Patent Publication Number: US-11645463-B2

Title: Natural language processing system, natural language processing method and non-transitory computer readable medium

Description:
BACKGROUND 
     Field of Invention 
     The present invention relates to natural language processing systems and methods. 
     DESCRIPTION OF RELATED ART 
     A semiconductor material has an electrical conductivity value falling between that of a conductor and an insulator. Normally, semiconductor equipment can be used to manufacture semiconductor devices. 
     However, the semiconductor equipment often fails due to various problems, and engineers usually waste a lot of time to fix the semiconductor equipment by their undependable experiences. 
     SUMMARY 
     An embodiment of the present disclosure is related to a natural language processing system includes a storage device and a processor. The storage device is configured to preload records of failure histories of semiconductor equipment, and the records of the failure histories of the semiconductor equipment include natural language. The processor is electrically connected to the storage device and is configured to perform a natural language process on the records of the failure histories of the semiconductor equipment to generate an abnormal model classification table. 
     Another embodiment of the present disclosure is related to. The natural language processing method includes steps of: preloading records of failure histories of semiconductor equipment, where the records of the failure histories of the semiconductor equipment comprise natural language; performing a natural language process on the records of the failure histories of the semiconductor equipment to generate an abnormal model classification table. 
     Yet another embodiment of the present disclosure is related to a non-transitory computer readable medium to store a plurality of instructions for commanding a server to execute a natural language processing method. The natural language processing method includes steps of: preloading records of failure histories of semiconductor equipment, where the records of the failure histories of the semiconductor equipment comprise natural language; performing a natural language process on the records of the failure histories of the semiconductor equipment to generate an abnormal model classification table. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention can be more fully understood by reading the following detailed description of the embodiment, with reference made to the accompanying drawings as follows: 
         FIG.  1    is a block diagram of a natural language processing system according to some embodiments of the present disclosure; and 
         FIG.  2    is a flow chart of a natural language processing method according to some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to the present embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the description to refer to the same or like parts. 
     As used in the description herein and throughout the claims that follow, the meaning of “a”, “an”, and “the” includes reference to the plural unless the context clearly dictates otherwise. Also, as used in the description herein and throughout the claims that follow, the terms “comprise or comprising”, “include or including”, “have or having”, “contain or containing” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. As used in the description herein and throughout the claims that follow, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise. 
     It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the embodiments. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. 
     It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present. 
     Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. 
       FIG.  1    is a block diagram of a natural language processing system  100  according to some embodiments of the present disclosure. As shown in  FIG.  1   , the natural language processing system  100  includes a storage device  110 , a processor  120  and a communication device  130 . For example, the natural language processing system  100  may be a server or the like, in which the storage device  110  may be storage hardware, such as a hard disk drive (HDD) or a solid-state drive (SSD), the processor  120  may be a central processing unit (CPU), and the communication device  130  may be wired or wireless communication circuit. 
     In structure, the communication device  130  is electrically connected to the processor  120 , and the processor  120  is electrically connected to the storage device  110 . The communication device  130  can communicate with a computer  170  and/or semiconductor equipment  190 . The computer  170  has a display screen  172 . For example, the display screen  172  may be a LCD or the like. 
     In practice, the semiconductor equipment  190  can be used to manufacture semiconductor devices. Every time the semiconductor equipment  190  fails, an engineer fixes the semiconductor equipment  190 ; accordingly, the engineer uses natural language to record a record of failure history of the semiconductor equipment  190  through the computer  170 , the semiconductor equipment  190  or another device. Therefore, the records of the failure histories of the semiconductor equipment include natural language. 
     For example, the record of the failure history of the semiconductor equipment  190  is described in below. 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                 EQP_LIST 
                 ISSUE 
                 EVENT 
                 TEXTCONTENT 
               
               
                   
               
             
            
               
                 FCV103N 
                 UV 
                 CHA-side1 
                 1. When tool run P94911700E 
               
               
                   
                 Lamp 
                 lamp B UV 
                 #23~24 occur tool alarm 
               
               
                   
                 fault 
                 lamp fault 
                 2. Check alarm message: side1 
               
               
                   
                   
                   
                 lamp B UV lamp fault: phase 
               
               
                   
                   
                   
                 control at limit 
               
               
                   
                   
                   
                 3. Reset and standby lamp ok 
               
               
                   
                   
                   
                 4. Re-curing #25~01 OK 
               
               
                   
                   
                   
                 5. Pass TA dummy OK and tool 
               
               
                   
                   
                   
                 keep run 
               
               
                   
                   
                   
                 Down time: 12/5 10::00~11:10 
               
               
                   
               
            
           
         
       
     
     In use, the communication device  130  receives the records of the failure histories of the semiconductor equipment  190  from the computer  170 , the semiconductor equipment  190  or another device. The storage device  110  preloads the records of failure histories of semiconductor equipment  190 . The processor  120  performs a natural language process on the records of the failure histories of the semiconductor equipment  190  to generate an abnormal model classification table. Then, the communication device  130  transmits the abnormal model classification table to the computer  170 , so that the display screen  172  of the computer  170  can display the abnormal model classification table, where the abnormal model classification table classifies topics of various problems of the semiconductor equipment  190  and provides corresponding solutions. Thus, the abnormal model classification table can help the engineer to quickly find the way to fix the semiconductor equipment  190  whenever the semiconductor equipment  190  fails again. 
     In some embodiments, the storage device  110  stores a dictionary file, and the processor  120  is according to the dictionary file to find valid words in the records of the failure histories of the semiconductor equipment  190 , so as to generate a record-word matrix that indicates word frequencies of valid words in each of the records of the failure histories of the semiconductor equipment. For example, the dictionary file defines invalid words, and therefore the remaining words in the records are the valid words. 
     In some embodiments, the storage device  110  stores a latent Dirichlet allocation (LDA) model, and the processor  120  is based on the LDA model to analyze the record-word matrix so as to generate a topic-word matrix and a record-topic matrix. 
     In some embodiments, the topic-word matrix indicates a respective ratio of each of the valid words to all of the valid words in each of different topics. For example, a portion of the topic-word matrix is described in below, in which t_1, t_2 and t_3 represent different topics, and asi_pca, psg_psg, and pressure_chart are different valid words. 
     
       
         
           
               
               
               
               
               
             
               
                   
                   
               
               
                   
                 asi_pca 
                 psg_psg 
                 pressure_chart 
                 . . . 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 t_1 
                 0.01848976 
                 0.01848967 
                 0.03143472 
                 . . . 
               
               
                 t_2 
                 0.02101414 
                 0.02101414 
                 0.03572662 
                 . . . 
               
               
                 t_3 
                 0.01974031 
                 0.01974032 
                 0.03356095 
                 . . . 
               
               
                 . 
                 . 
                 . 
                 . 
                 . 
               
               
                 . 
                 . 
                 . 
                 . 
                 . 
               
               
                 . 
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                 . 
                 . 
               
               
                   
               
            
           
         
       
     
     In some embodiments, the record-topic matrix indicates a respective relevance between each of the different topics and each of the records of the failure histories of the semiconductor equipment  190 . For example, a portion of the record-topic matrix is described in below, in which t_1, t_2 and t_3 represent different topics, and numbers 3, 6, and 33 represent corresponding records respectively. 
     
       
         
           
               
               
               
               
               
             
               
                   
                   
               
               
                   
                 t_1 
                 t_2 
                 t_3 
                 . . . 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 3 
                 0.244000000 
                 0.004000000 
                 0.004000000 
                 . . . 
               
               
                 6 
                 0.011111111 
                 0.455555556 
                 0.011111111 
                 . . . 
               
               
                 33 
                 0.011111111 
                 0.011111111 
                 0.011111111 
                 . . . 
               
               
                 . 
                 . 
                 . 
                 . 
                 . 
               
               
                 . 
                 . 
                 . 
                 . 
                 . 
               
               
                 . 
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                 . 
                 . 
                 . 
               
               
                   
               
            
           
         
       
     
     In some embodiments, the processor  120  is based on the topic-word matrix and the record-topic matrix to generate the abnormal model classification table that indicates a respective prevalence of each of the different topics and at least one relevant term of each of the different topics, where the relevant term is selected from the valid words; for instance, the most number of words in the valid words are selected as the relevant terms. For example, a portion of the abnormal model classification table is described in below, in which top terms represent the at least one relevant term of each topic. 
     
       
         
           
               
               
               
               
               
             
               
                   
               
               
                 topic 
                 label 
                 coherence 
                 prevalence 
                 top terms 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 t_4 
                 side_rf 
                 0.215 
                 14.478 
                 rf 
                 side 
                 impedance 
                 . . . 
               
               
                 t_6 
                 chart_step 
                 0.101 
                 13.769 
                 chart 
                 flow 
                 teos 
                 . . . 
               
               
                 t_3 
                 pca_chart 
                 0.134 
                 12.886 
                 chart 
                 pca 
                 pca_chart 
                 . . . 
               
               
                 t_7 
                 pca_chart 
                 0.079 
                 12.802 
                 chart 
                 hx 
                 suspect 
                 . . . 
               
               
                 t_5 
                 heater_temp 
                 0.348 
                 10.990 
                 temp 
                 heater 
                 heater_temp 
                 . . . 
               
               
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
               
               
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     For a more complete understanding of a natural language processing method performed by the natural language processing system  100 , referring  FIGS.  1 - 2   ,  FIG.  2    is a flow chart of the natural language processing method  200  according to an embodiment of the present disclosure. As shown in  FIG.  2   , the natural language processing method  200  includes operations S 201 , S 203 , S 205  and S 207 . However, as could be appreciated by persons having ordinary skill in the art, for the steps described in the present embodiment, the sequence in which these steps is performed, unless explicitly stated otherwise, can be altered depending on actual needs; in certain cases, all or some of these steps can be performed concurrently. 
     The natural language processing method  200  may take the form of a computer program product on a computer-readable storage medium having computer-readable instructions embodied in the medium. Any suitable storage medium may be used including non-volatile memory such as read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), and electrically erasable programmable read only memory (EEPROM) devices; volatile memory such as SRAM, DRAM, and DDR-RAM; optical storage devices such as CD-ROMs and DVD-ROMs; and magnetic storage devices such as hard disk drives and floppy disk drives. 
     In practice, every time the semiconductor equipment  190  fails, the engineer fixes the semiconductor equipment  190 ; accordingly, the engineer uses natural language to record a record of failure history of the semiconductor equipment  190  through the computer  170 , the semiconductor equipment  190  or another device. In operation S 201 , the records of the failure histories of the semiconductor equipment  190  are received from the computer  170 , the semiconductor equipment  190  or another device, where the records of the failure histories of the semiconductor equipment include natural language. 
     Then, in operation S 203 , the records of failure histories of semiconductor equipment  190  are preloaded. In operation S 205 , a natural language process is performed on the records of the failure histories of the semiconductor equipment  190  to generate an abnormal model classification table. In operation S 207 , the abnormal model classification table is transmitted to the computer  170 , so that the display screen  172  of the computer  170  displays the abnormal model classification table, where the abnormal model classification table classifies topics of various problems of the semiconductor equipment  190  and provides corresponding solutions. Thus, the abnormal model classification table can help the engineer to quickly find the way to fix the semiconductor equipment  190  whenever the semiconductor equipment  190  fails again. 
     In some embodiments, in operation S 205 , valid words are found in the records of the failure histories of the semiconductor equipment  190  according to a dictionary file, so as to generate a record-word matrix that indicates word frequencies of valid words in each of the records of the failure histories of the semiconductor equipment  190 . 
     In some embodiments, in operation S 205 , the record-word matrix is analyzed based on a LDA model, so as to generate a topic-word matrix and a record-topic matrix. 
     In some embodiments, the topic-word matrix indicates a respective ratio of each of the valid words to all of the valid words in each of different topics, and the record-topic matrix indicates a respective relevance between each of the different topics and each of the records of the failure histories of the semiconductor equipment  190 . 
     In some embodiments, the abnormal model classification table based on the topic-word matrix and the record-topic matrix is generated, where the abnormal model classification table indicates a respective prevalence of each of the different topics and at least one relevant term of each of the different topics, and the at least one relevant term is selected from the valid words. 
     In view of the above, the natural language processing system  100  and the natural language processing method  200  can classifies topics of various problems of the semiconductor equipment  190  and provides corresponding solutions, thereby helping the engineer to quickly find the way to fix the semiconductor equipment  190  whenever the semiconductor equipment  190  fails again. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the structure of the present invention without departing from the scope or spirit of the invention. In view of the foregoing, it is intended that the present invention cover modifications and variations of this invention provided they fall within the scope of the following claims.