Patent Publication Number: US-2006002606-A1

Title: Pattern recognition with the use of multiple images

Description:
BACKGROUND OF THE INVENTION  
      1. Field of the Invention  
      The present invention relates to the field of semiconductor fabrication and, more particularly to a method and apparatus that uses multiple images in a pattern recognition process used to detect defects in the manufacture of a semiconductor device.  
      2. Description of the Related Art  
      In the semiconductor industry, there is a continuing movement towards higher integration, density and production yield, all without sacrificing throughput or processing speed. The making of today&#39;s integrated circuits (ICs) requires a complex series of fabrication, inspection and testing steps interweaved throughout the entire process to ensure the proper balance between throughput, processing speed and yield. The inspections and tests are designed to detect unwanted variations in the wafers produced, as well as in the equipment and masks used in the fabrication processes. One small defect in either the devices produced or the process itself can render a finished device inoperable.  
      Many of the inspection steps once done manually by skilled operators have been automated. Automated systems increase the process efficiency and reliability as the machines performing the inspection are more consistent than human operators who vary in ability and experience and are subject to fatigue when performing repetitive tasks. The automated systems also provide greater amounts of data regarding the production and equipment, which enables process engineers to both better analyze and control the process.  
      One such automated inspection step is known as pattern recognition or pattern inspection. Many different “patterns” appear on both the wafer and the masks used to produce the ICs. Typical pattern inspection systems are image based, as described, for example, in U.S. Pat. Nos. 4,794,646; 5,057,689; 5,641,960; and 5,659,172. In U.S. Pat. No. 4,794,646, for example, the wafer, or part thereof,.is scanned and a highly resolved picture or image of the pertinent “pattern” is obtained. This pattern image is compared to other pattern images retrieved from the same or other wafers, or is compared to an ideal image stored in the inspection system database. Differences highlighted in this comparison identify possible defects in the IC or wafer.  
       FIG. 1  illustrates the conventional pattern recognition method  10  currently performed by today&#39;s pattern recognition or pattern inspection tools. The method  10  begins when a user places a wafer or other object to be inspected into the inspection apparatus (step  12 ). After scanning the wafer, the apparatus displays a “field of view” containing images from a portion of the scanned wafer (step  14 ). Theses images are to be inspected by the apparatus and thus, are referred to herein as the “inspected images.” The apparatus then displays a single pattern box within the field of view (step  16 ). This pattern box will be used by an operator of the apparatus to select a desired image from the inspected image. The selection is made by placing the pattern box over an image currently displayed in the field of view (step  18 ). The apparatus “learns” the pattern of the selected image and subsequently uses the learned pattern in a pattern recognition analysis to determine if the wafer has any defects (step  20 ). The use of “learns” or “learned” herein refers to the process of obtaining pattern information for the selected image and storing the information for subsequent use in a pattern recognition or critical dimension (CD) analysis. The process of learning a pattern and performing a pattern recognition analysis is well known and can be carried out in any known manner.  
      The method  10 , however, is not without its shortcomings. Referring to  FIG. 2 , exemplary inspected images  50  are illustrated. In this example, the images  50  are contacts formed within a wafer being inspected. Each image  50  contains a top surface  52  of the contact and a bottom surface  54  of the contact. The bottom surfaces  52  are the desired features, which must be inspected for defects. Typical defects include under etching and over etching of the contacts and what is sometimes referred to as “closed contacts,” which are partially etched contacts.  
      As can be seen from  FIG. 2 , the top surfaces  52  are larger and much more prominent than the bottom surfaces  54 . Often times, the inspection apparatus receives such strong signals from the contact top surfaces  52  that it is difficult to detect and properly inspect the contact bottom surfaces  54 , which, as described above, are the desired features. By way of example, it is presumed that during the method  10  ( FIG. 1 ) the operator placed a pattern box  70  over an image comprised of contact images  80 . The selected image and its pattern found within box  70  will be hereinafter referred to as the “pattern to recognize.” Like the images  50  to be inspected, the images  80  of the pattern to be recognized contain top surfaces  82  and bottom surfaces  84 . In this example, the user selected pattern to be recognized contains three contact bottom surfaces  84 , each with their own expected or desired shape. The user selected pattern to be recognized also contains portions of three top surfaces  82  that are much larger than the bottom surfaces  84 . The inspection apparatus learns the pattern to be recognized, which includes large signals associated with the top surfaces  82 , and subsequently uses the learned pattern for comparison with the inspected images, the apparatus detects three matches  60 ,  62 ,  64 .  
      As can be seen from  FIG. 2 , the three declared matches  60 ,  62 ,  64  do not contain bottom surfaces  54  that match the bottom surfaces  84  of the pattern to be recognized. Moreover, some of the matches  60 ,  62 ,  64  contain defects, e.g., under etched bottom surfaces  54   a ,  54   b ,  54   c . Thus, the apparatus has incorrectly detected three matches  60 ,  62 ,  64 , when there should have been zero matches and more importantly, the apparatus failed to detect three defects  54   a ,  54   b ,  54   c . This anomaly occurs since the apparatus receives such strong signals from the much larger and much more prominent top surfaces  52 ,  82 , which substantially match each other. By contrast, the apparatus receives weaker signals from the much smaller and less prominent bottom surfaces  54 ,  84 , which do not match each other and also contain defects. Since there is much more information associated with the top surfaces  52 ,  82  than the bottom surfaces  54 ,  84 , the apparatus detects the matches  60 ,  62 ,  64  based on the top surfaces  52 ,  82 , which results in improper pattern recognition results.  
      Typically, the inspection apparatus will allow a user to set pattern recognition thresholds. These thresholds are designed to reduce or increase the matching percentage required between the pattern to be recognized and the inspected images. Thus, a user may set a matching threshold to 100%, in which case, the apparatus will only declare matches when the inspected images contain patterns that exactly match the pattern to be recognized. This would ensure that defective images  54   a ,  54   b ,  54   c  are not matched to desired and non-defective images. However, due to variations in the manufacturing process, a matching threshold of 100% would most likely lead to no desired matches or too few desired matches than are actually present. The apparatus would not detect all of the proper matches, if it detects any at all (i.e., it is under inclusive). On the other hand, if the matching threshold is set too low, e.g., 50%, then too many matches will occur. These matches will include defective images  54   a ,  54   b ,  54   c  whose patterns are within the matching percentage (i.e., it is over inclusive). Typically, the matching threshold is set to approximately 65% to balance between the over inclusive and under inclusive matching thresholds.  
      Even with a threshold setting of 65%, the conventional pattern recognition process is still unreliable.  FIG. 3  illustrates another set of exemplary inspected images  50 . Four sample images  90 ,  92 ,  94 ,  96  are also illustrated. The sample images  90 ,  92 ,  94 ,  96  each contain three contact images  50 . The first three images  90 ,  92 ,  94  contain top surfaces  52  and bottom surfaces  54 , while the fourth image  96  only contains bottom surfaces  54 . The second and third images  92 , 94  also contain defective bottom surfaces  92   a ,  94   a , respectively. Using the current pattern recognition process, the first three images  90 ,  92 ,  94  would most likely match each other if one of the images  90 ,  92 ,  94  were used as a pattern to be recognized. This would happen even though the bottom surfaces  54  of the images  90 ,  92 ,  94  do not match at all and some of the surfaces  92   a ,  94   a  are defective.  
      It would be desirable to use the fourth sample image  96  as the pattern to be detected. As noted above, the fourth image  96  does not contain any top surfaces  52 . However, the fourth sample image  96 , which has bottom surfaces  54  substantially matching the bottom surfaces  54  of the first sample image  90 , would not match any of the other images  90 ,  92 ,  94  because the other images contain both top and bottom surfaces  52 ,  54 . Thus, even if it were possible to select the fourth sample image  96  as a pattern to be recognized, the pattern recognition analysis would be corrupted by the top surfaces  52 , of the inspected images (e.g., images  90 ,  92 ,  94 ) which are not part of the desired features.  
      Accordingly, there is a desire and need for a pattern recognition process that filters out undesirable features from the object being inspected prior to performing a pattern recognition analysis on the object. There is also a desire and need for a pattern recognition process that allows a user to select multiple desired images of the object being inspected to be used as a pattern to be recognized during a pattern recognition analysis on the object.  
     SUMMARY OF THE INVENTION  
      The present invention provides a pattern recognition technique that substantially filters out undesirable features of the object being inspected prior to performing a pattern recognition analysis on the object.  
      The present invention also provides a pattern recognition technique that allows multiple desired images of the object being inspected to be used as a pattern to be recognized during a pattern recognition analysis on the object.  
      The above and other features and advantages of the invention are achieved by a pattern inspection apparatus and method that uses multiple images in a pattern recognition process used to detect defects in an object being inspected. A user is provided with multiple image selection windows allowing the user to select multiple desired images from the object to form a pattern to be recognized within the object. The multiple desired images will be substantially free from undesired features of the object. Once the multiple desired images are selected, the relationship between them is determined and used to learn the pattern to be recognized. The relationships between the desired images further filters out undesired features. The pattern to be recognized is used in a subsequent pattern recognition analysis. Since the pattern to be recognized includes only desired images and their relationship, undesired features that could corrupt the pattern recognition analysis are not present during the analysis. Thus, the apparatus and method are more accurate than prior inspection tools. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      The foregoing and other advantages and features of the invention will become more apparent from the detailed description of the preferred embodiments of the invention given below with reference to the accompanying drawings in which:  
       FIG. 1  illustrates in flowchart form a conventional pattern recognition process;  
       FIG. 2  illustrates exemplary inspected images and a pattern to be recognized used in the process illustrated in  FIG. 1 ;  
       FIG. 3  illustrates exemplary inspected images used in the process illustrated in  FIG. 1 ;  
       FIG. 4  illustrates a pattern inspection apparatus constructed in accordance with the present invention;  
       FIG. 5  illustrates in flowchart form an exemplary pattern recognition method using multiple images in accordance with a first embodiment of the present invention;  
       FIGS. 6   a  and  6   b  illustrate exemplary images to be detected and their relationships;  
       FIG. 7  illustrates exemplary inspected images and a pattern to be recognized used in the processes illustrated in  FIGS. 5 and 8 ;  
       FIG. 8  illustrates in flowchart form another exemplary pattern recognition method using multiple images in accordance with a second embodiment of the present invention; and  
       FIGS. 9   a - 9   e  illustrate sample field of view images and a resultant pattern to be recognized in accordance with any of the embodiments of the present invention. 
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS  
      In an exemplary implementation, a pattern inspection or defect detection apparatus  100 , as shown in  FIG. 4 , can be constructed having a scanning electron-beam microscope (SEM)  102  used for viewing purposes, as is well known in the art. Although the invention is described herein as used during final wafer inspection of the IC dies, it can readily be appreciated that the invention has application to any other stage of manufacturing, e.g., inspection after the initial photomasking and baking of a wafer known as “development inspection,” where critical dimension measurements or pattern inspections are contemplated.  
      The SEM  102  is provided with an object support structure  108  in the form of a stage or chuck, which may be moveable or stationary. An object  120  under evaluation such as an IC sample die or a wafer containing many IC dies rests on support structure  108 . Under control of computer  110 , the structure  108  may be moveable in two (X-Y) or three (X-Y-Z) dimensions to facilitate the proper viewing of the object  120  (or parts thereof. A deflector  104  and detector  106 , whose operations will be described in detail below, are also provided within the SEM  102  to assist in the viewing of the object  120 . An image processor  112 , together with its accompanying image memory  114  are provided to process the image signals output by the SEM  102  and transform the signals into visual representations or data which can be viewed on a display monitor  116  (e.g., cathode ray tube (CRT)) or used for processing in computer  110 .  
      In operation, the SEM  102  uses a finely focused electron beam directed by deflector  104 , preferably under the control of the computer  110 , to scan the surface of the object  120  resting on the support  108 , typically in two dimensions (X-Y). For the purposes of discussion only, it will be assumed herein that the object  120  under evaluation is a silicon wafer having a plurality of contacts formed therein, such as contacts  50  illustrated in  FIGS. 2, 3  and  8 . The electrons striking the semiconductor surface of the object  120  collide with inner shell electrons of the object atoms causing inelastic collisions of low energy emitting so-called “secondary electrons” which are serially detected by the detector  106 .  
      The detected electron current is output as an image signal to the computer  110  and image processor  112  where an image representative of the surface of the object  120  can be formed based on the image signal. This image may be stored in the image memory  114  and can be viewed on the monitor  116  or otherwise processed by the computer  110 . The high resolution of the image is attributed to the small diameter (e.g., several nanometers) of the electron beam illuminator. The visual contrast achieved in the image originates mostly from variations in the extent of the secondary electron emissions from the topographic features of the surface of the object  120 .  
       FIG. 5  illustrates an exemplary method  200  of using multiple images in a pattern recognition process constructed in accordance with an embodiment of the present invention. The method  200  (with the exception of step  202 , described below) is preferably implemented in software and executed in the computer  110  of the pattern inspection apparatus  100  illustrated in  FIG. 4 . It should be noted that the method  200  of the present invention may also be implemented in a conventional CD-SEM apparatus such as the “IVS-200” made by IVS, Inc., the “Opal 7830si” made by Applied Materials, or the “S-8820/8620” made by Hitachi, by modifying the computer program used by the control computer within the CD-SEM apparatus such that the apparatus implements the operations of the method  200  (described below).  
      Referring to  FIGS. 4 and 5 , the method  200  begins when a user places a wafer or other object  120  to be inspected into the inspection apparatus  100  (step  202 ). It should be appreciated that the object  120  can be a semiconductor wafer at any stage of the manufacturing process or it can be a reticle used to create a mask, such as a phase shifting mask and that the invention should not be limited solely to wafers. However, to remain consistent with the preceding discussion, the object  120  will be a wafer having contacts formed therein. After scanning the wafer, the method  200  displays on the display  116  a field of view containing images from a portion of the scanned wafer (step  204 ). As noted above with reference to  FIG. 1 , theses images are to be inspected by the apparatus  100  and thus, are referred to herein as the “inspected images.” 
      The apparatus  100  then prompts the user to input the number of image selection windows required (step  206 ). Each image selection window is used by the method  200  to select an image, within the inspected images, to be used as part of the pattern to be detected. In the prior art method one large box is used to obtain an image having both desired (e.g., bottom surfaces of contacts) and undesired features (e.g., top surfaces of contacts). The present method  200  uses multiple sizable image selection windows. This way, instead of placing one large box over an image containing both desired and undesired features, the method  200  uses several multiple sizable image selection windows to select images with only desired features. As will become apparent below, this allows the method  200  to substantially filter out undesired features from the pattern to be recognized and thus, the pattern recognition analysis.  
      At step  208 , the user inputs the number of desired windows. By allowing the user to select more than one image selection window, the method  200  of the present invention allows multiple different images to be used in the pattern recognition analysis. It should be noted that these steps differ from the prior art, which only provides the user with one box, and thus, does not allow the user to select multiple images (see step  16  of  FIG. 1 ). Moreover, the prior art does not filter out undesired features.  
      Referring also to  FIGS. 9   a - 9   e , the apparatus  100  displays the appropriate number of image selection windows  300 ,  302 ,  304  within the field of view (step  210 ).  FIGS. 9   a - 9   e  illustrate an example where three image selection windows  300 ,  302 ,  304  have been chosen by a user at step  208 . These windows  300 ,  302 ,  304  are used to select desired images  310   a ,  310   b ,  310   c  from the inspected images. The selection is made by placing each image selection window  300 ,  302 ,  304  over a separate desired image  310   a ,  310   b ,  310   c  that is currently displayed in the field of view (step  212 ). It should be understood that depending upon how the method  200  is implemented, the user may be required to hit a “select” or enter “button” after positioning and sizing the image selection windows  300 .  302 ,  304  to select the images  310   a ,  310   b ,  310   c . By using multiple image selection windows  300 ,  302 ,  304  , the user can size them so that they only select desired features (e.g., bottom surfaces  54 ). That is, it is possible for the user to substantially filter out unwanted features, such as the top surfaces  52  of the contacts, from the pattern to be recognized at this step.  
      At step  214 , the apparatus  100  learns the selected images and the relationship between them. That is, the apparatus  100  obtains and stores information for the selected images. The information can be stored in the memory  114  or other computer readable medium connected to or contained within the apparatus  100 . The spatial relationship between the selected images is also obtained and stored. Learning the spatial relationship between the images will further filter out undesired features. This can best be illustrated by the following example. Referring to  FIG. 6   a , five exemplary images  250 ,  252 ,  254 ,  256 ,  258  are illustrated. These images  250 ,  252 ,  254 ,  256 ,  258  correspond to potential desired images within the inspected images. That is, these images  250 ,  252 ,  254 ,  256 ,  258  could represent the bottom surfaces of various contacts found on the object  120 . Referring to  FIG. 6   b , it can be seen that a pattern  260  to be detected has been selected by a user. The pattern  260  consists of three selected images  250 ,  254 ,  256 , the spatial relationship  266 ,  268  between image  250  and image  254  and the spatial relationship  262 ,  264  between image  250  and image  256 . It is desired that the relationships  262 ,  264 ,  266 ,  268  be two or three dimensional vectors. It should be appreciated, however, that any means for indicating the relationship between the selected images can be used. Another indicia of the relationship between the images could include the distance from an origin or test point on the object  120  (not shown).  FIG. 9   e  illustrates the pattern  310  to be recognized, the spatial relationships  312 ,  314  between images  310   a  and  310   c  and the spatial relationship between image  310   a  and  310   b.    
      Referring again to  FIGS. 4 and 5 , once the images and their relationship are learned, the method  200  continues at step  216 , where the pattern to be recognized is learned and subsequently used a pattern recognition analysis to determine if the object  120  has any defects. Here, unlike the prior art method  10  ( FIG. 1 ), the learned pattern includes the learned information of the multiple images, the relationship between the images and any other information required by the apparatus  100  to perform the pattern recognition analysis. Any required pattern information can be stored in the memory  114  or other computer readable medium connected to or contained within the apparatus  100 . At this point, the process of learning the pattern and performing the pattern recognition analysis can be carried out in any known manner.  
       FIG. 7  illustrates the exemplary inspected images  50  described above with reference to  FIG. 2 . However, a new pattern  310  to be recognized is illustrated. As illustrated in  FIGS. 9   a - 9   e , the pattern  310  was created using the method  200  ( FIG. 6 ) of the present invention. Unlike the pattern contained within box  70  of  FIG. 2 , the pattern  310  created in accordance with the present invention consists solely of images of the bottom surface  384  of the contacts. Each bottom surface  384  was individually selected by respective pattern windows  300 ,  302 ,  304 . As described above, the present invention learns the individual images within these windows  300 ,  302 ,  304  and their relationships ( FIG. 9   e ). By learning the images  384  and their relationship, the learned pattern  310  to be recognized is devoid of any undesired features, particularly the top surfaces  52 ,  82  (illustrated in  FIG. 2 ). As such, the present invention using pattern  310  detects zero matches from the inspected images  50 . This is the correct result, since none of the bottom surfaces  54  within the inspected images match the bottom surfaces  384  of the pattern  310  to be recognized.  
       FIG. 8  illustrates another exemplary method  400  of using multiple images in a pattern recognition process constructed in accordance with another embodiment of the present invention. The method  400  (with the exception of step  402 , described below) is preferably implemented in software and executed in the computer  110  of the pattern inspection apparatus  100  illustrated in  FIG. 4 . As with the method  200  ( FIG. 5 ), the method  400  may also be implemented in a conventional CD-SEM apparatus such as the “IVS-200” made by IVS, Inc., the “Opal 7830si” made by Applied Materials, or the “S-8820/8620” made by Hitachi, by modifying the computer program used by the control computer within the CD-SEM apparatus such that the apparatus implements the operations of the method  400  (described below).  
      Referring to  FIGS. 4 and 8 , the method  400  begins when a user places a wafer or other object  120  to be inspected into the inspection apparatus  100  (step  402 ). After scanning the wafer, the method  400  displays on the display  116  a field of view containing images from a portion of the scanned wafer (step  404 ). As noted above, these images are referred to as the inspected images. The apparatus  100  then displays an image selection window within the field of view (step  406 ). At step  408  the user uses the window to select a desired image from the inspected images. The selection is made by placing the image selection window over an image that is currently displayed in the field of view. It should be understood that depending upon how the method  400  is implemented, the user may be required to hit a “select” or enter “button” after positioning and sizing the window to select the image. At step  410 , the user is prompted to determine if more image selection windows are required.  
      If at step  412 , it is determined that more image selection windows are required, the method  400  continues at step  406  where a new image selection window is displayed on the field of view. This way, the user may select multiple images using multiple image selection windows. By using multiple image selection windows, the user can size these windows so that they only select desired features. That is, it is possible for the user to substantially filter out unwanted features, such as the top surfaces of the contacts, from the pattern to be recognized.  
      If at step  412  it is determined that no more image selection windows are required, the method continues at step  414 . At step  414 , the apparatus  100  learns the selected images and the relationship between them. That is, the apparatus  100  obtains and stores information for the selected images. The information can be stored in the memory  114  or other computer readable medium connected to or contained within the apparatus  100 . The spatial relationship between the selected images is also obtained and stored (as described above with reference to  FIG. 6 ). Once the images and their relationship are learned, the method  400  continues at step  416 , where the pattern to be recognized is learned and subsequently used a pattern recognition analysis to determine if the object  120  has any defects. Here, unlike the prior art method  10  ( FIG. 1 ), the learned pattern includes the learned information of the multiple images, the relationship between the images and any other information required by the apparatus  100  to perform the pattern recognition analysis. Any required pattern information can be stored in the memory  114  or other computer readable medium connected to or contained within the apparatus  100 . At this point, the process of learning the pattern and performing the pattern recognition analysis can be carried out in any known manner.  
      The present invention improves the pattern recognition process by allowing the user to create a pattern to be recognized using multiple images. By select multiple images, the user can substantially filter out unwanted features, such as the top surfaces of contacts formed in a wafer. More importantly, the present invention uses relationships between the selected images to hone in on the exact pattern to be recognized. Thus, the present invention can perform pattern recognition using the typical thresholds currently used in today&#39;s inspection systems, but with substantially better results.  
      It should be appreciated that the learned pattern created by the methods of the present invention can be used in the pattern recognition analysis to detect defects in the object as well as to detect desired (i.e., properly etched) patterns in the object. It should also be appreciated that the learned pattern created by the methods of the present invention can be used in both pattern recognition and critical dimension (CD) analysis processes. Moreover, the methods of the present invention can be used to inspect wafers, reticles or other semiconductor devices requiring pattern inspection/recognition.  
      While the invention has been described in detail in connection with the preferred embodiments known at the time, it should be readily understood that the invention is not limited to such disclosed embodiments. Rather, the invention can be modified to incorporate any number of variations, alterations, substitutions or equivalent arrangements not heretofore described, but which are commensurate with the spirit and scope of the invention. Accordingly, the invention is not to be seen as limited by the foregoing description, but is only limited by the scope of the appended claims.