CN113902357B - Automated quality management system, method, and computer-readable storage medium - Google Patents
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Abstract
The invention provides an automatic quality control system, a method and a computer readable storage medium, which realize the formulation, adjustment and improvement of each process parameter specification through a specification formulation module, a specification adjustment module, a specification improvement module and an abnormality feedback module, and the finally formed parameter specification accurately controls each process, and accurately and effectively performs the division arrangement of each process. Furthermore, the automatic quality control system provided by the invention can be customized and improved in the stages of parameter specification formulation, adjustment and improvement, meets the quality requirements of customers, is strictly controlled, and is continuously improved by self, so that the daily work of engineers is more efficient and convenient. In addition, the automatic quality management and control system provided by the invention can perform pre-calculation according to the requirements of engineers, and compared with manual calculation, errors caused by subjective factors can be reduced, and the accuracy of pre-calculation is improved.
Description
Technical Field
The present invention relates to the field of semiconductor technology, and in particular, to an automated quality management and control system, method, and computer-readable storage medium.
Background
For the wafer foundry industry, along with the factory energy promotion, the product types and product specifications are increasingly abundant, and the situation of product quality control is becoming severer and more attractive, wherein the most important part is to analyze data and control products by means of Statistical Process Control (SPC) of a mathematical statistical method, analyze the problems of process production from the data point of view, improve the process in time and achieve the purpose of quality control. With the detailed specifications of the process control tools, the current foundry rules refer to the following five aspects: 1 product type; 2, station; 3, machine type; 4 measuring type; and 5, manufacturing process. Generally, to eliminate interference from other aspects, a single product type, a single site, a single type of tool, a single measurement type is a specification. Fig. 1 is a FLOW of establishing a product parameter Specification (SPEC) in a semiconductor manufacturing process, and as shown in fig. 1, first, a product specification provided by a customer is obtained, then, a manufacturing process (FLOW) of a semiconductor product is determined, then, the product specification is manually established, and the product specification is manually adjusted at a product commissioning (Pi-Run) stage and a pre-commissioning (Pro-Run) stage to obtain a product rule. In the process of formulating the product rules, the steps of manually establishing and adjusting the product specifications are complex, the time consumption is long, the labor is consumed, manual adjustment is possible to cause inaccuracy in formulating the specification parameters due to subjective factors, the accuracy of quality monitoring is influenced, and when problems are subsequently tracked, the method is not beneficial to engineering units to quickly search and solve the problems.
Disclosure of Invention
The invention aims to provide an automatic quality control system, a method and a computer readable storage medium, so as to improve the accuracy of quality monitoring.
In order to achieve the above object, the present invention provides an automated quality control system, comprising:
the specification formulation module is used for customizing and formulating the parameter specification of the product in each manufacturing process by combining the product specification requirement of a customer and the product production rule;
and the specification adjusting module is used for adjusting the parameter specification of each process by using a 6Sigma management method during the test run period, and calculating the limit of the parameter specification of each process by using the standard deviation by combining the correlation among the processes.
The specification improving module is used for improving the parameter specification of each manufacturing procedure according to normal distribution and a 3Sigma principle during the pre-operation period and pre-calculating the improved parameter specification of each manufacturing procedure; and the number of the first and second groups,
and the abnormity feedback module is used for uninterruptedly feeding back the abnormal events of each manufacturing process to the specification improvement module so as to continuously improve the parameter specification of each manufacturing process of the product.
Optionally, the specification formulation module is further configured to formulate a failure mode and an influence analysis report by combining product specification requirements and production rules of a customer before customizing parameter specifications of a product in each manufacturing process.
Optionally, a reference value of the parameter specification of each process is determined according to the specified failure mode and the influence analysis report, and a certain proportion is retracted on the basis of the reference value to be used as a reference point for setting the limit of the parameter specification of each process.
Optionally, the 90% shrinkage is used as a reference point for defining the parameter specification limit of each process based on the reference value.
Optionally, the pre-calculating the parameter specification of each process includes: the parameter specification of each process is pre-calculated quarterly.
Optionally, the processes include exposing, etching and cleaning, wherein a parameter specification boundary in the exposing process is smaller than a parameter specification boundary in the etching process, and the parameter specification boundary in the etching process is smaller than a parameter specification boundary in the cleaning process.
The invention also provides an automatic quality control method, which comprises the following steps:
setting parameter specifications of each process by adopting the automatic quality control system;
carrying out mass production of products according to the parameter specification of each process, and collecting first data generated by a production control system during a test operation period and a pre-operation period;
referring to the data, carrying out mass production of products according to the parameter specification of each process;
and collecting the mass production data generated by the production control system of the product during the mass production period, feeding the mass production data back to the parameter adjustment engineering experiment, and revising the parameter specification of each process through the automatic mass control system.
Optionally, the method further includes: and automatically controlling each batch of products and machines according to the risk level during the mass production, and carrying out alarm monitoring and alarm operation.
Optionally, the method further includes: and continuously providing card control limit feedback for products in the production period and automatically correcting the failure mode after the failure mode changes according to the risk level control and alarm monitoring conditions.
Optionally, the method further includes: and performing abnormal feedback event tracking and advanced process control during the mass production period, feeding back production data generated by the abnormal feedback event tracking and the advanced process control, performing parameter adjustment engineering experiments, and feeding back the experiment results to a quality control system to revise the parameter specification of each process.
The present invention also provides a computer-readable storage medium having stored thereon a plurality of instructions executable by one or more processors to perform the steps of the automated quality management method described above.
In summary, the present invention provides an automated quality control system, method and computer readable storage medium, which implement the formulation, adjustment and improvement of each process parameter specification through a specification formulation module, a specification adjustment module, a specification improvement module and an exception feedback module, and the finally formed parameter specification accurately controls each process, and accurately and effectively performs the distribution of each process.
Furthermore, the automatic quality control system provided by the invention can be customized and improved in the stages of parameter specification formulation, adjustment and improvement, meets the quality requirements of customers, is strictly controlled, and is continuously improved by self, so that the daily work of engineers is more efficient and convenient.
Furthermore, the automatic quality management and control system provided by the invention can perform pre-calculation according to the requirements of engineers, and compared with manual calculation, errors caused by subjective factors can be reduced, and the accuracy of pre-calculation is improved.
Furthermore, the automatic quality management and control system provided by the invention can effectively track problems, and is convenient for engineering units to quickly search and solve the problems.
Drawings
FIG. 1 is a flow chart of establishing product parameter specification in a semiconductor manufacturing process;
FIG. 2 is a schematic diagram of an automated mass management system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an automated mass management system according to an embodiment of the present invention;
FIG. 4 is a flow chart of an automated quality control method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an automated quality management method according to an embodiment of the present invention.
Detailed Description
The motorized quality control system, method, and computer readable storage medium provided by the present invention are described in further detail below with reference to the accompanying drawings and the detailed description. The advantages and features of the present invention will become more apparent from the following description and drawings, it being understood, however, that the concepts of the present invention may be embodied in many different forms and should not be construed as limited to the specific embodiments set forth herein. The drawings are in simplified form and are not to scale, but are provided for convenience and clarity in describing embodiments of the invention.
The terms "first," "second," and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in other sequences than described or illustrated herein. Similarly, if the method described herein comprises a series of steps, the order in which these steps are presented herein is not necessarily the only order in which these steps may be performed, and some of the described steps may be omitted and/or some other steps not described herein may be added to the method. Although elements in one drawing may be readily identified as such in other drawings, the present disclosure does not identify each element as being identical to each other in every drawing for clarity of description.
Fig. 2 is a schematic structural diagram of the automated quality control system according to the embodiment. As shown in fig. 1, the automated quality control system according to the present embodiment includes:
the specification formulation module 100 is used for customizing and formulating the parameter specification of the product in each manufacturing process by combining the product requirement of a customer and the production rule of the product;
and the specification adjusting module 200 is used for adjusting the parameter specification of each process by using a 6Sigma management method during the test run period, and calculating the limit of the parameter specification of each process by using the standard deviation by combining the correlation among the processes.
The specification improving module 300 is used for improving the parameter specification of each manufacturing process according to normal distribution and the 3Sigma principle during the pre-operation period, and pre-calculating the improved parameter specification of each manufacturing process; and the number of the first and second groups,
the abnormal feedback module 400 is used for continuously feeding back the abnormal events of each process to the specification improvement module so as to continuously improve the parameter specification of each process of the product.
Further, the specification setting module 100 is further configured to perform Failure Mode and impact Analysis (FMEA) report before customizing the parameter specification of the product in each manufacturing process according to the product requirement and the production rule of the customer, obtain a reference value of the parameter Specification (SPEC) of each manufacturing process through the Failure Mode and impact Analysis report, then retract a certain proportion on the basis of the reference value as a reference point of the parameter specification limit of the customized manufacturing process, and obtain an initial value of the parameter specification of the product in each manufacturing process, for example, retract 90% on the basis of the reference value as a reference point of the parameter specification limit of the customized manufacturing process. Here, a single product type, a single site, a single type of tool, and a single metrology type are a parameter specification. For example, a product a obtains reference values (W1, W2) of the parameter specification with measurement type D on the machine C of the site B after automatically linking the customer failure modes, and then retracts 10% on the basis of the reference values as a reference point for customizing the parameter specification limit of each process, i.e., obtains initial values (110% W1, 90% W2) of the parameter specification of each process customized.
After the initial values of the parameter specifications of the product in each manufacturing process are obtained, different operation rules can be adopted according to the types of data (such as discrete type and aggregation type), and the parameter specifications after refining and customizing adjustment can be performed.
Failure mode and impact analysis (FMEA) is a method for analyzing all failure modes, reasons and impacts in product design and providing design improvement and using compensation measures for weak links. The FMEA method is suitable for being used in demonstration, scheme stage or early engineering development stage of products when the composition of the products is uncertain or incompletely determined. The method mainly comprises the steps of system definition and analysis, fault mode analysis, fault reason analysis, fault influence and severity analysis, fault detection method analysis design improvement measure analysis and compensation measure analysis, and finally the FMEA report of the product is obtained.
The specification adjusting module 200 adjusts the parameter specification of each process by using a 6Sigma management method during the test run, and calculates the parameter specification limit of each process by using the standard deviation in combination with the correlation between the processes. Specifically, firstly, the parameter specification of each process is adjusted by using a 6Sigma management method, data parameters during a trial Run (Pi-Run) period are obtained, an Upper Control limit (UCL, Upper Control Line) and a Lower Control limit (LCL, Lower Control Line) of process Control are obtained, UCL = Target +3Sigma, LCL = Target-3Sigma,
v1, V2, V3 are data parameters during the test Run (Pi-Run) and Target is the median of the limits in the parameter specification in FMEA. In addition, the standard deviation is used to calculate the parameter specification limit of each process, and the parameter specification limit of each process needs to be adjusted according to the correlation between each process by combining the correlation between each process. For example, in a related process exposure (PHOTO), ETCH (ETCH) and cleaning (WET), the parametric specification boundary in the exposure process is smaller than the parametric specification boundary in the ETCH process, the parametric specification boundary in the ETCH process is smaller than the parametric specification boundary in the cleaning process, the parametric specification boundary in the cleaning process is smaller than 8 times Sigma (also expressed by Sigma), PHOTO<ETCH<WET<8 (USL-LSL)/6, USL being the upper limit of SPEC and LSL being the lower limit of SPEC. That is, if a certain feature size (CD) required for a product is 40nm, the parameter specification boundary in the exposure process makes the certain feature size (CD) less than 40nm, such as 36nm, the parameter specification boundary in the etching process makes the certain feature size (CD) less than 38nm, such as 37nm, and the parameter specification boundary in the cleaning process makes the certain feature size (CD) 40 nm.
The specification improvement module 300 improves the parameter specification of each process according to the normal distribution and the 3Sigma principle during the pre-operation (Pro-Run), and pre-calculates the improved parameter specification of each process. First, a trial Run (Pi-Run) period is obtainedAcquiring upper control limit UCL and lower control limit LCL of the process control, wherein UCL = Target +3Sigma and LCL = Target-3Sigma, and Sigma =
， V1，V2，…，VNIs a data parameter during a commissioning (Pi-Run), N is the total amount of data parameters. The parameter specification limit obtained during commissioning is then refined based on the upper control limit UCL and the lower control limit LCL for process control obtained during pre-commissioning. Then, the improved parameter specification of each process is pre-calculated according to the normal distribution and control chart principle (3 Sigma principle), for example, the parameter specification of each process is pre-calculated in quarterly, and the parameter specification of each process is continuously adjusted. Illustratively, Sigma (standard deviation) and Avg (mean) calculations are performed using the data parameters for one quarter, and the control limit for the next quarter is calculated from the calculated 3Sigma and Avg, where UCL = Avg +3Sigma and LCL = Avg-3 Sigma.
The abnormal feedback module 400 is configured to continuously feed back the abnormal events of each process to the specification improving module 300, so as to continuously improve the parameter specification of the product in each process. For example, the abnormal event of each process during the pre-Run (Pro-Run) period is fed back to the specification improving module 300, and the specification improving module 300 analyzes the abnormal event and adjusts the parameter specification of each process according to the analysis result, so that the process is improved.
The embodiment also provides an automatic quality control method. Fig. 4 is a flowchart of an automated quality control method according to the present embodiment, and as shown in fig. 4, the automated quality control method includes the following steps:
step S01: setting parameter specifications of each process by adopting an automatic quality control system;
step S02: carrying out mass production of products according to the parameter specification of each process; and the number of the first and second groups,
step S03: and collecting the mass production data generated by the production control system during the mass production period of the product, feeding the mass production data back to the parameter adjustment engineering experiment, and revising the parameter specification of each process through the automatic mass control system.
FIG. 5 is a schematic diagram of an automated quality control method according to the present embodiment. Referring to fig. 4 and 5, first, in step S01, an automated quality control system is used to establish the parameter specification of each process. As described above, the specification setting module 100 performs failure mode and impact analysis report according to the product requirements and production rules of the customer, obtains the reference value of the parameter specification of each process, and customizes and adjusts the reference point of the boundary of the parameter specification of each process, so as to obtain the initial value of the parameter specification of the product in each process.
Then, a trial Run (Pi-Run) is performed according to the initial value of the parameter specification of each process, and the production control system performs production control according to the initial value of the parameter specification of each process to obtain trial Run data. The specification adjusting module 200 adjusts the parameter specification of each process by using a 6Sigma management method according to the trial run data, and calculates the parameter specification limit of each process by using the standard deviation in combination with the correlation between each process.
And then, performing pre-operation (Pro-Run) according to the adjusted parameter specification of each process, and performing production control by the production control system according to the adjusted parameter specification of each process to obtain pre-operation data. The specification improvement module 300 refers to the pre-operation data, adjusts the parameter specification of each process according to the normal distribution and the 3Sigma principle, and pre-calculates the adjusted value of the parameter specification of each process.
During the trial operation and the pre-operation, the production control system respectively performs production control (Auto Trigger) according to the established parameter specification of each process, and automatically collects trial operation data (including trial operation data and pre-operation data) before the mass production of the collection (Auto Collect). The production Control system comprises a statistical process Control system (SPC Monitor), a process error detection and classification system (FDC Monitor), a Defect detection management and Control system (Defect Inspection) and a product/machine flow management and Control system (Inline/ED Control). In other embodiments of the present invention, the production control system may also include a management and control system thereof, so as to provide more comprehensive data management and control and make more accurate parameter specification.
Next, step S02 and step S03: the mass production (Auto Applied) of the product is performed according to the parameter specification of each process, the mass production data generated by the production control system during the mass production period of the product is collected and fed back to the parameter adjustment engineering experiment, and the parameter specification of each process is revised through the automatic mass control system. In the mass production process, the parameter specification of each process can be corrected by referring to the trial data before mass production to realize card control. The method comprises the steps of automatically controlling products (Lot) and machines (EQP) in batches according to risk levels during mass production, carrying out Alarm monitoring and Alarm operation (Alarm Monitor & Action), realizing automatic evaluation (Auto Judge) on parameter specifications, continuously providing card control limit feedback and automatic correction after a failure mode changes for the products during mass production according to the risk level control and Alarm conditions, and further correcting the parameter specifications of each process. For example, if the failure mode is changed during the mass production of the product, automatically connecting the FMEA to pass through the more obvious failure mode; the card control limit feedback process sets different card control rules (Rule), such as: if the situation of exceeding the card control rule occurs in the mass production process, an alarm is given, and meanwhile, the corresponding processing is required to be carried out (machine testing, limit correction, formula replacement and the like).
In addition, during the mass production period, the method also comprises the steps of carrying out abnormal feedback event tracking (SLOTS) and Advanced Process Control (APC), feeding back production data generated by the abnormal feedback event tracking and the APC, carrying out parameter adjustment engineering experiments, and feeding back the experiment results to a quality control system so as to revise the parameter specification of each process. In the APC process, the APC is mainly used to calculate and adjust some specific parameters of each product production, for example, in the exposure process, the recipe parameters needed to be used for the current lot of goods are calculated according to the measurement values and the recipes used for the previous lot of goods and the expected values.
The present embodiments also provide a computer-readable storage medium having stored thereon a plurality of instructions executable by one or more processors to perform the steps of the automated quality management method described above.
In summary, the present invention provides an automated quality control system, method and computer readable storage medium, which implement the formulation, adjustment and improvement of each process parameter specification through a specification formulation module, a specification adjustment module, a specification improvement module and an exception feedback module, and the finally formed parameter specification accurately controls each process, and accurately and effectively performs the distribution of each process.
Furthermore, the automatic quality control system provided by the invention can be customized and improved in the stages of parameter specification formulation, adjustment and improvement, meets the quality requirements of customers, is strictly controlled, and is continuously improved by self, so that the daily work of engineers is more efficient and convenient.
Furthermore, the automatic quality management and control system provided by the invention can perform pre-calculation according to the requirements of engineers, and compared with manual calculation, errors caused by subjective factors can be reduced, and the accuracy of pre-calculation is improved.
Furthermore, the automatic quality management and control system provided by the invention can effectively track problems, and is convenient for engineering units to quickly search and solve the problems.
Claims (11)
1. An automated quality management system, comprising:
the specification formulation module is used for customizing and formulating the parameter specification of the product in each manufacturing process by combining the product specification requirement of a customer and the product production rule;
the specification adjusting module is used for adjusting the parameter specification of each process by using a 6Sigma management method during the test run period, and calculating the limit of the parameter specification of each process by using the standard deviation by combining the correlation among the processes;
the specification improving module is used for improving the parameter specification of each manufacturing procedure according to normal distribution and a 3Sigma principle during the pre-operation period and pre-calculating the improved parameter specification of each manufacturing procedure; and the number of the first and second groups,
and the abnormity feedback module is used for uninterruptedly feeding back the abnormal events of each manufacturing process to the specification improvement module so as to continuously improve the parameter specification of each manufacturing process of the product.
2. The automated quality management system of claim 1, wherein the specification formulation module is further configured to formulate failure modes and impact analysis reports in combination with customer product specification requirements and production rules prior to customizing the product to parameter specifications for each process.
3. The automated quality management system of claim 2, wherein a reference value of the parameter specification for each process is determined based on the specified failure mode and impact analysis report, and scaled back based on the reference value as a reference point for defining the parameter specification for each process.
4. The automated quality management system of claim 3, wherein a 90% reduction in the reference value is used as a reference point for establishing a limit for a parameter specification for each process.
5. The automated quality management system of claim 1, wherein pre-calculating parameter specifications for each process comprises: the parameter specification of each process is pre-calculated quarterly.
6. The automated quality management system of claim 1, wherein the processes comprise exposing, etching, and cleaning, wherein the parametric specification boundary in the exposing process is smaller than the parametric specification boundary in the etching process, and the parametric specification boundary in the etching process is smaller than the parametric specification boundary in the cleaning process.
7. An automated mass management method, comprising:
establishing a specification of parameters for each process using the automated quality management system of any one of claims 1-6;
carrying out mass production of products according to the parameter specification of each process, and collecting first data generated by a production control system during a test operation period and a pre-operation period;
referring to the data, carrying out mass production of products according to the parameter specification of each process;
and collecting the mass production data generated by the production control system of the product during the mass production period, feeding the mass production data back to the parameter adjustment engineering experiment, and revising the parameter specification of each process through the automatic mass control system.
8. The automated mass management method of claim 7, further comprising: and automatically controlling each batch of products and machines according to the risk level during the mass production, and carrying out alarm monitoring and alarm operation.
9. The automated mass management method of claim 8, further comprising: and continuously providing card control limit feedback for products in the production period and automatically correcting the failure mode after the failure mode changes according to the risk level control and alarm monitoring conditions.
10. The automated mass management method of claim 9, further comprising: and performing abnormal feedback event tracking and advanced process control during the mass production period, feeding back production data generated by the abnormal feedback event tracking and the advanced process control, performing parameter adjustment engineering experiments, and feeding back the experiment results to a quality control system to revise the parameter specification of each process.
11. A computer-readable storage medium having stored thereon instructions executable by one or more processors to perform the steps of the automated quality management method of any one of claims 7-10.
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