Establishing a care area for testing of samples

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 a specimen. [Background technology]

[0002] The following descriptions and examples are not admitted to be prior art by 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 sample that is of interest for inspection purposes. Care areas (CA) may be used to distinguish areas on a sample that are inspected from areas on the sample that are not inspected in the inspection process. Additionally, CA may be used to identify areas on a sample that are inspected with one or more different parameters. For example, if a first area of ​​a sample is more critical than a second area on the sample, the first area may be inspected with a higher sensitivity than the second area to detect defects with a higher sensitivity in the first area. Other parameters of the inspection process may be changed from CA to CA as well.

[0005] Currently, different categories of inspection CAs are in use. One category are 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 are design-based CAs. These are CAs derived based on heuristics on the chip design patterns printed on the specimen. Users try to derive methods / scripts that look at the chip design and help derive the CA. There are multiple techniques and tools available to define these design-based CAs. Since they are derived from the ground truth (chip design), they can provide CAs that are substantially smaller with high accuracy, and also allow the inspection system to store a large amount 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 of different noise behavior are grouped together and even one single CA can contain many different structures of different noise behavior. To identify areas where the noise is higher, several iterations of the so-called design-based search have to be performed many times. 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] Thus, currently used methods and systems for setting up an inspection CA have several drawbacks. For example, many methods and systems for setting up an inspection CA are computationally expensive methods that require 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 the CA at the target location. However, this method is tedious and time-consuming, as there may be more than 1000 hot spot locations on a process layer. Therefore, it would be advantageous to develop a system and method for setting up a CA 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 predefined cells in 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 the two or more predefined cells to a predefined threshold. In addition, the computer subsystem is configured to designate any one or more of the two or more predefined cells in which the number of defects is greater than the predefined 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 sample. The method includes the determining, comparing, assigning, and storing steps described above. The steps of the method are performed 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 steps 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 performing a computer-implemented method for setting up a CA for inspection of a sample. The computer-implemented method includes the steps of the method described above. 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. In addition, the computer-implemented method for which the program instructions are executable may include any other steps of any other method described herein. [Brief description 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. [Diagram 2] FIG. 1 is a schematic diagram showing a side view of an embodiment of a system configured as described herein. [Diagram 3] 4 is a flow chart illustrating one embodiment of steps performed by the computer subsystems described herein. [Figure 4] FIG. 1 is a schematic diagram illustrating a top view of example defects detected in a repeating patterned area of ​​a specimen and the results of various steps described herein for setting care areas (CAs) for inspection of the specimen based on the detected defects and predefined cells having different sizes; [Diagram 5] FIG. 13 is a schematic diagram illustrating a top view of an example of defects 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 predefined 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 PREFERRED EMBODIMENTS

[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 a user and / or an event that is detected on a specimen but is not actually an actual defect on the specimen. Nuisances that are not actually defects may be detected as events that are due to 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 of patterned attributes, thickness variations, etc.) and / or due to 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 that is detected on a specimen and is an actual defect on the specimen. Thus, DOIs are 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 of the actual defects on the specimen, which includes only the 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 the specimen. The terms "design," "design data," and "design information," as used interchangeably herein, generally refer to the physical design (layout) of an IC or other semiconductor device and the 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 (August 4, 2009, Zafar et al.), issued March 9, 2010 to Kulkami et al., both of which are incorporated by reference as if fully set forth herein. In addition, 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, "design," "design data," and "design information" as described herein refer to information and data generated by semiconductor device designers in the design process and thus 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 nature 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 described and illustrated elements 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 out across a die or other repetitive patterned area on a 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 settings to address systematic nuisance.

[0018] In one embodiment, the specimen is a wafer. The wafer may include any wafer known in the semiconductor art. In another embodiment, the specimen is a reticle. The reticle may 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 may be used. For example, the embodiments described herein may 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 a light-based imaging subsystem. For example, in the embodiment of the system shown in FIG. 1, the imaging subsystem 10 includes an illumination subsystem configured to direct light to 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 to 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 the light source 16 is directed through optical element 18 and then through lens 20 to beam splitter 21, which directs the light to 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 defects detected on the sample. The illumination subsystem may be configured to direct light to 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 to the sample at an angle of incidence different from the angle of incidence shown in FIG. 1. In one such example, the imaging subsystem can 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 direct light to the sample at multiple angles of incidence simultaneously. 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, and another of the illumination channels (not shown) may include similar elements that 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 further described herein. When such light is directed to the sample simultaneously with other light, one or more characteristics (e.g., wavelength, polarization, etc.) of the light directed to 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 the 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) of the illumination subsystem. The light in each of the different optical paths may then be directed to the sample. Multiple illumination channels may be configured to direct light to 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 to the sample at different times. For example, in some cases, the 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) such that light of different wavelengths may be directed to the sample at different times. The illumination subsystem may have any other suitable configuration known in the art for directing light having different or the same characteristics to the sample sequentially or simultaneously at different or the same angles of incidence.

[0022] In one embodiment, the light source 16 includes a broadband plasma (BBP) light source. In this manner, the light generated by the light source and directed to 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 can be configured to generate light at any suitable wavelength known in the art. The laser can be configured to generate light that is monochromatic or nearly monochromatic. In this manner, 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 the optical element 18 may be focused by a lens 20 to a beam splitter 21. Although the lens 20 is shown in FIG. 1 as a single refractive optical element, in reality, the lens 20 may include several refractive and / or reflective optical elements that combine to focus the 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, a polarization component, a spectral filter, a spatial filter, a reflective optical element, an apodizer, a beam splitter, an aperture, and the like, and may include any such suitable optical element known in the art. In addition, the system may be configured to alter one or more elements of the illumination subsystem based on the type of illumination used for imaging.

[0024] The imaging subsystem may also include a scanning subsystem configured to cause the light to be scanned over the sample. For example, the imaging subsystem may include a stage 22 on which the sample 14 is positioned during imaging. The scanning subsystem may include any suitable mechanical and / or robotic assembly (including the stage 22) that may be configured to move the sample such that the light may be scanned across the sample. Additionally or alternatively, the imaging subsystem may be configured such that one or more optical elements of the imaging subsystem perform some scanning of the light over the sample. The light may 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, the two or more detection channels can be configured to detect the same type of light (e.g., specularly reflected light) from the sample. 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 illustrated 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 detectors known in the art, such as photomultiplier tubes (PMTs), charge-coupled devices (CCDs), and time-delay integration (TDI) cameras. The detectors may also include non-imaging or imaging detectors. When the detectors are non-imaging detectors, each of the detectors may be configured to detect a particular characteristic of the scattered light, such as intensity, but may not be configured to detect such a characteristic as a function of position in 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 detectors. However, in other cases, the detectors may be configured as imaging detectors, configured to generate imaging signals 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 diagrammatically illustrate an imaging subsystem configuration that may be included in an embodiment of the system described herein. Obviously, the imaging subsystem configuration described herein may be modified to optimize the performance of the system, as is typically done when designing a commercially available imaging system. In addition, the system described herein may be implemented using an existing imaging system (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, Calif. 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 subsystem described herein may be designed "from scratch" to provide an entirely new imaging system.

[0028] The computer subsystem 36 of the system may 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) such that the computer subsystem may receive the output generated by the detector during scanning of the sample. The computer subsystem 36 may be configured to perform several functions using the output of the detector 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 image computer, a mainframe computer system, a workstation, a network appliance, an Internet appliance, or other devices. 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. The computer subsystem or system may also include any suitable processor known in the art, such as a parallel processor. In addition, the computer subsystem or system may include a computer platform having high speed processing and software, either as a stand-alone tool or a network tool.

[0029] Where a system includes multiple computer subsystems, the different computer subsystems may be coupled to one another such that images, data, information, instructions, and the like may 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 be an electron-based subsystem. For example, in one embodiment, the energy directed to the sample includes electrons and the energy detected from the sample includes electrons. In this manner, 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 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) may be focused onto a detector 134 by one or more elements 132. The one or more elements 132 may include a scanning subsystem, which may 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 April 8, 2014 to Kojima et al. (April 4, 2014 to Jiang et al.), 8,692,204 issued April 15, 2014 to Gubbens et al., and 8,716,662 issued May 6, 2014 to MacDonald et al., which are incorporated by reference as if fully set forth herein.

[0034] Although the electron column is shown in FIG. 2 as being configured such 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. Additionally, 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 output of the detector 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 diagrammatically illustrate configurations of electronic-based imaging subsystems that may be included in the embodiments described herein. As with the optical subsystems described above, the electron beam subsystem configurations described herein may be modified to optimize the performance of the subsystems, 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 an 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 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 to 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 the sample in multiple modes. In general, 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 the optical subsystem, different modes may use different wavelengths of light for illumination. The modes may differ in illumination wavelengths (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., an image, generated by the electron beam subsystem with 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 subsystems may be configured as a "virtual" inspection system as described in commonly assigned U.S. Patents 8,126,255 (February 28, 2012, Bhaskar et al.), issued August 29, 2015 to Duffy et al., and 9,222,895, 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 a number of defects detected within a given 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 given 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 given cell gives 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 hot spots will typically be concentrated in a particular location. Thus, by determining the number of defects detected within a given cell, the 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 may include performing a specimen inspection on a given process layer. In this manner, the embodiments described herein may be configured to perform defect detection using images generated by the imaging subsystem. For example, a reference image may be subtracted from an image for a one-time repeating patterned area on the specimen, thereby generating a difference image. The reference image may be a database type reference (generated from a design of the specimen without a specimen image), an image of the specimen from a different repeating patterned area on the specimen (such as 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 may 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 may be detected using any defect detection method or algorithm known in the art. In other words, the embodiments described herein are not limited to the manner in which the 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 in which the other 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 of some 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, a "defect" detected using an image may include actual defects or DOIs, nuisances, noise, etc. Thus, a "defect" as used in this process may be rephrased as a "detected event" or a "defect candidate" since it is not a defect that has been confirmed as a defect by a post-detection processing step 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" may be generally defined as a threshold used to detect potential defects that is intentionally set at or substantially near the noise floor of the image 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, to ensure that most or all of the defects of interest are captured, and / or to ensure that defects with relatively weak signals are captured. For example, in the embodiments described herein, a hot threshold is used to detect defects to ensure 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 area formed on the specimen is a repeating die formed on the specimen. However, the repeating patterned area may be any selected specific area on the specimen where systematic nuisance is observed or expected. For example, a given cell may overlap or be defined within an area of ​​a die formed on the specimen, although any other repeating patterned area on the specimen, such as those described herein, such as a field, only a specific area 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 size cells: x x y ...

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

[0048] The 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 the predefined cell boundaries are shown overlaid with the defect detection results 400 so that the area of ​​the predefined cell in the repeating patterned region may be easily seen by the user, the embodiments described herein do not necessarily need to generate such a visual representation of the location of the defect relative to the repeating patterned region and the predefined cell. For example, if the coordinates of the predefined cell in the repeating patterned region are known (based on how the predefined cell was set by the user or a computer subsystem), those coordinates and the coordinates of the detected defect may be used to assign the defect to a different predefined cell. Assigning the defect to a cell may include, for example, assigning the ID of the cell in which the defect is located to the defect. Once the 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 regardless of whether a visual representation of the predetermined cells and the detected defect locations 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 the defect location and without using design information of the specimen. Thus, unlike previously used systems and methods for setting up an inspection CA, the embodiments described herein are not computationally expensive and do not require access to intellectual property, such as GDS files, of the entity that designed the device, at least a portion of which is being inspected. In addition, the embodiments described herein can automatically identify systematic noise sources and designate the regions in which those systematic noise sources are located as CAs, thereby making the embodiments described herein much faster to set up 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 in 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 in the given cell from the stacked information. For example, a concentration of events from a systematic noise hotspot to a particular location is more noticeable in a die stack view. The defect detection results shown in Figures 4 and 5, for example results 400 and 500, can be die stack views generated by sample scanning. Although identification of systematic nuisance locations can be done using defect data from a single die, using multiple die information (in a die stack) can enhance nuisance sources and result in greater accuracy of 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 defect information for the multiple instances by aligning the defect information for the multiple instances of a 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 Figures 4 and 5 for defects detected in multiple dies. However, it is not necessary to generate such visual representations of overlaid defect information; instead, the defect information may be combined in another manner, such as a purely quantitative manner, for example, 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 the two or more predetermined cells to a predetermined threshold, as shown in step 302 of FIG. 3. A user may input a threshold number of defects per unit cell (T). The user may select any suitable value of T, such as more than five defects in a unit cell qualifying 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 may be determined, which may then be used to identify outlier cells (i.e., the predetermined cell contains a number of defects that are outliers compared to the number of defects detected in other predetermined cells) and determine an appropriate threshold (e.g., set T to be less than the number of defects per unit cell in the outlier cells). The inventors have found that the threshold number of defects per unit cell (T) is less than the number of defects detected in the outlier cells. 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 not present. For example, random defects tend to have less bias toward any particular location in the repeating patterned area. As a result, each cell will have a lower defect count when only random defects are present and detected. Conversely, defects from systematic noise sources tend to cluster around specific process or design features on the repeating patterned area. Thus, if a systematic noise source is present in a cell, that cell will have a higher defect count. Thus, the embodiments described herein provide a novel method aimed at identifying systematic noise hot spots based on defect locations from a wafer map. Once the 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 i,j The aim 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 the shaded given cells, satisfy condition C because the number of defects detected in these given cells is greater than the predetermined threshold value. 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 satisfying the criteria and may record the cell coordinates of the flagged cells.

[0056] Although the 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 the absolute number of defects per unit cell. For example, while using the absolute defect count per unit cell is the most simple and straightforward way to identify 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 region, the density of defects detected in a unit cell in one or more instances of a repeating patterned region, the spatial distribution of defects detected in a unit cell in one or more instances of a repeating patterned region, etc. In other words, one advantage of the embodiments described herein is their unexpected simplicity of effectively using the defect count per unit cell in a repeating patterned region to identify systematic nuisance locations on a sample. However, the embodiments described herein are not limited to simply using defect counts per unit cell for CA identification, and more complex or complicated methods of quantifying defects per unit cell may be used. Whether to quantify defects per unit cell using defect counts or some other measure 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 the 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 choose to assign new CAs to any of the predefined cells designated as "don't care areas" as described herein, or to inspect them with different sensitivity. In other words, the CAs identified as described herein are areas that are not inspected at all or inspected with lower sensitivity, such that systematic nuisance present in these CAs is not detected. Isolating the 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 areas in the repeating patterned area on the sample where no inspection is performed. In another embodiment, the one or more CAs are one or more areas in the repeating patterned area on the sample where inspection is performed with lower sensitivity than other areas in the repeating patterned area on the sample. 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 areas or not inspected at all for defects.

[0059] The computer subsystem is further configured to designate any one or more of the 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 the 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 of flagged predetermined cells 412, 414, and 416, respectively, that satisfy condition C.

[0060] As shown in FIG. 4, any flagged cells adjacent to each other may be combined into a single CA. However, this is not required, and each of the flagged cells adjacent to each other may be designated as a different CA. Additionally, 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, although a specific number of CAs are 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 so that an acceptable area on the sample 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 predefined cells have a first size, and the computer subsystem is configured to perform the determining, comparing, and designating steps for other predefined 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 predefined cell sizes smaller than those used in FIG. 4. For example, defect detection results 500 show the location of defects 502 detected within the repeating patterned area on the specimen. The defect detection results can include any of those described herein generated in any manner described herein. Results 504 can be generated by superimposing dashed line 506 onto defect detection results 500, which defines the area of ​​predefined cells 508 within the repeating patterned area. The predefined 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 results 510, cells containing a number of defects greater than a threshold can be flagged, and those cells are shown in the results as a shaded group of cells 512. Information about the flagged cells can be used to generate a result 514 indicative of a 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 the step having the smaller size 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 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. In addition, different optical or imaging configurations may have different nuisance sources, and thus CAs specific to those optical or imaging configurations may also be set 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, since the embodiments described herein can identify CA based only on defects detected per unit cell, the embodiments can be used to set CA 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 if the systematic nuisance position has changed (e.g., due to process drift). The embodiments described herein can also be re-performed if the process is intentionally changed, as this may cause the systematic nuisance position to change.

[0065] The computer subsystem is also configured to store information for one or more CAs for use in inspection of 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 auto-generate a CA having a location 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 in the main UI of the inspection tool, such that a user of the tool can gain an understanding of the information.

[0066] The computer subsystem may also be configured to store the 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 may then be used to generate instructions to perform 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 the 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 the 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 the specimen or other specimens of the same type may be used in a variety of ways by the 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 specimen or another specimen. For example, the computer subsystem described herein may be configured to determine one or more modifications to a process performed on the specimen based on a process performed on the inspected specimen 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 changes such that defects may be reduced or prevented on other specimens on which the modified process is performed, such that defects may be corrected or eliminated on the specimen in another process performed on the specimen, such that defects may be compensated for in another process performed on the specimen, etc. The computer subsystem described herein may determine such changes 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 etch tool, a chemical mechanical polishing (CMP) tool, a deposition tool, etc.

[0070] Thus, the embodiments as described herein can be used to set up a new inspection process or recipe. The embodiments may also be used to modify an existing inspection process or recipe, whether it is an inspection process or recipe that has been used for a sample, or an inspection process or recipe that has been created for one sample and is being adapted for another sample. In addition, 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 an inspection, such as mode selection made 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, any other parameters of the inspection process for which the CA is set up as described herein. The embodiments described herein provide several advantages over previously used methods and systems for setting up CAs for an inspection process. For example, the embodiments described herein can be used to help suppress systematic noise regardless of the inspection platform and process layer under study. In addition, the embodiments described herein can set up a CA using only defect coordinates, without requiring access to design files or any other form of restricted intellectual property. Thus, an inspection platform can use the embodiments described herein to suppress systematic nuisances without having access to restricted or sensitive intellectual property. The embodiments described herein can 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 real 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 of the 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 may 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-mentioned 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 method 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 inspection of samples are provided. This description should therefore be interpreted as merely illustrative and for the purpose of teaching those skilled in the art the general manner of carrying out the invention. It should be understood that the forms of the invention shown and described herein should be interpreted as 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 to the elements described herein without departing from the spirit and scope of the invention as set forth in the following claims.

Claims

1. A system configured to set a care area for inspection of a sample, an imaging subsystem configured to generate an image of the sample, a computer subsystem, determining the number of defects detected within a predetermined cell among one or more of the images generated within a repetitive patterned area formed on the sample; comparing the number of defects detected in each of two or more of the predetermined cells with a predetermined threshold, the predetermined threshold being selected to be less than the number of defects detected when systematic noise is present; designating as one or more care areas any one or more of the two or more predetermined cells in which the number of defects is greater than the predetermined threshold; storing information regarding one or more care areas for use in inspecting the sample; a computer subsystem configured to perform; A system comprising.

2. The system according to claim 1, wherein the predetermined threshold is further selected to be greater than the number of defects detected when no systematic noise is present.

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

4. The system according to claim 1, wherein the one or more care areas are one or more areas within the repetitive patterned area on the sample where the inspection is not performed.

5. The system according to claim 1, wherein the one or more care areas are one or more areas within the repetitive patterned area on the sample where the inspection is performed with lower sensitivity than other areas within the repetitive patterned area on the sample.

6. The system according to claim 1, wherein the predetermined cells include a regular array of the predetermined cells.

7. The system according to claim 1, wherein at least most of the predetermined cells have the same shape and size.

8. The system according to claim 1, wherein the characteristics of the predetermined cells are independent of the design of the repetitive patterned area.

9. The step of determining the number of defects comprises Step of stacking information about defects detected within the predetermined cell among one or more of the images corresponding to a plurality of instances of the repeated pattern region; Step of determining the number of defects detected within the predetermined cell from the stacked information; The system according to claim 1, characterized in that it comprises.

10. The predetermined cell has a first size, The computer subsystem is further configured to perform the determining step, the comparing step, and the specifying step for other predetermined cells having a second size different from the first size. The system according to claim 1, characterized in that it is configured to do so.

11. The computer subsystem is further configured to detect defects in one or more of the images using a hot threshold. The system according to claim 1, characterized in that it is configured to do so.

12. The imaging subsystem is further configured to generate the image of the sample in a plurality of modes, The computer subsystem is further configured to independently perform the determining step, the comparing step, and the specifying step for each of two or more of the plurality of modes. The system according to claim 1, characterized in that it is configured to do so.

13. One or more care areas for a first mode among two or more of the plurality of modes are different from one or more care areas for a second mode among two or more of the plurality of modes. The system according to claim 12, characterized in that it is different.

14. The repeated pattern region formed on the sample is a repeated die formed on the sample. The system according to claim 1, characterized in that it is so.

15. The sample is a wafer. The system according to claim 1, characterized in that it is so.

16. The imaging subsystem is an optical-based imaging subsystem. The system according to claim 1, characterized in that it is so.

17. The imaging subsystem is an electron-based imaging subsystem. The system according to claim 1, characterized in that it is so.

18. A computer-readable medium storing program instructions executable on a computer system for performing a computer-implemented method for setting a care area for inspection of a sample, the computer-implemented method comprising: Determining the number of defects detected within a predetermined cell in one or more images of the sample generated by an imaging subsystem within a repetitive patterned region formed on the sample; Comparing the number of defects detected in each of two or more of the predetermined cells with a predetermined threshold, wherein the predetermined threshold is selected to be less than the number of defects 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 regarding the one or more care areas for use in inspecting the sample; A computer-readable medium comprising the above.

19. A computer-implemented method for setting care areas for inspecting a sample, comprising: Determining the number of defects detected within a predetermined cell in one or more images of the sample generated by an imaging subsystem within a repetitive patterned region formed on the sample; Comparing the number of defects detected in each of two or more of the predetermined cells with a predetermined threshold, wherein the predetermined threshold is selected to be less than the number of defects 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 inspecting the sample; The method, wherein the determining step, the comparing step, the designating step, and the storing step are performed by a computer subsystem coupled to the imaging subsystem.

Images