Neural network for determining the endpoint in a process

Abstract

There is provided a system and method for pattern recognition of an endpoint curve for a dry etch process. The system trains a neural network with a group of training curves corresponding to the dry etch process, wherein the training curves contain normal and abnormal features. The system receives an endpoint curve at the neural network representing a dry etch process and detects an abnormal feature in the endpoint curve.

What is claimed is:  
     
         1 . A method for pattern recognition of an endpoint curve for a process comprising: 
 training a neural network with a group of training curves corresponding to the process, wherein the training curves contain normal and abnormal features;    receiving an endpoint curve at the neural network representing a process; and    detecting an abnormal feature in the endpoint curve.    
     
     
         2 . The method as set forth in  claim 1 , wherein the neural network uses a back-propagation learning rule.  
     
     
         3 . The method as set forth in  claim 1 , further comprising collecting the group of training curves.  
     
     
         4 . The method as set forth in  claim 1 , further comprising identifying a normal feature from the group of training curves.  
     
     
         5 . The method as set forth in  claim 1 , further comprising collecting an analog signal of the endpoint curve.  
     
     
         6 . The method as set forth in  claim 5 , further comprising transforming the analog signal to a digital signal.  
     
     
         7 . A system for pattern recognition of an endpoint curve for a process comprising: 
 means for training a neural network to learn a feature corresponding to the process;    means for receiving the endpoint curve; and    means for detecting an abnormal feature in the endpoint curve.    
     
     
         8 . The system as set forth in  claim 7 , further comprising a group of training curves corresponding to the process.  
     
     
         9 . The system as set forth in  claim 8 , further comprising means for collecting the group of training curves.  
     
     
         10 . The system as set forth in  claim 7 , further comprising means for collecting an analog signal of the endpoint curve.  
     
     
         11 . The system as set forth in  claim 10 , further comprising means for transforming the analog signal to a digital signal.  
     
     
         12 . The system as set forth in  claim 7 , further comprising means for comparing the endpoint curve with the feature.  
     
     
         13 . The system as set forth in  claim 7 , wherein the means for training the neural network uses a back-propagation learning rule.  
     
     
         14 . A computer readable medium including instructions for performing a method, when executed by a processor, for pattern recognition of an endpoint curve for a process comprising: 
 training a neural network with a group of training curves corresponding to the process, wherein the training curves contain normal and abnormal features;    receiving an endpoint curve at the neural network representing a process; and    detecting an abnormal feature in the endpoint curve.    
     
     
         15 . The computer readable medium as set forth in  claim 14 , wherein the neural network uses a back-propagation learning rule.  
     
     
         16 . The computer readable medium as set forth in  claim 14 , further comprising collecting the group of training curves.  
     
     
         17 . The computer readable medium as set forth in  claim 14 , further comprising identifying a normal feature from the group of training curves.  
     
     
         18 . The computer readable medium as set forth in  claim 14 , further comprising collecting an analog signal of the endpoint curve.  
     
     
         19 . The computer readable medium as set forth in  claim 18 , further comprising transforming the analog signal to a digital signal.  
     
     
         20 . A method for pattern recognition of an endpoint curve for a process comprising: 
 setting up a neural network;    collecting a group of analog training curves;    converting the group of analog training curves to a group of digital training curves;    training the neural network with the group of digital training curves;    collecting a group of analog experimental curves;    converting the group of analog training curves to a group of digital training curves;    inputting the group of digital experimental curves into the neural network; and    determining by the neural network the endpoint of the group of digital training curves.    
     
     
         21 . The method as set forth in  claim 20 , wherein the group of analog training curves includes at least one of a normal or abnormal curve.  
     
     
         22 . The method as set forth in  claim 20 , wherein the group of analog experimental training curves includes at least one of a normal or abnormal curve.  
     
     
         23 . The method as set forth in  claim 20 , wherein the neural network uses a back-propagation learning rule.  
     
     
         24 . The method as set forth in  claim 20 , wherein training the neural network comprises: 
 classifying the group of digital training curves;    dividing the group of digital training curves into a first and second set of digital training curves;    training the neural network with the first set of digital training curves;    testing the neural network;    inputting the second set of digital training curves into the neural network; and    determining whether the neural network is trained.    
     
     
         25 . The method as set forth in  claim 24 , wherein training the neural network further comprises: 
 adding a new classification to the group of digital training curves.    
     
     
         26 . A system for pattern recognition of an endpoint curve for a process comprising: 
 a process chamber for etching a semiconductor substrate;    a detector for detecting radiation from the process chamber during the etching of the semiconductor substrate and producing a radiation signal;    a recorder coupled to the detector for recording the radiation signal;    an analog to digital converter coupled to the recorder for converting the radiation signal; and    a pattern recognition apparatus coupled to the analog to digital converter for detecting the endpoint curve of the converted radiation signal.    
     
     
         27 . The system as set forth in  claim 26 , wherein the pattern recognition apparatus comprises a neural network using a back-propagation learning rule.