Detecting defects on a wafer

Information

  • Patent Grant
  • 8775101
  • Patent Number
    8,775,101
  • Date Filed
    Tuesday, August 2, 2011
    12 years ago
  • Date Issued
    Tuesday, July 8, 2014
    9 years ago
Abstract
Methods and systems for detecting defects on a wafer are provided.
Description
BACKGROUND OF THE INVENTION

1. Field of the Invention


This invention generally relates to detecting defects on a wafer. Certain embodiments relate to assigning individual output in raw output for a wafer generated by an inspection system to different segments.


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.


Wafer inspection, using either optical or electron beam technologies, is an important technique for debugging semiconductor manufacturing processes, monitoring process variations, and improving production yield in the semiconductor industry. With the ever decreasing scale of modern integrated circuits (ICs) as well as the increasing complexity of the manufacturing process, inspection becomes more and more difficult.


In each processing step performed on a semiconductor wafer, the same circuit pattern is printed in each die on the wafer. Most wafer inspection systems take advantage of this fact and use a relatively simple die-to-die comparison to detect defects on the wafer. However, the printed circuit in each die may include many areas of patterned features that repeat in the x or y direction such as the areas of DRAM, SRAM, or FLASH. This type of area is commonly referred to as an array area (the rest of the areas are called random or logic areas). To achieve better sensitivity, advanced inspection systems employ different strategies for inspecting the array areas and the random or logic areas.


To set up a wafer inspection process for array inspection, many currently used inspection systems require users to manually set up regions of interest (ROI) and apply the same set of parameters for defect detection in the same ROI. However, this method of set up is disadvantageous for a number of reasons. For example, as design rules shrink, region definition can be much more complicated and much smaller in area. With the limitations on stage accuracy and resolution of the inspection system, manual set up of ROI will become impossible eventually. On the other hand, if the distance between page breaks is larger than Fourier filtering can perform, the page break will not be suppressed in the array region.


In another method, intensity is used as a feature of segmentation to group similar intensity pixels together. Then, the same set of parameters are applied for the same group of pixels (intensity-based). However, this method also has a number of disadvantages. For example, an intensity-based segmentation algorithm can be used when a geometry feature scatters uniformly. Often, however, this is not enough. Therefore, other property-based segmentation is needed.


Accordingly, it would be advantageous to develop methods and systems for detecting defects on a wafer that can achieve better detection of defects by utilizing the knowledge that defects of interest and nuisance/noise reside in different segments geometrically.


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 detecting defects on a wafer. The computer-implemented method includes acquiring raw output for a wafer generated by an inspection system. The computer-implemented method also includes identifying one or more characteristics of the raw output that correspond to one or more geometrical characteristics of patterned features formed on the wafer. In addition, the computer-implemented method includes assigning individual output in the raw output to different segments based on the identified one or more characteristics of the raw output such that the one or more geometrical characteristics of the patterned features that correspond to each of the different segments are different. Furthermore, the computer-implemented method includes separately assigning one or more defect detection parameters to the different segments. The computer-implemented method also includes applying the assigned one or more defect detection parameters to the individual output assigned to the different segments to thereby detect defects on the wafer.


Each of the steps of the computer-implemented method described above may be performed as described further herein. The computer-implemented method described above may include any other step(s) of any other method(s) described herein. The computer-implemented method described above may be performed using any of the systems described herein.


Another embodiment relates to a computer-readable medium that includes program instructions executable on a computer system for performing a method for detecting defects on a wafer. The method includes the steps of the computer-implemented method described above. The computer-readable medium may be further configured as described herein. The steps of the method may be performed as described further herein. In addition, the method for which the program instructions are executable may include any other step(s) of any other method(s) described herein.


An additional embodiment relates to a system configured to detect defects on a wafer. The system includes an inspection subsystem configured to generate raw output for a wafer by scanning the wafer. The system also includes a computer subsystem configured to acquire the raw output. The computer subsystem is also configured to identify one or more characteristics of the raw output that correspond to one or more geometrical characteristics of patterned features formed on the wafer. In addition, the computer subsystem is configured to assign individual output in the raw output to different segments based on the identified one or more characteristics of the raw output such that the one or more geometrical characteristics of the patterned features that correspond to each of the different segments are different. The computer subsystem is further configured to separately assign one or more defect detection parameters to the different segments. Furthermore, the computer subsystem is configured to apply the assigned one or more defect detection parameters to the individual output assigned to the different segments to thereby detect defects on the wafer. 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 block diagram illustrating one embodiment of a computer-readable medium that includes program instructions executable on a computer system for performing one or more of the method embodiments described herein; and



FIG. 2 is a schematic diagram illustrating a side view of one embodiment of a system configured to detect defects on a wafer.





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

Although embodiments are described herein with respect to wafers, it is to be understood that the embodiments may be used for detecting defects on another specimen such as a reticle, which may also be commonly referred to as a mask or a photomask. Many different types of reticles are known in the art, and the terms “reticle,” “mask,” and “photomask” as used herein are intended to encompass all types of reticles known in the art.


One embodiment relates to a computer-implemented method for detecting defects on a wafer. The computer-implemented method includes acquiring raw output for a wafer generated by an inspection system. Acquiring the raw output for the wafer may be performed using the inspection system. For example, acquiring the raw output may include using the inspection system to scan light over the wafer and to generate raw output responsive to light scattered and/or reflected from the wafer detected by the inspection system during scanning. In this manner, acquiring the raw output may include scanning the wafer. However, acquiring the raw output does not necessarily include scanning the wafer. For example, acquiring the raw output may include acquiring the raw output from a storage medium in which the raw output has been stored (e.g., by the inspection system). Acquiring the raw output from the storage medium may be performed in any suitable manner, and the storage medium from which the output is acquired may include any of the storage media described herein. In any case, the method includes raw output (e.g., raw data) collection.


In one embodiment, the raw output is responsive to light scattered from the wafer. In particular, the raw output may be responsive to light scattered from the wafer and detected by the inspection system. Alternatively, the raw output may be responsive to light reflected from the wafer and detected by the inspection system. The raw output may include any suitable raw output and may vary depending on the configuration of the inspection system. For example, the raw output may include signals, data, image data, etc. In addition, the raw output may be generally defined as output for at least a portion (e.g., multiple pixels) of the entire output generated for the wafer by the inspection system. Furthermore, the raw output may include all of the raw output generated for the entire wafer by the inspection system, all of the raw output generated for the entire portion of the wafer that is scanned by the inspection system, all of the raw output generated for the wafer by one channel of the inspection system, etc., regardless of whether the raw output corresponds to defects on the wafer.


In contrast, individual output may be generally defined as output for an individual pixel of the entire output generated for the wafer by the inspection system. Therefore, the raw output may include multiple individual output. In other words, the individual output may be output separately generated for different locations on the wafer. For example, the individual output may include individual, discrete output generated for different locations on the wafer. In particular, the different locations may correspond to different “inspection points” on the wafer. In other words, the different locations may correspond to locations on the wafer for which output is separately generated by the inspection system. In this manner, the different locations may correspond to each location on the wafer at which a “measurement” is performed by the inspection system. As such, the different locations may vary depending on the configuration of the inspection system (e.g., the manner in which the inspection system generates output for the wafer). The individual output includes individual output that does and does not correspond to defects on the wafer.


The inspection system may be configured as described herein. For example, the inspection system may be configured for dark field (DF) inspection of the wafer. In this manner, the inspection system may include a DF inspection system. The DF inspection system may be configured as described further herein. In another example, the inspection system may be configured for bright field (BF) inspection of the wafer. In this manner, the inspection system may include a BF inspection system. The BF inspection system may have any suitable configuration known in the art. The inspection system may also be configured for BF and DF inspection. Furthermore, the inspection system may be configured as a scanning electron microscopy (SEM) inspection and review system, and such an inspection system may have any suitable configuration known in the art. In addition, the inspection system may be configured for inspection of patterned wafers and possibly also unpatterned wafers.


The computer-implemented method also includes identifying one or more characteristics of the raw output that correspond to one or more geometrical characteristics of patterned features formed on the wafer. In one embodiment, the identified one or more characteristics of the raw output include projections along lines within the raw output. A projection can be generally defined as a group, cluster, or summation of individual output that has some pattern within the raw output. For example, projections along horizontal and vertical lines of the raw output can be gathered. In this manner, x and y projections within the raw output can be identified that define or correspond to one or more geometrical characteristics of the patterned features. As such, identifying the one or more characteristics of the raw output may include performing two-dimensional (2D) projection of the raw output. However, the one or more characteristics of the raw output that correspond to the one or more geometrical characteristics of patterned features formed on the wafer may include any other characteristic(s) of the raw output. Identifying the one or more characteristics of the raw output as described above may be performed in any suitable manner using any suitable method and/or algorithm.


In one embodiment, the one or more geometrical characteristics of the patterned features include edges, shape, texture, a mathematical calculation that defines geometry of the patterned features, or some combination thereof. For example, characteristics that can be used for geometric-based segmentation, which may be performed as described further herein, include edges, shape, texture, any mathematical calculation/transformation that defines the geometry, or some combination thereof. Although all patterned features formed on a wafer may have some roughness and therefore some “texture,” texture is different than roughness in that roughness is generally used to refer to and describe roughness just on the periphery of patterned features white texture generally refers to the overall texture (e.g., as designed or not) of patterned features. One example of a mathematical calculation/transformation that can be used to define the geometry of the patterned features is a Fourier filtering algorithm, which can be used to describe a relationship between geometry and light scattering. For example, a Fourier filtering algorithm can be used to predict projections in the raw output that will correspond to one or more geometrical characteristics of the patterned features.


In one embodiment, identifying the one or more characteristics of the raw output is performed based on how a design layout of the patterned features will affect the one or more characteristics of the raw output. For example, a characteristic that can be used for segmentation, which can be performed as described herein, is the design layout. In particular, the design layout can be used to identify one or more geometrical characteristics of patterned features in the design layout. One or more characteristics (e.g., projections) of the raw output that correspond to the one or more identified geometrical characteristics can then be determined (e.g., empirically, theoretically, etc.). In this manner, one or more expected characteristics of the raw output that will correspond to one or more geometrical characteristics of the patterned features can be determined. Those one or more expected characteristics can then be compared to one or more characteristics of the raw output in any suitable manner to identify the one or more characteristics of the raw output that correspond to one or more geometrical characteristics of the patterned features. The design layout used in this step may be acquired in any suitable manner and may have any suitable format.


In another embodiment, identifying the one or more characteristics of the raw output is performed while acquiring the raw output is being performed. In this manner, identifying the one or more characteristics of the raw output may be performed on-the-fly as the wafer is being scanned by the inspection system. For example, identifying the one or more characteristics of the raw output can be performed using reference raw output for the wafer that will be compared to the raw output to detect defects on the wafer and that is acquired for the wafer in the same scan as the raw output. The reference raw output may include any of the references described herein. As such, other steps described herein (e.g., segmentation) that are performed using the one or more identified characteristics of the raw output may also be performed on-the-fly during acquisition of the raw output for the wafer.


The computer-implemented method also includes assigning individual output in the raw output to different segments based on the identified one or more characteristics of the raw output such that the one or more geometrical characteristics of the patterned features that correspond to each of the different segments are different. In this manner, the embodiments described herein are configured for geometry-based segmentation. More specifically, the embodiments described herein utilize how the geometrical characteristic(s) (e.g., shape) of wafer patterns will affect the raw output and separate the patterns that affect the raw output differently into different segments. In other words, the embodiments described herein utilize how the geometrical characteristic(s) (e.g., shape) of patterns on the wafer will affect the raw output to separate individual output in the raw output into different segments. For instance, patterned features that have one or more different geometrical characteristics may have different effects on light scattered from the wafer and thereby may have different effects on the raw output generated for the wafer. Those patterned features can be effectively separated into different segments by the embodiments described herein. Assigning the individual output in the raw output to different segments as described herein can be performed in any suitable manner using any suitable method and/or algorithm.


“Segments” can be generally defined as different portions of an entire range of possible values for the individual output. The segments may be defined based on values for different characteristics of the individual output depending on the defect detection algorithm that uses the segments. For instance, in the multiple die auto-thresholding (MDAT) algorithm, the value for the characteristic of the individual output that is used to define the segments may include median intensity value. In one such illustrative and non-limiting example, if the entire range of median intensity values is from 0 to 255, a first segment may include median intensity values from 0 to 100 and a second segment may include median intensity values from 101 to 255. In this manner, the first segment corresponds to darker areas in the raw output, and the second segment corresponds to brighter areas in the raw output. In some instances, the segments can be defined using one wafer, and for wafers having similar geometry as that one wafer, the predefined segments can be used.


In one embodiment, identifying the one or more characteristics of the raw output and assigning the individual output to the different segments is performed automatically without user input. For example, the embodiments described herein can utilize the geometrical characteristic(s) (e.g., shape) of patterns on the wafer and projection to automatically separate the individual output in the raw output into different segments. In this manner, unlike methods that include manually setting up regions of interest (ROI) and applying the same set of parameters for defect detection in the same ROI, as design rules shrink and as the different areas on the wafer to be segmented get smaller, segmentation will not become more complicated using the embodiments described herein. In addition, unlike manual methods, automatically identifying the one or more characteristics of the raw output and assigning the individual output to the different segments without user input is not affected by inspection system stage accuracy and resolution limitations. Therefore, using the embodiments described herein for segmentation, the inspection system stage accuracy and resolution limitations will not make segmentation impossible.


In another embodiment, assigning the individual output to the different segments is performed without regard to design data associated with the patterned features. For example, although the design layout may be used as described above to determine one or more expected characteristics of the raw output that will correspond to one or more geometrical characteristics of the patterned features, segmentation is not performed based on the design data itself. In other words, segmentation is based on how the one or more geometrical characteristics of the patterned features will affect the raw output, but is not based on the one or more geometrical characteristics of the patterned features themselves. In this manner, unlike other methods and systems that segment raw output based on the design data associated with patterned features, performing segmentation based on how the one or more geometrical characteristics of the patterned features will affect the raw output may result in patterned features associated with different design data, different electrical functions, different electrical characteristics, different criticalities to the performance of the device being formed using the patterned features, etc. being assigned to the same segment if those patterned features will affect the raw output in the same manner. For example, performing segmentation based on how the geometrical characteristic(s) will affect characteristic(s) (e.g., intensity) of the raw output instead of the geometry itself may result in patterned features that produce significant noise in the raw output being assigned to the same segment regardless of the design data associated with those pattern features and other patterned features that produce negligible noise in the raw output being assigned to a different segment again regardless of the design data associated with those other patterned features. In this manner, high noise patterned features can be segmented together, and low noise patterned features can be segmented together.


In an additional embodiment, assigning the individual output to the different segments is performed without regard to intensity of the individual output. In other words, although the segmentation is performed based on the one or more identified characteristics of the raw output, which may be identified based on intensity of multiple individual output in the raw output, the segmentation is not performed based on intensity of the individual output itself. For example, projections along lines within the raw output may include individual output that have a variety and possibly dramatically different intensities. Nevertheless, all of that individual output may correspond to the same one or more geometrical characteristics of patterned features such as page breaks. As such, all of the individual output that corresponds to the same one or more geometrical characteristics of the patterned features can be assigned to the same segment even though all of that individual output may have dramatically different intensities. In this manner, unlike methods for performing segmentation based on the intensity of individual pixels, the segmentation performed by the embodiments described herein will not be affected by non-uniform scattering from the patterned features.


In some embodiments, assigning the individual output to the different segments includes analyzing the identified one or more characteristics of the raw output and applying thresholds to the individual output. For example, as described above, projections along horizontal and vertical lines in the raw output can be gathered. The projections can then be analyzed, and thresholds can be set to separate the individual output in the raw output into different areas of interest (segments). Analyzing the identified one or more characteristics of the raw output and applying thresholds to the individual output may reduce the number of individual output corresponding to boundary regions from being inappropriately assigned to the segments.


In one embodiment, the one or more geometrical characteristics that correspond to one of the different segments include one or more geometrical characteristics of page breaks, and the one or more geometrical characteristics that correspond to another of the different segments include one or more geometrical characteristics of array areas. Page breaks are generally defined in the art as regions of a die separating substantially continuous regions of physical memory. Each of the continuous regions of physical memory may be commonly referred to as a page frame. Performing segmentation as described herein, one or more characteristics of the raw output (e.g., the x and/or y projections) that define the geometry for page breaks in array regions can be identified and used to assign individual output corresponding to the page breaks to one segment and to assign individual output corresponding to array regions to a different segment.


In another embodiment, the one or more characteristics of the raw output that correspond to the one or more geometrical characteristics of some of the patterned features cannot be suppressed by Fourier filtering. For example, unlike some methods for segmentation, even if the distance between page breaks is larger than Fourier filtering can perform, the page break can be suppressed in the array region. In one such example, for some inspection systems, if the width of a page break is about 5 μm and the spacing between page breaks is about 5 μm, Fourier filtering becomes impractical if not impossible while manual set up of ROI also becomes impractical if not impossible. Therefore, the signal (noise) produced in the raw output by the page breaks may not be suppressed and can thereby reduce the defect detection sensitivity that can be achieved using the raw output. However, using the embodiments described herein, the individual output that corresponds to the page breaks can be identified (e.g., based on projections within the raw output), and the individual output that corresponds to the page breaks can be assigned to one segment while other individual output can be assigned to other segments such that as described further herein different sensitivities can be used to detect defects in different segments.


The computer-implemented method further includes separately assigning one or more defect detection parameters to the different segments. One or more defect detection parameters can be separately assigned to all of the different segments. Therefore, some of the individual output may not be ignored when it comes to defect detection. Instead, defects can be detected using the individual output assigned to all of the different segments. In other words, defects can be detected using all segments of the raw output. In this manner, different segments can be treated differently with different inspection recipes. The different inspection recipes may be different in the defect detection algorithms that are assigned to the different segments. Alternatively, the different inspection recipes may be different in one or more parameters of the same defect detection algorithm that are assigned to the different segments. The defect detection algorithms that are assigned to the different segments or one or more parameters of which are assigned to the different segments may include any suitable detect detection algorithms. For example, the defect detection algorithm may be a segmented auto-thresholding (SAT) algorithm or a MDAT algorithm. Such defect detection algorithms may be particularly suitable for BF inspection. However, the defect detection algorithm may be a defect detection algorithm that is suitable for DF inspection. For example, the defect detection algorithm may be a FAST algorithm or an HLAT algorithm.


The different inspection recipes may also be different in one or more optical parameters of the inspection system that are used to acquire the raw output for the wafer. For example, in a multi-pass inspection, different passes may be performed with different values for at least one optical parameter (e.g., polarization, wavelength, angle of illumination, angle of collection, etc.) of the inspection system, and raw output generated in the different passes may be used to detect defects in different regions of the wafer in which patterned features having one or more different geometrical characteristics are formed. In this manner, regions of the wafer that include patterned features having one or more different geometrical characteristics can be inspected using raw output generated in different passes of a multi-pass inspection performed using one or more different optical parameters.


In one embodiment, the one or more defect detection parameters include a threshold to be applied to a difference between the individual output and a reference. In this manner, different thresholds can be applied to the difference between the individual output and the reference depending on the segment to which the individual output has been assigned. For example, a reference such as an 8-bit reference image) may be subtracted from the individual output in the raw output (such as an 8-bit test image) regardless of the segment to which the individual output has been assigned. The reference may include any suitable reference such as individual output corresponding to a die on the wafer that is different than the die in which the individual output, from which the reference is being subtracted, has been generated, a cell on the wafer that is different than the cell in which the individual output, from which the reference is being subtracted, has been generated, etc. Any individual output having a difference above the assigned threshold may be identified as a defect. In this manner, defects can be detected with different thresholds depending on the segment to which the individual output has been assigned.


In another embodiment, separately assigning the one or more defect detection parameters to the different segments is performed such that defects are detected using the individual output assigned to the different segments with different sensitivities. Therefore, the embodiments described herein can achieve better detection of defects by utilizing the knowledge that defects of interest (DOI) and nuisance/noise reside in different segments geometrically. For example, different geometries can exhibit different types of defects. In one such example, in an array pattern region, the raw output may include alternating line-like patterns of relatively bright individual output and relatively dark individual output. In some such instances, DOI may be located in portions of the raw output that include the relatively bright individual output while nuisance defects may be located in portions of the raw output that include the relatively dark individual output. In this manner, with segmentation using characteristic(s) that define the geometry (e.g., the x or y projection for page break in the array region), the sensitivity of a detection algorithm can be set up differently for better sensitivity in the array area and less nuisance from the page break. Therefore, the embodiments described herein advantageously allow an automatic way of separating different geometric patterns of the wafer into different segments. This segmentation makes it possible for these areas to be treated differently and better sensitivity can be achieved. Different geometries also scatter light differently. In this manner, some geometries may cause the raw output to be relatively noisy while other geometries may cause the raw output to be relatively quiet. However, using only intensity of the individual output for segmentation, individual output corresponding to relatively noisy and relatively quiet regions in the raw output can be grouped together (e.g., due to poorly defined boundaries). In contrast, in the embodiments described herein, for defects that are located in areas of the wafer that have one or more geometrical characteristics that correspond to less noise in the raw output, higher sensitivity can be achieved. In addition, for narrow band inspection systems, defects can often be buried in noise since patterns also scatter a significant amount of light. However, the embodiments described herein make it possible to detect those defects that are detuned by noise from nearby patterns.


The computer-implemented method further includes applying the assigned one or more defect detection parameters to the individual output assigned to the different segments to thereby detect defects on the wafer. As described above, different segments can be treated differently with different inspection recipes. In this manner, applying the assigned one or more defect detection parameters to the individual output may include inspecting segments with different recipes to thereby detect defects on the wafer. For example, the segment to which the individual output has been assigned can be used to determine the threshold that is to be applied to the difference between the individual output and the reference. After determining the segment to which the individual output has been assigned and assigning the one or more defect detection parameters to the different segments, the assigned one or more defect detection parameters can be applied to the individual output assigned to the different segments as would normally be performed.


In one embodiment, acquiring the raw output is performed in one pass of a multi-pass inspection of the wafer, and the computer-implemented method is not performed for raw output acquired in another pass of the multi-pass inspection. In this manner, segmentation as described herein may be performed for only one pass of a multi-pass inspection. Raw output acquired in other passes can be used for other purposes. For example, multi-pass inspection may serve the segmentation purpose with one pass having the optimum signal to defects and another pass providing the geometry-based segmentation. In particular, different passes of the multi-pass inspection may be performed with one or more different defect detection parameters and/or one or more different optical parameters such that the raw output and/or the defect detection results are different for different passes. In one such example, one optical mode used in one pass of the multi-pass inspection may allow segmentation while another optical mode of the inspection system used in another pass of the multi-pass inspection may provide the highest sensitivity to DOI.


In another embodiment, additional defects are detected using the raw output acquired in the other pass, and the method includes combining the defects and the additional defects to generate inspection results for the wafer. For example, as described above, one pass of a multi-pass inspection may be used for segmentation while another pass of the multi-pass inspection may be used to detect DOI with optimum signal. Therefore, different passes of the multi-pass inspection may detect different types of defects. In this manner, the results of the different passes of the multi-pass inspection can be combined to generate the overall inspection results for the wafer. The results of the defects detected using the raw output acquired in different passes may be combined after defect detection using the raw output generated in all of the different passes has been performed. Alternatively, the defect detection results generated using the raw output acquired in different passes may be combined on-the-fly or while some of the raw output is still being acquired.


In an additional embodiment, the method includes applying one or more predetermined defect detection parameters to the raw output to detect additional defects on the wafer and combining the defects and the additional defects to generate inspection results for the wafer. For example, a reference (such as an 8-bit reference image) may be subtracted from the individual output in the raw output (such as an 8-bit test image) regardless of the segment to which the individual output has been assigned. The reference may include any suitable reference such as those described above. In addition, the same reference can be used for detecting defects by applying the assigned one or more defect detection parameters to the individual output and by applying one or more predetermined defect detection parameters to the raw output. The result of the subtraction may be an absolute difference. A predetermined, direct difference threshold may then be applied to the absolute difference, and any individual output having an absolute difference above the threshold may be identified as a defect. In addition, the same predetermined, direct difference threshold may be applied to the absolute difference regardless of the segment to which the individual output has been assigned. Defects detected in this manner may then be combined with defects detected by applying the assigned one or more defect detection parameters to the individual output to generate the final inspection results for the wafer. For example, a defective mask may be separately generated for all defects detected in any manner. Region “grow” may be performed from both difference images, and a final mask for all defects may be generated.


Detecting defects in different manners as described above may provide defect redetection, which may be advantageous for a number of reasons. For example, automatic 2D projection and geometry-based segmentation provide robust defect redetection and ease of use for detect redetection. In addition, the segmentation described herein provides a dynamic way of mapping defect and reference images. For example, if the segment is noisy, the difference can be detuned. In contrast, if the segment is cleaner, the difference can be enlarged. In addition, double detection as described above lowers the possibility of false alarms from either detection method.


The method may also include storing results of any of the step(s) of the method 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. For example, the segments to which the individual output is assigned and/or the one or more defect detection parameters assigned to the different segments may be used to generate a data structure such as a look up table that is stored on a storage medium coupled to the inspection system. The storage medium may include any suitable storage medium known in the art. After the results have been stored, the results can be accessed in the storage medium and used as 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. For example, the storage medium may be random access memory (RAM), and the results may not necessarily persist indefinitely in the storage medium. Storing the results may also be performed as described in commonly owned U.S. patent application Ser. No. 12/234,201 by Bhaskar et al. filed Sep. 19, 2008, which published as U.S. Patent Application Publication No. 2009/0080759 on Mar. 26, 2009, and which is incorporated by reference as if fully set forth herein.


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.


Another embodiment relates to a computer-readable medium that includes program instructions executable on a computer system for performing a method (i.e., a computer-implemented method) for detecting defects on a wafer. One such embodiment is shown in FIG. 1. For example, as shown in FIG. 1, computer-readable medium 10 includes program instructions 12 executable on computer system 14 for performing the method for detecting defects on a wafer described above. The computer-implemented method for which the program instructions are executable may include any other step(s) of any other method(s) described herein.


Program instructions 12 implementing methods such as those described herein may be stored on computer-readable medium 10. The computer-readable medium may be a storage medium such as a read-only memory, a RAM, a magnetic or optical disk, or a magnetic tape or any other suitable computer-readable medium known in the art.


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 Matlab, Visual Basic, ActiveX controls, C, C++ objects, C#, JavaBeans, Microsoft Foundation Classes (“MFC”), or other technologies or methodologies, as desired.


Computer system 14 may take various forms, including a personal computer system, mainframe computer system, workstation, system computer, image computer, programmable 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.


An additional embodiment relates to a system configured to detect defects on a wafer. One embodiment of such a system is shown in FIG. 2. As shown in FIG. 2, system 16 includes inspection subsystem 18 and computer subsystem 20. The inspection subsystem is configured to generate raw output for a wafer by scanning the wafer. For example, as shown in FIG. 2, the inspection subsystem includes light source 22 such as a laser. Light source 22 is configured to direct light to polarizing component 24. In addition, the inspection subsystem may include more than one polarizing component (not shown), each of which may be positioned independently in the path of the light from the light source. Each of the polarizing components may be configured to alter the polarization of the light from the light source in a different manner. The inspection subsystem may be configured to move the polarizing components into and out of the path of the light from the light source in any suitable manner depending on which polarization setting is selected for illumination of the wafer during a scan. The polarization setting used for the illumination of the wafer during a scan may include p-polarized (P), s-polarized (S), or circularly polarized (C).


Light exiting polarizing component 24 is directed to wafer 26 at an oblique angle of incidence, which may include any suitable oblique angle of incidence. The inspection subsystem may also include one or more optical components (not shown) that are configured to direct light from tight source 22 to polarizing component 24 or from polarizing component 24 to wafer 26. The optical components may include any suitable optical components known in the art such as, but not limited to, a reflective optical component. In addition, the tight source, the polarizing component, and/or the one or more optical components may be configured to direct the light to the wafer at one or more angles of incidence (e.g., an oblique angle of incidence and/or a substantially normal angle of incidence). The inspection subsystem may be configured to perform the scanning by scanning the tight over the wafer in any suitable manner.


Light scattered from wafer 26 may be collected and detected by multiple channels of the inspection subsystem during scanning. For example, light scattered from wafer 26 at angles relatively close to normal may be collected by lens 28. Lens 28 may include a refractive optical element as shown in FIG. 2. In addition, lens 28 may include one or more refractive optical elements and/or one or more reflective optical elements. Light collected by lens 28 may be directed to polarizing component 30, which may include any suitable polarizing component known in the art. In addition, the inspection subsystem may include more than one polarizing component (not shown), each of which may be positioned independently in the path of the tight collected by the lens. Each of the polarizing components my be configured to alter the polarization of the light collected by the lens in a different manner. The inspection subsystem may be configured to move the polarizing components into and out of the path of the light collected by the lens in any suitable manner depending on which polarization setting is selected for detection of the light collected by lens 28 during scanning. The polarization setting used for the detection of the light collected by lens 28 during scanning may include any of the polarization settings described herein (e.g., P, S, and unpolarized (N)).


Light exiting polarizing component 30 is directed to detector 32. Detector 32 may include any suitable detector known in the art such as a charge coupled device (CCD) or another type of imaging detector. Detector 32 is configured to generate raw output that is responsive to the scattered light collected by lens 28 and transmitted by polarizing component 30 if positioned in the path of the collected scattered light. Therefore, lens 28, polarizing component 30 if positioned in the path of the light collected by lens 28, and detector 32 form one channel of the inspection subsystem. This channel of the inspection subsystem may include any other suitable optical components (not shown) known in the art such as a Fourier filtering component.


Light scattered from wafer 26 at different angles may be collected by lens 34. Lens 34 may be configured as described above. Light collected by lens 34 may be directed to polarizing component 36, which may include any suitable polarizing component known in the art. In addition, the inspection subsystem may include more than one polarizing component (not shown), each of which may be positioned independently in the path of the light collected by the lens. Each of the polarizing components may be configured to alter the polarization of the light collected by the lens in a different manner. The inspection subsystem may be configured to move the polarizing components into and out of the path of the light collected by the lens in any suitable manner depending on which polarization setting is selected for detection of the light collected by lens 34 during scanning. The polarization setting used for detection of the light collected by lens 34 during scanning may include P, S, or N.


Light exiting polarizing component 36 is directed to detector 38, which may be configured as described above. Detector 38 is also configured to generate raw output that is responsive to the collected scattered light that passes through polarizing component 36 if positioned in the path of the scattered light. Therefore, lens 34, polarizing component 36 if positioned in the path of the light collected by lens 34, and detector 38 may form another channel of the inspection subsystem. This channel may also include any other optical components (not shown) described above. In some embodiments, lens 34 may be configured to collect light scattered from the wafer at polar angles from about 20 degrees to about 70 degrees. In addition, lens 34 may be configured as a reflective optical component (not shown) that is configured to collect light scattered from the wafer at azimuthal angles of about 360 degrees.


The inspection subsystem shown in FIG. 2 may also include one or more other channels (not shown). For example, the inspection subsystem may include an additional channel, which may include any of the optical components described herein such as a lens, one or more polarizing components, and a detector, configured as a side channel. The lens, the one or more polarizing components, and the detector may be further configured as described herein. In one such example, the side channel may be configured to collect and detect light that is scattered out of the plane of incidence (e.g., the side channel may include a lens, which is centered in a plane that is substantially perpendicular to the plane of incidence, and a detector configured to detect light collected by the lens).


If inspection of the wafer includes more than one pass, the values of any optical parameter(s) of the inspection subsystem may be altered in any suitable manner if necessary between passes. For example, to change the illumination polarization states between passes, polarizing component 24 may be removed and/or replaced as described herein with a different polarizing component. In another example, to change illumination angles between passes, the position of the light source and/or any other optical components (e.g., polarizing component 24) used to direct the tight to the wafer may be altered between passes in any suitable manner.


Computer subsystem 20 is configured to acquire the raw output generated by the inspection subsystem. For example, raw output generated by the detectors during scanning may be provided to computer subsystem 20. In particular, the computer subsystem may be coupled to each of the detectors (e.g., by one or more transmission media shown by the dashed lines in FIG. 2, which may include any suitable transmission media known in the art) such that the computer subsystem may receive the raw output generated by the detectors. The computer subsystem may be coupled to each of the detectors in any suitable manner. The raw output generated by the detectors during scanning of the wafer may include any of the raw output described herein.


The computer subsystem is configured to identify one or more characteristics of the raw output that correspond to one or more geometrical characteristics of patterned features formed on the wafer according to any of the embodiments described herein. The one or more characteristics of the raw output may include any such characteristics described herein. The one or more geometrical characteristics may also include any such characteristics described herein. The patterned features may include any of the patterned features described herein.


In addition, the computer subsystem is configured to assign individual output in the raw output to different segments based on the identified one or more characteristics of the raw output such that the one or more geometrical characteristics of the patterned features that correspond to each of the different segments are different. The computer subsystem may be configured to assign the individual output to the different segments according to any of the embodiments described herein. The individual output may include any of the individual output described herein. The different segments may be configured as described herein. The identified one or more characteristics of the raw output may include any such characteristics described herein.


The computer subsystem is further configured to separately assign one or more defect detection parameters to the different segments according to any of the embodiments described herein. The one or more defect detection parameters may include any of the defect detection parameters described herein. The computer subsystem is also configured to apply the assigned one or more defect detection parameters to the individual output assigned to the different segments to thereby detect defects on the wafer, which may be performed according to any of the embodiments described herein. The assigned one or more defect detection parameters may include any such parameters described herein.


The computer subsystem may be configured to perform any other step(s) of any method embodiment(s) described herein. The computer subsystem, the inspection subsystem, and the system may be further configured as described herein.


It is noted that FIG. 2 is provided herein to generally illustrate one configuration of an inspection subsystem that may be included in the system embodiments described herein. Obviously, the inspection subsystem configuration described herein may be altered to optimize the performance of the inspection subsystem as is normally performed when designing a commercial inspection system. In addition, the systems described herein may be implemented using an existing inspection system (e.g., by adding functionality described herein to an existing inspection system) such as the Puma 90xx, 91xx, and 93xx series of tools that are commercially available from KLA-Tencor, Milpitas, Calif. For some such systems, the methods described herein may be provided as optional functionality of the system (e.g., in addition to other functionality of the system). Alternatively, the system described herein may be designed “from scratch” to provide a completely new system.


Further modifications and alternative embodiments of various aspects of the invention will be apparent to those skilled in the art in view of this description. For example, methods and systems for detecting defects on a wafer 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 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 detecting defects on a wafer, comprising: acquiring raw output for a wafer generated by an inspection system;identifying one or more characteristics of the raw output that correspond to one or more geometrical characteristics of patterned features formed on the wafer;assigning individual output in the raw output to different segments based on the identified one or more characteristics of the raw output such that the one or more geometrical characteristics of the patterned features that correspond to each of the different segments are different, wherein the one or more geometrical characteristics that correspond to one of the different segments comprise one or more geometrical characteristics of page breaks, and wherein the one or more geometrical characteristics that correspond to another of the different segments comprise one or more geometrical characteristics of array areas;separately assigning one or more defect detection parameters to the different segments; andapplying the assigned one or more defect detection parameters to the individual output assigned to the different segments to thereby detect defects on the wafer.
  • 2. The method of claim 1, wherein the raw output is responsive to light scattered from the wafer.
  • 3. The method of claim 1, wherein the identified one or more characteristics of the raw output comprise projections along lines within the raw output.
  • 4. The method of claim 1, wherein the one or more geometrical characteristics of the patterned features comprise edges, shape, texture, a mathematical calculation that defines geometry of the patterned features, or some combination thereof.
  • 5. The method of claim 1, wherein said identifying is performed based on how a design layout of the patterned features will affect the one or more characteristics of the raw output.
  • 6. The method of claim 1, wherein said identifying is performed while said acquiring is being performed.
  • 7. The method of claim 1, wherein said identifying and said assigning the individual output are performed automatically without user input.
  • 8. The method of claim 1, wherein said assigning the individual output is performed without regard to design data associated with the patterned features.
  • 9. The method of claim 1, wherein said assigning the individual output is performed without regard to intensity of the individual output.
  • 10. The method of claim 1, wherein said assigning the individual output comprises analyzing the identified one or more characteristics of the raw output and applying thresholds to the individual output.
  • 11. The method of claim 1, wherein the one or more characteristics of the raw output that correspond to the one or more geometrical characteristics of some of the patterned features cannot be suppressed by Fourier filtering.
  • 12. The method of claim 1, wherein the one or more defect detection parameters comprise a threshold to be applied to a difference between the individual output and a reference.
  • 13. The method of claim 1, wherein said separately assigning the one or more defect detection parameters is performed such that defects are detected using the individual output assigned to the different segments with different sensitivities.
  • 14. The method of claim 1, wherein said acquiring is performed in one pass of a multi-pass inspection of the wafer, and wherein the computer-implemented method is not performed for raw output acquired in another pass of the multi-pass inspection.
  • 15. The method of claim 1, wherein said acquiring is performed in one pass of a multi-pass inspection of the wafer, wherein the computer-implemented method is not performed for raw output acquired in another pass of the multi-pass inspection, wherein additional defects are detected using the raw output acquired in the other pass, and wherein the method further comprises combining the defects and the additional defects to generate inspection results for the wafer.
  • 16. The method of claim 1, further comprising applying one or more predetermined defect detection parameters to the raw output to detect additional defects on the wafer and combining the defects and the additional defects to generate inspection results for the wafer.
  • 17. A computer-readable medium, comprising program instructions executable on a computer system for performing a method for detecting defects on a wafer, wherein the method comprises: acquiring raw output for a wafer generated by an inspection system;identifying one or more characteristics of the raw output that correspond to one or more geometrical characteristics of patterned features formed on the wafer;assigning individual output in the raw output to different segments based on the identified one or more characteristics of the raw output such that the one or more geometrical characteristics of the patterned features that correspond to each of the different segments are different, wherein the one or more geometrical characteristics that correspond to one of the different segments comprise one or more geometrical characteristics of page breaks, and wherein the one or more geometrical characteristics that correspond to another of the different segments comprise one or more geometrical characteristics of array areas;separately assigning one or more defect detection parameters to the different segments; andapplying the assigned one or more defect detection parameters to the individual output assigned to the different segments to thereby detect defects on the wafer.
  • 18. A system configured to detect defects on a wafer, comprising: an inspection subsystem configured to generate raw output for a wafer by scanning the wafer; anda computer subsystem configured to: acquire the raw output;identify one or more characteristics of the raw output that correspond to one or more geometrical characteristics of patterned features formed on the wafer;assign individual output in the raw output to different segments based on the identified one or more characteristics of the raw output such that the one or more geometrical characteristics of the patterned features that correspond to each of the different segments are different, wherein the one or more geometrical characteristics that correspond to one of the different segments comprise one or more geometrical characteristics of page breaks, and wherein the one or more geometrical characteristics that correspond to another of the different segments comprise one or more geometrical characteristics of array areas;separately assign one or more defect detection parameters to the different segments; andapply the assigned one or more defect detection parameters to the individual output assigned to the different segments to thereby detect defects on the wafer.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation. of International Application No. PCT/US10/23802 filed Feb. 10, 2010, which application claims priority to U.S. Provisional Application No. 61/152,477 entitled “Methods and Systems for Detecting Defects on a Wafer,” filed Feb 13, 2009, which is incorporated by reference as if fully set forth herein.

US Referenced Citations (418)
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
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
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
6324298 O'Dell et al. Nov 2001 B1
6344640 Rhoads Feb 2002 B1
6363166 Wihl et al. Mar 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
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
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
7760929 Orbon et al. Jul 2010 B2
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
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
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
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
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
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
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
20100142800 Pak et al. Jun 2010 A1
20100146338 Schalick et al. Jun 2010 A1
20100150429 Jau et al. Jun 2010 A1
20110052040 Kuan Mar 2011 A1
20110184662 Badger et al. Jul 2011 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 (45)
Number Date Country
1339140 Mar 2002 CN
1398348 Feb 2003 CN
1646896 Jul 2005 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
2002365235 Dec 2002 JP
2003-215060 Jul 2003 JP
2004045066 Feb 2004 JP
2005-283326 Oct 2005 JP
2009-122046 Jun 2009 JP
10-2001-0007394 Jan 2001 KR
10-2001-0037206 May 2001 KR
10-2001-0101697 Nov 2001 KR
1020030055848 Jul 2003 KR
1020050092053 Sep 2005 KR
10-2006-0075691 Jul 2006 KR
10-2010-0061018 Jun 2010 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
2006063268 Jun 2006 WO
2010093733 Aug 2010 WO
Non-Patent Literature Citations (85)
Entry
Supplementary European Search Report and European Search Opinion for European Application No. 10 741 683.6 dated Oct. 18, 2012.
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.
International Preliminary Report on Patentability for PCT/US2010/023802 mailed Aug. 25, 2011.
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.
U.S. Appl. No. 60/418,994, filed Oct. 15, 2002 by Stokowski et al.
U.S. Appl. No. 60/419,028, filed Oct. 15, 2002 by Stokowski et al.
U.S. Appl. No. 60/451,707, filed Mar. 4, 2003 by Howard et al.
U.S. Appl. No. 60/485,233, filed Jul. 7, 2003 by Peterson et al.
U.S. Appl. No. 60/526,881, filed Dec. 4, 2003 by Hess et al.
U.S. Appl. No. 60/609,670, filed Sep. 14, 2004 by Preil et al.
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. 60/738,290, filed Nov. 18, 2005 by Kulkarni et al.
U.S. Appl. No. 60/772,418, filed Feb. 9, 2006 by Kirk 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. 12/102,343, filed Apr. 14, 2008 by Chen 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.
International Search Report for PCT/US2009/051961 mailed Mar. 16, 2010.
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.
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-R31.
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.
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.
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.
International Search Report and Written Opinion for PCT/US2010/023802 mailed Aug. 30, 2010.
Office Action for Chinese Patent Application No. 201080016422.8 mailed Sep. 30, 2013, No English.
Office Action for Japanese Patent Application 2011-550208 mailed on Nov. 5, 2013, No English.
U.S. Appl. No. 13/652,377, filed Oct. 15, 2012 by Wu et al.
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-Mar. 6, 2006 IEEE, 7 pages total.
Related Publications (1)
Number Date Country
20130035876 A1 Feb 2013 US
Provisional Applications (1)
Number Date Country
61152477 Feb 2009 US
Continuations (1)
Number Date Country
Parent PCT/US2010/023802 Feb 2010 US
Child 13196758 US