TWM619003U - Intelligent alarm platform using artificial intelligence - Google Patents

Abstract

This application proposes an intelligent early warning platform using artificial intelligence, wherein the intelligent early warning platform The station includes a huge data storage device and an arithmetic analysis system. The huge data storage device is used to store a plurality of manufacturing raw data, and the manufacturing raw data comes from a plurality of manufacturing equipment, and the arithmetic analysis system is used to automatically Generate a plurality of different graphical analysis results based on at least a part of multiple manufacturing raw data, and generate a graphical interface associated with the plurality of graphical analysis results, and a user can generate a corresponding user based on the graphical interface Select the information to get the associated graphical analysis results. This improves the convenience and efficiency of analyzing huge amounts of data, and quickly eliminates process abnormalities.

Description

Intelligent early warning platform using artificial intelligence

This application relates to a data analysis platform, especially an intelligent early warning platform using artificial intelligence.

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.

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 an intelligent early warning platform using artificial intelligence, which uses artificial intelligence to automatically analyze huge amounts of data on the production line, and generate multiple graphical analysis results, and graphically The interface provides a viewing environment. The user can clearly and accurately select the information to be viewed, and obtain the corresponding graphical analysis results, and quickly obtain the predictive analysis results corresponding to different manufacturing equipment, manufacturing processes, and semiconductor components, thereby enhancing intelligent early warning Convenience and efficiency to quickly eliminate process abnormalities and optimize the overall product process.

To achieve the above objective, this application proposes an intelligent early warning platform using artificial intelligence, which includes a huge data storage device and an arithmetic analysis system, wherein the huge data storage device is used to store a plurality of manufacturing raw data, and the The original manufacturing data comes from multiple manufacturing equipment, and the calculation analysis system is used to automatically generate multiple different graphical analysis results.

Further, the calculation analysis system further includes a plurality of data analysis engines and an interface generation system, and each of the data analysis engines includes: a pre-processor, an artificial intelligence analyzer, and a post-processor, wherein the pre-processing The device is used to obtain at least a part of multiple manufacturing raw data from the huge data storage device, and convert at least a portion of the multiple manufacturing raw data into multiple processed data, and the artificial intelligence analyzer is used to receive and analyze the multiple After processing the data and generating corresponding analysis result data, the post-processor is used for receiving the analysis result data and generating the graphical analysis result. The interface generation system receives a plurality of graphical analysis results from the plurality of data analysis engines, and is used to generate a graphical interface associated with the plurality of graphical analysis results, and according to the user associated with the graphical interface The selection information provides one of the multiple graphical analysis results.

With the above structure, the intelligent early warning platform using artificial intelligence can use the huge amount of data it collects to provide the required analysis results in a simple and clear graphical interface. The user does not need to analyze or search among the many analysis results. And can quickly obtain the required graphical analysis results. 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, thereby improving the convenience and efficiency of massive data analysis.

100: Intelligent warning platform

110: Central Control System

130: Massive data storage device

150: Operational Analysis System

151, 151a, 151b, 151n: data analysis engine

1511: preprocessor

1513: Artificial Intelligence Analyzer

1515: post processor

153: Interface Generation System

170: data input interface

190: I/O interface

200: Production line system

300: Input and output modules

S100, S200, S210, S230, S250, S270, S300: steps

Figure 1 is a schematic diagram of the implementation environment of an embodiment of an intelligent early warning platform using artificial intelligence according to this application; Figure 2 is a schematic diagram of the platform architecture of an embodiment of an intelligent early warning platform using artificial intelligence according to this application; Figure 3 is an application according to this application A schematic diagram of a platform architecture of an embodiment of an artificial intelligence computing analysis system; FIG. 4 is a diagram of a platform architecture of an embodiment of a data analysis engine according to this application; Schematic diagram of another step flow of the embodiment of the operating method according to the present application.

Please refer to Figure 1. Figure 1 is a schematic diagram of the implementation environment of the intelligent early warning platform using artificial intelligence in this application. It includes an intelligent early warning platform 100, a production line system 200, and an input and output module 300. The intelligent early warning platform 100 and The production line system 200 and the input/output module 300 are connected in communication. 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 intelligent early warning platform 100 communicates with the production line system 200 to receive raw manufacturing data and automatically analyze and predict the raw manufacturing data to generate multiple graphical analysis results and corresponding results. A graphical interface, where the graphical interface may include information associated with the plurality of graphical analysis results, and the information associated with the plurality of graphical analysis results, such as time, procedures (maintenance, mass production process, etc.) , Items (events, yield, manufacturing equipment), etc., and the graphical interface provides corresponding graphical analysis results based on user selection information generated by a user.

The input and output module 300 is used for receiving and displaying the graphical interface, and for receiving user selection information input by the user.

In this way, the user can quickly confirm the abnormal state or related information on the production line through the graphical interface displayed by the input and output module 300, and obtain the corresponding graphical analysis result, that is, the user does not need to perform numerous analyses. Find the information you need in the results, and you can quickly understand the causes of abnormal conditions in a graphical way, which improves the convenience and efficiency of massive data analysis.

In one embodiment, the intelligent early warning 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 to this system. In this way, the user can generate the user selection information by touching the touch display screen or operating the input device.

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 intelligent early warning platform 100 includes at least a central control system 110, a massive data storage device 130, an arithmetic analysis system 150, a data input interface 170, and an input/output interface 190. The central control system 110 is electrically connected to the massive data storage device 130, the calculation analysis system 150, the data input interface 170, and the input/output interface 190.

The central control system 110 is used to manage the arithmetic analysis system 150, monitor the data exchange among the massive data storage device 130, the arithmetic analysis system 150, the data input interface 170, and the input/output interface 190, and schedule the The overall computing, storage, and network resources of the intelligent early warning platform 100 improve the resource utilization efficiency and operating efficiency of the intelligent early warning platform 100.

In one embodiment, the central control system 110 is, for example, a Composer module, and the application is not limited thereto.

The data input interface 170 is used to communicate with the production line system 200 to receive the multiple pieces of manufacturing raw data.

In one embodiment, the data input interface 170 can be a serial data (RS232) communication interface, a universal serial bus (USB) standard connection port, and this application is not limited thereto.

The massive data storage device 130 is used to store multiple pieces of manufacturing raw data and their corresponding time. The manufacturing raw data are provided by the production line system 200 and come 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 device 130 can be implemented by multiple hard disk storage devices, and the application is not limited thereto.

The arithmetic analysis system 150 is used for reading at least a part of the multiple manufacturing raw data from the huge data storage device 130, and analyzing at least a part of the multiple manufacturing raw data to generate the Multiple graphical analysis results and the graphical interface.

The I/O interface 190 is communicatively connected with the I/O module 300 to provide the graphical interface to the I/O module 300 and receive user selections input by the user based on the information in the graphical interface News.

In one embodiment, the I/O interface 190 includes ports that comply with Universal Serial Bus (USB), High-Definition Multimedia Interface (HDMI), and Video Graphics Array (VGA) specifications, and the application is not limited thereto.

In one embodiment, the intelligent early warning platform 100 can be implemented by a blade server, and the application is not limited thereto.

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 calculation analysis system 150 further includes a plurality of data analysis engines 151 (151a, 151b...151n) and an interface generation system 153, as shown in FIG. 3, and the plurality of data analysis engines 151 (151a, 151b...151n) ) Is in communication with the interface generating system 153. Each data analysis cited Engine 151 (151a, 151b...151n) is used to automatically generate corresponding graphical analysis results according to the received manufacturing raw data, that is, multiple data analysis engines 151 (151a, 151b...151n) can each other to provide different graphical analysis result.

The interface generation system 153 is used to receive the graphical analysis results generated by each data analysis engine 151 (151a, 151b...151n), and to generate the graphical interface based on the information of the multiple graphical analysis results. The interface generation system 153 is used to receive the user selection information, and according to the user selection information, make the graphical interface include the graphical analysis result corresponding to the user selection information, that is, make the graphical interface include the use Graphical analysis results selected by the author. In this way, the user can view the visualization analysis result through the graphical interface.

In one embodiment, the graphical interface may tabularly provide information associated with the multiple graphical analysis results, and the application is not limited thereto.

In one embodiment, the graphical interface selectively includes information associated with at least one graphical analysis result. In other words, the graphical interface may not include all the information of the graphical analysis result at the same time. For example, the information included in the graphical interface corresponds to a graphical analysis result including an abnormal state.

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 generation system 153 determines that it is an abnormal state and makes the graphical interface include the information of the graphical analysis result, thereby Prompt the user immediately.

In one embodiment, each data analysis engine 151 can be implemented by a single board computer executing a corresponding analysis default program, and this application is not limited thereto.

In one embodiment, the interface generation system 153 can be implemented by a single-board computer executing a corresponding interface generation program, and this application is not limited thereto.

In one embodiment, the aforementioned program is used to store a computer-readable memory device of the single-board computer, such as a hard disk device, and this application is not limited thereto.

Please refer to FIG. 4, which is a platform architecture diagram of the data analysis engine 151. The data analysis engine 151 includes at least a pre-processor 1511, an artificial intelligence analyzer 1513, and a post-processor 1515.

The pre-processor 1511 is used for reading the required manufacturing raw data from the massive data storage device 130 according to the analysis and prediction to be performed by the artificial intelligence analyzer 1513. Furthermore, since the artificial intelligence analyzer 1512 of each data analysis engine 151 can be used to execute the same or different analysis and prediction programs, the preprocessor 1511 is based on the analysis and prediction programs executed by the artificial intelligence analyzer 1512. The massive data storage device 130 reads the required manufacturing raw data. For example, the artificial intelligence analyzer 1513 can be used to analyze and predict product yield according to the type of manufacturing equipment. Therefore, in this embodiment, the pre-processor 1511 is used to read the massive data storage device 130 The required manufacturing raw data (type information of manufacturing equipment) for analysis and prediction.

Further, the pre-processor 1511 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 1512.

In one embodiment, the pre-processor 1511 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 1513 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 1513 is also used to calculate a similarity index. The main purpose of the similarity index is to compare the degree of 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 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 1513 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 1513 can be implemented by Simple Recurrent Neural Networks (SRNN) and Multiple Regression Analysis (MRA), and this application is not limited to this .

In one embodiment, the manufacturing raw data received in time by the production line system 200 is further used to update and adjust or retrain the analysis and prediction model, so that the artificial intelligence analyzer 1513 can accurately grasp the operating status of the production line to improve The artificial intelligence analyzer 1513 analyzes and predicts accuracy.

The post-processor 1515 is used to receive the analysis result of the artificial intelligence analyzer 1513, and perform data graphing accordingly to generate the corresponding graphical analysis result, for example, a graph Graphical analysis results of tables, graphs and distribution diagrams. 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 1513 through the graphical analysis result.

In an embodiment, the post-processor 1515 can be implemented by a data statistical analysis program, and this application is not limited thereto.

The operation method of an intelligent early warning platform can be summarized from the above-mentioned embodiments and application methods of this application, as shown in Fig. 5, the steps include:

Step S100: Collect multiple pieces of manufacturing original data. An intelligent early warning platform 100 is used to receive multiple pieces of manufacturing raw data sent by a production line system 200 and store them in the massive data storage device 130 of the intelligent early warning platform 100.

Step S200: perform data analysis. The calculation analysis system 150 of the intelligent early warning platform 100 generates multiple graphical analysis results based on at least a part of the multiple pieces of manufacturing raw data.

Step S300: Generate a graphical interface, the graphical interface including information associated with at least one graphical analysis result. The calculation analysis system 150 is used to generate the graphical interface, and make the graphical interface include information of at least one of the graphical analysis results, and the calculation analysis system 150 is based on user selection information generated by a user, Make the graphical interface include graphical analysis results associated with the user's selection information.

Further, step 200 further includes the following steps:

Step S210: Obtain the required manufacturing original data. The calculation analysis system 150 includes a plurality of data analysis engines 151, and the preprocessor 1511 of each data analysis engine 151 is used to predict what is needed for reading from the massive data storage device 130 according to the analysis to be performed by an artificial intelligence analyzer 1513 The manufacturing source material.

Step S230: Perform pre-processing on multiple pieces of manufacturing original data. The pre-processor 1511 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 1513 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 1515 of the data analysis engine 151 performs post-processing on the analysis result to generate a corresponding graphical analysis result.

In summary, the embodiment of the intelligent early warning platform using artificial intelligence in this application can use the huge amount of data it collects to use artificial intelligence to provide the required analysis and prediction results in a simple and clear graphical interface, and according to the user The selection provides the required graphical analysis results. Users can quickly obtain the required process-related information without analyzing or searching among the many analysis results. In addition, by providing the graphics of the operation information of the multiple manufacturing equipment With a modernized interface, users can quickly grasp the overall status of the production line, thereby improving the convenience and efficiency of massive data analysis.

150: Operational Analysis System

151, 151a, 151b, 151n: data analysis engine

153: Interface Generation System

Claims (8)

An intelligent early warning platform using artificial intelligence, which includes: a huge data storage device for storing multiple manufacturing raw data, the manufacturing raw data comes from multiple manufacturing equipment; an arithmetic analysis system, which includes: multiple data Analysis engine, and each data analysis engine is used to automatically generate different graphical analysis results, and each data analysis engine includes: a pre-processor that obtains multiple manufacturing primitives from the huge data storage device At least part of the data, and convert at least part of the multiple pieces of manufacturing raw data into multiple pieces of processed data; an artificial intelligence analyzer for receiving and analyzing the multiple pieces of processed data, and generating corresponding analysis result data; And a post-processor to receive the analysis result data and generate the graphical analysis result; and an interface generation system to receive a plurality of graphical analysis results from the plurality of data analysis engines, and to generate the graphical analysis results A graphical interface associated with the graphical analysis result, and one of the plurality of graphical analysis results is provided according to user selection information associated with the graphical interface. The intelligent early warning platform according to claim 1, wherein the intelligent early warning platform further includes a central control system, which is in communication connection with the computing analysis system and the massive data storage device. The intelligent early warning platform according to claim 1, wherein the information of the graphical interface is only associated with the graphical analysis result including an abnormal state. The intelligent early warning platform according to claim 1, wherein the graphical interface only includes a graphical analysis result with an abnormal state. The intelligent early warning platform according to claim 1, wherein the preprocessor is a structured query language, a data warehouse, an R language or a Python language. The intelligent early warning platform according to claim 1, wherein the artificial intelligence analyzer includes a simple loop-like neural network and a multiple regression analysis. The intelligent early warning platform according to 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 equipment Type, a signal from a manufacturing equipment, a reaction chamber, and a unit measurement value. The intelligent early warning platform according to claim 1, wherein the intelligent early warning platform is a blade server.

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