CN116387208A - Chamber matching analysis method, system, equipment and medium based on threshold control - Google Patents
Chamber matching analysis method, system, equipment and medium based on threshold control Download PDFInfo
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Abstract
The invention discloses a cavity matching analysis method, a system, equipment and a medium based on threshold control, which comprise the following steps of S1: acquiring original process detection data and determining a reference data set; s2: calculating a control threshold value through the reference data set; s3: performing data alignment operation on the comparison data set and the control threshold curve; s4: calculating the passing rate of each detection parameter in the comparison data set, and sequencing from small to large; s5: analyzing the parameters with lower passing rate and calculating the difference of the parameters; s6: outputting the analysis conclusion and sending an alarm to an engineer. The chamber matching analysis method based on threshold control can quickly and accurately find out the unmatched chamber and give out a conclusion and an alarm, thereby helping a semiconductor engineer to quickly locate the problem, reducing the analysis range and finally achieving the purpose of improving the wafer yield.
Description
Technical Field
The invention relates to the technical field of semiconductor cavity matching analysis, in particular to a cavity matching analysis method based on threshold value control.
Background
For semiconductor wafer processing, hundreds of production runs may be required and often repeated passes are required. Based on the point of departure of maximizing throughput and productivity, semiconductor equipment suppliers offer multi-chamber machine equipment to allow production to be accomplished in parallel in different chambers of the same equipment. Manufacturers desire multiple chambers to have the same performance and produce similar product qualities, but often it is difficult to achieve ideal conditions in complex manufacturing environments for a variety of reasons. The situation that the performance of the chamber with variation is not matched with that of other chambers is called as a mismatching chamber, variability can be gradually accumulated in the whole production process, and as the variability is continuously increased, the stability of the electrical performance of the wafer circuit is finally affected, so that the yield is reduced. Therefore, the unmatched cavity is found in time, an alarm is given, the influence caused by process difference can be effectively reduced, and the yield is improved.
The existing cavity matching analysis methods mainly have the problems of data preprocessing, large data fragmentation, over-high dimensionality and the like; and detecting the characteristics of unequal length, asynchronous data and the like.
Disclosure of Invention
The invention provides a cavity matching analysis method based on threshold control, which can at least solve one of the technical problems.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a cavity matching analysis method based on threshold control comprises the following steps:
s1: acquiring original process detection data and determining a reference data set;
s2: calculating a control threshold value through the reference data set;
s3: performing data alignment operation on the comparison data set and the control threshold curve;
s4: calculating the passing rate of each detection parameter in the comparison data set, and sequencing from small to large;
s5: analyzing the parameters with lower passing rate and calculating the difference of the parameters;
s6: outputting the analysis conclusion and sending an alarm to an engineer.
Further, in step S1: the method comprises the steps of obtaining original process detection data, and determining a reference data set, wherein the determination of the reference data set specifically comprises the following steps:
s11: distinguishing data according to the dimension of the chamber, counting the corresponding wafer yield according to the daily aggregation, and calculating the variance of the yield according to the dimension of the chamber;
s12: and comprehensively considering and selecting a chamber with higher yield and lower variance as a reference chamber, and taking corresponding detection data as a reference data set.
Further, in step S2: through the reference data set, the control threshold is calculated, and the method specifically comprises the following steps:
s21: there are m independent data in the reference data set, and n detection parameters are included in each data set, wherein the i-th parameter is denoted as n_i. Counting the average length of the reference data set according to the dimension of the detection parameters to obtain an average length n_i_mean_length corresponding to each detection parameter;
s22: according to the dimension of the detection parameters, calculating a mean value of m data at the point by point, namely calculating a standard deviation standard_displacement of m data at the point, wherein each parameter corresponds to the calculated length, namely n_i_mean_length obtained in S21, and the part exceeding the length does not include a calculation range;
s23: and obtaining a final control threshold curve according to the mean value and the standard deviation obtained in the step S22, wherein the lower control limit corresponding to each point is lcl and is equal to mean_value minus k times standard_displacement, and the upper control limit corresponding to each point is ucl and is equal to mean_value plus k times standard_displacement. Where k is an artificially set integer, the general experience takes a value of 3.
Further, in the data alignment operation of comparing the data set and the control curve, two methods can be adopted, and optionally, the purpose of data alignment can be achieved. The method specifically comprises the following steps:
s31: according to the method one, DTW (dynamic time warping) is a dynamic time warping algorithm, and the algorithm is utilized to align data of the comparison data set with the control threshold curve obtained in the step S2 according to the dimension of the detection parameter, so that the subsequent calculation of the passing rate is facilitated;
s32: and in the second method, a manual translation method is used for translating a certain detection parameter of the comparison data set through a user-defined coordinate axis value (x, y). Where x is greater than 0 means panning to the right and less than 0 means panning to the left; y greater than 0 indicates an upward translation and less than 0 indicates a downward translation.
Further, in step S4: the method specifically comprises the following steps of:
s41: s3, obtaining an aligned comparison data set and a control threshold curve, calculating whether a threshold is met or not point by point according to the dimension of the detection parameter, judging as fail when the point is higher than ucl or lower than lcl, otherwise judging as pass, and counting the number of pass as pass_count;
s42: the pass rate of each detection parameter of each data in the comparison data set is calculated as pass_ratio, which is equal to the pass_count divided by the length of the threshold curve for the parameter, i.e. the average length n_i_mean_length obtained in S21. And then calculating the average value of the pass_ratio according to the dimension of the detection parameter, and recording the average value as the final pass rate of the detection parameter as final_pass_ratio. And finally, sorting according to the sequence from small to large of final_pass_ratio.
Further, in step S5: the method for analyzing the parameters with lower passing rate and calculating the difference comprises the following steps:
s51: taking N minimum detection parameters of final_pass_ratio as analysis objects, drawing a box line graph according to the dimension of the detection parameters, and visually observing the data distribution condition;
s52: the analysis object in S51, i.e., N detection parameters, is subjected to K-W (Kruskal-Wallis test) inspection according to the dimension of the detection parameters to obtain the significance level p-value of the parameter, thereby obtaining the variability of the parameter.
Further, in step S6: outputting analysis conclusion and sending alarm to engineer, including the following steps:
s61: comprehensively analyzing the passing rate of the detection parameters obtained in the step S4 and the difference of the detection parameters obtained in the step S5, giving a detection parameter list needing to be focused on, and giving an alarm to the parameters with lower passing rate.
On the other hand, the invention also discloses a cavity matching analysis system based on threshold control, which comprises an acquisition module, a control threshold calculation module, a data alignment module, a pass rate calculation module and a comparison analysis module;
the acquisition module is used for acquiring original process detection parameters and determining a reference data set;
the control threshold calculation module is used for calculating a control threshold through the reference data set to obtain a control curve;
the data alignment module is used for carrying out data alignment on the comparison data set and the control curve, so that subsequent calculation is facilitated;
the passing rate calculation module is used for calculating the passing rate of each detection parameter in the comparison data set and sequencing from small to large;
the comparison analysis module is used for analyzing the parameters with lower passing rate, calculating the difference of the parameters and giving out conclusion and alarm information.
In yet another aspect, the invention also discloses a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the method as described above.
In yet another aspect, the invention also discloses a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the method as described above.
According to the technical scheme, the chamber matching analysis method based on threshold control can quickly and accurately find out the unmatched chambers and give out conclusions and alarms, so that a semiconductor engineer is helped to quickly locate the problems, the analysis range is reduced, and the purpose of improving the wafer yield is finally achieved.
The cavity matching analysis method and system based on threshold control provided by the invention have the advantages that:
(1) The program adopting the method has short running time, and the system module is simple and clear, so that the unmatched cavity can be quickly and accurately found out, and the detection parameter with lower passing rate can be positioned;
(2) The control threshold curve can be conveniently obtained by operating the control threshold calculation module and is used as a reference for subsequent control;
(3) The problem of unequal data length can be solved by operating the data alignment module, and the workload of data preprocessing is reduced;
(4) The operation comparison analysis module can help engineers to reduce the problem investigation range and improve the working efficiency of the engineers.
Drawings
FIG. 1 is a schematic diagram of the structure of the present invention;
FIG. 2 is a flow chart for calculating a policing threshold;
fig. 3 is a flowchart of calculating the pass rate.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention.
As shown in fig. 1, the chamber matching analysis method based on threshold control according to the present embodiment includes the following steps S1 to S6:
s1: acquiring original process detection data and determining a reference data set;
after the original process detection data are obtained, firstly checking the data quality, and filtering part of abnormal data, such as over-high or over-low parameter values; and for example, the loss ratio of the parameter value is too large.
Because the purpose of the chamber matching problem is to find out the unmatched chamber and further analyze the parameters in the unmatched chamber, finally reduce the investigation scope, and facilitate the semiconductor engineer to find out the reason affecting the process. It should first be determined that the reference chamber, i.e. the matched target chamber, has its corresponding dataset called the baseline dataset. Wherein the determination of the reference data set specifically comprises the following steps S11 to S12:
s11: take a period of data, for example, one month or one quarter. And counting the yield of the wafers produced by the chamber by taking the 'day' as a unit according to the dimension of the chamber to obtain a list consisting of yield, and counting the variance of data in the list.
S12: the chamber with higher yield and lower variance is comprehensively considered and selected as a reference chamber, and the accuracy and the stability are considered. This step requires a certain engineering experience and cooperation of the semiconductor engineer. And taking the detection data corresponding to the reference chamber as a reference data set for subsequent steps.
S2: calculating a control threshold value through the reference data set;
as shown in fig. 2, calculating the control threshold specifically includes the following steps S21 to S23:
s21: there are m independent data in the reference data set, and n detection parameters are included in each data set, wherein the i-th parameter is denoted as n_i. Counting the average length of the reference data according to the dimension of the detection parameters to obtain an average length n_i_mean_length corresponding to each detection parameter;
s22: according to the dimension of the detection parameters, calculating a mean value of m data at the point by point, namely calculating a standard deviation standard_displacement of m data at the point, wherein each parameter corresponds to the calculated length, namely n_i_mean_length obtained in S21, and the part exceeding the length does not include a calculation range;
s23: and obtaining a final control threshold curve according to the mean value and the standard deviation obtained in the step S22, wherein the lower control limit corresponding to each point is lcl and is equal to mean_value minus k times standard_displacement, and the upper control limit corresponding to each point is ucl and is equal to mean_value plus k times standard_displacement. Where k is an artificially set integer, the general empirical value 3, the 3sigma principle, can be described simply as: if the data obeys a normal distribution, an outlier is defined as a value in the set of resulting values that deviates from the mean by more than three times the standard deviation. This principle is relatively common in engineering science. Of course, the value of k can be determined according to a specific service scene, so that the method is flexible.
S3: the data alignment operation is carried out on the comparison data set and the control curve, and one of the following two methods is optional:
s31: the method one, DTW (dynamic time warping) dynamic time warping algorithm, is a dynamic programming algorithm that can calculate the similarity of two time series data (especially two different time series data). The main idea is that in the process of calculating the similarity of two sequences, an optimal path can be obtained, so that the distance between the two sequences is minimum. The path is utilized to align the data of the comparison data set with the control threshold curve obtained in the step S2 according to the dimension of the detection parameter, so that the subsequent calculation of the passing rate is facilitated;
s32: and in the second method, a manual translation method is used for translating a certain detection parameter of the comparison data set through a user-defined coordinate axis value (x, y). Wherein x is greater than 0 for right translation and x is less than 0 for left translation; y greater than 0 indicates an upward translation and y less than 0 indicates a downward translation. It should be noted that the point beyond the leftmost boundary of the control curve when moving left and the point added for ease of calculation when moving right are both determined to exceed the threshold, which is a requirement for unified calculation logic.
S4: calculating the passing rate of each detection parameter in the comparison data set, and sequencing from small to large;
as shown in fig. 3, calculating the passing rate specifically includes the following steps S41 to S42:
s41: s3, obtaining an aligned comparison data set and a control threshold curve, calculating whether a threshold is met or not point by point according to the dimension of the detection parameter, judging as fail when the point is higher than ucl or lower than lcl, otherwise judging as pass, and counting the number of pass as pass_count;
s42: the pass rate of each detection parameter of each data in the comparison data set is calculated as pass_ratio, which is equal to the pass_count divided by the length of the threshold curve for the parameter, i.e. the average length n_i_mean_length obtained in S21. And then calculating the average value of the pass_ratio according to the dimension of the detection parameter, and recording the average value as the final pass rate of the detection parameter as final_pass_ratio. And finally, sorting according to the sequence from small to large of final_pass_ratio.
S5: analyzing the parameters with lower passing rate and calculating the difference of the parameters;
the analysis of the lower pass rate parameters and the variability specifically includes the following steps S51 to S52:
s51: taking N minimum detection parameters of final_pass_ratio as analysis objects, and drawing a corresponding box line diagram according to the dimension of the detection parameters, so that the data distribution situation can be intuitively seen;
s52: the analysis object in S51, i.e., N detection parameters, is subjected to K-W (Kruskal-Wallis test) inspection according to the dimension of the detection parameters to obtain the significance level p-value of the parameter, thereby obtaining the variability of the parameter.
Kruskal-Wallis test is also known as the K-W test, which is a nonparametric test in a significance analysis method, and is applicable to data where the overall distribution is unknown. The smaller the significance level p-value, the greater the numerical variability of this parameter is considered.
S6: outputting analysis conclusion, sending alarm to engineer, comprehensively analyzing the passing rate of the detection parameters obtained in S4 and the difference of the detection parameters obtained in S5, giving a detection parameter list needing to be focused on, and giving alarm to the parameters with lower passing rate, so that the semiconductor engineer can be reminded by means of automatic mail sending.
Through the steps S1 to S6 above, a non-matching chamber, a list of detection parameters that need to be focused on, and an alarm can be found. The working efficiency of a semiconductor engineer can be greatly improved, the problem investigation range is reduced, and an alarm prompt is timely and accurately given.
In yet another aspect, the invention also discloses a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of any of the methods described above.
In yet another aspect, the invention also discloses a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of any of the methods described above.
In yet another embodiment provided herein, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the steps of any of the methods of the above embodiments.
It may be understood that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and explanation, examples and beneficial effects of the related content may refer to corresponding parts in the above method.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The cavity matching analysis method based on threshold control is characterized by comprising the following steps of:
s1: acquiring original process detection data and determining a reference data set;
s2: calculating a control threshold value through the reference data set;
s3: performing data alignment operation on the comparison data set and the control threshold curve;
s4: calculating the passing rate of each detection parameter in the comparison data set, and sequencing from small to large;
s5: analyzing the parameters with lower passing rate and calculating the difference of the parameters;
s6: outputting the analysis conclusion and sending an alarm to an engineer.
2. The threshold-control-based chamber matching analysis method according to claim 1, wherein: the determining of the reference data set in the step S1 specifically includes the following steps:
s11: distinguishing data according to the dimension of the chamber, counting the corresponding wafer yield according to the daily aggregation, and calculating the variance of the yield according to the dimension of the chamber;
s12: and comprehensively considering and selecting a chamber with higher yield and lower variance as a reference chamber, and taking corresponding detection data as a reference data set.
3. The threshold-control-based chamber matching analysis method according to claim 2, wherein: the step S2: through the reference data set, a control threshold is calculated, and the method specifically comprises the following steps:
s21: setting m independent data in a reference data set, wherein n detection parameters exist in each data set, and the i-th parameter is expressed as n_i; counting the average length of the reference data set according to the dimension of the detection parameters to obtain an average length n_i_mean_length corresponding to each detection parameter;
s22: according to the dimension of the detection parameters, calculating a mean value of m data at the point by point, namely calculating a standard deviation standard_displacement of m data at the point, wherein each parameter corresponds to the calculated length, namely n_i_mean_length obtained in S21, and the part exceeding the length does not include a calculation range;
s23: obtaining a final control threshold curve according to the mean value and the standard deviation obtained in the step S22, wherein the control lower limit corresponding to each point is lcl which is equal to the mean value minus k times the standard deviation standard_displacement, and the control upper limit corresponding to each point is ucl which is equal to the mean value plus k times the standard deviation standard_displacement; where k is an artificially set integer.
4. A method of threshold-based chamber matching analysis as claimed in claim 3, wherein: the step S3: in the data alignment operation of comparing the data set with the control threshold curve, two methods can be adopted, and optionally one method can achieve the aim of data alignment; the method specifically comprises the following steps:
s31: the DTW dynamic time warping algorithm is utilized to align the data of the comparison data set with the control threshold curve obtained in the step S2 according to the dimension of the detection parameter, so that the subsequent calculation of the passing rate is facilitated;
s32: the second method is a manual translation method, namely, translating a certain detection parameter of the comparison data set through a user-defined coordinate axis value (x, y); where x is greater than 0 means panning to the right and less than 0 means panning to the left; y greater than 0 indicates an upward translation and less than 0 indicates a downward translation.
5. The threshold-control-based chamber matching analysis method according to claim 4, wherein: the step S4: the method specifically comprises the following steps of:
s41: s3, obtaining an aligned comparison data set and a control threshold curve, calculating whether a threshold is met or not point by point according to the dimension of the detection parameter, judging as fail when the point is higher than ucl or lower than lcl, otherwise judging as pass, and counting the number of pass as pass_count;
s42: calculating the passing rate of each detection parameter of each data in the comparison data set as pass_ratio, which is equal to the pass_count divided by the length of the control threshold curve of the parameter, namely the average length n_i_mean_length obtained in S21; then calculating an average value of the pass_ratio according to the dimension of the detection parameter, and marking the average value as the final pass ratio of the detection parameter; and finally, sorting according to the sequence from small to large of final_pass_ratio.
6. The threshold-control-based chamber matching analysis method of claim 5, wherein: step S5: the method for analyzing the parameters with lower passing rate and calculating the difference comprises the following steps:
s51: taking N minimum detection parameters of final_pass_ratio as analysis objects, drawing a box line graph according to the dimension of the detection parameters, and visually observing the data distribution condition;
s52: and (3) carrying out K-W detection on the analysis object in S51, namely N detection parameters according to the dimension of the detection parameters to obtain the significance level p-value of the parameters, thereby obtaining the difference of the parameters.
7. The threshold-control-based chamber matching analysis method of claim 6, wherein: the step S6: outputting analysis conclusion and sending alarm to engineer, including the following steps:
s61: comprehensively analyzing the passing rate of the detection parameters obtained in the step S4 and the difference of the detection parameters obtained in the step S5, giving a detection parameter list needing to be focused on, and giving an alarm to the parameters with lower passing rate.
8. A cavity matching analysis system based on threshold control is characterized in that: the system comprises an acquisition module, a control threshold calculation module, a data alignment module, a pass rate calculation module and a comparison analysis module;
the acquisition module is used for acquiring original process detection parameters and determining a reference data set;
the control threshold calculation module is used for calculating a control threshold through the reference data set to obtain a control curve;
the data alignment module is used for carrying out data alignment on the comparison data set and the control curve, so that subsequent calculation is facilitated;
the passing rate calculation module is used for calculating the passing rate of each detection parameter in the comparison data set and sequencing from small to large;
the comparison analysis module is used for analyzing the parameters with lower passing rate, calculating the difference of the parameters and giving out conclusion and alarm information.
9. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method of any of claims 1 to 7.
10. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the method of any one of claims 1 to 7.
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