Patent Publication Number: US-2022237451-A1

Title: Semiconductor process prediction method and semiconductor process prediction apparatus for heterogeneous data

Description:
This application claims the benefit of People&#39;s Republic of China application Serial No. 202110118097.6, 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 for heterogeneous data. 
     BACKGROUND 
     With the development of semiconductor technology, various types of complex semiconductor products are constantly being introduced. In the semiconductor process, the wafer needs to go through tens of thousands of processes to produce the final product. Therefore, researchers use appropriate prediction methods in the semiconductor process to predict the electrical function and yield of the final product, so as to avoid a large number of defective products in the final products. 
     The TOAD simulation system is traditionally used to estimate the electrical function of the final product. However, this method is used in a single line process, under very strict boundary conditions through electromagnetic theory for prediction. With the trend of increasing complexity of the semiconductor process, it has been difficult to produce prediction results with higher accuracy. 
     SUMMARY 
     The disclosure is directed to a semiconductor process prediction method and a semiconductor process prediction apparatus for heterogeneous data. The equipment recipe data, the equipment sensing data, and the metrology inspection data which are heterogeneous data are obtained from the pipe line to obtain highly accurate prediction results. 
     According to one embodiment, a semiconductor process prediction method for heterogeneous data is provided. A plurality of equipment recipe data of a plurality of pieces of equipment are obtained. The equipment recipe data are inputted into a first Neural Network model, to obtain a first prediction result. A plurality of equipment sensing data are obtained. The equipment sensing data are inputted into a second Neural Network model to obtain a second prediction result. A plurality of metrology inspection data are obtained. The equipment recipe data, the equipment sensing data and the metrology inspection data are heterogeneous data. The metrology inspection data are inputted into a third Neural Network model, to obtain a third prediction result. A total prediction result is obtained according to the first prediction result, the second prediction result and the third prediction result. 
     According to another embodiment, a semiconductor process prediction apparatus for heterogeneous data is provided. The semiconductor process prediction apparatus includes a first database, a first Neural Network model, a second database, a second Neural Network model, a third database, a third Neural Network model and a total prediction unit. The first database is configured to storing a plurality of equipment recipe data of a plurality of pieces of equipment. The first Neural Network model is configured to receive the equipment recipe data to obtain a first prediction result. The second database is configured to storing a plurality of equipment sensing data. The second Neural Network model is configured to receive the equipment sensing data to obtain a second prediction result. The third database is configured to storing a plurality of metrology inspection data. The equipment recipe data, the equipment sensing data and the metrology inspection data are heterogeneous data. The third Neural Network model is configured to receive the metrology inspection data to obtain a third prediction result. The total prediction unit is configured to obtain a total prediction result according to the first prediction result, the second prediction result and the third prediction result. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a schematic diagram of a semiconductor process using a pipe line production according to an embodiment. 
         FIG. 2  shows a block diagram of a semiconductor process prediction apparatus according to an embodiment. 
         FIG. 3  shows a flowchart of a semiconductor process prediction method for the heterogeneous data according to an embodiment. 
         FIG. 4  illustrates the steps in  FIG. 3 . 
         FIG. 5  shows a flowchart of the detailed steps of step S 140 . 
         FIG. 6  illustrates the steps of  FIG. 5 . 
     
    
    
     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 schematic diagram of a semiconductor process using a pipe line production according to an embodiment. In the semiconductor process using the pipeline production, several processes executed by several pieces of equipment  910 ,  920 ,  930 , etc., are executed successively. When a wafer  500  enters the equipment  910  for semiconductor process, a wafer  510  also enters the equipment  920  for semiconductor process. The pieces of equipment  910 ,  920 ,  930 , etc., are operated continuously without stopping to maximize the efficiency of the pieces of equipment  910 ,  920 ,  930 , etc. Various information in the process can be transmitted to the semiconductor process prediction apparatus  100  through the network  800  to predict the electrical function and yield of the final product. 
     Please refer to  FIG. 2 , which shows a block diagram of a semiconductor process prediction apparatus  100  according to an embodiment. The semiconductor process prediction apparatus  100  includes a first database  111 , a first Neural Network model  112 , a second database  121 , a second Neural Network model  122 , a third database  131 , a third Neural Network model  132 , a filtering unit  140 , a metrology inspection unit  150  and a total prediction unit  160 . The functions of the components are summarized as follows. The first database  111 , the second database  121  and the third database  131  are used to store various data. The first database  111 , the second database  121  and the third database  131  are, for example, a memory, a hard disk or a cloud storage center. The first Neural Network model  112 , the second Neural Network model  122  and/or the third Neural Network model  132  are used for data prediction. The filtering unit  140  is used for data filtering. The first Neural Network model  112 , the second Neural Network model  122 , the third Neural Network model  132 , the filtering unit  140 , the metrology inspection unit  150  and/or the total prediction unit  160  are, for example, program code, a circuit, a chip, and a circuit board or a storage device that stores the program code. Through the above components, the semiconductor process prediction apparatus  100  can use various heterogeneous data obtained from the pipeline production to obtain highly accurate prediction results. 
     Please refer to  FIGS. 3 and 4 .  FIG. 3  shows a flowchart of a semiconductor process prediction method for the heterogeneous data according to an embodiment.  FIG. 4  illustrates the steps in  FIG. 3 . As shown in  FIG. 4 , the wafer  500  needs to go through the semiconductor processes P 1 , P 2 , P 3 , and so on, to obtain the final product  590 . When the wafer  500  is performed the semiconductor process P 1 , the wafer  510  is performed the semiconductor process P 2 , the wafer  520  is performed semiconductor process P 3 , and so on. 
     Each piece of equipment  910 ,  920 ,  930 , etc., has set equipment recipe data ED 1 , ED 2 , ED 3 , etc. The equipment recipe data ED 1 , ED 2 , ED 3 , etc., are, for example, pressure setting value, valve opening time, heating time, etc. The equipment recipe data ED 1 , ED 2 , ED 3 , etc., are discrete numerical data. The setting items of the pieces of equipment  910 ,  920 ,  930 , etc., are different, and the contents of equipment recipe data ED 1 , ED 2 , ED 3 , etc., are also different. 
     Each piece of equipment  910 ,  920 ,  930 , etc., will also obtain equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., through sensors  911 ,  921 ,  931 , etc. The equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., are for example, pressure, gas concentration, temperature, etc. The equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., are continuous numerical data. The equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., are continuously collected in data pool  700 . 
     When the semiconductor processes P 1 , P 2 , P 3 , etc., respectively output the wafers  510 ,  520 ,  530  etc., the physical measurement will also be performed to obtain metrology inspection data MI 1 , MI 2 , MI 3 , etc. The metrology inspection data MI 1 , MI 2 , MI 3 , etc., are, for example, as thickness, wire width, perforation distance, etc. The metrology inspection data MI 1 , MI 2 , MI 3 , etc., are discrete numerical data. 
     The above-mentioned equipment recipe data ED 1 , ED 2 , ED 3 , etc., the equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., and the metrology inspection data MI 1 , MI 2 , MI 3 , etc., are heterogeneous data. These data have their own advantages in process prediction, and this disclosure combines the advantages of these heterogeneous data to improve prediction accuracy. 
     First, in step S 111 , the equipment recipe data ED 1 , ED 2 , ED 3 , etc., of the pieces of equipment  910 ,  920 ,  930 , etc., are obtained from the first database  111 . 
     Next, in step S 112 , the equipment recipe data ED 1 , ED 2 , ED 3 , etc., are inputted into the first Neural Network model  112  to obtain a first prediction result R 1 . The first Neural Network model  112  is, for example, Supervised Learning Network, Unsupervised Learning Network, Hybrid Learning Network, Associate Learning Network, Optimization Application Network, etc. The first Neural Network model  112  receives the equipment recipe data ED 1 , ED 2 , ED 3 , etc., from the combination of the equipment recipe data ED 1 , ED 2 , ED 3 , etc., the electrical function and yield of the final product  590  can be predicted. 
     Then, in step S 121 , the equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., of the pieces of equipment  910 ,  920 ,  930 , etc., are obtained from the second database  121 . 
     Next, in step S 140 , the filtering unit  140  filters out part of the equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., according to the correlations among the equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc. Since the equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., have a huge amount of data and has co-linearity, this step can be used to filter out representative content to eliminate the effect of co-linearity. 
     Please refer to  FIGS. 5 and 6 .  FIG. 5  shows a flowchart of the detailed steps of step S 140 .  FIG. 6  illustrates the steps of  FIG. 5 . The step S 140  includes steps S 141  and S 142 . In the example shown in  FIG. 6 , the equipment sensing data FDC 1  has 6 sensing factors X 1  to X 6 , which are temperature, pressure, gas concentration, etc. In step S 141 , the filtering unit  140  classifies the sensing factors X 1  to X 6  into a number of 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 sensing factors X 1  to X 6 . Those whose relationship coefficient is greater than a predetermined threshold are classified into the same group. As shown in  FIG. 6 , the sensing factors X 1  to X 3  are classified as the group G 1 ; the sensing factors X 4  to X 5  are classified as the group G 2 ; the sensing factor X 6  is classified as the group G 3 . 
     Next, in step S 142 , the filtering unit  140  selects one from the sensing factors in each of the groups G 1 , G 2 , G 3 . That is, the sensing factors X 1 , X 5 , X 6  are selected from the groups G 1 , G 2 , G 3  respectively. Only one sensing factor is selected from each of the groups G 1 , G 2 , G 3 . In the group G 1 , the sensing factor X 1  having the largest correlation coefficient (as shown by the dashed double arrow in  FIG. 6 ) to prediction target Y 0  is selected. In the group G 2 , the sensing factor X 5  having the largest correlation coefficient (as shown by the dashed double arrow in  FIG. 6 ) to prediction target Y 0  is selected. As a result, the selected sensing factors X 1 , X 5 , and X 6  have a low correlation and no co-linearity. In addition, the selected sensing factors X 1 , X 5 , X 6  have higher correlation coefficients relative to the prediction target Y 0 , and are the most representative. 
     Each of the equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., can be reduced to representative content by performing the above-mentioned filtering step. 
     Afterwards, in the step S 122  of  FIG. 3 , the equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., are inputted into the second Neural Network model  122  to obtain a second prediction result R 2 . The second Neural Network model  122  is, for example, Supervised Learning Network, Unsupervised Learning Network, Hybrid Learning Network, Associate Learning Network, Optimization Application Network, etc. 
     Next, in step S 131 , the metrology inspection data MI 1 , MI 3 , MI 4 , etc., are obtained from the third database  131 . As shown in  FIG. 4 , the metrology inspection data MI 1  includes a plurality of actual measurement data MI 11  and a plurality of virtual measurement data MI 12 . The large number of the wafers  510  makes it difficult to perform the physical measurements one by one. Therefore, the measuring unit  913  can perform the physical measurements on a small part of the wafers  510 , and obtain actual measurement data MI 11 . The metrology inspection unit  150  can perform a simulation procedure based on the actual measurement data MI 11  and the equipment sensing data FDC 1  (and/or the equipment recipe data ED 1 ) to obtain the virtual measurement data MI 12 . Similarly, the measuring unit  923 ,  933  can perform the physical measurements on a small part of the wafers  520 ,  530 , and obtain actual measurement data MI 21 , MI 31 . The metrology inspection unit  150  can perform simulation procedures based on the actual measurement data MI 21 , MI 31  and the equipment sensing data FDC 2 , FDC 3  (and/or the equipment recipe data ED 2 , ED 3 ) to obtain virtual measurement data MI 22 , MI 32 , and so on. 
     Afterwards, in step S 132 , the metrology inspection data MI 1 , MI 2 , MI 3 , etc., are inputted into the third Neural Network model  132  to obtain a third prediction result R 3 . The third Neural Network model  132  is, for example, Supervised Learning Network, Unsupervised Learning Network, Hybrid Learning Network, Associate Learning Network, Optimization Application Network, etc. 
     Next, in step S 150 , the total prediction unit  160  obtains a total prediction result RS according to the first prediction result R 1 , the second prediction result R 2 , and the third prediction result R 3 . In this step, the total prediction unit  160  can obtain the total prediction result RS through a voting procedure. 
     According to the above embodiments, the semiconductor process prediction apparatus  100  and the semiconductor process prediction method can utilize the equipment recipe data ED 1 , ED 2 , ED 3 , etc., the equipment sensing data FDC 1 , FDC 2 , FDC 3 , etc., the metrology inspection data MI 1 , MI 2 , MI 3 , etc. obtained from pipeline production, which are heterogeneous data to obtain highly accurate prediction results. 
     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.