JP7366775B2 - Data processing method, data processing device and program - Google Patents

Description

The present invention relates to a data processing method, a data processing device, and a program.

2. Description of the Related Art Substrate processing apparatuses that process substrates are known. Typically, the substrate processing apparatus is suitably used for processing semiconductor substrates. 2. Description of the Related Art Determining an abnormality in a substrate processing apparatus based on data output from the substrate processing apparatus in a time-series manner has been considered (for example, see Patent Document 1). In the semiconductor manufacturing equipment disclosed in Patent Document 1, a two-axis coordinate system is created from correlation data between a specific monitoring target and another related monitoring target, and an abnormality in the semiconductor manufacturing equipment is detected based on whether or not it is included in an abnormal area. Judging.

Japanese Patent Application Publication No. 2006-228911

However, in the method of Patent Document 1, it is necessary to identify two monitoring targets that have a desired correlation, but in reality, this identification is difficult and the state of semiconductor manufacturing equipment cannot be easily determined. There wasn't.

The present invention has been made in view of the above problems, and its purpose is to provide data processing that can reduce the amount of calculation in matching target time series data obtained from a substrate processing device and time series data included in a learning database. An object of the present invention is to provide a method, a data processing device, and a program.

According to one aspect of the present invention, a data processing method includes the steps of obtaining time series data, obtaining evaluation values, classifying, and matching. In the step of acquiring time series data, a plurality of time series data obtained by the substrate processing apparatus is acquired. In the step of acquiring the evaluation value, an evaluation value of each of the plurality of time series data is acquired. In the classifying step, each of the plurality of time series data is classified into one of a plurality of categories based on the evaluation value. In the matching step, target time series data corresponding to any one of the plurality of categories is matched with time series data included in the learning database.

In one embodiment, the learning database stores cause countermeasure information corresponding to time series data. In the data processing method, in the matching step, if a matching rate between the target time series data and at least one time series data included in the learning database is higher than a threshold, a cause countermeasure corresponding to the time series data is performed. The method further includes the step of reading the information.

In one embodiment, in the step of obtaining the evaluation value, the evaluation value of each of the plurality of time-series data is obtained by comparing each of the plurality of time-series data with reference data.

In one embodiment, in the step of obtaining the evaluation value, the evaluation value when the difference between the values of the time series data and the reference data is large is determined by the evaluation value when the difference between the values of the time series data and the reference data is small. is larger than the above evaluation value.

In one embodiment, the data processing method further includes the step of storing the target time series data.

According to another aspect of the present invention, a data processing device includes a data acquisition section, an evaluation value acquisition section, a classification section, and a matching section. The data acquisition unit acquires a plurality of time series data obtained by the substrate processing apparatus. The evaluation value acquisition unit acquires evaluation values of each of the plurality of time series data. The classification unit classifies the plurality of time series data into one of a plurality of categories based on the evaluation value. The matching unit matches target time series data corresponding to any of the plurality of categories with time series data included in the learning database.

According to yet another aspect of the present invention, the program causes the computer to execute the steps of acquiring time series data, acquiring evaluation values, classifying, and matching. In the step of acquiring time series data, a plurality of time series data obtained by the substrate processing apparatus is acquired. In the step of acquiring the evaluation value, an evaluation value of each of the plurality of time series data is acquired. In the classifying step, the plurality of time series data are classified into one of a plurality of categories based on the evaluation value. In the matching step, target time series data corresponding to any one of the plurality of categories is matched with time series data included in the learning database.

According to the present invention, it is possible to reduce the amount of calculation in matching target time series data obtained from a substrate processing apparatus and time series data included in a learning database.

FIG. 1 is a schematic diagram of a data processing device and a substrate processing device according to the present embodiment. It is a flow diagram of the data processing method of this embodiment. (a) is a graph showing time series data used in the data processing method of this embodiment, and (b) is a graph showing reference data used in the data processing method of this embodiment. (a) is a schematic diagram showing time series data used in the data processing method of this embodiment, and (b) is a schematic diagram showing a comparison between the time series data and reference data in the data processing method of this embodiment. (c) is a schematic diagram showing changes in evaluation values in the data processing method of this embodiment. (a) is a schematic diagram showing classification based on evaluation values in the data processing method of this embodiment, and (b) is a schematic diagram showing extraction of time series data in the data processing method of this embodiment. FIG. 1 is a schematic diagram of a data processing device and a substrate processing device according to the present embodiment. (a) to (c) are schematic diagrams showing evaluation value graphs and time series data showing changes in evaluation values displayed on the display unit in the data processing device of the present embodiment. (a) to (c) are schematic diagrams showing time-series data displayed on a display unit in the data processing device of this embodiment. (a) to (c) are schematic diagrams showing extracted evaluation value graphs and corresponding time series data displayed on the display unit in the data processing device of the present embodiment. FIG. 1 is a schematic diagram of a data processing device and a substrate processing device according to the present embodiment. (a) to (c) are schematic diagrams showing time series data extracted by the data processing method of the present embodiment. It is a flow diagram of the data processing method of this embodiment. It is a flow diagram of the data processing method of this embodiment. FIG. 3 is a schematic diagram showing a table of a learning database in the data processing device of the present embodiment. FIG. 1 is a schematic diagram of a data processing device and a substrate processing device according to the present embodiment. It is a flow diagram of the data processing method of this embodiment. It is a flow diagram of the data processing method of this embodiment. (a) is a diagram showing the waveform of a control signal, and (a) is a diagram showing the waveform of time-series data. It is a schematic diagram which shows the value of several time series data. (a) It is a graph which shows evaluation value distribution, and (b) It is a graph which shows the standardization result of evaluation value distribution. FIG. 1 is a schematic diagram of a data processing device and a substrate processing device according to the present embodiment. FIG. 1 is a schematic diagram of a data processing device and a substrate processing device according to the present embodiment. FIG. 1 is a schematic diagram of a data processing device and a substrate processing device according to the present embodiment. FIG. 1 is a schematic diagram of a data processing device and a substrate processing device according to the present embodiment. FIG. 1 is a schematic diagram of a data processing device and a substrate processing device according to the present embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of a data processing method, a data processing apparatus, and a program according to the present invention will be described below with reference to the drawings. In addition, in the drawings, the same or corresponding parts are given the same reference numerals and the description will not be repeated.

First, an embodiment of a data processing device 100 according to the present invention will be described with reference to FIG. FIG. 1 is a schematic diagram of a data processing apparatus 100 and a substrate processing apparatus 200 of this embodiment.

The data processing device 100 performs data processing. Specifically, the data processing apparatus 100 processes time series data TD generated in the substrate processing apparatus 200.

Typically, substrate processing apparatus 200 includes at least one processing unit 210. The processing units 210 each generate time series data TD.

The time series data TD is data indicating changes in physical quantities in the substrate processing apparatus 200 over time. The time-series data TD indicates time-based changes in physical quantities (values) that change over time over a predetermined period. For example, the time-series data TD is data indicating changes over time in physical quantities regarding processing performed on a substrate by the substrate processing apparatus 200. Alternatively, the time series data TD is data indicating changes over time in physical quantities regarding the characteristics of the substrate processed by the substrate processing apparatus 200.

Note that the value shown in the time series data TD may be a value directly measured with a measuring device. Alternatively, the value shown in the time series data TD may be a value obtained by processing a value directly measured by a measuring device. Alternatively, the value shown in the time series data TD may be calculated from values measured by a plurality of measuring devices.

Typically, time series data TD includes 10 or more values. The time series data TD may be composed of 100 or more values, or may be composed of 1000 or more values.

The data processing apparatus 100 may be communicably connected to the substrate processing apparatus 200. Alternatively, the time series data TD generated in the substrate processing apparatus 200 may be transferred to the data processing apparatus 100 via a storage element.

The data processing device 100 includes a processing section 10. The processing unit 10 has a processor. The processing unit 10 includes, for example, a central processing unit (CPU). Alternatively, the processing unit 10 may include a general-purpose computing machine.

The data processing device 100 may further include a storage unit 20. Storage unit 20 stores data and computer programs. The storage unit 20 includes a main storage device and an auxiliary storage device. The main storage device is, for example, a semiconductor memory. The auxiliary storage device is, for example, a semiconductor memory and/or a hard disk drive. The storage unit 20 may include removable media. The processing unit 10 executes a computer program stored in the storage unit 20.

Here, the computer program is stored on a non-transitory computer readable storage medium. Non-transitory computer-readable storage media include ROM (Read Only Memory), RAM (Random Access Memory), CD-ROM, magnetic tape, magnetic disk, or optical data storage devices.

The storage unit 20 stores time-series data generated in the substrate processing apparatus 200. The data processing apparatus 100 and the substrate processing apparatus 200 are communicably connected to each other, and each time the time series data TD is generated in the substrate processing apparatus 200, it is transferred to the data processing apparatus 100, and the storage unit 20 is The time series data TD may be stored sequentially. Alternatively, each time a predetermined number of time series data TD is generated in the substrate processing apparatus 200, the time series data TD is collectively transferred to the data processing apparatus 100, and the storage unit 20 stores the time series data TD together. You can.

The processing unit 10 includes a data acquisition unit 11, an evaluation value acquisition unit 12, a classification unit 13, and an extraction unit 14. The data acquisition unit 11 acquires a plurality of time series data. Here, the data acquisition unit 11 acquires time series data TD from the storage unit 20. Note that in this specification, the data acquisition unit 11 may be simply referred to as acquisition unit 11.

The evaluation value acquisition unit 12 acquires evaluation values for each of the plurality of time series data TD. In this specification, the evaluation value acquisition unit 12 may be simply referred to as acquisition unit 12.

The evaluation value acquisition unit 12 acquires evaluation values for a plurality of time series data TD according to the same evaluation standard. The evaluation value acquisition unit 12 generates one evaluation value corresponding to one time series data TD according to a specific evaluation criterion. For example, the evaluation value acquisition unit 12 acquires evaluation values for the entire time series data TD according to the same evaluation criteria. Alternatively, the evaluation value acquisition unit 12 acquires evaluation values for a specific portion of the time series data TD according to the same evaluation criteria. Note that the evaluation value acquisition unit 12 may generate an evaluation value by integrating a plurality of evaluation criteria using the entire time series data TD or a specific portion.

Typically, the evaluation value acquisition unit 12 generates the evaluation value of the time series data TD by calculating the evaluation value according to the evaluation criteria set in the data processing device 100. For example, the evaluation value acquisition unit 12 generates an evaluation value by digitizing the waveform of a graph representing the time series data TD. However, the evaluation value acquisition unit 12 may cause other components to calculate evaluation values and acquire only the calculated evaluation values. The evaluation value may be generated by comparing the time series data TD with reference data. Note that the reference data is preferably stored in the storage unit 20.

The number of possible evaluation values is smaller than the number of time series data TD. Typically, the number of time-series data TD is 100 or more, whereas the possible number of evaluation values is 20 or more and less than 100.

Note that the upper limit of the time series data TD is not particularly limited. As long as the data processing device 100 is capable of calculation or storage, the upper limit of the number of time series data TD may be arbitrary. In one example, the upper limit of time series data TD may be 10,000, and the upper limit of time series data TD may be 100 million.

The classification unit 13 classifies each of the plurality of time series data TD into one of the plurality of categories based on the evaluation value. The number of the plurality of divisions is smaller than the number of time series data TD or the possible number of evaluation values generated according to the time series data TD. Typically, the number of time series data TD is 100 or more, the possible number of evaluation values is 20 or more and less than 100, whereas the number of multiple divisions is 2 or more and 10 or less.

The evaluation value of the time series data TD is generated according to certain evaluation criteria. Therefore, the categories into which the time series data TD is classified using the evaluation values may indicate the level of the evaluation criteria. In the following description of this specification, a classification indicating high or low evaluation criteria may be referred to as a level.

For example, the classification unit 13 classifies the time series data TD into one of levels 1 to 4 based on the evaluation value. Typically, after the acquisition unit 12 acquires an evaluation value corresponding to the time series data TD based on the time series data TD and reference data, the classification unit 13 classifies the time series data based on the evaluation value. Classify TD into one of levels 1 to 4. Preferably, the reference data is stored in the storage unit 20.

In one example, level 1 indicates that the evaluation value is reference data or a category relatively close to the reference data, and level 2 indicates that the evaluation value is the next closest category to the reference data after level 1. Furthermore, level 3 indicates that the evaluation value is the next closest to the standard data after level 2, and level 4 indicates that the evaluation value is further away from the standard data than any of levels 1 to 3. shows.

Note that the classification unit 13 may classify the time series data TD into two categories based on the evaluation value. For example, time series data TD classified into Category 1 indicates reference data or relatively close to the reference data, and time series data TD classified into Category 2 indicates that it is further away from the reference data than Category 1. .

The extraction unit 14 extracts time series data corresponding to any of the categories classified by the classification unit 13 as extracted time series data. Thereby, specific time series data indicating evaluation values in a specific range can be collected as extracted time series data from a plurality of time series data.

For example, it is preferable that the extraction unit 14 extracts time-series data corresponding to the division furthest from the reference data among the divisions classified by the classification unit 13. Thereby, time-series data corresponding to evaluation values far from the ideal value or average value can be easily extracted. Therefore, changes in the substrate processing apparatus 200 can be detected before a substantial abnormality occurs in the substrate processing apparatus 200. In this case, it is preferable to change the control of the substrate processing apparatus 200 so as to suppress the change in the substrate processing apparatus 200 before an abnormality occurs in the substrate processing in the substrate processing apparatus 200.

As described above, it is preferable that the extraction unit 14 extracts time-series data corresponding to the division furthest from the reference data among the divisions classified by the classification unit 13. Further, when time series data indicating an evaluation value that is different from the reference data is extracted as the extracted time series data, it is preferable that the extracted time series data is further subjected to clustering processing. However, the extracted time series data extracted by the extraction unit 14 does not have to be time series data corresponding to the segment farthest from the reference data.

According to the data processing apparatus 100 of this embodiment, specific time series data can be easily extracted from a plurality of time series data generated in the substrate processing apparatus.

Next, the data processing method of this embodiment will be explained with reference to FIGS. 1 and 2. FIG. 2 shows a flow diagram of the data processing method of this embodiment.

As shown in FIG. 2, in step S2, time series data TD is acquired. The time series data TD is data generated in the substrate processing apparatus 200. The acquisition unit 11 acquires a plurality of time series data TD. The plurality of time series data TD may be data generated in the same processing unit 210 or may be data generated in different processing units 210. Each of the plurality of time series data TD indicates a change over time in a specific unit of physical quantity in the substrate processing apparatus 200.

The acquisition unit 11 acquires a plurality of time series data TD from the storage unit 20. Alternatively, the acquisition unit 11 may directly acquire the time series data TD from the substrate processing apparatus 200. For example, the time-series data is sequentially generated as the substrate processing apparatus 200 processes the substrate or the substrate processing apparatus 200 is controlled. Next, the process proceeds to step S4.

In step S4, an evaluation value (score) is obtained. The acquisition unit 12 acquires an evaluation value Ev for each of the plurality of time series data TD. For example, the acquisition unit 12 generates an evaluation value Ev for each of the plurality of time series data TD according to a specific evaluation criterion. The evaluation criteria may be stored within the data processing device 100 or may be stored outside the data processing device 100. One evaluation value Ev is obtained for one time series data TD. However, the evaluation value Ev can indicate a plurality of values depending on the time series data TD.

Note that the range between the maximum value and the minimum value of the evaluation value may be determined. For example, the evaluation values may be normalized such that the difference between the maximum value and the minimum value of the evaluation values is 1.

Evaluation values are generated according to specific evaluation criteria. For example, the evaluation value may be determined such that the higher the evaluation value, the higher the evaluation, and the lower the evaluation value, the lower the evaluation. Alternatively, the evaluation value may be determined such that the higher the evaluation value, the lower the evaluation, and the lower the evaluation value, the higher the evaluation. Alternatively, the evaluation value may be determined such that the closer the evaluation value is to the specific value, the higher the evaluation, and the farther the evaluation value is from the specific value, the higher the evaluation. Next, the process proceeds to step S6.

In step S6, each of the time series data TD is classified into one of a plurality of categories using the evaluation value Ev. The number of categories that can be classified is smaller than the number of values that the evaluation value Ev can indicate. Next, the process proceeds to step S8.

In step S8, specific time series data TD classified into the target category among the plurality of categories is extracted as extracted time series data from the plurality of time series data TD. Typically, the extracted time series data is stored in the storage unit 20. However, the extracted time series data may be stored in an external storage element.

The data processing method of this embodiment is performed as described above. According to the data processing method of this embodiment, specific time series data can be easily extracted from a plurality of time series data generated in the substrate processing apparatus 200. Note that among the plurality of time series data TD acquired in step S2, the extracted time series data is stored in the storage unit 20, while the time series data TD that is not extracted is stored in the storage unit 20. It may be deleted without being deleted. This allows necessary information to be stored efficiently.

The evaluation value of the time series data TD may be generated by comparing it with reference data. For example, the reference data may be generated based on a plurality of time series data showing good substrate processing results. In one example, the reference data is generated by averaging multiple time series data that have shown good substrate processing results. When the reference data is ideal data or time series data indicating a good substrate processing result, the closer the time series data is to the reference data, the higher the evaluation, and the farther away from the reference data, the lower the evaluation.

Next, time series data TD and reference data RD will be explained with reference to FIG. 3. FIG. 3(a) is a graph representing time series data TD, and FIG. 3(b) is a graph representing reference data RD. In the graphs of FIGS. 3(a) and 3(b), the horizontal axis indicates time, and the vertical axis indicates physical quantity. For example, the time series data TD and the reference data RD each indicate changes over time in 100 physical quantities (values).

The time-series data TD and the reference data RD show temporal changes in the same physical quantity, and the shape of the graph shown by the time-series data TD is generally the same as the shape of the graph of the reference data RD. However, strictly speaking, the time series data TD does not match the reference data RD.

As shown in FIG. 3(a), in the time series data TD, the physical quantity is almost zero at the start point. The physical quantity starts increasing from zero after a predetermined period of time has elapsed. A physical quantity rapidly increases and reaches a peak in a short period of time. Then, the physical quantity maintains its peak for a certain period of time. Over the peak period, the physical quantity fluctuates slightly but remains approximately constant. After that, the physical quantity rapidly decreases in a short period of time and returns to almost zero.

In FIG. 3(b), the reference data RD is created based on time-series data when substrate processing in the substrate processing apparatus 200 progresses in an ideal state. For example, the reference data RD may be time-series data when substrate processing in the substrate processing apparatus 200 progresses under the most ideal conditions. Alternatively, the reference data RD may indicate an average value of time-series data during a period in which substrate processing in the substrate processing apparatus 200 progressed under ideal conditions.

As shown in FIG. 3(b), in the reference data, the physical quantity is almost zero at the starting point. The physical quantity starts increasing from zero after a predetermined period of time has elapsed. A physical quantity rapidly increases and reaches a peak in a short period of time. Then, the physical quantity maintains its peak for a predetermined period of time. After that, the physical quantity rapidly decreases in a short period of time and returns to almost zero.

In this way, the graph shown by the time series data TD shows the same tendency as the graph shown by the reference data RD, but strictly speaking, the time series data is not the same as the reference data RD. For example, the rising period until reaching the peak in the graph of time series data TD is different from the rising period until reaching the peak in the graph of reference data RD. Further, the peak value in the graph of the time series data TD is different from the peak value in the graph of the reference data RD. Furthermore, the peak in the graph of the time series data TD fluctuates more than in the graph of the reference data RD.

Next, the data processing method of this embodiment will be explained with reference to FIGS. 1 to 5. FIG. 4(a) is a schematic diagram showing time series data TD1 to TD100. Each of the time series data TD1 to TD100 indicates a change in the same physical quantity over time. For example, the acquisition unit 11 acquires time series data TD1 to TD100 from the storage unit 20.

The acquisition unit 12 acquires the evaluation value Ev from each of the time series data TD1 to TD100. For example, the acquisition unit 12 acquires the evaluation values Ev1 to Ev100 based on the differences between each of the time series data TD1 to TD100 and the reference data RD. For example, the acquisition unit 12 generates the evaluation value Ev by integrating the squares of the differences between each value of the time series data TD and each value of the reference data RD. In this case, the closer the time-series data is to the reference data, the smaller the evaluation value becomes, and the farther the time-series data is from the reference data, the larger the evaluation value becomes.

FIG. 4(b) is a graph showing the results of comparing the time series data TD and the reference data RD. The diagonal lines in FIG. 4(b) indicate differences between the time series data TD and the reference data RD. When obtaining the evaluation value by integrating the squares of the differences, the evaluation value Ev corresponds to the area of the hatched portion in FIG. 4(b).

FIG. 4(c) is an evaluation value graph GE showing changes in evaluation values Ev1 to Ev25 corresponding to time series data TD1 to TD25. In FIG. 4C, due to the drawing, the evaluation value graph GE shows changes in the evaluation values Ev1 to Ev25 corresponding to the time series data TD1 to TD25 among the time series data TD1 to TD100. As shown in FIG. 4(c), the evaluation value Ev changes depending on the order of the time series data TD. Here, the evaluation value Ev6 is considerably higher than the evaluation values Ev1 to Ev5 and Ev7 to Ev10. Furthermore, the evaluation value Ev20 is considerably higher than the evaluation values Ev15 to Ev19 and Ev21 to Ev25.

FIG. 5(a) is a graph for explaining the classification of the evaluation value graph GE showing changes in the evaluation values Ev1 to Ev25. As shown in FIG. 5(a), the evaluation values Ev are classified according to their sizes. For example, the evaluation value is classified into one of a plurality of categories based on the size.

Here, the classification unit 13 classifies the time series data TD1 to TD25 into one of four levels based on the evaluation values Ev1 to Ev25. The classification unit 13 classifies the time series data TD whose evaluation value Ev does not exceed the threshold Th1 as level 1. The classification unit 13 classifies time-series data TD, in which the magnitude of the evaluation value Ev is greater than or equal to the threshold Th1 and does not exceed the threshold Th2, as level 2. The classification unit 13 classifies time-series data TD, in which the magnitude of the evaluation value Ev is greater than or equal to the threshold Th2 and does not exceed Th3, as level 3. The classification unit 13 classifies time-series data TD whose evaluation value Ev is equal to or greater than a predetermined value Th3 as level 4.

In the graph of FIG. 5(a), a boundary line B1 is shown at the boundary between level 1 and level 2. Similarly, a boundary line B2 is shown at the boundary between level 2 and level 3, and a boundary line B3 is shown at the boundary between level 3 and level 4.

In the graph of FIG. 5A, evaluation values Ev3, Ev8, Ev12, Ev13, Ev15, Ev17, Ev21, and Ev23 correspond to level 1. The evaluation values Ev1, Ev2, Ev4, Ev5, Ev7, Ev9, Ev10, Ev14, Ev16, Ev18, Ev19, Ev22, Ev24, and Ev25 correspond to level 2. The evaluation value Ev11 corresponds to level 3. Evaluation values Ev6 and Ev20 correspond to level 4.

FIG. 5B is a graph for explaining the extraction of time series data TD6 and time series data TD20 corresponding to evaluation values Ev6 and Ev20 by the extraction unit 14. As shown in FIG. 5(b), when the evaluation values Ev acquired according to the specific evaluation criteria are compared, the evaluation value Ev6 and the evaluation value Ev20 are larger than the other evaluation values. At this time, time series data TD6 and time series data TD20 corresponding to evaluation value Ev6 and evaluation value Ev20 are considered to exhibit a unique tendency compared to other time series data TD. Therefore, the extraction unit 14 extracts time series data TD6 and time series data TD20 corresponding to evaluation value Ev6 and evaluation value Ev20.

According to the present embodiment, time series data TD6 and time series data TD20 showing a peculiar tendency among the plurality of time series data TD1 to TD100 are selected based on evaluation values corresponding to the plurality of time series data TD1 to TD100. Can be extracted suitably. Therefore, by extracting the time series data TD6 and TD20 classified into the target categories from the plurality of time series data TD1 to TD100, it is possible to understand and study the unique state of the substrate processing apparatus 200 without performing excessive calculations.

Note that in the above explanation (particularly the explanation with reference to FIGS. 3 to 5), the time series data TD and the reference data RD were compared when generating the evaluation value Ev, but the present embodiment is limited to this. Not done. The evaluation value Ev may be generated from the time series data TD without using the reference data RD.

For example, the acquisition unit 12 may generate the evaluation value Ev by comparing some values of the time series data TD with a specific value stored in the storage unit 20. For example, the storage unit 20 stores a value corresponding to the peak value of the graph of the time series data TD (peak value equivalent value), and the acquisition unit 12 stores the peak value of the time series data TD and the storage unit 20. The evaluation value Ev may be generated by comparing the value corresponding to the peak value stored in the evaluation value Ev.

Alternatively, the storage unit 20 stores a value corresponding to the rising period of the graph of time series data (rising period equivalent value), and the acquisition unit 12 stores the rising period of the time series data TD and the value in the storage unit 20. The evaluation value Ev may be generated by comparing it with a stored value corresponding to the rising period. Alternatively, the storage unit 20 stores a value corresponding to the fluctuation value during the peak period of the graph of time series data (peak fluctuation equivalent value), and the acquisition unit 12 stores the peak fluctuation value and the peak fluctuation value of the time series data TD. , and the peak fluctuation equivalent value stored in the storage unit 20 may be compared to generate the evaluation value Ev.

Note that it is preferable that instructions from an operator or administrator can be input in the data processing apparatus 100. Further, in the data processing device 100, it is preferable that information can be displayed to the operator or administrator. For example, it is particularly preferable to be able to display evaluation values or time series data (particularly extracted time series data) to the operator or administrator.

Next, with reference to FIG. 6, the data processing apparatus 100 of this embodiment will be described. FIG. 6 is a schematic diagram of the data processing apparatus 100 and the substrate processing apparatus 200. The data processing apparatus 100 in FIG. 6 has the same configuration as the data processing apparatus 100 described above with reference to FIG. 1, except that it further includes a display section 30 and an input section 40. Therefore, duplicate descriptions will be omitted to avoid redundancy.

The display unit 30 displays an operation screen or results of various processes. The display unit 30 also displays a graph showing time series data or an evaluation value graph GE showing changes in evaluation values.

The display section 30 has a display. For example, the display includes a liquid crystal display or an organic EL display.

Note that the display unit 30 preferably displays an evaluation value graph GE indicating changes in evaluation values. Further, when displaying the evaluation value graph GE, when the operator or administrator specifies one value (evaluation value) of the evaluation value graph GE via the input section 140, the display section 30 displays the specified value. It is preferable to display time series data corresponding to the calculated values.

The input unit 40 includes, for example, various keys for instructing the type of job and the content of the job. Input unit 40 includes a keyboard and a mouse. Alternatively, the input unit 40 may include a touch sensor. Note that the display section 30 and the input section 40 may be an integrated touch panel.

Next, switching of the display between the evaluation value graph GE and the graph showing the time series data TD by the display unit 30 will be described with reference to FIGS. 4 to 7.

7(a) and FIG. 7(b) show the evaluation value graph GE displayed by the display unit 30. As shown in FIG. 7(a), the display unit 30 displays an evaluation value graph GE showing changes in the evaluation values Ev1 to Ev100 corresponding to the time series data TD1 to TD100. The operator or administrator specifies the time series data TD corresponding to the evaluation value Ev via the input unit 40 in order to confirm the time series data TD corresponding to the evaluation value Ev.

As shown in FIG. 7(b), the operator or administrator moves the cursor CU via the input unit 40 to the target evaluation value Ev, and selects a specific evaluation value in the evaluation value graph GE. Specify the point indicating Ev. Here, the cursor CU specifies the evaluation value Ev6. In that case, the display unit 30 displays time series data TD6 corresponding to the specified evaluation value Ev6.

FIG. 7(c) is a graph showing the time series data TD displayed by the display unit 30. Here, the display unit 30 displays time series data TD6 corresponding to the evaluation value Ev6.

Note that, even when displaying the time series data TD, the display unit 30 preferably displays a button BG for returning to the evaluation value graph GE. When the button BG is selected, the display section 30 switches to a screen that displays the evaluation value graph GE again. In this way, it is preferable that the display unit 30 switches between and displays the evaluation value graph GE showing changes in the evaluation value Ev and the graph showing the time series data TD.

Note that, typically, the storage unit 20 stores a plurality of time series data. It is preferable that the storage unit 20 stores a plurality of time series data in the order in which the time series data are generated. In this case, it is preferable that the display unit 30 switches and displays a plurality of time series data according to a predetermined operation.

Next, time series data displayed on the display section 30 will be explained with reference to FIGS. 1 to 8. FIG. 8 is a schematic diagram showing changes in the display of a plurality of time-series data on the display unit 30. 8(a) to 8(c) show the display screen 32 of the display unit 30. FIG.

Display screen 32 includes a display area 33 and an operation area 34. The display area 33 displays one piece of time series data. Here, the display area 33 extends in the horizontal direction. The length of the display area 33 in the horizontal direction is larger than the length of the display area 33 in the vertical direction.

The operation area 34 allows switching of the time series data displayed in the display area 33. Here, the operation area 34 is arranged below the display area 33.

In FIG. 8, the operation area 34 is a scroll bar. The operation area 34 includes an arrow 34a and a knob 34b. The arrow 34a extends linearly in the horizontal direction. Knob 34b is movable along arrow 34a so as to overlap a portion within arrow 34a. The time series data displayed in the display area 33 changes depending on the position of the knob 34b within the arrow 34a.

The knob 34b moves horizontally within the arrow 34a in response to input from the input section 40. For example, when the knob 34b is moved leftward or rightward while the knob 34b is selected, the time series data TD displayed in the display area 33 is switched.

For example, as shown in FIG. 8A, when the knob 34b is located at the left end of the arrow 34a, the first generated time series data TD1 is displayed in the display area 33. The knob 34b moves horizontally within the arrow 34a in response to input from the input section 40.

As shown in FIG. 8(b), when the knob 34b is moved to the right with the knob 34b selected, the time series data displayed in the display area 33 is switched. For example, when the knob 34b moves to the center of the arrow 34a, the time series data displayed in the display area 33 is switched to the time series data TD50.

Further, as shown in FIG. 8(c), when the knob 34b is further moved to the right while the knob 34b is selected, the time series data displayed in the display area 33 is switched to the time series data TD100. As described above, the display unit 30 can switch and display a plurality of time series data according to a predetermined operation.

Next, with reference to FIG. 9, switching of the display of a graph showing changes in evaluation values and time-series data on the display unit 30 will be described. 9(a) and FIG. 9(b) show the extraction evaluation value graph EGE displayed by the display unit 30. The extracted evaluation value graph EGE shows temporal changes in evaluation values corresponding to extracted time series data extracted from a plurality of time series data TD. In this case, it is possible to display changes over time in the evaluation values of the extracted time-series data.

As shown in FIG. 9(a), the display unit 30 displays an extracted evaluation value graph EGE showing changes in the evaluation value Ev corresponding to the extracted time series data. The operator or administrator specifies the time series data corresponding to the evaluation value Ev via the input unit 40 in order to confirm the extracted time series data corresponding to the evaluation value Ev.

As shown in FIG. 9(b), the operator or administrator moves the cursor CU to the target evaluation value Ev to designate a specific evaluation value Ev. Here, the cursor CU specifies the evaluation value Ev. In that case, the display unit 30 displays extracted time series data corresponding to the specified evaluation value Ev.

FIG. 9(c) is a graph showing extracted time series data displayed by the display unit 30. In this way, it is preferable that the display unit 30 switches between displaying the graph showing the change in the evaluation value Ev and the extracted evaluation value graph EGE showing the extracted time series data. Thereby, it is possible to display temporal changes in evaluation values for extracted time series data.

Note that in the above description with reference to FIGS. 1 to 9, extracted time series data is extracted, but it is preferable to process the extracted time series data. For example, it is preferable to perform clustering processing on the extracted time series data.

Next, with reference to FIG. 10, the data processing apparatus 100 of this embodiment will be described. FIG. 10 is a schematic diagram of the data processing apparatus 100 and the substrate processing apparatus 200. The data processing device 100 in FIG. 10 has the same configuration as the data processing device 100 described above with reference to FIG. 6, except that it further includes the clustering processing section 15. Therefore, duplicate descriptions will be omitted to avoid redundancy.

The data processing device 100 further includes a clustering processing section 15. The clustering processing unit 15 is included in the processing unit 10.

The clustering processing unit 15 performs clustering processing on the extracted time series data. For example, the clustering processing unit 15 classifies the extracted time series data into one of a plurality of clusters according to the characteristics of the extracted time series data. In one example, the clustering processing unit 15 classifies the extracted time series data into one of a plurality of clusters according to the characteristics of the graph shown in the extracted time series data.

It is preferable that the clustering processing unit 15 performs clustering processing on the extracted time series data by so-called unsupervised learning. For example, the clustering processing unit 15 may perform clustering processing using all values of the extracted time series data. For example, the clustering processing unit 15 may perform clustering processing using any one of hierarchical clustering, k-means clustering, Gaussian mixture model clustering, self-organizing mapping clustering, and hidden Markov model clustering.

For example, the plurality of clusters include a rise delay cluster where the graph rises slowly, a peak fluctuation cluster where the graph peaks fluctuate widely, and a low peak cluster where the graph peak values are low. In this case, the clustering processing unit 15 performs clustering processing to classify the extracted time series data into at least one of a rise delay cluster, a peak fluctuation cluster, and a low peak cluster. Note that the plurality of clusters may further include another cluster. For example, the plurality of clusters may include falling delay clusters whose graph falls slowly.

Note that the clustering processing unit 15 may perform the clustering process using all the values of the extracted time series data. For example, the clustering processing unit 15 may process all the values of the extracted time series data by vector analysis. Alternatively, the clustering processing unit 15 may perform clustering processing using some of the values of the extracted time series data.

Next, clusters obtained by clustering the extracted time series data will be described with reference to FIG. 11. FIG. 11(a) is a graph showing the extracted time series data ETD1 classified into the rise delay cluster, and FIG. 11(b) is a graph showing the extracted time series data ETD2 classified into the peak fluctuation cluster. FIG. 11(c) is a graph showing the extracted time series data ETD3 classified into the low peak cluster. Note that each of FIGS. 11(a) to 11(c) shows the reference data RD together with the extracted time series data.

As shown in FIG. 11(a), when the extracted time series data and the reference data are compared, the rise of the graph of the extracted time series data ETD1 is delayed with respect to the graph of the reference data RD. Such extracted time series data is classified as a rising delay cluster.

As shown in FIG. 11(b), when the extracted time series data and the reference data are compared, the peak of the graph of the extracted time series data ETD2 varies significantly compared to the graph of the reference data RD. Such extracted time series data is classified as a peak fluctuation cluster.

As shown in FIG. 11(c), when the extracted time series data and the reference data are compared, the peak of the graph of the extracted time series data ETD3 is lower than that of the reference data RD. Such extracted time series data is classified as a low peak cluster.

Although the clusters into which the extracted time series data are classified have been explained with reference to FIGS. 11(a) to 11(c), in this embodiment, the clusters into which the extracted time series data are classified are , but not limited to. The extracted time series data may be classified into other clusters.

Next, the data processing method of this embodiment will be explained with reference to FIGS. 1 to 12. FIG. 12 is a flow diagram of the data processing method of this embodiment. The flow diagram in FIG. 12 is similar to the flow diagram described above with reference to FIG. 2, except that the clustering process in step S10 is added. Therefore, duplicate descriptions will be omitted to avoid redundancy.

As shown in FIG. 12, as shown in step S8, extracted time series data is extracted. The extraction unit 14 extracts extracted time series data from a plurality of time series data based on the classification results of the evaluation values. Next, the process proceeds to step S10.

In step S10, the extracted time series data is subjected to clustering processing. The clustering processing unit 15 classifies the extracted time series data into one of a plurality of clusters.

The data processing method of this embodiment is performed as described above. According to the data processing method of the present embodiment, extracted time series data showing unique evaluation values can be classified into clusters from a plurality of time series data.

In general, if clustering processing is performed on all time series data, since the time series data also includes normal time series data, non-specific clusters will be formed unnecessarily, and There is a risk that the situation may not be fully understood. On the other hand, according to the data processing method of the present embodiment, clustering processing is performed on extracted time series data showing a unique evaluation value from a plurality of time series data. states can be classified into clusters more accurately. Furthermore, according to the data processing method of this embodiment, clustering processing is not performed on all time series data, so unnecessary calculations can be avoided.

Note that the display unit 30 preferably displays an evaluation value graph showing changes in the corresponding evaluation value for each cluster. In this case, when the operator or administrator specifies one evaluation value of the graph, it is preferable that the display unit 30 displays time-series data corresponding to the specified evaluation value.

For example, in the above description with reference to FIG. 9, the display unit 30 showed the extracted evaluation value graph EGE that shows the temporal change in the evaluation value for the extracted time series data extracted by the extraction unit 14, but in this embodiment is not limited to this. The display unit 30 may display a graph showing changes over time in evaluation values for time series data classified into clusters by the clustering processing unit 15. In this case, it is possible to display changes over time in evaluation values within a specific cluster.

Note that, as described above with reference to FIGS. 10 to 12, it is preferable to perform clustering processing on the extracted time series data. Needless to say, the extracted time series data reflects the singular state of the substrate processing apparatus 200, and the results of the clustering process determine the state of the substrate processing apparatus 200 according to the type of the peculiar state of the substrate processing apparatus 200. It shows. Therefore, it is preferable to use the results of the clustering process to learn the state of the substrate processing apparatus 200.

Next, the data processing method of this embodiment will be explained with reference to FIGS. 1 to 13. FIG. 13 is a flow diagram of the data processing method of this embodiment. The flowchart in FIG. 13 is similar to FIG. 12 except that steps S2B to S6B, step S9, and steps S22 to S24 are added in addition to determining whether or not extracted time series data has been extracted in step S8A. This is similar to the flow diagram described above. Therefore, duplicate descriptions will be omitted to avoid redundancy. The data processing method shown in FIG. 13 is suitably used to create a learning database in the substrate processing apparatus 200.

In step S2, time series data is acquired. Note that here, the time series data is used to learn the state of the substrate processing apparatus 200. Therefore, in this specification, time series data may be referred to as learning time series data.

Here, the acquisition unit 11 acquires one piece of learning time series data generated in the substrate processing apparatus 200. Next, the process proceeds to step S4. Note that step S4 and step S6 are similar to the flowchart described above with reference to FIG. After step 6, the process then proceeds to step S8A.

In step S8A, it is determined whether the learning time series data is extracted as extracted time series data. When the classification unit 13 classifies the learning time series data into target categories based on the evaluation value Ev, the extraction unit 14 extracts the learning time series data as extracted time series data. On the other hand, when the classification unit 13 classifies the learning time series data into non-target categories based on the evaluation value Ev, the extraction unit 14 does not extract the learning time series data as extracted time series data.

For example, when the classification unit 13 classifies the learning time series data into level 4 based on the evaluation value Ev of the learning time series data, the extraction unit 14 extracts the learning time series data as extracted time series data. Further, when the classification unit 13 classifies the learning time series data into one of levels 1 to 3, the extraction unit 14 does not extract the learning time series data as extracted time series data.

If the extraction time series data is not extracted (No in step S8A), the process returns to step S2. If the extracted time series data is extracted (Yes in step S8A), the process proceeds to step S10. Note that if clustering processing has not been performed in advance, the process may proceed to step S10 after a number of data that enable unsupervised learning are extracted.

In step S10, clustering processing is performed on the extracted time series data. Thereby, the learning time series data is classified into one of a plurality of clusters. Next, the process proceeds to step S22.

In step S22, control of the substrate processing apparatus 200 is changed. Specifically, the control of the substrate processing apparatus 200 or processing unit 210 that generated the learning time series data is changed. The control of the substrate processing apparatus 200 may be changed according to a program that controls driving of the substrate processing apparatus 200. Alternatively, changes in the control of the substrate processing apparatus 200 may be manually input in the substrate processing apparatus 200.

Typically, control of the substrate processing apparatus 200 is performed while substrate processing is temporarily stopped in the substrate processing apparatus 200. For example, if the time-series data TD shows changes in the supply amount of processing liquid for processing the substrate, and the supply amount of processing liquid is in an unusual state, substrate processing may be temporarily stopped in order to return the supply amount to normal. , adjust the valve. Note that the control of the substrate processing apparatus 200 may be changed while the substrate processing apparatus 200 continues processing the substrate. Next, the process proceeds to step S2B.

In step S2B, time series data is acquired from the substrate processing apparatus 200 whose control has been changed. In this specification, time series data acquired from the substrate processing apparatus 200 whose control has been changed may be referred to as changed time series data. Note that here, the acquisition unit 11 acquires one piece of time series data generated in the substrate processing apparatus 200 as changed time series data. Next, the process proceeds to step S4B.

In step S4B, an evaluation value (score) is obtained. The acquisition unit 12 acquires evaluation values for the changed time series data for the substrate processing apparatus 200 whose control has been changed. For example, similarly to the learning time series data, the acquisition unit 12 generates evaluation values for the modified time series data according to specific evaluation criteria. Next, the process proceeds to step S6B.

In step S6B, the modified time series data is classified into one of a plurality of categories using the evaluation value. Next, the process proceeds to step S9.

In step S9, it is determined whether or not the modified time series data is extracted as extracted time series data. When the classification unit 13 classifies the modified time series data into target categories, the extraction unit 14 extracts the modified time series data as extracted time series data. On the other hand, when the classification unit 13 classifies the changed time series data into a non-target category, the extraction unit 14 does not extract the target time series data as extracted time series data.

For example, when the classification unit 13 classifies the modified time series data into level 4 based on the evaluation value Ev of the modified time series data, the extraction unit 14 extracts the modified time series data as extracted time series data. Furthermore, when the classification unit 13 classifies the modified time series data into one of levels 1 to 3, the extraction unit 14 does not extract the modified time series data as extracted time series data.

If the extracted time series data has been extracted (Yes in step S9), the process returns to step S22. In this case, the substrate processing apparatus 200 is controlled again to further change the control of the substrate processing apparatus 200. On the other hand, if the extracted time series data is not extracted (No in step S9), the process proceeds to step S24.

In step S24, information regarding the change in the control of the substrate processing apparatus 200 performed in step S22 is stored in the storage unit 20 as cause countermeasure information. The cause countermeasure information is information regarding the details of the change in the control of the substrate processing apparatus 200 performed in step S22. For example, the cause countermeasure information may be information indicating the content of change in the control of the substrate processing apparatus 200 itself. Alternatively, the cause countermeasure information may be information that is considered to be the cause of the learning time series data showing the singular state.

For example, the operator or administrator of the data processing apparatus 100 and/or the substrate processing apparatus 200 inputs cause countermeasure information via the input unit 40, and the storage unit 20 stores the cause countermeasure information. At this time, it is preferable that the storage unit 20 stores cause countermeasure information together with learning time series data for each cluster subjected to the clustering process in step S10. Note that the clusters, learning time series data, and cause countermeasure information stored in the storage unit 20 are suitably used as a learning database.

The data processing method of this embodiment is performed as described above. According to the data processing method of this embodiment, the acquisition unit 11, acquisition unit 12, classification unit 13, and extraction unit 14 of the data processing device 100 are used to determine the peculiar state of the substrate processing device 200, and After the control of the substrate processing apparatus 200 is changed, it can be determined whether the peculiar state of the substrate processing apparatus 200 has been resolved.

Further, according to the data processing method of the present embodiment, time-series data when the substrate processing apparatus 200 is in a specific state is extracted and subjected to clustering processing, and then cause countermeasure information is stored. Therefore, cause countermeasure information that is considered effective for returning the time-series data of the substrate processing apparatus 200 from a peculiar state to its original state can also be stored.

Note that the storage unit 20 preferably stores clusters, extracted time series data, and cause countermeasure information as a learning database.

Next, the learning database in the storage unit 20 will be explained with reference to FIG. FIG. 14 is a schematic table for explaining the learning database in the storage unit 20.

As shown in FIG. 14, the learning database includes clusters classified by the clustering processing unit 15, time-series data included in the clusters, and cause countermeasure information corresponding to the clusters. For example, in the table shown in FIG. 14, the clusters include a rise delay cluster, a peak variation cluster, and a low peak cluster.

Specifically, the rise delay cluster includes time series data TD6 and TD40 as time series data, and includes processing liquid concentration decrease/valve retightening as cause countermeasure information. The peak fluctuation cluster includes time series data TD20 and TD41 as time series data, and includes substrate holding force reduction/overcurrent suppression as cause countermeasure information. The low peak cluster includes time series data TD80 and TD95 as time series data, and includes atmospheric non-uniformity/sensor restart as cause countermeasure information.

Note that in the explanation with reference to FIG. 14, one set of cause countermeasure information is shown corresponding to one cluster in order to avoid overly complicating the invention, but one cluster may have two or more cause countermeasures. The information may correspond. In this case, it is preferable that the correspondence between cluster generation and cause countermeasure information is appropriately learned using so-called machine learning.

In this embodiment, the learning database is used to change the control of the substrate processing apparatus 200 and/or the processing unit 210 using time series data generated from the substrate processing apparatus 200 and/or the processing unit 210 later. The data processing apparatus 100 may change the control of the substrate processing apparatus 200 according to the time series data generated in the substrate processing apparatus 200 based on the learning content stored in the learning database of the storage unit.

Next, the data processing device 100 of this embodiment will be explained with reference to FIGS. 1 to 15. FIG. 15 is a schematic diagram of the data processing device 100 of this embodiment. Data processing device 100 includes a processing section 110, a storage section 120, a display section 130, and an input section 140. Here, the storage unit 120 stores a learning database. The display section 130 and the input section 140 correspond to the display section 30 and the input section 40 described above with reference to FIG.

The processing section 110 includes a data acquisition section 111, an evaluation value acquisition section 112, a classification section 113, a matching section 114, and a reading section 115. The data acquisition unit 111, the evaluation value acquisition unit 112, and the classification unit 113 correspond to the data acquisition unit 11, the evaluation value acquisition unit 12, and the classification unit 13 described above with reference to FIG.

The matching unit 114 matches the time series data classified into target categories by the classification unit 113 and the extracted time series data stored in the learning database of the storage unit 120. Note that in this specification, time series data classified into target categories by the classification unit 113 may be referred to as target time series data. Further, among the time series data of the learning database, time series data similar to the target time series data may be referred to as similar time series data.

The matching unit 114 identifies time-series data similar to the target time-series data from among the time-series data in the learning database as similar time-series data. Typically, among the time-series data in the learning database, the time-series data closest to the target time-series data is the similar time-series data. However, the learning database may not have time series data that is similar time series data.

Typically, the matching unit 114 matches the target time series data and the time series data stored in the learning database according to specific evaluation criteria. For example, the matching unit 114 matches the target time series data and the time series data of the learning database for the entire time series data. In one example, the matching unit 114 may process the target time series data and the time series data of the learning database by vector analysis. Alternatively, the matching unit 114 may match the target time series data with the time series data stored in the learning database for a specific portion of the time series data. The matching unit 114 identifies similar time series data close to the target time series data among the time series data stored in the learning database of the storage unit 120. Note that matching between the target time series data and similar time series data may be performed according to the same evaluation criteria as the evaluation value acquisition unit 112, or may be performed according to a different evaluation standard from the evaluation value acquisition unit 112. .

Thereafter, the reading unit 115 identifies clusters corresponding to similar time series data in the learning database of the storage unit 120, and reads cause countermeasure information corresponding to the identified clusters. Typically, the display unit 130 displays cause countermeasure information.

According to the data processing device 100 of this embodiment, similar time series data close to the target time series data and corresponding cause countermeasure information are read from the learning database. Therefore, the data processing device 100 can acquire appropriate cause countermeasure information for target time series data classified into a specific category.

Next, the data processing method of this embodiment will be explained with reference to FIGS. 1 to 16. FIG. 16 shows a flow diagram of the data processing method of this embodiment. Steps S102 to S106 in FIG. 16 correspond to steps S2 to S6 in FIG.

As shown in FIG. 16, time series data is acquired in step S102. The time series data is data generated in the substrate processing apparatus 200. Next, the process proceeds to step S104.

In step S104, an evaluation value (score) of the time series data is obtained. The acquisition unit 12 acquires evaluation values for time-series data. For example, the acquisition unit 12 acquires evaluation values of time-series data according to the same evaluation criteria used when creating the learning database. Next, the process proceeds to step S106.

In step S106, the time series data is classified into one of a plurality of categories using the evaluation value of the time series data. For example, the classification unit 13 uses evaluation values to classify time-series data into one of a plurality of categories in the same way as when creating a learning database. Next, the process proceeds to step S106a.

In step S106a, it is determined whether the time series data has been classified into the target category. For example, the target classification is level 4.

If the time series data is not classified into the target category (No in step S106a), the process returns to step S102. If the time series data is classified into the target category (Yes in step S106a), the process proceeds to step S108.

In step S108, the target time series data and the time series data stored in the learning database of the storage unit 120 are matched. The matching unit 114 matches the target time series data with the time series data stored in the learning database of the storage unit 120. The matching unit 114 identifies similar time series data among the extracted time series data stored in the learning database of the storage unit 120. Next, the process proceeds to step S110.

In step S110, cause countermeasure information corresponding to similar time series data is read from the learning database. The reading unit 115 identifies clusters corresponding to similar time series data in the learning database of the storage unit 120, and reads cause countermeasure information corresponding to the identified clusters. Typically, the display unit 130 then displays cause countermeasure information. Note that, if necessary, the operator or administrator of the data processing apparatus 100 and/or the substrate processing apparatus 200 may change the control of the substrate processing apparatus 200 based on the displayed cause countermeasure information.

The data processing method of this embodiment is performed as described above. According to the data processing method of the present embodiment, matching is performed on target time series data that is recognized as unique through the evaluation value. Therefore, the amount of calculations related to matching processing can be reduced.

Furthermore, according to the present embodiment, cause countermeasure information corresponding to clusters of target time series data can be acquired from the learning database. Therefore, even if the time series data is unique, cause countermeasure information corresponding to the time series data can be obtained from the learning database. Therefore, the unique state of the substrate processing apparatus 200 can be efficiently grasped.

Typically, an experienced operator of a substrate processing equipment can estimate the condition of the substrate processing equipment from the time series data obtained from the substrate processing equipment, but an inexperienced operator is The condition of the substrate processing equipment cannot be estimated from the data. However, according to the data processing method of the present embodiment, if the target time series data of the substrate processing apparatus 200 is unique, the learning database created in the past is used to acquire cause countermeasure information of the substrate processing apparatus 200. can. Therefore, even if the operator of the substrate processing apparatus 200 is inexperienced, problems with the substrate processing apparatus 200 can be resolved.

In the explanation with reference to FIG. 16, the cause countermeasure information is read out in step S110, and then the operator or administrator changes the control of the substrate processing apparatus 200 based on the displayed cause countermeasure information. However, this embodiment is not limited to this. The substrate processing apparatus 200 may change the control of the substrate processing apparatus 200 based on the read cause countermeasure information without involving an operator or administrator.

In addition, in the above explanation with reference to FIG. 16, similar time series data is identified from the learning database in the matching in step S108, but if the time series data acquired in step S102 is extremely unique, similar time series data is identified from the learning database in the matching in step S108. It is also possible that the time series data is not specified. In this case, the time series data acquired in step S102 may be stored separately in the storage unit 120.

Next, the data processing method of this embodiment will be explained with reference to FIGS. 1 to 17. FIG. 17 shows a flow diagram of the data processing method of this embodiment. The flow diagram of the data processing method in FIG. 17 is the same as the flow diagram in FIG. 16 except that step S108a and steps S112 to S114 are added, so duplicate descriptions will be omitted to avoid redundancy.

As described above, in step S108, the target time series data and the time series data stored in the learning database of the storage unit 120 are matched. The matching unit 114 matches the target time series data with the time series data stored in the learning database of the storage unit 120. For example, matching is performed by obtaining a matching rate between the target time series data and the time series data of the learning database. Processing then proceeds to step 108a.

In step S108a, it is determined whether there is similar time series data similar to the target time series data in the learning database. For example, the matching unit 114 may determine whether there is similar time series data similar to the target time series data in the learning database, depending on whether the matching rate exceeds a threshold value. For example, if the matching rate is higher than the threshold, the matching unit 114 determines that similar time series data exists. On the other hand, if the matching rate is less than or equal to the threshold, the matching unit 114 determines that there is no similar time series data.

If it is determined that there is similar time series data (Yes in step S108a), the process proceeds to step S110. If it is determined that there is no similar time series data (No in step S108a), the process proceeds to step S114.

In step S110, cause countermeasure information corresponding to similar time series data is read from the learning database. The reading unit 115 reads cause countermeasure information corresponding to similar time series data from the learning database in the storage unit 120. The process proceeds to step S112.

In step S112, control of the substrate processing apparatus 200 is changed based on the cause countermeasure information. For example, the processing unit 110 notifies the operator or administrator of cause countermeasure information. In one example, the display unit 130 displays cause countermeasure information.

In step S114, the target time series data is stored in the learning database of the storage unit 120. At this time, it is preferable that the storage unit 120 stores information indicating a new cluster corresponding to the target time series data together with the target time series data. Further, in this case, the display unit 130 may display that the matching rate is less than or equal to a threshold value.

The data processing method of this embodiment is performed as described above. According to the data processing method of the present embodiment, in order to read and display cause countermeasure information corresponding to clusters of target time series data from the learning database, the operator or administrator of the data processing apparatus 100 can You can obtain cause and countermeasure information for.

Next, the time series data TD will be explained in detail with reference to FIGS. 18 to 20. Typically, the time series data TD can be decomposed into a rising portion, a stable period portion, and a falling portion depending on the shape of the graph. Further, typically, the time series data TD indicates a physical quantity in response to a predetermined control signal.

First, time series data TD will be explained with reference to FIGS. 1 and 18. FIG. 18A is a graph showing temporal changes in the control signal CS in the substrate processing apparatus 200. FIG. 18(b) is a graph showing time-series data TD representing physical quantities controlled according to the control signal CS.

As shown in FIG. 18(a), the control signal CS is at a low level in the initial state (time t0). The control signal CS changes from low level to high level at time t1, and changes from high level to low level at time t2.

As shown in FIG. 18(b), the time series data TD changes between an initial level L0 and a target level L1 (L0<L1) according to the control signal CS.

The time series data TD has an initial level L0 in an initial state (time t0). When the control signal CS changes from a low level to a high level at time t1, the time series data TD begins to rise from the initial level L0 toward the target level L1. Here, the time series data TD rises above the target level L1 and then falls. The time series data TD repeatedly rises and falls around the target level L1, and finally stabilizes around the target level L1.

Thereafter, when the control signal CS changes from a high level to a low level at time t2, the time series data TD begins to fall from around the target level L1 toward the initial level L0. The time series data TD rises after falling to or near the initial level L0. The time series data TD repeatedly rises and falls near the initial level L0, and finally stabilizes at the initial level L0.

For example, a first range R1 including the target level L1 and a second range R2 including the initial level L0 are set in the time series data TD. For example, a range from 90% to 110% of the target level L1 is set as the first range R1, and a range from -10% to 10% of the target level L1 is set as the second range R2. The upper and lower limits of the first range R1 and the upper and lower limits of the second range R2 are arbitrarily determined by the user.

The evaluation value acquisition unit 12 may acquire evaluation values from the time series data TD as follows. First, the evaluation value acquisition unit 12 defines the period from when the control signal CS starts changing from a low level to a high level until the time series data TD falls within the first range R1 as a "period A (rising period)". The period from when the control signal CS changes from high level to low level until the time series data TD falls within the second range R2 is determined as "period C (falling period)", and the rising period and falling period are calculated. The period between is calculated as "Period B (stable period)". Note that "the time series data falls within a certain range" means that the time series data does not take values outside the range after that point.

The evaluation value acquisition unit 12 may acquire the evaluation value from any one of “Period A,” “Period B,” and “Period C.” For example, the evaluation value acquisition unit 12 may acquire the evaluation value from "Period A." Alternatively, the evaluation value acquisition unit 12 may acquire the evaluation value from “Period B” or “Period C”.

For example, the evaluation value acquisition unit 12 obtains the score in the rising period, the score in the stable period, and the score in the falling period using a predetermined method. For example, the evaluation value acquisition unit 12 may obtain the length of the rising period as the score for the rising period, and may obtain the length of the falling period as the score for the falling period. In this way, the evaluation value acquisition unit 12 may use the lengths of "Period A," "Period B," and "Period C" themselves as evaluation values. Alternatively, the evaluation value acquisition unit 12 may use the average or composite value of the lengths of "Period A", "Period B", and "Period C" as the evaluation value.

Alternatively, the evaluation value acquisition unit 12 acquires some values (scores) from each of "Period A," "Period B," and "Period C," and evaluates by determining, comparing, and/or combining the scores. You can also get the value.

The evaluation value acquisition unit 12 obtains statistics of time-series data TD within “period A,” “period B,” and “period C,” as evaluation values or scores for “period A,” “period B,” and “period C.” You can also find the value.

For example, the evaluation value acquisition unit 12 may use a plurality of time series data to obtain a sudden value during the stable period as the score during the stable period. The evaluation value acquisition unit 12 obtains the average value, median value, or variance of the time series data TD within the stable period as the score during the stable period.

FIG. 19 is a diagram showing values of a plurality of time series data. The n pieces of time series data TD1, TD2, ..., TDn shown in FIG. 19 each include m values. Here, when i is an integer from 1 to n and j is an integer from 1 to m, the j-th data included in the time series data TDi is referred to as x _ij . In this case, the evaluation value acquisition unit 12 calculates the score Sp of the time series data TDp according to the following equations (1) to (4).

Note that in equation (1), the value μ _pj indicates the average value of the j-th data included in the (n-1) time series data excluding the target time series data TDp. In equation (2), the value μ _p indicates the average value of all data included in the (n-1) time series data excluding the time series data TDp. In equation (3), the value σ _p ² indicates the variance of (n−1) pieces of time series data excluding the time series data TDp.

Furthermore, the evaluation value acquisition unit 12 may obtain the overshoot amount of the time series data TD as the score in the rising period. When the target level of the time series data TD is L1 and the maximum value of the time series data TD is M, the evaluation value acquisition unit 12 may obtain the overshoot amount V of the time series data according to equation (5).
V=(M-L1)/L1×100...(5)

Alternatively, the evaluation value acquisition unit 12 may calculate the overshoot amount V of the time series data according to equation (6).
V=M−L1…(6)

Note that the above-described evaluation value acquisition method is merely an example, and it goes without saying that the method is not limited thereto. For example, as described above with reference to FIG. 4, the evaluation value may be acquired by integrating the squares of the differences between the value of the time series data TD and the value of the reference data RD.

Note that the classification unit 13 (see FIG. 1, etc.) may classify the plurality of time-series data TD into any one of a plurality of categories by processing the evaluation value Ev of each of the plurality of time-series data TD.

Next, an example of classification processing by the classification unit 13 will be described with reference to FIG. 20. FIG. 20(a) is a graph showing the evaluation value distribution. The evaluation value distribution indicates the frequency (frequency) of the evaluation value Ev for each of the plurality of time series data TD. In the graph of FIG. 20(a), the horizontal axis indicates the magnitude of the evaluation value Ev, and the vertical axis indicates the frequency (frequency or number of appearances) of the evaluation value Ev.

In FIG. 20(a), μ indicates the average value of the plurality of evaluation values Ev, and σ indicates the standard deviation. As shown in FIG. 20(a), the evaluation value distribution shows a peak near the average value μ. The evaluation value distribution has a shape close to a normal distribution.

The classification unit 13 standardizes the evaluation value distribution. Specifically, the classification unit 13 generates a standardized distribution by standardizing the evaluation value distribution according to equation (7).

Here, Sold indicates the evaluation value distribution, and New indicates the standardized distribution.

FIG. 20(b) is a graph showing the standardized distribution. The standardized distribution is generated by performing standardization processing so that the average value μ of the evaluation value distribution becomes 0 and the standard deviation becomes 1.

For example, the standardized distribution is divided into L1 to L4. In the standardized distribution, if it is less than 1, it is considered level 1 (L1), if it is 1 or more and less than 2, it is considered level 2 (L2), if it is 2 or more and less than 3, it is considered level 3 (L3), and if it is 3 or more, it is considered level 3 (L3). In this case, it is set to level 4 (L4). Here, the level indicates the degree of abnormality.

Note that here, classification is performed using standard deviation. Therefore, the operator or administrator does not have to individually input the threshold value for abnormality determination.

Note that the explanation with reference to FIGS. 18 to 20 mainly describes the evaluation value acquisition section 12 and the classification section 13, but it may also be applied to the evaluation value acquisition section 112 and the classification section 113. Further, it may be applied to the clustering processing section 15 and/or the matching section 114.

Note that in the data processing apparatus 100 in FIG. 10, the processing unit 10 stores a learning database indicating the state of the substrate processing apparatus 200 in the storage unit 20, while in the data processing apparatus 100 in FIG. However, the processing unit of the data processing apparatus 100 causes the storage unit to store a learning database indicating the state of the substrate processing apparatus 200, and also uses the learning database stored in the same storage unit. It's okay.

Next, with reference to FIG. 21, the data processing apparatus 100 and substrate processing apparatus 200 of this embodiment will be described. FIG. 21 is a schematic diagram of the data processing apparatus 100 and the substrate processing apparatus 200.

As shown in FIG. 21, the data processing device 100 includes a processing section 10 and a storage section 20. The processing unit 10 includes, in addition to a data acquisition unit 11, an evaluation value acquisition unit 12, a classification unit 13, an extraction unit 14, and a clustering processing unit 15, a data acquisition unit 111, an evaluation value acquisition unit 112, and a classification unit. 113, a matching section 114, and a reading section 115. The storage unit 20 stores a learning database.

The processing unit 10 can cause the storage unit 20 to store the learning database using the data acquisition unit 11, the evaluation value acquisition unit 12, the classification unit 13, the extraction unit 14, and the clustering processing unit 15. Further, the processing unit 10 can utilize the learning database stored in the storage unit 20 by the data acquisition unit 111, the evaluation value acquisition unit 112, the classification unit 113, the matching unit 114, and the reading unit 115.

Note that the substrate processing apparatus 200 is generally classified into a single-wafer type or a batch type. The substrate processing apparatus 200 may be either a single wafer type or a batch type.

Next, with reference to FIG. 22, the data processing apparatus 100 and substrate processing apparatus 200 of this embodiment will be described. FIG. 22 is a schematic diagram of the data processing apparatus 100 and the substrate processing apparatus 200.

The data processing device 100 processes time series data TD generated in the substrate processing device 200. Here, the substrate processing apparatus 200 is a single-wafer type.

The substrate processing apparatus 200 processes a substrate W. The substrate processing apparatus 200 processes the substrate W to perform at least one of etching, surface treatment, imparting properties, forming a treated film, removing at least a portion of the film, and cleaning.

The substrate W is, for example, a semiconductor wafer, a substrate for a liquid crystal display device, a substrate for a plasma display, a substrate for a field emission display (FED), a substrate for an optical disk, a substrate for a magnetic disk, a substrate for a magneto-optical disk, or a photomask. including solar cell substrates, ceramic substrates, and solar cell substrates. For example, the substrate W has a substantially disk shape. The substrate processing apparatus 200 processes the substrates W one by one.

The substrate processing apparatus 200 includes a chamber 220, a substrate holding section 230, and a liquid supply section 240 as a processing unit 210. Chamber 220 accommodates substrate W. The substrate holding section 230 holds the substrate W.

Chamber 220 is approximately box-shaped with an internal space. Chamber 220 accommodates substrate W. Typically, chamber 220 is an air atmosphere. However, an airflow may be formed in the chamber 220 by downflow or the like.

Here, the substrate processing apparatus 200 is a single-wafer type that processes substrates W one by one, and the chamber 220 accommodates one substrate W at a time. The substrate W is accommodated within the chamber 220 and processed within the chamber 220. The chamber 220 accommodates at least a portion of each of the substrate holding section 230 and the liquid supply section 240.

The substrate holding section 230 holds the substrate W. For example, the substrate holding section 230 holds the ends of the substrate W. The substrate holding unit 230 holds the substrate W horizontally so that the upper surface (front surface) Wa of the substrate W faces upward and the back surface (lower surface) Wb of the substrate W faces vertically downward. Further, the substrate holding unit 230 rotates the substrate W while holding the substrate W. For example, the substrate W rotates counterclockwise when viewed from vertically above.

For example, the substrate holder 230 includes a spin base 231, a chuck member 232, a shaft 233, an electric motor 234, and a base 235. For example, the spin base 231 has a plate shape extending in the XY plane. Here, the spin base 231 has a disk shape (thin disk shape). The spin base 231 faces the substrate W.

The chuck member 232 is provided on the spin base 231. The chuck member 232 fixes (chucks) the substrate W. Typically, the spin base 231 is provided with a plurality of chuck members 232.

The shaft 233 may be a hollow shaft. The shaft 233 extends vertically along the rotation axis Ax. A spin base 231 is coupled to the upper end of the shaft 233. The substrate W is placed above the spin base 231.

The shaft 233 extends downward from the center of the spin base 231. Electric motor 234 provides rotational force to shaft 233. Shaft 233 rotates relative to base 235. The base 235 rotatably supports the shaft 233. The electric motor 234 rotates the substrate W and the spin base 231 around the rotation axis Ax by rotating the shaft 233 in the rotational direction. Electric motor 234 is an example of a rotating member.

The liquid supply unit 240 supplies liquid to the substrate W. Typically, the liquid supply unit 240 supplies liquid to the upper surface Wa of the substrate W. For example, the liquid includes a rinsing liquid or a medicinal liquid.

The rinsing liquid can be deionized water (DIW), carbonated water, electrolyzed ionized water, ozonated water, ammonia water, hydrochloric acid water at a diluted concentration (for example, about 10 ppm to 100 ppm), or reduced water (hydrogen water). It may contain either.

The chemical solution contains hydrofluoric acid. For example, hydrofluoric acid may be heated to 40°C or more and 70°C or less, or may be heated to 50°C or more and 60°C or less. However, hydrofluoric acid does not need to be heated. Moreover, the chemical solution may contain water or phosphoric acid.

Furthermore, the chemical solution may further contain a hydrogen peroxide solution. Further, the chemical solution may include SC1 (ammonia hydrogen peroxide solution mixture), SC2 (hydrochloric acid hydrogen peroxide solution mixture), or aqua regia (mixture of concentrated hydrochloric acid and concentrated nitric acid).

The substrate processing apparatus 200 further includes a cup 250. The cup 250 collects liquid scattered from the substrate W. The cup 250 moves up and down. For example, the cup 250 rises vertically upward to the side of the substrate W over a period in which the liquid supply section 240 supplies the liquid to the substrate W. In this case, the cup 250 collects the liquid scattered from the substrate W due to the rotation of the substrate W. Furthermore, when the period in which the liquid supply unit 240 supplies the liquid to the substrate W ends, the cup 250 descends vertically downward from the side of the substrate W.

Control device 201 includes a control section 201a and a storage section 201b. The control unit 201a controls the substrate holding unit 230, the liquid supply unit 240, and/or the cup 250. In one example, the control unit 201a controls the electric motor 234, the valve 240b, and/or the cup 250.

The substrate processing apparatus 200 of this embodiment is suitably used for manufacturing a semiconductor device provided with a semiconductor. The substrate processing apparatus 200 is suitably used for cleaning and/or processing (eg, etching, changing characteristics, etc.) of a semiconductor device during manufacturing of the semiconductor device.

As described above, the substrate processing apparatus 200 may be of a single-wafer type. In this case, the substrate processing apparatus 200 preferably includes a plurality of processing units 210.

Next, with reference to FIG. 23, the data processing apparatus 100 and substrate processing apparatus 200 of this embodiment will be described. FIG. 23 is a schematic diagram of the data processing apparatus 100 and the substrate processing apparatus 200. Here, the substrate processing apparatus 200 includes a plurality of processing units 210. Each of the processing units 210 processes a substrate W. The plurality of processing units 210 are arranged in a predetermined arrangement.

As shown in FIG. 23, the substrate processing apparatus 200 includes a plurality of processing units 210, a fluid cabinet LC, a fluid box LB, a plurality of load ports LP, an indexer robot IR, a center robot CR, and a control device. 201. The control device 201 controls the load port LP, indexer robot IR, and center robot CR. Control device 201 includes a control section 201a and a storage section 201b.

Each of the load ports LP accommodates a plurality of stacked substrates W. The indexer robot IR transports the substrate W between the load port LP and the center robot CR. The center robot CR transports the substrate W between the indexer robot IR and the processing unit 210. Each of the processing units 210 processes the substrate W by discharging a liquid onto the substrate W. For example, the liquid includes a processing liquid, a rinsing liquid, and/or a chemical liquid. The fluid cabinet LC contains liquid. Note that the fluid cabinet LC may accommodate gas.

Specifically, the plurality of processing units 210 form a plurality of towers TW (four towers TW in FIG. 1) arranged so as to surround the center robot CR in plan view. Each tower TW includes a plurality of processing units 210 (three processing units 210 in FIG. 1) stacked one above the other. Each fluid box LB corresponds to a plurality of towers TW. The liquid in the fluid cabinet LC is supplied via one of the fluid boxes LB to all the processing units 210 included in the tower TW corresponding to the fluid box LB. Further, the gas in the fluid cabinet LC is supplied to all the processing units 210 included in the tower TW corresponding to the fluid box LB via any one of the fluid boxes LB.

The substrate processing apparatus 200 further includes a control device 201. The control device 201 controls various operations of the substrate processing apparatus 200.

Control device 201 includes a control section 201a and a storage section 201b. The control unit 201a has a processor. The control unit 201a includes, for example, a central processing unit (CPU). Alternatively, the control unit 201a may include a general-purpose computing machine.

The storage unit 201b stores data and computer programs. The data includes recipe data. The recipe data includes information indicating multiple recipes. Each of the plurality of recipes defines processing contents and processing procedures for the substrate W.

The storage unit 201b includes a main storage device and an auxiliary storage device. The main storage device is, for example, a semiconductor memory. The auxiliary storage device is, for example, a semiconductor memory and/or a hard disk drive. The storage unit 201b may include removable media. The control unit 201a executes a computer program stored in the storage unit 201b to perform a substrate processing operation.

Note that in the above description with reference to FIGS. 22 and 23, the substrate processing apparatus 200 and/or the processing unit 210 are of a single-wafer type, but the present embodiment is not limited thereto. The substrate processing apparatus 200 and/or the processing unit 210 may be of a batch type.

The data processing apparatus 100 and substrate processing apparatus 200 of this embodiment will be described with reference to FIG. 24. FIG. 24 is a schematic diagram of the data processing apparatus 100 and the substrate processing apparatus 200 of this embodiment. Here, the substrate processing apparatus 200 is of a batch type and can process a plurality of substrates W at once.

The substrate processing apparatus 200 includes a processing tank 260, a substrate holding section 270, and a control device 201. The processing tank 260 stores a processing liquid L for processing the substrate W.

The substrate holding section 270 holds the substrate W. The normal direction of the main surface of the substrate W held by the substrate holding section 270 is parallel to the Y direction. The substrate holding unit 270 moves the substrate W while holding it. For example, the substrate holding unit 270 moves vertically upward or vertically downward along the vertical direction while holding the substrate W.

Typically, the substrate holding unit 270 holds a plurality of substrates W together. Here, the plurality of substrates W are arranged in a line along the Y direction. Note that the substrate holding section 270 may hold only one substrate W.

Although the data processing apparatus 100 shown in FIGS. 1 to 24 processes time-series data generated in one substrate processing apparatus 200, the present embodiment is not limited thereto. The data processing apparatus 100 may process time-series data generated in substrate processing apparatuses 200 located at different locations.

Next, with reference to FIG. 25, the data processing apparatus 100 and substrate processing apparatus 200 of this embodiment will be described. FIG. 25 is a schematic diagram of the data processing apparatus 100 and the substrate processing apparatus 200 of this embodiment. As shown in FIG. 25, the substrate processing apparatus 200 includes a substrate processing apparatus 200A and a substrate processing apparatus 200B. The substrate processing apparatus 200A and the substrate processing apparatus 200B are communicably connected to the data processing apparatus 100.

Typically, the substrate processing apparatus 200A and the substrate processing apparatus 200B are arranged at locations separated from each other. For example, the substrate processing apparatus 200A and the substrate processing apparatus 200B may be located at different locations in the same country. Alternatively, the substrate processing apparatus 200A and the substrate processing apparatus 200B may be located in different countries.

Data processing device 100 is, for example, a server. For example, the substrate processing apparatus 200A can communicate information with the substrate processing apparatus 200B via the data processing apparatus 100.

Note that in the above description with reference to FIG. 25, the data processing apparatus 100 was able to communicate with the substrate processing apparatuses 200A and 200B, but the substrate processing apparatus 200A and the substrate processing apparatus 200B may not exist at the same time. good.

The embodiments of the present invention have been described above with reference to the drawings. However, the present invention is not limited to the above-described embodiments, and can be implemented in various forms without departing from the spirit thereof. Moreover, various inventions can be formed by appropriately combining the plurality of components disclosed in the above embodiments. For example, some components may be deleted from all the components shown in the embodiments. Furthermore, components of different embodiments may be combined as appropriate. For ease of understanding, the drawing mainly shows each component schematically, and the thickness, length, number, spacing, etc. of each component shown in the diagram may differ from the actual one for convenience of drawing. may be different. Furthermore, the materials, shapes, dimensions, etc. of each component shown in the above embodiments are merely examples, and are not particularly limited, and various changes can be made without substantially departing from the effects of the present invention. be.

INDUSTRIAL APPLICATION This invention is suitably used for a data processing method, a data processing apparatus, and/or a program.

10 processing section 11 data acquisition section 12 evaluation value acquisition section 13 classification section 14 extraction section 15 clustering processing section 20 storage section 30 input section 40 display section 100 data processing device 110 processing section 111 data acquisition section 112 evaluation value acquisition section 113 classification section 114 Matching section 115 Reading section 120 Storage section 130 Input section 140 Display section

Claims (7)

a step of acquiring a plurality of time series data obtained by the substrate processing apparatus;
obtaining evaluation values for each of the plurality of time series data;
classifying each of the plurality of time series data into one of a plurality of categories based on the evaluation value;
A data processing method comprising the step of matching target time series data corresponding to any of the plurality of classifications with time series data included in a learning database. The learning database stores cause countermeasure information corresponding to time series data,
In the matching step, if the matching rate between the target time series data and at least one time series data included in the learning database is higher than a threshold, the step of further reading cause countermeasure information corresponding to the time series data. The data processing method according to claim 1, comprising: The data processing method according to claim 1 or 2, wherein in the step of obtaining the evaluation value, the evaluation value of each of the plurality of time series data is obtained by comparing each of the plurality of time series data with reference data. . In the step of obtaining the evaluation value, the evaluation value when the difference between the values of the time series data and the reference data is large is greater than the evaluation value when the difference between the values of the time series data and the reference data is small. 4. The data processing method according to claim 3, wherein the data processing method is also large. 5. The data processing method according to claim 1, further comprising the step of storing the target time series data. a data acquisition unit that acquires a plurality of time series data obtained by the substrate processing apparatus;
an evaluation value acquisition unit that acquires evaluation values of each of the plurality of time series data;
a classification unit that classifies the plurality of time series data into one of a plurality of categories based on the evaluation value;
A data processing device, comprising: a matching unit that matches target time series data corresponding to any of the plurality of categories with time series data included in a learning database. a step of acquiring a plurality of time series data obtained by the substrate processing apparatus;
obtaining evaluation values for each of the plurality of time series data;
classifying the plurality of time series data into one of a plurality of categories based on the evaluation value;
A program that causes a computer to execute a step of matching target time series data corresponding to any of the plurality of classifications with time series data included in a learning database.

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