CN107679163B - System and method for analyzing significant difference of manufacturing factors in single-step process - Google Patents
System and method for analyzing significant difference of manufacturing factors in single-step process Download PDFInfo
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- CN107679163B CN107679163B CN201710900981.9A CN201710900981A CN107679163B CN 107679163 B CN107679163 B CN 107679163B CN 201710900981 A CN201710900981 A CN 201710900981A CN 107679163 B CN107679163 B CN 107679163B
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Abstract
The invention relates to the technical field of semiconductor manufacturing, in particular to a system and a method for analyzing a significant difference of manufacturing factors in a single step process, the system and the method collect the test data of the wafers in batch in real time from the test machine of the key process through the data collection system, meanwhile, the difference analysis system acquires the manufacturing history data of the sampled wafer on the key process from the manufacturing execution system MES, associates the test data with the manufacturing history data one by one, grouping according to the specific items of the manufacturing factors, including variance and homogeneity verification for calculation and analysis to obtain significant difference indexes of the manufacturing factors, such as obvious index difference, prompting an engineer to verify the influence of certain equipment, certain operators or certain batch of materials in the manufacturing factors on the process capability of the product, and providing a basis for later equipment maintenance and operator training; if the index difference is not significant, the engineer is prompted to search for other factors causing the significant difference.
Description
Technical Field
The invention belongs to the technical field of semiconductor manufacturing, and particularly relates to a system and a method for analyzing a significant difference of manufacturing factors in a single step process.
Background
The compound semiconductor manufacturing has the characteristics of multiple varieties and large batch, and the difference in manufacturing capability of the same type of equipment in a single step process can be caused by frequent process switching and uninterrupted equipment operation; in addition, the automation degree of the compound semiconductor production line is low, a plurality of devices are manually conveyed and operated by operators, and different operators have different operation methods; even materials of different batches or different manufacturers have material difference factors, and the small difference causes the performance of the product on a single-step process to fluctuate, thereby finally influencing the product yield.
In order to ensure that the final product yield can be stabilized at a high level, the performance fluctuation of the products in a single-step process must be solved, and the wafers in each product batch must be subjected to a single process test according to a sampling rule, such as line width, film thickness, alignment degree and the like.
In the existing single process analysis system and method, statistical algorithms such as statistical management charts, descriptive statistical values and the like are usually adopted to analyze the test parameters of a single process, and even if the method analyzes an abnormal condition or a problem, the method cannot accurately position which manufacturing factor in a production machine, an operator and materials causes. The lack of the analysis system and method results in faulty equipment, operators without post-working capability and materials which do not meet the process requirements being still applied to the process line manufacturing, resulting in low final product yield and failure to meet the customer delivery period.
Disclosure of Invention
The invention aims to provide a system and a method for realizing the analysis of the significant difference of each condition of a single-step process manufacturing factor by combining test data with manufacturing history and utilizing a statistical algorithm.
In order to meet the requirements, the technical scheme adopted by the invention is as follows: the difference analysis system acquires test data of wafers in a batch in a manufacturing process from a test machine table of a key process in real time through a data acquisition system, acquires manufacturing history data of a sampled wafer on the key process from a Manufacturing Execution System (MES) and associates the test data and the manufacturing history data one by one to form integrated data; acquiring integrated data meeting the data conditions from the integrated data according to the input data conditions, wherein the data conditions comprise time periods, products, processes and manufacturing factors, and the manufacturing factors can be production machines, operators or material batches; depending on manufacturing factors, the data conditions also include different confidence levels; grouping the screened integrated data according to specific items of the manufacturing factors, including variance homogeneity verification for calculation and analysis, obtaining significance difference indexes of the manufacturing factors, and prompting an engineer to verify the influence of certain equipment, certain operators or certain batches of materials in the manufacturing factors on the process capability of products in the case of significant index difference, so as to provide a basis for later equipment maintenance and operator training; if the index difference is not significant, the engineer is prompted to search other reasons causing the significant difference.
Compared with the prior art, the invention has the following advantages: after the single process capability deviation occurs in a certain period of time, the significance difference analysis and verification of various manufacturing factors are carried out, so that an engineer can be ensured to accurately position the specific reason of the single process capability deviation, and manufacturing factors such as equipment with poor process capability, operators with poor manufacturing capability or materials with poor quality are prevented from being continuously put into production, and the manufacturing yield of a process line can be greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a block diagram of a one-step process manufacturing factor significant difference analysis system of the present invention;
FIG. 2 is a flow chart of a method for analyzing the significant difference of the manufacturing factors in a single step process according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings and specific embodiments. Certain features that are well known to those skilled in the art have been omitted from the following description for the sake of simplicity.
As shown in fig. 1, the present embodiment provides a one-step process manufacturing factor significant difference analysis system 1, which includes a difference analysis system 1 and a data acquisition system 5.
The data acquisition system 5 includes:
the sampling rule setting module 52 is used for setting a sampling rule of the collected test data and transmitting the sampling rule to the process measuring machine 6 through a semiconductor standard communication protocol; the sampling rule of the embodiment is as follows: collecting test data of 10 points for each wafer, and selecting the first 10 wafers in each batch;
a data collection module 51, configured to collect test data of wafers in a batch from the process metrology tool 6 according to the sampling rule, and synchronize the data to the test data collection module 17 through the data transmission system 3; in this embodiment, the test data is CD linewidth test data.
The difference analysis system 1 includes:
the test data acquisition module 17 is used for receiving the test data acquired by the data acquisition system 5;
an MES interface module 15, configured to collect manufacturing history data of all sampled wafers in the manufacturing execution system 4, where the manufacturing history data is a serial number of a lithography machine in a T-grid process in this embodiment, and the manufacturing history data further includes manufacturing time and products;
the data integration module 13 is configured to associate the test data of the sampled wafers with the manufacturing history data one by one, that is, associate the test data of the non-batch wafers with the manufacturing history data corresponding to the batch wafers one by one to form integrated data;
a database 11 for storing test data, manufacturing history data, and integration data;
a user access module 16, configured to receive data conditions for significant difference analysis, where the data conditions in this embodiment include: time period (2017, 5/1/2017 to 2017, 8/31/2017), product (GaAs pHemt), process (PPA25001), manufacturing factor (lithography machine), confidence level (95%); selecting integrated data of which the manufacturing history data is matched with the data conditions from the integrated data, namely the manufacturing time recorded in the manufacturing history data is between 5 months and 1 days in 2017 and 8 months and 31 days in 2017, the manufactured product is GaAs pHemt, the manufacturing process is PPA25001, and the manufacturing factor is a photoetching machine;
the factor grouping module 14 is used for grouping the CD line width test data according to the photoetching machines used in the T-shaped grid process of each batch, and exposing all batches of wafers on the photoetching machine No. 1 or the photoetching machine No. 2 in the T-shaped grid process, so that the data are integrated and grouped according to the numbers of the photoetching machines;
the significant difference analysis module 12 calculates the F statistic of the two groups to be 6.12 by using the homogeneity of variance test, the P value is 0.0014, and the P value is less than the significant level of 0.05, so that the difference between the manufacturing capacities of the lithography machine No. 1 and the lithography machine No. 2 can be determined to be significant, and therefore an engineer should pay attention to the manufacturing capacity of the lithography machine in the process and should perform maintenance on the lithography machine No. 2 with large variation. The homogeneity test of variance belongs to the prior art, and the specific calculation method is not described herein.
The system further comprises an interactive system 2 for an operator to input data conditions for significant difference analysis, the significant difference analysis conditions selected at the interactive system 2 being communicated to the user access module 16 via the HTTP protocol; and highlights the manufacturing factor specific items that cause significant differences in process capabilities.
The method for analyzing the significant difference of the manufacturing factors in the single-step process by adopting the system comprises the following steps as shown in FIG. 2:
s1, collecting CD linewidth test data of the wafers in batch according to a sampling rule, where the sampling rule in this embodiment is: collecting test data of 10 points for each wafer, and selecting the first 10 wafers in each batch; collecting the manufacturing history data of all the sampled wafers at the same time, wherein the manufacturing history data are the number of the photoetching machine in the T-shaped gate process, the manufactured time and the product;
s2, associating the test data with the manufacturing history data one by one to form integrated data;
s3, acquiring integrated data meeting data conditions from the integrated data according to the input data conditions to be subjected to significant difference analysis; the data conditions in this embodiment include: time period (2017, 5/1/2017 to 2017, 8/31/2017), product (GaAs pHemt), process (PPA25001), manufacturing factor (lithography machine), confidence level (95%);
s4, grouping the integrated data meeting the data condition according to specific items in the manufacturing factors, wherein the manufacturing factors are lithography machines, and therefore the integrated data are grouped according to the numbers of the adopted lithography machines;
s5, calculating the F statistic value to be 6.12 by adopting the homogeneity test of the variance, the P value to be 0.0014, and the P value to be less than the significance level of 0.05, so that the difference between the manufacturing capacities of the photoetching machine No. 1 and the photoetching machine No. 2 can be judged to be significant, and therefore an engineer should pay attention to the manufacturing capacity of the photoetching machine in the process and should maintain the photoetching machine No. 2 with large variation.
S6, highlighting the manufacturing factors that have significant differences in manufacturing capabilities to alert engineers to improve the manufacturing factors of the lithography machine.
Before step S5, the mean and standard deviation of the CD linewidth test data in each group may be calculated to see if significant differences can be judged by the mean and standard deviation, and if not, the homogeneity test of the variance is used. In this example, the average value of the lithography machine No. 1 was 1.4195 μm, and the standard deviation was 0.0035; the average value of the photoetching machine No. 2 is 1.4092 μm, the standard deviation is 0.0443, the two groups of data are within the specification limit of the parameter and close to the target, and whether the significant difference exists can not be judged through the indexes of the average value and the standard deviation.
The above examples are merely illustrative of several embodiments of the present invention, which are described in more detail and detail, but are not to be construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the claims.
Claims (10)
1. A single-step process manufacturing factor significant difference analysis system is characterized by comprising a difference analysis system and a data acquisition system, wherein the data acquisition system acquires test data of wafers in batches from a process measurement machine according to a sampling rule;
the difference analysis system includes:
the test data acquisition module is used for acquiring the test data acquired by the data acquisition system;
the MES interface module is used for collecting manufacturing history data of all sampled wafers in the manufacturing execution system, wherein the manufacturing history data comprises process information and manufacturing factor information;
the data integration module is used for associating the test data of the sampled wafer with the manufacturing history data one by one to form integrated data;
the system comprises a user access module, a data analysis module and a data analysis module, wherein the user access module is used for collecting data conditions for significant difference analysis and acquiring integrated data meeting the data conditions from the integrated data, and the data conditions comprise processes and manufacturing factors for significant difference analysis;
the factor grouping module is used for grouping the integrated data meeting the data condition according to specific items in the manufacturing factors;
and the significant difference analysis module is used for carrying out the homogeneity test on the variances of the groups so as to judge whether the specific items of the manufacturing factors have significant differences in the manufacturing capability of the process.
2. The system of claim 1, wherein the data acquisition system specifically comprises:
the sampling rule setting module is used for setting a sampling rule for acquiring test data;
and the data acquisition module is used for acquiring the test data of the wafers in the batch from the process measurement machine according to the sampling rule.
3. The single-step process manufacturing factor significant difference analysis system of claim 1 or 2, further comprising an interactive system for an operator to input data conditions for significant difference analysis and to highlight manufacturing factors that have significant differences in manufacturing capability.
4. The single-step process manufacturing factor significant difference analysis system of claim 3, further comprising a database for storing the test data, the manufacturing history data, and the integration data.
5. The system of claim 1, wherein the test data comprises CD line width test data.
6. The single-step process manufacturing factor significant difference analysis system of claim 1 or 5, wherein the manufacturing history data further comprises time information and product information, and the data conditions further comprise time period, product, and confidence level.
7. A method for analyzing the significant difference of manufacturing factors in a single-step process is characterized by comprising the following steps of:
s1, collecting the test data of the wafers in the batch according to the sampling rule, and collecting the manufacturing history data of all the sampled wafers, wherein the manufacturing history data comprises process information and manufacturing factor information;
s2, associating the test data with the manufacturing history data one by one to form integrated data;
s3, acquiring integrated data meeting data conditions from the integrated data according to the input data conditions to be subjected to the significant difference analysis, wherein the data conditions comprise processes and manufacturing factors to be subjected to the significant difference analysis;
s4, grouping the integrated data meeting the data condition according to specific items in the manufacturing factors;
and S5, adopting a homogeneity test of variance to the groups to judge whether the specific items of the manufacturing factors have significant difference in the manufacturing capability of the process.
8. The method for analyzing significant differences in manufacturing factors according to claim 7, further comprising step S6: manufacturing factors that differ significantly in their manufacturing capabilities are highlighted.
9. The method of claim 7, wherein the test data comprises CD line width test data.
10. The single-step process manufacturing factor significant difference analysis method according to claim 7 or 9, wherein the manufacturing history data further includes time information and product information, and the data conditions further include time period, product, and confidence level.
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| CN114461457A (en) * | 2020-11-10 | 2022-05-10 | 长鑫存储技术有限公司 | Detection method and detection device for wafer tester |
| CN114912898A (en) * | 2022-05-27 | 2022-08-16 | 上海哥瑞利软件股份有限公司 | System for rapidly analyzing equipment difference root cause in semiconductor manufacturing |
| CN117476083A (en) * | 2022-07-22 | 2024-01-30 | 长鑫存储技术有限公司 | Method for determining data reading time length, testing method, device and storage medium |
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| 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 |
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| 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 |
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