Establishing a care area for specimen testing

Abstract

A method and system for setting care areas (CAs) for inspection of a specimen is provided. One system includes an imaging subsystem configured to generate images of the specimen and a computer subsystem configured to determine a number of defects detected in a predetermined cell in one or more of the generated images in a repeating patterned area formed on the specimen. The computer subsystem is also configured to compare the number of defects detected in each of two or more of the predetermined cells to a predetermined threshold and designate any one or more of the two or more of the predetermined cells having a number of defects greater than the predetermined threshold as one or more CAs. Additionally, the computer subsystem is configured to store information about the one or more CAs for use in inspection of the specimen.

Description

[Technical Field]

[0001] The present invention relates generally to methods and systems for establishing care areas for testing of specimens. [Background technology]

[0002] The following descriptions and examples are not admitted to be prior art by virtue of their inclusion in this section.

[0003] Inspection processes are used at various steps during the semiconductor manufacturing process to detect defects on reticles and wafers, promoting higher yields and therefore higher profits in the manufacturing process. Inspection has always been an important part of manufacturing 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 device failure.

[0004] A "care area," as commonly referred to in the art, is an area on a specimen that is of interest for inspection purposes. Care areas (CAs) may be used to distinguish between areas on a specimen that are inspected and areas on a specimen that are not inspected by the inspection process. Additionally, CAs may be used to identify areas on a specimen that are inspected with one or more different parameters. For example, if a first area on a specimen is more critical than a second area on the specimen, the first area may be inspected with a higher sensitivity than the second area to detect defects in the first area with a higher sensitivity. Other parameters of the inspection process may be similarly varied from CA to CA.

[0005] There are different categories of inspection CAs in use today. One category is legacy CAs, which are traditionally handwritten. With almost all users adopting design-guided inspection, there are very few legacy CAs in use today. Another category is design-based CAs. These are CAs derived based on heuristics about the chip design patterns printed on the specimen. Users look at the chip design and attempt to derive methods / scripts to help derive the CAs. There are multiple techniques and tools available to define these design-based CAs. Because they are derived from ground truth (the chip design), they can provide highly accurate and substantially smaller CAs, allowing inspection systems to store a large number of CAs. These CAs are not just important from a defect detection perspective, but are often important for noise suppression.

[0006] Some currently used inspection methods also use regular groups of CAs, where CAs with different noise behaviors are grouped together and even one single CA can contain many different structures with different noise behaviors. To identify areas with higher noise, several iterations of a so-called design-based search must be performed. This procedure takes a lot of time. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] U.S. Patent No. 6,466,895 [Patent Document 2] U.S. Patent Application Publication No. 2006-0182333 Summary of the Invention [Problem to be solved by the invention]

[0008] Therefore, currently used methods and systems for setting up inspection CAs have several drawbacks. For example, many methods and systems for setting up inspection CAs are computationally expensive, requiring access to intellectual property, such as CDS files, of the entity that designed the device being inspected, at least in part. In addition, systematic noise sources can be suppressed by manually drawing CAs at target locations. However, this method is tedious and time-consuming, as there may be over 1,000 hot spot locations on a process layer. Therefore, it would be advantageous to develop a system and method for setting up CAs for inspection of a specimen that does not have one or more of the above-mentioned drawbacks. [Means for solving the problem]

[0009] The following description of various embodiments is not to be construed in any way as limiting the subject matter of the appended claims.

[0010] One embodiment relates to a system configured to set care areas (CAs) for inspection of a specimen. The system includes an imaging subsystem configured to generate images of the specimen and a computer subsystem configured to determine a number of defects detected within predetermined cells within one or more of the generated images within a repeating patterned area formed on the specimen. The computer subsystem is also configured to compare the number of defects detected in each of two or more predetermined cells with a predetermined threshold. Additionally, the computer subsystem is configured to designate any one or more of the two or more predetermined cells in which the number of defects is greater than the predetermined threshold as one or more CAs. The computer subsystem is further configured to store information regarding the one or more CAs for use in inspection of the specimen. The system may be further configured as described herein.

[0011] Another embodiment relates to a computer-implemented method for setting up a CA for inspection of a specimen. The method includes the determining, comparing, designating, and storing steps described above. The steps of the method are executed by a computer subsystem coupled to the imaging subsystem described above. Each of the steps of the method may be performed as further described herein. The method may include any other step of any other method described herein. The method may be performed by any of the systems described herein. Another embodiment relates to a non-transitory computer-readable medium storing program instructions executable on a computer system for executing a computer-implemented method for setting up a CA for inspection of a specimen. The computer-implemented method includes the steps of the above-described method. The computer-readable medium may be further configured as described herein. The steps of the computer-implemented method may be performed as further described herein. Additionally, the computer-implemented method for which the program instructions are executable may include any other step of any other method described herein. [Brief explanation of the drawings]

[0012] Further advantages of the present invention will become apparent to those skilled in the art with the benefit of the following detailed description of the preferred embodiments, and with reference to the accompanying drawings, in which: [Figure 1] FIG. 1 is a schematic diagram showing a side view of an embodiment of a system configured as described herein. [Figure 2] FIG. 1 is a schematic diagram showing a side view of an embodiment of a system configured as described herein. [Figure 3] 4 is a flowchart illustrating one embodiment of steps performed by the computer subsystems described herein. [Figure 4] 1 is a schematic diagram illustrating a top view of an example of defects detected in a repeating patterned area of ​​a specimen and the results of various steps described herein for establishing care areas (CAs) for inspection of the specimen based on the detected defects and predetermined cells having different sizes. [Figure 5] FIG. 10 is a schematic diagram illustrating a plan view of an example of a defect detected within a repeating patterned area of ​​a sample, and the results of various steps described herein for setting care areas (CAs) for inspection of the sample based on the detected defects and predetermined cells having different sizes. [Figure 6] FIG. 1 is a block diagram illustrating one embodiment of a non-transitory computer-readable medium storing program instructions for causing a computer system to perform the computer-implemented methods described herein. DETAILED DESCRIPTION OF THE INVENTION

[0013] While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are herein described in detail. The drawings may not be to scale. It should be understood, however, that the drawings and detailed description are not intended to limit the invention to the particular forms disclosed, but on the contrary, are intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.

[0014] As used herein, a "nuisance" (sometimes used interchangeably with "nuisance defect") is generally defined as a defect that is not noticed by the user and / or an event that is detected on the specimen but is not actually an actual defect on the specimen. Nuisances that are not actually defects may be detected as events that result from non-defect noise sources on the specimen (e.g., grains in metal lines on the specimen, signals from underlying layers or materials on the specimen, line edge roughness (LER), relatively small critical dimension (CD) variations in patterned attributes, thickness variations, etc.) and / or that result from limitations in the inspection system itself or its configuration used for inspection.

[0015] As used herein, the term "defect of interest (DOI)" is defined as a defect detected on a specimen and is an actual defect on the specimen. DOIs are therefore of interest to a user because the user generally cares about the number and type of actual defects on the specimen being inspected. In some contexts, the term "DOI" is used to refer to a subset of all actual defects on a specimen, including only those actual defects that the user cares about. For example, there may be multiple types of DOIs on any given specimen, one or more of which may be more interesting to the user than one or more other types. However, in the context of the embodiments described herein, the term "DOI" is used to refer to any actual defect on a specimen. The terms "design," "design data," and "design information," used interchangeably herein, generally refer to the physical design (layout) of an IC or other semiconductor device and data derived from the physical design through complex simulations or simple geometric and Boolean operations. The design may include any other design data or design data proxies described in commonly owned U.S. Patents 7,570,796 (August 4, 2009, Zafar et al.) and 7,676,077 (March 9, 2010, Kulkami et al.), both of which are incorporated by reference as if fully set forth herein. Additionally, the design data may be standard cell library data, integrated layout data, design data for one or more layers, derivatives of design data, and complete or partial chip design data. Furthermore, the terms "design," "design data," and "design information" as used herein refer to information and data generated by semiconductor device designers in the design process and therefore available for use in the embodiments described herein prior to printing the design on any physical specimens, such as reticles and wafers.

[0016] Referring now to the drawings, it should be noted that the figures are not drawn to scale. In particular, the scale of some of the elements in the figures has been greatly exaggerated to emphasize the characteristics of the elements. It should also be noted that the figures are not drawn to scale. Elements shown in multiple figures that may be similarly configured are indicated using the same reference numerals. Unless otherwise noted herein, any of the elements described and shown may include any suitable commercially available elements.

[0017] One embodiment relates to a system configured to set care areas (CAs) for inspection of a specimen. As described further herein, the embodiments advantageously provide an advanced method for identifying systematic noise locations for inspection CA generation. Generally, the embodiments described herein take advantage of the fact that noise sources can be identified after inspecting a process wafer or another specimen as described herein. For example, random defect locations are expected to be relatively spread throughout a die or other repetitively patterned area on the specimen. In contrast, events from systematic noise hotspots are concentrated in specific locations. Unique embodiments described herein have been developed to identify such concentrated noise locations and separate them from random defects. In this manner, the embodiments described herein provide methods and systems for advanced CA setting to address systematic nuisance.

[0018] In one embodiment, the specimen is a wafer. The wafer can include any wafer known in the semiconductor art. In another embodiment, the specimen is a reticle. The reticle can include any reticle known in the semiconductor art. Although some embodiments may be described herein with respect to one or more wafers, the embodiments are not limited to the specimens with which they can be used. For example, the embodiments described herein can be used with specimens such as reticles, flat panels, personal computer (PC) boards, and other semiconductor specimens.

[0019] One embodiment of such a system includes an imaging subsystem configured to generate an image of the sample. In some embodiments, the imaging subsystem includes at least an energy source and a detector. The energy source is configured to generate energy that is directed at the sample. The detector is configured to detect energy from the sample and generate an output in response to the detected energy. In one embodiment, the imaging subsystem is an optical-based imaging subsystem. For example, in the system embodiment shown in FIG. 1 , the imaging subsystem 10 includes an illumination subsystem configured to direct light toward the sample 14. The illumination subsystem includes at least one light source, e.g., light source 16. The illumination subsystem is configured to direct light toward the sample at one or more angles of incidence, which may include one or more oblique angles and / or one or more normal angles. As shown in FIG. 1 , light from light source 16 passes through optical element 18 and then through lens 20 to beam splitter 21, which directs the light toward the sample 14 at a normal angle of incidence. The angle of incidence may include any suitable angle of incidence, which may vary depending, for example, on the characteristics of the sample and the defects detected on the sample. The illumination subsystem may be configured to direct light toward the sample at different angles of incidence at different times. For example, the imaging subsystem may be configured to change one or more characteristics of one or more elements of the illumination subsystem to direct light toward the sample at angles of incidence different from the angle of incidence shown in FIG. 1 . In one such example, the imaging subsystem may be configured to move the light source 16, the optical element 18, and the lens 20 so that light is directed at the sample at different angles of incidence.

[0020] In some cases, the imaging subsystem can be configured to simultaneously direct light at multiple angles of incidence toward the sample. For example, the imaging subsystem may include multiple illumination channels, one of which may include light source 16, optical element 18, and lens 20, as shown in FIG. 1, while another of the illumination channels (not shown) may include similar elements, which may be configured differently or may be the same, or may include at least a light source and, in some cases, one or more other components, such as those described further herein. When such light is directed toward the sample simultaneously with other light, one or more characteristics (e.g., wavelength, polarization, etc.) of the light directed toward the sample at different angles of incidence may be different such that light resulting from illumination of the sample at different angles of incidence can be distinguished from one another at a detector.

[0021] In another example, the illumination subsystem may include only one light source (e.g., light source 16 shown in FIG. 1 ), and the light from the light source may be separated into different optical paths (e.g., based on wavelength, polarization, etc.) by one or more optical elements (not shown) in the illumination subsystem. The light in each of the different optical paths may then be directed toward the sample. Multiple illumination channels may be configured to direct light toward the sample simultaneously or at different times (e.g., when different illumination channels are used to sequentially illuminate the sample). In other examples, the same illumination channel may be configured to direct light with different characteristics toward the sample at different times. For example, in some cases, optical element 18 may be configured as a spectral filter, and the characteristics of the spectral filter may be changed in a variety of different ways (e.g., by swapping out the spectral filter) so that light of different wavelengths can be directed toward the sample at different times. The illumination subsystem may have any other suitable configuration known in the art for directing light with different or the same characteristics toward the sample sequentially or simultaneously at different or the same angles of incidence.

[0022] In one embodiment, light source 16 includes a broadband plasma (BBP) light source. In this manner, the light generated by the light source and directed toward the sample can include broadband light. However, the light source can include any other suitable light source, such as a laser, which can be any suitable laser known in the art and configured to generate light at any suitable wavelength known in the art. The laser can be configured to generate monochromatic or nearly monochromatic light. As such, the laser can be a narrowband laser. The light source can also include a polychromatic light source that generates light at multiple discrete wavelengths or wavelength bands.

[0023] Light from optical element 18 may be focused by lens 20 onto beam splitter 21. While lens 20 is shown in FIG. 1 as a single refractive optical element, in reality, lens 20 may include several refractive and / or reflective optical elements that combine to focus light from the optical elements onto the sample. The illumination subsystem shown in FIG. 1 and described herein may include any other suitable optical elements (not shown). Examples of such optical elements include, but are not limited to, polarization components, spectral filters, spatial filters, reflective optical elements, apodizers, beam splitters, apertures, etc., and may include any such suitable optical elements known in the art. Additionally, the system may be configured to vary one or more elements of the illumination subsystem based on the type of illumination used for imaging.

[0024] The imaging subsystem can also include a scanning subsystem configured to scan the light over the sample. For example, the imaging subsystem can include a stage 22 on which the sample 14 is positioned during imaging. The scanning subsystem can include any suitable mechanical and / or robotic assembly (including the stage 22) that can be configured to move the sample so that the light can be scanned across the sample. Additionally or alternatively, the imaging subsystem can be configured such that one or more optical elements of the imaging subsystem perform some scanning of the light over the sample. The light can be scanned over the sample in any suitable manner.

[0025] The imaging subsystem further includes one or more detection channels. At least one of the one or more detection channels includes a detector configured to detect light from the sample resulting from illumination of the sample by the imaging subsystem and generate an output in response to the detected light. For example, the imaging subsystem shown in FIG. 1 includes two detection channels: one formed by collector 24, element 26, and detector 28, and the other formed by collector 30, element 32, and detector 34. As shown in FIG. 1, the two detection channels are configured to collect and detect light at different collection angles. In some cases, one detection channel is configured to detect specularly reflected light, and the other detection channel is configured to detect light that is not specularly reflected from the sample (e.g., scattered, diffracted, etc.). However, two or more detection channels can be configured to detect the same type of light from the sample (e.g., specularly reflected light). Although FIG. 1 shows an embodiment of an imaging subsystem including two detection channels, the imaging subsystem may include a different number of detection channels (e.g., only one detection channel or two or more detection channels). Although each of the collectors is shown in FIG. 1 as a single refractive optical element, each of the collectors may include one or more refractive optical elements and / or one or more reflective optical elements.

[0026] The one or more detection channels may include any suitable detector known in the art, such as a photomultiplier tube (PMT), a charge-coupled device (CCD), and a time-delay integration (TDI) camera. The detectors may also include non-imaging or imaging detectors. When the detectors are non-imaging detectors, each detector may be configured to detect a particular characteristic of the scattered light, such as intensity, but may not be configured to detect such characteristic as a function of position within the imaging plane. Thus, the output generated by each detector included in each detection channel may be a signal or data rather than an image signal or image data. In such cases, a computer subsystem, such as computer subsystem 36, may be configured to generate an image of the sample from the non-imaging output of the detector. However, in other cases, the detector may be configured as an imaging detector, configured to generate an imaging signal or image data. Thus, the imaging subsystem may be configured to generate an image in several ways.

[0027] It should be noted that FIG. 1 is provided herein to schematically illustrate imaging subsystem configurations that may be included in system embodiments described herein. Clearly, the imaging subsystem configurations described herein may be modified to optimize system performance, as is typically done when designing commercially available imaging systems. In addition, the systems described herein may be implemented using existing imaging systems (e.g., by adding the functionality described herein to an existing inspection system), such as the 29xx and 39xx series of tools commercially available from KLA Corp., Milpitas, California. For some such systems, the embodiments described herein may be provided as optional functionality of the imaging system (e.g., in addition to other functions of the imaging system). Alternatively, the imaging subsystems described herein may be designed “from scratch” to provide an entirely new imaging system.

[0028] The computer subsystem 36 of the system can be coupled to the detector of the imaging subsystem in any suitable manner (e.g., via one or more transmission media, which may include “wired” and / or “wireless” transmission media) so that the computer subsystem can receive output generated by the detector during scanning of the sample. The computer subsystem 36 may be configured to perform several functions using the detector output as described herein and any other functions further described herein. This computer subsystem may be further configured as described herein. This computer subsystem (as well as other computer subsystems described herein) may also be referred to herein as a computer system. Each of the computer subsystems or systems described herein may take various forms, including a personal computer system, an imaging computer, a mainframe computer system, a workstation, a network appliance, an Internet appliance, or other device. In general, the term “computer system” may be broadly defined to encompass any device having one or more processors that execute instructions from a memory medium. A computer subsystem or system may also include any suitable processor known in the art, such as a parallel processor. In addition, a computer subsystem or system may include a computer platform with high-speed processing and software, either as a standalone tool or a network tool.

[0029] Where a system includes multiple computer subsystems, the different computer subsystems may be coupled to one another so that images, data, information, instructions, etc., can be transmitted between the computer subsystems, as described further herein. For example, computer subsystem 36 may be coupled to computer subsystem 102 (as shown by the dashed line in FIG. 1 ) by any suitable transmission medium, which may include any suitable wired and / or wireless transmission medium known in the art. Two or more of such computer subsystems may also be effectively coupled by a shared computer-readable storage medium (not shown).

[0030] Although the imaging subsystem is described above as being an optical or light-based subsystem, the imaging subsystem may also be an electron-based subsystem. For example, in one embodiment, the energy directed at the sample includes electrons, and the energy detected from the sample includes electrons. Thus, the energy source may be an electron beam source. In one such embodiment, shown in FIG. 2, the imaging subsystem includes an electron column 122 coupled to a computer subsystem 124.

[0031] 2, the electron column includes an electron beam source 126 configured to generate electrons that are focused onto a sample 128 by one or more elements 130. The electron beam source may include, for example, a cathode source or an emitter tip, and the one or more elements 130 may include, for example, a gun lens, an anode, a beam-limiting aperture, a gate valve, a beam current selection aperture, an objective lens, and a scanning subsystem, all of which may include any such suitable elements known in the art.

[0032] Electrons returning from the sample (e.g., secondary electrons) can be focused onto a detector 134 by one or more elements 132. The one or more elements 132 can include a scanning subsystem, which can be, for example, the same scanning subsystem included in element 130.

[0033] The electron column may include any other suitable elements known in the art. Additionally, the electron column may be further configured as described in U.S. Patents 8,664,594 issued to Kojima et al. on April 8, 2014 (Jiang et al. on April 4, 2014), 8,692,204, 8,698,093 issued to Gubbens et al. on April 15, 2014, and 8,716,662 issued to MacDonald et al. on May 6, 2014, which are incorporated by reference as if fully set forth herein.

[0034] Although the electron column is shown in FIG. 2 as being configured so that electrons are directed at the sample at an oblique angle of incidence and scattered from the sample at another oblique angle, it should be understood that the electron beam may be directed at the sample and scattered from the sample at any suitable angle. Furthermore, the electron beam subsystem is configured to use multiple modes to generate images of the sample (e.g., having different illumination angles, collection angles, etc.). The multiple modes of the electron beam subsystem may differ in any image-generating parameter of the subsystem.

[0035] The computer subsystem 124 may be coupled to the detector 134 as described above. The detector may detect electrons returning from the surface of the sample, thereby forming an electron beam image of the sample. The electron beam image may include any suitable electron beam image. The computer subsystem 124 may be configured to perform any of the functions described herein using the detector output and / or the electron beam image. The computer subsystem 124 may be configured to perform any additional steps described herein. A system including the imaging subsystem shown in FIG. 2 may be further configured as described herein.

[0036] It should be noted that FIG. 2 is provided herein to schematically illustrate the configuration of an electronic-based imaging subsystem that may be included in the embodiments described herein. As with the optical subsystem described above, the electron beam subsystem configuration described herein may be modified to optimize the performance of the subsystem, as is typically done when designing a commercially available imaging system. In addition, the systems described herein may be implemented using existing imaging systems (e.g., by adding the functionality described herein to the existing imaging system). For some such systems, the embodiments described herein may be provided as optional functionality of the system (e.g., in addition to other functions of the system). Alternatively, the systems described herein may be designed “from scratch” to provide an entirely new system.

[0037] Although the imaging subsystem is described above as being an optical-based or electron-beam-based subsystem, the imaging subsystem may also be an ion-beam-based subsystem. Such an imaging subsystem may be configured as shown in FIG. 2 , except that the electron beam source may be replaced with any suitable ion beam source known in the art. Thus, in one embodiment, the energy directed at the sample includes ions. Additionally, the imaging subsystem may be any other suitable ion-beam-based imaging subsystem, such as those included in commercially available focused ion beam (FIB) systems, helium ion microscope (HIM) systems, and secondary ion mass spectrometry (SIMS) systems.

[0038] The imaging subsystems described herein can be configured to generate outputs, e.g., images, of a sample in multiple modes. Generally, a "mode" is defined by the values ​​of the parameters of the imaging subsystem used to generate the output and / or image of the sample (or the output used to generate an image of the sample). Thus, the modes may differ in the value of at least one of the parameters of the imaging subsystem (other than the location on the sample where the output is generated). For example, in an optical subsystem, different modes may use different wavelengths of light for illumination. The modes may differ in illumination wavelength (e.g., by using different light sources, different spectral filters, etc. for the different modes), as further described herein. In another example, different modes may use different illumination channels of the optical subsystem. For example, as described above, the optical subsystem may include multiple illumination channels. Thus, different illumination channels can be used for different modes. The modes may additionally or alternatively differ in one or more collection / detection parameters of the optical subsystem. The modes may differ in any one or more modifiable parameters of the imaging subsystem (e.g., illumination polarization, angle, wavelength, etc., detection polarization, angle, wavelength, etc.). The imaging subsystem may be configured to scan the sample in different modes in the same scan or in different scans, for example, depending on the ability to use multiple modes to simultaneously scan the sample.

[0039] Similarly, the output generated by the electron beam subsystem may include output, e.g., images, generated by the electron beam subsystem using two or more different values ​​of a parameter of the electron beam subsystem. Multiple modes of the electron beam subsystem may be defined by the values ​​of the parameter of the electron beam subsystem used to generate the output and / or image of the specimen. Thus, the modes may differ in the value of at least one of the electron beam parameters of the electron beam subsystem. For example, the different modes may use different angles of incidence for illumination.

[0040] As described above, the optical, electron, and ion beam subsystems are configured to scan energy (e.g., light, electrons, etc.) across a physical version of the specimen, thereby generating an output for the physical version of the specimen. In this manner, the optical, electron, and ion beam subsystems may be configured as “real” subsystems rather than “virtual” subsystems. However, the storage medium (not shown) and computer subsystem 102 shown in FIG. 1 may be configured as a “virtual” system. In particular, the storage medium and computer subsystem may be configured as a “virtual” inspection system as described in commonly assigned U.S. Patents Nos. 8,126,255 (February 28, 2012, Bhaskar et al.) and 9,222,895 (August 29, 2015, Duffy et al.), both of which are incorporated by reference as if fully set forth herein. The embodiments described herein may be further configured as described in these patents.

[0041] The computer subsystem is configured to determine the number of defects detected within a predetermined cell within one or more of the images generated within a repeating patterned region formed on the specimen, as shown in step 300 of FIG. 3 . The number of defects per each predetermined cell can be used as a kind of local spatial distribution, or local defect density, of the detected defects in different regions on the specimen. That is, the number of defects within each predetermined cell provides an indication of how the detected defects are spread across some regions on the specimen and / or how they are concentrated in other regions on the specimen. As described further herein, the locations of random defects are expected to be relatively spread throughout a die or other repeating patterned region on the specimen. In contrast, events from systematic noise hotspots will typically be concentrated in specific locations. Therefore, by determining the number of defects detected within a predetermined cell, these numbers can be used to identify such concentrated noise locations and separate them from random defects.

[0042] Determining the number of defects detected in a given cell can include performing specimen inspection on a given process layer. In this manner, the embodiments described herein can be configured to perform defect detection using images generated by the imaging subsystem. For example, a reference image can be subtracted from an image for a single repeating patterned area on the specimen, thereby generating a difference image. The reference image can be a database-type reference (generated from a design of the specimen without a specimen image), an image of the specimen from different repeating patterned areas on the specimen (such as in die-to-die inspection), an image generated from multiple images of multiple repeating patterned areas (e.g., a standard reference die image), or any other suitable reference image known in the art. A threshold can then be applied to the difference image to detect defects. In particular, any signal or image portion having a value above the threshold may be designated as a defect, and all other signals or image portions having a value below the threshold may not be designated as a defect. Of course, such defect detection is perhaps the simplest type of defect detection, and the defects described herein can be detected using any defect detection method or algorithm known in the art. In other words, the embodiments described herein are not limited by the manner in which defects are detected.

[0043] The defects, the number of which is determined as described herein, may be detected by the computer subsystem described herein using images generated by the imaging subsystem. However, another computer subsystem may perform the defect detection, and the computer subsystem described herein may use the detected defects to perform the steps described herein. For example, one computer subsystem may perform the defect detection, and the computer subsystem described herein may obtain the defect detection results from that computer subsystem or from a storage medium on which another computer subsystem stored the defect detection results. In one such example, one computer subsystem may export a defect detection or inspection result file, such as KLARF, a standard proprietary file format for several inspection tools commercially available from KLA for exporting results, and such a file may be provided to or obtained by the computer subsystem described herein to perform the steps described herein. The defect detection results used to determine the number of defects as described herein may include any information generated by the defect detection, such as repeating patterned area size (e.g., die size) and defect coordinates (e.g., die-based coordinates).

[0044] The defects detected on the specimen, the number of which is determined as described herein, include all events detected on the specimen. For example, "defects" detected using an image may include actual defects or DOIs, nuisances, noise, etc. Therefore, "defects" as used in this process may alternatively be referred to as "detected events" or "defect candidates," as they are not defects confirmed as defects by post-detection processing steps such as nuisance filtering or defect classification. In another embodiment, the computer subsystem is configured to detect defects in one or more of the images using a hot threshold. For example, the imaging and computer subsystem may perform a substantial hot scan for a given layer. A "hot threshold" can generally be defined as a threshold used to detect potential defects that is intentionally set at or substantially near the noise floor of the images generated by the imaging subsystem. A "hot scan" performed using a hot threshold is typically performed to detect as many potential and actual defects as possible, ensuring that most or all of the defects of interest are captured and / or ensuring that defects with relatively weak signals are captured. For example, in the embodiments described herein, detecting defects using a hot threshold ensures that all random defects are detected and systematic nuisances are found, so that CA can be accurately defined based on these results. The hot threshold may be applied to the difference image generated as described herein, and any image signal or data that exceeds the hot threshold may be identified as a potential defect, and signals or data that do not exceed the hot threshold are not identified as a potential defect.

[0045] In one embodiment, the repeating patterned region formed on the specimen is a repeating die formed on the specimen. However, the repeating patterned region may be any selected specific region on the specimen where a systematic nuisance is observed or expected. For example, a given cell may overlap or be defined within a region of a die formed on the specimen, although any other repeating patterned region on the specimen, such as a field, only a specific region within a die, etc., may be used.

[0046] In one embodiment, the predetermined cells include a regular array of predetermined cells. In another embodiment, at least a majority of the predetermined cells have the same shape and size. For example, the entire repeating patterned area is divided into unit-sized cells: x = ∑ ∑ ∑ ∑ ∑ n ∑ ...

[0047] The entire repeating patterned region can then be divided into (m × n) cells, as shown in result 404. For example, the repeating patterned region is divided into predetermined cells 408 defined by dashed lines 406 extending in both the X and Y directions. Each cell has a dimension x in the X direction and a dimension y in the Y direction. In this manner, at least a majority of the predetermined cells have the same dimensions in both the X and Y directions and are arranged in a regular array within the repeating patterned region. In particular, as shown in FIG. 4, some of the predetermined cells at the top and bottom of the repeating patterned region have smaller dimensions in the Y direction, which may also occur in the X direction if the dimensions of the repeating patterned region are not evenly divisible by the predetermined cell unit width or length (not shown in FIG. 4). Regions that cannot be evenly divided into the predetermined cell unit size may not be evaluated in the steps described herein. As shown in FIG. 4, the predetermined cells may have the same size in both the X and Y directions and therefore may have a square shape, although this is not required. For example, the predetermined cells can have different dimensions in the X and Y directions, so that they each have a rectangular shape. In either case, each predetermined cell preferably has the same unit size in X and Y as each other predetermined cell, so that the number of defects per predetermined cell can be used to assess whether each predetermined cell contains systematic noise.

[0048] Cells may then be referred to as cell(i,j) by their location in X and Y, where i is defined in X from i=1 to i=m and j is defined in Y from j=1 to j=n, as shown in FIG. 4 . While FIG. 4 shows a certain number of cells in the repeating patterned region, the number of cells may vary from that shown and may depend on the cell unit size, which may be selected by the user or in any other suitable manner. Additionally, although predetermined cell boundaries are shown overlaid with the defect detection results 400 so that the area of ​​a given cell within the repeating patterned region can be easily seen by the user, the embodiments described herein do not necessarily need to generate such a visual representation of the repeating patterned region and the location of the defect relative to the given cell. For example, if the coordinates of a given cell within the repeating patterned region are known (based on how the given cell was set by the user or a computer subsystem), those coordinates and the coordinates of the detected defect can be used to assign the defect to a different predetermined cell. Assigning a defect to a cell may include, for example, assigning the ID of the cell in which the defect is located to the defect. Once predetermined cells are defined within the repeating patterned region, the number of defects detected in each of the predetermined cells can be readily determined by the computer subsystem, as shown in result 404 of Figure 4. This is true whether or not a visual representation of the predetermined cells and the locations of the detected defects within the repeating patterned region are generated and used to identify the predetermined cells in which the defects are located.

[0049] In further embodiments, the characteristics of a given cell are independent of the design of the repeating patterned region. In this manner, the embodiments described herein can define a CA based solely on defect location and without using design information about the specimen. Therefore, unlike previously used systems and methods for establishing an inspection CA, the embodiments described herein are not computationally expensive and do not require access to intellectual property, such as a GDS file, of the entity that designed at least a portion of the device being inspected. Additionally, the embodiments described herein can automatically identify systematic noise sources and designate the regions where those systematic noise sources are located as CAs, thereby making the embodiments described herein much faster for establishing an inspection CA compared to other currently used methods and systems.

[0050] In some embodiments, determining the number of defects includes stacking information about defects detected within a given cell in one or more of the images corresponding to multiple instances of the repeating patterned region and determining the number of defects detected within the given cell from the stacked information. For example, a concentration of events at a specific location from a systematic noise hotspot becomes more noticeable in a die stack view. The defect detection results shown in FIGS. 4 and 5, e.g., results 400 and 500, may be die stack views generated by sample scanning. While identification of systematic nuisance locations can be performed using defect data from a single die, using information from multiple dies (within a die stack) can enhance nuisance sources and result in greater accuracy in systematic noise source identification.

[0051] Die stacking may be performed in any suitable manner known in the art. For example, defect information for multiple instances of a die may be stacked by combining the defect information for the multiple instances by aligning the defect information for the multiple instances of the die with one another and then overlaying the defect information on top of one another. In this manner, die stacking may generate visual representations such as those shown in FIGS. 4 and 5 for defects detected in multiple dies. However, generating such visual representations of overlaid defect information is not necessary; instead, the defect information may be combined in another manner, such as a purely quantitative manner, e.g., to generate a single value for the total number of defects per unit cell stack.

[0052] The computer subsystem is also configured to compare the number of defects detected in each of two or more predetermined cells with a predetermined threshold, as shown in step 302 of FIG. 3 . A user can input a threshold value for the number of defects per unit cell (T). The user can select any appropriate value for T, such as having more than five defects in a unit cell qualify the unit cell as containing a systematic nuisance. However, the predetermined threshold may be defined in several ways, depending on the embodiments described herein. For example, the number of defects detected in each predetermined cell can be determined, which can then be used to identify outlier cells (i.e., the predetermined cell contains a number of defects that is an outlier compared to the number of defects detected in other predetermined cells) and determine an appropriate threshold (e.g., setting T less than the number of defects per unit cell in the outlier cells). The inventors have found that N i,j Define , as the number of defects located in any given cell (i,j).

[0053] In one embodiment, the predetermined threshold is selected to be lower than the number of defects detected when systematic noise is present. In another embodiment, the predetermined threshold is selected to be higher than the number of defects detected when systematic noise is absent. For example, random defects tend to have less bias toward any particular location within a repeating patterned area. As a result, each cell will have a lower defect count if only random defects are present and detected. Conversely, defects from systematic noise sources tend to cluster around particular process or design features on a repeating patterned area. Thus, if a systematic noise source is present within a cell, that cell will have a higher defect count. Thus, the embodiments described herein provide a novel method for identifying systematic noise hotspots based on defect locations from a wafer map. Once systematic nuisances are separated from other detected defects based on their locations, the computer subsystem can create CAs on target locations for optimal sensitivity, as further described herein.

[0054] Thus, the embodiments described herein provide a method for determining N for T. i,j The goal is to distinguish cells based on the number of defects in a given cell. The output from this operation is the location coordinates of cells that satisfy the following condition (C):

number

[0055] In the example shown in FIG. 4, results 410 visually indicate the results of comparing the number of defects detected in a given cell to a threshold value. In particular, once the repeating patterned area layout is divided into cells as shown in results 404, condition C may be applied to each of the cells. A group of given cells 412, 414, and 416, shown in results 410 as shaded given cells, satisfy condition C because the number of defects detected in these given cells is greater than a predetermined threshold. The remaining given cells shown in FIG. 4 do not satisfy condition C and therefore are not flagged as true for the systematic noise location condition. In this manner, the computer subsystem may flag the shaded cells for meeting the criteria and record the cell coordinates of the flagged cells.

[0056] Although embodiments are described herein as designating a given cell as a CA based on a defect count per given cell, which is the actual number of defects per unit cell, the defect count may be something other than an absolute number of defects per unit cell. For example, while using the absolute defect count per unit cell is the simplest and most straightforward method for identifying areas containing systematic nuisance, other methods of quantifying the number of defects per unit cell may be used, such as the average or total number of defects in a unit cell in multiple instances of a repeating patterned area, the density of defects detected in a unit cell in one or more instances of a repeating patterned area, the spatial distribution of defects detected in a unit cell in one or more instances of a repeating patterned area, etc. In other words, one advantage of the embodiments described herein is their unexpected simplicity in effectively using the defect count per unit cell in a repeating patterned area to identify systematic nuisance locations on a sample. However, the embodiments described herein are not limited to using simply a defect count per unit cell for CA identification, and more complex or complicated methods for quantifying defects per unit cell may be used. Whether defect counts or some other measure is used to quantify defects per unit cell for CA identification can be determined in any suitable manner and can vary depending on the expected properties of the systematic nuisance.

[0057] In other embodiments, inspection of a specimen is performed to detect random defects on the specimen. For example, the embodiments described herein are particularly useful for providing better separation between random defects and systematic nuisance. Once systematic nuisance locations are identified, this information can be used to generate target CAs. The user can then assign new CAs to any of the predetermined cells designated as "don't care areas" as described herein, or choose to inspect them with different sensitivity. In other words, CAs identified as described herein are areas that are not inspected at all or inspected with lower sensitivity, so that systematic nuisance present in these CAs is not detected. Isolating systematic nuisance locations from other locations on the specimen allows the user to have higher sensitivity to the remainder of the specimen for random defect detection.

[0058] Thus, as described above, in one embodiment, the one or more CAs are one or more regions within a repeating patterned region on a specimen where inspection is not performed. In another embodiment, the one or more CAs are one or more regions within a repeating patterned region on a specimen where inspection is performed with lower sensitivity than other regions within the repeating patterned region on the specimen. One advantage of the embodiments described herein is that inspection sensitivity can be improved by identifying systematic noise hotspot locations for CA generation. The CAs identified as described herein can then be inspected with lower sensitivity than other regions, or cannot be inspected for defects at all.

[0059] The computer subsystem is further configured to designate any one or more of two or more predetermined cells having a number of defects greater than a predetermined threshold as one or more CAs, as shown in step 304 of Figure 3. For example, the computer subsystem may generate CAs based on the flagged cell coordinates in result 410 shown in Figure 4. In other words, the computer subsystem may generate CA information 418, which may include CAs 420, 422, and 424 having the same characteristics as groups 412, 414, and 416, respectively, of flagged predetermined cells that satisfy condition C.

[0060] As shown in FIG. 4 , any adjacent flagged cells may be combined into a single CA. However, this is not required, and adjacent flagged cells may each be designated as a different CA. Furthermore, while each of the CAs shown in FIG. 4 has a square or rectangular shape, such regular shapes are not limiting. For example, depending on which cells are flagged, a CA generated from multiple flagged cells may have an L-shape, a U-shape, an O-shape, etc. Thus, a CA generated as described herein may have an irregular polygonal shape. Additionally, while a specific number of CAs is shown in FIG. 4 , the number of cells designated as a CA is not limited in the embodiments described herein. In some cases, a user may place an upper limit on the number of CAs designated in the embodiments described herein to ensure that an acceptable area on the specimen is inspected in the process. If the number of cells flagged as CA exceeds an upper limit, this can also be used as an indicator that the parameters of the imaging or computer subsystem used to detect the defects may need to be adjusted (because too many systematic nuisances are being detected), or that a different sample may need to be inspected (because the inspected sample may have problems with too many systematic nuisances to be detected).

[0061] In one embodiment, the predetermined cell has a first size, and the computer subsystem is configured to perform the determining, comparing, and designating steps for other predetermined cells having a second size different from the first size. For example, dividing the entire repeating patterned area into unit-sized cells can be repeated with different (x, y) values ​​appropriate to the inspection platform. FIG. 5 illustrates an example of performing the steps described herein but with a smaller predetermined cell size than that used in FIG. 4. For example, defect detection result 500 shows the location of a defect 502 detected within a repeating patterned area on a specimen. The defect detection result can include any of those described herein generated by any method described herein. Result 504 can be generated by overlaying dashed line 506 on defect detection result 500, which defines the area of ​​predetermined cell 508 within the repeating patterned area. The predetermined cells can be further configured as described herein, except that they have different x and y dimensions than those used in FIG. 4. As shown in result 510, cells containing a number of defects greater than a threshold can be flagged, and those cells are shown in the result as a shaded group of cells 512. The information about the flagged cells can be used to generate result 514, which indicates CA 516 located within the repeating patterned region. As shown by a comparison of results 510 and 514, the CA has the same characteristics as the given flagged cell.

[0062] Performing the determining, comparing, and designating steps having smaller sizes may be further configured as described herein.

[0063] In another embodiment, the imaging subsystem is configured to generate images of the specimen in multiple modes, and the computer subsystem is configured to perform the determining, comparing, and designating steps independently for each of two or more of the multiple modes. In one such embodiment, one or more CAs for a first mode of two or more of the multiple modes are different from one or more CAs for a second mode of two or more of the multiple modes. In this manner, different CAs may be used for the different modes. For example, systematic nuisance locations depend in large part on the architecture of the inspection platform. The embodiments described herein act only on defect locations and thus can be applied to any inspection platform, regardless of its architecture and optics. Different inspection (e.g., optical or electron beam) tools will perceive the presence of systematic nuisance differently based on their architecture. The embodiments described herein can work across platforms to identify and isolate such locations. Additionally, different optical or imaging configurations may have different nuisance sources, and therefore CAs specific to those optical or imaging configurations may also be established by the embodiments described herein and used in inspections using multiple modes. In this manner, the embodiments described herein may be configured to perform the steps described herein multiple times for multiple modes, thereby generating different CAs for different imaging modes or settings.

[0064] The embodiments also find their application across all nodes and process layers. For example, because the embodiments described herein can identify CAs based solely on defects detected per unit cell, the embodiments can be used to establish CAs for any or all layers on a specimen. The embodiments also find application across time and / or process changes. For example, the embodiments described herein can be re-run at specified intervals to check whether the systematic nuisance location has changed (e.g., due to process drift). The embodiments described herein can also be re-performed if the process is intentionally changed, which may cause a change in the systematic nuisance location.

[0065] The computer subsystem is also configured to store information for one or more CAs for use in inspecting the specimen, as shown in step 306 of FIG. 3 . For example, the computer subsystem can generate a CA file, which can have any suitable file format or extension, such as a .bin file. The information for the one or more CAs can include any suitable information, such as CA location and size. During inspection, the tool performing the inspection, which may or may not include the imaging and computer subsystems described herein, may automatically generate CAs having locations and dimensions specified by the stored information. In some such cases, the inspection tool can import the stored information, or a representation thereof, into a CA UI within the main UI of the inspection tool so that a user of the tool can gain an understanding of the information.

[0066] The computer subsystem may also be configured to store CA information in a recipe or by generating a recipe for an inspection in which the CA is used. As used herein, the term "recipe" is defined as a set of instructions that can be used by a tool to perform a process on a specimen. In this manner, generating a recipe may include generating information about how a process should be performed, which can then be used to generate instructions for performing the process. The information about the CA stored by the computer subsystem may include any information that can be used to identify, access, and / or use the selected CA (e.g., file name, storage location, etc.). The information about the CA stored may also include the actual code, instructions, algorithms, etc. for applying the CA and detecting defects based on the applied CA.

[0067] The computer subsystem may be configured to store information about the CA in any suitable computer-readable storage medium. The information may be stored with any of the results described herein or in any manner known in the art. The storage medium may include any storage medium described herein or any other suitable storage medium known in the art. After the information is stored, it may 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. For example, the embodiments described herein may generate an inspection recipe as described above. This inspection recipe may then be stored and used by a system or method (or another system or method) to inspect a sample or other sample, thereby generating information (e.g., defect information) about the sample or other sample.

[0068] Results and information generated by performing inspections on a sample or other samples of the same type can be used in various ways by embodiments and / or other systems and methods described herein. Such functions include, but are not limited to, modifying a process, such as a manufacturing process or step, that has been or will be performed in a feedback or feedforward manner on the inspected sample or another sample. For example, the computer subsystems described herein can be configured to determine one or more modifications to a process performed on a sample based on a process performed on the inspected sample as described herein and / or a detected defect. The modifications to the process may include any suitable modifications to one or more parameters of the process. The computer subsystem preferably determines these modifications so that defects may be reduced or prevented on other samples on which the modified process is performed, so that defects may be corrected or eliminated on the sample in another process performed on the sample, so that defects may be compensated for in another process performed on the sample, etc. The computer subsystems described herein can determine such modifications in any suitable manner known in the art.

[0069] These changes can then be transmitted to a semiconductor manufacturing system (not shown) or to a storage medium (not shown) accessible to the computer subsystems and semiconductor manufacturing systems described herein. The semiconductor manufacturing system may or may not be part of the system embodiments described herein. For example, the systems described herein may be coupled to the semiconductor manufacturing system via one or more common elements, such as, for example, an enclosure, a power supply, a sample handling device or mechanism, etc. The semiconductor manufacturing system may include any semiconductor manufacturing system known in the art, such as a lithography tool, an etching tool, a chemical-mechanical polishing (CMP) tool, a deposition tool, etc.

[0070] Thus, as described herein, embodiments can be used to configure a new inspection process or recipe. The embodiments can also be used to modify an existing inspection process or recipe, whether it be an inspection process or recipe used for a sample or an inspection process or recipe created for one sample and being adapted for another sample. Additionally, the embodiments described herein are not limited to only creating or modifying CAs. For example, the embodiments described herein can also be used to select one or more other parameters for inspection, such as mode selection based on systematic noise sources identified by the embodiments described herein, output processing parameter selection such as defect detection sensitivity used in systematic nuisance source regions versus other regions, or any other parameters of the inspection process for which the CA is configured as described herein. The embodiments described herein offer several advantages over previously used methods and systems for configuring CAs for inspection processes. For example, the embodiments described herein can be used to help suppress systematic noise regardless of the inspection platform and process layer under study. Additionally, the embodiments described herein can configure CAs using only defect coordinates and do not require access to design files or any other form of restricted intellectual property. Thus, an inspection platform can use the embodiments described herein to deter systematic nuisances without having access to restricted or sensitive intellectual property. The embodiments described herein may also be applied to both advanced technology and legacy node layers alike. Furthermore, using the CAs generated with the embodiments described herein, an inspection platform can reduce nuisance rates, thus increasing the chances of finding actual defects and reducing the defect review burden on users.

[0071] Each of the embodiments of each of the above systems can be combined together in one single embodiment.

[0072] Another embodiment relates to a computer-implemented method for setting up a CA for inspection of a specimen, the method including determining a number of defects, comparing the number of defects, designating any one or more of two or more predetermined cells as a CA, and storing the information steps described above.

[0073] Each of the steps of the method may be performed as further described herein. The method may also include any other steps that can be performed by the imaging subsystem, computer subsystem, and / or system described herein. The steps of the method are performed by a computer subsystem coupled to the imaging subsystem, both of which may be configured according to any of the embodiments described herein. In addition, the above-described method may be performed by any of the system embodiments described herein.

[0074] A further embodiment relates to a non-transitory computer-readable medium storing program instructions executable on a computer system for performing a computer-implemented method for configuring a CA for testing a sample. One such embodiment is shown in Figure 6. In particular, as shown in Figure 6, a non-transitory computer-readable medium 600 includes program instructions 602 executable on a computer system 604. The computer-implemented method may include any step of any of the methods described herein.

[0075] Program instructions 602 implementing methods such as those described herein may be stored on a computer-readable medium 600. The computer-readable medium may be a storage medium such as a magnetic or optical disk, magnetic tape, or any other suitable non-transitory computer-readable medium known in the art.

[0076] The program instructions may be implemented in any of a variety of 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, JavaBeans, Microsoft Foundation Casses ("MFC"), Streaming SIMD Extensions (SSE), or other technologies or methodologies, as desired.

[0077] The computer system 604 may be configured according to any of the embodiments described herein.

[0078] 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 setting up CA for testing samples are provided. Accordingly, this description is to be construed as merely illustrative and 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 shown 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 those 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 set forth in the following claims.

Claims

1. 1. A system configured to establish a care area for testing of a specimen, comprising: an imaging subsystem configured to generate an image of the sample; 1. A computer subsystem comprising: determining a number of defects detected within a given cell within one or more of the images generated within a repeating patterned area formed on the specimen; comparing the number of defects detected in each of two or more of the predetermined cells to a predetermined threshold, the predetermined threshold being selected to be less than the number of defects that would be detected in the presence of systematic noise; designating any one or more of the two or more predetermined cells in which the number of defects is greater than the predetermined threshold as one or more care areas; storing information about one or more care areas for use in testing the sample; a computer subsystem configured to execute A system comprising:

2. 2. The system of claim 1, wherein the predetermined threshold is further selected to be greater than the number of defects that would be detected in the absence of systematic noise.

3. 10. The system of claim 1, wherein the inspection of the specimen is performed to detect random defects on the specimen.

4. 10. The system of claim 1, wherein the one or more care areas are one or more areas within the repeating patterned area on the specimen where the inspection is not performed.

5. 10. The system of claim 1, wherein the one or more care areas are one or more areas within the repeating patterned area on the specimen where the inspection is performed with less sensitivity than other areas within the repeating patterned area on the specimen.

6. 2. The system of claim 1, wherein the predetermined cells comprise a regular array of the predetermined cells.

7. 10. The system of claim 1, wherein at least a majority of said predetermined cells have the same shape and size.

8. 10. The system of claim 1, wherein the characteristics of the predetermined cell are independent of the design of the repeating patterned region.

9. The step of determining the number of defects comprises: stacking information about defects detected in the given cell in one or more of the images corresponding to multiple instances of the repeating patterned region; determining the number of defects detected in the given cell from the accumulated information; The system of claim 1 , comprising:

10. the predetermined cell has a first size; 2. The system of claim 1, wherein the computer subsystem is further configured to perform the determining, comparing, and designating steps for other predetermined cells having a second size different from the first size.

11. The system of claim 1 , wherein the computer subsystem is further configured to detect defects in one or more of the images using a hot threshold.

12. The imaging subsystem is further configured to generate the images of the sample in a plurality of modes; 2. The system of claim 1, wherein the computer subsystem is further configured to perform the determining, comparing, and specifying steps independently for each of two or more of the plurality of modes.

13. 13. The system of claim 12, wherein one or more care areas for a first mode of two or more of the plurality of modes are different from one or more care areas for a second mode of two or more of the plurality of modes.

14. 10. The system of claim 1, wherein the repeating patterned area formed on the specimen is a repeating die formed on the specimen.

15. The system of claim 1 , wherein the specimen is a wafer.

16. The system of claim 1 , wherein the imaging subsystem is an optical-based imaging subsystem.

17. 10. The system of claim 1, wherein the imaging subsystem is an electronic-based imaging subsystem.

18. 1. A computer-readable medium storing program instructions executable on a computer system for performing a computer-implemented method for establishing care areas for testing of a specimen, the computer-implemented method comprising: determining a number of defects detected within a given cell within one or more images of the specimen generated by an imaging subsystem within a repeating patterned area formed on the specimen; comparing the number of defects detected in each of two or more of the predetermined cells to a predetermined threshold, the predetermined threshold being selected to be less than the number of defects that would be detected in the presence of systematic noise; designating any one or more of the two or more predetermined cells in which the number of defects is greater than the predetermined threshold as one or more care areas; storing information about the one or more care areas for use in testing the sample; 1. A computer-readable medium comprising:

19. 1. A computer-implemented method for establishing care areas for testing of a specimen, comprising: determining a number of defects detected within a given cell within one or more images of the specimen generated by an imaging subsystem within a repeating patterned area formed on the specimen; comparing the number of defects detected in each of two or more of the predetermined cells to a predetermined threshold, the predetermined threshold being selected to be less than the number of defects that would be detected in the presence of systematic noise; designating any one or more of the two or more predetermined cells in which the number of defects is greater than the predetermined threshold as one or more care areas; storing information about the one or more care areas for use in testing the sample; wherein the determining, comparing, designating, and storing steps are performed by a computer subsystem coupled to the imaging subsystem.

Images