Setting up the test for the sample

Abstract

The present invention provides methods and systems for setting up an inspection of a sample. A system includes one or more computer subsystems configured to acquire a reference image of a sample and modify the reference image to make the reference image suitable for a design grid to thereby produce a golden grid image. The one or more computer subsystems are also configured to store the golden grid image for inspection of the sample. The inspection includes aligning a test image of the sample generated from an output of an inspection subsystem with the golden grid image.

Description

Technical Field

[0001] The present invention generally relates to methods and systems for setting up an assay of a sample. Background Art

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

[0003] Inspection processes are used to detect defects on the mask and wafer at various steps during the semiconductor manufacturing process to promote higher yields and therefore higher profits in the manufacturing process. Inspection has always been an important part of manufacturing semiconductor devices. However, as the size of semiconductor devices decreases, inspection becomes more important to successfully manufacturing acceptable semiconductor devices because smaller defects can cause device failures.

[0004] There are many factors that affect the defect count stability of an inspection tool from scan to scan. One major factor is the pixel position change from scan to scan. For example, many currently used inspections are set up and / or performed based on design information of the sample being inspected. In one such example, the region of interest can be generated from the design in setup and then applied to the test image at runtime using a pixel to design alignment (PDA) offset (runtime test image to design offset). In another such example, the test image can be aligned to the design and then the properties of the defects detected in the test image can be determined from the design to which it is aligned.

[0005] Therefore, the success of such design-based inspection may depend at least in part on how well the test image can be aligned to the design and / or pattern-to-pattern, sample-to-sample, and tool-to-tool alignment differences. For example, not only can it be difficult to align the test image to the design with substantially high accuracy, but the accuracy with which such alignment can be performed may vary from scan to scan, pattern to pattern, tool to tool, etc. due to process and sample variations. With currently used methods, this variation may cause defects to be assigned to different regions from scan to scan, which may result in changes in the inspection region or even defect loss and unstable defect attributes.

[0006] Therefore, currently used methods and systems have several disadvantages. For example, the run-time background map (RTCM) region of interest from the design may be shifted due to the pixel-to-design alignment (PDA) offset between the run-time test image and the design. Then, the RTCM region of interest is reproduced on top of the run-time test image. Due to the inter-scan pixel position variation, the RTCM partition map may change to cause the inter-scan defect count variation. In another example, some defect attributes may be calculated from a reference screenshot image of the defect. These defect attributes may capture the basic design structural information of the defect, such as a reference background pattern. Reference contrast is another useful defect attribute that can be used to filter out interference. Due to inter-scan variation, the attributes based on the reference screenshot are unstable.

[0007] Therefore, it would be advantageous to develop systems and methods for setting up an inspection of a sample that do not suffer from one or more of the above-mentioned disadvantages. Summary of the invention

[0008] The following description of various embodiments should in no way be construed as limiting the subject matter of the appended claims.

[0009] One embodiment relates to a system configured to set up an inspection of a sample. The system includes an inspection subsystem configured to generate an output in response to energy detected from a sample. The system also includes one or more computer subsystems configured to acquire a reference image of the sample and modify the reference image to make the reference image suitable for a design grid to thereby generate a golden grid image. The one or more computer subsystems are further configured to store the golden grid image for inspection of the sample. The inspection includes aligning a test image of the sample generated from the output of the inspection subsystem with the golden grid image. The system may be further configured as described herein.

[0010] Another embodiment is directed to a computer-implemented method for setting up an inspection of a sample. The method includes: acquiring a reference image of the sample; and modifying the reference image to adapt the reference image to a design grid to thereby produce a golden grid image. The method further includes storing the golden grid image for use in inspecting the sample. The inspection includes aligning a test image of the sample generated from an output of an inspection subsystem responsive to energy detected from the sample with the golden grid image. The acquiring, modifying, and storing steps are performed by one or more computer subsystems.

[0011] Each of the steps of the above 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.

[0012] Another embodiment relates to a non-transitory computer-readable medium storing program instructions executable on a computer system to perform a computer-implemented method for setting up an inspection of a sample. The computer-implemented method includes the steps of the above method. The computer-readable medium may be further configured as described herein. The steps of the computer-implemented method may be executed as further described herein. In addition, the computer-implemented method executable by the program instructions may include any other steps of any other method described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Further advantages of the present invention will become apparent to those skilled in the art from the benefit of the following detailed description of the preferred embodiments and with reference to the accompanying drawings, in which:

[0014] Figure 1 and 2 is a schematic diagram illustrating a side view of an embodiment of a system configured as described herein;

[0015] Figure 3 is a flow chart illustrating an embodiment of steps performed by one or more computer subsystems described herein in a setup phase;

[0016] Figure 4 is a flow chart illustrating an embodiment of steps performed in a runtime phase by one or more computer subsystems described herein; and

[0017] Figure 5 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.

[0018] Although the present invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown in the drawings by way of example and described in detail herein. The drawings may not be drawn to scale. However, it should be understood that the drawings and their detailed description are not intended to limit the invention to the specific forms disclosed, but on the contrary, the invention covers all modifications, equivalents and alternatives falling within the spirit and scope of the invention as defined by the appended claims. DETAILED DESCRIPTION

[0019] As used herein, the term "noise" (which may sometimes be used interchangeably with "noise defect") is generally defined as a defect that is not of interest to the user and / or an event detected on a sample but which is not, in fact, an actual defect on the sample. Noise that is not, in fact, a defect may be detected as an event due to non-defect noise sources on the sample (e.g., metal line die on the sample, signals from underlying layers or materials on the sample, line edge roughness (LER), relatively small critical dimension (CD) variations of patterned features, thickness variations, etc.) and / or due to marginality of the inspection system itself or its configuration for inspection.

[0020] As used herein, the term "defect of interest (DOI)" is defined as a defect that is detected on a sample and is, in fact, an actual defect on the sample. Therefore, DOI is of interest to users because users generally care about how many and what kind of actual defects there are on the sample being inspected. In some contexts, the term "DOI" is used to refer to a subset of all actual defects on a sample that only includes actual defects that the user is concerned about. For example, there may be multiple types of DOI on any given sample, and one or more of them may be of greater concern 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 and all real defects on a sample.

[0021] The terms "design" and "design data" as used herein generally refer to the physical design (layout) of an IC and data derived from the physical design by complex simulations or simple geometric and Boolean operations. The physical design may be stored in a data structure, such as a Graphic Data Stream (GDS) file, any other standard machine-readable file, any other suitable file known in the art, and a design database. A GDSII file is one of a class of files used to represent design layout data. Other examples of such files include GL1 and OASIS files and proprietary file formats, such as RDF data, which is proprietary to KLA, Inc. of Milpitas, California. In addition, the "design" and "design data" described herein refer to information and data generated by a semiconductor device designer during the design process and thus can be used to well use in the embodiments described herein before the design is printed on any physical wafer. The design may include any other design data or design data proxies described in commonly owned U.S. Patent No. 7,570,796, issued on August 4, 2009 to Zafar et al. and commonly owned U.S. Patent No. 7,676,077, issued on March 9, 2010 to Kulkarni et al., both of which are incorporated herein by reference as if fully set forth.

[0022] Turning now to the drawings, it should be noted that the drawings are not drawn to scale. In particular, the proportions of some elements of the drawings are greatly exaggerated to emphasize the characteristics of the elements. It should also be noted that the drawings are not drawn to the same scale. Elements shown in more than one figure that may be similarly configured have been indicated using the same element number. Unless otherwise specified herein, any element described and shown may include any suitable commercially available element.

[0023] One embodiment relates to a system configured to set up an inspection of a sample.Some embodiments relate to inspection tool process variation control using a golden grid.

[0024] In one embodiment, the sample is a wafer. The wafer may include any wafer known in semiconductor technology. In another embodiment, the sample is a mask. The mask may include any mask known in semiconductor technology. Although some embodiments may be described herein with respect to one or several wafers, the embodiments are not limited to samples thereof that may be used. For example, the embodiments described herein may be used for samples such as masks, flat panels, personal computer (PC) boards, and other semiconductor samples.

[0025] The system includes an inspection subsystem configured to generate an output in response to energy detected from the sample. Figure 1An embodiment of such a system is shown in . In some embodiments, the inspection subsystem includes at least an energy source and a detector. The energy source is configured to generate energy directed to the sample. The detector is configured to detect energy from the sample and generate an output in response to the detected energy.

[0026] In one embodiment, the inspection subsystem is a light-based inspection subsystem. Figure 1 In the embodiment of the system shown, the inspection subsystem 10 includes an illumination subsystem configured to direct light to the sample 14. The illumination subsystem includes at least one light source. Figure 1 , the illumination subsystem includes a light source 16. In one embodiment, the illumination subsystem is configured to direct light to the sample at one or more incident angles (which may include one or more oblique angles and / or one or more normal angles). Figure 1 , light from light source 16 is directed through optical element 18 and then lens 20 to beam splitter 21, which directs the light at a normal angle of incidence to sample 14. The angle of incidence may include any suitable angle of incidence, which may vary depending on, for example, the characteristics of the sample and the defects to be detected on the sample.

[0027] The illumination subsystem may be configured to direct light to the sample at different incident angles at different times. For example, the inspection subsystem may be configured to modify one or more characteristics of one or more elements of the illumination subsystem so that the light may be directed differently to the sample. Figure 1 In one such example, the inspection subsystem may be configured to move light source 16, optical element 18, and lens 20 so that light is directed to the sample at different angles of incidence.

[0028] In some examples, the inspection subsystem may be configured to direct light to the sample at more than one incident angle simultaneously. For example, the inspection subsystem may include more than one illumination channel, one of which may include Figure 1 , and another of the illumination channels (not shown) may include similar elements that may be configured differently or identically or may include at least a light source and possibly one or more other components, such as those described further herein. If this light is directed to the sample at the same time as the 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 illuminating the sample at different angles of incidence may be distinguished from one another at a detector.

[0029] In another example, the illumination subsystem may include only one light source (eg, Figure 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 another example, the same illumination channel may be configured to direct light to the sample with different characteristics at different times. For example, in some examples, the optical element 18 may be configured as a spectral filter and the properties of the spectral filter may be changed in a variety of different ways (e.g., by swapping out the spectral filter) so that different wavelengths of light 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 with different or the same characteristics to the sample sequentially or simultaneously at different or the same angles of incidence.

[0030] In one embodiment, the light source 16 may include a broadband plasma (BBP) light source. In this way, the light generated by the light source and directed to the sample may include broadband light. However, the light source may include any other suitable light source, such as a laser, which may be any suitable laser known in the art and may be configured to generate light at any suitable wavelength known in the art. In addition, the laser may be configured to generate monochromatic or near-monochromatic light. In this way, the laser may be a narrow-band laser. The light source may also include a polychromatic light source that generates light at multiple discrete wavelengths or bands.

[0031] Light from optical element 18 may be focused by lens 20 to beam splitter 21. Although lens 20 is Figure 1 2 is shown as a single refractive optical element, but in reality, lens 20 may include several refractive and / or reflective optical elements that combine to focus light from the optical element onto the sample. Figure 1 The illumination subsystem shown in and described herein may include any other suitable optical elements (not shown in the figures). Examples of such optical elements include, but are not limited to, polarization components, spectral filters, spatial filters, reflective optical elements, apodizers, beam splitters, apertures, and the like, which may include any such suitable optical elements known in the art. Additionally, the system may be configured to alter one or more elements of the illumination subsystem based on the type of illumination used for inspection.

[0032] The inspection subsystem may also include a scanning subsystem configured to cause light to be scanned over the sample. For example, the inspection subsystem may include a stage 22 on which the sample 14 is placed during inspection. The scanning subsystem may include any suitable mechanical and / or robotic assembly (including the stage 22) that may be configured to move the sample so that light can be scanned over the sample. Additionally or alternatively, the inspection subsystem may be configured so that one or more optical elements of the inspection subsystem perform a certain scan of light over the sample. The light may be scanned over the sample in any suitable manner.

[0033] The inspection 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 due to illumination of the sample by the inspection subsystem and to generate an output in response to the detected light. For example, Figure 1 The inspection subsystem shown in FIG. 1 includes two detection channels, one formed by a light collector 24, an element 26, and a detector 28, and the other formed by a light collector 30, an element 32, and a detector 34. Figure 1 , the two detection channels are configured to collect and detect light at different collection angles. In some examples, 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 may be configured to detect the same type of light from the sample (e.g., specularly reflected light). Although Figure 1 An embodiment of an inspection subsystem including two detection channels is shown, but the inspection subsystem may include a different number of detection channels (e.g., only one detection channel or two or more detection channels). Figure 1 , but each light collector may include one or more refractive optical elements and / or one or more reflective optical elements.

[0034] One or more detection channels may include any suitable detector known in the art, such as a photomultiplier tube (PMT), a charge coupled device (CDD), and a time delay integration (TDI) camera. The detector may also include a non-imaging detector or an imaging detector. If the detector is a non-imaging detector, each detector may be configured to detect a specific characteristic of the scattered light (e.g., intensity), but cannot be configured to detect a characteristic that varies with position in the imaging plane. Thus, the output generated by each detector included in each detection channel may be a signal or data, but not an image signal or image data. In such examples, a computer subsystem (e.g., the computer subsystem 36 of the system) may be configured to generate an image of the sample from the non-imaging output of the detector. However, in other examples, the detector may be configured as an imaging detector configured to generate an imaging signal or image data. Thus, the system may be configured to generate an image in a number of ways.

[0035] It should be noted that this article provides Figure 1 to generally illustrate the configuration of the inspection subsystem that may be included in the system embodiments described herein. Obviously, the inspection subsystem configuration described herein may be modified to optimize the performance of the system that normally performs when designing a commercial inspection system. In addition, the system described herein may be implemented using an existing inspection system (e.g., by adding the functionality described herein to an existing inspection system), such as the 29xx and 39xx series tools available from Kelei. For some such systems, the embodiments described herein may be provided as an optional function of the inspection system (e.g., in addition to other functions of the inspection system). Alternatively, the inspection subsystem described herein may be designed "from scratch" to provide an entirely new inspection system.

[0036] The computer subsystem 36 of the system may be coupled to the detector of the inspection 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 may receive the output generated by the detector during scanning of the sample. The computer subsystem 36 may be configured to use the output of the detector to perform a number of functions (as described herein) and any other functions further described herein. This computer subsystem may be further configured as described herein.

[0037] This computer subsystem (and 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 be in various forms, including personal computer systems, image computers, mainframe computer systems, workstations, network appliances, Internet appliances, 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 with high-speed processing and software as a stand-alone or network tool.

[0038] If the system includes more than one computer subsystem, the different computer subsystems may be coupled to each other so that images, data, information, instructions, etc. may be sent between the computer subsystems, as further described herein. For example, computer subsystem 36 may be coupled to computer subsystem 102 (e.g., by a computer subsystem 104) via any suitable transmission media (which may include any suitable wired and / or wireless transmission media known in the art). Figure 1 Two or more such computer subsystems may also be effectively coupled by a shared computer-readable storage medium (not shown).

[0039] Although the inspection subsystem is described above as an optical or light-based subsystem, the inspection subsystem may be an electron-based inspection 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. Figure 2 In one such embodiment shown, the inspection subsystem includes an electronic column 122 coupled to a computer subsystem 124 .

[0040] Also like Figure 2 , the electron column includes an electron beam source 126 configured to produce electrons that are focused by one or more elements 130 to a sample 128. 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 selecting aperture, an objective lens, and a scanning subsystem, all of which may include any such suitable elements known in the art.

[0041] Electrons returning from the sample (eg, secondary electrons) may be focused by one or more elements 132 onto a detector 134. One or more elements 132 may include, for example, a scanning subsystem, which may be the same scanning subsystem included in element 130.

[0042] The electron column may include any other suitable elements known in the art. In addition, the electron column may be further configured as described in U.S. Pat. No. 8,664,594 issued on April 4, 2014 to Jiang et al., U.S. Pat. No. 8,692,204 issued on April 8, 2014 to Kojima et al., U.S. Pat. No. 8,698,093 issued on April 15, 2014 to Gubbens et al., and U.S. Pat. No. 8,716,662 issued on May 6, 2014 to MacDonald et al., which are incorporated herein by reference as if fully set forth.

[0043] Although the electron column Figure 2 The electron beam subsystem is shown as being configured so that electrons are directed to the sample at an oblique angle of incidence and scattered from the sample at another oblique angle, but it should be understood that the electron beam can be directed to the sample and scattered from the sample at any suitable angle. In addition, the electron beam subsystem can be configured to use multiple modes to generate an image of the sample (e.g., using different illumination angles, collection angles, etc.). The multiple modes of the electron beam subsystem can differ in any image generation parameter of the subsystem.

[0044] The computer subsystem 124 may be coupled to a detector 134, as described above. The detector may detect electrons returning from the surface of the sample to thereby form 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 use the output of the detector and / or the electron beam image to perform any of the functions described herein. The computer subsystem 124 may be configured to perform any additional steps described herein. Figure 2 The system of inspection subsystems shown in may be further configured as described herein.

[0045] It should be noted that this article provides Figure 2 1 and 2 to generally illustrate configurations of electronic-based inspection 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 subsystem as would normally be expected when designing a commercial inspection system. Additionally, the systems described herein may be implemented using existing inspection systems (e.g., by adding the functionality described herein to an existing inspection system). For some such systems, the embodiments described herein may be provided as optional functionality of the system (e.g., in addition to other functionality of the system). Alternatively, the systems described herein may be designed "from the ground up" to provide an entirely new system.

[0046] Although the inspection subsystem is described above as a light-based or electron-beam-based subsystem, the inspection subsystem may be an ion-beam-based subsystem. In addition to the electron beam source being replaceable by any suitable ion beam source known in the art, the inspection subsystem may be configured as follows: Figure 2 . Thus, in one embodiment, the energy directed to the sample includes ions. Additionally, the inspection subsystem may be any other suitable ion beam-based inspection subsystem, such as those included in commercially available focused ion beam (FIB) systems, helium ion microscope (HIM) systems, and secondary ion mass spectrometer (SIMS) systems.

[0047] The inspection subsystem described herein may be configured to use multiple modes to generate outputs, such as images, of a sample. In general, a "mode" is defined by the value of a parameter of the inspection subsystem used to generate an output and / or image of a sample (or an output for generating an image of a sample). Thus, a mode may differ in the value of at least one parameter of the inspection 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. Modes may differ in illumination wavelength, as further described herein (e.g., by using different light sources, different spectral filters, etc. for different modes). In another example, different modes may use different illumination channels of the optical subsystem. For example, as mentioned above, the optical subsystem may include more than one illumination channel. Thus, different illumination channels may be used for different modes. Modes may also or alternatively differ in one or more collection / detection parameters of the optical subsystem. Modes may differ in any one or more modifiable parameters of the inspection subsystem (e.g., illumination polarization, angle, wavelength, etc., detection polarization, angle, wavelength, etc.). The inspection subsystem may be configured to scan a sample using different modes in the same scan or in different scans, eg, depending on the ability to scan a sample using multiple modes simultaneously.

[0048] In a similar manner, the output generated by the electron beam subsystem may include output, such as an image, 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 parameters of the electron beam subsystem used to generate the output and / or image of the sample. Thus, the modes may differ in the value of at least one electron beam parameter of the electron beam subsystem. For example, different modes may use different illumination incident angles.

[0049] This article describes and Figure 1 and 2 The subsystems shown in may modify one or more parameters to provide different output generation capabilities depending on the application in which they will be used. In one such example, Figure 1 The inspection subsystem shown in can be configured to have higher resolution when it is used for defect review or metrology rather than inspection. In other words, Figure 1 and 2 The embodiments of the inspection subsystem shown in describe some common and various configurations of the inspection subsystem, which can be adapted in a number of ways that should be apparent to those skilled in the art to produce an inspection subsystem with different output generation capabilities that are nearly suitable for different applications.

[0050] As mentioned above, the optical, electron, and ion beam subsystems are configured to scan energy (e.g., light, electrons, etc.) across a physical version of a sample to thereby produce an output of a physical version of the sample. In this way, the optical, electron, and ion beam subsystems may be configured as "real" subsystems rather than "virtual" subsystems. However, Figure 1 The storage medium (not shown) and computer subsystem 102 shown in the figure can be configured as a "virtual" system. In particular, the storage medium and computer subsystem can be configured as a "virtual" inspection system, as described in commonly assigned U.S. Patent No. 8,126,255 issued on February 28, 2012 to Bhaskar et al. and commonly assigned U.S. Patent No. 9,222,895 issued on December 29, 2015 to Duffy et al., both of which are incorporated herein by reference as if set forth in their entirety. The embodiments described herein can be further configured as described in these patents.

[0051] The computer subsystem is configured to acquire a reference image of the sample. Figure 3 As shown in step 300 of , the computer subsystem is configured to acquire a reference image. In general, the reference image may be any reference image that is not generated from the design of the sample. For example, as further described herein, an embodiment uses the reference image to generate a golden grid image by fitting the reference image to a design grid.

[0052] Acquiring a reference image may include using the inspection subsystem to scan a sample to produce an image of the sample to be used as a reference image. Acquiring a reference image in this manner may be performed in any suitable manner known in the art, such as by scanning one or more printed instances of a die or other repeating structure on the sample. The reference image used in the steps described herein may be the raw output of a detector of the inspection subsystem, which may or may not have been processed in some manner. Additionally, the acquired reference image may be generated from multiple printed instances of repeating structures on the sample in any suitable manner, such as by averaging, robust averaging that excludes outliers (defects), and the like. Thus, the computer subsystem may acquire a reference image simply by causing the inspection subsystem to produce a detector output for use as a reference image or by causing the detection subsystem to physically scan the sample and perform one or more image processing steps on the detector output.

[0053] However, acquiring the reference image may not include scanning the sample. For example, another system or method may generate a reference image and store the reference image in a storage medium (not shown) accessible to the computer subsystem. Then, acquiring the reference image may include accessing and retrieving the reference image from the storage medium.

[0054] As further described herein, some inspection processes are performed using more than one mode of the inspection subsystem, and the modes may be defined as further described herein. If the inspection process is performed using more than one mode, each mode may require a different reference image. In other words, due to the different modes that provide inspection advantages, the images produced by the modes will be quite different, so that each mode requires a different reference image. Therefore, in such examples, acquiring a reference image may include acquiring a first reference image for a first mode, acquiring a second reference image for a second mode, and so on. Each reference image may be acquired by the above-mentioned inspection subsystem. For example, the computer subsystem may cause the inspection subsystem to scan a repeating structure on a sample using each mode to thereby acquire a reference image for each mode. Each reference image page for each mode may be processed as described herein. In other words, the steps described herein may be performed separately and independently for each mode used in the inspection process.

[0055] Acquiring a reference image as described above may be performed during a setup phase that is performed prior to runtime of the inspection process. Acquiring a reference image (and other steps described herein) may also be performed at any time the inspection process is being updated, modified, calibrated, retrained, etc. In this manner, "setting up an inspection process" as described herein may include setting up a new inspection process or modifying an existing process.

[0056] The computer subsystem is also configured to modify the reference image so that the reference image fits the design grid to thereby generate a golden grid image. The term "design grid" used herein may generally be defined as a uniform grid defined between sample designs and within the design of a sample. The term "design grid" may also be used interchangeably with the term "golden grid". Each pixel within the design grid has the same size, and the pixels along the x and y directions are perfectly aligned. Thus, the design grid is like a digital coordinate system. In other words, the entire design (or at least a portion of the design to be inspected during the inspection process) may be subdivided into significantly smaller areas (referred to herein as pixels) in the design space, each of which has the same size and is arranged in a uniform two-dimensional (2D) array to thereby define the design grid. In one hypothetical example, the design grid of a die may include a 2D array of pixels arranged in 256 rows by 256 columns, and each pixel may have the same x and y dimensions. Of course, different design grids may be defined for a design or for different designs, depending on various factors, such as the design itself, the characteristics of the inspection image to be produced by the sample on which the design is formed (e.g., resolution), the inspection process to be performed on the sample using the image, user-specified parameters, etc. However, for the embodiments described herein, the design grid should be uniform in pixel arrangement and size. In contrast, in the sample image, pixel size may vary slightly from pixel to pixel and pixels along the x and y directions may be slightly skewed due to inherent marginalities of the sample and / or the inspection subsystem (e.g., optical distortion and image jitter).

[0057] As further described herein, embodiments provide a golden grid method that has various applications for quality control tools and methods, such as inspection tool stability control. A golden grid image can be generated and saved during a training (also called a setup) step or phase of an inspection. The golden grid image can be a golden grid die image or a golden grid image of another repeated printing example on a sample. For example, the golden grid image can be a golden grid field image, a golden grid memory array image, a golden grid device area image, a golden grid cell image, etc.

[0058] The reference image may undergo one or more optional processing steps before, during, or after the modification step. For example, in one embodiment, generating a golden grid image includes removing random defects from the reference image. Figure 3 As shown in step 302 of , one or more computer subsystems may remove random defects from a reference image. (Optional steps are shown in dashed lines in the figure.) Removing random defects from a reference image may be performed in any suitable manner known in the art. One example of how random defects may be removed from a reference image is by robustly averaging detector outputs from multiple examples of a repeating structure on a sample (e.g., a die). The robust averaging performed in the embodiments described herein may rank multiple examples of a repeating structure, remove outliers (which may correspond to defects), and then average the remaining examples. In this manner, a golden grid image may be a processed image that may be produced in part by removing random defects.

[0059] In another embodiment, generating the golden grid image includes removing features specific to the inspection subsystem from the reference image. Figure 3 As shown in step 304 of , the computer subsystem may remove tool features from the reference image. For example, the golden grid image is preferably free of tool specific features such as tool optical distortion, stage movement related uncertainties, color variations, etc. This distortion correction may be performed in any suitable manner known in the art.

[0060] In a further embodiment, generating the golden grid image includes removing sample-specific features from the reference image. Figure 3 As shown in step 306 of , the computer subsystem may remove sample features from the reference image. For example, the golden grid image is preferably free of sample-specific features such as color variations. This distortion correction may also be performed in any suitable manner known in the art.

[0061] In some embodiments, modifying the reference image includes stretching the reference image along one or more dimensions. In this way, a golden grid image can be a processed image produced by stretching the reference image to a design grid. The reference image can be stretched along either or both of the x and y dimensions and to the same or different degrees in x and y. Although the modification step may generally involve changing the overall size of the reference image by stretching it along one or more dimensions, it is contemplated that in some examples, modifying the reference image may include shrinking the reference image along one or more dimensions or some combination of stretching along one dimension and shrinking along another dimension. In either case, stretching and / or shrinking the reference image along one or more dimensions can be performed in any suitable manner known in the art.

[0062] In another embodiment, modifying the reference image includes rotating the reference image. The reference image may be rotated in any suitable direction (e.g., clockwise or counterclockwise). Rotating the reference image along one or more dimensions to make it suitable for the design grid may be performed in combination with one or more other ways of modifying the reference image described herein.

[0063] In another embodiment, modifying the reference image includes interpolating pixels of the reference image to a suitable design grid. For example, a golden grid image can be stretched by interpolating pixels in a uniform design grid to correct for optical distortion and image jitter caused by wafer stage movement. Interpolation can be performed in any suitable manner using any suitable method, algorithm, function, etc.

[0064] Two or more of the above steps may also be used in combination to produce a golden grid image. For example, a golden grid image may be produced by removing random defects from a reference image and by stretching the reference image to a design grid. Other steps may also be performed in combination to produce a golden grid image and which steps to perform may be determined based on information about the inspection subsystem, sample, etc. and / or by comparing the reference image to the design grid and determining which steps may be advantageously performed on the reference image to produce the golden grid image.

[0065] In some such embodiments, Figure 3 The processed reference image 308 shown in FIG. 300 may be produced by step 300, possibly in conjunction with one or more of steps 302, 304, and 306. If the computer subsystem is not configured to perform steps 302, 304, and 306, then the processed reference image 308 will simply be an acquired reference image.

[0066] like Figure 3As further shown in , the design 310 of the sample may be acquired by a computer subsystem or another system or method. The computer subsystem or another system or method may generate or determine a design grid 312 from the design. Then, one or more computer subsystems may use the processed reference image 308 (or acquired reference image) and the design grid 312 as inputs to step 314, where the computer subsystem modifies the (processed or acquired) reference image to fit the reference image to the design grid. The modification performed in step 314 may include any of the modification steps described herein, such as stretching and / or rotating the reference image along one or more dimensions and / or interpolating pixels of the reference image to fit to the design grid. Thus, the output of the modification step may be a golden grid image 316.

[0067] The computer subsystem is further configured to store the golden grid image for use in verifying the sample. Figure 3 As shown in step 324 of , the computer subsystem is configured to store the golden grid image. The golden grid image can then be retrieved and used during run-time sample inspection. Inspection includes aligning a test image of the sample generated from the output of the inspection subsystem with the golden grid image. During run-time inspection, the test image is shifted, stretched, rotated, interpolated, or some combination thereof to align with the golden grid image. The computer subsystem can be configured to store the golden grid image in a recipe or by generating an inspection recipe in which the alignment is to be performed. The term "recipe" as used herein is defined as a set of instructions that can be used by a tool to perform a process on a sample. In this manner, generating a recipe can include generating information on how to perform a process, which can then be used to generate instructions for performing the process. The computer subsystem can also be configured to store the golden grid image in a computer-readable storage medium separate from the inspection process recipe and store any information that can be used to identify, access and / or use the golden grid image in the inspection process recipe, such as, for example, a file name and where it is stored.

[0068] The computer subsystem may be configured to store the golden grid image in any suitable computer-readable storage medium. The golden grid image may be stored with any of the results described herein and may be stored in any manner known in the art. The storage medium may include any of the storage media described herein or any other suitable storage medium known in the art. After the golden grid image is stored, the golden grid image 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.

[0069] In one embodiment, storing the golden grid image includes storing the golden grid image for use in inspecting the sample and inspecting at least one other sample of the same type as the sample. For example, embodiments described herein may generate the inspection recipe described above. The inspection recipe may then be stored and used by embodiments described herein (and / or another system or method) to inspect the sample and / or other samples to thereby generate information (e.g., defect information) of the sample and / or other samples. In this manner, a golden grid image may be generated and stored once per sample layer, and the golden grid image may be used to inspect multiple samples of the same layer. Of course, as with any inspection process, if the inspection of a sample layer is updated, calibrated, modified, retrained, etc., then the golden grid image may also be updated, calibrated, modified, retrained, etc. in the same manner as any other inspection process parameters. Updating, calibrating, modifying, retraining, etc. the golden grid image may be performed in the same manner as described herein to generate and store the golden grid image. In this manner, embodiments described herein may be configured to repeat the steps described herein at various times to modify a previously set inspection process.

[0070] A "region of interest" as often referred to in the art is a region on a sample that is of interest for inspection purposes. Regions of interest are sometimes used to distinguish regions on a sample that are inspected from regions on a sample that are not inspected during the inspection process. Additionally, regions of interest are sometimes used to distinguish regions on a sample that are inspected using one or more different parameters. For example, if a first region of a sample is more important than a second region on the sample, the first region may be inspected with a higher sensitivity than the second region, so that defects are detected in the first region with a higher sensitivity. Other parameters of the inspection process may be altered in a similar manner with the regions of interest.

[0071] In some embodiments, the one or more computer subsystems are configured to define one or more region of interest areas in the golden grid image, the inspection subsystem and the one or more computer subsystems are configured to perform inspection of the sample, and the inspection includes defining one or more region of interest areas in the test image based on the results of aligning the test image with the golden grid image and the one or more region of interest areas defined in the golden grid image. For example, the one or more computer subsystems may perform region of interest area definition on the golden grid image. In one such example, Figure 3 As shown in step 318 of , the computer subsystem or another system or method may generate a region of interest region from the design. The region of interest region may be generated from the design in any suitable manner known in the art, and the region of interest region may have any suitable characteristics known in the art. As shown in step 320, the computer subsystem may use the region of interest region from the design and the golden grid image 316 to define the region of interest region in the golden grid image. The computer subsystem may then store the region of interest region in the golden grid image and the golden grid image in step 324.

[0072] Generally, because the golden grid image has been modified to fit the design grid, the region of interest region will have the same placement, coordinates, size, etc. in the design and golden grid images. However, by defining the region of interest region in the golden grid image, when the test image used for inspection is aligned with the golden grid image, the region of interest region will be effectively defined in the test image. Because the same golden grid image can be used between different scans in the inspection, the region of interest region definition will be advantageously consistent. The inspection subsystem and computer subsystem can perform the inspection and align the test image with the golden grid image, as further described herein.

[0073] In another embodiment, one or more computer subsystems are configured to render a runtime context map (RTCM) from a golden grid image and store the RTCM for use in inspecting samples. For example, as shown in step 322, the computer subsystem may render the RTCM from the golden grid image 316. Rendering the RTCM in step 322 may be performed using the region of interest information generated in step 318, the masking information from the design 310, and the golden grid image. In particular, the RTCM data is generated from the design, so the design may be input to step 322. The RTCM rendering generates an RTCM zone map, an RTCM mask map, etc. (using the pixel size in the golden grid) on the golden grid. The computer subsystem may then store the rendered RTCM with the golden grid image in step 324. In step 324, the golden grid image may be stored with the rendered RTCM region / mask map. Figure 1 The rendered RTCM region / shield map may be saved in the recipe along with the golden grid image for use in the inspection run time. The RTCM rendering may be performed to assign each pixel to a specific region or regions if possible. RTCM may also be performed in any suitable manner known in the art, some examples of which are described in the above-referenced patents of Kulkarni et al. The embodiments described herein may be further configured for RTCM rendering as described in the patents.

[0074] RTCM may use geometric information, primarily in rectangular coordinates, to define regions of interest and / or other information, such as shielding information for supercells / nanocells. RTCM region of interest definition is from design and is independent of the golden grid. During setup, RTCM may be rendered on top of the golden grid to produce an RTCM partition map, where each pixel on the golden grid is assigned to one (or more) regions. The rendered RTCM partition map may be saved with the golden grid image and used during runtime. Existing systems perform RTCM rendering at runtime, which has a throughput hiccup. Runtime RTCM rendering may be synchronized with the saved RTCM partition map. Figure 1 The RTCM partition map is saved together and is consistent between runs, tools, etc. as long as the same recipe (with the same RTCM partition map) is used.

[0075] In another embodiment, the one or more computer subsystems are configured to define one or more masking regions in the golden grid image, the inspection subsystem and the one or more computer subsystems are configured to perform inspection of the sample, and the inspection includes defining one or more masking regions in the test image based on the results of aligning the test image with the golden grid image and the one or more masking regions defined in the golden grid image. In one such embodiment, masking is used in super cells and nano cells. Each array region is subdivided into masking sub-regions so that different recipes can be used for different sub-regions to, for example, separate NMOS and PMOS into different sub-regions. These steps can be performed as described above, and the one or more masking regions can be defined in the test image by applying RTCM to the aligned or modified test image, as further described herein. For example, the one or more masking regions can be defined in the RTCM reproduced and stored as described herein. Thus, by applying RTCM to the test image aligned with the golden grid image, the masking regions will be defined in the aligned test image. Alternatively, the masked regions may be defined separately from the RTCM, such as from the design 310 in the same manner as the regions of interest were defined from the design in step 318. The masked regions may be defined in the golden grid image in the same manner as the regions of interest, and the masked regions may be stored with the golden grid image as further described herein so that they may be accessed and applied during inspection, as further described herein.

[0076] Using the golden grid, if the image to design alignment is perfect, then the definition of regions of the same pixel size will be the same. Background Based Inspection (CBI) RTCM generation can be performed only once during the training step as the RTCM region of interest region rendering performed on the golden grid image. Performing the RTCM rendering in this manner can save RTCM rendering time at run time. The golden grid image and RTCM partition map can be generated and saved during setup and then used at run time, as further described herein. For example, the RTCM partition map can be retrieved and used to define the region of interest area for per-region defect detection.

[0077] In one such embodiment, RTCM is used to verify at least one other sample of the same type as the sample. For example, if an RTCM zoning map is reproduced on a golden grid image during setup and saved in a database, the RTCM zoning map can be retrieved at runtime to thereby advantageously provide substantially consistent region definitions between scans.

[0078] In additional embodiments, aligning the test image with the golden grid image includes modifying the test image to align the test image with the golden grid image. Figure 4, the computer subsystem may use a test image 400 and a stored golden grid image 402 as inputs to step 404, where the computer subsystem aligns the test image with the golden grid image to thereby produce a modified test image 406. For example, all runtime test images (e.g., test die images) may be shifted / interpolated to align with the golden grid image. In one such embodiment, the test frames may be interpolated and / or shifted to align with the golden grid image using offsets measured between the test images and the golden grid image during runtime. The test images may be shifted, interpolated, stretched, etc. relative to the reference image, as further described herein.

[0079] In one such embodiment, the inspection subsystem and one or more computer subsystems are configured to perform inspection of the sample, and the inspection includes determining one or more parameters of a defect detection method based on the results of aligning the test image with the golden grid image and detecting defects on the sample by applying the defect detection method with the determined one or more parameters to the modified test image. For example, as further described herein, a region of interest area can be defined in the golden grid image and RTCM rendering can be performed using the golden grid image. Then, the region of interest area and / or the rendered RTCM can be used to determine and control one or more parameters of the defect detection method applied to the modified test image.

[0080] In one such example, Figure 4 As shown in step 410 of , the computer subsystem may use the modified test image 406 and the region of interest area 408 in the golden grid image to define the region of interest in the modified test image. In particular, once the test image is modified to align with the golden grid image, the region of interest area defined in the golden grid image may be mapped to the modified test image. In another such example, Figure 4 As shown in step 414 of , one or more computer subsystems may apply the rendered RTCM 412 to the modified test image 406. In the same manner that a region of interest area defined in a golden grid image may be mapped to a modified test image aligned therewith, the rendered RTCM from the golden grid image may be mapped to the modified test image.

[0081] Although the region of interest 408 and the rendered RTCM 412 are Figure 4, but the region of interest area may be defined in the RTCM, as further described herein. Thus, the region of interest area may be defined in the rendered RTCM mapped to the golden grid image. In this way, the embodiments described herein do not require the execution of the define region of interest in the modified test image step 410. Rather, the region of interest may also be defined in the modified test image by executing the apply RTCM to the modified test image step 414. However, it should be understood that the RTCM may include background information in addition to any region of interest information and in addition to any region of interest information, and other and additional information may also be applied to the modified test image in step 414. Thus, whether the region of interest is defined and applied using RTCM related steps or defined and applied in steps separate from the RTCM steps may depend on the specific details of the user and implementation.

[0082] In general, when setting up the region of interest areas and / or rendering the RTCM, a user, one or more computer subsystems, or another method or system will determine one or more parameters of the inspection process to be used with the region of interest areas and / or the RTCM. For example, during setup, the one or more computer subsystems may determine which region of interest areas will be inspected and which defect detection parameter(s), such as thresholds, to use. In this way, by varying defect detection parameters, such as thresholds, based on the characteristics of the region of interest areas, different types of region of interest areas may be inspected with different sensitivities. In a similar manner, during setup, the one or more computer subsystems may use the RTCM to assign different defect detection parameters to portions of the golden grid image with different backgrounds. Thus, after the test image is modified to align it with the golden grid image, the RTCM is also effectively mapped to the test image, and defect detection parameters applied to different portions of the test image may be determined from the RTCM mapped thereto.

[0083] Because the RTCM zone map is generated on top of the golden grid image during setup, the RTCM zone map is the same from scan to scan, tool to tool, etc. Similarly, the same is true for the RTCM shield map. Similar to the RTCM region data, the shield data in the RTCM can be reproduced during setup to produce a consistent RTCM shield map. The RTCM shield map can be defined in the array area to separate different array areas into separate shield areas similar to the RTCM areas. Different recipes can then be applied to different RTCM shield areas. Using consistent RTCM zone maps and shield maps, defect detection results will advantageously be more stable between scans, tools, etc.

[0084] Thus, in general, the computer subsystem may use various inputs (which include at least the modified test image 406, and possibly also the stored golden grid image 402, the region of interest defined in the modified test image in step 410, and the RTCM applied to the modified test image in step 414) to perform defect detection on the modified test image, such as Figure 4 Although some methods are described herein as using these inputs for defect detection, it should be noted that Figure 4 The inputs to step 416 shown in can be used by various defect detection methods and algorithms for detecting defects on a sample. Such defect detection methods and algorithms include those used by inspection tools available from KLA-Television and other sources, which may be in either or both of their one-dimensional (1D) and 2D versions. Defect detection methods that produce a 1D histogram of the detector output may be referred to as 1D defect detection methods. In contrast, the term "2D defect detection algorithm" used herein is an algorithm that uses a 2D histogram, where one axis is the median grayscale (y-axis) of, for example, an n>1 reference system and the x-axis is the different grayscales. However, such defect detection methods and algorithms may also include defect detection methods and algorithms that do not necessarily require a reference for defect detection, such as single image defect detection methods, which may be based on deep learning (DL) or machine learning (ML) techniques. Some examples of ML-based defect detection methods are described by Karsenti et al. in U.S. Patent No. 10,186,026, issued on January 22, 2019, which is incorporated herein by reference as if fully set forth. The embodiments described herein may be further configured as described in such patent.

[0085] In another such embodiment, the inspection subsystem and one or more computer subsystems are configured to perform inspection of the sample, and the inspection includes detecting defects on the sample by subtracting the golden grid image from the modified test image. In this way, defect detection can be performed using a modified test image that has been interpolated, shifted, stretched, etc. to fit the test image to the golden grid image and thus to the designed golden grid. In one such example, the golden grid image can be subtracted from the modified test image to produce a difference image, and in some defect detection methods, a threshold can be applied to pixels in the difference image. Pixels with values ​​above the threshold can be identified as defects or potential defects, and pixels that do not have values ​​above the threshold can not be identified as defects or potential defects. However, in general, any defect detection method that uses a test image as input can use the modified test image as input for defect detection. Therefore, the modified test image can be used in the same manner as any other test image after it is generated, but the inventors believe that the modifications to the test image described herein have not been previously used to generate modified test images for defect detection.

[0086] In a further embodiment, the inspection subsystem and the one or more computer subsystems are configured to perform inspection of the sample, and the inspection includes detecting defects on the sample based on the golden grid image and the test image and determining one or more properties of the defect based on the golden grid image. For example, the golden grid image can be used to calculate defect properties during inspection. In one such example, the golden grid image can be saved in a database during setup and retrieved at runtime to calculate defect properties based on the golden grid. For example Figure 4 As shown in step 418 of , one or more computer subsystems may determine defect attributes using the results of step 416 and the stored golden grid image 402. The defect attributes may be attributes of the stored golden grid image at the location of the detected defect, which is easy to determine because the modified test image has been aligned with the golden grid image.

[0087] However, defect attributes may also be information determined for or using a golden grid image. For example, as further described herein, RTCM reproduction may be performed using a golden grid image. The reproduced RTCM may determine various contexts (e.g., design information) for portions of the golden grid image that vary with location within the golden grid image. The defect attribute may then be the context assigned to the location in the golden grid image corresponding to the location of the defect, which is also easily determined because the modified test image is aligned with the golden grid image. In general, any information that can be determined from a golden grid image about one or more of the golden grid image, the sample, or the design of the sample may be used as a defect attribute by the embodiments described herein.

[0088] Thus, these attributes are the same from scan to scan and are therefore extremely stable. In other words, these defect attributes will be the same for each defect detected at the same location within a printed instance on a sample (e.g., a die, etc.). Thus, these attributes can be particularly advantageously used in other steps such as interference filtering, as interference and DOI can be stably, repeatably, and substantially accurately separated from each other using such attributes. Additionally, the golden grid image can be used to extract additional defect attributes. Such defect attributes can include, for example, differential grayscale when the golden grid image is subtracted from the modified test image.

[0089] In one such embodiment, the inspection also includes performing nuisance filtering of the defect based on one or more attributes determined for the defect. Figure 4 As shown in step 420 of , one or more computer subsystems may perform interference filtering using the defect attributes determined in step 418. Interference filtering may be performed in any suitable manner known in the art. For example, the defect attributes determined based on the golden grid image described herein may be used for interference filtering in the same manner as any other defect attributes.

[0090] Because these attributes are the same from scan to scan, the attributes are extremely stable in interference removal. Specifically, for the golden grid images described herein, both RTCM region definitions and pixel locations can be consistent from scan to scan. Consistent RTCM region definitions and pixel locations from scan to scan result in more stable defect attributes and reduced process variations from scan to scan in terms of defect count stability. For example, in some currently used methods, defect attributes are calculated from the image generated from the current scan. Because the image changes from scan to scan, the defect attributes also change. Interference filtering uses defect attributes. Therefore, if the defect attributes change from scan to scan, the defect count after interference filtering will also change. However, in the embodiments described herein, the defect attributes from the golden grid images are constant from scan to scan. Therefore, the results of interference filtering using golden grid based attributes will also be stable.

[0091] In some embodiments, one or more computer subsystems are configured to retrain the inspection by: acquiring new reference images of additional samples of the same type as the sample; modifying the new reference images so that the new reference images fit the design grid to thereby produce a new golden grid image; and storing the new golden grid image for inspection of the additional samples. Because the golden grid image is stretched to the design grid with pixels in the uniform design grid, the golden grid image is consistent on the same tool or on different tools from different modes, even after retraining (golden grid image regeneration). Acquiring new reference images, modifying new reference images, and storing new golden grid images may be performed in other ways, as further described herein. The additional samples may be of the same type as the sample in that they are manufactured using the same manufacturing process steps as the sample and therefore have the same layers and type as the sample. In this way, the inspection process (i.e., the settings described herein for the sample) can be updated, modified, calibrated, etc. by performing the steps on different samples of the same type. This updating, modification, calibration, etc. of the inspection process may be performed periodically or whenever it is deemed appropriate or necessary.

[0092] In a further embodiment, the inspection subsystem is configured to generate an output of the sample using a first mode and a second mode of the inspection subsystem, a reference image is acquired for the first mode, one or more computer subsystems are configured to acquire additional reference images of the sample and the second mode, modify the additional reference images to fit the additional reference images to the design grid to thereby generate additional golden grid images, and store the additional golden grid images for inspection of the sample, and the inspection includes aligning a test image of the sample generated from the output of the inspection subsystem generated using the second mode with the additional golden grid image. In this manner, the embodiments described herein may generate different golden grid images for different modes. For example, the computer subsystem may generate a first golden grid image for the DF mode and a second golden grid image for the BF mode. In another example, for two DF modes using different scattering angles, the computer subsystem may generate a first golden grid image for one of the two DF modes and a second golden grid image for the other of the two DF modes. When the inspection is performed using multiple modes, different golden grid images may be generated by the computer subsystem because different modes typically require different reference images due to differences in images generated using different modes.

[0093] By generating a golden grid image with pixels in a uniform design grid between different modes, inspection using multiple modes can have the same pixelation. Having the same pixelation in multiple modes can benefit multi-mode sensitivity. For example, because the reference images of different modes can fit to the same uniform design grid, different resulting golden grid images can have the same pixelation, which means that corresponding pixels in different golden grid images correspond to the same area in the design of the sample. Therefore, pixels in different golden grid images corresponding to the same area in the design of the sample can be inspected with the same (optimal) sensitivity. Therefore, multi-mode inspection performed using golden grid images generated as described herein can detect more DOIs and suppress more interference than other methods and systems for multi-mode inspection. In addition, because the multi-mode golden grid images fit to the same uniform design grid, any other steps performed using the multi-mode images or information determined therefrom (such as defect classification or filtering) can perform better than currently used methods and systems because the inter-mode images or information are better aligned with each other via the golden grid.

[0094] In additional embodiments, storing the golden grid image includes storing the golden grid image for use in inspecting the sample and inspecting at least one other sample of the same type as the sample, and inspecting the at least one other sample includes aligning a test image of the at least one other sample generated from an output of an additional inspection subsystem responsive to energy detected from the at least one other sample with the golden grid image. In this manner, the same golden grid image can be used to inspect different samples (of the same type) on different inspection tools. Because the golden grid image has pixels in a uniformly designed grid between different tools, inspections using different inspection subsystems can have the same pixelation. Having the same pixelation in different inspection subsystems can benefit multi-tool sensitivity.

[0095] like Figure 4 As shown in step 422 of , one or more computer subsystems may generate test results, which may include Figure 4 The results of any step shown in . The inspection results may include information of the detected defects, such as the defect ID, location, etc., of the bounding box of the detected defects, size, detection score, information about the defect classification (such as a class label or ID, etc.), or any such suitable information known in the art. The results of the defects may be generated by the computer subsystem in any suitable manner. The results of the defects may have any suitable form or format, such as a standard file type. The computer subsystem may generate the results and store the results so that the results can be used by the computer subsystem and / or another system or method to perform one or more functions on the sample or another sample of the same type.

[0096] The computer subsystem may be configured to store information of detected defects in any suitable computer-readable storage medium. The information may be stored with any results described herein and may be stored 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, the information may be accessed in the storage medium and used by any method or system embodiment described herein, formatted for display to a user, used by another software module, method or system, etc.

[0097] The results and information generated by performing an inspection on a sample or other samples of the same type may be used in various ways by the embodiments and / or other systems and methods described herein. Such functions include, but are not limited to, changing a process, such as a manufacturing process or step, that has been or will be performed on the inspected sample or another sample in a feedback or feedforward manner. For example, the computer subsystem may be configured to determine one or more changes to a process that has been or will be performed on a sample inspected as described herein based on the detected defects. The change in the process may include any suitable change in one or more parameters of the process. The computer subsystem preferably determines the change so that defects can be reduced or prevented on other samples (corrective processes can be performed on other samples), defects on the sample can be corrected or eliminated in another process performed on the sample, defects can be compensated in another process performed on the sample, etc. The computer subsystem can determine such changes in any suitable manner known in the art.

[0098] The changes may then be sent to a semiconductor manufacturing system (not shown) or a storage medium (not shown) accessible to the computer subsystem and the semiconductor manufacturing system. The semiconductor manufacturing system may or may not be part of the system embodiments described herein. For example, the computer subsystem and / or the inspection subsystem described herein may be coupled to the semiconductor manufacturing system, for example, via one or more common elements such as a housing, 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, and the like.

[0099] Thus, as described herein, embodiments may be used to set up a new inspection process or recipe. Embodiments may also be used to modify an existing inspection process or recipe, whether it is an inspection process or recipe that is used for a sample or that is generated for one sample and adapted for another sample. However, the embodiments described herein are not limited to inspection process generation or modification. For example, the embodiments described herein may also be used in a similar manner to set up or modify processes for measurement, defect review, and the like. In particular, the generation of a golden grid image as described herein may be performed regardless of the process that is set up or modified. Thus, the embodiments described herein may be used not only to set up or modify an inspection process, but may also be used to set up or modify any quality control type process performed on the samples described herein.

[0100] The embodiments described herein provide several advantages over previously used methods and systems for setting up inspection of samples. For example, empirical data generated by the inventors show improved defect count stability using golden grid images and golden grid-based RTCM zone maps. The embodiments described herein may also improve inspection tool stability in terms of defect count stability before and after interference filtering. In one such example, the golden grid images described herein provide consistent region definitions that can improve stability of most defect attributes to thereby improve defect count stability after interference filtering.

[0101] In another example, as further described herein, a golden grid image is a processed sample image generated during a training step and stretched to a design grid. RTCM regions of interest can be reproduced directly on the golden grid image and are the same between scans. Test frames in run time are shifted / interpolated to align with the golden grid with pixels in a uniform design grid. Thus, the RTCM partition map is consistent between scans and improves inspection tool stability when the same recipe is used between different sample scans. In addition, RTCM reproduction can be performed only once on the golden grid image during setup, which can save a significant amount of time typically required for run-time RTCM reproduction. RTCM reproduction can also be performed only once in a pixel-to-design alignment (PDA) setup. Thus, run-time RTCM reproduction time can be saved, which is particularly important for back-end-of-line (BEOL) wafers and other relatively complex samples. For example, denser and / or more complex RTCM region of interest areas require more RTCM reproduction time.

[0102] In a further example, the golden grid image can be used to extract defect golden grid based contrast and background features, which can be used to replace the existing reference based contrast and background features. The golden grid image is the same from scan to scan. Therefore, the golden grid based attributes are the same (will not have variations) from scan to scan. When the golden grid based defect attributes are used in interference filtering, there is no defect count variation due to these attributes.

[0103] In another example, because the golden grid image is stretched using pixels in a uniform design grid, the pixel positions (relative to the design after the image and design are aligned) are consistent across different tools, different scans, and different modes, which should result in optimal inspection tool process variation stability across scans, tools, and modes. For example, if the PDA alignment between the reference image and the design is "perfect," the golden grid image coordinates will be "perfectly" aligned with the digital coordinate system (or coordinates in the design space). The intensity of the pixels in the golden grid image is from the reference image acquired during the setup scan. However, the golden grid image coordinates are consistent in terms of the "perfect" digital coordinate system for the same runtime pixel size (e.g., 50 nm) across tools and modes. The embodiments described herein may also improve the stability of the dynamic range of the runtime images at the frame level. For example, during runtime, before inspection, the test image may first be matched with the golden grid image at a smaller block level (e.g., 128 pixels by 128 pixels) of each frame. Thus, the runtime matched image may have a stable dynamic range, like the golden grid image.

[0104] The above advantages of the embodiments are provided by several new features of the systems and methods described herein. One such new feature is a golden grid image, which is an image stretched to the design grid. Another such feature is an RTCM rendering on the golden grid image, which results in consistent region definition. An additional such feature is that the RTCM rendering only needs to be done once in the setup and the runtime RTCM rendering can be saved. A further such feature is that the background and contrast properties based on the golden grid can be used for interference filtering. An additional such feature is that the golden grid image can be used as a y-axis in 2D-based defect detection. For example, some defect detection methods use a reference to detect defects on a sample. Some such methods use a reference to generate a difference image by subtracting the reference from a test image. In addition, some such methods can use a reference and a difference image to detect defects. In one such example, a characteristic of the reference (e.g., median grayscale) can be plotted on the y-axis, the difference image grayscale can be plotted on the x-axis, and a threshold can be applied to the resulting 2D map to detect defects on the sample. An example of a defect detection method performed in this manner is the multi-die automatic limit (MDAT) algorithm used by some inspection tools available from KLA. By using the golden grid image feature of defect detection rather than currently using a reference, the embodiments described herein will provide greater stability of defect detection. Other such features include providing better defect attribute stability by using a golden grid image.

[0105] Each of the embodiments of each of the systems described above may be combined together into a single embodiment.

[0106] Another embodiment is directed to a computer-implemented method for setting up an inspection of a sample. The method includes: acquiring a reference image of the sample; and modifying the reference image to fit the reference image to a design grid to thereby produce a golden grid image. The method further includes storing the golden grid image for use in inspecting the sample. The inspection includes aligning a test image of the sample generated from an output of an inspection subsystem responsive to energy detected from the sample with the golden grid image. The acquiring, modifying, and storing steps are performed by one or more computer subsystems that may be configured according to any of the embodiments described herein.

[0107] Each step of the method may be performed as further described herein. The method may also include any other steps that may be performed by the inspection subsystem and / or computer subsystem described herein. The above method may be performed by any system embodiment described herein.

[0108] Additional embodiments are directed to a non-transitory computer-readable medium storing program instructions executable on a computer system to perform a computer-implemented method for setting up an assay of a sample. Figure 5 Specifically, Figure 5 , non-transitory computer readable medium 500 includes program instructions 502 executable on a computer system 504. The computer-implemented method may include any of the steps of any of the methods described herein.

[0109] Program instructions 502 implementing methods such as those described herein may be stored on computer readable medium 500. The computer readable medium may be a storage medium such as a magnetic or optical disk, tape, or any other suitable non-transitory computer readable medium known in the art.

[0110] The program instructions may be implemented in any of a variety of ways, including process-based techniques, component-based techniques, and / or object-oriented techniques, etc. For example, the program instructions may be implemented using ActiveX controls, C++ objects, JavaBeans, Microsoft Foundation Classes ("MFC"), SSE (Streaming SIMD Extensions), or other techniques or methods as desired.

[0111] Computer system 504 may be configured according to any of the embodiments described herein.

[0112] Those skilled in the art will understand further modifications and alternative embodiments of various aspects of the present invention in view of the specific embodiments. For example, methods and systems for setting the inspection of samples are provided. Therefore, the specific embodiments should be interpreted as being only for illustration and for teaching the general manner in which the technical personnel of the art implement the present invention. It should be understood that the form of the present invention shown and described herein should be regarded as the current preferred embodiment. Elements and materials can replace the elements and materials illustrated and described herein, parts and processes can be reversed, and the specific features of the present invention can be used independently, all of which will be understood by those skilled in the art after benefiting from the specific embodiments of the present invention. Changes can be made to the elements described herein without departing from the spirit and scope of the present invention described in the appended claims.

Claims

1. A system configured for setting up an inspection of a sample, comprising: an inspection subsystem configured to generate an output in response to energy detected from the sample; and One or more computer subsystems configured to: acquiring a reference image of the sample; modifying the reference image to fit the reference image to a design grid to thereby generate a golden grid image, wherein the golden grid image is an image having a uniform grid defined as an inter-sample design and defined within a sample's design, and each pixel within the design grid has the same size, and pixels along x and y directions are perfectly aligned; and The golden grid image is stored for use in inspecting the sample, wherein the inspecting includes aligning a test image of the sample generated from the output of the inspection subsystem with the golden grid image. 2 . The system of claim 1 , wherein generating the golden grid image comprises removing random defects from the reference image. 3 . The system of claim 1 , wherein generating the golden grid image comprises removing features specific to the inspection subsystem from the reference image. 4 . The system of claim 1 , wherein generating the golden grid image comprises removing features specific to the sample from the reference image.

5. The system of claim 1, wherein modifying the reference image comprises stretching the reference image along one or more dimensions. The system of claim 1 , wherein modifying the reference image comprises rotating the reference image.

7. The system of claim 1, wherein modifying the reference image comprises interpolating pixels of the reference image to fit the design grid.

8. The system of claim 1 , wherein the one or more computer subsystems are further configured to define one or more region of interest areas in the golden grid image, wherein the inspection subsystem and the one or more computer subsystems are further configured to perform the inspection of the sample, and wherein the inspection further includes defining one or more region of interest areas in the test image based on a result of aligning the test image with the golden grid image and the one or more region of interest areas defined in the golden grid image.

9. The system of claim 1 , wherein the one or more computer subsystems are further configured to define one or more masking regions in the golden grid image, wherein the inspection subsystem and the one or more computer subsystems are further configured to perform the inspection of the sample, and wherein the inspection further comprises defining one or more masking regions in the test image based on a result of aligning the test image with the golden grid image and the one or more masking regions defined in the golden grid image.

10. The system of claim 1, wherein the one or more computer subsystems are further configured to render a runtime context map from the golden grid image and store the runtime context map for the verification of the sample.

11. The system of claim 10, wherein the runtime context map is used to examine at least one other sample of the same type as the sample.

12. The system of claim 1 , wherein the inspection subsystem and the one or more computer subsystems are further configured to perform the inspection of the sample, and wherein the inspection further comprises detecting defects on the sample based on the golden grid image and the test image and determining one or more properties of the defect based on the golden grid image.

13. The system of claim 12, wherein the inspecting further comprises performing nuisance filtering of the defect based on the one or more attributes determined for the defect.

14. The system of claim 1, wherein aligning the test image with the golden grid image comprises modifying the test image to align the test image with the golden grid image.

15. The system of claim 14, wherein the inspection subsystem and the one or more computer subsystems are further configured to perform the inspection of the sample, and wherein the inspection further comprises determining one or more parameters of a defect detection method based on the result of aligning the test image with the golden grid image and detecting defects on the sample by applying the defect detection method with the determined one or more parameters to the modified test image.

16. The system of claim 14, wherein the inspection subsystem and the one or more computer subsystems are further configured to perform the inspection of the sample, and wherein the inspection further comprises detecting defects on the sample by subtracting the golden grid image from the modified test image.

17. The system of claim 1, wherein storing the golden grid image comprises storing the golden grid image for use in the inspection of the sample and inspection of at least one other sample of the same type as the sample.

18. The system of claim 1, wherein the one or more computer subsystems are further configured to retrain the inspection by: acquiring a new reference image of an additional sample of the same type as the sample; modifying the new reference image to fit the new reference image to the design grid to thereby produce a new golden grid image; and storing the new golden grid image for inspection of the additional sample.

19. The system of claim 1 , wherein the inspection subsystem is further configured to generate the output of the sample using a first mode and a second mode of the inspection subsystem, wherein the reference image is acquired for the first mode, wherein the one or more computer subsystems are further configured to acquire additional reference images of the sample and the second mode, modify the additional reference images to fit the additional reference images to the design grid to thereby generate additional golden grid images, and store the additional golden grid images for inspection of the sample, and wherein the inspection further includes aligning a test image of the sample generated from the output of the inspection subsystem generated using the second mode with the additional golden grid image.

20. The system of claim 1, wherein storing the golden grid image comprises storing the golden grid image for the inspection of the sample and inspection of at least one other sample of the same type as the sample, and wherein the inspection of the at least one other sample comprises aligning a test image of the at least one other sample generated from an output of an additional inspection subsystem responsive to energy detected from the at least one other sample with the golden grid image.

21. The system of claim 1, wherein the sample is a wafer.

22. The system of claim 1, wherein the inspection subsystem is a light-based inspection subsystem.

23. The system of claim 1, wherein the inspection subsystem is an electronic-based inspection subsystem.

24. A non-transitory computer readable medium storing program instructions executable on a computer system to perform a computer implemented method for setting up an assay of a sample, wherein the computer implemented method comprises: Get a reference image of the sample; modifying the reference image to fit the reference image to a design grid to thereby generate a golden grid image, wherein the golden grid image is an image having a uniform grid defined as an inter-sample design and defined within a sample's design, and each pixel within the design grid has the same size, and pixels along x and y directions are perfectly aligned; and The golden grid image is stored for use in inspecting the sample, wherein the inspecting comprises aligning a test image of the sample generated from an output of an inspection subsystem responsive to energy detected from the sample with the golden grid image, and wherein the acquiring, the modifying, and the storing are performed by the computer system.

25. A computer-implemented method for setting up an inspection of a sample, comprising: Get a reference image of the sample; modifying the reference image to fit the reference image to a design grid to thereby generate a golden grid image, wherein the golden grid image is an image having a uniform grid defined as an inter-sample design and defined within a sample's design, and each pixel within the design grid has the same size, and pixels along x and y directions are perfectly aligned; and The golden grid image is stored for use in inspecting the sample, wherein the inspecting comprises aligning a test image of the sample generated from an output of an inspection subsystem responsive to energy detected from the sample with the golden grid image, and wherein the acquiring, the modifying, and the storing are performed by one or more computer subsystems.

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