TWM618958U - Development platform capable of realizing big data analysis - Google Patents

Abstract

A developable huge data analysis platform, which includes a master huge data analysis device, which is electrically connected to a plurality of slave huge data analysis devices, wherein the master huge data analysis device is used to allocate the hardware of the huge data analysis platform Based on physical resources, multiple pieces of manufacturing raw data are generated based on the calculation of multiple data analysis engines to generate multiple graphical analysis results, and a development environment is built based on multiple pieces of manufacturing raw data to generate a self-built data analysis engine. In this way, the self-built data analysis engine can be developed in the actual application environment and applied to the huge data analysis platform immediately after the development is completed, so as to quickly expand the application field of the huge data analysis platform and reduce the development environment and The construction cost caused by different actual application environments improves the convenience and efficiency of massive data analysis.

Description

A large amount of data analysis platform that can be developed

This application relates to a data analysis platform, especially a large data analysis platform that can be developed.

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

With the advancement of the manufacturing process, multiple manufacturing equipment on the production line will generate tens of thousands of real-time monitoring data, nearly 10,000 on-line sampling and inspection measurements (metrology), and hundreds of electrical tests measured at different positions on semiconductor components At the same time, coupled with various integrated circuit production modes, the production line can generate more than billions of huge amounts of data in one 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 correlation problems, and cannot accurately locate the cause of the abnormality of the process. Also, because the production line generates 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 to find related problems in the results of statistical correlation analysis, and analyze the causes of abnormal processes on their own. As a result, the current statistical analysis methods cannot quickly eliminate 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 power. However, the analysis system with a single hardware architecture is no longer adequate.

In addition, in order to build an analysis engine, conventional knowledge is often divided into a development phase and a deployment phase. In the development phase, simulation data is usually used to train the analysis engine, and in the deployment phase, the trained analysis engine is uploaded to the running platform for operation. However, based on the hardware and software settings of the platform itself, development often occurs during the deployment phase. Unpredictable errors in the stage. Therefore, in the deployment stage, additional time and labor costs are required to improve the analysis engine.

Based on the above-mentioned problems in the prior art, it is indeed necessary to propose a better solution.

In view of the above-mentioned shortcomings of the prior art, the main purpose of this application is to provide a large-scale data analysis platform that can be developed, which uses artificial intelligence to analyze the large-scale data on the production line, and generates multiple graphical analysis results, and selectively Provides one of multiple graphical analysis results for viewing. Users can quickly obtain prediction results corresponding to different manufacturing equipment, manufacturing processes, and semiconductor component products through the graphical analysis results. In addition, the expandable structure can be flexible Increase its computing power according to computing needs to quickly eliminate process abnormalities and optimize the overall product process. Moreover, through the development environment settings, users can build customized self-built data analysis engines based on the application environment. To expand the application field of the huge data analysis platform, reduce the construction cost caused by the difference between the development environment and the actual application environment, and improve the convenience and efficiency of the huge data analysis.

In order to achieve the above-mentioned purpose, this application proposes a developable huge data analysis platform. The developable huge data analysis platform includes a master huge data analysis device for electrically connecting with multiple slave huge data analysis devices. And the main huge data analysis equipment includes: an input and output interface module, a huge data storage module and a processor module, wherein the processor module and the input and output interface module and the huge data The storage module is electrically connected to execute multiple programs to allocate the hardware resources of the huge data analysis platform, so that at least a part of the multiple manufacturing raw data generates multiple graphical analyses based on the calculations of multiple data analysis engines As a result, a graphical interface is made to selectively include at least one of the plurality of graphical analysis results, the graphical interface is used to display data associated with the plurality of manufacturing equipment, and is based on the plurality of manufacturing original data Build a development environment, which is used to generate a self-built data analysis engine.

With the above structure, the developable massive data analysis platform can expand the computing power of the developable 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 graphical analysis results provide the required analysis results in a simple and clear manner. Users do not need to analyze or search among the numerous analysis results to quickly obtain the required process-related information. In addition, , By providing the graphical interface of the operation information of the multiple manufacturing equipment, the user can quickly grasp the overall status of the production line, and by setting the development environment, the user can create customized self-built data analysis The engine quickly expands the application field of the huge data analysis platform, reduces the construction cost caused by the difference between the development environment and the actual application environment, thereby improving the convenience and efficiency of the huge data analysis.

Please refer to FIG. 1. FIG. 1 is a schematic diagram of the implementation environment of the massive data analysis platform that can be developed in this application. It includes a massive data analysis platform 100, a production line system 200, and an input and output module 300. The data analysis platform 100 is in communication connection with the production line system 200 and the input and output module 300. The production line system 200 is used to monitor multiple manufacturing equipment on the production line, and collect the manufacturing raw data related to the production line and the multiple manufacturing equipment in real time, and the manufacturing raw data has a time series relationship before and after, so as to control the production line in real time. All states on the production line. The massive data analysis platform 100 is in communication connection with the production line system 200 to receive manufacturing raw data and analyze and predict the manufacturing raw data to generate a plurality of graphical analysis results and a graphical interface. 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, but only specific graphical analysis results. 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 user-selected information. The input and output module 300 is used for receiving and displaying the graphical interface generated by the massive data analysis platform 100, and for receiving user selection information input by the user.

In this way, a user can quickly confirm the abnormal state or related information of 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 required information among the numerous analysis results. And quickly understand the cause of the abnormal state in a graphical way of data. In addition, you can input user selection information corresponding to the graphical interface to perform operations or selections required during viewing, thereby improving the convenience of large-scale data analysis Sex and efficiency.

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

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

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

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

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

In an embodiment, the device 130 for analyzing massive data can be implemented by a computing server, and this application is not limited thereto.

In an embodiment, the plurality of mass data analysis devices 130 may be electrically connected in series or in parallel with each other.

Please refer to FIG. 3, the main bulk data analysis device 110 includes at least a processor module 111, a bulk 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 to the huge data storage module 113, the memory module 115, and the input/output interface module 117.

The huge data storage module 113 is used to store multiple pieces of manufacturing raw data and their corresponding time. The manufacturing raw data is provided by the production line system 200 and comes from multiple manufacturing equipment, where each manufacturing equipment is The production line generates corresponding manufacturing raw data over time and process.

In one embodiment, the massive data storage module 113 can be implemented by multiple hard disk storage devices, and this application is not limited thereto.

The memory module 115 is used to store a plurality of programs. As shown in FIG. 4, the memory module 115 at least stores a plurality of data analysis programs 1151 (1151a, 1151b...1151n), an interface generation program 1153, and a resource allocation Programs 1155, an analysis engine development program 1157, and a deployment engine program 1159 are used to be executed by the processor module 111 to implement 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 application is not limited thereto.

Please return to FIG. 3 and refer to FIG. 1 at the same time. The input/output interface module 117 is used for electrically connecting with external devices of the main massive data analysis device 110 to exchange data 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 input from the user through the I/O module 300 User selection information for. For example, the input/output interface module 117 is electrically connected to the production line system 200 to receive manufacturing raw data from the production line system 200. For another example, the input and output interface module 117 is used to electrically connect the slave huge data analysis device 130 to exchange data with the slave huge data analysis device 130, thereby enabling the master huge data analysis device 110 can use the computing power of the massive data analysis device 130 to perform different analysis and predictions.

In one embodiment, the input/output interface module 117 may include a serial data (RS232) communication interface, a universal serial bus (USB) specification port, and an external connection standard (Peripheral Component Interconnect, PCI) port , Ports that comply with the High Definition Multimedia Interface (HDMI) standard, and ports that comply with the Accelerated Graphics Port (AGP) standard, and this application is not limited by this.

The processor module 111 is used to execute the multiple programs. As shown in FIGS. 3 and 4, further, the processor module 111 is used to execute the resource allocation program 1155 to schedule the overall computing, storage, and network resources of the huge data analysis platform 100, thereby improving the The resource utilization efficiency and operational efficiency of the massive data analysis platform 100, and the computing power of second-level computing is realized. In one embodiment, the resource allocation program 1155 is, for example, a program for implementing a virtual machine monitor (VMM), and this application is not limited thereto. Further, the processor module 111 is used to execute at least one of the plurality of data analysis programs 1511 (1151a, 1151b...1151n), and according to the manufacturing primitives required by each of the data analysis programs (1151a, 1151b...1151n) Data, read at least a part of the multiple manufacturing raw data in the huge data storage module 113, and analyze and predict at least a part of the multiple manufacturing raw data to correspond to different data analysis The programs (1151a, 1151b...1151n) generate the multiple graphical analysis results, that is, the multiple data analysis programs (1151a, 1151b...1151n) are used to provide different graphical analysis results from each other.

In one embodiment, the processor module 111 is further used to execute a self-built data analysis engine program 1152, the operation of which is similar to at least one of the aforementioned execution of the plurality of data analysis programs 1511 (1151a, 1151b...1151n), The self-built data analysis engine program 1152 reads at least part of the multiple pieces of manufacturing raw data in the huge data storage module 113, and analyzes and predicts at least a portion of the multiple pieces of raw data. A graphical analysis result is generated according to the analysis and prediction result, so it will not be repeated here.

Further, the processor module 111 is used to execute the interface generation program 1153, receive the graphical analysis results generated by the multiple data analysis programs 1511, and generate the graphical analysis results including the graphical analysis results and corresponding multiple data Analyze the graphical interface of the program 1511 (1151a, 1151b...1151n), and selectively make the graphical interface include only one of the multiple graphical analysis results, for example, the graphical interface only includes abnormal conditions Graphical analysis results, or only the graphical analysis results corresponding to the information selected by the user.

In one embodiment, the abnormal state may be a state in which the process step/manufacturing equipment/product component is abnormal according to the analysis and prediction result of the data analysis engine, and this application is not limited thereto. For example, according to the graphical analysis result, the analysis and prediction result shows that the production time is higher than a threshold value, the interface generating program 1153 determines that it is an abnormal state and causes the graphical interface to include the graphical analysis result, thereby prompting the use in real time By.

In one embodiment, the user selection information is the information that the user wants to actively view, which includes the graphical analysis result of the manufacturing raw data obtained by the production line system 200 by the mass data analysis platform 100. For example, the user selection information is selection information corresponding to the number of prescriptions, and the interface generating program 1153 makes the graphical interface include the graphical analysis result corresponding to the number of prescriptions according to the user selection information, thereby providing the user for viewing.

In one embodiment, the processor module 111 is used to execute the interface generation program 1153, and update the graphical interface based on the self-built data analysis engine program 1152 so that the graphical interface includes a corresponding self-built data analysis engine program 1152 operation interface. In this way, the user can update the graphical interface, input the user selection information corresponding to the self-built data analysis engine program 1152, and execute the corresponding self-built data analysis engine.

In one embodiment, the multiple pieces of manufacturing source data may include product yield (for example, maximum value, intermediate value, minimum value), electrical parameters (WAT), inline metrology, and defects. (defects), production time (time) (for example: on-machine time, off-machine time, treatment time), recipe (for example: prescription type, prescription quantity), type of manufacturing equipment (Model), manufacturing equipment (Equipment) ) (For example: manufacturing equipment signal), reaction chamber (for example: temperature, humidity, no wind state), unit measurement value, etc., and this application is not limited by this.

Further, the processor module 111 is used to execute the analysis engine development program 1157 based on the multiple manufacturing raw data, the hardware architecture of the huge data analysis platform 100, and the multiple data analysis programs 1511 (1151a , 1151b...1151n) library to build a development environment for the development of data analysis engine, by which users can develop the self-built in the actual application environment (that is, the massive data analysis platform 100) based on the development environment The code of the data analysis engine program 1152 reduces the construction cost caused by the difference between the development environment and the actual application environment.

Further, the processor module 111 is used to execute the deployment engine program 1159, to continuously and automatically build, test, analyze, and deploy the code to quickly build the self-built data analysis engine program 1152, And deployed in the massive data analysis platform 100.

Therefore, based on the aforementioned massive data analysis platform 100, a massive data analysis system architecture can be realized. As shown in FIG. 5, the massive data analysis platform 100 can define a massive data layer 120 and a data analysis layer. 140. A development layer 180 and a graphical interface layer 160, wherein the huge data layer 120 is implemented by the huge data storage module 113, and the data analysis layer 140 includes a plurality of data analysis engines 141 (1411, 1412... 141n), 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 engines 141 are different from each other, and the development layer 180 includes programs developed by the analysis engine The implemented analysis engine development module 181 and a deployment engine 183 implemented by the deployment engine program 1159 and the graphical interface layer 160 are implemented by the interface generation program 1153.

Please refer to FIG. 6, which is a diagram of the architecture 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 for reading the required manufacturing raw data from the massive data storage module 113 according to the analysis and prediction to be performed by the artificial intelligence analyzer 1413. Furthermore, 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 preprocessor 1411 is based on the analysis and prediction programs executed by the artificial intelligence analyzer 1512. The huge 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 the massive data storage module 113 Take 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, and transforming (Transform) the manufacturing raw data. ) And so on to remove abnormal data and ensure data quality and high prediction accuracy to generate processed data to be provided to the artificial intelligence analyzer 1413.

In an embodiment, the pre-processor 1411 can be implemented in a structured query language (SQL), a data warehouse (Hive), an R language, or a Python language, and this application is not limited thereto. .

The artificial intelligence analyzer 1413 is used to receive 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 indicates 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 to determine 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 indicator value is greater than the confidence indicator threshold, it means that the analysis result can be trusted; on the contrary, when the confidence indicator value is lower than the confidence indicator threshold, a warning is issued. Therefore, users as equipment engineers can use this to inspect manufacturing equipment, or users as process engineers can adjust parameters to confirm whether the process is stable.

The artificial intelligence analyzer 1413 is also used to calculate a similarity index. The main purpose of the similarity index is to compare the similarity of the process parameter data between 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 second is the individual process parameter similarity index (ISI). The overall similarity index of the process parameters is the degree of similarity between the process parameters in the prediction section and all the parameters in the modeling section. 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 the parameter in the modeling section. The similarity index is also used as an auxiliary confidence index for judgment. Therefore, a user as a process engineer can determine whether to perform parameter adjustment based on the analysis and prediction result of the similarity index to confirm whether the process is stable.

For example, if the confidence index of the prediction point is higher than the confidence index threshold, and the overall similarity index of the process parameter at the prediction point is higher than the overall similarity index threshold of the process parameter, check the individual similarity index display 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 overall similarity index threshold of the process parameters, the predicted value may be inaccurate, but due to the prediction The overall similarity index of the process parameters of the point is low, indicating that the new component (such as wafer) has a high similarity with the modeling parameter data. At this time, there may be a situation 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 confidence index threshold, and the overall similarity index of the process parameters of the prediction point is higher than the overall similarity index threshold 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 predicted point is high, it means that the new component and the modeling process parameter data are low in similarity, so it can be considered that the predicted value is inaccurate.

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

In addition, since all the manufacturing raw data have a time series relationship before and after, the artificial intelligence analyzer 1413 can accurately and effectively locate the problem. For example, when the time points of abnormal occurrence are denser, the problem can be located.

In one embodiment, the artificial intelligence analyzer 1413 can be implemented by Simple Recurrent Neural Networks (SRNN) and Multiple Regression Analysis (MRA) operations, and this application is not based on this. limit.

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

The post-processor 1415 is used to receive the analysis results of the artificial intelligence analyzer 1413, and perform data graphics accordingly to generate the corresponding graphical analysis results, for example, to generate charts, graphs, and distribution diagrams. Graphical analysis results. For example, use a graph to show the yield of multiple manufacturing equipment on the production line. In this way, the user can quickly understand the state or trend of the analysis result of the artificial intelligence analyzer 1413 through the graphical analysis result.

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

According to the above-mentioned embodiments and application methods of this application, the operation method of a massive data analysis platform can be summarized, as shown in Fig. 7, the steps include:

Step S10: Receive a program code. The massive data analysis platform 100 is used to receive a code input by a user based on a development environment.

Step S20: Perform integrated deployment on the code to build a self-built data analysis engine. The massive data analysis platform 100 performs integrated deployment of the code, generates a self-built data analysis engine program 1152 stored in the massive data analysis platform 100, and implements the self-built data analysis based on the self-built data analysis engine program 1152 engine.

In step S30, a graphical interface including the self-built data analysis engine is generated. The massive data analysis platform 100 updates the graphical interface based on the self-built data analysis engine program 1152 so that the graphical interface includes the self-built data analysis engine.

The operation method of another massive data analysis platform can also be summarized from the above-mentioned embodiments and application methods of this application, as shown in Fig. 8, the steps include:

Step S100: Collect multiple pieces of manufacturing original data. A huge 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 huge data storage module 113 of the huge data analysis platform 100.

Step S200: perform data analysis. The massive data analysis platform 100 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 including 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 information of the multiple manufacturing equipment Operational information.

Further, step 200 further includes the following steps, as shown in FIG. 9:

Step S210: Obtain the required manufacturing original data. 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 predict 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 raw materials.

Step S230: Perform pre-processing on multiple pieces of manufacturing original 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: Perform post-processing on the analysis result. The post-processor 1415 of the data analysis engine 141 performs post-processing on the analysis result to generate a corresponding graphical analysis result.

In summary, the embodiment of the massive data analysis platform that can be developed in this application can expand its computing power by adding massive data analysis equipment, and use artificial intelligence to graphically use the massive data collected by it. The chemical analysis results provide the required analysis and prediction results in a simple and clear manner. Users can quickly obtain the required process-related information without having to analyze or search among the many 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. In addition, the self-built data analysis engine can be developed in the actual application environment and applied to the massive data analysis platform immediately after the development is completed. Expand the application field of the huge data analysis platform, reduce the construction cost caused by the difference between the development environment and the actual application environment, and improve the convenience and efficiency of the huge data analysis.

100: Mass data analysis platform 110: Main massive data analysis equipment 111: processor module 113: Massive Data Storage Module 115: memory module 1151, 1151a, 1151b, 1151n: data analysis engine program 1152: Self-built data analysis engine program 1153: Interface generation program 1155: Resource Allocation Program 1157: Analysis engine development program 1159: Deploy the engine program 117: Input and output interface module 120: Massive Data Layer 130, 130a, 130b, 130n: analysis equipment from huge amounts of data 140: data analysis layer 141, 141a, 141b …141n: data analysis engine 1411: preprocessor 1413: Artificial Intelligence Analyzer 1415: post processor 160: Graphical interface layer 180: Development layer 181: Analysis Engine Development Module 183: Deployment Engine 200: Production line system 300: Input and output modules S10, S20, S30, S100, S200, S210, S230, S250, S270, S300: steps

Fig. 1 is a schematic diagram of the implementation environment of a massive data analysis platform that can be developed according to an embodiment of the present application; Figure 2 is a schematic diagram of the platform architecture of a massive data analysis platform that can be developed according to this application example; Figure 3 is a schematic diagram of the architecture of the main massive data analysis device according to an embodiment of the present application; FIG. 4 is a schematic diagram of the structure of a memory module according to an embodiment of the present application; Figure 5 is a schematic diagram of the architecture of a massive data analysis platform that can be developed according to an embodiment of the present application; FIG. 6 is a schematic diagram of the structure of a data analysis engine according to an embodiment of the present application; FIG. 7 is a schematic diagram of a step flow diagram of an embodiment of an operating method according to the present application; FIG. 8 is a schematic flowchart of another step according to an embodiment of the operating method of the present application; and FIG. 9 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: Massive Data Storage Module

115: memory module

117: Input and output interface module

Claims (7)

A large amount of data analysis platform that can be developed, which includes: A master huge data analysis device for electrically connecting with a plurality of slave huge data analysis devices, the master huge data analysis device includes; An input and output interface module for electrical connection with at least one of the plurality of analysis equipment from huge amounts of data; A huge data storage module for storing multiple pieces of manufacturing raw data, which come from multiple manufacturing equipment; A processor module, electrically connected to the input/output interface module and the huge data storage module, is used to execute a plurality of programs to allocate the hardware resources of the huge data analysis platform so that the plurality of original manufacturing At least a part of the data generates a plurality of graphical analysis results based on the operation of a plurality of data analysis engines, and a graphical interface selectively includes at least one of the plurality of graphical analysis results, and the graphical interface is used for displaying and Based on the data associated with the multiple manufacturing equipment, a development environment is built based on the multiple manufacturing raw data, and the development environment is used to generate a self-built data analysis engine. The massive data analysis platform according to claim 1, wherein the plurality of slave massive data analysis devices are connected in series or parallel with each other. The massive data analysis platform described in claim 1, wherein each data analysis engine includes: A pre-processor that obtains at least a part of the multiple manufacturing raw data from the huge data storage module, and converts at least a part of the multiple manufacturing raw data into multiple processed data; An artificial intelligence analyzer for receiving and analyzing the multiple processed data and generating corresponding analysis result data; and A post-processor receives the analysis result data and generates the graphical analysis result. The massive data analysis platform according to claim 1, wherein the development environment includes an analysis engine development module and a deployment engine. The massive data analysis platform according to claim 3, wherein the preprocessor is a structured query language, a data warehouse, an R language or a Python language. The massive data analysis platform according to claim 3, wherein the artificial intelligence analyzer includes a simple loop-like neural network and a multiple regression analysis operation. The massive data analysis platform described in claim 1, wherein the multiple pieces of manufacturing raw data include a product yield, an electrical parameter, an online measurement parameter, a defect, a production time, a prescription, and a manufacturing The type of equipment, the signal of a manufacturing equipment, a reaction chamber, and the measured value of a unit.

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