WO1996026539A1 - Procede et dispositif destine a l'analyse d'anomalies et la verification de chaines de production - Google Patents

Procede et dispositif destine a l'analyse d'anomalies et la verification de chaines de production Download PDF

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Publication number
WO1996026539A1
WO1996026539A1 PCT/JP1995/000288 JP9500288W WO9626539A1 WO 1996026539 A1 WO1996026539 A1 WO 1996026539A1 JP 9500288 W JP9500288 W JP 9500288W WO 9626539 A1 WO9626539 A1 WO 9626539A1
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WIPO (PCT)
Prior art keywords
data
abnormality
manufacturing
file
factor
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Application number
PCT/JP1995/000288
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English (en)
Japanese (ja)
Inventor
Isao Miyazaki
Yoshihiko Kiyokuni
Ken Uchikawa
Tetsuo Noguchi
Jun Nakazato
Masao Sakata
Seiji Ishikawa
Kazuko Ishihara
Naoki Go
Masaki Koma
Koichi Kubouchi
Tsutomu Okabe
Kazunori Nemoto
Original Assignee
Hitachi, Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hitachi, Ltd. filed Critical Hitachi, Ltd.
Priority to PCT/JP1995/000288 priority Critical patent/WO1996026539A1/fr
Publication of WO1996026539A1 publication Critical patent/WO1996026539A1/fr

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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor
    • H01L22/20—Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Definitions

  • the present invention provides an abnormality analysis technology that automatically detects an abnormality that has occurred in a manufacturing line of an industrial product, tracks the cause of the detected abnormality, and predicts an influence of the detected abnormality on a subsequent process.
  • the present invention relates to a control technique for utilizing the results obtained in a control method of the production line. Background technology
  • a semiconductor integrated circuit device which is an example of a semiconductor product among industrial products, is a semiconductor wafer (hereinafter, referred to as a wafer) in a so-called pre-process (hereinafter, referred to as an IC manufacturing line) of an IC manufacturing line.
  • IC manufacturing line a semiconductor wafer
  • pre-process hereinafter, referred to as an IC manufacturing line
  • integrated circuit devices are manufactured through a wide variety of manufacturing processes such as photolithography, oxide film formation, thin film formation, and impurity implantation.
  • IC manufacturing lines have many different manufacturing processes.
  • each manufacturing process of the IC manufacturing line is an ultra-fine processing, a dimensional variation of a wafer, a foreign substance attached to a wafer, a defect of a transferred pattern, and the like cause a product defect. Therefore, in the IC manufacturing line, the film thickness of the thin film formed on the wafer, the dimension of the pattern, the foreign matter adhering to the wafer and the defect in the appearance of the pattern are quantitatively inspected. In addition, defects in each manufacturing process are quickly found through inspections in each manufacturing process, and prevention of the flow of defective wafers in subsequent manufacturing processes is being implemented. In addition, manufacturing equipment for each manufacturing process and its surrounding ring Strict environmental controls are in place. With this strict management, it is implemented to prevent defects from being created in the wafer in each 3R fabrication process.
  • a final inspection is performed to determine whether many products (pellets) formed on a wafer are good or defective. That is, a so-called probe inspection is performed in which a probe needle is brought into contact with the wafer to perform an electrical characteristic inspection of the product. In other words, product defects are ultimately found by probe inspection. Therefore, a failure analysis method for analyzing the cause of product failure by investigating the correlation between probe inspection data and inspection data for each manufacturing process has been proposed.
  • the first product failure analysis method is a method in which foreign matter inspection data and appearance defect inspection data are collated by a pattern matching method, and a product failure is predicted based on the overlap of both data.
  • geometric foreign matter inspection data and appearance defect inspection data, and logical one-dimensional probe inspection data that is compressible characteristic data are converted into a data array. After being transformed by a data transformation processing method such as data coordinate transformation, it is collated by a pattern matching method, and a product defect is analyzed by overlapping of both data.
  • a first object of the present invention is to provide a production line abnormality analysis technique capable of detecting an abnormality occurring in a production line by using production data and inspection data.
  • a second object of the present invention is to provide a production line abnormality analysis technology that can automatically determine the cause of the detected abnormality.
  • a third object of the present invention is to provide a production line abnormality analysis technique capable of predicting the influence of a detected abnormality on a subsequent production process.
  • a fourth object of the present invention is to provide a computer-based production line control technique that improves control accuracy by utilizing abnormality analysis data. Disclosure of the invention
  • the present invention has an abnormality detection method including the following steps. That is, manufacturing data and inspection data necessary for abnormality detection processing for a production line to be subjected to abnormality analysis are selected as an abnormality detection parameter, and an edited abnormality detection file is recorded in a file database in advance. An operation method required for the abnormality detection processing is selected for the production line to be prepared, and an edited operation method file is prepared in advance. A step in which data and inspection data are recorded in a process database; and a step in which the abnormality detection file is searched and an abnormality detection parameter to be subjected to abnormality detection processing is specified by a human operation or automatically.
  • the manufacturing data and / or inspection data corresponding to the specified abnormality detection The step read from the process database and the calculation method to be used from the calculation method file are specified manually or automatically by a human operation, and the calculation method is applied to the read data, causing an error. Are detected, and a step in which the calculation result is output.
  • the production line is constantly monitored dynamically, and any abnormalities that occur should be detected dynamically and quickly and accurately. For this reason, not only can rigorous and monotonous monitoring of the production line be performed, which can relieve humans from the work of detecting abnormalities, but also monitoring of the production line. Can greatly improve the reliability of the abnormality detection work.
  • the present invention includes an abnormality factor tracking method including the following steps in addition to the abnormality detection method.
  • the manufacturing data and inspection data required for the abnormality factor tracking process for the manufacturing line to be analyzed are selected as the factor tracking parameters, and the edited factor tracking file is recorded in the file database in advance.
  • a step in which the factor tracking parameter to be processed is specified by a human operation or automatically, and the manufacturing data and Z or inspection data corresponding to the specified factor tracking parameter are based on the process data.
  • the operation method to be used from the operation method file is determined by a human operation. Te or automatically specified, the steps of the calculation method to read de one data is applied has been factor is additionally & a step, the tracking result is output.
  • this abnormal factor tracking method if an abnormal event is detected, the cause of the abnormal event is automatically and quickly and accurately tracked and specified. By identifying the cause of this anomaly, it is possible to quickly take countermeasures for anomalies in the production line, thereby shortening the period during which an anomaly occurs and ensuring that the same product defect continues in large quantities. Accidents can be avoided and ultimately product yield can be increased. Also, when launching a new product, unexpected abnormalities tend to occur. It can be raised to the standard and new products can be launched early.
  • the present invention provides a fluctuation prediction including the following steps in addition to the abnormality detection method. It has a measurement method. That is, the manufacturing data and inspection data required for the fluctuation prediction process for the production line to be analyzed for abnormality are selected as the fluctuation prediction parameters, and the edited fluctuation prediction file is recorded in the file database in advance. A calculation method file selected and edited in advance for a fluctuation prediction process relating to the production line is recorded in a file database in advance; and the fluctuation prediction parameter is searched for the fluctuation prediction file and processed. Is specified, a step in which manufacturing data and / or inspection data corresponding to the specified fluctuation prediction parameter is read out from the process database, and a step in which the calculation method file is searched and used from now on. Operation method is specified, and the read data Calculation method is applied, and scan Tetsubu abnormal fluctuations in the process of manufacturing the manufacturing process or later after being detected is predicted, a step, the prediction result is output.
  • FIG. 1 shows an embodiment of the present invention equipped with an abnormality analysis device for an IC manufacturing line.
  • FIG. 2 is a block diagram showing an IC IS construction line control device. Fig. 2 shows an example of a file recorded on a file basis.
  • A is an error detection file
  • (b) is a factor addition file
  • (c) is a fluctuation prediction file.
  • FIG. 3 is a block diagram showing an abnormality detection system of an abnormality analysis device for an IC manufacturing line according to one embodiment of the present invention.
  • FIG. 4 is an explanatory diagram showing an abnormality detection method using a control value deviation test method.
  • FIG. 5 is an explanatory diagram showing an abnormality detection method using the repetition test method.
  • FIG. 6 is an explanatory diagram showing an abnormality detection method using another repetition test method.
  • FIG. 7 is an explanatory diagram showing an abnormality detection method using the histogram outlier test method.
  • FIG. 8 is an explanatory diagram showing an abnormality detection method using the histogram mean difference test method.
  • FIG. 9 is a block diagram showing a factor tracking system of an abnormality analyzer for an IC manufacturing line according to an embodiment of the present invention.
  • FIG. 10 is an explanatory diagram showing a factor tracking method for identifying an abnormal factor by comparing inspection data with probe inspection data.
  • FIG. 11 is an explanatory diagram showing a factor tracking method for identifying an abnormal factor manufacturing apparatus by comparing probe inspection data for each lot of the manufacturing apparatus.
  • FIG. 10 is an explanatory diagram showing a factor tracking method for identifying an abnormal factor manufacturing apparatus by comparing probe inspection data for each lot of the manufacturing apparatus.
  • FIG. 12 is an explanatory diagram showing a factor tracking method for identifying an abnormal cause charge position by comparing probe inspection data for each lot of a charge position.
  • FIG. 13 is an explanatory diagram showing a factor tracking method for tracking an abnormality caused by maintenance of a manufacturing apparatus.
  • FIG. 14 is an explanatory diagram showing a factor tracking method for tracking an abnormality caused by a change in manufacturing conditions of a manufacturing apparatus.
  • FIG. 15 is a flowchart showing an embodiment of a factor tracking method for automatically identifying the cause of an abnormality by comparing probe inspection data with inspection data in a manufacturing process.
  • FIG. 16 is a flowchart showing an embodiment of a factor tracking method for dynamically identifying an abnormal factor manufacturing apparatus by comparing probe inspection data for each lot of the manufacturing apparatus.
  • FIG. 17 is a flowchart showing an embodiment of a factor tracking method for automatically specifying an abnormal cause charger position by comparing probe inspection data for each mouth of a charging position of a manufacturing apparatus.
  • Fig. 18 5 is a flowchart showing an embodiment of a factor tracking method for automatically tracking an abnormality caused by maintenance of a manufacturing apparatus.
  • FIG. 19 is a flowchart showing an embodiment of a factor tracking method for automatically tracking an abnormality caused by a change in manufacturing conditions of a manufacturing apparatus.
  • FIG. 20 is a block diagram showing a fluctuation prediction system of an abnormality analyzer for an IC manufacturing line according to an embodiment of the present invention.
  • FIG. 21 is an explanatory diagram showing a fluctuation prediction method for predicting an influence after the manufacturing process on the basis of inspection data of the manufacturing process in which an abnormality is detected.
  • FIG. 22 is an explanatory diagram showing a fluctuation prediction method for predicting the influence of a maintenance operation on a manufacturing apparatus.
  • FIG. 23 is an explanatory diagram showing a fluctuation prediction method for predicting the influence of a change in manufacturing conditions on a manufacturing apparatus.
  • the control device of the manufacturing line according to the present invention is constructed as a control device for automatically controlling a so-called front-end process in a manufacturing line for manufacturing an IC which is an example of a semiconductor product by a computer.
  • An abnormality analysis device for an IC production line (hereinafter, referred to as an abnormality analysis device), which is an embodiment of the present invention, for analyzing an abnormality occurring in an IC production line is incorporated.
  • the so-called pre-process (hereinafter referred to as the "manufacturing line") in the IC manufacturing line 1 has a wide variety of processes such as a photolithography process, an oxide film forming process, a thin film forming process, and an impurity implantation process.
  • Each of the manufacturing steps 2 includes various semiconductor manufacturing apparatuses (hereinafter referred to as manufacturing apparatuses) 3 and various inspection apparatuses 4.
  • the final process of the production line 1 has a probe inspection process 5 In this probe inspection step 5, a probe inspection device 6 is provided.
  • the integrated circuit elements are built into the wafer 7, and the wafer 7 is produced by the product of the production line 1.
  • a pellet 8 is manufactured. Note that, in FIG. 1, the arrow 9 indicates the flow of the wafer 7.
  • the control device of the production line comprehensively monitors the entire production line based on the production measurement surface measured in advance, and controls the production line 1 by issuing commands as needed to the supervisor of each production process 2
  • the host computer 10 includes an abnormality analyzer 12 via a communication network 11 and a manufacturing data collection terminal device (not shown) attached to each manufacturing device 3. ) And an inspection data collection terminal device (not shown) attached to each inspection device 4 as appropriate.
  • the anomaly analyzer 12 is constructed by a personal computer or the like, and includes a central processing unit (hereinafter, referred to as a CPU) 13 and an input device for executing data input composed of a mouse, a keyboard, and the like. 1 and 4, an output device 15 composed of a display device for displaying data and a printer for outputting data, etc., and an external memory 16 are provided. Access database 17 and file database 18 are defined respectively. Note that the abnormality analysis device 12 can also be constructed by a part of the host computer 10.
  • a CPU central processing unit
  • the manufacturing apparatus 3 installed in each manufacturing process 2 performs predetermined processing on the wafer 7 in the manufacturing process 2, and then stores product information, manufacturing conditions, manufacturing history, maintenance history, and failure history (hereinafter, manufacturing data).
  • the information is transmitted in real time to the abnormality analyzer 12 via the network 11.
  • the abnormality analyzer 12 records each manufacturing data transmitted from each manufacturing apparatus 3 in the process database 17.
  • the inspection equipment 4 installed in the manufacturing process 2 is configured to quantitatively inspect the processing state of the wafer 7 in the manufacturing process 2.
  • the inspection device 4 installed in the photolithography process performs pattern dimension inspection, film thickness inspection, foreign matter inspection, and appearance inspection.
  • Each inspection device 4 transmits the inspection data to the abnormality analysis device 12 via the network 11 in real time.
  • the abnormality analyzer 12 records each inspection data sent from each inspection device 4 in the process database 17.
  • the same inspection device may be shared by the plurality of manufacturing processes 2 for the inspection device 4.
  • the probe inspection device 6 in the probe inspection process 5 measures the electrical characteristics of the product pellets 8 by bringing the probe needles into contact with the wafer 7 and checks whether each pellet 8 is good or defective based on the measurement results. I do.
  • the probe inspection device 6 transmits the inspection data (hereinafter referred to as probe inspection data) of the thermal characteristics inspection for each pellet 8 to the abnormality analysis device 12 via the network 11 in real time.
  • the abnormality analysis device 12 records the probe inspection data transmitted from the probe inspection device 6 in the process database 17.
  • Fig. 2 shows an embodiment of the files recorded in the file database 18.
  • (a) is an abnormality detection file
  • (b) is a factor tracking file
  • (c) is a fluctuation prediction file. is there.
  • the CPU 13 is configured to execute the abnormality analysis method based on the data of the process database 17 and the data of the file database 18.
  • the CPU 13 is an abnormality detection system for the production line It is called a normal detection system. 20), a production line abnormality tracking system (hereinafter referred to as a factor tracking system) 30 and a production line fluctuation prediction system (hereinafter referred to as a fluctuation prediction system) 40.
  • the abnormality analysis method includes abnormality detection processing, factor tracking processing, fluctuation prediction processing, processing that links abnormality detection processing and factor tracking processing, and also links abnormality detection processing and fluctuation prediction processing. There is a processing that was done.
  • the output contents of the abnormality detection system 20 include the type of the abnormality, the manufacturing process of the abnormality, the time of the abnormality, the content of the abnormality, the manufacturing data, and the inspection data.
  • the output contents of the factor tracking system 30 include the type of the variable factor, the manufacturing process, the manufacturing device, the inspection device, the changed content, and the timing.
  • the output contents of the fluctuation prediction system 40 include the type, the manufacturing process, the time, the content, and the manufacturing data whose fluctuation is predicted.
  • the abnormality detection system 20 includes a start unit 21, an abnormality detection parameter specification unit 22, a data reading unit 23, a sorting unit 24, an abnormality detection calculation unit 25, and a detection result.
  • An output unit 26 is provided.
  • the starting unit 21 includes a manual mode unit 21a and an automatic mode unit 21b.
  • the manual mode is activated when the manual mode section 21a is selected in the activation section 21 and all processing steps proceed in an interactive manner between a person and a computer.
  • the automatic mode is activated when the automatic mode unit 21b is selected by the activation unit 21 and all processing steps automatically proceed.
  • an embodiment of the automatic mode will be described.
  • the abnormality detection parameter specification part 22 detects an abnormality in the file database 18.
  • Access the file 27 and automatically specify the abnormality detection record 28 of the abnormality detection processing to be executed in the abnormality detection file 27, and the item to be processed first in the abnormality detection record 28 28 a is transferred to the data reading section 23.
  • the data reading section 23 reads the manufacturing data and the inspection data from the process database 17 as parameters (characters) relating to the designated item 28a.
  • the sorting unit 24 sorts the read data into data necessary in the abnormality detection calculation unit 25.
  • the anomaly detection calculation unit 25 reads out a predetermined test method (calculation method) that matches the footed data from the calculation method file 29 containing the test method as described later, and executes the calculation based on the read. Then, based on the sorted data, a singular point / special point is extracted and detected as abnormal. The detected abnormality is output by the detection result output unit 26 to an output device 15 such as a printer / display device, and is also output to the factor tracking system 30 and the fluctuation prediction system 40, respectively. Further, the result output unit 26 transmits an update signal to the abnormality detection parameter designating unit 22 to start the next abnormality detection process.
  • the calculation method of abnormality detection by each test method of the abnormality detection calculation unit 25 will be specifically described with reference to FIGS.
  • This control value deviation test method is an example of a test method for detecting sudden fluctuations.
  • the parameters related to the item specified by the abnormality detection parameter specifying unit 22 are each lot in the source-gate photo process (hereinafter referred to as SG photo process) for the type 4 MSRAM. This is development dimension inspection data for each development.
  • the abnormality detection parameter designating section 22 reads the file data in order to specify the abnormality detection record 28 to be subjected to the next abnormality detection processing.
  • the error detection record 28 specified at the end of the error detection file 27 in Fig. 2 stored in the base 18 is searched, and the next error detection item 28 a is specified to read the data.
  • the data reading unit 23 reads necessary data from the process database 17 according to the specified item, and transfers the data to the sorting unit 24.
  • the sorting section 24 sorts the read data to virtually create the graph of FIG. 4 and transfers it to the abnormality detection calculation section 25.
  • the abscissa indicates the lot numbers
  • the ordinate indicates the development dimension inspection values for each lot.
  • the abnormality detection calculation unit 25 reads the control value deviation test method from the calculation method file 29 and calculates whether or not the control value deviation has occurred in the graph of FIG. 4, and the control value deviation has occurred. In this case, it is detected as abnormal.
  • an abnormality of the lot number A23 is detected in the development dimension inspection in the SG photo process for the product type 4 MSRAM.
  • the detection result output unit 26 outputs the detected abnormality to an output device 15 such as a printer or a display device, and transmits an abnormality detection signal to the factor tracking system 30 and the fluctuation prediction system 40.
  • the detection result output unit 26 transmits an update signal to the abnormality detection file designating unit 22.
  • This repetition test method is an example of a test method for detecting a trend change.
  • the parameters of the item specified by the abnormality detection parameter specifying section 22 are the same as in FIG. 4 for each unit in the SG photo process for the type 4 MSRAM.
  • This is the developed dimension inspection data.
  • Anomaly detection parameter specification unit 2 2 searches anomaly detection record 28 of anomaly detection file 27 of file database 18, specifies anomaly detection item to be processed from now on, and transfers it to data read unit 23
  • Data read unit 2 3 process data according to this designated item The necessary data is read from the overnight base 17 and transferred to the sorting section 24.
  • the sorting section 2 sorts the read data and virtually creates the graph of FIG.
  • the abnormality detection calculation unit 25 reads the repetition test method from the calculation method file 29 and determines whether the developed dimension value is continuously lower than the average value or continuously higher than the average value in the graph of FIG. Calculation is performed, and if a linked part occurs, it is detected as abnormal.
  • abnormalities of the lot numbers A12 to A20 are detected in the development dimension inspection in the SG photo process.
  • the processing of the detection result output unit 26 is the same as that of the control value outlier test method, and a description thereof will be omitted.
  • FIG. 6 shows another embodiment of the repeated test method.
  • the parameter of the item specified by the abnormality detection parameter specifying unit 22 is the same as in FIGS. 4 and 5 for each lot in the SG photo process for the type 4 MSR AM. This is development dimension inspection data.
  • the abnormality detection parameter specification unit 22 searches the abnormality detection record 28 of the abnormality detection file 27 of the file database 18, specifies the next abnormality detection item, and transfers it to the data reading unit 23.
  • the data reading unit 23 reads necessary data from the process database 17 according to the specified item, and transfers the data to the sorting unit 24.
  • the sorting unit 24 sorts the read data, and displays the graph of Fig.
  • the abnormality detection calculation unit 25 reads out the second run test method from the calculation method file 29, and the developed dimension values are continuously displayed in the graph of FIG. Calculates whether it has risen or has fallen continuously, and a continuous point occurs If so, it is detected as abnormal. In the embodiment shown in FIG. 6, abnormalities of lot numbers A12 to A20 are detected. Note that the abnormality detection calculation unit 25 carries out the combination of the above-described control value deviation test method and the two consecutive test methods, and generates an abnormality detection signal regardless of which test method detects an abnormality. .
  • This histogram outlier test method is an example of a test method for detecting sudden fluctuation.
  • the parameter specified by the abnormality detection parameter setting unit 22 is a deviation value related to the yield of the probe inspection process at the present time for the type 4 MECL.
  • the yield of the probe inspection process is the percentage of the number of non-defective products to the number of berets on one wafer.
  • the abnormality detection parameter specification unit 22 searches the abnormality detection record 28 of the abnormality detection file 27 of the file database 18, specifies the next abnormality detection item, and transfers the parameters to the data readout unit 23. .
  • the data reading section 23 reads necessary data from the access database 17 in accordance with the designated parameters, and transfers it to the sorting section 24.
  • the sorting unit 24 sorts the read data and virtually creates the graph in Fig. 7 with the horizontal axis indicating each lot number and the vertical axis indicating the inspection value of the development dimension for each lot. Then, the data is transferred to the abnormality detection calculation unit 25. In FIG. 7, the yield value is plotted on the horizontal axis, and the frequency is plotted on the vertical axis.
  • the abnormality detection calculation unit 25 reads the histogram test method from the error test method file 29 and calculates whether or not there is a deviation value outside the reference range in FIG. In the case of the embodiment shown in FIG. 7, since there is a deviation value, an abnormality detection signal is generated.
  • FIG. 8 shows a mean difference test method which is another embodiment of the histogram test method. ing.
  • the parameters of the item specified by the abnormality detection parameter overnight specification unit 22 are two types of periods corresponding to the yield of the probe inspection process for the type 4 ME. It is the deviation value of.
  • the abnormality detection parameter specification section 22 searches the abnormality detection record 28 of the error detection file 27 of the file database 18, specifies the next abnormality detection item, and transfers it to the data reading section 23.
  • the data reading unit 23 reads necessary data from the process database 17 according to the designated item, and transfers the data to the sorting unit 24.
  • the sorting unit 24 sorts the read data and virtually creates the graph in Fig.
  • the abnormality detection calculation unit 25 reads the second histogram test method from the calculation method file 29, obtains the average value of the deviation values of the upper and lower graphs, and determines whether the difference between the average values is within the reference range. Is calculated, and if out of range, it is detected as abnormal. In the case of the embodiment shown in FIG. 8, no abnormality detection signal is generated because it is within the range.
  • the abnormality detection calculation unit 25 performs the above-described two types of histogram test methods in combination, and generates an abnormal signal regardless of which test method detects an abnormality.
  • the IC manufacturing line 1 is automatically and constantly monitored by the abnormality detection system 20 of the abnormality analysis device 12, and any abnormality that occurs is detected automatically and quickly and accurately. This not only relieves humans from the rigorous and monotonous monitoring or abnormality detection of the IC manufacturing line 1, but also monitors or monitors the IC manufacturing line 1. The reliability of the normal detection work can be greatly improved.
  • the factor tracking system 30 is a system that tracks and investigates the cause of the abnormality detected by the abnormality detection system 20.As shown in Fig. 9, the activation unit 31 and the factor tracking parameter It has a designation unit 32, a data readout unit 33, a sorting unit 34, a factor tracking calculation unit 35, and a tracking result output unit 36. Further, the activation unit 31 includes a manual mode unit 31 a and an automatic mode unit 31 b as in the abnormality detection system 20. First, the factor tracking method in the case of the manual mode will be described.
  • the monitor of the production line activates the activation unit 31 manually.
  • the factor tracking system S 0 is automatically activated.
  • the factor tracking parameter designation unit 32 accesses the factor tracking file 37 of the file database 18 and causes the factor tracking record 38 to be displayed on the display device of the output device 15.
  • the monitor designates the item 38 a of the factor tracking process to be executed from among the displayed factor tracking records 38 with the input device 14 such as a mouse, and transfers the item 38 a to the data reading unit 33.
  • the data reading unit 33 reads necessary data from the process database 17 according to the item 38a designated in this way and transfers it to the sorting unit 34.
  • the sorting section 34 sorts the read data into necessary data in the factor tracking operation section 35, creates the graphs shown in FIGS. 10 to 14 and displays them on the output device 15 Let it.
  • the factor tracking calculation unit 35 reads a factor tracking method described later from a calculation method file 39 in which a factor tracking method described later is recorded as a calculation method. The observer observes this graph while confirming the factor tracking method of the computation file 39, and observes the factor abnormality. Identify the cause.
  • the purging result output unit 36 outputs the specified abnormal cause to an output device 15 such as a printer or a display device, and updates the next factor purging process.
  • FIG. 10 shows an embodiment of a factor tracking method for identifying the cause of an abnormality by comparing the inspection data of a predetermined manufacturing process with the probe inspection data.
  • the parameters corresponding to the items designated by the observer in the factor tracking parameter designation section 32 are the developed dimension inspection data and the completed dimension inspection data for each lot in the SG photo, and the lot dimension. This is probe inspection data for each case.
  • the data read unit 33 reads predetermined data from the process database 17 according to the designated parameters, and transfers the read data to the sorting unit 34.
  • the sorting unit 3 creates the graph shown in FIG. 10 based on the transferred data and displays the graph on the monitor of the output device 15. In FIG. 10, the horizontal axis indicates each lot number, and the vertical axis indicates the inspection value for each lot.
  • the upper graph shows the development dimensions
  • the middle graph shows the completed dimensions
  • the lower graph shows the deviation in the probe inspection yield.
  • the observer reads the calculation method file 39 of the factor tracking calculation unit 35, and checks the inspection data against the probe inspection data to confirm the factor tracking method for identifying the cause of the abnormality. . Then, the observer tracks the common points of these graph changes as abnormal factors. For example, in FIG. 10, since the development dimension, the completed dimension, and the yield deviation value decrease at the lot number A22, immediately before the development dimension inspection is performed on the lot number A22, the SG photo process is performed. It is concluded that there is an anomaly factor in the work before the development dimension inspection.
  • the tracking result output unit 36 sends this result to the output device 15 Output.
  • Figure 11 shows that when the same processing in the same manufacturing process was performed by different manufacturing equipment of the same model, the probe inspection data of each lot passed through each manufacturing equipment was compared with each other.
  • An embodiment of a factor tracking method for identifying a manufacturing apparatus that has caused an abnormality is shown.
  • the parameter specified by the monitor in the factor tracking parameter specifying unit 32 is a probe related to the yield of each of the first, second, and third exposure apparatuses in the SG photo process. This is inspection data.
  • the data reading unit 33 reads out predetermined data from the process database 17 and transfers it to the sorting unit 34.
  • the sorting unit 34 creates the graph shown in FIG. 11 based on the transferred data, and displays the graph on the monitor of the output device 15.
  • the process database 17 contains the history of manufacturing equipment that records which unit has passed which machine and which manufacturing machine, and also contains probe inspection data for each lot.
  • the sorting unit 34 can create the graph shown in FIG. 11 by matching the manufacturing device history and the probe inspection data for each lot.
  • the horizontal axis represents the deviation value of the yield, and the vertical axis represents the frequency.
  • the upper graph is for Unit 1
  • the middle graph is for Unit 2
  • the lower graph is for Unit 3.
  • the observer reads the calculation method file 39 of the factor tracking calculation unit 35, and checks the probe inspection data for each port after passing through each manufacturing device. Check the factor tracking method to identify the manufacturing equipment that was used. Then, the observer tracks the differences between these graph changes as abnormal factors. For example, in FIG. 11, since the deviation value of the second unit is reduced, it is concluded that there is a cause of abnormality in the second unit of the exposure apparatus in the SG photo process.
  • the tracking result output unit 36 outputs this result. Force device 15 to output.
  • Fig. 12 shows that when the same processing in the same manufacturing process is charged to different positions in the same manufacturing equipment and executed, the probe inspection data for each port passing through each charge position is compared with each other.
  • This shows an embodiment of a factor tracking method for specifying the position of a charger of a manufacturing apparatus that has caused an abnormality.
  • the parameters specified by the observer in the factor tracking parameter specification unit 32 are the upper charge position, the middle charge position, and the lower charge position of the high-temperature low-pressure CVD device (hereinafter referred to as HLDCVD device) in the SG photo process.
  • Probe inspection data for each yield for each position.
  • the data reading unit 33 reads out predetermined data from the process database 17 and transfers it to the sorting unit 34.
  • the sorting section 34 creates the graph shown in FIG. 12 based on the transferred data, and displays the graph on the monitor of the output device 15.
  • the process database 17 contains the history of manufacturing equipment that records which lot was charged to which manufacturing equipment and at which location, and the probe inspection data for each lot. Therefore, the factor tracking calculation unit 35 can create the graph shown in FIG. 12 by matching the history of the manufacturing apparatus with the probe inspection data by mouth.
  • the horizontal axis represents the deviation value of the yield
  • the vertical axis represents the frequency.
  • the upper graph shows the upper charge position
  • the middle graph shows the upper charge position
  • the lower graph shows the upper charge position.
  • the observer read the calculation method file 39 of the factor tracking calculation unit 35 and collated the probe test data for each mouth after passing through each change position, which caused the abnormality. Check the factor tracking method to identify the position of the manufacturing equipment charge. The observer then tracks the differences in these graphs as anomalies. For example, in Fig. 12 Since the deviation value is decreasing, it is concluded that there is an abnormal factor in the middle charger position of the HLDCVD equipment in the SG photo process.
  • the tracking result output unit 36 causes the output device 15 to output this result.
  • FIG. 13 shows an embodiment of a factor tracking method for tracking an abnormality caused by maintenance of a manufacturing apparatus.
  • the parameters specified by the monitoring person in the factor tracking parameter specifying unit 32 are manufacturing apparatus history data, film thickness inspection data, and yield of probe inspection, which indicate the cleaning time for the HLDCVD apparatus in the SG photo process. Data.
  • the data reading unit 33 reads predetermined data from the process database 17 and transfers it to the sorting unit 34.
  • the sorting section 34 creates the graph shown in FIG. 13 based on the transferred data and displays it on the monitor of the output device 15.
  • the process database 17 records the maintenance history that records when cleaning work etc. were performed for each manufacturing device, and the manufacturing time that recorded when each lot passed through each manufacturing device.
  • the reading unit 34 matches the maintenance history with the manufacturing equipment history in chronological order, and the manufacturing equipment S history and probe.
  • the graph shown in Figure 13 can be created.
  • the upper part shows the time series of the cleaning work for the HLDCVD equipment in the SG photo process
  • the middle part shows the graph on the film thickness inspection value
  • the lower part shows the graph on the deviation value of the yield in the probe inspection.
  • the horizontal axis represents the lot number
  • the vertical axis represents the film thickness value.
  • the horizontal axis shows the lot number
  • the vertical axis shows the deviation value.
  • the K-viewer reads the calculation method file 39 of the factor tracking calculation unit 35 and checks the factor tracking method for pursuing an abnormality caused by the maintenance of the manufacturing apparatus. Then, the observer tracks the displacement point as an abnormal factor in the graph of the film thickness inspection value before and after the cleaning time and the graph of the yield deviation value. For example, in FIG. 13, since there is a displacement point after cleaning, it can be concluded that there is an abnormal factor in the cleaning work of the HLDCVD apparatus in the SG photo process.
  • the additional output unit 36 causes the output device 15 to output this result.
  • FIG. 14 shows an embodiment of a factor tracking method for tracking an abnormality caused by a change in manufacturing conditions in a manufacturing apparatus.
  • the parameters specified by the observer in the factor tracking parameter specification section 32 include the manufacturing apparatus history data, the development dimension inspection data, and the probe inspection regarding the change of the exposure condition of the exposure apparatus in the SG photo process. This is yield data.
  • the data read section 33 reads predetermined data from the process database 17 and transfers the read data to the sorting section 34.
  • the sorting section 34 creates the graph shown in FIG. 14 based on the transferred data, and displays it on the monitor of the output device 15.
  • the manufacturing equipment history in the process database 17 records when and how the manufacturing conditions were changed for each manufacturing equipment, and the time when each lot passed through each manufacturing equipment.
  • the sorting section 34 has the manufacturing equipment history and development dimension inspection data and data.
  • the graph shown in FIG. 14 can be created.
  • the upper part shows the time series of changes in the exposure conditions of the exposure equipment in the SG photo process
  • the middle part shows the graph related to the development dimension inspection values
  • the lower part shows the professional 6 is a graph relating to a deviation value of the yield of the probe inspection.
  • the horizontal axis represents the lot number
  • the vertical axis represents the developed dimension value.
  • the horizontal axis represents the lot number
  • the vertical axis represents the deviation value.
  • FIG. 15 is a flowchart showing an embodiment of a factor tracking method for automatically specifying a cause of abnormality by comparing probe inspection data with inspection data of a predetermined manufacturing process.
  • the parameters specified by the additional factor parameter setting unit 32 are first the probe inspection data for each lot and the development dimension inspection data for each lot in the SG photo process. That is, when an abnormality detection signal based on the probe inspection data is transmitted from the abnormality detection system 20, the factor tracking parameter setting unit 32 initially specifies a parameter to be subjected to factor tracking processing. Then, the factor tracking file 37 in FIG. 3 stored in the file database 18 is searched to specify the factor tracking record 38, and the developed dimension inspection data for each mouth in the SG photo process is firstly obtained.
  • the data readout unit 33 reads out the development dimension inspection inspection data in the SG photo process, which is the abnormal factor candidate parameter, from the process database 17 together with the probe inspection data, and transfers it to the factor tracking operation unit 35.
  • the factor additional calculation unit 35 calculates a correlation coefficient between the probe inspection data and the developed dimension inspection data.
  • the distribution diagram shown in FIG. 15 schematically illustrates the calculation of the correlation coefficient.
  • the horizontal axis represents the yield value
  • the vertical axis represents the development dimension.
  • Each plot shows the yield value (horizontal axis) and development size (vertical axis) of each lot.
  • the straight line is the calculated correlation coefficient straight line.
  • the factor tracking calculation unit 35 compares whether the calculated correlation coefficient is equal to or greater than a preset reference value. If the value is equal to or higher than the reference value, it is concluded that the exposure process, which is a process prior to the development dimension inspection in the SG photo process, is one of the abnormal factor candidates.
  • the follow-up result output unit 36 outputs this conclusion to the output device 15 and registers it as a factor candidate in the factor tracking file 37.
  • the item relating to the probe inspection yield abnormality retrieved earlier by the factor tracking parameter specification unit 32 is updated, and the correlation coefficient between the next inspection data and the probe inspection data is calculated by the factor tracking calculation unit 35.
  • the routine in which the calculated correlation coefficient is compared with the reference value is repeated. This routine is repeated for all the abnormal cause candidate parameters corresponding to the items related to the probe inspection yield abnormality.
  • the parameters concluded as abnormal cause candidates are determined and output in order of ascending and descending correlation coefficients. Further, as the output of the tracking result, the correlation coefficient may be output according to the order of ascending and descending for all the parameters examined.
  • Figure 16 shows that when the same process in the same manufacturing process was performed by different manufacturing equipment of the same model, probe inspection data for each lot passed through each manufacturing equipment was compared with each other to detect abnormalities.
  • 5 is a flowchart illustrating an embodiment of a factor tracking method for automatically specifying a manufacturing apparatus that has caused a cause.
  • the parameters of the item specified first by the factor tracking parameter specifying section 32 are the first, second, and third units of the exposure device in the SG photo process.
  • Probe inspection data on yield That is, when an abnormality detection signal based on the probe test data is transmitted from the abnormality detection system 20, the factor tracking parameter specification unit 32 searches the factor tracking file 37 and probes as a factor tracking record 38.
  • probe inspection data for each of the first, second, and third exposure equipment in the SG Hote process is used as the parameter of the first abnormality factor candidate item. And transfer it to the data readout unit 33.
  • the data reading unit 33 reads from the process database 17 probe inspection data for each yield of each of the first, second, and third exposure equipment in the SG photo process, which is one of the parameters for this abnormal factor candidate.
  • the factor tracking calculator 35 calculates the deviation value of the yield for each unit, calculates the average value of all the deviation values, and obtains the difference between the average value and the deviation value of each unit.
  • the graph shown in FIG. 16 is a schematic diagram of this arithmetic processing. In Fig.
  • the horizontal axis shows the yield deviation and the vertical axis shows the frequency.
  • (A) is the graph for the first unit
  • (b) is the graph for the second unit
  • (c) is the graph for the third unit
  • (d) is the graph showing the difference between the average value and the second unit. It is.
  • the factor tracking calculation unit 35 compares whether the calculated difference is equal to or greater than a preset reference value. For example, as shown in Figure 16, the difference value of Unit 2 If is greater than or equal to the reference value, it is concluded that the second exposure apparatus in the SG photo process is one of the candidate abnormal factors.
  • the tracking result output unit 36 outputs this conclusion to the output device 15 and registers it in the factor tracking file 37 as a factor candidate.
  • the probe inspection data on the yield of each unit of the manufacturing equipment in the next process is the next abnormality factor candidate.
  • the difference between the average value of all designated units and the deviation value of each unit is specified by the factor tracking calculation unit 35, and the routine of comparing the calculated difference value with the reference value is repeated. It is.
  • This routine is repeated for the manufacturing equipment of all the processes specified in the items of the cause tracking record of the probe inspection yield abnormality.
  • the manufacturing equipments concluded as abnormal cause candidates are ranked and output in the order of the difference value. Further, as the output of the arterial vein result, the difference value may be output according to the order of the magnitude with respect to all the inspected manufacturing apparatuses.
  • Figure 17 compares probe inspection data for each port after each charge position when the same process in the same manufacturing process is charged to different positions in the same manufacturing equipment.
  • 6 is a flowchart showing an embodiment of a factor tracking method for automatically specifying a charge position of a manufacturing apparatus that has caused an abnormality.
  • the parameter of the item specified first by the factor tracking parameter specifying unit 32 is the upper charging position, the middle charging position, and the lower charging position of the HLDCVD apparatus in the SG photo process. This is a probe inspection day for yield.
  • the factor tracking parameter specification unit 32 Search the factor tracking file 37 and read out the record of the probe inspection yield abnormality as the factor tracking record 38.Also, regarding the yield at each of the upper charge position, middle charge position, and lower charge position of the HLDCVD equipment in the SG photo process
  • the probe inspection data is designated as a parameter of the first abnormality factor candidate item, and is transferred to the data reading unit 33.
  • the data reading unit 33 processes the probe data for each of the yield factors at the upper, middle, and lower stages of the HLDCVD system in the SG photo process, which are the candidate parameters for the abnormal factors, in the process database. Read from 17 and transfer to factor addition calculation unit 35.
  • the factor addition calculation unit 35 calculates the deviation value of the yield for each charge position, calculates the average value of all the deviation values, and calculates the difference between the average value and the deviation value of each unit. Ask.
  • the graph shown in FIG. 17 is a schematic diagram of this arithmetic processing. In Fig. 17, the deviation value of the yield is plotted on the horizontal axis, and the frequency is plotted on the vertical axis. Curve A is the deviation value of the middle charging position, and curve B is the average value. Accordingly, the factor tracking calculation unit 35 compares whether the calculated difference value is equal to or larger than a preset reference value. For example, when the difference value at the middle charge position is equal to or larger than the reference value as shown in the graph of FIG. 17, the middle charge position of the HLDCVD apparatus in the SG photo process is one of the abnormal cause candidates. It is concluded. The tracking result output unit 36 outputs this conclusion to the output device 15 and registers it as a factor candidate in the factor tracking file 37.
  • the probe inspection data on the yield at each charge position of the manufacturing equipment in the next process is obtained from the items of the factor tracking record of the probe inspection yield abnormality previously searched by the factor tracking parameter specifying unit 32.
  • the difference between the average value of all the designated charge positions and the deviation value of each charge position is designated as an abnormal cause candidate parameter overnight. Is calculated, and the routine in which the calculated difference value is compared with the reference value is returned. This routine is repeated for all manufacturing equipment specified in the items of the tracking record for the cause of the probe inspection yield abnormality.
  • the charging positions of the manufacturing apparatuses concluded as the abnormal cause candidates are ranked and output in the order of the difference value.
  • the difference value may be output according to the order of the magnitude for each charging position for all the inspected manufacturing apparatuses.
  • FIG. 18 is a flowchart showing an embodiment of a factor tracking method for automatically tracking an abnormality caused by maintenance of a manufacturing apparatus.
  • the parameters specified first by the factor additional parameter specifying unit 32 are manufacturing apparatus history data, film thickness inspection data, and processing data relating to the cleaning timing of the HLDCVD apparatus in the SG photo process. This is the yield data for one inspection. That is, when an abnormality detection signal based on the probe inspection data is transmitted from the abnormality detection system 20, the factor tracking parameter specification unit 32 searches the factor tracking file 37 and obtains the probe inspection yield as the factor tracking record 38.
  • the data readout unit 33 processes the abnormality parameter candidate parameters, such as manufacturing equipment history data on the cleaning timing of the HLDCVD equipment in the SG photo process, film thickness inspection data, and yield data of the probe inspection, on a process data basis. From the source 17 and transferred to the factor tracking calculation unit 35. Factor tracking calculation unit 35 is the average of the deviations of the lots processed before cleaning by the HLDCVD equipment and the average of the deviations of the lots processed after cleaning. And calculate the difference between the two averages. The graph shown in FIG.
  • the factor tracking calculation unit 35 compares whether the calculated difference value is equal to or larger than a preset reference value. For example, as shown in the graph of Fig. 18, when the average value after cleaning is larger than the average value before cleaning and the difference value is equal to or more than the reference value, cleaning work of the HLDCVD apparatus in the SG photo process is performed. It is concluded that this is one of the possible causes of the anomaly.
  • the tracking result output unit 36 outputs this conclusion to the output device 15 and registers it in the factor tracking file 37 as a factor candidate.
  • the data related to the maintenance of the manufacturing equipment in the next process is specified as the next candidate parameter of the abnormality factor and specified.
  • the difference between the average value before and after the maintenance work of all the manufacturing apparatuses thus calculated is calculated by the factor tracking calculation unit 35, and the routine of comparing the calculated difference value with the reference value is repeated.
  • This routine is returned for all equipment maintenance operations for all processes specified in the items in the probe tracking yield factor anomaly tracking record. Then, when the tracking of the maintenance work of all the specified manufacturing equipment is completed, the maintenance work of the manufacturing equipment concluded as a candidate for an abnormal factor is ranked and output in the order of the difference value. You.
  • the difference value may be output in accordance with the order of the magnitude for each maintenance operation for all the inspected manufacturing apparatuses.
  • Figure 19 shows dynamic changes in manufacturing equipment caused by changes in manufacturing conditions.
  • the parameters initially specified by the factor additional parameter specification section 32 are manufacturing apparatus history data, development dimension inspection data, and probe data relating to changes in the exposure conditions of the SS optical apparatus in the SG photo process. This is the inspection yield data. That is, when an abnormality detection signal based on the probe inspection data is transmitted from the abnormality detection system 20, the factor tracking parameter specification unit 32 searches the factor tracking file 37 and performs the probe inspection as the factor tracking record 38.
  • the manufacturing equipment history data, the current dimension inspection data, and the yield data of the probe inspection regarding the change of the exposure condition of the exposure equipment in the SG Photo process were specified as the first abnormality cause candidate parameter. Then, the data is transferred to the data reading unit 33.
  • the data readout unit 33 processes the process parameters based on the manufacturing equipment history data, development dimension inspection data, and probe inspection yield data related to changes in the exposure conditions of the exposure equipment in the SG photo process, which are the abnormal parameter candidate parameters. From the source 17 and transferred to the factor tracking calculation unit 35.
  • the factor tracking calculation unit 35 calculates the average value of the deviation values of the lot groups processed before the change of the exposure condition of the exposure apparatus and the average value of the deviation values of the lot groups processed after the change.
  • the graph shown in FIG. 19 is a schematic diagram of this arithmetic processing.
  • the horizontal axis shows the deviation value of the yield
  • the vertical axis shows the frequency.
  • Curve A is the average of the deviations of the lots processed before the change
  • curve B is the average of the deviations of the lots processed after the change.
  • the factor tracking calculator 35 compares whether the calculated difference value is equal to or larger than a preset reference value. For example, as shown in the graph of Fig. 19, when the average value after the change is larger than the average value before the change and the difference value is equal to or larger than the reference value, the exposure condition of the exposure apparatus in the SG photo process is used. Change is an abnormal factor It is concluded that it is one of the supplements.
  • the tracking result output unit 36 outputs this conclusion to the output device 15 and registers it in the factor tracking file 37 as a factor candidate.
  • the data relating to the change in the condition of the manufacturing equipment in another process is designated as the next abnormal factor candidate parameter, and all of the specified manufacturing
  • the difference between the average values before and after the device condition change is calculated by the factor tracking calculation unit 35, and the routine for comparing the calculated difference value with the reference value is repeated.
  • This routine is repeated for changes in the manufacturing equipment conditions for all processes specified in the items of the probe inspection yield abnormality record.
  • the change of the conditions of the manufacturing equipment that is concluded as a candidate for the cause of abnormality is determined and output in the order of the difference value. You. Further, as the output of the additional result, the difference value may be output in accordance with the order of the magnitude every time the condition is changed for all the manufacturing apparatuses examined.
  • the IC manufacturing line is automatically and constantly monitored by the abnormality analyzer, and if an abnormality is detected, the cause of the abnormality is automatically and quickly and accurately tracked and identified.
  • the cause of this abnormality is automatically and quickly and accurately tracked and identified.
  • the fluctuation prediction system 40 predicts, for the abnormality detected by the abnormality detection system 20, how the abnormality will affect the processes after the abnormality occurrence process.
  • the fluctuation prediction system 40 includes a start unit 41, a fluctuation prediction parameter overnight setting unit 42, a data reading unit 43, a reading unit 44, a fluctuation prediction calculation unit 45, and a prediction result output unit 46. I have.
  • the starter 41 has only an automatic mode, and all steps automatically proceed.
  • the fluctuation prediction parameter specification unit 42 stores the fluctuation prediction file of FIG. 20 stored in the file database 18. 4 Search for 7. As a result of this search, a fluctuation prediction record 48 used in the fluctuation prediction processing to be executed in the fluctuation prediction file 47 is searched, and the first item to be processed is automatically read out. Transferred to 3.
  • the data reading section 43 reads necessary data from the process database 17 according to the parameters of the transferred items and transfers the data to the sorting section 44.
  • the sorting section 44 sorts the read data, virtually creates the graphs shown in FIGS. 21 to 23 necessary for the fluctuation prediction operation section 45, and appropriately creates the fluctuation prediction operation section 45.
  • the fluctuation prediction calculation unit 45 reads the fluctuation prediction methods shown in FIGS. 21 to 23 described later from the calculation method file 49, calculates the fluctuation amount of the created graph, and calculates the calculation result as the prediction result. Output as The prediction result output unit 46 outputs the prediction result to the output device 15 and updates the next fluctuation prediction process. Also, in order to automatically verify whether the predicted parameters in the subsequent process change as predicted, the prediction result output unit 46 stores the prediction results in the abnormality detection record 28 of the abnormality detection file 27. register. This error detection file By registering the file 27 in the abnormality detection record 28, the fluctuation prediction system 40 is linked to the abnormality detection system 20.
  • the abnormality detection system 20 finds the prediction result of the fluctuation prediction system 40 as an abnormality detection item when searching the abnormality detection file 27 described above, it executes the abnormality detection processing described above for the abnormality detection item. .
  • FIG. 21 shows an embodiment of a fluctuation prediction method for predicting an influence after a manufacturing process based on inspection data of a manufacturing process in which an abnormality is detected.
  • the inspection data in which the abnormality is detected is the actual dimension inspection data in the SG photo process.
  • the fluctuation prediction parameter designating section 42 searches the fluctuation prediction file 47 to execute the SG photo process.
  • the parameter of the first item is read from the fluctuation prediction record 48 that causes the development dimension to be abnormal, and is transferred to the data reading unit 43.
  • the data reading section 43 reads necessary data from the process database 17 in accordance with the transferred parameters and transfers it to the sorting section 44.
  • the sorting unit 44 sorts the read data to virtually create the graph shown in FIG. 21 and transfers it to the fluctuation prediction calculation unit 45.
  • a product of type 4 MECL corresponding to the product of lot A22 was used.
  • Figure 21 illustrates this fluctuation prediction method.
  • the abscissa indicates each lot number, and the ordinate indicates the inspection value for each lot.
  • the upper part is a graph relating to the deterioration of the development dimension, and the lower part is a graph relating to the fluctuation of the deviation value of the yield of the probe inspection target for fluctuation prediction.
  • the amount of variation in the probe inspection yield for which variation is to be predicted is expressed by the difference from the preceding and following lots.
  • the prediction result output unit 46 outputs the prediction result to the output device 15 and updates the next fluctuation prediction process. Also, in order to automatically verify whether the predicted parameters of the post-process that have been predicted change as predicted, the prediction result output unit 46 stores the prediction results in the abnormality detection record 28 of the abnormality detection specification file 27. register.
  • FIG. 22 shows an embodiment of a fluctuation prediction method for predicting the influence of a maintenance operation on a manufacturing apparatus.
  • the inspection data in which an abnormality is detected is the film thickness inspection data after the processing by the HLDCVD apparatus in the SG photo process.
  • the fluctuation prediction parameter designation unit 4 2 changes the fluctuation prediction file 4. 7 is retrieved, the parameters of the first item are read from the fluctuation prediction record 48 relating to the film thickness abnormality after processing by the HLDCVD apparatus in the SG photo process, and transferred to the data reading unit 43.
  • the data reading section 43 reads necessary data from the process database 17 according to the transferred parameters and transfers the data to the sorting section 44.
  • the sorting unit 44 sorts the read data to virtually create the graph shown in FIG. 22 and transfers it to the fluctuation prediction calculation unit 45.
  • the fluctuation prediction calculation unit 45 receives the method for the manufacturing equipment from the calculation method file 49.
  • the fluctuation prediction method for predicting the effect of the maintenance work is read, and the fluctuation amount of the parameter is calculated using the fluctuation coefficient specified in the calculation method file 49.
  • the process of the fluctuation prediction specified by the fluctuation prediction operation unit 45 is a probe inspection process, and the parameter of the fluctuation prediction corresponds to the product of the lot A22. Yield in the probe inspection process for Type 4 MECL.
  • the upper part is a time series of the cleaning operation for the HLDCVD apparatus in the SG photo process
  • the middle part is a graph relating to the film thickness inspection value
  • the lower part is a graph showing the deviation value of the yield in the probe inspection.
  • the horizontal axis represents the lot number
  • the vertical axis represents the film thickness value.
  • the horizontal axis represents the lot number
  • the vertical axis represents the deviation value.
  • the time series of the cleaning operation and each lot number are matched with the passage time of the HLDCVD equipment that has been cleaned.
  • the fluctuation amount of the probe inspection yield for fluctuation prediction is expressed by the difference from the preceding and following lots.
  • the prediction result output unit 46 outputs the prediction result to the output device 15 and updates the next fluctuation prediction process.
  • the prediction result output unit 46 registers the prediction result in the abnormality detection record 28 of the abnormality detection specification file 27 in order to automatically verify whether the predicted parameter in the post-process changes as predicted. I do.
  • FIG. 23 shows an embodiment of a fluctuation prediction method for predicting the influence of a change in manufacturing conditions on a manufacturing apparatus.
  • the inspection data in which an abnormality is detected is development dimension inspection data in the SG photo process.
  • the fluctuation prediction parameter specifying unit 42 searches the fluctuation prediction file 47 to execute the SG photo processing.
  • Abnormal development dimension in process The first parameter is read from the fluctuation prediction record 48 and the data is transferred to the data reading unit 43.
  • the data reading section 43 reads necessary data from the process database 17 according to the transferred parameters and transfers the data to the sorting section 44.
  • the sorting section 44 sorts the read data, virtually creates the graph shown in FIG.
  • the fluctuation prediction calculation unit 45 reads the fluctuation prediction method for predicting the effect of the change in the manufacturing conditions on the manufacturing equipment from the calculation method file 49, and calculates the fluctuation amount of the parameter by using the fluctuation coefficient specified in the calculation method file 49. Use to calculate.
  • the process of the fluctuation prediction designated by the fluctuation prediction operation unit 45 is a probe inspection process
  • the parameter of the fluctuation prediction is a product of the cutout A22. This is the yield in the probe inspection process of the corresponding product type 4 MECL.
  • the fluctuation predicting operation unit 45 calculates the yield fluctuation amount-deterioration amount XI.
  • the upper part is a time series relating to the change of the exposure condition of the exposure apparatus in the SG photo process
  • the middle part is a graph relating to the development dimension inspection value
  • the lower part is a graph relating to the deviation value of the yield of the probe inspection.
  • the horizontal axis represents the lot number
  • the vertical axis represents the developed dimension value.
  • the horizontal axis represents the lot number
  • the vertical axis represents the deviation value.
  • the prediction result output unit 46 outputs the prediction result to the output device 15 and updates the next fluctuation prediction process. In addition, in order to automatically verify whether the predicted parameters in the post-process that have been predicted fluctuate as predicted, the prediction result output unit 46 stores the prediction in the abnormality detection record 28 of the abnormality detection specification file 27. Register the measurement results.
  • the IC manufacturing line is automatically and constantly monitored by the abnormality analyzer, and if an abnormality is detected, the effect of the abnormality on the subsequent process is automatically and quickly and accurately predicted. . Prediction of post-process fluctuations caused by these abnormalities makes it possible to take prompt measures for abnormalities in the IC manufacturing line, thereby shortening the period during which abnormalities occur and increasing the number of identical product defects. It is possible to avoid accidents that cause a spliced gun, and ultimately increase the product yield. In addition, unexpected abnormalities tend to occur when new products are launched. Can be launched early. Industrial applicability
  • the method and apparatus for analyzing a production line abnormality and the method and apparatus for controlling a production line according to the present invention include an IC, other semiconductor products, a liquid crystal display device, a printed wiring board, a photomask, a magnetic disk, and a compact. It is useful as a method and apparatus for analyzing abnormalities in production lines of industrial products such as disks and automobiles, and is particularly suitable for use in production lines with a large number of production lines and a wide variety of production types.

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Abstract

Technique d'analyse d'anomalies qui permet de détecter automatiquement des anomalies dans la chaîne de fabrication d'un produit, de rechercher la cause de l'anomalie relevée et de prévoir l'impact de celle-ci sur les étapes ultérieures. Pour pouvoir détecter les anomalies, les données de fabrication et de contrôle de qualité sont recueillies puis enregistrées dans une base de données de processus. A partir de la base de données de processus, on lit les données de fabrication et de contrôle de qualité relatives à un paramètre de détection d'anomalie désigné dans un fichier de détection d'anomalie qui est enregistré dans une base de données de fichiers. On désigne ensuite un procédé de détection d'anomalie enregistré dans un fichier de procédé arithmétique préenregistré que l'on applique aux données. Lorsqu'une anomalie est détectée, on désigne un paramètre de recherche de cause provenant d'un fichier de recherche de cause enregistré dans la base de données de fichiers, puis on lit les données de fabrication et de contrôle de qualité relatives au paramètre à partir de la base de données de processus. On recherche la cause de l'anomalie en appliquant aux données un procédé de recherche de cause lu à partir d'un fichier de procédé arithmétique préenregistré. Une fois l'anomalie détectée, on désigne en outre un paramètre de prévision de variation appartenant à un fichier de prévision de variation préenregistré puis on lit les données de fabrication et de contrôle de qualité relatives au paramètre à partir de la base de données de processus. On prévoit la variation en appliquant aux données un procédé de prévision de variation lu à partir d'un fichier de procédé arithmétique.
PCT/JP1995/000288 1995-02-24 1995-02-24 Procede et dispositif destine a l'analyse d'anomalies et la verification de chaines de production WO1996026539A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0910123A1 (fr) * 1996-03-19 1999-04-21 Hitachi, Ltd. Systeme de commande de processus
JP2007527612A (ja) * 2003-07-07 2007-09-27 アドバンスト・マイクロ・ディバイシズ・インコーポレイテッド 異常検知に基づき計測ディスパッチを実行するための方法および装置
JP2009164292A (ja) * 2007-12-28 2009-07-23 Fujitsu Microelectronics Ltd 半導体デバイス試験装置及び半導体デバイス試験方法

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS544078A (en) * 1977-06-13 1979-01-12 Hitachi Ltd Inspection method of performance of circuit element
JPS58216414A (ja) * 1982-06-11 1983-12-16 Hitachi Ltd 半導体製造プラント異常検出システム
JPH02234451A (ja) * 1989-03-07 1990-09-17 Nippon Telegr & Teleph Corp <Ntt> Ｌｓｉ製造工程の不良工程抽出方法
JPH04204115A (ja) * 1990-11-30 1992-07-24 Nissan Motor Co Ltd 故障対応支援装置
JPH05253797A (ja) * 1992-03-11 1993-10-05 Hitachi Ltd ライン異常判定方式
JPH0618297A (ja) * 1992-07-03 1994-01-25 Hitachi Ltd 装置異常判定方式
JPH0750235A (ja) * 1993-08-06 1995-02-21 Toshiba Corp 半導体製造装置の管理装置

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS544078A (en) * 1977-06-13 1979-01-12 Hitachi Ltd Inspection method of performance of circuit element
JPS58216414A (ja) * 1982-06-11 1983-12-16 Hitachi Ltd 半導体製造プラント異常検出システム
JPH02234451A (ja) * 1989-03-07 1990-09-17 Nippon Telegr & Teleph Corp <Ntt> Ｌｓｉ製造工程の不良工程抽出方法
JPH04204115A (ja) * 1990-11-30 1992-07-24 Nissan Motor Co Ltd 故障対応支援装置
JPH05253797A (ja) * 1992-03-11 1993-10-05 Hitachi Ltd ライン異常判定方式
JPH0618297A (ja) * 1992-07-03 1994-01-25 Hitachi Ltd 装置異常判定方式
JPH0750235A (ja) * 1993-08-06 1995-02-21 Toshiba Corp 半導体製造装置の管理装置

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0910123A1 (fr) * 1996-03-19 1999-04-21 Hitachi, Ltd. Systeme de commande de processus
EP0910123A4 (fr) * 1996-03-19 2000-08-23 Hitachi Ltd Systeme de commande de processus
US6542830B1 (en) 1996-03-19 2003-04-01 Hitachi, Ltd. Process control system
US6757621B2 (en) 1996-03-19 2004-06-29 Hitachi, Ltd. Process management system
JP2007527612A (ja) * 2003-07-07 2007-09-27 アドバンスト・マイクロ・ディバイシズ・インコーポレイテッド 異常検知に基づき計測ディスパッチを実行するための方法および装置
JP2009164292A (ja) * 2007-12-28 2009-07-23 Fujitsu Microelectronics Ltd 半導体デバイス試験装置及び半導体デバイス試験方法

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