Patent ID: 12242500

DETAILED DESCRIPTION

The inventive concept may be variously modified and may have various forms, and specific embodiments thereof will be illustrated in the drawings and described in detail. The embodiment is provided to more fully explain the inventive concept to a person with average knowledge in the art. However, the embodiments according to the concept of the inventive concept are not intended to limit the specific disclosed forms, and it should be understood that the present inventive concept includes all transforms, equivalents, and replacements included in the spirit and technical scope of the inventive concept. In a description of the inventive concept, a detailed description of related known technologies may be omitted when it may make the essence of the inventive concept unclear. Also, the same sign is used through the drawings for parts that have similar functions and actions.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the inventive concept. It will be further understood that the terms “comprises”, “comprising,”, “includes”, and/or “including” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

Singular expressions include plural expressions unless they are explicitly meant differently in context. In addition, the shapes and sizes of elements in the drawings may be exaggerated for clearer description.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as those generally understood by those skilled in the art to which the inventive concept belongs. Terms such as those defined in commonly used dictionaries should be interpreted as consistent with the context of the relevant technology and not as ideal or excessively formal unless clearly defined in this application.

FIG.4toFIG.5illustrate a data comparing method according to the inventive concept.

Referring toFIG.4, an embodiment of comparing the data of sample 1 and sample 2 which are each different from each other is disclosed. Sample 1 and sample 2 show different result samples obtained for the same process. According to an embodiment, sample 1 may be a result data processed in a first substrate treating facility. According to an embodiment, sample 2 may be a result data processed in a second substrate treating facility. In other words, the sample 1 and the sample 2 may be samples obtained from the same treating process performed by the first substrate treating facility and the second substrate treating facility, respectively.

Sample 1 and sample 2 may be a time series data of each treating facility, respectively. Since a processing time of each treating facility varies from time to time, a size of the data may not match when compared on its own. To solve this problem, in the inventive concept, when determining whether sample 1 and sample 2 are the same, the data sample may be cut and compared according to each process step of each data sample. However, referring toFIG.4, there may be a problem that a length of each respective process step does not match either.

According to the inventive concept, the data may be converted by using a 1D convolution in a method for dividing the data according to each respective process step and for substantially equally adjusting the length of the data according to each respective process step. This will be described in more detail with reference toFIG.6andFIG.7.

Referring toFIG.4, sample data 1 and sample data 2 may be respectively divided according to each process step. According to an embodiment ofFIG.4, sample data 1 and sample data 2 may be divided into three process steps. Referring toFIG.4, it may be seen that the length of the data divided according to each process step for sample data 1 and the length of the data divided according to each process step for sample data 2 are different.

In order to adjust a length of the data which differs according to each step, the length of the data in each process step may respectively be converted through the 1D convolution to adjust the length of the data.

Conventionally, when the size of the data is not adjusted, there is a problem that a learning through the Siamese network may not performed, but in the inventive concept, a data pre-processing may be performed to facilitate a learning through the Siamese network through a length adjusting operation. According to an embodiment, since there are nearly 1000 pieces of data, the higher the numbers of the data, the more efficiently the data may be compared.

Referring toFIG.5, a data divided according to the process steps from sample data 1 and a data divided according to the process steps from sample data 2 may be selected to be compared, and only a data of the corresponding steps may be size-converted.

According to an embodiment, when comparing a current data with a normal data to find an abnormal I/O, a data of a specific I/O may be selected. Referring toFIG.5, a selected process step may be step 2. That is, a conversion may be performed only on a data of a process step which a determination is to be made on whether a change has occurred without performing the conversion of all steps. In the following embodiment, it will be assumed and described as an example of performing a data size conversion for all of the process steps.

Referring toFIG.4toFIG.5, the sizes of the data divided according to each process step may all be different. The conditions for determining a reference size for adjusting them may be as follows. According to an embodiment, it may be converted to a size suitable for inputting to the Siamese network. That is, it may be converted to a size matching an input size of a deep learning neural network. According to another embodiment, it may be converted to a size matching a largest size of the data of the process step selected among the process steps.

In this case, the data conversion may be performed using the 1D convolution.

FIG.6illustrates the data comparison through a data comparing method according to the inventive concept.

Referring toFIG.6, sample data 1 (top) and the sample data 2 (bottom) may be divided according to each process step. The sizes of the data according to each process step may differ even within the first sample data, and they may each differ even within the corresponding process steps of sample data 1 and sample data 2. To match these, it is possible to create an input value of a fixed size through the 1D convolution. This may be processed as an input value of the Siamese network.

FIG.7illustrates an embodiment of the 1D convolution.

Referring toFIG.7, a time series data according to each process step is disclosed. Referring toFIG.7, an embodiment of converting the data in step 4 through the 1D convolution is disclosed. In this case, a parameter of the 1D convolution may be provided as a fixed value. In this way, by performing a data conversion of the data according to each process step through the ID convolution, it may be possible to derive a result value of a fixed size. A data value and a parameter value in the 1D convolution inFIG.7are only an embodiment, and the parameter value of the 1D convolution may be differently applied according to characteristics to be extracted.

FIG.8illustrates a data assembling according to the data comparing method according to an embodiment of the inventive concept.

Referring toFIG.8, there is an effect of being able to input into a Siamese network by converting a time series data of a substrate treating facility into a pre-processed image data form. According to an embodiment, all input sizes may be set to be the same to be converted, and one input data may be set by assembling according to each process step. A plurality of input data may be a result of performing a same process step in different facilities. Alternatively, the plurality of input data may be a result of performing the same process step with a time difference in the same facility.

As shown inFIG.8, after converting lengths of the process steps having different lengths respectively, they may be assembled in one plane and thereby changed to an optimal shape that may be input to a deep learning, therefore making an application easier.

FIG.9illustrates an intermediate process of converting from 2D to 3D ofFIG.8.FIG.10illustrates an application of an assembled data according to the inventive concept.

According to the inventive concept, when a matching rate (similarity) is calculated for log data 1 and log data 2 of a facility, a similarity that reflects characteristics of the data for each process step may be calculated by testing according to segmented process steps, while the conventional way was to test according to one process unit.

Referring toFIG.9, in the conventional technology, the similarity is calculated by dividing a log data into I/O data units and then further subdividing according to each process step. When performing a test, starting points for each process step may be the same. According to the inventive concept, it is possible to test whether the data according to the process step is matched according to each I/O data. According to the inventive concept, when inspecting a similarity of the log data of two facilities through a deep learning, the similarity of the log data may be divided according to I/O and each process step and inspected by a data pre-processing.

Referring to the first figure ofFIG.9, when the process data is cut according to each process step, the data size of each step is not constant. Referring toFIG.10, sizes of input 1 and input 2 of the Siamese network are the same and should be provided constantly. According to the inventive concept, going through the pre-processing process, the sizes of different process step data may become the same as the input size.

Referring back to the second figure ofFIG.9, when pre-processing the log data of a facility, the process step data in various sizes may be converted into an input size of the deep learning to perform the pre-processing. In this case, the input size for preprocessing may be set to a maximum value among the sizes of the process step data. Alternatively, the pre-processing may be performed at a size suitable for the input size of the deep learning to be applied. When converting the log data of the facility to the input size, it may be converted using the 1D convolution.

Referring to the third figure ofFIG.9, the process step data obtained by a first pre-processing (1D convolution) may be combined according to each I/O to generate a 2D data. That is, it may be made into a state for inputting data through assembling the process step data. Referring toFIG.10, the 3D data may be finally set as one input data by combining all the 2D data of the remaining I/Os.

FIG.11is a flowchart illustrating the data processing method according to an embodiment of the inventive concept.

The method of performing a processing of a data generated during a substrate treating process may include: a step for dividing the data according to each process step, a step for converting the data divided according to each process step to a same size through 1D convolution, and a step for assembling a data divided according to each process step. In this way, the data divided according to each process step can be converted to the same size, and by assembling them they can be converted to an input data applicable in deep learning.

FIG.12is a flowchart illustrating the data comparing method according to an embodiment of the inventive concept.

For convenience, each data of the first facility and the second facility will be described as an example of comparison. In this case, a step for pre-processing the first data of the first facility, a step for pre-processing the second data of the second facility, and a step for determining whether a pre-processed data of the first facility and a pre-processed data of the second facility are the same may be included.

The step for pre-processing the first data of the first facility may include a step for dividing the first data of the first facility according to each process step, and a step for converting the first data divided according to each process step to a same size.

The step for pre-processing the second data of the second facility may include a step for dividing the second data according to each process step, and a step for converting the second data divided according to each process step to a same size.

In this case, the size conversion may be converted using the 1D convolution.

Each of the converted data may be assembled according to each of the first data and the second data and may be provided in the form of the 2D data. An assembled data may be input as an input value of the Siamese network and used to determine a similarity.

According to the inventive concept, by proposing a pre-processing method of a data for learning a Siamese network, a problem where a learning using a deep learning cannot be performed due to many data being provided in different lengths may be resolved.

Meanwhile, the data processing method and the data comparing method according to an embodiment of the inventive concept described above may be implemented in the form of a program command that may be performed through various computer means and recorded in a computer-readable recording medium. In this case, the computer-readable recording medium may include a program command, a data file, a data structure, or the like alone or in combination. Meanwhile, the program command recorded on the recording medium may be specially designed and configured for the inventive concept or may be known to and usable by those skilled in the computer software.

The computer-readable recording medium may include hardware devices specifically configured to store and execute program instructions such as a magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as a CD-ROM and a DVD, magneto-optical media such as a floptical disk, and a ROM, a RAM, a flash memory, and the like. In addition, program instructions include machine language codes such as those created by compilers, as well as advanced language codes that can be executed by computers using interpreters, etc. The above-described hardware device may be configured to operate as one or more software modules to perform the operation of the inventive concept.

The effects of the inventive concept are not limited to the above-mentioned effects, and the unmentioned effects can be clearly understood by those skilled in the art to which the inventive concept pertains from the specification and the accompanying drawings.

Although the preferred embodiment of the inventive concept has been illustrated and described until now, the inventive concept is not limited to the above-described specific embodiment, and it is noted that an ordinary person in the art, to which the inventive concept pertains, may be variously carry out the inventive concept without departing from the essence of the inventive concept claimed in the claims and the modifications should not be construed separately from the technical spirit or prospect of the inventive concept.