defects based on CAA pre-diagnosis database | |
| CN100375258C (en) | Method for detecting again fault | |
| CN115360116B (en) | Wafer defect detection method and system | |
| CN107689335B (en) | method for analyzing defects of wafers of various products | |
| US6551847B2 (en) | Inspection analyzing apparatus and semiconductor device | |
| US7071011B2 (en) | Method of defect review | |
| CN101738400B (en) | Method and device for judging repeated defects on surface of wafer | |
| JP4080087B2 (en) | Analysis method, analysis system, and analysis apparatus | |
| US6424881B1 (en) | Computer generated recipe selector utilizing defect file information | |
| US6828776B2 (en) | Method for analyzing defect inspection parameters | |
| US7363098B2 (en) | Method to identify machines causing excursion in semiconductor manufacturing | |
| US6978041B2 (en) | Review work supporting system | |
| JP2008113027A (en) | Method of manufacturing device | |
| US6423557B1 (en) | ADC based in-situ destructive analysis selection and methodology therefor | |
| CN1673991A (en) | Defect control method | |
| US20090299669A1 (en) | Method of yield management for semiconductor manufacture and apparatus thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20080312 Termination date: 20100329 |