TW202248870A - Expandable mass data analysis platform and operation method thereof wherein the platform includes a master mass data analysis device and multiple slave mass data analysis devices - Google Patents

Abstract

An expandable mass data analysis platform includes a master mass data analysis device to electrically connect with multiple slave mass data analysis devices. The master mass data analysis device is configured to distribute the hardware resources of the mass data analysis platform to generate a plurality of graphical analysis results from at least a part of a plurality of manufacturing raw data, and selectively include at least one of the plurality of graphical analysis results in a graphical interface. The graphical interface is configured to display the data associated with a plurality of manufacturing devices. Therefore, the computing capability of the expandable mass data analysis platform can be expanded by multiple slave data analysis devices. Accordingly, the convenience and efficiency of massive data analysis are improved to quickly eliminate the abnormalities in manufacturing processes and optimize the overall process of products.

Description

Scalable massive data analysis platform and its operation method

This application is about a data analysis platform and its operation method, especially an expandable massive data analysis platform and its operation method.

Generally speaking, the production of semiconductor components needs to go through more than a thousand manufacturing and testing procedures, and the yield rate of the final product is maintained through sophisticated manufacturing equipment and process design. Therefore, in order to effectively control the manufacturing process of semiconductor components, the process All information and data in the process must be collected and analyzed in real time.

As the manufacturing process advances, multiple manufacturing equipment on the production line will generate tens of thousands of real-time monitoring data, nearly 10,000 online sampling measurements (metrology), and hundreds of electrical tests measured at different positions on the semiconductor device. parameters, and at the same time, coupled with the production mode of various integrated circuits, the production line can produce a huge amount of data exceeding billions within a month.

Therefore, in order to analyze the huge amount of data generated on the production line, commercial statistical analysis methods are often used in order to quickly find out the cause of process abnormalities. However, the statistical correlation analysis method can only show possible related problems, and cannot accurately locate the cause of the process abnormality, and because the production line will generate huge amounts of data at any time, the statistical correlation analysis method cannot complete the corresponding analysis in real time, and the user needs to spend extra Time is spent on searching for related problems in the results of statistical correlation analysis, and analyzing the causes of process abnormalities by themselves, which leads to the inability of current statistical analysis methods to quickly rule out process abnormalities.

In addition, in order to analyze huge amounts of data, the hardware specifications of the analysis system are required to have huge and fast computing capabilities. However, an analysis system with a single hardware architecture is no longer sufficient.

There are many above-mentioned problems based on the prior art, and it is indeed necessary to propose a better solution.

In view of the above-mentioned deficiencies in the prior art, the main purpose of this application is to provide an expandable massive data analysis platform and its operation method, which utilizes artificial intelligence to analyze the massive data on the production line and generate multiple graphical analysis results, And selectively provide one of multiple graphical analysis results for viewing, users can quickly obtain prediction results corresponding to different manufacturing equipment, manufacturing processes, and finished semiconductor components through graphical analysis results. In addition, the expandable The architecture can flexibly increase its computing power according to computing needs, thereby improving the convenience and efficiency of massive data analysis, quickly eliminating abnormalities in the process, and optimizing the overall product process.

In order to achieve the above purpose, this application proposes an expandable mass data analysis platform, which includes a master mass data analysis device for electrical connection with multiple slave mass data analysis devices, And the main mass data analysis equipment includes: an input and output interface module, a mass data storage module, a memory module, storing a plurality of programs and a processor module, wherein the processor module It is electrically connected with the input/output interface module, the mass data storage module and the memory module, and is used to execute the multiple programs, so as to sense in real time whether there are other devices establishing a connection with the main mass data analysis device line, and allocate the hardware resources of the massive data analysis platform, and automatically perform data backup, so that at least a part of the multiple manufacturing raw data can generate multiple graphical analysis results, and selectively enable a graphical interface to select Optionally includes at least one of the plurality of graphical analysis results, and the graphical interface is used to display data associated with the plurality of manufacturing equipment.

According to the above structure, the scalable massive data analysis platform can expand the computing power of the scalable massive data analysis platform by electrically connecting the master massive data analysis device with at least one slave massive data analysis device, and With the huge amount of data collected, the required analysis results are provided simply and clearly with graphical analysis results. Users can quickly obtain the required process-related information without analyzing or searching among the numerous analysis results. , by providing a graphical interface for the operation information of the multiple manufacturing equipment, users can quickly grasp the overall status of the production line, thereby improving the convenience and efficiency of massive data analysis.

In order to achieve the above purpose, this application also proposes an operation method of an expandable massive data analysis platform, the steps of which include: obtaining multiple manufacturing raw data from multiple manufacturing equipment; performing data analysis on raw data, generating a plurality of graphical analysis results, the plurality of graphical analysis results are different from each other; and generating a graphical interface, selectively enabling the graphical interface to include at least one of the graphical analysis results , and the graphical interface is used to display the operation information of the plurality of manufacturing equipments.

Through the above steps, the huge amount of data analysis platform can provide the required analysis results in a simple and clear graphical analysis result based on the huge amount of data collected by it. Users do not need to analyze or search for themselves among the many analysis results. The required process-related information can be quickly obtained. In addition, by providing a graphical interface for the operation information of the multiple manufacturing equipment, users can quickly grasp the overall status of the production line, quickly eliminate process abnormalities, and thereby improve the huge amount of data Analysis convenience and efficiency.

Please refer to FIG. 1. FIG. 1 is a schematic diagram of the implementation environment of the scalable massive data analysis platform of the present application, which includes a massive data analysis platform 100, a production line system 200, and an input and output module 300. The data analysis platform 100 communicates with the production line system 200 and the input/output module 300 . The production line system 200 is used to monitor multiple manufacturing devices on the production line, and collect the manufacturing raw data related to the production line and the multiple manufacturing devices in real time, and the manufacturing raw data has a time series relationship, so as to control the production line in real time. All statuses on the production line. The massive data analysis platform 100 communicates with the production line system 200 to receive manufacturing raw data, and analyze and predict the manufacturing raw data to generate multiple graphical analysis results and a graphical interface, wherein the graphical The graphical interface optionally includes at least one graphical analysis result. In other words, the graphical interface may not include all graphical analysis results at the same time. For example, the graphical analysis result included in the graphical interface is a graphical analysis result corresponding to an abnormal state or a graphical analysis result corresponding to information selected by a user. The input and output module 300 is used to receive and display the graphical interface generated by the massive data analysis platform 100, and is used to receive user selection information input by the user.

In this way, a user can quickly confirm the abnormal status or related information on the production line through the graphical analysis results of the graphical interface displayed by the input and output module 300, without having to search for the required information among numerous analysis results, And quickly understand the cause of the abnormal state in a graphical way of data. In addition, user selection information corresponding to the graphical interface can be input to perform operations or selections required for viewing, thereby improving the convenience of massive data analysis performance and efficiency.

In one embodiment, the mass data analysis platform 100 is a yield and quality engineering analysis and decision system.

In one embodiment, the production line system 200 is, for example, a production execution platform, an equipment engineering platform, or a precision process engineering platform, and the present application is not limited thereto.

In one embodiment, the input and output module 300 is, for example, a touch display screen, or a display equipped with an input device, such as a keyboard and a mouse, and the present application is not limited thereto.

In one embodiment, the production line is a semiconductor device production line, and the application is not limited thereto.

Please refer to FIG. 2 , the massive data analysis platform 100 includes at least one main massive data analysis device 110 , and the main massive data analysis device 110 can operate independently to generate the plurality of graphical analysis results and the graphical interface. In one embodiment, the master massive data analysis device 110 can be electrically connected to one or more slave massive data analysis devices 130 (130a, 130b...130n), so as to flexibly expand the massive data analysis platform 100 B.

In an embodiment, the slave massive data analysis device 130 can be implemented by a computing server, and the present application is not limited thereto.

In an embodiment, the plurality of slave bulk data analysis devices 130 may be electrically connected in series or in parallel.

Please refer to FIG. 3, the main mass data analysis device 110 at least includes a processor module 111, a mass data storage module 113, a memory module 115 and an input and output interface module 117, wherein the processor The module 111 is electrically connected with the mass data storage module 113 , the memory module 115 and the input/output interface module 117 .

The mass data storage module 113 is used to store multiple pieces of manufacturing raw data and their corresponding times. These manufacturing raw data are provided by the production line system 200 and come from a plurality of manufacturing equipment, wherein each manufacturing equipment is The production line generates corresponding manufacturing raw data with time and process.

In one embodiment, the mass data storage module 113 can be realized by multiple hard disk storage devices, and the present application is not limited thereto.

The memory module 115 is used to store multiple programs. As shown in FIG. 4, the memory module 115 stores at least a plurality of data analysis programs 1151 (1151a, 1151b...1151n), an interface generation program 1153, and a device management program. A program 1155, a resource allocation program 1157 and an automatic backup program 1159 are used to be executed by the processor module 111 to realize the massive data analysis platform 100, which will be further described later.

In one embodiment, the memory module 115 is a computer-readable memory device, such as a hard disk device or a memory device, and the present application is not limited thereto.

The input-output interface module 117 is used to electrically connect with the external devices of the main bulk data analysis device 110 to perform data exchange between different devices or devices. For example, the I/O interface module 117 is electrically connected to the I/O module 300 to provide the graphical interface to the I/O module 300 and receive user input through the I/O module 300 User selection information for . For example, the I/O interface module 117 is electrically connected with the production line system 200 to receive the manufacturing raw data from the production line system 200 . For another example, the input-output interface module 117 is used to electrically connect with the slave mass data analysis device 130, so as to exchange data with the slave mass data analysis device 130, thereby utilizing the slave mass data analysis device 130 computing power.

In one embodiment, the I/O interface module 117 may include a serial data (RS232) communication interface, a connection port conforming to the Universal Serial Bus (USB) specification, and a connection port conforming to the Peripheral Component Interconnect (PCI) standard , A connection port conforming to the High Definition Multimedia Interface (HDMI) standard, and a connecting port conforming to the Accelerated Graphics Port (AGP) standard, and this application is not limited thereto.

The processor module 111 is used to execute the programs. Further, the processor module 111 is used to execute the device management program 1155 to detect in real time whether there are other devices (such as the slave mass data analysis equipment 130) via the input and output interface module 117 and the master The bulk data analysis device 110 establishes a connection, wherein the connection can be established in a hot-swappable manner, that is, the master bulk data analysis device 110 can establish a connection with other devices in the state of performing calculations without powering off.

Further, the processor module 111 is used to execute the resource allocation program 1157 to schedule the overall computing, storage and network resources of the massive data analysis platform 100, thereby improving the resource usage of the massive data analysis platform 100 Efficiency and operational performance, and the ability to achieve second-level calculations. In this embodiment, the resource allocation program 1157 can dynamically allocate resources according to the connection status between the master bulk data analysis device 110 and the slave bulk data analysis device 130 at any time, so as to achieve the best resource utilization efficiency and operational efficiency. In one embodiment, the resource allocation program 1157 is, for example, a program for implementing a virtual machine monitor (Virtual Machine Monitor, VMM), and the present application is not limited thereto.

Further, the processor module 111 is used to execute the automatic backup program 1159 to automatically back up the data of the massive data analysis platform 100, that is, the master massive data analysis device 110 and the slave massive data analysis device 130 They are backed up automatically and dynamically (for example, according to time and condition settings), so as to ensure the preservation of huge amounts of data and calculation results, and avoid troubles caused by data damage.

Further, the processor module 111 is used to execute the multiple data analysis programs 1511, and read the multiple records in the massive data storage module 113 according to the manufacturing raw data required by each of the data analysis programs 1511. Manufacture at least a part of the raw data, and analyze at least a part of the multiple manufacturing raw data, so as to generate the multiple graphical analysis results corresponding to different data analysis programs 1511, that is, the multiple data analysis The programs 1511 are used to provide different graphical analysis results.

Further, the processor module 111 is used to execute the interface generation program 1153, receive the graphical analysis results generated by the plurality of data analysis programs 1511, and generate a graphical interface including the graphical analysis results, and Selectively make the graphical interface include only one of the plurality of graphical analysis results, for example, the graphical interface only includes the graphical analysis results of the abnormal state, or only includes the graphical analysis results corresponding to the information selected by the user Analyze the results.

In one embodiment, the abnormal state may be a state in which the process steps/manufacturing equipment/product components are abnormal according to the analysis and prediction results of the data analysis engine, and the present application is not limited thereto. For example, according to the graphical analysis results, the analysis and prediction results show that the production time is higher than a threshold value, the interface generation program 1153 judges that it is an abnormal state and makes the graphical interface include the graphical analysis results, thereby prompting the user in real time By.

In one embodiment, the user-selected information is the information that the user wants to view actively, which includes the graphical analysis results generated by the massive data analysis platform 100 from the manufacturing raw data obtained by the production line system 200 . For example, the user selection information is selection information corresponding to the prescription quantity, and the interface generation program 1153 makes the graphical interface include the graphical analysis result corresponding to the prescription quantity according to the user selection information, thereby providing it to the user for viewing.

In one embodiment, the multiple manufacturing raw data may include product yield (Yield) (for example: maximum value, median value, minimum value), electrical parameters (WAT), online measurement parameters (Inline Metrology), defects (defects), production time (time) (for example: on-board time, off-machine time, treatment time), prescription (Recipe) (for example: type of prescription, quantity of prescription), type of manufacturing equipment (Model), manufacturing equipment (Equipment ) (for example: manufacturing equipment signal), reaction chamber (for example: temperature, humidity, windless state), unit (Unit) measurement value, etc., and this application is not limited thereto.

In this embodiment, the system architecture of each slave massive data analysis device 130 may be the same as that of the master massive data analysis device 110 , and this application is not limited thereto.

Based on the aforementioned massive data analysis platform 100, a massive data analysis system architecture can be realized, as shown in Figure 5, which includes a massive data layer 120, a data analysis layer 140 and a graphical interface layer 160, the The data analysis layer 140 includes a plurality of data analysis engines 141 (1411, 1412...141n), wherein the plurality of data analysis engines 141 are individually implemented by one of the plurality of data analysis programs 1511, and the plurality of data analysis The engines 141 are different from each other.

Please refer to FIG. 6 , which is a structural diagram of a single data analysis engine 141 . The data analysis engine 141 includes at least a pre-processor 1411 , an artificial intelligence analyzer 1413 and a post-processor 1415 .

The pre-processor 1411 is used to read the required manufacturing raw data from the massive data storage module 113 according to the analysis prediction to be performed by the artificial intelligence analyzer 1413 . Further, since the artificial intelligence analyzer 1413 of each data analysis engine 141 can be used to execute the same or different analysis and prediction programs, the pre-processor 1411 is based on the analysis and prediction programs executed by the artificial intelligence analyzer 1512. The mass data storage module 113 reads the required manufacturing raw data. For example, the artificial intelligence analyzer 1413 can be used to analyze and predict product yield according to the type of manufacturing equipment. Therefore, in this embodiment, the pre-processor 1411 is used to read from the mass data storage module 113 Obtain the required manufacturing raw data (type information of manufacturing equipment) for analysis and prediction.

Further, the pre-processor 1411 is used for pre-processing the received manufacturing raw data, that is, screening, cleaning (Clean), unified format (Format), missing value (Missing value) processing, transform (Transform) on the manufacturing raw data. ) etc. to remove abnormal data and ensure data quality and high prediction accuracy to generate processed data for providing to the artificial intelligence analyzer 1413 .

In one embodiment, the pre-processor 1411 can be implemented in a Structured Query Language (Structured Query Language, SQL), a data warehouse (Hive), an R language or a Python language, and the present application is not limited thereto .

The artificial intelligence analyzer 1413 is used to receive the processed data, and use its analysis and prediction model to perform second-level operations to calculate and predict the corresponding prediction value (Conjecture value), confidence index (Reliance Index) and overall similarity index of process parameters ( Global Similarity Index) and other analysis results.

Further, the confidence index represents the credibility of the accuracy of the predicted value. The purpose of the confidence index is to calculate a confidence value between 0 and 1 by analyzing the process parameter data of the manufacturing equipment, so as to judge whether the analysis result can be trusted. And use the maximum tolerable error upper limit (EL) to correspond to the confidence index to obtain the confidence index threshold (RIT). When the confidence index value is greater than the threshold value of the confidence index, it means that the analysis result can be trusted; otherwise, when the value of the confidence index is lower than the threshold value of the confidence index, a warning signal is issued. Therefore, the user as an equipment engineer can check the manufacturing equipment, or the user as a process engineer can perform parameter adjustment to confirm whether the process is stable.

The artificial intelligence analyzer 1413 is also used to calculate the similarity index. The main purpose of the similarity index is to compare the similarity between the process parameter data of the prediction section and the modeling section. The similarity index includes two parts, one is the overall similarity index of the process parameters, and the other is the individual similarity index (ISI) of the process parameters. The overall similarity index of the process parameters is the degree of similarity between the process parameters in the prediction segment and all the parameters in the modeling segment. The individual similarity index of the process parameter is the standardized absolute similarity between any process parameter in the prediction section and all samples of that parameter in the modeling section, and this similarity index is also used as an auxiliary confidence index for judgment. Therefore, the user as a process engineer can judge whether to perform parameter adjustment according to the analysis and prediction results of the similarity index, so as to confirm whether the process is stable.

For example, if the confidence index of the prediction point is higher than the threshold value of the confidence index, and the overall similarity index of the process parameters of the prediction point is higher than the threshold value of the overall similarity index of the process parameters, it is necessary to check the display of the individual similarity index of the process parameter Whether the process parameters are abnormal.

For example, if the confidence index of the prediction point is lower than the threshold value of the confidence index, and the overall similarity index of the process parameters of the prediction point is lower than the threshold value of the overall similarity index of the process parameters, it indicates that the predicted value may be inaccurate, but due to the The overall similarity index of the process parameters of the points is low, indicating that the new components (such as wafers) have a high similarity with the modeling parameter data. At this time, there may be situations where the prediction value of the analysis and prediction model is not good.

For example, if the confidence index of the prediction point is lower than the threshold value of the confidence index, and the overall similarity index of the process parameters of the prediction point is higher than the threshold value of the overall similarity index of the process parameters, it means that the prediction value of the analysis and prediction model is not good, and Since the overall similarity index value of the process parameters at the prediction point is high, it means that the similarity between the new components and the modeled process parameter data is low, so it can be determined that the predicted value is inaccurate.

For example, when the overall similarity index of the process parameter at each prediction point is higher than the threshold value of the overall similarity index of the process parameter, it indicates that the process parameter may be abnormal.

In addition, since all manufacturing raw data have a time series relationship, the artificial intelligence analyzer 1413 can locate problems accurately and effectively, for example, when the time point of abnormal occurrence is denser, the problem can be located.

In one embodiment, the artificial intelligence analyzer 1413 can be realized by simple recurrent neural networks (Simple Recurrent Neural Networks, SRNN) and multiple regression analysis (Multiple Regression Analysis, MRA), and this application is not limited thereto .

In one embodiment, the manufacturing raw data received by the production line system 200 in time is used to update and adjust or retrain the analysis and prediction model, so that the artificial intelligence analyzer 1413 can accurately grasp the operation status of the production line to improve The analysis and prediction accuracy of the artificial intelligence analyzer 1413.

The post-processor 1415 is used to receive the analysis result of the artificial intelligence analyzer 1413, and perform data graphing based on it, so as to generate the corresponding graphical analysis result, for example, to generate graphs, graphs and distribution graphs. Graphical analysis results. For example, a graph showing the yield rate of multiple manufacturing equipment on the production line. In this way, the user can quickly understand the status or trend of the analysis results of the artificial intelligence analyzer 1413 through the graphical analysis results.

In one embodiment, the post-processor 1415 can be implemented by a data statistical analysis program, and the present application is not limited thereto.

From the above-mentioned embodiments and application methods of the present application, an operation method of a massive data analysis platform can be summarized, as shown in Figure 7, the steps include:

Step S100: Collect multiple pieces of manufacturing raw data. A mass data analysis platform 100 is used to receive multiple pieces of manufacturing raw data sent by a production line system 200 and store them in the mass data storage module 113 of the mass data analysis platform 100 .

Step S200: Perform data analysis. The mass data analysis platform 1000 generates a plurality of graphical analysis results according to at least a part of the plurality of manufacturing raw data.

Step S300: Generate a graphical interface, the graphical interface includes at least one graphical analysis result. The massive data analysis platform 100 is used to generate the graphical interface, and selectively make the graphical interface include at least one of the graphical analysis results, and the graphical interface is used to display the plurality of manufacturing equipment operational information.

Further, step 200 further includes the following steps:

Step S210: Obtain the required manufacturing raw materials. The massive data analysis platform 100 includes a plurality of data analysis engines 141, and the pre-processor 1411 of each data analysis engine 141 is used to send the massive data storage module 113 to the massive data storage module 113 according to the analysis and prediction to be performed by an artificial intelligence analyzer 1413 Read the required manufacturing sources.

Step S230: Execute pre-processing on multiple pieces of manufacturing raw data. The pre-processor 1411 further performs pre-processing on the received multiple pieces of manufacturing raw data, and generates corresponding processed data.

Step S250: Perform data analysis on the processed data. The artificial intelligence analyzer 1413 performs analysis and prediction on the processed data, and generates corresponding analysis results.

Step S270: Execute post-processing on the analysis results. The post-processor 1415 of the data analysis engine 141 performs post-processing on the analysis results to generate corresponding graphical analysis results.

To sum up, the embodiment of the scalable massive data analysis platform of this application can expand its computing power by adding massive data analysis equipment, and use artificial intelligence to graphically The results of chemical analysis provide the required analysis and prediction results in a simple and clear way. Users can quickly obtain the required process-related information without analyzing or searching among the numerous analysis results. In addition, by providing the operation of the multiple manufacturing equipment With the graphical interface of information, users can quickly grasp the overall status of the production line, thereby improving the convenience and efficiency of massive data analysis.

100: Huge data analysis platform 110: Main massive data analysis equipment 111: Processor module 113: Huge data storage module 115:Memory module 1151, 1151a, 1151b, 1151n: data analysis engine program 1153:Interface generation program 1155: Device management program 1157: Resource allocation program 1159: Automatic backup program 117: Input and output interface module 120: Huge data layer 130, 130a, 130b, 130n: analyzing equipment from massive data 140:Data analysis layer 141, 141a, 141b ... 141n: data analysis engine 1411: pre-processor 1413: AI Analyzer 1415: post processor 160: Graphical interface layer 200: Production line system 300: input and output module S100, S200, S210, S230, S250, S270, S300: steps

Figure 1 is a schematic diagram of the implementation environment of the scalable massive data analysis platform according to the embodiment of the present application; Figure 2 is a schematic diagram of the platform architecture of the scalable massive data analysis platform according to this application example; FIG. 3 is a schematic diagram of the architecture of the main massive data analysis device according to the embodiment of the present application; FIG. 4 is a schematic structural diagram of a memory module according to an embodiment of the present application; FIG. 5 is a schematic structural diagram of an expandable massive data analysis platform according to an embodiment of the present application; FIG. 6 is a schematic structural diagram of a data analysis engine according to an embodiment of the present application; Fig. 7 is a schematic flow chart of steps according to an embodiment of the operation method of the present application; and FIG. 8 is a schematic flowchart of another step of the operation method embodiment according to the present application.

110: Main massive data analysis equipment

111: Processor module

113: Huge data storage module

115:Memory module

117: Input and output interface module

Claims (10)

An expandable massive data analysis platform, which includes: A master massive data analysis device is used to electrically connect multiple slave massive data analysis devices, the master massive data analysis device includes; An input-output interface module, used for electrical connection with at least one of the plurality of slave massive data analysis devices; A massive data storage module, used to store multiple pieces of manufacturing raw data, the manufacturing raw data comes from multiple manufacturing equipment; A processor module, electrically connected with the input/output interface module and the massive data storage module, is used to execute a plurality of programs, so as to sense in real time whether there is connection between other devices and the main massive data analysis equipment , and allocate the hardware resources of the massive data analysis platform, and automatically perform data backup, so that at least a part of the multiple manufacturing raw data can generate multiple graphical analysis results, and selectively enable a graphical interface At least one of the plurality of graphical analysis results is included, and the graphical interface is used to display data associated with the plurality of manufacturing equipment. The massive data analysis platform as described in Claim 1, wherein the multiple slave massive data analysis devices are connected in series or in parallel. The massive data analysis platform as described in Claim 1, wherein the processor module is used to implement multiple data analysis engines, and each of the data analysis engines is used to generate different analysis results, and each of the data analysis engines Analysis engines include: a pre-processor, which obtains at least a part of the plurality of manufacturing raw data from the mass data storage module, and converts at least a part of the plurality of manufacturing raw data into a plurality of processed data; An artificial intelligence analyzer, used to receive and analyze the multiple pieces of processed data, and generate corresponding analysis result data; and A post-processor receives the analysis result data and generates the graphical analysis result. The huge amount of data analysis platform as described in Claim 1, wherein the graphical interface only includes graphical analysis results in abnormal states. The massive data analysis platform as described in Claim 3, wherein the pre-processor is a structured query language, a data warehouse, an R language or a Python language. The massive data analysis platform as described in Claim 3, wherein the artificial intelligence analyzer includes a simple recurrent neural network and a complex regression analysis. The huge amount of data analysis platform as described in Claim 1, wherein the multiple manufacturing raw data include a product yield rate, an electrical parameter, an online measurement parameter, a defect, a production time, a prescription, and a manufacturing A type of equipment, a signal of a manufacturing equipment, a reaction chamber, and a unit of measurement. An operation method of an expandable massive data analysis platform, the steps of which include: Obtain multiple pieces of manufacturing raw data, the multiple pieces of manufacturing raw data come from multiple manufacturing equipment; performing data analysis on the plurality of manufacturing raw data to generate a plurality of graphical analysis results, the plurality of graphical analysis results being different from each other; and A graphical interface is generated, selectively enabling the graphical interface to include at least one of the graphical analysis results, and the graphical interface is used to display the operation information of the plurality of manufacturing equipment. The operation method as described in Claim 8, wherein the data analysis is performed on the multiple pieces of manufacturing raw data to generate multiple graphical analysis results, and the steps of the multiple graphical analysis results being different from each other include: Obtain the required manufacturing raw materials; Perform pre-processing on the required manufacturing raw data and generate corresponding post-processing data; Perform data analysis on the processed data and generate corresponding analysis results; and Post-process the analysis results and generate corresponding graphical analysis results. The operation method as described in Claim 8, wherein the graphical interface only includes graphical analysis results of abnormal states.

Images