CN110895404A - Method and system for automatically detecting correlation between integrated circuit parameters - Google Patents
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Abstract
The invention provides a method and a system for automatically detecting the correlation among integrated circuit parameters, which can select the parameters to be analyzed from the captured data information related to the production and the manufacture of integrated circuits to form parameter sets to be analyzed, divide the parameter sets to be analyzed into one or more groups of parameter subsets to be analyzed, and set statistical factors for comparing the correlation among the parameters of the parameter subsets to be analyzed, thereby calculating the correlation among the parameters of the parameter subsets to be analyzed, and further highlighting and displaying the parameters with strong correlation among the parameter subsets to be analyzed according to the calculation result.
Description
Technical Field
The present invention relates to the field of integrated circuit manufacturing technologies, and in particular, to a method and a system for automatically detecting correlation between parameters of an integrated circuit.
Background
In the integrated circuit manufacturing process, a large amount of data, such as machine parameters under each process and online (inline) measurement data on a production line, are generated in real time, and the data need to be analyzed to find abnormal data in time, so as to adjust or optimize the process or machine equipment, and further ensure that the produced product has high yield and reliability. At present, manual analysis methods are mostly adopted for analyzing the data. However, as the data amount of the machine parameters and the measured data is larger and larger, the current manual analysis method cannot process these large data comprehensively and quickly, and when the inspection of the result is also more expensive, engineers spend a lot of time on data analysis with insignificant correlation to the key parameters of the subsequent process improvement or the electrical performance of the final device, and the judgment of abnormal data is not sensitive enough, and the abnormal data cannot be found quickly and accurately, so that the online abnormal condition on the production line cannot be found in time, and then the corresponding adjustment or optimization cannot be performed on the process or the machine equipment in time, and the performance of the final device cannot be improved in time. Obviously, the current manual data analysis method cannot meet the analysis requirement of large data in integrated circuit manufacturing.
Disclosure of Invention
The present invention provides a method and system for automatically detecting the correlation between parameters of an integrated circuit, which can automatically detect the correlation between the parameters of the integrated circuit to quickly find the cause of the occurrence of an abnormality in the production and manufacturing process of the integrated circuit.
In order to achieve the above object, the present invention provides a method for automatically detecting correlation between parameters of an integrated circuit, comprising:
capturing data information, wherein the data information comprises content values of various parameters related to the production and the manufacture of the integrated circuit;
selecting parameters to be analyzed from the captured data to form a parameter set to be analyzed, and dividing the parameter set to be analyzed into one or more parameter subsets to be analyzed; and the number of the first and second groups,
setting statistical factors for comparing the correlation among the parameters of the parameter subsets to be analyzed, calculating the correlation among the parameters of the parameter subsets to be analyzed, and highlighting and displaying the parameters with strong correlation among the parameter subsets to be analyzed according to the calculation result.
Optionally, in the method, the plurality of parameters include two or more of a lot number of the product, a production time, a parameter of the process tool, process monitoring test data, and measurement data of the metrology tool.
Optionally, in the method, the parameter to be analyzed includes at least one of direct data measured by any metrology tool, parameter or data for performance test, parameter or data for functional test, wafer acceptance test parameter or test data, failure test parameter or data, failure pattern analysis data, digitized parameter, and process tool parameter.
Optionally, in the method, the parameters that are digitized include yield and/or quality factor.
Optionally, in the method, the statistical factor includes at least one of a difference value, a standard deviation, an assumed probability value, and a correlation coefficient.
Optionally, the method for detecting correlation between parameters of an integrated circuit further presents the correlation between strongly correlated parameters among the subsets of parameters to be analyzed as a graphical result.
The present invention also provides a computer storage medium having a computer program stored thereon, characterized in that: the program, when executed by a processor, implements the method for automatically detecting correlations between parameters of an integrated circuit of the present invention.
The present invention also provides a system for automatically detecting correlation between parameters of an integrated circuit, comprising:
a data capture device configured to capture data material including content values of a plurality of parameters associated with integrated circuit manufacturing;
the parameter selecting and grouping device is configured to select parameters to be analyzed to form a parameter set to be analyzed, and divide the parameter set to be analyzed into one group or more than one group of parameter subsets to be analyzed; and the number of the first and second groups,
and the correlation analysis device is configured to set statistical factors for comparing the correlation among the parameters of the parameter subsets to be analyzed, calculate the correlation among the parameter subsets to be analyzed, and highlight and display the parameters with strong correlation among the parameter subsets to be analyzed according to the calculation result.
Optionally, in the system for automatically detecting correlation between parameters of an integrated circuit, the plurality of parameters include two or more of lot number of a product, production time, process tool parameters, and metrology data of a metrology tool.
Optionally, in the system for automatically detecting correlation between parameters of an integrated circuit, the parameter to be analyzed includes at least one of direct data measured by any metrology tool, parameter or data for performance test, parameter or data for functional test, wafer acceptance test parameter or test data, failure test parameter or data, failure pattern analysis data, digitized parameter, and process tool parameter.
Optionally, in the system for automatically detecting correlation between parameters of an integrated circuit, the parameters to be digitized include yield and/or quality factor.
Optionally, in the system for automatically detecting correlation between parameters of an integrated circuit, the statistical factor includes at least one of a difference value, a standard deviation, an assumed probability value, and a correlation coefficient.
Optionally, the system for automatically detecting the correlation between the parameters of the integrated circuit further comprises a user interface device configured to provide an interface for operating the data capture device, the parameter selection and grouping device and the correlation analysis device for a user, and present the data captured by the data capture device, the parameters and the grouping conditions selected by the parameter selection and grouping device and the portion of the calculation result of the correlation analysis device, which is required to be presented, to the user.
Optionally, the user interface device is further configured to present the correlation of the strongly correlated parameters among the subsets of parameters to be analyzed as a graphical result.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the data analysis method can capture required data information from existing mass data, and can further perform correlation comparison only on data corresponding to the selected parameters, so that the data volume of one-time analysis can be reduced, and the pertinence of data analysis can be improved.
2. The method can select a proper statistical factor and the specification (namely the threshold value) for judging the strong correlation, can further calculate the value of the statistical factor among one group or more than one group of parameter subsets to be analyzed, and can find the online abnormal condition on the production line in time when the calculated value of the statistical factor meets the set specification, namely the strong correlation parameter and data, so that the process or machine equipment and the like can be adjusted or optimized correspondingly in time, and the final device performance is improved.
3. According to the technical scheme, the correlation analysis among the parameters can be automatically realized, the reason of the abnormal occurrence in the production process can be rapidly found by means of the strongly correlated parameters and data, the method and the device are suitable for any automatic machine production process or any industry which can generate a large amount of data, and the application range is wide.
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FIG. 1 is a flow chart of a method for automatically detecting correlation between parameters of an integrated circuit according to an embodiment of the present invention.
FIG. 2 is a data diagram illustrating the execution of step S1 of the method for automatically detecting correlation between parameters of an integrated circuit according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of parameter grouping when step S2 is executed by the method for automatically detecting correlation between parameters of an integrated circuit according to an embodiment of the present invention.
Fig. 4 is a parameter grouping diagram illustrating the method for automatically detecting the correlation between the parameters of the integrated circuit according to the embodiment of the invention when the step S3 is executed.
Fig. 5a to 5c are graphs illustrating the calculation results when the method for automatically detecting the correlation between the parameters of the integrated circuit according to the embodiment of the present invention performs step S3.
FIG. 6 is a block diagram of a system for automatically detecting correlations between parameters of an integrated circuit, in accordance with an embodiment of the present invention.
FIG. 7 is an interface for user interface device to present to a user the selection and grouping of parameters in accordance with an embodiment of the present invention.
FIG. 8 is an interface of statistical factor selection and specification set presented to a user by the user interface device according to the embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings in order to make the objects and features of the present invention more comprehensible, however, the present invention may be realized in various forms and should not be limited to the embodiments described above. Furthermore, it should be noted that the techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.).
Referring to fig. 1, the present invention provides a method for automatically detecting correlation between parameters of an integrated circuit, comprising:
s1, capturing data information, wherein the data information comprises content values of various parameters related to the production and manufacturing of the integrated circuit;
s2, selecting the parameters to be analyzed from the captured data to form the parameter set to be analyzed, and dividing the parameter set to be analyzed into one or more parameter subsets to be analyzed;
s3, setting statistical factors for comparing the correlation among the parameters of the parameter subsets to be analyzed, calculating the correlation among the parameters of the parameter subsets to be analyzed, and highlighting and displaying the parameters with strong correlation among the parameter subsets to be analyzed according to the calculation result.
During the manufacturing process of an integrated circuit, a large amount of data is generated and stored in one or more databases. In step S1, data to be used may be captured from the corresponding database, where the data includes a plurality of data generated by the integrated circuit manufacturing process, including two or more of lot number of the product, production time, parameters of the process tool, process monitoring test data, and metrology data of the metrology tool. The process tool parameters may include data collected by sensors in the tool such as power, pressure, temperature, gas, etc. The process monitoring test data may include various test data, defect scan data, and the like. The measurement data of the metrology tool is typically Inline measurement data, which mainly includes line widths of various structures, thicknesses and flatness and roughness of formed layers, critical dimensions such as depths of holes and trenches and diameters of holes, doping concentration, defect number, particle number, and the like. Referring to fig. 2 and fig. 5a to 5c, in the embodiment, the captured data includes lot numbers (lot numbers) AB000001, AB000002, AB000003, and AB000004 … … of the product, and further includes a machine number: the delivery time of each lot on the corresponding machine, for example, the delivery times are respectively recorded as: 018. 019, 020, 021, 022, 023, 024, 025, 123, 124, 129, 203, 209, 213, 227 and the like.
Referring to fig. 3, in step S2, parameters to be analyzed are selected from the captured data to form a parameter set to be analyzed, and the parameter set to be analyzed is divided into one or more parameter subsets to be analyzed. The parameters to be analyzed include at least one of direct data (inline) measured by any metrology tool, parameters or data for performance testing (BIN), parameters or data for functional testing, parameters to be digitized, and process tool parameters. Wherein the parameters to be digitized include, for example, Yield (Yield) and/or quality factor (Q), the Yield further includes a direct current parameter Yield YDC, a functional parameter Yield YF, a non-functional parameter Yield YF, a margin parameter Yield YM, and a Pre-repair Yield YP (Pre-Fuse Yield), the parameters or data for performance testing may be divided into various test items by test type, such as a Direct Current (DC) parameter test item and an Alternating Current (AC) parameter test item, the direct current parameter test item includes: an open circuit test (which may be defined as test item 1 and denoted as BIN1), a short circuit test (which may be defined as test item 2 and denoted as BIN2), an input current test (which may be defined as test item 3 and denoted as BIN3), a leakage current test (which may be defined as test item 4 and denoted as BIN4), a supply current test (which may be defined as test item 5 and denoted as BIN5), a threshold voltage test (which may be defined as test item 6 and denoted as BIN6), and the like; the communication parameter test items comprise: rise time, fall time, delay time, hold time, pause time, access time, function speed time, etc. In this embodiment, five yield rates of YDC, YF, YM, and YP to be analyzed and six parameters (or called test items) for performance test in BIN1, BIN2, BIN3, BIN4, BIN5, and BIN6 are selected to form a parameter set to be analyzed, and all the parameters in the parameter set to be analyzed are further divided into two parameter subsets to be analyzed, where the parameter subset to be analyzed Group a is composed of five yield rates of YDC, YF, YM, and YP, and the parameter subset to be analyzed Group pb is composed of six parameters for performance test, BIN1, BIN2, BIN3, BIN4, BIN5, and BIN 6.
In step S3, first, a statistical factor for the correlation between any two parameters of the parameter subset to be analyzed Group a and the parameter subset to be analyzed Group pb, and a specification corresponding to the statistical factor need to be set. Wherein the statistical factor may include at least one of a difference value (abbreviated as "Diff"), a standard deviation (STD), an assumed probability value (P-value), and a correlation coefficient (correlation coefficient). The correlation coefficient may be a pearson correlation coefficient. Referring to fig. 4, in the present embodiment, the statistical factors selected are an assumed probability value (P-value, i.e., P-value) and a difference value (difference), and only the assumed probability value (P-value, i.e., P-value) is specified as P <0.05, that is, an assumed probability value P of any two parameters between the to-be-analyzed parameter subset Group a and the to-be-analyzed parameter subset Group pb is calculated, when the calculated P value is less than 0.05, it is indicated that the two parameters corresponding to the P value are strongly correlated or that there is a significant difference between the two parameters, and data information (i.e., content values of the two parameters) corresponding to the two parameters are also strongly correlated.
In step S3, the value of the statistical factor between any of the parameters in the parameter subset to be analyzed Group a and any of the parameters in the parameter subset to be analyzed Group pb is then calculated. In this embodiment, since the selected statistical factor is a P value, a P value between any parameter in the parameter subset to be analyzed Group a and any parameter in the parameter subset to be analyzed Group b is calculated. In step S1, the captured data includes data samples corresponding to each parameter of the to-be-analyzed parameter subset Group a and the to-be-analyzed parameter subset Group pb, so in step S3, the P value between any parameter X in the to-be-analyzed parameter subset Group a and any parameter Y in the to-be-analyzed parameter subset Group pb can be calculated by using the statistical P value calculation method based on the data in step S1, and the specific calculation process is as follows:
first, the pearson correlation coefficient r, r of two parameters (X, Y) is calculated equal to the product (σ X, σ Y) of the covariance cov (X, Y) between them divided by their respective standard deviations, for example:
where n is the number of samples of the two parameters (X, Y) in the data file captured in S1.
Then, calculating a T value according to a significance T test formula, and looking up a table according to the T value to obtain a P value, wherein the specific significance T test formula is as follows:
where r is the pearson correlation coefficient of two parameters (X, Y).
In step S3, the results of the correlation calculation are further displayed in a report form, and all the results of the correlation calculation in the report form are arranged in an ascending or descending manner according to the value of the selected statistical factor, and the specification satisfying the set statistical factor is automatically highlighted (highlight) to display the table data where the strongly correlated parameters are located. Referring to fig. 4, in the present embodiment, the result of calculating the P values between the parameters of the to-be-analyzed parameter subset Group a and the to-be-analyzed parameter subset Group B is presented in a report form, the data in the report form are arranged in an ascending order of the P values (i.e. from small to large), and further the table data with the P value less than 0.05 (i.e. the set specification) is highlighted, that is, the first 4 rows of data in the table, as can be seen from fig. 4, the P values of the parameter YD in the to-be-analyzed parameter subset Group a and the parameter BIN1 in the to-be-analyzed parameter subset Group B are 0.004<0.05, which are the most strongly related parameters, the P values of the parameter YF in the to-be-analyzed parameter subset Group a and the parameter BIN2 in the to-be-analyzed parameter subset Group B are 0.006<0.05, which are also strongly related parameters, and the P values of the parameter YF in the to-be-analyzed parameter subset Group a and the parameter subset B are 3 <0.05, the parameters YM in the parameter subset to be analyzed Group a and the P value of the parameter BIN4 in the parameter subset to be analyzed Group B are 0.02<0.05, which are also strongly correlated parameters. That is, the data corresponding to the parameter YD in the parameter subset Group a to be analyzed and the parameter BIN1 in the parameter subset Group B to be analyzed in the data captured in step S1 are the most strongly correlated data, the data corresponding to the parameter YF in the parameter subset Group a to be analyzed and the parameter BIN2 in the parameter subset Group B to be analyzed in the data captured in step S1 are the strongly correlated data, the data corresponding to the parameter YF in the parameter subset Group a to be analyzed and the parameter BIN3 in the parameter subset Group B to be analyzed in the data captured in step S1 are also the strongly correlated data, and the data corresponding to the parameter YM in the parameter subset Group a to be analyzed and the parameter BIN4 in the parameter subset Group B to be analyzed in the data captured in step S1 are also the strongly correlated data, which include the data that are most likely to cause an abnormal problem. Further pointing the chart field on the report can directly present the data as a graphical result, wherein the graphical result comprises at least one of a graph (trand chart), a box chart (BOXPLOT), an all-in-one chart (3 in1 chart) and a scatter chart (scatter). The form of the all-in-one chart is, for example, the all-in-one graph shown in fig. 5a, or the box chart shown in fig. 5b, or the table shown in fig. 5 c. For example, when clicking on the column of the graph in the YP-BIN5 row in fig. 4, the graph shown in fig. 5a may be presented, where the content in fig. 5a illustrates the time when the data captured in step S1 corresponding to the two parameters YP and BIN5 includes the time when the slave machine 1, machine 2, and machine 3 are located and the YP yield information of the wafer in the wafer lot in the corresponding machine within the time, and fig. 5a may further indicate that the data captured in step S1 corresponding to the two parameters YP and BIN5 includes the wafer lot in the slave machine 1, machine 2, and machine 3 and the YP yield information of the wafer lot, and the total number of wafer lots from the machine 1, machine 2, and machine 3 is 283; fig. 5c may also present a table, where the content in fig. 5c illustrates that the data corresponding to the YP and BIN5 parameters in the data captured in step S1 includes the numbers of the wafer lots coming from the machine 1, the machine 2, and the machine 3, the YP yield information of each wafer lot, and the like, and there are 283 wafer lots coming from the machine 1, the machine 2, and the machine 3, where there are 9 wafer lots coming from the machine 1, the average value of the YP yields of these 9 wafer lots is 51.6, the standard deviation of the YP yields is 32.27, the median value is 63.2, there are 264 wafer lots coming from the machine 2, the average value of the YP yields of these 264 wafer lots is 70.1, the standard deviation of the YP yields is 15.75, the median value is 70.8, there are 10 wafer lots coming from the machine 3, the average value of the YP yields of these 10 wafer lots is 70.2, the standard deviation of the YP yields is 3.87, and the median value is 69.9.
In other embodiments of the present invention, when the statistical factor selected in step S3 is a pearson correlation coefficient, an r value between any parameter X in the to-be-analyzed parameter subset Group a and any parameter Y in the to-be-analyzed parameter subset Group pb is further calculated according to the pearson correlation coefficient r, and then which two parameters are strongly correlated is determined according to a set r specification. When the statistical factor selected in step S3 is a standard deviation, one way is to further set a difference between the standard deviations in step S3, set specifications of the standard deviation and the difference, then calculate a standard deviation of any parameter X in the to-be-analyzed parameter subset Group a and a standard deviation of any parameter Y in the to-be-analyzed parameter subset Group pb, and then calculate a difference between a standard deviation of any parameter X in the to-be-analyzed parameter subset Group a and a standard deviation of any parameter Y in the to-be-analyzed parameter subset Group pb, and when both the standard deviation of the parameter X in the to-be-analyzed parameter subset Group a and the standard deviation of the parameter Y in the to-be-analyzed parameter subset Group pb satisfy the set specification of the standard deviation and a difference between the two also satisfy the specification of the difference, the two parameters are strongly correlated; another way is to set only the standard deviation specification in step S3, then mix the data of any parameter X in the parameter subset Group a to be analyzed and any parameter Y in the parameter subset Group pb to be analyzed, and then calculate the standard deviation of the mixed data, and when the calculated standard deviation meets the set standard deviation specification, it indicates that the parameter X in the parameter subset Group a to be analyzed and the parameter Y in the parameter subset Group pb to be analyzed that correspond to the standard deviation are strongly correlated.
In other embodiments of the present invention, when the statistical factor selected in step S3 is a difference value, a T test method may be adopted to test the significance of the difference between any parameter X in the to-be-analyzed parameter subset Group a and any parameter Y in the to-be-analyzed parameter subset Group pb, to calculate a T value, and then a table is looked up according to the calculated T value until the difference value D is reached, where a calculation formula of the T value is as follows;
in the formula (I), the compound is shown in the specification,
is the arithmetic mean of the parameters X in the parameter subset Group a to be analyzed,
is the arithmetic mean of the parameters Y in the parameter subset to be analyzed Group B,
is a standard deviation of an arithmetic mean of the parameter X and the parameter Y, and when the data amounts of the parameter X and the parameter Y are the same,
the calculation formula of (a) is as follows:
and when the data quantity of the parameter X is n1And the quantity of data of parameter Y is n2When the temperature of the water is higher than the set temperature,
the calculation formula of (a) is as follows:
with continued reference to fig. 1, the present invention further provides a computer storage medium having stored thereon a computer program, which may include code/computer-executable instructions, which, when executed by a processor, performs steps S1 to S4 of the method for automatically detecting correlations between integrated circuit parameters shown in fig. 1, and any variations thereof. The computer storage medium may be any medium that can contain, store, communicate, propagate, or transport the instructions. For example, the computer storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Specific examples of the computer storage medium include: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and/or wired/wireless communication links.
Referring to fig. 6, the present invention further provides a system for automatically detecting correlation between parameters of an integrated circuit, comprising: data fetching means 11, parameter selecting and grouping means 12, correlation analysis means 13 and user interface means 14.
The data capture device 11 is configured to capture data to be used from a data storage location (e.g., one or more databases) during the ic manufacturing process, where the data includes a plurality of data generated by the ic manufacturing process, including two or more of lot number of a product, production time, process tool parameters, process monitoring test data, and metrology data of a metrology tool. The process tool parameters may include data collected by sensors in the tool such as power, pressure, temperature, gas, etc. The process monitoring test data may include various test data, defect scan data, and the like. The measurement data of the metrology tool is typically Inline measurement data, which mainly includes line widths of various structures, thicknesses and flatness and roughness of formed layers, critical dimensions such as depths of holes and trenches and diameters of holes, doping concentration, defect number, particle number, and the like. The data captured by the data capture device 11 may be displayed to the user through the user interface device 14, and may display specific data to the user in a list manner, and the specific data may be sorted in the list in an ascending manner or a descending manner according to the wafer number, for example, as shown in fig. 2, the specific content in fig. 2 may refer to the above description of the content in step S1, and is not described herein again.
The parameter selecting and grouping device 12 is configured to select parameters to be analyzed to form parameter sets to be analyzed, and to group the parameter sets to be analyzed into one or more parameter subsets to be analyzed; the parameters to be analyzed include at least one of direct data (inline) measured by any metrology tool, parameters or data (BIN) for performance test, parameters or data for functional test, digitalized parameters and process tool parameters, the digitalized parameters include Yield (Yield) and/or quality factor (Q), the Yield further includes DC parameter Yield YDC, functional parameter Yield YF, non-functional parameter Yield YF, margin parameter Yield YM and Yield before repair YP, the parameters or data for performance test can be divided into various test items according to test types, such as DC (DC) parameter test item and AC (AC) parameter test item, the DC parameter test item includes: an open circuit test (which may be defined as test item 1 and denoted as BIN1), a short circuit test (which may be defined as test item 2 and denoted as BIN2), an input current test (which may be defined as test item 3 and denoted as BIN3), a leakage current test (which may be defined as test item 4 and denoted as BIN4), a supply current test (which may be defined as test item 5 and denoted as BIN5), a threshold voltage test (which may be defined as test item 6 and denoted as BIN6), and the like; the communication parameter test items comprise: rise time, fall time, delay time, hold time, pause time, access time, function speed time, etc. Referring to fig. 7, the interface for selecting the parameters to be analyzed by the parameter selecting and grouping device 12, the interface for forming the parameter set to be analyzed by the selected parameters to be analyzed, and the interface for grouping the parameter set to be analyzed into one or more than one parameter subsets to be analyzed can all be displayed to the user by the user interface device 14. For example, after the user selects the Yield parameter Yield and the test item parameter BIN on the interface of the user interface device 14 for displaying the parameter selection and selecting the parameter to be analyzed by the grouping device 12, the various yields (e.g., YDC, YF, YM, YP, … …) included in the Yield parameter Yield and the various test items (e.g., BIN1, BIN2, BIN3, BIN4, BIN5, BIN6, … …) included in the test item parameter BIN constitute a parameter set to be analyzed, all the yields and test items included in the parameter set to be analyzed may be displayed in a list manner on the interface of the parameter set to be analyzed, the user may further select YDC, YF, YM, YP from all parameter lists in the interface of the parameter set to be analyzed as a parameter subset to be analyzed, and select a subset of BIN1, BIN2, BIN3, BIN 637, BIN 68562, BIN6 as a subset of the parameter to be analyzed.
The correlation analysis device 13 is configured to set a statistical factor for comparing the correlations among the parameters of each to-be-analyzed parameter subset, calculate the correlations among the parameters of each to-be-analyzed parameter subset, and display the highlighted parameters with strong correlations in each to-be-analyzed parameter subset according to the calculation result. Wherein the statistical factor may include at least one of a difference value (abbreviated as "Diff"), a standard deviation (STD), an assumed probability value (P-value), and a correlation coefficient (correlation coefficient). The correlation coefficient may be a pearson correlation coefficient. The interface of the specification corresponding to the statistical factor selected by the correlation analysis device 13 and the statistical factor set can be displayed to the user through the user interface device 14, the displayed interface is shown in fig. 8, and the drop-down arrow on the right side of the "P-value" is clicked
Other statistical factors can be shown, in an embodiment of the present invention, boxes on the left and right sides of "or (or logic)" represent the upper and lower limits of the specification, click "<(less than) "Right Pull-down arrow
Can be combined ">The mathematical operators such as "not"or "Right side Pull-down arrow
The logical "and (and)" and the like may be switched. In another embodiment of the present invention, the boxes on the left and right sides of "or (or logic)" indicate that two statistical factors are set, and the other statistical factor and its specification are set on the right side of "or (or logic)".
The correlation analysis device 13 is configured to calculate, based on the data information captured by the data capture device 11, the correlation between the parameter selection and each parameter subset to be analyzed set by the grouping device 12, and specifically calculate the value of a statistical factor between any two parameters, where two parameters corresponding to the value of the statistical factor meeting the set specification are strongly correlated parameters, and data corresponding to the strongly correlated parameters are strongly correlated data. The process of calculating the parameter correlation between the parameter subset to be analyzed Group a and the parameter subset to be analyzed Group B by the correlation analysis device 13 in this embodiment may refer to the related content in step S3, and is not described herein again. The calculation results of the correlation analysis means 13 are presented to the user via the user interface means 14.
As can be seen from the above, the user interface device 14 has an editing window for displaying configuration files, a viewing window for viewing various editing and calculating results, and a graphic window for displaying reports, graphics, etc. and may be configured to provide the user with an interface for operating the data capture device 11, the parameter selection and grouping device 12, and the correlation analysis device 13, and display the data captured by the data capture device 11, the parameters and grouping conditions selected by the parameter selection and grouping device 12, the statistical factors selected by the correlation analysis device 13, and the specifications set for the statistical factors and the portions of the correlation calculation results to be displayed. Preferably, the user interface device 14 is further configured to display all or part of the results calculated by the correlation analysis device 13 in a report form, and arrange all the related data contents in the report form according to the ascending (i.e. from small to large) or descending (i.e. from large to small) order of the calculated statistical factor values. In this embodiment, referring to fig. 4 and fig. 5a to 5c, the user interface device 14 arranges the calculation results of the correlation analysis device 13 with the P value not greater than 0.8 according to the ascending power of the P value (i.e. from small to large), and further highlights the table data with the P value less than 0.05 (i.e. the set specification), so as to directly prompt that the highlighted data in the user report needs special attention, is a strongly correlated parameter, and the corresponding data information includes data that is most likely to cause an abnormal problem. When the user further clicks on the chart field on the report shown in fig. 4, the data corresponding to the strongly related parameters can be directly presented as a graphical result, for example, the all-in-one graph shown in fig. 5a, or the box diagram shown in fig. 5b, or the table shown in fig. 5 c. For example, when clicking on the column of the graph in the YP-BIN5 line in fig. 4, the graph shown in fig. 5a may be presented, and the specific contents in fig. 5a to 5c may refer to the above detailed description of step S3, which is not repeated herein.
In order to provide the summarized results to the user for the convenience of the user to view at any time, the user interface device 14 is further configured to convert the report forms and graphs corresponding to all or part of the results of the analysis and calculation into report forms, and may further output the report forms to an external device.
It will be understood that the data fetching means 11, the parameter selecting and grouping means 12, the correlation analysis means 13 and the user interface means 14 may be combined in one module, or any one of them may be split into a plurality of modules, or at least part of the functions of one or more of these means may be combined with at least part of the functions of the other means and implemented in one module. According to an embodiment of the present invention, at least one of the data grabbing means 11, the parameter selection and grouping means 12, the correlation analysis means 13 and the user interface means 14 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in a suitable combination of three implementations of software, hardware and firmware. Alternatively, at least one of the data fetching means 11, the parameter selection and grouping means 12, the correlation analysis means 13 and the user interface means 14 may be at least partly implemented as a computer program module, which, when being executed by a computer, may perform the functions of the respective module.
In summary, the method, system and computer storage medium for automatically detecting correlation between parameters of an integrated circuit according to the present invention can capture required data information from existing data related to a large number of integrated circuit manufacturing processes, and further perform correlation comparison only on data corresponding to selected parameters, thereby reducing the data amount of one-time analysis and improving the pertinence of data analysis; the method can select a proper statistical factor and the specification (namely the threshold value) for judging the strong correlation, further calculate the value of the statistical factor between any two selected parameters, and when the calculated value of the statistical factor meets the set specification, the calculated value is the strong correlation parameter and data, so that the online abnormal condition on the production line can be found in time, and then the process or the machine equipment and the like can be adjusted or optimized in time, and the final device performance is improved. The technical scheme of the invention can automatically realize the correlation analysis among the parameters of the integrated circuit, can quickly find the reasons of the abnormal occurrence in the production process of the integrated circuit by the strongly correlated parameters and data, is suitable for any automatic machine production process or any industry which can generate a large amount of data, and has wide application range.
It will be appreciated by a person skilled in the art that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure may be made, even if such combinations or combinations are not explicitly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure. That is, various modifications and alterations of this invention may be made by those skilled in the art without departing from the spirit and scope of this invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (12)
1. A method for automatically detecting correlations between parameters of an integrated circuit, comprising:
capturing data information, wherein the data information comprises content values of various parameters related to the production and the manufacture of the integrated circuit;
selecting parameters to be analyzed from the captured data to form a parameter set to be analyzed, and dividing the parameter set to be analyzed into one or more parameter subsets to be analyzed; and the number of the first and second groups,
setting statistical factors for comparing the correlation among the parameters of the parameter subsets to be analyzed, calculating the correlation among the parameters of the parameter subsets to be analyzed, and highlighting and displaying the parameters with strong correlation among the parameter subsets to be analyzed according to the calculation result.
2. The method of claim 1, wherein the plurality of parameters include at least two of lot number of the product, production time, process tool parameters, process monitoring test data, and metrology data from metrology tools.
3. The method of claim 1, wherein the parameters to be analyzed comprise at least one of direct data measured by any metrology tool, parameters or data for performance testing, parameters or data for functional testing, wafer acceptance test parameters or test data, failure test parameters or data, failure pattern analysis data, digitized parameters including yield and/or quality factor, and process tool parameters.
4. The method of automatically detecting correlations between integrated circuit parameters of claim 1, wherein the statistical factors include at least one of difference values, standard deviations, hypothesis probability values, and correlation coefficients.
5. The method of any of claims 1 to 4, further comprising graphically presenting the strong correlation between the parameters of the subset to be analyzed.
6. A computer storage medium having a computer program stored thereon, characterized in that: the program, when executed by a processor, implements the method of automatically detecting correlations between integrated circuit parameters of any one of claims 1 to 5.
7. A system for automatically detecting correlations between parameters of an integrated circuit, comprising:
a data capture device configured to capture data material including content values of a plurality of parameters associated with integrated circuit manufacturing;
the parameter selecting and grouping device is configured to select parameters to be analyzed to form a parameter set to be analyzed, and divide the parameter set to be analyzed into one group or more than one group of parameter subsets to be analyzed; and the number of the first and second groups,
and the correlation analysis device is configured to set statistical factors for comparing the correlation among the parameters of the parameter subsets to be analyzed, calculate the correlation among the parameters of the parameter subsets to be analyzed, and highlight and display the parameters with strong correlation among the parameter subsets to be analyzed according to the calculation result.
8. The system of claim 7, wherein the plurality of parameters include at least two of lot number of the product, production time, process tool parameters, and metrology data from metrology tools.
9. The system of claim 7, wherein the parameters to be analyzed comprise at least one of direct data measured by any metrology tool, parameters or data for performance testing, parameters or data for functional testing, wafer acceptance test parameters or test data, failure test parameters or data, failure pattern analysis data, digitized parameters including yield and/or quality factor, and process tool parameters.
10. The system for automatically detecting correlations between integrated circuit parameters according to claim 7, wherein the statistical factors include at least one of difference values, standard deviations, hypothesis probability values, and correlation coefficients.
11. The system according to any of claims 7 to 10, further comprising a user interface device configured to provide a user with an interface for operating the data capture device, the parameter selection and grouping device, and the correlation analysis device, and to present to the user the data captured by the data capture device, the parameters and grouping conditions selected by the parameter selection and grouping device, and the calculation results of the correlation analysis device.
12. The system for automatically detecting correlations between integrated circuit parameters according to claim 11, wherein the user interface device is further configured to graphically present the correlations between strongly correlated parameters among the subsets of parameters to be analyzed.
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