Data processing method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a data processing method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining the measurement data of a pattern on a wafer when the wafer is scanned by an electron microscope; selecting a first screening condition matched with each type of measurement data from a database, and performing a screening operation on the corresponding type of measurement data based on the first screening condition to obtain first screening data corresponding to each type of measurement data; selecting a second screening condition matched with each type of measurement data from the database, and based on the second screening condition, performing screening operation on the first screening data to obtain second screening data corresponding to each type of measurement data, the second screening condition is obtained by correcting the first screening condition. By adopting the technical scheme, the processing efficiency and precision requirements can be considered.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor manufacturing technology, and in particular to a data processing method, device, electronic equipment, storage medium and program product. Background Art

[0002] With the continuous improvement of semiconductor production process technology, the technology nodes are shrinking, and the accuracy requirements for measurement data are gradually increasing.

[0003] Currently, to improve the accuracy of measurement data, two approaches are usually taken: improving the smoothness of the measured figure contours and performing post-processing data analysis. Data analysis is more common and less expensive, but it suffers from low efficiency and high error rates. Summary of the Invention

[0004] In view of this, the present invention provides a data processing method, device, electronic device, storage medium and program product, which take into account both processing efficiency and accuracy requirements.

[0005] The present invention provides a data processing method, comprising: obtaining measurement data of a pattern on a wafer when the wafer is scanned by an electron microscope; selecting a first filtering condition that is compatible with each type of measurement data from a database, and performing a filtering operation on the corresponding type of measurement data based on the first filtering condition to obtain first filtering data corresponding to each type of measurement data; selecting a second filtering condition that is compatible with each type of measurement data from the database, and performing a filtering operation on the first filtering data based on the second filtering condition to obtain second filtering data corresponding to each type of measurement data, wherein the second filtering condition is obtained by modifying the first filtering condition.

[0006] Optionally, the first screening condition is determined based on a standard range corresponding to the process technology in which the wafer is located and a credibility score of a processing equipment that executes the process technology.

[0007] Optionally, determining the first filtering condition includes: determining the credibility score of the processing equipment based on the standard deviation of the historical measurement data, calibration status and maintenance records of the processing equipment; determining the compensation value based on the credibility score of the processing equipment and a preset compensation coefficient; and determining the first filtering condition based on the compensation value and the standard range.

[0008] Optionally, the second screening condition is applied for multiple rounds of screening, with the screening interval decreasing in each round, and each screening interval is included in the screening interval of the first screening condition;

[0009] The performing of a screening operation on the first screening data based on the second screening condition to obtain second screening data corresponding to each type of measurement data includes:

[0010] According to the interval range corresponding to each screening interval, the previous screening result is compared with the interval of the current screening round according to the screening scheme from large to small until the comparison of all screening intervals is completed, and the remaining first screening data is used as the second screening data.

[0011] Optionally, when the number of the first filtered data is greater than a preset number, the ratio between the number of the first filtered data and the preset number is rounded up as the number of times the filtering operation is performed on the first filtered data, and the set of data obtained from each round of filtering operations is used as the second filtered data.

[0012] Optionally, the data processing method also includes: determining the 3σ corresponding to the second screening data; determining the ratio between the number of data within the 3σ in the second screening data and the second screening data; when it is determined that the ratio is not less than 99.73%, taking the data within the 3σ as the final second screening data.

[0013] Optionally, the data processing method also includes: extracting the parameter type and measurement data value of each third screening data respectively to obtain feature data, wherein the third screening data is data other than the first screening data and the second screening data in the measurement data; inputting the feature data into the knowledge graph, determining the abnormal cause node associated with the feature data by traversing the knowledge graph, and determining and outputting the fusion confidence of the abnormal cause based on the multi-parameter joint trigger condition and the rule base matching and case base matching method; wherein the knowledge graph includes: measurement parameter nodes, abnormal cause nodes and equipment process nodes, as well as the association relationship between the nodes; determining the abnormal cause according to the fusion confidence of the abnormal cause.

[0014] The present invention also provides a data processing device, comprising:

[0015] an acquisition unit, configured to acquire measurement data of a pattern on the wafer when the wafer is scanned by an electron microscope;

[0016] a first screening unit, configured to select a first screening condition adapted to each type of measurement data from the database, and perform a screening operation on the corresponding type of measurement data based on the first screening condition to obtain first screening data corresponding to each type of measurement data;

[0017] The second screening unit is used to select a second screening condition that is compatible with each type of measurement data from the database, and perform a screening operation on the first screening data based on the second screening condition to obtain second screening data corresponding to each type of measurement data, wherein the second screening condition is obtained by modifying the first screening condition.

[0018] The present invention also provides an electronic device, comprising a memory and a processor, wherein: the memory is suitable for storing one or more computer instructions, and when the processor runs the computer instructions, it executes the steps of the data processing method as described in any one of the above items.

[0019] The present invention also provides a computer storage medium, wherein the storage medium stores one or more computer instructions, and the one or more computer instructions are used to implement the steps of any of the data processing methods described above.

[0020] The present invention also provides a computer program product, comprising computer instructions, which are used to implement the steps of any of the data processing methods described above when executed by a processor.

[0021] Compared with the prior art, the technical solution of the present invention has the following advantages:

[0022] In the data processing method provided by the present invention, a first screening condition is used to filter the measured data, thereby obtaining first filtered data that satisfies the first screening condition. A second screening condition is used to filter the first filtered data, thereby obtaining second filtered data that satisfies the second screening condition. The second filtering condition is obtained by modifying the first filtering condition, and the second filtering condition has a higher screening requirement than the first filtering condition. Thus, through the "coarse screening" and "fine screening" screening steps, the accuracy of the second filtered data is improved. Furthermore, the entire screening process does not require manual processing, thereby improving processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a data processing method according to an embodiment of the present invention;

[0024] Figure 2 This is a flow chart of a method for determining a first screening condition in one embodiment of the present invention;

[0025] Figure 3 This is a schematic structural diagram of a data processing device according to an embodiment of the present invention;

[0026] Figure 4 The figure is a schematic diagram of an optional hardware structure of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] As described in the background art, current measurement data processing solutions have the disadvantages of low efficiency and high error.

[0029] To solve the above-mentioned technical problems, the present invention provides a data processing method that, through a first filtering condition, can perform a filtering operation on measurement data to obtain first filtered data that meets the first filtering condition. Through a second filtering condition, a filtering operation can be performed on the second filtered data to obtain second filtered data that meets the second filtering condition. The second filtering condition is obtained by modifying the first filtering condition, and the screening requirement of the second filtering condition is higher than that of the first filtering condition. Thus, through the "coarse screening" and "fine screening" screening steps, the accuracy of the second filtered data is improved. Furthermore, the entire screening process does not require manual processing, thereby improving processing efficiency.

[0030] In order to enable those skilled in the art to have a clearer understanding of the technical concepts, technical principles, advantages, etc. contained in the present invention, the following is a detailed introduction with reference to the accompanying drawings, through specific embodiments, and in combination with specific application scenarios.

[0031] See also Figure 1 A flow chart of a data processing method according to an embodiment of the present invention is shown in FIG. Figure 1 As shown, steps S11 to S13 are included.

[0032] S11, obtaining measurement data of a pattern on the wafer when the wafer is scanned by an electron microscope.

[0033] In some embodiments, when some processes on a wafer are completed, characteristic parameters related to the current process can be measured using an electron microscope, thereby obtaining various types of measurement data related to the current pattern.

[0034] Specifically, electron microscopes can capture high-resolution images, which can be displayed on measurement equipment. Software tools built into the equipment can then be used to obtain different types of measurement data.

[0035] In some embodiments, the current process may be a patterning process, and accordingly, the measurement parameters may include the critical dimensions of two edges of a pattern, the spacing between two patterns, and the shape of the pattern, such as an asymmetrical circle and a symmetrical circle.

[0036] In other words, by executing step S11 , different types of measurement data can be obtained.

[0037] It should be noted that in the process of acquiring measurement data, other parameters of the wafer during processing, such as temperature, etc., can also be acquired.

[0038] S12, selecting a first screening condition that is compatible with each type of measurement data from the database, and performing a screening operation on the corresponding type of measurement data based on the first screening condition to obtain first screening data corresponding to each type of measurement data.

[0039] In some embodiments, different types of measurement data may have different specific values, such as units and sizes. First filtering conditions are configured for each type of measurement data. Thus, the corresponding measurement data is filtered based on the first filtering conditions, thereby eliminating outliers and using the remaining measurement data as the first filtered data.

[0040] In other words, each type of measurement data is filtered separately.

[0041] It should be noted that the database is pre-established and the types of data stored are limited. During the execution of steps S11 and S12, the data types in the measurement data can also be verified to eliminate data types that are not stored.

[0042] In some embodiments, for the same processing equipment, the impact on the measurement parameters is different when in different processing time periods. Therefore, when determining the first screening condition, the credibility score of the processing equipment needs to be considered.

[0043] In some embodiments, the first screening condition may be determined based on a standard range corresponding to the process technology in which the wafer is located and a credibility score of a processing equipment that executes the process technology.

[0044] Specifically, on the one hand, the process technology defines the current process of the wafer processing, and different processing steps require different standards. For example, the critical dimensions corresponding to the two edges will change at different stages of the photolithography process.

[0045] On the other hand, the operating status of the processing equipment also directly affects the accuracy of the measurement parameters. The reliability score of the processing equipment represents the degree of influence of the processing equipment on the accuracy of the measurement parameters.

[0046] For example, a higher credibility score indicates a higher processing performance of the processing equipment, and a lower correlation between the low precision of the measurement parameters and the processing equipment; a lower credibility score indicates a lower processing performance of the processing equipment, and a higher correlation between the low precision of the measurement parameters and the processing equipment.

[0047] In some embodiments, see Figure 2 A flowchart of a method for determining a first screening condition in one embodiment of the present invention is shown in FIG. Figure 2 As shown, you can perform the following steps:

[0048] S21 , determining a reliability score of the processing equipment based on a standard deviation of historical measurement data, calibration status, and maintenance records of the processing equipment.

[0049] In some embodiments, if the difference between the standard deviations of the historical measurement data of the processing equipment is small, it means that the wafer processing processes of different batches of the processing equipment are relatively stable, and a higher credibility score can be given; conversely, if the difference between the standard deviations of the historical measurement data is large, a lower credibility score is given.

[0050] If the processing equipment is in a calibrated state, it means that all operating indicators of the processing equipment are normal and a higher credibility score can be assigned; on the contrary, if the processing equipment is in an uncalibrated state, a lower credibility score is assigned.

[0051] The maintenance records of processing equipment represent the current operating performance of the processing equipment. The more maintenance records there are, the more aged the processing equipment is or the higher the failure rate is, and thus a lower credibility score can be assigned. Conversely, if there are fewer maintenance records, a higher credibility score is assigned.

[0052] In this way, the credibility scores corresponding to the standard deviation of measurement data, calibration status and maintenance records can be determined based on the historical data of the processing equipment, and then the credibility scores of the processing equipment can be determined based on the weight coefficients corresponding to the standard deviation of measurement data, calibration status and maintenance records.

[0053] The weight coefficients corresponding to the standard deviation of the measurement data, the calibration status, and the maintenance record may be determined based on experience or data analysis.

[0054] It should be noted that, considering the frequency of use of the processing equipment, the weight coefficients corresponding to the measurement data standard deviation, calibration status and maintenance records can be updated according to the measurement data standard deviation, calibration status and maintenance records within the preset window period.

[0055] S22, determining a compensation value according to the reliability score of the processing equipment and a preset compensation coefficient.

[0056] In an optional embodiment, the compensation value D=1+η*(1-W), where: η represents a preset compensation coefficient, and W represents a credibility score.

[0057] As the above formula shows, the lower the credibility score W and the larger the compensation value D, the more unstable the device status. By actively expanding the effective threshold range, the risk of misjudgment is reduced, avoiding the situation where valid data is incorrectly filtered due to device errors.

[0058] Furthermore, by negatively correlating the device status with the screening strictness, we comply with the reliability engineering principle, giving priority to ensuring data continuity when uncontrollable errors exist.

[0059] S23: Determine the first screening condition according to the compensation value and the standard interval.

[0060] In some embodiments, the compensation value takes into account the impact of the processing equipment's own state on the measured data, while the standard range defines the required range to be achieved when using this processing equipment. Based on both the compensation value and the standard range, the first screening condition more realistically reflects the impact of the processing equipment on the measured parameters.

[0061] As an optional example, the sum of the compensation value and the standard interval can be used as the interval range of the first screening condition.

[0062] For example, the sum of the two endpoint values ​​of the standard interval and the compensation value can be used as the two endpoint values ​​of the first screening condition.

[0063] It should be noted that after the first screening process is performed, if the ratio of the number of data within the 3σ range to the measured data is determined to be no less than 99.73%, the first screened data will be further screened. Otherwise, the range of the first screening condition is adjusted and verification is performed again until the first screened data meets the verification criteria. Alternatively, if the verification criteria are still not met after multiple adjustments (e.g., 8 times), the data obtained in the last adjustment will be used as the first screened data.

[0064] S13, selecting a second filtering condition that is compatible with each type of measurement data from the database, and performing a filtering operation on the first filtering data based on the second filtering condition to obtain second filtering data corresponding to each type of measurement data, wherein the second filtering condition is obtained by modifying the first filtering condition.

[0065] In some embodiments, second screening conditions are configured for different types of measurement data. Thus, the corresponding first screening data are screened according to the second screening conditions, thereby further eliminating some abnormal values ​​and using the remaining measurement data as the second screening data.

[0066] The second screening condition is obtained by modifying the first screening condition. The screening requirements of the second screening condition are higher than those of the first screening condition. In this way, after the screening steps of "coarse screening" and "fine screening", the accuracy of the second screening data is improved, and the entire screening process does not require manual processing, thereby improving processing efficiency.

[0067] In some embodiments, the second screening condition is applied for multiple rounds of screening, the screening interval is reduced in each round, and each screening interval is included in the screening interval of the first screening condition.

[0068] Specifically, after obtaining the first screening data, multiple rounds of screening are performed according to the second screening condition, and the screening interval of each round is within the screening interval of the first screening condition, so that the screening accuracy of each round is higher than the accuracy of the first screening condition, thereby improving the accuracy of the measurement parameters.

[0069] Accordingly, the step of obtaining the second screening data corresponding to each type of measurement data may include:

[0070] According to the interval range corresponding to each screening interval, the previous screening result is compared with the interval of the current screening round according to the screening scheme from large to small until the comparison of all screening intervals is completed, and the remaining first screening data is used as the second screening data (that is, the first screening data that meets the second screening condition).

[0071] In other words, each round of screening corresponds to a screening interval, and the ranges of these screening intervals are inconsistent. Therefore, screening can be performed in descending order according to the screening interval.

[0072] More specifically, the previous screening result is compared with the range of the current screening interval until the comparison operation on all screening intervals is completed.

[0073] It should be pointed out that for the screening round corresponding to the maximum interval range, the previous screening result refers to the "first screening data".

[0074] In some embodiments, the second screening condition is obtained by modifying the first screening condition, so the second screening condition is uncertain, and thus the amount of screening data obtained in each round of screening is also different.

[0075] Based on this, when performing each round of screening processing, the following can also be performed: determining the 3σ corresponding to the round screening data determined by each round of screening operation; determining the number of data within the 3σ in the round screening data and the ratio between the round screening data; when it is determined that the ratio is not less than 99.73%, the data within the 3σ will be used as the screening data for this round.

[0076] In other words, for multiple rounds of screening under the second screening condition, a 3σ check is performed after each round of screening. If the 3σ check is met, the screening round is considered normal. Otherwise, it indicates that there is a problem with the screening round, and the range values ​​of each screening condition need to be adjusted and checked again until the screening data obtained each time meets the check standard. Alternatively, if the check standard is still not met after multiple adjustments (for example, 8 times), the data obtained in the last round is used as the second screening data.

[0077] In some embodiments, if the amount of data to be filtered first is large, the second filtering operation may be performed in batches to reduce the computational complexity of each batch of filtering operations.

[0078] For example, when the number of the first filtered data is greater than the preset number, the ratio between the number of the first filtered data and the preset number is rounded up as the number of times the filtering operation is performed on the first filtered data, and the set of data obtained from each round of filtering operations is used as the second filtered data.

[0079] In other words, the first screening data are processed in batches, and each batch can be screened multiple times under the second screening conditions.

[0080] In some embodiments, after performing the first and second screenings, data that meets the requirements can be obtained from the original measurement data. Naturally, there will still be some data that does not meet the process requirements. By analyzing this data, the cause of the anomaly can be determined.

[0081] Specifically, the data processing method in this solution may also include:

[0082] Parameter types and measurement data values ​​of each third screening data are extracted respectively to obtain characteristic data, wherein the third screening data is data other than the first screening data and the second screening data in the measurement data.

[0083] In some embodiments, the third screening data may be a set of unqualified data, including various types of measurement data. By performing an extraction operation, the type of each measurement parameter in the third screening data and the corresponding measurement data value can be determined.

[0084] The causes of abnormal conditions vary for different types of measurement parameters. By determining the type of each third screening data item, the cause of the abnormality can be easily determined. The measurement data value indicates the degree of deviation.

[0085] The feature data is input into the knowledge graph, and the associated abnormal cause nodes in the knowledge graph are traversed, and the fusion confidence of the abnormal cause is determined and output based on the multi-parameter joint trigger conditions and the rule base matching and case base matching methods; wherein, the knowledge graph includes: measurement parameter nodes, abnormal cause nodes and equipment process nodes, as well as the association relationship between the nodes, and each association relationship has its own corresponding association confidence weight.

[0086] Specifically, the knowledge graph can be understood as a "relationship network" that includes the following core elements:

[0087] Nodes, namely: measurement parameter nodes (for example, voltage, leakage current, wafer thickness, temperature, etc.), abnormality cause nodes (for example, high temperature interference, equipment calibration error, material contamination, etc.) and equipment process nodes (for example, lithography machine model, deposition process steps, etc., used to associate parameters with production links).

[0088] Relationships (association between connected nodes), including: causal relationships: for example, excessive leakage current may be caused by a high temperature environment; accompanying relationships: for example, wafer thickness variations are accompanied by deposition rate fluctuations; exclusion relationships: for example, if the operation log is normal, voltage anomalies may rule out human error.

[0089] In some embodiments, for different association relationships, the corresponding association confidence weights are determined in different ways.

[0090] For example: For causal relationships, the associated confidence weight can be determined through experimental verification, historical data statistics, and physical model derivation.

[0091] For experimental verification, the fault is reproduced in a controlled environment to observe whether the corresponding parameters exceed the standard. If the leakage current is abnormal in 8 out of 10 experiments, the confidence level of causality is set to 80%.

[0092] For historical data statistics, analyze the co-occurrence frequency of parameters and faults in the historical data. For example, if 85 of the past 100 leakage current exceeding the limit were accompanied by a temperature greater than 30°C, the confidence level is set at 85%.

[0093] For the derivation of the physical model, based on the principles of semiconductor physics (such as hot carrier effect), a theoretical relationship between temperature and leakage current is established as a supplementary basis.

[0094] For the accompanying relationship, the association confidence weight can be determined through correlation analysis and process flow association.

[0095] For the correlation analysis, the correlation coefficient (such as the Pearson correlation coefficient) between the parameters can be calculated, and the correlation coefficient can be used as the confidence level.

[0096] Corresponding process flow associations determine whether parameters belong to the same process step based on the production process steps. For example, photoresist coating thickness and exposure energy both affect line width and are considered accompanying parameters of the same process step.

[0097] Then, the correlation coefficient is modified through the process correlation to determine the correlation confidence weight.

[0098] For exclusion relationships, the association confidence weight can be determined through logical reasoning and expert rule base.

[0099] For logical reasoning, exclusion conditions are established based on physical principles or process constraints. For example, if a photoresist batch is qualified, line width deviation cannot be caused by expired materials.

[0100] For the expert rule base, engineers define exclusion rules. For example, if the device log shows no manual operation record, human error is ruled out.

[0101] Therefore, through the above method, a knowledge graph can be constructed that can reflect domain knowledge and adapt to changes in production lines.

[0102] After obtaining the third screening data, the associated abnormal cause nodes in the knowledge graph can be traversed based on the extracted parameter types and values.

[0103] At the same time, since different abnormal cause nodes may be caused by multiple factors, multi-parameter joint triggering conditions are also considered to determine the fusion confidence of the abnormal cause.

[0104] Furthermore, the fusion confidence under multiple parameters is determined based on the preset rule base weight ratio and the historical case base weight ratio by linear weighting.

[0105] For example, for the same parameter, there may be two reasons leading to its anomaly, corresponding to two confidence levels. The two confidence levels can be weighted separately according to the weight ratio of the rule library and the weight ratio of the historical case library, and the anomaly cause with the highest confidence level can be output.

[0106] In one embodiment, if the following third screening data is determined: leakage current = 2 μA (standard value ≤ 1 μA), temperature = 32° C. (standard value ≤ 25° C.).

[0107] By matching the rule base, it can be determined that the initial confidence level that the cause of the leakage current > 1 μA is a power module failure is 70%, and the initial confidence level that the cause of the leakage current > 1 μA and the temperature > 30°C is high temperature environment interference is 85%.

[0108] Using the historical case library, the initial confidence level is 33% that the cause of the leakage current > 1μA is a power module failure, and the initial confidence level is 67% that the cause of the leakage current > 1μA and temperature > 30°C is high temperature environment interference.

[0109] Assuming the current rule base weight is set to 70% and the historical case base weight is set to 30%, the comprehensive confidence level for high-temperature interference is 85% × 70% + 30% × 67% = 79.6%. The comprehensive confidence level for power module failure is 70% × 70% + 30% × 33% = 58.9%.

[0110] The cause of the abnormality is determined according to the fusion confidence of the abnormality cause.

[0111] In some embodiments, the cause of the abnormality may be determined based on the maximum fusion confidence or the first few higher fusion confidences.

[0112] For example, in the aforementioned example, the comprehensive confidence level of high temperature environment interference is 79.6%, which is greater than the comprehensive confidence level of power module failure, 58.9%, and thus it can be determined that the cause of the abnormality is high temperature environment interference.

[0113] It should be pointed out that as the process progresses, the relationships in the knowledge graph, as well as the cases in the rule base and case base, need to be updated in real time to update the corresponding weight information.

[0114] It is understandable that the above-mentioned embodiments provide multiple implementation plans, and the various implementation plans can be combined and cross-referenced with each other without conflict, thereby extending multiple possible implementation plans, which can all be considered as embodiment plans disclosed and open in the embodiments of this application.

[0115] The present invention also provides a data processing device corresponding to the above data processing method, which will be described in detail below through specific embodiments with reference to the accompanying drawings.

[0116] See also Figure 3 The structural diagram of a data processing device in one embodiment of the present invention is shown in FIG. Figure 3 As shown, the data processing device 100 may include:

[0117] An acquisition unit 110 is used to acquire measurement data of a pattern on a wafer when the wafer is scanned by an electron microscope;

[0118] a first screening unit 120 configured to select a first screening condition that matches each type of measurement data from the database, and perform a screening operation on the corresponding type of measurement data based on the first screening condition to obtain first screening data corresponding to each type of measurement data;

[0119] The second filtering unit 130 is used to select a second filtering condition that is compatible with each type of measurement data from the database, and perform a filtering operation on the first filtering data based on the second filtering condition to obtain second filtering data corresponding to each type of measurement data, wherein the second filtering condition is obtained by modifying the first filtering condition.

[0120] The specific working principles and processes of the acquisition unit 110 , the first screening unit 120 and the second screening unit 130 may refer to the aforementioned examples.

[0121] It is understandable that the division of the above units is only a division of logical functions, and in actual implementation, they can be fully or partially integrated into one physical entity, or physically separated. In addition, the above modules can be implemented in the form of processor calling software.

[0122] See also Figure 4 , shows a schematic diagram of an optional hardware structure of an electronic device provided by an embodiment of the present invention.

[0123] The device of the present invention includes: at least one processor 41 , at least one communication interface 42 , at least one memory 43 and at least one communication bus 44 .

[0124] In some embodiments, the number of each of the processor 41 , the communication interface 42 , the memory 43 and the communication bus 44 is at least one, and the processor 41 , the communication interface 42 and the memory 43 communicate with each other via the communication bus 44 .

[0125] The communication interface 42 may be an interface of a communication module for network communication, such as an interface of a GSM module.

[0126] The processor 41 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the data processing method of this embodiment.

[0127] The memory 43 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0128] The memory 43 stores one or more computer instructions, and the one or more computer instructions are executed by the processor 41 to implement the data processing method provided in the aforementioned embodiment.

[0129] It should be noted that the above-mentioned electronic device may also include other devices (not shown) that may not be necessary for understanding the disclosure of the embodiments of the present invention. Since these other devices may not be necessary for understanding the disclosure of the embodiments of the present invention, the present invention will not introduce them one by one.

[0130] Accordingly, the present invention further provides a computer program product, comprising a computer program / instructions, which are used to implement the data processing method of the present invention when executed by a processor.

[0131] The present invention also provides a storage medium, wherein the storage medium stores one or more computer instructions, and the one or more computer instructions are used to implement the data processing method provided in the above embodiment.

[0132] The embodiments of the present invention described above are combinations of elements and features of the present invention. Unless otherwise mentioned, elements or features may be considered as optional. Each element or feature may be put into practice without being combined with other elements or features. In addition, embodiments of the present invention may be constructed by combining some elements and / or features. The order of operations described in the embodiments of the present invention may be rearranged. Some configurations of any one embodiment may be included in another embodiment, and may be replaced by the corresponding configuration of another embodiment. It is obvious to those skilled in the art that claims that do not have a clear reference relationship to each other in the appended claims may be combined into embodiments of the present invention, or may be used as new claims in amendments after submitting this application.

[0133] The embodiments of the present invention may be implemented by various means such as hardware, firmware, software, or a combination thereof. In a hardware configuration, the method according to the exemplary embodiment of the present invention may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, etc.

[0134] In a firmware or software configuration, embodiments of the present invention may be implemented in the form of modules, procedures, functions, and the like. Software code may be stored in a memory unit and executed by a processor. The memory unit is located inside or outside the processor and may send data to and receive data from the processor via various known means. The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0135] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the scope defined by the claims.

Claims

1. A data processing method, characterized in that: include: acquiring measurement data of a pattern on the wafer when the wafer is scanned by an electron microscope; Selecting a first screening condition that matches each type of measurement data from the database, and performing a screening operation on the corresponding type of measurement data based on the first screening condition to obtain first screening data corresponding to each type of measurement data; A second filtering condition that is compatible with each type of measurement data is selected from the database, and based on the second filtering condition, a filtering operation is performed on the first filtering data to obtain second filtering data corresponding to each type of measurement data, wherein the second filtering condition is obtained by modifying the first filtering condition.

2. The data processing method according to claim 1, wherein: The first screening condition is determined based on a standard range corresponding to the process technology in which the wafer is located and a credibility score of a processing equipment that executes the process technology.

3. The data processing method according to claim 2, characterized in that: Determining the first screening condition includes: Determining a reliability score for the processing equipment based on a standard deviation of historical measurement data, calibration status, and maintenance records of the processing equipment; Determining a compensation value based on the credibility score of the processing equipment and a preset compensation coefficient; The first screening condition is determined according to the compensation value and the standard interval.

4. The data processing method according to claim 1, wherein: Applying the second screening condition for multiple rounds of screening, with the screening interval decreasing in each round, and each screening interval being included in the screening interval of the first screening condition; The step of performing a screening operation on the first screening data based on the second screening condition to obtain second screening data corresponding to each type of measurement data includes: According to the interval range corresponding to each screening interval, the previous screening result is compared with the interval of the current screening round according to the screening scheme from large to small until the comparison of all screening intervals is completed, and the remaining first screening data is used as the second screening data.

5. The data processing method according to claim 1 or 4, characterized in that: When the number of the first filtered data is greater than the preset number, the ratio between the number of the first filtered data and the preset number is rounded up as the number of times the filtering operation is performed on the first filtered data, and the set of data obtained from each round of filtering operations is used as the second filtered data.

6. The data processing method according to claim 1 or 4, characterized in that: Also includes: Determine 3σ corresponding to the second screening data; determine the ratio of the number of data in the second screening data that is within the 3σ to the second screening data; When it is determined that the ratio is not less than 99.73%, the data within the 3σ range is used as the final second screening data.

7. The data processing method according to claim 1, wherein: Also includes: respectively extracting parameter types and measurement data values ​​of respective third screening data to obtain characteristic data, wherein the third screening data is data other than the first screening data and the second screening data in the measurement data; Input the feature data into a knowledge graph, traverse the knowledge graph, determine the abnormality cause node associated with the feature data, and determine and output the fusion confidence of the abnormality cause based on multi-parameter joint trigger conditions and rule base matching and case base matching; wherein the knowledge graph includes: measurement parameter nodes, abnormality cause nodes and equipment process nodes, as well as the association relationship between the nodes; The cause of the abnormality is determined according to the fusion confidence of the abnormality cause.

8. A data processing device, characterized in that: include: an acquisition unit, configured to acquire measurement data of a pattern on the wafer when the wafer is scanned by an electron microscope; a first screening unit, configured to select a first screening condition adapted to each type of measurement data from the database, and perform a screening operation on the corresponding type of measurement data based on the first screening condition to obtain first screening data corresponding to each type of measurement data; The second screening unit is used to select a second screening condition that is compatible with each type of measurement data from the database, and perform a screening operation on the first screening data based on the second screening condition to obtain second screening data corresponding to each type of measurement data, wherein the second screening condition is obtained by modifying the first screening condition.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory is suitable for storing one or more computer instructions, and when the processor runs the computer instructions, the method executes the steps of the data processing method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that The storage medium stores one or more computer instructions, and the one or more computer instructions are used to implement the steps of the data processing method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The method comprises computer instructions, which are used to implement the steps of the data processing method according to any one of claims 1 to 7 when the computer instructions are executed by a processor.

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