Patent Publication Number: US-2022214287-A1

Title: Automatic defect classification

Description:
CROSS REFERENCE 
     This application claims priority from U.S. provisional patent Ser. No. 62/519,516 filing date Jun. 14, 2017. 
    
    
     BACKGROUND OF THE INVENTION 
     For every industry a high yield is a pivot for maintaining competitiveness, not least in the highly developed microelectronics industry. Every yield fraction can have a dramatic influence on the ability to earn or lose during the production phase. 
     In order to maintain and constantly improve its yield, a factory would have to invest in inspection for both QA (quality assurance) purposes (which die is good and which ones are bad) and process control. 
     An efficient process control system could recognize an abnormality in an early stage and so prevent a deterioration and enable the engineering staff to perform a corrective action. 
     The quicker the corrective action is, the less loss would it be for the factory. 
     SUMMARY 
     There may be provided a method for automatic defect classification, the method may include (i) acquiring, by a first camera, at least one first image of at least one area of an object; (ii) processing the at least one first image to detect a group of suspected defects within the at least one area; (iii) performing a first classification process for initially classifying the group of suspected defects; (iii) determining whether a first subgroup of the suspected defects requires additional information from a second camera for a completion of a classification; (iv) when determining that the first subgroup of the suspected defects requires additional information from the second camera then: (a) acquiring second images, by the second camera, of the first subgroup of the suspected defects; and (b) performing a second classification process for classifying the first subgroup of suspected defects. 
    
    
     
       BRIEF DESCRIPTION OF THE INVENTION 
       The present invention will be understood and appreciated more fully from the following detailed description taken in conjunction with the drawings: 
         FIG. 1  illustrates an example of a method; 
         FIG. 2  illustrates an example of a system; 
         FIG. 3  illustrates an example of a system; 
         FIG. 4  illustrates an example of a system; 
         FIG. 5  illustrates an example of a generation of a reference image; 
         FIG. 6  illustrates an example of a defect detection process; 
         FIG. 7  illustrates a top view of a bump and its surroundings, and a gray level distribution of pixels along an imaginary line; 
         FIG. 8  illustrates an image of a bump and its surroundings and of various processing steps for extracting parameters of the bump; 
         FIG. 9  illustrates examples of different recipe rules; and 
         FIG. 10  illustrates various determination rules. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Because the apparatus implementing the present invention is, for the most part, composed of optical components and circuits known to those skilled in the art, circuit details will not be explained in any greater extent than that considered necessary as illustrated above, for the understanding and appreciation of the underlying concepts of the present invention and in order not to obfuscate or distract from the teachings of the present invention. 
     In the following specification, the invention will be described with reference to specific examples of embodiments of the invention. It will, however, be evident that various modifications and changes may be made therein without departing from the broader spirit and scope of the invention as set forth in the appended claims. 
     Inspection machines can check for a variety of defects and abnormalities. However, in order to have a clear cause and effect, a classification of the abnormalities (defects) is required. 
     For example, a defect can be caused by a defective CMP process, an etching or contamination issues. A defect classification process can distinguish between the different defects. 
     While a manual classification process can be performed, it is both slow and costly. 
     An Automatic Defect Classification (ADC) can work cheaply and quickly. In the last few years, some major advancements in the machine learning technology, enabled to achieve very good results using ADC systems. 
     However, in many cases, the data that is used during scanning is insufficient for the clear separation between different kinds of defects. For instance, when a color difference appears—if it is red—it may be a copper layer, however if it is black it may be just a foreign material. 
     But, getting this additional data after the detection process, costs time and resources. 
     There may be provided a method that may include:
         a. Loading the wafer on the chuck.   b. Acquiring by the first camera (such as a high performance scanning camera—may be a black and white camera) images of the wafer and send it to the computer for processing. This may include scanning the wafer with a main acquisition module (scanner module) (continuous, usually with superior rate but reduces sensitivity with respect to the second camera).   c. Using a predefined set of detection processes, defect are located and stored in the computer.   d. A first classification process assigns an initial class to the defect.   e. After the end of the inspection process, the defects are loaded again (are fed to the computer—especially to a decision circuit of the computer). For each defect, based on a predefined rule (or based on any other decision process), the system automatically determines if an additional data is required.   f. If so, the defect location is brought within the field of view of a second camera (a verification camera) such as the color camera (can be a 3d profiler, an IR camera, a NIR camera, or a high magnification microscope) and the additional data about the defect is acquired.   g. Based on the full data, a final classification is performed for the defects and a final class is attached to the defect.   h. The data is then stored (reported if necessary) and the wafer can be unloaded from the system.       

     This process enables a fabrication plant (FAB) to enjoy the high speed of the inspection system combined with the high resolution and detailed information of the verification system. The selective application of the verification system (only to those defects that require additional data) speeds up the entire process. 
     The combination of the two (or more) classification phases enables, while using the same loading/unloading mechanism, to optimize the time of the wafer processing and end up with a final comprehensive data of the wafer status. 
     This in turn enables a quick and effective measures to be performed in case of a fault in the previous steps. 
       FIG. 1  illustrates a method  900  for automatic defect classification, the method may include: 
     Step  904  of loading a wafer. The wafer may be loaded on a chuck. 
     Step  904  may be followed by step  905  of maintaining the wafer on the chuck until the completion of steps  908 ,  910 ,  912 ,  914  and  916 . 
     Step  905  is followed by step  907  of unloading the wafer. 
     The method may also include step  906  of acquiring, by a first camera, at least one first image of at least one area of an object. The at least one area may span along the entire object or may include only one or more parts of the object. 
     Step  906  may be followed by step  908  of processing the at least one first image to detect a group of suspected defects within the at least one area. 
     Step  908  may be followed by step  910  of performing a first classification process for initially classifying the group of suspected defects. 
     Step  910  may be followed by step  912  of determining whether a first subgroup of the suspected defects requires additional information from a second camera for a completion of a classification. 
     Step  912  may be executed without human intervention. 
     The determining may be responsive to at least one of the following:
         a. A difference between image acquisition parameters of the first camera and second camera. These image acquisition parameters may include frequency (visible light versus IR, NIR, color versus black and white, narrowband versus wide band), resolution, throughput, and the like.   b. A criticality and/or importance of the suspected defects. More critical defects (such as killer defects that may render a circuit inoperable) may be examined with more resources.   c. An accuracy of the first classification process.   d. A difference between a reliability of the first classification process and a reliability, of the second classification process. The reliability may be reflected by a success rate, a false alarm rate, false positive rate, and the like.   e. A type of the defect. The type of the defect may be taken in account in combination of all other factors mentioned above. For example—if a certain defect can be detected in a more reliable manner when using IR radiation—then the reliability of a classification process that uses an IR camera is higher than a classification process that uses a black and white camera.       

     Step  912  may include calculating a cost function that will take into account one of more benefits of the execution of step  914  (for example—more reliable classification process), one or more merits of the execution of step  914  (for example—higher time consumption), and may also take into account parameters related to limitations (such as resolution) of the first camera, throughput considerations, and the like. 
     If the answer of step  912  is negative, then step  912  is followed by step  916 . 
     If the answer of step  912  is positive, then step  912  is followed by step  914 . 
     Step  914  includes:
         a. Acquiring second images, by the second camera, of the first subgroup of the suspected defects.   b. Performing a second classification process for classifying the first subgroup of suspected defects.       

     Step  914  may include acquiring the second images without acquiring images of suspected defects that do not belong to the first subgroup of suspected defects. 
     Step  914  may be followed by step  916  by providing classification results. This may include storing the classification results, communicating the classification results to another system, and the like. 
     Steps  908 ,  910 ,  912  and the second classification process may be executed by the same computer, by the same processor, by different computers, by different processors, by the inspection and verification system, by one or more computers located outside the inspection and verification systems. 
     The Combined ADC 
     The combined ADC system generates an effective ADC process while optimizing the resources to minimize the analysis time of the wafer. 
     The combined system may include:
         a. A wafer handling system for loading/unloading a wafer.   b. An inspection module for full/partial wafer inspection. The inspection module includes a first camera (such as an inspection camera).   c. An additional data acquisition component. For example—a Color Camera, infrared (IR) Camera or 3D profiler.   d. A computer (also referred to as processor) for data analysis.   e. Data storage for results.       

     The throughput of the first camera may exceed a throughput of the second camera. For example—by a factor of 2, 3, 4, 5, 6 and even more. 
     The resolution of the first camera may be coarser than resolution of the second camera. For example—by a factor of 2, 3, 4, 5, 6 and even more. 
     The first camera may differ from the second camera by one or more image acquisition parameter—such as frequency (visible light versus IR, NIR, color versus black and white, narrowband versus wide band), resolution, throughput, dark field versus bright field, and the like. 
     The first camera may be a black and white camera and the second camera is selected out of an infrared camera, a near infrared camera and a three dimension profiler. 
       FIG. 2  illustrates an object (such as wafer  90 ) and a system  101  that includes:
         a. Storage unit  190 .   b. Computer  180 .   c. Color illumination  173  for illuminating the object with radiation that may be reflected and/or scattered and detected by the color camera  172 —the term “color” in relation to the illumination merely links the illumination to the color camera.   d. Color camera  172 .   e. Color optics  171  that precede color camera  172 .   f. Inspection illumination  163  for illuminating the object with radiation that may be reflected and/or scattered and detected by the inspection camera  162 .   g. Inspection camera  162 .   h. Inspection optics  161  that precede inspection camera  162 .   i. Chuck  150 .   j. Stage  140  for moving the chuck (and hence moving the object).   k. Wafer handling system  130 . It may include a robots for fetching a wafer from a cassette (or other interface) and place it on chuck  150 .       

     The computer  180  includes one or more hardware processing circuits (such as one or more processors) that may perform image processing, defect detection, classification and may determine whether an additional classification is required. 
     Each optics out of color optics  171  and inspection optics  161  may include at least one out of lenses, apertures, beam splitters, polarizers, collimators, scanners, and the like. 
     These optics (color optics  171  and inspection optics  161 ) may be used in a collection path and/or in an illumination path. Thus—these optics may be used to manipulate/direct/control/affect radiation from color illumination  173  and/or inspection illumination  163 . 
     Color optics  171  and inspection optics  161  may share one or more optical component, may not share any optical component, may be combined to a single optics unit, and the like. 
     Color illumination  173  and inspection illumination  163  may share one or more optical component, may not share any optical component, may be combined to a single illumination unit, and the like. 
     Data/images acquired before each one of the classification phases may involve introducing movement between the object and the relevant camera and/or relevant optics and/or the relevant illumination. The movement can be done by the stage  140  and/or by a movement mechanism (such as a scanner) that moves the relevant camera and/or relevant optics and/or the relevant illumination. 
     The acquiring, by the inspection camera, at least one first image of at least one area of an object may include scanning the one or more areas using a first scan pattern. 
     The acquiring of second images, by the color camera, of a first subgroup of the suspected defects may include introducing movement (moving between one defect to another)—so that the suspected defects of the first subgroup are within the field of view of the color camera—one or more suspected defects at a time. 
       FIG. 3  illustrates an example a system  102 . System  102  differs from system  101  by the following:
         a. System  102  includes first camera  821  and second camera  822  which may differ from the color camera and the inspection camera of system  101 .   b. System  102  includes first illumination  211  and second illumination  212  that may differ from color illumination  173  and inspection illumination  163 .   c. System  102  includes a shared optic  210  that precedes these cameras. It should be noted that first and second optics may precede first and second cameras.       

     It should be noted that the system may include more than two cameras that differ from each other. Thus—there may be more than two classification sessions. Additionally or alternatively, any classification phase may be preceded by selecting which camera will be used to acquire data to be used during that classification. The defect detection that precedes the initial classification may also be preceded by selecting which camera will be used to acquire the images to be processed during the defect detection phase. 
       FIG. 4  illustrates an example a system  103 . System  103  differs from system  102  by the following:
         a. System  103  includes more than two cameras—first till N&#39;th cameras (for example first camera  821  and N&#39;th camera  828 )—where N exceeds two.       

       FIG. 4  also provide more details—it (a) illustrates a scanner  125  for moving one or more cameras, (b) illustrates a controller  120  for controlling the system, and (c) illustrates computer  180  as including classifier  182 , and defect detector  181 . The controller  120  and/or the scanner  125  and or defect detector  181  and/or the classifier  182  may be includes in system  101  and system  102 . 
     The classifier  182  is a processing circuit that is programmed and/or constructed and arrange to perform one or more classification processes. 
     The defect detector  181  is a processing circuit that is programmed and/or constructed and arrange to perform one or more defect detection processes. 
       FIGS. 5-10  illustrate a non-limiting example of decision rules that dictate when an additional data is required. These figures illustrates how a reference image is generated, how first images are processed and then illustrates some recipe rules for defining a defect, and then show when additional data is required. 
       FIG. 5  illustrates an example of a generation of a reference image. 
     Out of wafer  200  some dies are selected. The selection can be done in any manner. 
     A reference die is generated based on the properties of pixels in the images  201 ,  202 ,  203  and  204  of the selected dies. The properties (for example—minimum value, maximal value and nominal value) of each pixel are based on the values (gray level values) of corresponding pixels within the images of dies  201 - 204 —after the images are aligned. 
     Indexes i and j represent the row and column of each pixel.
         a. The minimal value of the (i,j)th pixel of the reference image equals the minimal gray level value out of the values of the (i,j)th pixels of the images of dies  201 - 204 .   b. The maximal value of the (i,j)th pixel of the reference image equals the maximal gray level value out of the values of the (i,j)th pixels of the images of dies  201 - 204 .   c. The nominal value of the (i,j)th pixel of the reference image equals the medial gray level value out of the values of the (i,j)th pixels of the images of dies  201 - 204 .       

       FIG. 6  illustrates an example of a defect detection process. 
     The defect detection process includes:
         a. Grabbing  222 —acquiring an image of an area of an object—see image  242 .   b. Alignment  224 —aligning the acquired image and the reference image.   c. Segmentation  226 —the defect detection process marks locations (pixels) that may represent (or may be included in) suspected defects—in image  244  these pixels are white.   d. Blob analysis  228 —attempting to group suspected defect pixels that are connected to each other—form a continuous arrangement of suspected defect pixels. In image  246  a while line represents the edge of a blob that is formed by suspected defect pixels.   e. Measurement  230 —measuring one or more properties of the blob. For example, assuming that the blob includes M pixels (B1 . . . BM) then the following attributes may be measured:
           i. Defect area—number of pixels (M).   ii. Defect average deviation from reference=Sum(Bi,j−Ri,j)/M, wherein Bi,j belongs to B1 . . . BM, whereas (i,j) is the location of the pixel within the acquired image, and Ri,j if the (i,j)th pixel of the reference image.   iii. Defect position is the center of mass  249  of the blob (see image  248 ).   
               

     Any other defect analysis can be used. 
       FIG. 7  illustrates a top view of a bump  250  and its surroundings  252 , and a gray level distribution  260  of pixels along an imaginary line  253  that passes through the bump and its surroundings. 
       FIG. 8  illustrates an image of a bump and its surroundings and of various processing steps for extracting parameters of the bump. 
     Image  270  of the image of the bump and its surroundings. 
     The image is processed (steps  272  and  274 ) by sampling the edges of the bump at different locations (at different angles—at intersections between the edge and lines that extend from a common point). 
     Ignoring a sample that differs from other samples by a predefined amount to provide an estimate of the bump—reconstructed hump circle ( 276 ). 
       FIG. 9  illustrates examples of different recipe rules. 
     In this example size x1 is smaller than x2 that is smaller than x3 that is smaller than x4: x1&lt;x2&lt;x3&lt;x4. 
     The different recipe rules are used to classify defects. 
     These rules define a defect if:
         a. The defect ( 280 ) contacts the bump ( 250 ) (distance from bump is zero) and the defect size exceeds x1.   b. The distance between the defect and the bump does not exceed 0.25 of the diameter of the bump and the defect size exceeds x2.   c. The defect is not dust, the distance between the defect and the bump exceeds 0.25 of the diameter of the bump and the defect size exceeds x3.   d. The defect is not dust and the defect size exceeds x4.       

       FIG. 10  illustrates various determination rules. 
     These determination rules are arrange in table  300 . 
     Table  300  includes five columns—decision (defect, no defect, suspected defect), distance from bump, size of defect, average gray level (used to differentiate dust—as dust has a unique GL—usually the darkest GL), and additional data ? (indicates if additional data is required—whether the suspected defect belongs to a first subgroup of suspected defects—that require an additional classification process). 
     Table  300  includes a term “resolution” which is the resolution of the first camera. The resolution introduced an uncertainty—as the accuracy of distance measurements and/or size measurements are limited by the resolution. 
     A processing of a first image acquired by a first camera introduces an uncertainty (of Resolution) regarding whether the defect contacts the bump and regarding the size of the defect. 
     For example, referring to the second rule listed in the table—if the first image indicates that the defect contacts the bump and the size of the defect ranges between (x1−Resolution) and (x1+Resolution)—then the defect is a suspected defect and more information should be acquired—for example by using a finer resolution camera, using a color camera that may determine with more certainty whether the defect touches the bump, and the like. 
     For example, referring to the third rule listed in the table—if the first image indicates that the distance between the defect and the bump is lower than Resolution and the size of the defect exceeds x1+Resolution—then the defect is a suspected defect and more information should be acquired—for example by using a finer resolution camera, using a color camera that may determine with more certainty whether the defect touches the bump, and the like. 
     In the table—decisions of a “defect” or “no defect” are an outcome of the first classification process, and decisions “suspected defect” are indicative of suspected defects (of a first subgroup of suspected defects) that require an acquisition of second images and an additional classification process. 
     The terms “first camera”, “inspection camera”, “black and white camera” are used in an interchangeable manner. 
     The terms “second camera”, “verification camera”, “color camera” are used in an interchangeable manner. 
     The terms processor and computer are used in an interchangeable manner. 
     In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present invention. 
     It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. 
     Any reference in the specification to a system should be applied mutatis mutandis to a method that can be executed by the system. 
     Because the illustrated embodiments of the present invention may for the most part, be implemented using electronic components and circuits known to those skilled in the art, details will not be explained in any greater extent than that considered necessary as illustrated above, for the understanding and appreciation of the underlying concepts of the present invention and in order not to obfuscate or distract from the teachings of the present invention. 
     Any reference in the specification to a method should be applied mutatis mutandis to a system capable of executing the method and should be applied mutatis mutandis to a non-transitory computer program product that stores instructions that once executed by a computer result in the execution of the method. The non-transitory computer program product may be a chip, a memory unit, a disk, a compact disk, a non-volatile memory, a volatile memory, a magnetic memory, a memristor, an optical storage unit, and the like. 
     Any reference in the specification to a system should be applied mutatis mutandis to a method that can be executed by the system and should be applied mutatis mutandis to a non-transitory computer program product that stores instructions that once executed by a computer result in the execution of the method. 
     Furthermore, those skilled in the art will recognize that boundaries between the functionality of the above described operations are merely illustrative. The functionality of multiple operations may be combined into a single operation, and/or the functionality of a single operation may be distributed in additional operations. Moreover, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be altered in various other embodiments. 
     Thus, it is to be understood that the architectures depicted herein are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In an abstract, but still definite sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality. 
     However, other modifications, variations, and alternatives are also possible. The specifications and drawings are, accordingly, to be regarded in an illustrative rather than in a restrictive sense. 
     The term “comprising” is synonymous with (means the same thing as) “including,” “containing” or “having” and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. 
     The term “consisting” is a closed (only includes exactly what is stated) and excludes any additional, unrecited elements or method steps. 
     The term “consisting essentially of” limits the scope to specified materials or steps and those that do not materially affect the basic and novel characteristics. 
     In the claims and specification any reference to the term “comprising” (or “including” or “containing”) should be applied mutatis mutandis to the term “consisting” and should be applied mutatis mutandis to the phrase “consisting essentially of”. 
     In the claims and specification any reference to the term “consisting” should be applied mutatis mutandis to the term “comprising” and should be applied mutatis mutandis to the phrase “consisting essentially of”. 
     In the claims and specification any reference to the phrase “consisting essentially of” should be applied mutatis mutandis to the term “comprising” and should be applied mutatis mutandis to the term “consisting”. 
     Furthermore, the terms “a” or “an,” as used herein, are defined as one or more than one. Also, the use of introductory phrases such as “at least one” and “one or more” in the claims should not be construed to imply that the introduction of another claim element by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim element to inventions containing only one such element, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an.” The same holds true for the use of definite articles. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. 
     Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage.