CN107679163A - A kind of one step process manufacture factor significant difference analysis system and analysis method - Google Patents
A kind of one step process manufacture factor significant difference analysis system and analysis method Download PDFInfo
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Abstract
The present invention relates to technical field of manufacturing semiconductors,More particularly to a kind of one step process manufacture factor significant difference analysis system and analysis method,The system and method gather the test data in batch wafer processed by data collecting system from the tester table of critical process in real time,Variance analysis system obtains manufacture history data of the sampling wafer in the critical process from Manufacturing Executive System MES simultaneously,Test data is associated one by one with manufacture history data,And it is grouped according to the specific item of manufacture factor,Homogeneity of variance checking is included to be calculated,Analysis,Draw the significant difference index of each manufacture factor,As indicator difference is notable,Then prompting engineer verifies certain equipment in the manufacture factor,Certain operative employee or the influence to caused by the technological ability of product of certain batch materials,For the plant maintenance in later stage,Operative employee, which trains, provides foundation；As indicator difference is not notable, then engineer is prompted to search other factors for causing significant difference.
Description
Technical field
The invention belongs to technical field of manufacturing semiconductors, and in particular to a kind of one step process manufacture factor significant difference analysis
System and analysis method.
Background technology
Compound semiconductor manufacture with multi items, large batch of feature, more frequently technique switching, continual set
Received shipment guild cause the same type equipment on one step process manufacturing capacity be present in terms of difference；In addition, compound semiconductor produces
The automaticity of line is relatively low, and many equipment pass piece, manual operations by hand by operator, and there is also manipulator by different operators
Difference in method；Materials variances factor also occurs in the even material of different batches or different manufacturers, and these small differences are all
The performance in product in single-step process will be caused to occur fluctuating and finally influence product yield.
Product yield to ensure final can be stablized in higher level, it is necessary to solve in product in single-step process
Performance inconsistency, it is necessary to by the wafer under every product batches according to sampling prescription carry out process test, as line width, thickness,
Aligning degree etc..
In existing individual event procedure analysis system and method, the system such as generally use statistical control chart, descriptive statistic value
Calculating method is analyzed the test parameter of process, even if the above method analyzes exception or problem, still can not accurately be determined
Position is produced caused by which kind of manufacture factor in board, operator, material.The missing of analysis system and method causes faulty
Equipment, do not possess ability on duty operator, do not reach technological requirement material still be applied to processing line manufacture, cause final
Product yield is relatively low, can not meet that client hands over the phase.
The content of the invention
Combined it is an object of the invention to provide one kind by test data with manufacture resume and utilize statistical algorithms real
The system and method for the significant difference analysis of each condition of existing single-step process manufacture factor.
To reach above-mentioned requirements, the present invention adopts the technical scheme that：A kind of one step process manufacture factor significance difference is provided
Different analysis system and analysis method, variance analysis system are adopted from the tester table of critical process in real time by data collecting system
Collect the test data in batch wafer processed, while variance analysis system obtains sampling wafer at this from Manufacturing Executive System MES
Manufacture history data in critical process, test data is associated to form integral data one by one with manufacture history data；Root
According to the data qualification of input, the integral data for meeting data qualification is obtained from integral data, data qualification includes period, production
Product, process and manufacture factor, manufacture factor can be production board, operator or batches of materials；According to the difference of manufacture factor,
Data qualification also includes different confidence levels；The integral data screened is divided according to the specific item of manufacture factor
Group, include homogeneity of variance checking and calculated, analyzed, the significant difference index of each manufacture factor is drawn, as indicator difference shows
Write, then prompt engineer to verify certain equipment, certain operative employee or certain batch materials in the manufacture factor to the technique energy in product
Influence caused by power, foundation is provided for the plant maintenance in later stage, operative employee's training；As indicator difference is not notable, then engineering is prompted
Teacher searches the reason for other cause significant difference.
Compared with prior art, the present invention has advantages below：After the skew of process ability occurs within certain time
Significant difference analysis, the checking of every manufacture factor are carried out, to ensure that engineer can be accurately positioned the skew of process ability
Concrete reason, prevent the poor operative employee of the poor equipment of technological ability, manufacturing capacity or second-rate material etc. manufacture
Factor persistently puts into production, so as to greatly promote the fine ratio of product of processing line.
Brief description of the drawings
Accompanying drawing described herein is used for providing further understanding of the present application, the part of the application is formed, at this
Same or analogous part, the schematic description and description of the application are represented using identical reference number in a little accompanying drawings
For explaining the application, the improper restriction to the application is not formed.In the accompanying drawings：
Fig. 1 is the block diagram of the one step process manufacture factor significant difference analysis system of the present invention；
Fig. 2 is the schematic flow sheet of the one step process manufacture factor significant difference analysis method of the present invention.
Embodiment
To make the purpose, technical scheme and advantage of the application clearer, below in conjunction with drawings and the specific embodiments, to this
Application is described in further detail.For the sake of simplicity, eliminate that well known to a person skilled in the art some skills in describing below
Art feature.
As shown in figure 1, the present embodiment provides a kind of one step process manufacture factor significant difference analysis system 1, including difference
Analysis system 1 and data collecting system 5.
Data collecting system 5 includes：
Sampling prescription setup module 52, pass through for setting the sampling prescription of collecting test data, and by the sampling prescription
Semiconductor standard communications protocol is assigned to process measurement platform 6；The present embodiment sampling prescription is：10 points of collection per wafer
Test data, 10 wafers before selection per batch；
Data acquisition module 51, for according to the sampling prescription, the survey in batch wafer processed to be gathered from process measurement platform 6
Data are tried, and the data are synchronized to test data collection module 17 by data transmission system 3；Test data in the present embodiment
For CD line width test datas.
Variance analysis system 1 includes：
Test data collection module 17, the test data collected for receiving data collecting system 5；
MES interface modules 15, for gathering the manufacture history data of all sampling wafers in manufacturing execution system 4, this reality
Apply and history data is manufactured in example as the litho machine numbering in T-shaped grid process, manufacturing history data also includes manufacturing time and production
Product；
Data Integration module 13, for the test data for wafer of sampling to be associated one by one with manufacture history data, i.e.,
The test data for there be not batch wafer manufacture history data corresponding with the batch wafer is associated one by one, is formed and integrates number
According to；
Database 11, for storing test data, manufacture history data and integral data；
User Access Module 16, the data qualification of significant difference analysis is carried out for receiving, data strip in the present embodiment
Part includes：Period (1 day to 2017 May in 2017 of on August 31), product (GaAs pHemt), process (PPA25001), system
Make factor (litho machine), confidence level (95%)；Identical whole of manufacture history data and data qualification is selected from integral data
Data are closed, that is, manufactures the manufacturing time described in history data and is in August 1 day to 2017 May in 2017 between 31 days, manufacture
Product be GaAs pHemt, manufacturing process PPA25001, manufacture factor is litho machine；
Factor grouping module 14, CD line width test datas are entered according to the litho machine that every batch is used in T-shaped grid process
Row packet, all batch wafers are exposed on No. 1 litho machine or No. 2 litho machines in T-shaped grid process, therefore according to light
Quarter machine numbering progress integral data packet；
Significant difference analysis module 12, homogeneity test of variance is used to calculate the F statistical values of two packets as 6.12, P values
For 0.0014, the P values are less than notable level 0.05, therefore can determine that No. 1 litho machine and the manufacturing capacity difference of No. 2 litho machines show
Write, therefore engineer should pay close attention to the manufacturing capacity of litho machine in this process, to making a variation, No. 2 larger litho machines should be carried out
Maintaining.Homogeneity test of variance belongs to prior art, and its circular does not repeat herein.
The system also includes interactive system 2, and the data qualification of significant difference analysis is carried out for operator's input, is passed through
Http protocol will be delivered to User Access Module 16 in the significant difference analysis condition selected by interactive system 2；And to process capability
The specific item of manufacture factor of significant difference is caused to be highlighted.
Using the one step process manufacture factor significant difference analysis method of said system, as shown in Fig. 2 including following step
Suddenly：
S1, according to sampling prescription collection in the CD line width test datas of batch wafer processed, the present embodiment sampling prescription is：Often
Wafer collects the test data of 10 points, 10 wafers before selection per batch；The manufacture for gathering all sampling wafers simultaneously is carried out
Count evidence one by one, manufacture history data is the litho machine numbering in T-shaped grid process, manufacturing time and product；
S2, by the test data with manufacture history data associated one by one, formation integral data；
S3, the data qualification that carry out significant difference analysis according to input, obtained from integral data and meet data strip
The integral data of part,；Data qualification includes in the present embodiment：Period (1 day to 2017 May in 2017 of on August 31), product
(GaAs pHemt), process (PPA25001), manufacture factor (litho machine), confidence level (95%)；
The integral data for meeting the data qualification is grouped by S4, the specific item in the manufacture factor, this
The manufacture factor of application is litho machine, therefore integral data is grouped according to the numbering of the litho machine of use；
S5, to use homogeneity test of variance to calculate F statistical values as 6.12, P values be 0.0014, and the P values are less than notable level
0.05, therefore the manufacturing capacity significant difference of No. 1 litho machine and No. 2 litho machines is can determine that, therefore engineer should in this process
The manufacturing capacity of litho machine is paid close attention to, No. 2 larger litho machines should carry out maintaining to making a variation.
S6, the manufacture factor that manufacturing capacity significant difference be present is highlighted, to remind engineer to litho machine
This manufacture factor is improved.
Before step S5, can first calculate the average value and standard deviation of CD line widths test data in each packet, see whether
Significant difference can be judged by average value and standard deviation, if it can not judge, then using homogeneity test of variance.The present embodiment
In, the average value of No. 1 litho machine is 1.4195 μm, standard deviation 0.0035；The average value of No. 2 litho machines is 1.4092 μm, mark
Quasi- difference is 0.0443, and two groups of data are all within the specification limitation of the parameter, and close to target, referred to by average value and standard deviation
Mark can not judge whether that there were significant differences.
Above example only represents the several embodiments of the present invention, and its description is more specific and detailed, but can not manage
Solve as limitation of the scope of the invention.It should be pointed out that for the person of ordinary skill of the art, this hair is not being departed from
On the premise of bright design, various modifications and improvements can be made, these belong to the scope of the present invention.Therefore the present invention
Protection domain should be defined by claim.
Claims (10)
1. a kind of one step process manufacture factor significant difference analysis system, it is characterised in that including variance analysis system and data
Acquisition system, the data collecting system gather the test number in batch wafer processed according to sampling prescription from process measurement platform
According to；
The variance analysis system includes：
Test data collection module, the test data collected for gathered data acquisition system；
MES interface modules, for gathering the manufacture history data of all sampling wafers in manufacturing execution system, the manufacture resume
Data include process information and manufacture factor information；
Data Integration module, for the test data for wafer of sampling to be associated one by one with manufacture history data, formed and integrated
Data；
User Access Module, the data qualification of significant difference analysis will be carried out for gathering, and is obtained from the integral data
Meet the integral data of the data qualification, the data qualification include process and to carry out the manufacture of significant difference analysis because
Element；
Factor grouping module, the integral data for meeting the data qualification is entered for the specific item in the manufacture factor
Row packet；
Significant difference analysis module, homogeneity test of variance is used to the packet, so as to judge the specific item of the manufacture factor
It whether there is significant difference in the manufacturing capacity of the process.
2. one step process manufacture factor significant difference analysis system according to claim 1, it is characterised in that the data
Acquisition system specifically includes：
Sampling prescription setup module, for setting the sampling prescription of collecting test data；
Data acquisition module, for according to the sampling prescription, the test number in batch wafer processed to be gathered from process measurement platform
According to.
3. one step process manufacture factor significant difference analysis system according to claim 1 or 2, it is characterised in that also wrap
Interactive system is included, the interactive system is used for the data qualification that operator's input will carry out significant difference analysis, and is made to existing
The manufacture factor for making ability significant difference is highlighted.
4. one step process manufacture factor significant difference analysis system according to claim 3, it is characterised in that the difference
Analysis system also includes database, for storing the test data, the manufacture history data and the integral data.
5. one step process manufacture factor significant difference analysis system according to claim 1, it is characterised in that the test
Data include CD line widths test data or THK thickness metric data.
6. one step process manufacture factor significant difference analysis system according to claim 1 or 5, it is characterised in that described
Manufacture history data also includes temporal information and product information, and the data qualification also includes period, product and confidence level.
7. a kind of one step process manufacture factor significant difference analysis method, it is characterised in that comprise the following steps：
S1, according to sampling prescription collection batch wafer processed test data, while gather it is all sampling wafers manufacture resume
Data, the manufacture history data include process information and manufacture factor information；
S2, by the test data with manufacture history data associated one by one, formation integral data；
S3, the data qualification that carry out significant difference analysis according to input, obtained from integral data and meet data qualification
Integral data, the data qualification include process and to carry out the manufacture factor of significant difference analysis；
The integral data for meeting the data qualification is grouped by S4, the specific item in the manufacture factor；
S5, homogeneity test of variance is used to the packet, so as to judge system of the specific item of the manufacture factor in the process
Make ability and whether there is significant difference.
8. one step process manufacture factor significant difference analysis method according to claim 7, it is characterised in that also include step
Rapid S6：The manufacture factor that manufacturing capacity significant difference be present is highlighted.
9. one step process manufacture factor significant difference analysis method according to claim 7, it is characterised in that the test
Data include CD line widths test data or THK thickness metric data.
10. the one step process manufacture factor significant difference analysis system according to claim 7 or 9, it is characterised in that described
Manufacture history data also includes temporal information and product information, and the data qualification also includes period, product and confidence level.
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| CN201710900981.9A CN107679163B (en) | 2017-09-28 | 2017-09-28 | System and method for analyzing significant difference of manufacturing factors in single-step process |
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| CN201710900981.9A CN107679163B (en) | 2017-09-28 | 2017-09-28 | System and method for analyzing significant difference of manufacturing factors in single-step process |
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| CN107679163B CN107679163B (en) | 2020-04-21 |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022100139A1 (en) * | 2020-11-10 | 2022-05-19 | 长鑫存储技术有限公司 | Detection method and detection apparatus for wafer testing machines |
| WO2023226675A1 (en) * | 2022-05-27 | 2023-11-30 | 上海哥瑞利软件股份有限公司 | System for rapidly analyzing equipment difference root cause in semiconductor manufacturing |
| WO2024016423A1 (en) * | 2022-07-22 | 2024-01-25 | 长鑫存储技术有限公司 | Method and apparatus for determining data reading duration, and test method and apparatus and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0831887A (en) * | 1994-07-15 | 1996-02-02 | Hitachi Ltd | System for detecting difference of fabrication process and method for detecting the difference |
| CN101211168A (en) * | 2006-12-28 | 2008-07-02 | 财团法人工业技术研究院 | Real time failure diagnosis and classification system applies to semiconductor preparation method |
| CN102117730A (en) * | 2009-12-31 | 2011-07-06 | 中芯国际集成电路制造(上海)有限公司 | Method for processing parameter data of machine station in manufacturing process of semiconductor and device thereof |
| CN105489524A (en) * | 2015-12-08 | 2016-04-13 | 成都海威华芯科技有限公司 | Process validation method in manufacturing process of compound semiconductor product |
-
2017
- 2017-09-28 CN CN201710900981.9A patent/CN107679163B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0831887A (en) * | 1994-07-15 | 1996-02-02 | Hitachi Ltd | System for detecting difference of fabrication process and method for detecting the difference |
| CN101211168A (en) * | 2006-12-28 | 2008-07-02 | 财团法人工业技术研究院 | Real time failure diagnosis and classification system applies to semiconductor preparation method |
| CN102117730A (en) * | 2009-12-31 | 2011-07-06 | 中芯国际集成电路制造(上海)有限公司 | Method for processing parameter data of machine station in manufacturing process of semiconductor and device thereof |
| CN105489524A (en) * | 2015-12-08 | 2016-04-13 | 成都海威华芯科技有限公司 | Process validation method in manufacturing process of compound semiconductor product |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022100139A1 (en) * | 2020-11-10 | 2022-05-19 | 长鑫存储技术有限公司 | Detection method and detection apparatus for wafer testing machines |
| WO2023226675A1 (en) * | 2022-05-27 | 2023-11-30 | 上海哥瑞利软件股份有限公司 | System for rapidly analyzing equipment difference root cause in semiconductor manufacturing |
| WO2024016423A1 (en) * | 2022-07-22 | 2024-01-25 | 长鑫存储技术有限公司 | Method and apparatus for determining data reading duration, and test method and apparatus and storage medium |
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