Patent Publication Number: US-2022237450-A1

Title: Semiconductor process prediction method and semiconductor process prediction apparatus considering overall features and local features

Description:
This application claims the benefit of People&#39;s Republic of China application Serial No. 202110118090.4, filed Jan. 28, 2021, the disclosure of which is incorporated by reference herein in its entirety. 
     TECHNICAL FIELD 
     The disclosure relates in general to a semiconductor process prediction method and a semiconductor process prediction apparatus, and more particularly to a semiconductor process prediction method and a semiconductor process prediction apparatus considering overall features and local features. 
     BACKGROUND 
     With the development of semiconductor technology, various types of complex semiconductor products are constantly being introduced. In the semiconductor manufacturing process, the wafer needs to go through tens thousands of processes to produce the final product. Therefore, researchers must use appropriate prediction methods for the semiconductor process to predict the occurrence of process abnormalities to avoid a large number of defective products in the final products. 
     Traditionally, statistical data such as average or standard deviation is monitored to estimate the abnormalities that may occur in the process. However, this method only considers the overall features, and under the trend of increasing complexity of the semiconductor process, it has been difficult to obtain prediction results with higher accuracy. 
     SUMMARY 
     The disclosure is directed to a semiconductor process prediction method and a semiconductor process prediction apparatus considering overall features and local features. The local features and the overall features are analyzed by using a dynamic time warping (DTW), a Convolutional Neural Network (CNN) model and an Artificial Neural Network (ANN) model to improve the prediction accuracy. 
     According to one embodiment, a semiconductor process prediction method considering overall features and local features is provided. The semiconductor process prediction method includes the following steps. A plurality of equipment sensing curves are obtained. The equipment sensing curves are filtered to reduce a co-linearity of the equipment sensing curves. A dynamic time warping (DTW) is performed to align the equipment sensing curves. The equipment sensing curves which are aligned are inputted into a Convolutional Neural Network (CNN) model, to obtain a first prediction result considering the local features. A statistical analysis procedure is performed on the equipment sensing curves to obtain a plurality of statistical data. The statistical data are inputted into an Artificial Neural Network (ANN) model, to obtain a second prediction result considering the overall features. A total prediction result is obtained according to the first prediction result and the second prediction result. 
     According to another embodiment, a semiconductor process prediction apparatus considering overall features and local features is provided. The semiconductor process prediction apparatus includes a database, a filtering unit, a filtering unit, an aligning unit, a Convolutional Neural Network (CNN) model, a statistical unit, an Artificial Neural Network (ANN) model and a total prediction unit. The database is configured to storing a plurality of equipment sensing curves. The filtering unit is configured to filter the equipment sensing curves to reduce a co-linearity of the equipment sensing curves. The aligning unit is configured to perform a dynamic time warping (DTW) to align the equipment sensing curves. The CNN model is configured to receive the equipment sensing curves which are aligned to obtain a first prediction result considering the local features. The statistical unit is configured to perform a statistical analysis procedure on the equipment sensing curves to obtain a plurality of statistical data. The ANN model is configured to receive the statistical data to obtain a second prediction result considering the overall features. The total prediction unit is configured to obtain a total prediction result according to the first prediction result and the second prediction result. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a semiconductor process prediction apparatus according to an embodiment. 
         FIG. 2  shows a block diagram of the semiconductor process prediction apparatus according to an embodiment. 
         FIG. 3  illustrates a flow chart of a semiconductor process prediction method simultaneously considering overall features and local features according to an embodiment. 
         FIG. 4  illustrates the steps in  FIG. 3 . 
         FIG. 5  shows a flowchart of the detailed steps of step S 120 . 
         FIG. 6  illustrates the steps in  FIG. 5 . 
         FIG. 7  shows an implementation of a Convolutional Neural Network (CNN) model. 
         FIG. 8  shows another implementation of the CNN model. 
     
    
    
     In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. It will be apparent, however, that one or more embodiments may be practiced without these specific details. In other instances, well-known structures and devices are schematically shown in order to simplify the drawing. 
     DETAILED DESCRIPTION 
     Please refer to  FIG. 1 , which shows a semiconductor process prediction apparatus  100  according to an embodiment. In the semiconductor process, the wafer needs to go through various pieces of equipment  900  for different processes, such as deposition, etching, or annealing. Every process of every equipment  900  requires precise monitoring. Therefore, various sensors  910  are set on the equipment  900  to capture sensing data S 01  to S 06  such as temperature, pressure, gas concentration (the number is not to limit the present invention). These sensing data S 01  are continuously captured over time and transmitted to the semiconductor process prediction apparatus  100  through network  800  to record as an equipment sensing curve S 11  (shown in  FIG. 2 ); these sensing data S 02  are continuously captured over time and recorded as an equipment sensing curve S 12  (shown in  FIG. 2 ), and so on. The semiconductor process prediction apparatus  100  can perform Fault Detection and Classification (FDC) with these equipment sensing curves S 11  to S 16  (the number is not used to limit the present invention). When abnormal conditions of the process are predicted, the process can be stopped or modified immediately to avoid process defects. 
     Please refer to  FIG. 2 , which shows a block diagram of the semiconductor process prediction apparatus  100  according to an embodiment. The semiconductor process prediction apparatus  100  is, for example, a server, a computer or a cloud computing center. The semiconductor process prediction apparatus  100  includes a database  110 , a filtering unit  120 , an aligning unit  130 , a Convolutional Neural Network (CNN) model  140 , a statistical unit  150 , an Artificial Neural Network (ANN) model  160  and a total prediction unit  170 . The functions of the components are summarized as follows. The database  110  is used to store data, such as a memory, a hard disk or a cloud storage center. The filtering unit  120  is used for data filtering. The aligning unit  130  is used for data alignment. The CNN model  140 , the ANN model  160  and the total prediction unit  170  are used for data prediction. The statistical unit  150  is used for data statistics. The filtering unit  120 , the aligning unit  130 , the CNN model  140 , the statistical unit  150 , the ANN model  160  and/or the total prediction unit  170  are, for example, program codes, a circuit, a chip, a circuit board, or a storage device that stores program codes. In this embodiment, the semiconductor process prediction apparatus  100  can perform time alignment of the curve through the aligning unit  130 , and directly analyze the curve through the CNN model  140  to consider the local features. In addition, the semiconductor process prediction apparatus  100  uses the statistical unit  150  to perform statistics on the curve, and analyzes the statistical data through the ANN model  160  to consider the overall features. In other words, the semiconductor process prediction apparatus  100  can consider the local features and the overall features at the same time to improve prediction accuracy. The detail of the operation of the semiconductor process prediction apparatus  100  is described through a flowchart as follows. 
     Please refer to  FIGS. 3 and 4 .  FIG. 3  illustrates a flow chart of a semiconductor process prediction method simultaneously considering the overall features and the local features according to an embodiment.  FIG. 4  illustrates the steps in  FIG. 3 . In step S 110 , the equipment sensing curves S 11  to S 16  are obtained from the database  110 . Each of the equipment sensing curves S 11  to S 16  is respectively composed of the sensing data S 01  to S 06  captured continuously over time. 
     Next, in step S 120 , the filtering unit  120  filters the equipment sensing curves S 11  to S 16  to reduce the co-linearity of the equipment sensing curves S 11  to S 16 . For example, an increase in temperature will cause pressure to rise; a drop in temperature will also cause pressure to drop. Therefore, there is co-linearity between the temperature factor and the pressure factor, and they are essentially the same factor. If the temperature sensing curve and the pressure sensing curve are included in the subsequent analysis, the learning and prediction of the CNN model  140  will be overly focused on the same factor, which will reduce the accuracy. If the temperature sensing curve and the pressure sensing curve are included in the subsequent analysis, the learning and prediction of the CNN model  140  will be overly focused on the same factor, which will reduce the accuracy. Therefore, reducing the co-linearity of equipment sensing curve S 1  through appropriate filtering steps can ensure the accuracy of prediction. 
     Refer to  FIGS. 5 and 6 .  FIG. 5  shows a flowchart of the detailed steps of step S 120 .  FIG. 6  illustrates the steps of  FIG. 5 . The step S 120  includes steps S 121  and S 122 . In the example shown in  FIG. 6 , there are 6 equipment sensing curves S 11  to S 16 . In the step S 121 , the filtering unit  120  classifies the equipment sensing curves S 11  to S 16  into several groups G 1  to G 3  according to a correlation matrix MX. The correlation matrix MX records the correlation coefficients (as shown by the solid double arrow in  FIG. 6 ) between any two of the equipment sensing curves S 11  to S 16 . Those whose relationship coefficient is greater than a predetermined threshold are classified into the same group. As shown in  FIG. 6 , the equipment sensing curves S 11  to S 13  are classified into the group G 1 ; the equipment sensing curves S 14  to S 15  are classified into the group G 2 ; the equipment sensing curve S 16  is classified into the group G 3 . 
     Next, in the step S 122 , the filtering unit  120  selects one from the equipment sensing curves in each of the groups G 1 , G 2 , G 3 . For example the equipment sensing curves S 11 , S 15 , S 16  are respectively selected from the groups G 1 , G 2 , G 3 . Only one equipment sensing curve is selected for each of the groups G 1 , G 2 , G 3 . In the group G 1 , the equipment sensing curve S 11  having the largest correlation coefficient (as shown by the dashed double arrow in  FIG. 6 ) to the prediction target Y 0  is selected. In the group G 2 , the equipment sensing curve S 15  having the largest correlation coefficient (as shown by the dashed double arrow in  FIG. 6 ) to the prediction target Y 0  is selected. As a result, the selected equipment sensing curves S 11 , S 15 , and S 16  have a low correlation and no co-linearity. In addition, the selected equipment sensing curves S 11 , S 15 , and S 16  have higher correlation coefficients to the prediction target Y 0 , and are the most representative. 
     Next, in step S 130  of  FIG. 3 , the aligning unit  130  performs a dynamic time warping (DTW) to align the equipment sensing curves S 11 , S 15 , S 16 . As shown in  FIG. 4 , the equipment sensing curve S 11  will be compared with a template curve S 11 ′, and the equipment sensing curve S 11  can be effectively mapped to the template curve S 11 ′ through alignment points. Similarly, the dynamic time warping (DTW) will also be performed for other equipment sensing curves S 15 , S 16 . 
     Then, in step S 140 , the equipment sensing curves S 11 , S 15 , S 16  which are aligned are inputted into the CNN model  140  to obtain a first prediction result R 1  that considers the local features. The CNN model  140  is, for example, a LeNet model, an AlexNet model, a VGG model, a GoogLeNet model or a ResNet model. 
     The data inputted into the CNN model  140  in this step is a continuous curve, which can take into account the detailed features of the continuous curve, including bursts, drift, oscillations, etc. 
     Please refer to  FIG. 7 , which shows an implementation of the CNN model  140 . The CNN model  140  is, for example, a single-channel model. The single-channel model analyzes only one factor at a time, which can avoid the interference of other factors. 
     Please refer to  FIG. 8 , which shows another implementation of the CNN model  140 . The CNN model  140  is, for example, a multi-channel model. The multi-channel model can analyze multiple factors at the same time and consider the interaction of all factors at the same time. 
     Then, in step S 150  of  FIG. 2 , the statistical unit  150  performs a statistical analysis procedure on the equipment sensing curves S 11 , S 15 , and S 16  to obtain several statistical data ST 1 , ST 5 , ST 6 . Each of the statistical data ST 1 , ST 5 , ST 6  is, for example, mean, standard deviation, median, etc. 
     Next, in step S 160 , the statistical data ST 1 , ST 5 , ST 6  are inputted into the ANN model  160  to obtain a second prediction result R 2  considering the overall features. The ANN model  160  is, for example, a Supervised Learning Network, a Unsupervised Learning Network, a Hybrid Learning Network, an Associate Learning Network, an Optimization Application Network, etc. The data inputted into the ANN model in this step are statistical values which are average, standard deviation, median, etc., which can take into account the overall features of the continuous curve, including overall deviation, overall stability, etc. 
     Then, in step S 170 , the total prediction unit  170  obtains a total prediction result RS according to the first prediction result R 1  and the second prediction result R 2 . In this step, the total prediction unit  170  can obtain the total prediction result RS through a voting procedure. 
     According to the above embodiment, the semiconductor process prediction apparatus  100  and the semiconductor process prediction method can perform the time alignment of the curve, and directly analyze the curve through the CNN model  140  to consider the local features. In addition, the semiconductor process prediction apparatus  100  and the semiconductor process prediction method also perform the data statistics of the curve, and analyze the statistical data through the ANN model  160  to consider the overall features. In other words, the semiconductor process prediction apparatus  100  can consider the local features and the overall features at the same time to improve prediction accuracy. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the disclosed embodiments. It is intended that the specification and examples be considered as exemplary only, with a true scope of the disclosure being indicated by the following claims and their equivalents.