Design-based inspection using repeating structures

Information

  • Patent Grant
  • 9170211
  • Patent Number
    9,170,211
  • Date Filed
    Wednesday, March 14, 2012
    12 years ago
  • Date Issued
    Tuesday, October 27, 2015
    9 years ago
Abstract
Systems and methods for design-based inspection using repeating structures are provided.
Description
BACKGROUND OF THE INVENTION

1. Field of the Invention


This invention generally relates to systems and methods for design-based inspection of wafers using repeating structures.


2. Description of the Related Art


The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.


Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.


Inspection processes can be limited by various noise sources on the wafer. For example, one common inspection method is the die-to-die method that involves comparing output of an inspection system generated for corresponding positions in different dies formed on the wafer. In this manner, output generated for similar structures in multiple dies can be compared and the results of the comparison can be used to detect defects in those structures. However, due to process variations across the wafer, corresponding positions in different dies may have different characteristics such as film thickness and color variation that, while not actually defects, can be misidentified as defects in the die-to-die methods. The variations between dies can be accommodated by increasing the thresholds that are used to detect defects. However, increasing the threshold will obviously eliminate the detection of the smallest defects on the wafers.


Some inspection methods and/or systems detect defects by comparing output generated for multiple locations within a single die. There are generally two types of methods that utilize this concept in current inspection systems. However, their applicability is limited. For example, for array areas such as SRAM or DRAM areas of a die, by knowing the cell size within the array, a cell-to-cell inspection can be performed on wafer inspectors. There are also some existing systems that analyze the image stream during inspection and look for repeating structures by performing auto-correlation analysis and looking for periodic patterns. However, both of these methods are limited to layouts that have some periodicity (e.g., in the x direction) with a certain period.


Accordingly, it would be advantageous to develop inspection systems and/or methods that do not have one or more of the disadvantages described above.


SUMMARY OF THE INVENTION

The following description of various embodiments is not to be construed in any way as limiting the subject matter of the appended claims.


One embodiment relates to a computer-implemented method for inspecting a wafer. The method includes identifying multiple instances of structures in a design for a wafer. The structures have the same or substantially the same geometrical characteristics. Identifying the multiple instances is performed using design data for the design. The method also includes comparing output of an inspection system generated for two or more of the multiple instances formed on the wafer to each other. The two or more of the multiple instances are located within the same die on the wafer. In addition, the method includes detecting defects on the wafer based on results of the comparing. The identifying, comparing, and detecting steps are performed using a computer system.


Each of the steps of the method described above may be further performed as described herein. In addition, each of the steps of the method may be performed using any of the system(s) described herein. Furthermore, the method may include any other step(s) described herein.


Another embodiment relates to a non-transitory computer-readable medium storing program instructions executable on a computer system for performing a computer-implemented method for inspecting a wafer. The computer-implemented method executable by the program instructions includes the steps of the above-described computer-implemented method. The computer-readable medium may be further configured as described herein.


An additional embodiment relates to a system configured to inspect a wafer. The system includes an inspection subsystem configured to generate output for a wafer. The system also includes a computer subsystem configured for performing the steps of the computer-implemented method described above. The system may be further configured as described herein.





BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the invention will become apparent upon reading the following detailed description and upon reference to the accompanying drawings in which:



FIG. 1 is a schematic diagram illustrating one embodiment of cell instantiation analysis;



FIG. 2 is a schematic diagram illustrating one example of similar structures that are self-evident from design “layer” intent itself;



FIG. 3 is a schematic diagram illustrating one embodiment of how an iterative algorithm can rank and detect repeating geometries;



FIG. 4 is a block diagram illustrating one embodiment of a non-transitory computer-readable medium; and



FIG. 5 is a block diagram illustrating one embodiment of a system.





While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims.


DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Turning now to the drawings, it is noted that the figures are not drawn to scale. In particular, the scale of some of the elements of the figures is greatly exaggerated to emphasize characteristics of the elements. It is also noted that the figures are not drawn to the same scale. Elements shown in more than one figure that may be similarly configured have been indicated using the same reference numerals.


Various embodiments for design-based inspection using repeating structures are described herein. One embodiment relates to a computer-implemented method for inspecting a wafer. The method includes identifying multiple instances of structures in a design for a wafer. The structures have the same or substantially the same geometrical characteristics. “Geometrical characteristics” as that term is used herein is intended to mean any characteristics of the structure that are related to the geometry of the structure. Therefore, “geometrical characteristics” may include such characteristics as width, height, side profile, shape of the structure, two-dimensional shape, three-dimensional shape, and the like. Therefore, structures that have the same geometrical characteristics would have the same width, height, side profile, shape of the structure, two-dimensional shape, three-dimensional shape, and the like. The term “substantially the same geometrical characteristics” references geometrical characteristics that are not different to such a degree that the differences themselves would be detected as defects in the methods described herein. In addition, the extent to which different geometrical characteristics can vary and still be considered substantially the same may vary depending upon the geometrical characteristic itself. For example, geometrical characteristics that are different by about 1% may be considered substantially the same for one characteristic while geometrical characteristics that are different by about 5% may be considered substantially the same for another characteristic.


A “structure” as that term is used herein is intended to mean a patterned structure that is formed on a wafer. In other words, the “structures” described herein are not simply films formed on a wafer. In addition, a “structure” as that term is used herein refers to a single continuous structure. In other words, a “structure” as that term is used herein does not refer to a collection of individual structures such as an array of lines, an array of contacts, or any other set of discrete structures. Instead, each of the lines, contacts, or discrete structures included in a set would each be a structure as that term is used herein.


Identifying the multiple instances is performed using design data for the design. For example, the embodiments described herein may use the design layout of a semiconductor layer to determine “similar” structures in relatively close proximity to one another in a die layout so that during inspection, these similar structures can be compared with each other and “outliers” marked as defects, the basic assumption being that such defect locations will be a substantially small fraction of all such similar structures. In one embodiment, identifying the multiple structures is not performed using output of an inspection system generated for a wafer. Instead, as described above, the repeating structures may be identified using the design data and in some cases only the design data. The design data may not include or be data or information acquired using a physical wafer. In other words, design data is not output generated by scanning a physical wafer or information or data generated based on such output. Since the repeating structures are identified in the embodiments described herein from analysis of design data, there is no image noise introduced into the algorithm. The embodiments described herein exploit the fact that some image computer systems can read in a large swath of data across a die and one can use the embodiments described herein to determine all locations of certain groups of geometries within that image buffer.


Several techniques may be used to find repeating structures in the design. For example, in one embodiment, identifying the multiple structures is performed without using periodicity of the structures. In another embodiment, the multiple instances do not occur in the design with any periodicity. For example, the embodiments described herein may look for similar patterns even if they are not periodic.


In some embodiments, identifying the multiple structures is not performed based on cell size of the design. In this manner, the embodiments described herein may be independent of cell size, which is different than some methods used for array inspection. For example, in some currently used methods for array areas such as SRAM or DRAM areas of a die, by knowing the cell size within the array, a cell-to-cell inspection is performed on wafer inspectors. However, these methods are limited to layouts that have some periodicity (e.g., in the x or scanning direction) with a certain period. In contrast, the embodiments described herein can utilize the repeating nature of certain structures within the design regardless of the cell size, periodicity, frequency, and any other cell characteristics. Therefore, the embodiments described herein may be much more flexible in the types of structures that can be used as repeating structures, the algorithms that are used to detect defects in those repeating structures, and the way defects in different repeating structures are classified.


In some embodiments, each of the multiple instances includes only one structure in the design. For example, each repeating structure itself may be identified and used as a single instance of the structure. In this manner, for defect detection purposes, the output for one repeating structure may be used for comparison to output for at least one other of the same or substantially the same repeating structure.


In another embodiment, each of the multiple instances includes multiple structures in the design. For example, the method may find groups of geometries that occur in various places in the layout. The groups of geometries do not have to occur with any fixed periodicity. Such groups of geometries may be identified as described further herein.


In one embodiment, identifying the multiple instances includes identifying multiple instances of the same design cell in the design using a design layout hierarchy for the design. In this manner, identifying the multiple instances may include cell instantiation analysis. For example, in this method, the design layout hierarchy is examined and instances of the same design “cell” in the layout are identified. In particular, as shown in FIG. 1, the method may include defining cells 100 (e.g., Cell A, Cell B, Cell C, etc) in the design. The method may also include using the design hierarchy with cell references (instantiations) 110 to search in design data for die 120 for the various cells (e.g., Cell A, Cell B, etc.). These areas can be assumed to look similar on the scanned die.


In some embodiments, identifying the multiple instances includes searching the design data for any instances of a known pattern of interest by performing a polygon match of the structures that make up the known pattern of interest and the structures in the design data. For example, if a pattern of interest (POI) is known a priori, the design can be searched for all locations of the same pattern by performing a polygon match of the figures that make up the POI.


In an additional embodiment, identifying the multiple instances includes performing Fourier analysis of the design data. For example, Fourier analysis of the design may be performed either by rendering it or by analyzing the polygonal representation and looking for periodicities in the x (comparing) direction.


In one embodiment, identifying the multiple instances is performed using design layer intent for the design data. For example, certain structures that are similar may be self-evident from the design “layer” intent itself. In one such example, a via/contact layer is simply a set of identical small structures and other than the center location and dimensions of each via/contact, no further processing is required to identify “similarity.” In one particular example, as shown in FIG. 2, one layer may be the metal layer with vias 200, and the corresponding via design layer 210 may be used to identify the center location and dimensions of each via/contact.


In a further embodiment, identifying the multiple instances includes analyzing a design layout for the design data at a polygonal level to identify the multiple instances of the structures. In this manner, the design layout can be analyzed at the polygonal level to find similar collections of neighboring geometries that repeat in several places in the design. These can be marked as belonging to one “care area group” and can be compared with each other within a die instead of in a die-to-die comparison. One example of an algorithm that can be used to find repeating structures is described in Gu et al., “Lossless compression algorithms for hierarchical IC layouts,” IEEE Transactions on Semiconductor Manufacturing, Vol. 21, No. 2, May 2008, which is incorporated by reference as if fully set forth herein. Although the algorithm described in this paper is used for the purposes of compressing the design representation, this algorithm, or a similar one, may be used to identify repeating structures for comparison purposes during inspection.


In some embodiments, identifying the multiple instances is performed by identifying a first structure in the design data that can be included in the multiple instances and then iteratively searching the design data for other structures that can also be included in the multiple instances with the first structure. For example, FIG. 3 is taken from the above-referenced paper and shows how an iterative algorithm ranks and detects repeating geometries. In particular, FIG. 3 shows an intracell subcell detection example in which (a) shows the 0th iteration; (b) shows the first iteration; (c) shows the second iteration; and (d) shows the final result. In this example, as shown in the 0th iteration, the algorithm may search the cell for a single selected structure and find 5 instances of the single structure within the cell. Each of the 5 instances has the same geometrical characteristics (e.g., shape, dimensions, and orientation) in the cell. The algorithm may then define the subcell as SC(0) shown in FIG. 3, 5 instances of which were found, each containing one instance of the structure and some space around the structure.


In the next iteration, the algorithm searches the cell for instances of other structures that are near more than one instance of the first structure included in SC(0) (i.e., other structures having the same geometrical characteristics as each other and that are repeating with the first structure). For example, as shown in FIG. 3, the algorithm may search the cell again and identify two other structures that are located near more than one instance of the first “L” shaped structure. Each of the two other identified structures have the same geometrical characteristics as each other (e.g., the same shape as each other and the same spatial relationship with respect to the first structure). The algorithm may then define the subcell SC(1) shown in FIG. 3, 4 instances of which were found, each containing one instance of 3 different structures and some space around the structures.


The algorithm may perform any additional number of iterations. For example, as shown in FIG. 3, the algorithm may search the cell again and identify one other structure that is located near more than one instance of the first “L” shaped structure. Each instance of this other identified structure has the same geometrical characteristics as each other (e.g., the same shape and the same spatial relationship with respect to the first structure). The algorithm may then define the subcell as SC(2) shown in FIG. 3, 4 instances of which were found, each containing one instance of 4 different structures and some space around the structures. As also shown in this example, the algorithm may search the cell in the space in and around one of the subcells and identify another structure that is near the first structure. However, upon searching the other subcells the corresponding areas in which that additional structure was found in the first instance, that other structure was not found in the other subcells. In this manner, there is only one instance of the structure and it is not repeating. Therefore, this other structure is not included in the subcell results (i.e., SC(2)), and this subcell is not included in the results (e.g., it is not another repeating subcell). Therefore, as shown in the final result, the subcell SC(2) defines each of the multiple (4) instances of the repeating structures.


In another embodiment, the multiple instances include at least three instances of the structures. For example, different inspection methods may require a different minimum number of repeats of a structure. In one such example, in a die-to-die type method, defect arbitration is performed by comparing three adjacent die locations by doing two die-pair comparisons, where each comparison is preceded by an alignment/interpolation step to align the pixels to a sub-pixel (typically less than 0.05 pixels) accuracy. The method performs interpolations of a sub-frame (256 pixels×256 pixels) at a time in bright field (BF) tools, the alignment offset (delta x, delta y) being provided by a real time alignment (RTA) system. One could therefore argue that the minimum number of repeats of a structure required to arbitrate a defect location is three. Therefore, to perform a similar defect arbitration using the multiple instances described herein, at least three instances of the structures would be needed. However, having many more occurrences of the structure within an image frame allows one to use statistics to flag outliers in a more robust manner. For example, in one embodiment, the multiple instances include all instances of the structures in the design data.


The method also includes comparing output of an inspection system generated for two or more of the multiple instances formed on the wafer to each other. The two or more of the multiple instances are located within the same die on the wafer. In this manner, the embodiments described herein bypass the die-to-die method of random mode defect detection and in some way mimic the array-mode detection algorithm. The advantage of the embodiments described herein over die-to-die random mode is that the die-to-die noise sources are removed and one can potentially lower the detection threshold thereby enabling more sensitive inspection.


In one embodiment, prior to the comparing step, the method includes interpolating the multiple instances of the structures to a common pixel grid. For example, all occurrences of the structure may be interpolated to a common pixel grid before performing the comparisons (differencing their gray level values from corresponding pixel locations). Clearly, interpolation errors will introduce a noise source. However, for a properly sampled image, this noise source may be much less than the die-to-die noise, particularly in electron beam based inspection systems.


In a further embodiment, during inspection and prior to the comparing step, the method includes aligning the output generated for the two or more of the multiple instances in corresponding pixel streams of the output. For example, the embodiments described herein assume one can accurately align the similar areas (detected from analyzing the design) with the corresponding pixel stream during inspection.


The method further includes detecting defects on the wafer based on results of the comparing. For example, detecting the defects may include applying some threshold to the results of the comparing step, and any output determined to be above the threshold may be identified as corresponding to a defect. The threshold may include any suitable threshold known in the art.


The identifying, comparing, and detecting steps are performed using a computer system. The computer system may be further configured as described herein.


The embodiments described herein have a number of advantages over other inspection systems and methods. For example, by comparing two structures within the same die, rather than die-to-die comparisons as is done for inspecting logic areas, the die-to-die noise such as changes in film thickness, focus, etc. are eliminated. In addition, the embodiments described herein may find all repeats of a certain set of geometries within a die, while the current methods look for periodicity (in the scanning (and comparison) direction). Thus, the embodiments described herein are more general than the current methods. Furthermore, the embodiments described herein use design data rather than trying to infer from the wafer image where the repeating structures are located. Using the design has the advantage that it is the ideal representation (intent) of what the wafer image should look like, uncorrupted by image noise sources in the image acquisition system.


All of the methods described herein may include storing results of one or more steps of the methods in a storage medium. The results may include any of the results described herein and may be stored in any manner known in the art. The storage medium may include any suitable computer-readable storage medium known in the art. After the results have been stored, the results can be accessed in the storage medium and used by any of the method or system embodiments described herein, formatted for display to a user, used by another software module, method, or system, etc. Furthermore, the results may be stored “permanently,” “semi-permanently,” temporarily, or for some period of time.



FIG. 4 illustrates one embodiment of non-transitory computer-readable medium 400 storing program instructions 402 executable on computer system 404 for performing a computer-implemented method for inspecting a wafer. The method for which program instructions 402 are executable on computer system 404 may include any step(s) of any method(s) described herein. In some embodiments, computer system 404 may be a computer system of an inspection system as described further herein. In some alternative embodiments, the computer system may be connected to the inspection system by a network. However, in other embodiments, computer system 404 may not be coupled to or included in an inspection system. In some such embodiments, computer system 404 may be configured as a stand alone computer system. Computer-readable medium 400, program instructions 402, and computer system 404 may be further configured as described herein.


Program instructions 402 implementing methods such as those described herein may be stored on computer-readable medium 400. The computer-readable medium may be a storage medium such as a read-only memory, a random access memory, a magnetic or optical disk, a magnetic tape, or other non-transitory computer-readable medium.


The program instructions may be implemented in any of various ways, including procedure-based techniques, component-based techniques, and/or object-oriented techniques, among others. For example, the program instructions may be implemented using ActiveX controls, C++ objects, C#, JavaBeans, Microsoft Foundation Classes (“MFC”), or other technologies or methodologies, as desired.


The computer system may include any suitable computer system known in the art. For example, computer system 404 may take various forms, including a personal computer system, mainframe computer system, workstation, image computer, parallel processor, or any other device known in the art. In general, the term “computer system” may be broadly defined to encompass any device having one or more processors, which executes instructions from a memory medium.


Another embodiment relates to a system configured to inspect a wafer. For example, as shown in FIG. 5, the system includes inspection subsystem 500 configured to generate output for a wafer. The inspection subsystem may include an inspection subsystem of any commercially available wafer inspection system. The inspection subsystem may also be configured for any suitable inspection methods such as bright field inspection, dark field inspection, optical (light-based) inspection, electron beam based inspection, etc., or some combination thereof. The system also includes computer subsystem 502 configured for performing the steps of the methods described above. Computer subsystem 502 may be further configured as described above with respect to computer system 404. The computer subsystem and the system may be further configured as described herein.


Further modifications and alternative embodiments of various aspects of the invention may be apparent to those skilled in the art in view of this description. For example, systems and methods for design-based inspection using repeating structures are provided. Accordingly, this description is to be construed as illustrative only and is for the purpose of teaching those skilled in the art the general manner of carrying out the it) invention. It is to be understood that the forms of the invention shown and described herein are to be taken as the presently preferred embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed, and certain features of the invention may be utilized independently, all as would be apparent to one skilled in the art after having the benefit of this description of the invention. Changes may be made in the elements described herein without departing from the spirit and scope of the invention as described in the following claims.

Claims
  • 1. A computer-implemented method for inspecting a wafer, comprising: identifying multiple instances of structures in a design for a wafer, wherein the structures have the same or substantially the same geometrical characteristics, wherein each of the structures included in the multiple instances comprises a single continuous structure, and wherein said identifying is performed using design data for the design;comparing output of an inspection system generated for two or more of the multiple instances of the single continuous structures formed on the wafer to each other, wherein the two or more of the multiple instances are located within the same die on the wafer, and wherein the output that is compared for the two or more of the multiple instances comprises all of the output generated for the single continuous structures included in the two or more of the multiple instances; anddetecting defects on the wafer based on results of said comparing, wherein said identifying, said comparing, and said detecting are performed using a computer system.
  • 2. The method of claim 1, wherein said identifying is further performed without using periodicity of the structures.
  • 3. The method of claim 1, wherein said identifying is not performed using output of an inspection system generated for a wafer.
  • 4. The method of claim 1, wherein said identifying is not performed based on cell size of the design.
  • 5. The method of claim 1, wherein the multiple instances do not occur in the design with any periodicity.
  • 6. The method of claim 1, wherein each of the multiple instances comprise only one structure in the design.
  • 7. The method of claim 1, wherein each of the multiple instances comprise multiple structures in the design.
  • 8. The method of claim 1, wherein said identifying comprises identifying multiple instances of the same design cell in the design using a design layout hierarchy for the design.
  • 9. The method of claim 1, wherein said identifying comprises searching the design data for any instances of a known pattern of interest by performing a polygon match of the structures that make up the known pattern of interest and the structures in the design data.
  • 10. The method of claim 1, wherein said identifying comprises performing Fourier analysis of the design data.
  • 11. The method of claim 1, wherein said identifying is further performed using design layer intent for the design data.
  • 12. The method of claim 1, wherein said identifying comprises analyzing a design layout for the design data at a polygonal level to identify the multiple instances of the structures.
  • 13. The method of claim 1, wherein said identifying is performed by identifying a first structure in the design data that can be included in the multiple instances and then iteratively searching the design data for other structures that can also be included in the multiple instances with the first structure.
  • 14. The method of claim 1, wherein the multiple instances comprise at least three instances of the structures.
  • 15. The method of claim 1, wherein the multiple instances comprise all instances of the structures in the design data.
  • 16. The method of claim 1, further comprising prior to said comparing, interpolating the multiple instances of the structures to a common pixel grid.
  • 17. The method of claim 1, further comprising during inspection and prior to said comparing, aligning the output generated for the two or more of the multiple instances in corresponding pixel streams of the output.
  • 18. A non-transitory computer-readable medium storing program instructions executable on a computer system for performing a computer-implemented method for inspecting a wafer, wherein the computer-implemented method comprises: identifying multiple instances of structures in a design for a wafer, wherein the structures have the same or substantially the same geometrical characteristics, wherein each of the structures included in the multiple instances comprises a single continuous structure, and wherein said identifying is performed using design data for the design;comparing output of an inspection system generated for two or more of the multiple instances of the single continuous structures formed on the wafer to each other, wherein the two or more of the multiple instances are located within the same die on the wafer, and wherein the output that is compared for the two or more of the multiple instances comprises all of the output generated for the single continuous structures included in the two or more of the multiple instances; anddetecting defects on the wafer based on results of said comparing.
  • 19. A system configured to inspect a wafer, comprising: an inspection subsystem configured to generate output for a wafer; anda computer subsystem configured for: identifying multiple instances of structures in a design for the wafer, wherein the structures have the same or substantially the same geometrical characteristics, wherein each of the structures included in the multiple instances comprises a single continuous structure, and wherein said identifying is performed using design data for the design;comparing output of the inspection subsystem generated for two or more of the multiple instances of the single continuous structures formed on the wafer to each other, wherein the two or more of the multiple instances are located within the same die on the wafer, and wherein the output that is compared for the two or more of the multiple instances comprises all of the output generated for the single continuous structures included in the two or more of the multiple instances; anddetecting defects on the wafer based on results of said comparing.
  • 20. The system of claim 19, wherein said identifying is further performed without using periodicity of the structures.
  • 21. The system of claim 19, wherein said identifying is not performed using output of the inspection subsystem generated for a wafer.
  • 22. The system of claim 19, wherein said identifying is not performed based on cell size of the design.
  • 23. The system of claim 19, wherein the multiple instances do not occur in the design with any periodicity.
  • 24. The system of claim 19, wherein each of the multiple instances comprise only one structure in the design.
  • 25. The system of claim 19, wherein each of the multiple instances comprise multiple structures in the design.
  • 26. The system of claim 19, wherein said identifying comprises identifying multiple instances of the same design cell in the design using a design layout hierarchy for the design.
  • 27. The system of claim 19, wherein said identifying comprises searching the design data for any instances of a known pattern of interest by performing a polygon match of the structures that make up the known pattern of interest and the structures in the design data.
  • 28. The system of claim 19, wherein said identifying comprises performing Fourier analysis of the design data.
  • 29. The system of claim 19, wherein said identifying is further performed using design layer intent for the design data.
  • 30. The system of claim 19, wherein said identifying comprises analyzing a design layout for the design data at a polygonal level to identify the multiple instances of the structures.
  • 31. The system of claim 19, wherein said identifying is performed by identifying a first structure in the design data that can be included in the multiple instances and then iteratively searching the design data fir other structures that can also be included in the multiple instances with the first structure.
  • 32. The system of claim 19, wherein the multiple instances comprise at least three instances of the structures.
  • 33. The system of claim 19, wherein the multiple instances comprise all instances of the structures in the design data.
  • 34. The system of claim 19, wherein the computer subsystem is further configured for, prior to said comparing, interpolating the multiple instances of the structures to a common pixel grid.
  • 35. The system of claim 19, wherein the computer subsystem is further configured for, during inspection and prior to said comparing, aligning the output generated for the two or more of the multiple instances in corresponding pixel streams of the output.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No. 61/467,964 entitled “Design-based inspection using repeating structures,” filed Mar. 25, 2011, which is incorporated by reference as if fully set forth herein.

US Referenced Citations (445)
Number Name Date Kind
3495269 Mutschler et al. Feb 1970 A
3496352 Jugle Feb 1970 A
3909602 Micka Sep 1975 A
4015203 Verkuil Mar 1977 A
4247203 Levy et al. Jan 1981 A
4347001 Levy et al. Aug 1982 A
4378159 Galbraith Mar 1983 A
4448532 Joseph et al. May 1984 A
4475122 Green Oct 1984 A
4532650 Wihl et al. Jul 1985 A
4555798 Broadbent, Jr. et al. Nov 1985 A
4578810 MacFarlane et al. Mar 1986 A
4579455 Levy et al. Apr 1986 A
4595289 Feldman et al. Jun 1986 A
4599558 Castellano, Jr. et al. Jul 1986 A
4633504 Wihl Dec 1986 A
4641353 Kobayashi Feb 1987 A
4641967 Pecen Feb 1987 A
4734721 Boyer et al. Mar 1988 A
4748327 Shinozaki et al. May 1988 A
4758094 Wihl et al. Jul 1988 A
4766324 Saadat et al. Aug 1988 A
4799175 Sano et al. Jan 1989 A
4805123 Specht et al. Feb 1989 A
4812756 Curtis et al. Mar 1989 A
4814829 Kosugi et al. Mar 1989 A
4817123 Sones et al. Mar 1989 A
4845558 Tsai et al. Jul 1989 A
4877326 Chadwick et al. Oct 1989 A
4926489 Danielson et al. May 1990 A
4928313 Leonard et al. May 1990 A
5046109 Fujimori et al. Sep 1991 A
5124927 Hopewell et al. Jun 1992 A
5189481 Jann et al. Feb 1993 A
5355212 Wells et al. Oct 1994 A
5444480 Sumita Aug 1995 A
5453844 George et al. Sep 1995 A
5481624 Kamon Jan 1996 A
5485091 Verkuil Jan 1996 A
5497381 O'Donoghue et al. Mar 1996 A
5528153 Taylor et al. Jun 1996 A
5544256 Brecher et al. Aug 1996 A
5563702 Emery et al. Oct 1996 A
5572598 Wihl et al. Nov 1996 A
5578821 Meisberger et al. Nov 1996 A
5594247 Verkuil et al. Jan 1997 A
5608538 Edgar et al. Mar 1997 A
5619548 Koppel Apr 1997 A
5621519 Frost et al. Apr 1997 A
5644223 Verkuil Jul 1997 A
5650731 Fung et al. Jul 1997 A
5661408 Kamieniecki et al. Aug 1997 A
5689614 Gronet et al. Nov 1997 A
5694478 Braier et al. Dec 1997 A
5696835 Hennessey et al. Dec 1997 A
5703969 Hennessey et al. Dec 1997 A
5716889 Tsuji et al. Feb 1998 A
5737072 Emery et al. Apr 1998 A
5742658 Tiffin et al. Apr 1998 A
5754678 Hawthorne et al. May 1998 A
5767691 Verkuil Jun 1998 A
5767693 Verkuil Jun 1998 A
5771317 Edgar Jun 1998 A
5773989 Edelman et al. Jun 1998 A
5774179 Chevrette et al. Jun 1998 A
5795685 Liebmann et al. Aug 1998 A
5822218 Moosa et al. Oct 1998 A
5831865 Berezin et al. Nov 1998 A
5834941 Verkuil Nov 1998 A
5852232 Samsavar et al. Dec 1998 A
5866806 Samsavar et al. Feb 1999 A
5874733 Silver et al. Feb 1999 A
5884242 Meier et al. Mar 1999 A
5889593 Bareket Mar 1999 A
5917332 Chen et al. Jun 1999 A
5932377 Ferguson et al. Aug 1999 A
5940458 Suk Aug 1999 A
5948972 Samsavar et al. Sep 1999 A
5955661 Samsavar et al. Sep 1999 A
5965306 Mansfield et al. Oct 1999 A
5978501 Badger et al. Nov 1999 A
5980187 Verhovsky Nov 1999 A
5986263 Hiroi et al. Nov 1999 A
5991699 Kulkarni et al. Nov 1999 A
5999003 Steffan et al. Dec 1999 A
6011404 Ma et al. Jan 2000 A
6014461 Hennessey et al. Jan 2000 A
6040911 Nozaki et al. Mar 2000 A
6040912 Zika et al. Mar 2000 A
6052478 Wihl et al. Apr 2000 A
6060709 Verkuil et al. May 2000 A
6072320 Verkuil Jun 2000 A
6076465 Vacca et al. Jun 2000 A
6078738 Garza et al. Jun 2000 A
6091257 Verkuil et al. Jul 2000 A
6091846 Lin et al. Jul 2000 A
6097196 Verkuil et al. Aug 2000 A
6097887 Hardikar et al. Aug 2000 A
6104206 Verkuil Aug 2000 A
6104835 Han Aug 2000 A
6117598 Imai Sep 2000 A
6121783 Horner et al. Sep 2000 A
6122017 Taubman Sep 2000 A
6122046 Almogy Sep 2000 A
6137570 Chuang et al. Oct 2000 A
6141038 Young et al. Oct 2000 A
6146627 Muller et al. Nov 2000 A
6171737 Phan et al. Jan 2001 B1
6175645 Elyasaf et al. Jan 2001 B1
6184929 Noda et al. Feb 2001 B1
6184976 Park et al. Feb 2001 B1
6191605 Miller et al. Feb 2001 B1
6201999 Jevtic Mar 2001 B1
6202029 Verkuil et al. Mar 2001 B1
6205239 Lin et al. Mar 2001 B1
6215551 Nikoonahad et al. Apr 2001 B1
6224638 Jevtic et al. May 2001 B1
6233719 Hardikar et al. May 2001 B1
6246787 Hennessey et al. Jun 2001 B1
6248485 Cuthbert Jun 2001 B1
6248486 Dirksen et al. Jun 2001 B1
6259960 Inokuchi Jul 2001 B1
6266437 Eichel et al. Jul 2001 B1
6267005 Samsavar et al. Jul 2001 B1
6268093 Kenan et al. Jul 2001 B1
6272236 Pierrat et al. Aug 2001 B1
6282309 Emery Aug 2001 B1
6292582 Lin et al. Sep 2001 B1
6295374 Robinson et al. Sep 2001 B1
6324298 O'Dell et al. Nov 2001 B1
6344640 Rhoads Feb 2002 B1
6363166 Wihl et al. Mar 2002 B1
6366687 Aloni et al. Apr 2002 B1
6373975 Bula et al. Apr 2002 B1
6388747 Nara et al. May 2002 B2
6393602 Atchison et al. May 2002 B1
6407373 Dotan Jun 2002 B1
6415421 Anderson et al. Jul 2002 B2
6445199 Satya et al. Sep 2002 B1
6451690 Matsumoto et al. Sep 2002 B1
6459520 Takayama Oct 2002 B1
6466314 Lehman Oct 2002 B1
6466315 Karpol et al. Oct 2002 B1
6470489 Chang et al. Oct 2002 B1
6483938 Hennessey et al. Nov 2002 B1
6513151 Erhardt et al. Jan 2003 B1
6526164 Mansfield et al. Feb 2003 B1
6529621 Glasser et al. Mar 2003 B1
6535628 Smargiassi et al. Mar 2003 B2
6539106 Gallarda et al. Mar 2003 B1
6569691 Jastrzebski et al. May 2003 B1
6581193 McGhee et al. Jun 2003 B1
6593748 Halliyal et al. Jul 2003 B1
6597193 Lagowski et al. Jul 2003 B2
6602728 Liebmann et al. Aug 2003 B1
6608681 Tanaka et al. Aug 2003 B2
6614520 Bareket et al. Sep 2003 B1
6631511 Haffner et al. Oct 2003 B2
6636301 Kvamme et al. Oct 2003 B1
6642066 Halliyal et al. Nov 2003 B1
6658640 Weed Dec 2003 B2
6665065 Phan et al. Dec 2003 B1
6670082 Liu et al. Dec 2003 B2
6680621 Savtchouk Jan 2004 B2
6691052 Maurer Feb 2004 B1
6701004 Shykind et al. Mar 2004 B1
6718526 Eldredge et al. Apr 2004 B1
6721695 Chen et al. Apr 2004 B1
6734696 Horner et al. May 2004 B2
6738954 Allen et al. May 2004 B1
6748103 Glasser et al. Jun 2004 B2
6751519 Satya et al. Jun 2004 B1
6753954 Chen Jun 2004 B2
6757645 Chang et al. Jun 2004 B2
6759655 Nara et al. Jul 2004 B2
6771806 Satya et al. Aug 2004 B1
6775818 Taravade et al. Aug 2004 B2
6777147 Fonseca et al. Aug 2004 B1
6777676 Wang et al. Aug 2004 B1
6778695 Schellenberg et al. Aug 2004 B1
6779159 Yokoyama et al. Aug 2004 B2
6784446 Phan et al. Aug 2004 B1
6788400 Chen Sep 2004 B2
6789032 Barbour et al. Sep 2004 B2
6803554 Ye et al. Oct 2004 B2
6806456 Ye et al. Oct 2004 B1
6807503 Ye et al. Oct 2004 B2
6813572 Satya et al. Nov 2004 B2
6820028 Ye et al. Nov 2004 B2
6828542 Ye et al. Dec 2004 B2
6842225 Irie Jan 2005 B1
6859746 Stirton Feb 2005 B1
6879403 Freifeld Apr 2005 B2
6879924 Ye et al. Apr 2005 B2
6882745 Brankner et al. Apr 2005 B2
6884984 Ye et al. Apr 2005 B2
6886153 Bevis Apr 2005 B1
6892156 Ye et al. May 2005 B2
6902855 Peterson et al. Jun 2005 B2
6906305 Pease et al. Jun 2005 B2
6918101 Satya et al. Jul 2005 B1
6919957 Nikoonahad et al. Jul 2005 B2
6937753 O'Dell et al. Aug 2005 B1
6948141 Satya et al. Sep 2005 B1
6959255 Ye et al. Oct 2005 B2
6966047 Glasser Nov 2005 B1
6969837 Ye et al. Nov 2005 B2
6969864 Ye et al. Nov 2005 B2
6983060 Martinent-Catalot et al. Jan 2006 B1
6988045 Purdy Jan 2006 B2
6990385 Smith et al. Jan 2006 B1
7003755 Pang et al. Feb 2006 B2
7003758 Ye et al. Feb 2006 B2
7012438 Miller et al. Mar 2006 B1
7026615 Takane et al. Apr 2006 B2
7027143 Stokowski et al. Apr 2006 B1
7030966 Hansen Apr 2006 B2
7030997 Neureuther et al. Apr 2006 B2
7053355 Ye et al. May 2006 B2
7061625 Hwang et al. Jun 2006 B1
7071833 Nagano et al. Jul 2006 B2
7103484 Shi et al. Sep 2006 B1
7106895 Goldberg et al. Sep 2006 B1
7107517 Suzuki et al. Sep 2006 B1
7107571 Chang et al. Sep 2006 B2
7111277 Ye et al. Sep 2006 B2
7114143 Hanson et al. Sep 2006 B2
7114145 Ye et al. Sep 2006 B2
7117477 Ye et al. Oct 2006 B2
7117478 Ye et al. Oct 2006 B2
7120285 Spence Oct 2006 B1
7120895 Ye et al. Oct 2006 B2
7123356 Stokowski et al. Oct 2006 B1
7124386 Smith et al. Oct 2006 B2
7133548 Kenan et al. Nov 2006 B2
7135344 Nehmadi et al. Nov 2006 B2
7136143 Smith Nov 2006 B2
7152215 Smith et al. Dec 2006 B2
7162071 Hung et al. Jan 2007 B2
7170593 Honda et al. Jan 2007 B2
7171334 Gassner Jan 2007 B2
7174520 White et al. Feb 2007 B2
7194709 Brankner Mar 2007 B2
7207017 Tabery et al. Apr 2007 B1
7231628 Pack et al. Jun 2007 B2
7236847 Marella Jun 2007 B2
7271891 Xiong et al. Sep 2007 B1
7379175 Stokowski et al. May 2008 B1
7383156 Matsusita et al. Jun 2008 B2
7386839 Golender et al. Jun 2008 B1
7388979 Sakai et al. Jun 2008 B2
7418124 Peterson et al. Aug 2008 B2
7424145 Horie et al. Sep 2008 B2
7440093 Xiong et al. Oct 2008 B1
7570796 Zafar et al. Aug 2009 B2
7676077 Kulkarni et al. Mar 2010 B2
7683319 Makino et al. Mar 2010 B2
7738093 Alles et al. Jun 2010 B2
7739064 Ryker et al. Jun 2010 B1
7752584 Yang Jul 2010 B2
7760929 Orbon et al. Jul 2010 B2
7774153 Smith Aug 2010 B1
7877722 Duffy et al. Jan 2011 B2
7890917 Young et al. Feb 2011 B1
7904845 Fouquet et al. Mar 2011 B2
7968859 Young et al. Jun 2011 B2
8073240 Fischer et al. Dec 2011 B2
8112241 Xiong Feb 2012 B2
8126255 Bhaskar et al. Feb 2012 B2
20010017694 Oomori et al. Aug 2001 A1
20010019625 Kenan et al. Sep 2001 A1
20010022858 Komiya et al. Sep 2001 A1
20010043735 Smargiassi et al. Nov 2001 A1
20020010560 Balachandran Jan 2002 A1
20020019729 Chang et al. Feb 2002 A1
20020026626 Randall et al. Feb 2002 A1
20020033449 Nakasuji et al. Mar 2002 A1
20020035461 Chang et al. Mar 2002 A1
20020035641 Kurose et al. Mar 2002 A1
20020035717 Matsuoka Mar 2002 A1
20020054291 Tsai et al. May 2002 A1
20020088951 Chen Jul 2002 A1
20020090746 Xu et al. Jul 2002 A1
20020134936 Matsui et al. Sep 2002 A1
20020144230 Rittman Oct 2002 A1
20020145734 Watkins et al. Oct 2002 A1
20020164065 Cai et al. Nov 2002 A1
20020168099 Noy Nov 2002 A1
20020176096 Sentoku et al. Nov 2002 A1
20020181756 Shibuya et al. Dec 2002 A1
20020186878 Hoon et al. Dec 2002 A1
20020192578 Tanaka et al. Dec 2002 A1
20030004699 Choi et al. Jan 2003 A1
20030014146 Fujii et al. Jan 2003 A1
20030017664 Pnueli et al. Jan 2003 A1
20030022401 Hamamatsu et al. Jan 2003 A1
20030033046 Yoshitake et al. Feb 2003 A1
20030048458 Mieher et al. Mar 2003 A1
20030048939 Lehman Mar 2003 A1
20030057971 Nishiyama et al. Mar 2003 A1
20030076989 Maayah et al. Apr 2003 A1
20030086081 Lehman May 2003 A1
20030094572 Matsui et al. May 2003 A1
20030098805 Bizjak et al. May 2003 A1
20030128870 Pease et al. Jul 2003 A1
20030138138 Vacca et al. Jul 2003 A1
20030138978 Tanaka et al. Jul 2003 A1
20030169916 Hayashi et al. Sep 2003 A1
20030173516 Takane et al. Sep 2003 A1
20030192015 Liu Oct 2003 A1
20030207475 Nakasuji et al. Nov 2003 A1
20030223639 Shlain et al. Dec 2003 A1
20030226951 Ye et al. Dec 2003 A1
20030227620 Yokoyama et al. Dec 2003 A1
20030228050 Geshel et al. Dec 2003 A1
20030228714 Smith et al. Dec 2003 A1
20030229410 Smith et al. Dec 2003 A1
20030229412 White et al. Dec 2003 A1
20030229868 White et al. Dec 2003 A1
20030229875 Smith et al. Dec 2003 A1
20030229880 White et al. Dec 2003 A1
20030229881 White et al. Dec 2003 A1
20030237064 White et al. Dec 2003 A1
20040030430 Matsuoka Feb 2004 A1
20040032908 Hagai et al. Feb 2004 A1
20040049722 Matsushita Mar 2004 A1
20040052411 Qian et al. Mar 2004 A1
20040057611 Lee et al. Mar 2004 A1
20040066506 Elichai et al. Apr 2004 A1
20040091142 Peterson et al. May 2004 A1
20040094762 Hess et al. May 2004 A1
20040098216 Ye et al. May 2004 A1
20040102934 Chang May 2004 A1
20040107412 Pack et al. Jun 2004 A1
20040119036 Ye et al. Jun 2004 A1
20040120569 Hung et al. Jun 2004 A1
20040133369 Pack et al. Jul 2004 A1
20040147121 Nakagaki et al. Jul 2004 A1
20040174506 Smith Sep 2004 A1
20040179738 Dai et al. Sep 2004 A1
20040199885 Lu et al. Oct 2004 A1
20040223639 Sato et al. Nov 2004 A1
20040228515 Okabe et al. Nov 2004 A1
20040234120 Honda et al. Nov 2004 A1
20040243320 Chang et al. Dec 2004 A1
20040246476 Bevis et al. Dec 2004 A1
20040254752 Wisniewski et al. Dec 2004 A1
20050004774 Volk et al. Jan 2005 A1
20050008218 O'Dell et al. Jan 2005 A1
20050010890 Nehmadi et al. Jan 2005 A1
20050013474 Sim Jan 2005 A1
20050062962 Fairley et al. Mar 2005 A1
20050069217 Mukherjee Mar 2005 A1
20050117796 Matsui et al. Jun 2005 A1
20050132306 Smith et al. Jun 2005 A1
20050141764 Tohyama et al. Jun 2005 A1
20050166174 Ye et al. Jul 2005 A1
20050184252 Ogawa et al. Aug 2005 A1
20050190957 Cai et al. Sep 2005 A1
20050198602 Brankner et al. Sep 2005 A1
20060000964 Ye et al. Jan 2006 A1
20060036979 Zurbrick et al. Feb 2006 A1
20060038986 Honda et al. Feb 2006 A1
20060048089 Schwarzband Mar 2006 A1
20060051682 Hess et al. Mar 2006 A1
20060062445 Verma et al. Mar 2006 A1
20060066339 Rajski et al. Mar 2006 A1
20060082763 Teh et al. Apr 2006 A1
20060159333 Ishikawa Jul 2006 A1
20060161452 Hess Jul 2006 A1
20060193506 Dorphan et al. Aug 2006 A1
20060193507 Sali et al. Aug 2006 A1
20060236294 Saidin et al. Oct 2006 A1
20060236297 Melvin, III et al. Oct 2006 A1
20060239536 Shibuya et al. Oct 2006 A1
20060265145 Huet et al. Nov 2006 A1
20060266243 Percin et al. Nov 2006 A1
20060269120 Nehmadi et al. Nov 2006 A1
20060273242 Hunsche et al. Dec 2006 A1
20060273266 Preil et al. Dec 2006 A1
20060277520 Gennari Dec 2006 A1
20060291714 Wu et al. Dec 2006 A1
20060292463 Best et al. Dec 2006 A1
20070002322 Borodovsky et al. Jan 2007 A1
20070011628 Ouali et al. Jan 2007 A1
20070013901 Kim et al. Jan 2007 A1
20070019171 Smith Jan 2007 A1
20070019856 Furman et al. Jan 2007 A1
20070031745 Ye et al. Feb 2007 A1
20070032896 Ye et al. Feb 2007 A1
20070035322 Kang et al. Feb 2007 A1
20070035712 Gassner et al. Feb 2007 A1
20070035728 Kekare et al. Feb 2007 A1
20070052963 Orbon et al. Mar 2007 A1
20070064995 Oaki et al. Mar 2007 A1
20070133860 Lin et al. Jun 2007 A1
20070156379 Kulkarni et al. Jul 2007 A1
20070230770 Kulkarni et al. Oct 2007 A1
20070248257 Bruce et al. Oct 2007 A1
20070280527 Almogy et al. Dec 2007 A1
20070288219 Zafar et al. Dec 2007 A1
20080013083 Kirk et al. Jan 2008 A1
20080015802 Urano et al. Jan 2008 A1
20080016481 Matsuoka et al. Jan 2008 A1
20080018887 Chen et al. Jan 2008 A1
20080049994 Rognin et al. Feb 2008 A1
20080058977 Honda Mar 2008 A1
20080072207 Verma et al. Mar 2008 A1
20080081385 Marella et al. Apr 2008 A1
20080163140 Fouquet et al. Jul 2008 A1
20080167829 Park et al. Jul 2008 A1
20080250384 Duffy et al. Oct 2008 A1
20080295047 Nehmadi et al. Nov 2008 A1
20080295048 Nehmadi et al. Nov 2008 A1
20080304056 Alles et al. Dec 2008 A1
20090024967 Su et al. Jan 2009 A1
20090037134 Kulkarni et al. Feb 2009 A1
20090041332 Bhaskar et al. Feb 2009 A1
20090043527 Park et al. Feb 2009 A1
20090055783 Florence et al. Feb 2009 A1
20090067703 Lin et al. Mar 2009 A1
20090080759 Bhaskar et al. Mar 2009 A1
20090210183 Rajski et al. Aug 2009 A1
20090257645 Chen et al. Oct 2009 A1
20090284733 Wallingford et al. Nov 2009 A1
20090290782 Regensburger Nov 2009 A1
20090299681 Chen et al. Dec 2009 A1
20090310864 Takagi et al. Dec 2009 A1
20090323052 Silberstein et al. Dec 2009 A1
20100142800 Pak et al. Jun 2010 A1
20100146338 Schalick et al. Jun 2010 A1
20100150429 Jau et al. Jun 2010 A1
20100188657 Chen et al. Jul 2010 A1
20100226562 Wu et al. Sep 2010 A1
20110013825 Shibuya et al. Jan 2011 A1
20110052040 Kuan Mar 2011 A1
20110184662 Badger et al. Jul 2011 A1
20110188733 Bardos et al. Aug 2011 A1
20110251713 Teshima et al. Oct 2011 A1
20110276935 Fouquet et al. Nov 2011 A1
20110311126 Sakai et al. Dec 2011 A1
20120308112 Hu et al. Dec 2012 A1
20120319246 Tan et al. Dec 2012 A1
20130009989 Chen et al. Jan 2013 A1
20130027196 Yankun et al. Jan 2013 A1
Foreign Referenced Citations (55)
Number Date Country
1339140 Mar 2002 CN
1398348 Feb 2003 CN
1646896 Jul 2005 CN
101275920 Oct 2008 CN
0032197 Jul 1981 EP
0370322 May 1990 EP
1061358 Dec 2000 EP
1061571 Dec 2000 EP
1065567 Jan 2001 EP
1066925 Jan 2001 EP
1069609 Jan 2001 EP
1093017 Apr 2001 EP
1329771 Jul 2003 EP
1480034 Nov 2004 EP
1696270 Aug 2006 EP
7-159337 Jun 1995 JP
2002071575 Mar 2002 JP
2002-365235 Dec 2002 JP
2003-215060 Jul 2003 JP
2004-045066 Feb 2004 JP
2005-283326 Oct 2005 JP
2007-234798 Sep 2007 JP
2009-122046 Jun 2009 JP
2010-256242 Nov 2010 JP
2012-225768 Nov 2012 JP
10-2001-0007394 Jan 2001 KR
10-2001-0037026 May 2001 KR
10-2001-0101697 Nov 2001 KR
1020030055848 Jul 2003 KR
10-2005-0092053 Sep 2005 KR
10-2006-0075691 Jul 2006 KR
10-2006-0124514 Dec 2006 KR
10-0696276 Mar 2007 KR
10-2010-0061018 Jun 2010 KR
10-2012-0068128 Jun 2012 KR
9857358 Dec 1998 WO
9922310 May 1999 WO
9925004 May 1999 WO
9959200 May 1999 WO
9938002 Jul 1999 WO
9941434 Aug 1999 WO
0003234 Jan 2000 WO
0036525 Jun 2000 WO
0055799 Sep 2000 WO
0068884 Nov 2000 WO
0070332 Nov 2000 WO
0109566 Feb 2001 WO
0140145 Jun 2001 WO
03104921 Dec 2003 WO
2004027684 Apr 2004 WO
2004097903 Nov 2004 WO
2006012388 Feb 2006 WO
2006063268 Jun 2006 WO
2009152046 Sep 2009 WO
2010093733 Aug 2010 WO
Non-Patent Literature Citations (71)
Entry
Phan et al., “Comparison of Binary Mask Defect Printability Analysis Using Virtual Stepper System and Aerial Image Microscope System,” Proceedings of SPIE—The International Society for Optical Engineering 1999 Society of Photo-Optical Instrumentation Engineers, vol. 3873, 1999, pp. 681-692.
Sahouria et al., “Full-chip Process Simulation for Silicon DRC,” Mentor Graphics, Mar. 2000, 6 pages.
Schroder et al., Corona-Oxide-Semiconductor Device Characterization, 1998, Solid-State Electronics, vol. 42, No. 4, pp. 505-512.
Schroder, “Surface voltage and surface photovoltage: history, theory and applications,” Measurement Science and Technology, vol. 12, 2001, pp. R16-31.
Schroder, Contactless Surface Charge Semiconductor Characterization, Apr. 2002, Materials Science and Engineering B, vol. 91-92, pp. 196-228.
Schurz et al., “Simulation Study of Reticle Enhancement Technology Applications for 157 nm Lithography,” SPIE vol. 4562, 2002, pp. 902-913.
Svidenko et al. “Dynamic Defect-Limited Yield Prediction by Criticality Factor,” ISSM Paper: YE-O-157, 2007.
Verkuil et al., “A Contactless Alternative to MOS Charge Measurements by Means of a Corona-Oxide-Semiconductor (COS) Technique,”Electrochem. Soc. Extended Abstracts, 1988, vol. 88-1, No. 169, pp. 261-262.
Verkuil, “Rapid Contactless Method for Measuring Fixed Oxide Charge Associated with Silicon Processing,” IBM Technical Disclosure Bulletin, vol. 24, No. 6, 1981, pp. 3048-3053.
Volk et al. “Investigation of Reticle Defect Formation at DUV Lithography,” 2002, BACUS Symposium on Photomask Technology.
Volk et al. “Investigation of Reticle Defect Formation at DUV Lithography,” 2003, IEEE/SEMI Advanced Manufacturing Conference, pp. 29-35.
Volk et al., “Investigation of Smart Inspection of Critical Layer Reticles using Additional Designer Data to Determine Defect Significance,” Proceedings of SPIE vol. 5256, 2003, pp. 489-499.
Weinberg, “Tunneling of Electrons from Si into Thermally Grown SiO2,” Solid-State Electronics, 1977, vol. 20, pp. 11-18.
Weinzierl et al., “Non-Contact Corona-Based Process Control Measurements: Where We've Been, Where We're Headed,” Electrochemical Society Proceedings, Oct. 1999, vol. 99-16, pp. 342-350.
Yan et al., “Printability of Pellicle Defects in DUV 0.5 um Lithography,” SPIE vol. 1604, 1991, pp. 106-117.
U.S. Appl. No. 13/652,377, filed Oct. 15, 2012 by Wu et al.
Huang et al., “Using Design Based Binning to Improve Defect Excursion Control for 45nm Production,” IEEE, International Symposium on Semiconductor Manufacturing, Oct. 2007, pp. 1-3.
Sato et al., “Defect Criticality Index (DCI): A new methodology to significantly improve DOI sampling rate in a 45nm production environment,” Metrology, Inspection, and Process Control for Microlithography XXII, Proc. of SPIE vol. 6922, 692213 (2008), pp. 1-9.
Tang et al., “Analyzing Volume Diagnosis Results with Statistical Learning for Yield Improvement” 12th IEEE European Test Symposium, Freiburg 2007, IEEE European, May 20-24, 2007, pp. 145-150.
U.S. Appl. No. 60/681,095, filed May 13, 2005 by Nehmadi et al.
U.S. Appl. No. 60/684,360, filed May 24, 2005 by Nehmadi et al.
U.S. Appl. No. 10/778,752, filed Feb. 13, 2004 by Preil et al.
U.S. Appl. No. 10/793,599, filed Mar. 4, 2004 by Howard et al.
U.S. Appl. No. 11/139,151, filed Feb. 10, 2005 by Volk.
U.S. Appl. No. 11/154,310, filed Feb. 10, 2005 by Verma et al.
U.S. Appl. No. 12/394,752, filed Feb. 27, 2009 by Xiong et al.
U.S. Appl. No. 12/403,905, filed Mar. 13, 2009 by Xiong.
Allan et al., “Critical Area Extraction for Soft Fault Estimation,” IEEE Transactions on Semiconductor Manufacturing, vol. 11, No. 1, Feb. 1998.
Barty et al., “Aerial Image Microscopes for the inspection of defects in EUV masks,” Proceedings of SPIE, vol. 4889, 2002, pp. 1073-1084.
Budd et al., “A New Mask Evaluation Tool, the Microlithography Simulation Microscope Aerial Image Measurement System,” SPIE vol. 2197, 1994, pp. 530-540.
Cai et al., “Enhanced Dispositioning of Reticle Defects Using the Virtual Stepper With Automated Defect Severity Scoring,” Proceedings of the SPIE, vol. 4409, Jan. 2001, pp. 467-478.
Comizzoli, “Uses of Corona Discharges in the Semiconductor Industry,” J. Electrochem. Soc., 1987, pp. 424-429.
Contactless Electrical Equivalent Oxide Thickness Measurement, IBM Technical Disclosure Bulletin, vol. 29, No. 10, 1987, pp. 4622-4623.
Contactless Photovoltage vs. Bias Method for Determining Flat-Band Voltage, IBM Technical Disclosure Bulletin, vol. 32, vol. 9A, 1990, pp. 14-17.
Cosway et al., “Manufacturing Implementation of Corona Oxide Silicon (COS) Systems for Diffusion Furnace Contamination Monitoring,” 1997 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, pp. 98-102.
Diebold et al., “Characterization and production metrology of thin transistor gate oxide films,” Materials Science in Semiconductor Processing 2, 1999, pp. 103-147.
Dirksen et al., “Impact of high order aberrations on the performance of the aberration monitor,” Proc. of SPIE vol. 4000, Mar. 2000, pp. 9-17.
Dirksen et al., “Novel aberration monitor for optical lithography,” Proc. of SPIE vol. 3679, Jul. 1999, pp. 77-86.
Garcia et al., “New Die to Database Inspection Algorithm for Inspection of 90-nm Node Reticles,” Proceedings of SPIE, vol. 5130, 2003, pp. 364-374.
Granik et al., “Sub-resolution process windows and yield estimation technique based on detailed full-chip CD simulation,” Mentor Graphics, Sep. 2000, 5 pages.
Hess et al., “A Novel Approach: High Resolution Inspection with Wafer Plane Defect Detection,” Proceedings of SPIE—International Society for Optical Engineering; Photomask and Next-Generation Lithography Mask Technology 2008, vol. 7028, 2008.
Huang et al., “Process Window Impact of Progressive Mask Defects, Its Inspection and Disposition Techniques (go/no-go criteria) Via a Lithographic Detector,” Proceedings of SPIE—The International Society for Optical Engineering; 25th Annual Bacus Symposium on Photomask Technology 2005, vol. 5992, No. 1, 2005, p. 6.
Hung et al., Metrology Study of Sub 20 Angstrom oxynitride by Corona-Oxide-Silicon (COS) and Conventional C-V Approaches, 2002, Mat. Res. Soc. Symp. Proc., vol. 716, pp. 119-124.
International Search Report for PCT/US2003/021907 mailed Jun. 7, 2004.
International Search Report for PCT/US2004/040733 mailed Dec. 23, 2005.
International Search Report for PCT/US2006/061112 mailed Sep. 25, 2008.
International Search Report for PCT/US2006/061113 mailed Jul. 16, 2008.
International Search Report for PCT/US2008/050397 mailed Jul. 11, 2008.
International Search Report for PCT/US2008/062873 mailed Aug. 12, 2008.
International Search Report for PCT/US2008/062875 mailed Sep. 10, 2008.
International Search Report for PCT/US2008/063008 mailed Aug. 18, 2008.
International Search Report for PCT/US2008/066328 mailed Oct. 1, 2009.
International Search Report for PCT/US2008/070647 mailed Dec. 16, 2008.
International Search Report for PCT/US2008/072636 mailed Jan. 29, 2009.
International Search Report for PCT/US2008/073706 mailed Jan. 29, 2009.
Karklin et al., “Automatic Defect Severity Scoring for 193 nm Reticle Defect Inspection,” Proceedings of SPIE—The International Society for Optical Engineering, 2001, vol. 4346, No. 2, pp. 898-906.
Lo et al., “Identifying Process Window Marginalities of Reticle Designs for 0.15/0.13 μm Technologies,” Proceedings of SPIE vol. 5130, 2003, pp. 829-837.
Lorusso et al. “Advanced DFM Applns. Using design-based metrology on CDSEM,” SPIE vol. 6152, Mar. 27, 2006.
Lu et al., “Application of Simulation Based Defect Printability Analysis for Mask Qualification Control,” Proceedings of SPIE, vol. 5038, 2003, pp. 33-40.
Mack, “Lithographic Simulation: A Review,” Proceedings of SPIE vol. 4440, 2001, pp. 59-72.
Martino et al., “Application of the Aerial Image Measurement System (AIMS(TM)) to the Analysis of Binary Mask Imaging and Resolution Enhancement Techniques,” SPIE vol. 2197, 1994, pp. 573-584.
Miller, “A New Approach for Measuring Oxide Thickness,” Semiconductor International, Jul. 1995, pp. 147-148.
Nagpal et al., “Wafer Plane Inspection for Advanced Reticle Defects,” Proceedings of SPIE—The International Society for Optical Engineering; Photomask and Next-Generation Lithography Mask Technology. vol. 7028, 2008.
Numerical Recipes in C. The Art of Scientific Computing, 2nd Ed.,© Cambridge University Press 1988, 1992, p. 683.
O'Gorman et al., “Subpixel Registration Using a Concentric Ring Fiducial,” Proceedings of the International Conference on Pattern Recognition, vol. ii, Jun. 16, 1990, pp. 249-253.
Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-9, No. 1, Jan. 1979, pp. 62-66.
Pang et al., “Simulation-based Defect Printability Analysis on Alternating Phase Shifting Masks for 193 nm Lithography,” Proceedings of SPIE, vol. 4889, 2002, pp. 947-954.
Pettibone et al., “Wafer Printability Simulation Accuracy Based on UV Optical Inspection Images of Reticle Defects,” Proceedings of SPIE—The International Society for Optical Engineering 1999 Society of Photo-Optical Instrumentation Engineers, vol. 3677, No. II, 1999, pp. 711-720.
International Search Report and Written Opinion for PCT/US2012/029676 mailed Oct. 29, 2012.
Guo et al., “License Plate Localization and Character Segmentation with Feedback Self-Learning and Hybrid Binarization Techniques,” IEEE Transactions on Vehicular Technology, vol. 57, No. 3, May 2008, pp. 1417-1424.
Liu, “Robust Image Segmentation Using Local Median,” Proceedings of the 3rd Canadian Conference on Computer and Robot Vision (CRV'06) 0-7695-2542-3/06, 2006 IEEE, 7 pages total.
Related Publications (1)
Number Date Country
20120243773 A1 Sep 2012 US
Provisional Applications (1)
Number Date Country
61467964 Mar 2011 US