Printed inspection repeater defect detection

Abstract

Systems and methods are provided for detecting defects on a demagnification mask. One system is configured to generate different stacked difference images for a plurality of instances of a first patterned region in different rows on a wafer based on images generated for the first patterned region in the different rows. The system is also configured to perform double detection based on the different stacked difference images. The system then identifies defects on the demagnification mask based on defects detected by the double detection. As described further herein, the systems and methods detect defects from multiple demagnification mask rows printed on a wafer, which can reduce noise and enable detection of substantially small reticle defects. Embodiments are particularly useful for extreme ultraviolet (EUV) demagnification mask and multi-die demagnification mask (MDR) high sensitivity reticle defect detection.

Description

Technical Field

[0001] The present invention generally relates to systems and methods for performing repeater defect detection on printed inspection wafers to detect defects on, for example, extreme ultraviolet (EUV) single-wafer and multi-wafer reduction masks. Background Technology

[0002] The following descriptions and examples are not acknowledged as prior art simply because they are included in this section.

[0003] Inspection processes are used at various steps during semiconductor manufacturing to detect defects on wafers, thereby promoting higher throughput and, consequently, higher profits. Some current inspection methods detect repeater defects on wafers to detect defects on the photomask. For example, if a defect (“repeater defect”) is repeatedly detected at multiple locations on the wafer corresponding to the same location on the photomask, then the defect may be caused by the photomask itself. Therefore, the repeater defect can be analyzed to determine whether it is caused by a photomask defect rather than some other cause.

[0004] Generally, repeater defect detection (RDD) is performed as a wafer post-processing (PP) operation. For example, inspection tools can perform normal die-to-die defect detection (DD) in the case of multiple die-magnification masks, and after reporting all wafer defects, RDD can be performed in the post-processing step. In this filing, a repeater defect is defined as a defect located at the same position (within a specific tolerance) in several magnification masks.

[0005] However, current RDD methods and systems have several drawbacks. For example, many currently used RDD methods and systems have been developed in an attempt to reduce noise, enabling more accurate detection of repeater defects with fewer detected obfuscators and higher sensitivity. However, many attempts to reduce wafer noise fail to detect defects in the smaller-sized EUV repeaters required by next-generation design rules. For instance, some currently used RDD methods use setup wafers for steps such as image alignment. The setup wafer and test wafer may have relatively small differences due to variations in process conditions, for example. Those differences in the wafers can be sufficient to cause errors in image alignment, which can in turn introduce noise into the defect detection results. This noise can then actually cause errors in the defect detection results, such as missed defects, obfuscators, and noise being detected as defects and the like.

[0006] Therefore, it would be advantageous to develop a system and method for detecting repeater defects on a wafer and thereby detecting defects on a magnifying mask that does not have one or more of the above-mentioned disadvantages. Summary of the Invention

[0007] The following description of the various embodiments should in no way be construed as limiting the subject matter of the appended claims.

[0008] One embodiment relates to a system configured to detect defects on a reduction photomask. The system includes an inspection subsystem configured to generate an image of a wafer. The reduction photomask is used to print patterned regions onto the wafer during a photolithography process. The patterned regions include first patterned regions corresponding to the same region on the reduction photomask and printed in different rows on the wafer. The system further includes a computer subsystem configured to generate different stacking difference images of multiple examples of the first patterned regions in the different rows based on the images generated by the inspection system for the first patterned regions in the different rows. The computer subsystem is further configured to detect defect candidates among the multiple examples of the first patterned regions on the wafer by applying a defect detection method to the different stacking difference images. The defect detection method determines that a defect candidate exists at the location in each of the multiple examples of the first patterned regions only when the defect detection method detects a defect candidate at a location in the different stacking difference images. The computer subsystem is further configured to identify defects on the reduction photomask based on the detected defect candidates. The system can be further configured as described herein.

[0009] Another embodiment relates to a computer-implemented method for detecting defects on a reduction photomask. The method includes acquiring an image of a wafer generated by an inspection subsystem. The reduction photomask is used to print patterned regions onto the wafer during a photolithography process. The patterned regions include a first patterned region corresponding to the same region on the reduction photomask and printed in a different row on the wafer. The method further includes the generation, detection, and identification steps described above, which are performed by a computer subsystem coupled to the inspection subsystem.

[0010] Each step of the method may be performed as further described herein. The method may include any other steps of any other method described herein. The method may be performed by any of the systems described herein.

[0011] Another embodiment relates to a non-transitory computer-readable medium storing program instructions executable on a computer system for performing a computer-implemented method for detecting defects on a reduction-size photomask. The computer-implemented method includes the steps of the method described above. The computer-readable medium may be further configured as described herein. The steps of the computer-implemented method may be performed as further described herein. Additionally, a computer-implemented method for which program instructions are executable may include any other steps of any other method described herein. Attached Figure Description

[0012] Further advantages of the invention will become apparent to those skilled in the art upon the following detailed description of preferred embodiments and upon reference to the accompanying drawings, wherein:

[0013] Figure 1 and 2 This is a schematic diagram of a side view illustrating an embodiment of a system configured as described herein;

[0014] Figure 3 This is a schematic plan view illustrating an example of a wafer having patterned areas printed thereon using a reduction photomask and an embodiment of an interlaced scan of different rows on the wafer performed by an inspection subsystem.

[0015] Figures 4 to 7 This is a flowchart illustrating an embodiment of steps that can be performed to generate different stacked difference images and detect defect candidates for detecting defects on a magnified photomask;

[0016] Figure 8 This is a flowchart illustrating an embodiment of identifying defects on a reduction mask based on defect candidates detected on the wafer;

[0017] Figure 9 This is a schematic diagram illustrating a test image generated from three rows of patterned areas printed on a wafer that can be co-processed to detect defects on a reduction mask.

[0018] Figure 10 This explains that when processing three rows of a patterned region together, they can be combined. Figure 4 The flowchart shown illustrates an embodiment of the steps performed to generate different stacked difference images and detect defect candidates for detecting defects on a magnified mask;

[0019] Figure 11 This is a flowchart illustrating an embodiment of identifying defects on a scaled-down mask based on defect candidates detected on the wafer, executable when processing three rows of a patterned region; and

[0020] Figure 12This is a block diagram illustrating an embodiment of a non-transitory computer-readable medium containing program instructions stored on a computer system for executing one or more of the computer implementation methods described herein.

[0021] While the invention is open to various modifications and alternatives, specific embodiments of the invention are shown by way of example in the drawings and described in detail herein. The drawings may not be 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; on the contrary, they are intended to cover all modifications, equivalents, and alternatives that fall within the spirit and scope of the invention as defined by the claims. Detailed Implementation

[0022] Referring now to the figures, it should be noted that the figures are not drawn to scale. In particular, some elements in the figures are enlarged to emphasize their characteristics. It should also be noted that the figures are not drawn to the same scale. The same element symbols have been used to indicate elements that can be shown in similar configurations in more than one figure. Unless otherwise stated herein, any of the elements described and shown may include any suitable commercially available element.

[0023] One embodiment relates to a system configured to detect defects on a reduction photomask. The embodiments described herein provide new and improved methods and systems for high-sensitivity repeater defect detection (RDD) to detect mask defects by inspecting wafers printed with a mask. The terms "reduction photomask" and "mask" are used interchangeably herein. The term "repeater" is used interchangeably herein with the term "repeater defect."

[0024] exist Figure 1 An embodiment of this system is illustrated herein. The system includes an inspection subsystem configured to generate an image of a wafer. The inspection subsystem described herein may include at least an energy source and a detector. The energy source is configured to generate energy directed to the wafer. The detector is configured to detect the energy from the wafer and generate an output (e.g., an image) in response to the detected energy.

[0025] In one embodiment, the energy directed to the wafer includes light, and the energy detected from the wafer includes light. For example, in Figure 1 In an embodiment of the system shown, the testing subsystem 10 includes an illumination subsystem configured to direct light to the sample 14. The illumination subsystem includes at least one light source. For example, as in... Figure 1 As shown, the illumination subsystem includes a light source 16. In one embodiment, the illumination subsystem is configured to direct light to the wafer at one or more incident angles, said incident angles may include one or more tilt angles and / or one or more normal angles. For example, as in... Figure 1As shown, light from light source 16 is guided through optical element 18 and then through lens 20 to beam splitter 21, which guides the light to wafer 14 at a normal angle of incidence. The angle of incidence can include any suitable angle of incidence, which can vary depending on, for example, the characteristics of the wafer and the defects to be detected on the wafer.

[0026] The illumination subsystem can be configured to direct light onto the wafer at different incident angles at different times. For example, the inspection subsystem can be configured to change one or more characteristics of one or more components of the illumination subsystem, so that the light is directed at different angles than... Figure 1 The incident angle shown is directed to the wafer. In one example, the inspection subsystem can be configured to move the light source 16, optical element 18, and lens 20 so that light is directed to the wafer at different incident angles.

[0027] In some examples, the inspection system can be configured to direct light onto the wafer at more than one incident angle at the same time. For example, the illumination subsystem may include more than one illumination channel, one of which may include features such as... Figure 1 The light source 16, optical element 18, and lens 20 are shown in the diagram, and another element in the illumination channel (not shown) may contain similar elements (which may be configured differently or identically), or may contain at least one light source and possibly one or more other elements (such as those further described herein). If this light and another light are simultaneously directed to the wafer, then one or more characteristics (e.g., wavelength, polarization, etc.) of the light directed to the wafer at different incident angles may be different, such that the light originating from the wafer illuminating at different incident angles can be distinguished from each other at the detector.

[0028] In another example, the lighting subsystem may contain only one light source (e.g., Figure 1 The light from the light source (source 16) shown in the diagram can 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. Light in each of the different optical paths can then be directed to the wafer. Multiple illumination channels can be configured to direct light to the wafer at the same time or at different times (e.g., when different illumination channels are used to sequentially illuminate the wafer). In another example, the same illumination channel can be configured to direct light with different characteristics to the wafer at different times. For example, in some examples, optical element 18 can be configured as a spectral filter, and the properties of the spectral filter can be changed in various ways (e.g., by swapping spectral filters) such that light of different wavelengths can be directed to the wafer at different times. The illumination subsystem can have any other suitable configuration known in the art for sequentially or simultaneously directing light with different or the same characteristics to the wafer at different or the same incident angles.

[0029] In one embodiment, light source 16 may comprise a broadband plasma (BBP) light source. In this way, the light generated by the light source and directed to the wafer may comprise broadband light. However, the light source may comprise any other suitable light source, such as a laser. The laser may comprise any suitable laser known in the art and may be configured to produce light of any suitable wavelength or wavelengths known in the art. Additionally, the laser may be configured to produce monochromatic or near-monochromatic light. In this way, the laser may be a narrow-band laser. The light source may also comprise a multicolor light source that produces light of multiple discrete wavelengths or bands.

[0030] Light from optical element 18 can be focused onto beam splitter 21 by lens 20. Although lens 20 is... Figure 1 While shown as a single refractive optical element, lens 20 may actually comprise several refractive and / or reflective optical elements that combine to focus light from the optical element onto the wafer. Figure 1 The illumination subsystem shown and described herein may include any other suitable optical element (not shown). Examples of such optical elements include, but are not limited to, polarizing elements, spectral filters, spatial filters, reflective optics, apodizers, beam splitters, apertures, and the like, which may include any such suitable optical element known in the art. Additionally, the system may be configured to change one or more elements of the illumination subsystem based on the type of illumination to be tested.

[0031] The inspection subsystem also includes a scanning subsystem configured to scan light across the wafer. For example, the inspection subsystem may include a stage 22 on which the wafer 14 is placed during inspection. The scanning subsystem may include any suitable mechanical and / or robotic assembly (including the stage 22) configured to move the wafer so that light can scan across it. Alternatively, the inspection subsystem may be configured such that one or more optical elements of the inspection subsystem perform some form of scanning of the wafer. Light can scan across the wafer in any suitable manner.

[0032] The inspection subsystem further includes one or more detection channels. At least one of the detection channels includes a detector configured to detect light from the wafer due to illumination of the sample by the inspection subsystem and to generate an output in response to the detected light. For example, in Figure 1 The inspection subsystem shown includes two detection channels: one channel is formed by a light collector 24, element 26, and detector 28, and the other channel is formed by a light collector 30, element 32, and detector 34. (As shown in...) Figure 1The diagram illustrates two detection channels configured to collect and detect light at different collection angles. In some examples, one detection channel is configured to detect specularly reflected light, and another detection channel is configured to detect light that is not reflected from the wafer's surface (e.g., scattered, diffracted, etc.). However, two or more detection channels may be configured to detect the same type of light (e.g., specularly reflected light) from the wafer. Although Figure 1 An embodiment of an inspection subsystem comprising two detection channels is shown, but the inspection subsystem may contain a different number of detection channels (e.g., only one detection channel or two or more detection channels). Although each of the light collectors... Figure 1 The optical element is shown as a single refractive optical element, but each of the optical collectors may contain one or more refractive optical elements and / or one or more reflective optical elements.

[0033] One or more detection channels may contain any suitable detector known in the art, such as a photomultiplier tube (PMT), charge-coupled device (CCD), and time-delay integration (TDI) camera. The detector may also contain non-imaging detectors or imaging detectors. If the detector is a non-imaging detector, each of the detectors may be configured to detect specific characteristics (e.g., intensity) of the scattered light, but may not be configured to detect such characteristics that vary depending on the position within the imaging plane. Therefore, the output generated by each detector in each detection channel of the inspection subsystem may be a signal or data, rather than an image signal or image data. In such examples, the computer subsystem further described herein may be configured to generate an image of the wafer from the non-imaging output of the detector. In other examples, the detector may be configured as an imaging detector, which is configured to generate an image signal or image data. Thus, the system may be configured to generate the images described herein in several ways.

[0034] It should be noted that the information provided in this article Figure 1 The configuration of the inspection subsystems that may be included in the system embodiments described herein is explained in general terms. It is evident that the configuration of the inspection subsystems described herein can be modified to optimize system performance as is typically performed when designing commercial inspection systems. Alternatively, the systems described herein can be implemented using existing inspection systems (e.g., by adding the functionality described herein to an existing inspection system) (e.g., tools from the 29xx / 39xx series available from KLA Corporation, Milpitas, California). For some such systems, the methods described herein may be provided as optional functionality of the system (e.g., in addition to other system functionalities). Alternatively, the systems described herein can be designed "from scratch" to provide entirely new systems.

[0035] The system's computer subsystem 100 can be coupled to the detector of the verification subsystem in any suitable manner (e.g., via one or more transmission media, which may include "wired" and / or "wireless" transmission media), such that the computer subsystem can receive the output generated by the detector during wafer scanning. The computer subsystem 100 can be configured to perform several functions using the output of the detector as described herein, and any other functions further described herein. The computer subsystem 100 can be further configured as described herein.

[0036] Computer subsystem 100 (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 take various forms, including personal computer systems, graphics computers, mainframe computer systems, workstations, network devices, Internet devices, or other devices adapted according to the various embodiments described herein. Generally, the term "computer system" may be broadly defined to cover any device having one or more processors that execute instructions from memory media. A computer subsystem or system may also include any suitable processor known in the art, such as a parallel processor. Additionally, a computer subsystem or system may include a computer platform (as a standalone tool or a network tool) with high-speed processing and switching capabilities.

[0037] If the system comprises more than one computer subsystem, these subsystems can be coupled to each other to enable the transmission of images, data, information, instructions, etc., between the various computer subsystems. For example, different computer subsystems can be coupled to each other via any suitable transmission medium (which may include any appropriate wired and / or wireless transmission medium known in the art). Two or more computer subsystems can also be effectively coupled by sharing a computer-readable and writable storage medium (not shown).

[0038] Although the inspection subsystem is described above as an optical or light-based inspection subsystem, it can be an electron beam-based inspection subsystem. For example, in one embodiment, the energy directed to the wafer contains electrons, and the energy detected from the wafer contains electrons. In this way, the energy source can be an electron beam source. Figure 2 In one embodiment shown, the inspection subsystem includes an electronic column 122 coupled to the computer subsystem 200.

[0039] Similarly, Figure 2As shown, the electron column includes an electron beam source 126 configured to generate electrons that are focused onto a sample 128 by one or more elements 130. The electron beam source may include, for example, a cathode source or an emitter tip, and the one or more elements 130 may include, for example, a gun lens, an anode, a beam-limiting aperture, a gate valve, a beam current selection aperture, an objective lens, and a scanning subsystem, all of which may include any such suitable elements known in the art.

[0040] Electrons (e.g., secondary electrons) returning from the sample can be focused onto detector 134 by one or more elements 132. One or more elements 132 may include, for example, a scanning subsystem, which may be the same scanning subsystem included in element 130.

[0041] The electron column may comprise any other suitable element known in the art. Furthermore, the electron column may be configured as described in U.S. Patent No. 8,664,594, issued April 4, 2014 to Jiang et al., U.S. Patent No. 8,692,204, issued April 8, 2014 to Kojima et al., U.S. Patent No. 8,698,093, issued April 15, 2014 to Gubbens et al., and U.S. Patent No. 8,716,662, issued May 6, 2014 to MacDonald et al., all of which are incorporated herein by reference as if their entirety were stated herein.

[0042] Despite the electron column Figure 2 The diagram illustrates a configuration where electrons are guided to the wafer at an angle of incidence and scattered from the wafer at another angle. However, the electron beam can be guided to and scattered from the wafer at any suitable angle. Furthermore, the electron beam-based subsystem can be configured to generate images of the wafer using multiple modes (e.g., employing different illumination angles, collection angles, etc.). These multiple modes of the electron beam-based subsystem can differ in any image generation parameters of the subsystem.

[0043] Computer subsystem 200 may be coupled to detector 134 as described above. The detector detects electrons returning from the surface of the wafer, thereby forming an electron beam image of the wafer. The electron beam image may include any suitable electron beam image. Computer subsystem 300 may be configured to use the detector output and / or the electron beam image to perform any of the functions described herein. Computer subsystem 200 may be configured to perform any additional steps described herein. Figure 2 The inspection subsystem shown in the document can be further configured as described herein.

[0044] It should be noted that the information provided in this article... Figure 2The configuration of the electron beam-based inspection subsystem that may be included in the embodiments described herein is explained in general terms. As with the optical inspection subsystem described above, the configuration of the electron beam-based inspection subsystem described herein can be modified to optimize the performance of the inspection subsystem as typically done when designing a commercial inspection system. Alternatively, the system described herein can be implemented using an existing inspection system (e.g., by adding the functionality described herein to an existing inspection system). For some such systems, the methods described herein may provide optional functionality for the system (e.g., in addition to other system functionalities). Alternatively, the system described herein can be designed "from scratch" to provide a completely new system.

[0045] Although the inspection subsystem is described above as a light- or electron-beam-based inspection subsystem, it can also be an ion-beam-based inspection subsystem. Figure 2 The example shown illustrates the typical configuration of this testing subsystem, which can be used in place of any suitable ion beam source known in the art instead of an electron beam source. Alternatively, the testing subsystem can be any other suitable ion beam-based subsystem, such as those included in commercially available focused ion beam (FIB) systems, helium ion microscopy (HIM) systems, and secondary ion mass spectrometry (SIMS) systems.

[0046] As mentioned above, the inspection subsystem can be configured to direct energy (e.g., light, electrons) to a physical version of the wafer and / or to scan the physical version of the wafer with energy (e.g., light, electrons), thereby generating an actual image of the physical version of the wafer. In this way, the inspection subsystem can be configured as an "actual" imaging system rather than a "virtual" system. Figure 1 The storage media (not shown) and computer subsystem 100 shown herein can be configured as a “virtual” system. The system and method configured as a “virtual” verification system are described in U.S. Patent No. 8,126,255, jointly assigned to Bhaskar et al., February 28, 2012, and U.S. Patent No. 9,222,895, issued to Duffy et al., December 29, 2015, both of which are incorporated herein by reference in their entirety. The embodiments described herein can be further configured as described in those patents.

[0047] As further mentioned above, the testing subsystem can be configured to have multiple modes. Generally, a "mode" is defined by the values ​​of the parameters of the testing subsystem used to generate the output of the sample. Therefore, different modes can differ in the values ​​of at least one of the optical or electron beam parameters of the testing subsystem (other than the location of the output generating the image on the sample). For example, for a light-based testing subsystem, different modes can use light of different wavelengths. Modes can differ in the wavelength of light directed to the sample, as further described herein for different modes (e.g., by using different light sources, different spectral filters, etc.). In another embodiment, different modes can use different illumination channels. For example, as mentioned above, the testing subsystem can include more than one illumination channel. Thus, different illumination channels can be used in different modes.

[0048] Multiple modes can also differ in illumination and / or collection / detection. For example, as further described above, the testing subsystem may include multiple detectors. Thus, one detector can be used in one mode and another detector can be used in another mode. Furthermore, modes can differ from each other in more than one way as described herein (e.g., different modes may have one or more different illumination parameters and one or more different detection parameters). For example, depending on the ability to use multiple modes to scan samples simultaneously, the testing subsystem may be configured to scan samples with different modes in the same scan or different scans.

[0049] A photomask is used to print patterned areas onto a wafer during a photolithography process. The photolithography process can include any suitable photolithography procedure. Generally, the photolithography process uses some form of energy (e.g., light, electrons, etc.) to transfer a pattern formed on the photomask to the material on the wafer. The material on the wafer can be a form of photoresist, one or more of whose properties change after exposure to the energy used in the photolithography process. In this way, the pattern of energy (light, electrons, etc.) can be guided from or through the photomask to the photoresist on the wafer, thereby transferring the pattern from the photomask to the photoresist. One or more additional steps (e.g., post-exposure baking, development, etc.) can be performed on the photoresist to complete the pattern transfer process. Therefore, as is clearly apparent from this general description of the photolithography process, defects present on the photomask can be transferred to the wafer during the photolithography process, which is obviously disadvantageous. In the process of inspecting a scaled-down mask, sometimes direct inspection of the mask is impossible or impractical. In such cases, the scaled-down mask is printed onto the wafer, defects on the wafer are inspected, and the wafer defects are used to determine whether the defects are caused by the scaled-down mask. This determination can be performed as further described herein.

[0050] In one embodiment, the reduction mask is a single-die reduction mask (SDR). In another embodiment, the reduction mask is a multi-die reduction mask (MDR). For example, the embodiments described herein can be used for both SDR inspection where the mask contains only one die and MDR inspection where one mask contains several identical dies.

[0051] In a further embodiment, the reduction mask is configured for use at extreme ultraviolet (EUV) wavelengths. This mask may then be referred to as an EUV mask. In other words, the mask may be configured for use in a photolithography process that uses one or more EUV wavelengths of light to transfer a pattern from the mask to the wafer (e.g., by reflecting EUV light from the mask onto the wafer). In this way, the photochemical wavelength of the mask (i.e., the wavelength of light used to transfer the pattern from the mask to the wafer, thereby causing a photochemical reaction in one or more materials (e.g., photoresist) on the wafer) may be an EUV wavelength. While the embodiments described herein are particularly useful for defect detection on EUV masks, they can also be used for defect detection on other types of SDR and MDR, such as masks designed for use at one or more deep ultraviolet (DUV) wavelengths of light (i.e., single-wafer or multi-wafer, optical or DUV masks).

[0052] The embodiments described herein provide substantially high-sensitivity repeater defect detection (RDD) for EUV print inspection. “Print inspection” is defined herein as a type of mask inspection involving printing a mask onto a wafer, inspecting the wafer, and identifying defects on the mask based on the wafer inspection results. Print inspection can be used in various applications where routine or periodic inspections of EUV masks intended for production use can be performed to check for potential contamination, including the identification of scaled-down mask repeaters for EUV mask conformity verification and / or monitoring. Print inspection can be used for both SDR and MDR. In the embodiments described herein, reference images from multiple scaled-down mask rows are used to generate difference images, and difference images from or across scaled-down mask rows are stacked for improved noise reduction, enabling the detection of smaller-sized EUV MDR repeater defects on the print inspection wafer.

[0053] The patterned area includes a first patterned area corresponding to the same area on the resize mask and printed in a different row on the wafer. In this way, a “row” described herein refers to a “resize mask row” printed on the wafer, and each “resize mask row” is defined as a row of multiple examples of the entire resize mask printed on the wafer. For a single die resize mask, each row of dies printed on the wafer may correspond to a row of resize mask examples printed on the wafer. However, more than one row of dies printed on the wafer may correspond to a single row of resize mask examples printed on the wafer using MDR. For example, when an MDR containing multiple rows of dies is printed on the wafer at once, the single resize mask example will contain multiple rows of dies. In this way, when this resize mask is printed multiple times in a single row across the wafer, the row will contain multiple rows of dies. For the embodiments described herein, different rows refer to different resize mask rows that may or may not be different die rows.

[0054] The image of the first patterned area generated by the inspection subsystem is mapped to different examples of the same area on a resizing mask printed on the wafer. Depending on whether the resizing mask is SDR or MDR, the different examples of the same area on the resizing mask printed on the wafer can vary. For example, for SDR, different examples of the same area on the resizing mask printed on the wafer could be corresponding areas printed individually on different dies using the resizing mask. In the case of MDR, different examples of the same area on the resizing mask printed on the wafer could be corresponding areas among different examples of the resizing mask printed on the wafer. In such examples, the corresponding area in MDR could be multiple examples of the same area on only one die in the MDR.

[0055] In one embodiment, the inspection subsystem is configured to generate an image by interleaving scans of a first patterned region in different rows on the wafer with scans of a second patterned region in different rows on the wafer. Figure 3 This describes one embodiment of a mask-based interleaved scan-to-scan (i.e., scanning) strategy that can be used in the embodiments described herein. The mask-based interleaved scan-to-scan strategy scans the same Y-position on a printed mask example on a wafer across multiple mask rows, such as... Figure 3 Displayed in the middle. For example, such as Figure 3As shown, wafer 300 may have a reduction mask example 302 formed thereon. In the case of MDR, each reduction mask example contains a plurality of dies 304 that can be configured as further described herein. In this embodiment, the inspection subsystem can sequentially scan S1, S2, S3, S4, S5, and S6. S1, S3, and S5 all correspond to the same Y position of the reduction mask example in each of different rows. Similarly, S2, S4, and S6 all correspond to the same Y position of the reduction mask example in each of different rows, which is different from the Y position of S1, S3, and S5. (Of course, there may be some overlap in the Y positions between S1 and S2, S3 and S4, and S5 and S6, but S1 and S2, S3 and S4, and S5 and S6 still have different Y positions). This mask-based interleaved scan-band scanning strategy differs from the currently used die-based interleaved scan-band scanning strategy. The staggered scan-band scanning strategy based on the mask ensures that reference images across mask rows can be used for computational reference (CR) image generation and difference image stacking, which are further described in this paper.

[0056] As described above, Figure 3 The scanning strategy shown herein scans scan bands in the following order: S1, S2, S3, S4, S5, and S6 (of course, more than six scan bands are possible). Next, the system can simultaneously inspect S1 and S3, and simultaneously inspect S2 and S4, as further described herein. In other words, scan bands generated at different Y positions relative to the magnified mask area are processed separately for defect detection. In this way, defect detection is performed on S1, S3, and S5 independently of the defect detection performed on S2, S4, and S6. Once the first set of two magnified mask rows is completed, the system can then proceed to inspect the third row (e.g., inspect S1 and S3, then inspect S3 and S5). However, as further described herein, more than two (e.g., three) magnified mask rows can also be inspected together (e.g., simultaneously inspect S1, S3, and S5). For the embodiments described herein, at least two magnified mask rows are scanned and then processed simultaneously. In this way, the scanning of the first set of scan bands processed together is interleaved with the scanning of other scan bands processed together but independent of the first set of scan bands.

[0057] Despite the descriptions in this article and Figure 3 The interleaved scan-band scanning strategy shown herein is particularly advantageous for the embodiments described herein, but the systems and methods described herein can be used with any other suitable type of scan-band scanning strategy known in the art. A suitable scan-band scanning strategy can be selected based on several system layout factors, such as available memory for temporarily storing images of scan bands to be processed together for defect detection.

[0058] When any two or more images generated at different locations on a wafer are used together for a particular purpose, some type of image alignment should be performed to ensure that the images are correctly aligned with each other (e.g., for image subtraction, for CR generation, etc.). A particularly advantageous way to perform this image alignment in the embodiments described herein is by using an alignment die positioned in a first calibrated mask row scanned in scan bands to align with other calibrated mask rows. For example, an image generated in the first die scanned in scan band S1 by an inspection subsystem can be used as an alignment die for aligning images generated in said scan band with each other and with images generated in other scan bands. Alignment using this image as an alignment die can be performed in any other suitable manner known in the art. While this is a particularly useful method for aligning images between different rows or scan bands, the embodiments can also be used with any other suitable alignment method known in the art.

[0059] In one embodiment, the first patterned region and the second patterned region correspond to different portions of the die on the reduction photomask. For example, when the size of the scan band across the wafer in the Y direction is smaller than the size of the die printed on the wafer in the Y direction, more than one scan band can be scanned on the wafer to cover the entire die. In this way, different patterned regions in different rows on the wafer (e.g., different patterned regions in S1 and S2) corresponding to different portions of the same die on the reduction photomask can have different patterned features formed therein. Therefore, in the embodiments described herein, such patterned regions will be processed separately for defect detection.

[0060] In another embodiment, the first patterned region and the second patterned region correspond to different examples of dies printed on a reduction mask on the wafer. For example, when the size of the scan band across the wafer in the Y direction is equal to or greater than the size of the die printed on the wafer in the Y direction, a scan band can be scanned on the wafer to cover the entire die. In this way, different patterned regions in different rows on the wafer (e.g., different patterned regions in S1 and S2) can correspond to different dies on the wafer. In some such examples, different dies on the wafer covered by different scan bands can be printed in the same reduction mask row (e.g., when a single printing of the reduction mask on the wafer results in multiple rows of dies, and each scan band covers the rows of those dies). In other such examples, different dies on the wafer covered by different scan bands can be printed in different reduction mask rows (e.g., when a single printing of the reduction mask on the wafer results in a single row of dies, and each scan band covers those rows of dies).

[0061] In some embodiments, the reduction mask is an MDR, and the first patterned region comprises at least a portion of only a first die of the MDR printed on the wafer. For example, the first patterned region may correspond to only one die of the MDR, and each first patterned region is a different example of the MDR printed on the wafer. In this case, each of those first patterned regions may correspond to the entire die or less of the die printed on the wafer. In this way, in a row of repeating patterned regions printed on the wafer with MDR, the first patterned region may correspond to a first die of the MDR printed in the row, the second patterned region may correspond to a second die of the MDR printed in the row, and so on.

[0062] The system also includes a computer subsystem configured to generate different stacked difference images of multiple examples of the first patterned region in different rows based on images generated by the examination subsystem for the first patterned region in different rows. The embodiments described herein provide novel methods and systems for CR image generation and difference image stacking. In the embodiments described herein, reference images from multiple magnification mask rows are used to generate difference images and / or stack difference images from and / or across magnification mask rows. Several ways of performing this step are described herein, but all different methods share the common feature that the image used to generate the input to the defect candidate detection step comprises images generated from patterned regions formed on the wafer in different rows. These novel ways of generating the input for the defect candidate detection step provide several important improvements to the embodiments described herein compared to currently used systems and methods for magnification mask defect detection. For example, four new methods further described herein offer advantages such as maximum noise reduction and maximum disturbance point suppression.

[0063] Contrary to the embodiments described herein, some currently used methods and systems for print inspection use reference images from the same magnified mask row and stack difference images within the magnified mask row to achieve noise reduction. However, this wafer noise reduction is insufficient to detect defects in smaller EUV repeaters required by next-generation design rules. Some currently used methods for print inspection-type magnified mask inspection require training that stores a complete die image from the setup wafer and uses it to align with the production wafer. Because setup wafers and production wafers may differ for various reasons (e.g., being processed under different process conditions due to process drift), the aligned die image from the setup wafer may have color variations compared to the production wafer image, and any resulting image misalignment can introduce false defects during operation.

[0064] In one embodiment, generating a first of the different stacked difference images (“stacked difference 1”) includes generating a first and a second difference image and combining at least a portion of the first and second difference images to generate stacked difference 1, and generating a second of the different stacked difference images (“stacked difference 2”) includes generating a third and a fourth difference image and combining at least a portion of the third and fourth difference images to generate stacked difference 2. This step is common to the first three methods described herein.

[0065] Figure 4 The first method (“Type 1”) shown herein uses reference images from two rows to generate a CR image for each test image and stacks the resulting difference images across the two rows to maximize noise reduction. In this embodiment and other embodiments described herein, A1 and A2 refer to the process of printing a scaled-down mask onto a wafer during photolithography. Figure 4 Different examples of patterned areas (not shown) on the wafer, corresponding to the same area on the reduction mask and printed in different rows on the wafer. Similarly, B1 and B2 (and C1 and C2) refer to different examples of patterned areas printed on the wafer using a reduction mask during photolithography, corresponding to the same area on the reduction mask and printed in different rows on the wafer. In this way, A1 and A2 correspond to different examples of the same portion of a die printed on the wafer using MDR, B1 and B2 correspond to different examples of the same portion of a different die printed on the wafer using the MDR, and C1 and C2 correspond to different examples of the same portion of another different die printed on the wafer using MDR. In this way, test images 400 of A1, A2, B1, B2, C1, and C2 can be generated by scanning two different rows on the wafer. For example, in Figure 3 The S1 shown can generate test images A1, B1, and C1, and can generate test images for a single MDR example printed on the chip. Figure 4 The image shows each group A1, B1, and C1 in test image 400. Figure 3 In S3, test images A2, B2, and C2 can be generated, and test images can be generated for a single MDR example printed on the chip. Figure 4 The test images 400 shown in the image are each group A2, B2, and C2. Similarly, in... Figure 3 The S5 shown in the image can generate test images A3, B3, and C3 (e.g., ...). Figure 9 (as shown in the figure), and can generate each set of A3, B3 and C3 for a single MDR example printed on the chip.

[0066] Figure 4Each of A1, A2, B1, B2, C1, and C2 shown in the other figures described herein may contain only a portion of the die in the Y direction and may contain the entire die in the X direction. In other words, it can be generated by scanning the scanning belt. Figure 4 And each of A1, A2, B1, B2, C1, and C2 shown in the other figures described herein, the scan band has a height less than the height of the die and a height in the Y direction covering the entire width of the die in the X direction relative to the height of the scan band. Alternatively, Figure 4 Each of A1, A2, B1, B2, C1, and C2 shown in the other figures described herein may be included in all the bare wafers in both the X and Y directions.

[0067] In one embodiment, generating a first difference image includes subtracting a first CR from images generated individually from multiple examples of a first patterned region in a first of different rows on the wafer, and generating a second difference image includes subtracting the first CR from images individually from multiple examples of a first patterned region in a second of different rows on the wafer. For example, in Figure 4 In this process, generating a first difference image (e.g., the top row of set 402 for difference 1) involves subtracting a first CR separately from the image generated for A1, and generating a second difference image (e.g., the bottom row of set 402) involves subtracting a first CR separately from the image generated for A2, as shown in step 404. All image subtractions described herein can be performed in any manner known in the art.

[0068] In some such embodiments, the computer subsystem is configured to generate a first CR from images of multiple examples of a second type of patterned region in different rows on a wafer. For example, as... Figure 4 As shown, the first CR subtracted from A1 in step 404 can be generated as CR(B1,B2). This same CR can be subtracted from A2 alone, as shown in step 404. This CR and all other CRs described herein can be generated in any suitable manner known in the art.

[0069] In one embodiment, CR(B1,B2) indicates that the CR is calculated from the image of each example of B1 and B2 scanned in two rows. However, the B1 and B2 images used to generate this CR may contain fewer images than all the patterned regions of B1 and B2 scanned in two rows. For example, in some instances, this CR may be generated from a single B1 image and a single B2 image, but the quality of the CR generally increases as the number of reference images used to generate the CR increases. Therefore, it is desirable, but not necessary, to use all available reference images to generate the CR. This also applies to all other CRs described herein.

[0070] In an additional embodiment, generating a third difference image includes subtracting a second CR from an image generated individually from multiple examples of a first patterned region in a first of different rows on the wafer, and generating a fourth difference image includes subtracting a second CR from an image individually from an image generated individually from multiple examples of a first patterned region in a second of different rows on the wafer. For example, in Figure 4 In step 408, generating a third difference image (e.g., the top row of set 406 of difference 2) involves subtracting the second CR separately from the image generated for A1, and generating a fourth difference image (e.g., the bottom row of set 406) involves subtracting the second CR separately from the image generated for A2, as shown in step 408.

[0071] In another embodiment, the computer subsystem is configured to generate a second CR from images of multiple examples of a third party in patterned regions in different rows on a wafer. For example, as Figure 4 As shown in step 408, the second CR subtracted from A1 can be generated as CR(C1,C2). This same CR can be subtracted separately from A2, as shown in step 408.

[0072] like Figure 4 As shown, the computer subsystem can generate stacked difference 1 410 by combining at least a portion of the first and second difference images (i.e., at least some differences 1 in the two rows of set 402) to generate stacked difference 1, and stacked difference 2 412 by combining at least a portion of the third and fourth difference images (i.e., at least some differences 2 in the two rows of set 406) to generate stacked difference 2. The generation of stacked difference 1 can be performed using all differences 1 generated by A1-CR(B1,B2) and A2-CR(B1,B2), and the generation of stacked difference 2 can be performed using all differences 2 generated by A1-CR(C1,C2) and A2-CR(C1,C2). In other words, stacked difference 1 410 can be generated from all differences 1 in set 402, and stacked difference 2 412 can be generated from all differences 2 in set 406. Therefore, in this method, each of the stacked difference images is generated from a different image generated by CR generated across rows. In other words, the stacked difference image is generated from an image from more than one row on the wafer. This multi-row source of images used to generate a single stacked difference image has several advantages, which are further described herein.

[0073] While it may be advantageous to use as many difference images as possible (i.e., all difference images) to generate a stacked difference image, fewer than all difference images may be used to generate a stacked difference image when appropriate (e.g., when one or more of the difference images clearly contain some outlier characteristics). Combining at least a portion of multiple difference images to generate the stacked difference images described herein and any other can be performed in other ways in any suitable manner known in the art. For example, any suitable mathematical operation (e.g., linear combination) can be used to combine multiple difference images into a stacked difference image.

[0074] The computer subsystem is also configured to detect defect candidates among multiple examples of a first patterned region on the wafer by applying a defect detection method to different stacked difference images. The defect detection method determines that a defect candidate exists at said location in each of the multiple examples of the first patterned region only when the defect detection method detects a defect candidate at a location in the different stacked difference images. For example, the computer subsystem may be configured to perform dual detection 414 using stacked difference 1 410 and stacked difference 2 412. Dual detection means comparing a test image with two different references to generate two different difference images, performing defect detection on the two difference images, and then performing arbitration such that only defects detected at the same location by both defect detections are reported in the defect detection results.

[0075] In the embodiments described herein, two distinct difference images are generated prior to the dual detection step 414. Therefore, in step 414, each of the stacked difference images can be compared individually with a certain defect detection threshold. Any signal or pixel in each stacked difference image with a value higher than the threshold can be identified as a defect candidate (conversely, a signal or pixel with a value lower than the threshold may not be identified as a potential defect). Dual detection then compares the defect candidates detected in the two stacked difference images and reports a defect candidate only if a defect is detected at the same location in both stacked difference images. Clearly, this is likely the simplest method that can be used for the dual detection step, and more complex or difficult-to-understand methods can be used. The stacked difference image generated using one of the novel methods described herein can be input and used for defect detection in the same manner as any other difference image known in the art. In other words, the novel stacked difference image described herein is not specific to any kind of dual detection.

[0076] The output of the dual detection step can be defect detection result 416, where the location of any defect candidate detected by dual detection is identified in each of a patterned region with some kind of marking (in this case, an asterisk). In this way, the output of the dual detection step is a total correlation of the reported defect candidates, without individual frame information. In other words, since dual detection is performed on the stacked difference images, the computer subsystem cannot determine whether each individual test image contains a defect candidate based solely on the result of the detection step. Therefore, when the computer subsystem detects a defect candidate at a location via dual detection, the computer subsystem assigns the defect candidate (or marks the location or something equivalent) at the corresponding location in each test image. The additional steps described herein can then be performed to determine whether a defect is detected at the location of the defect candidate in each test image location.

[0077] although Figure 4 (And other figures described herein) show that only one defect candidate is detected through the dual detection step, but it is clear that more than one defect candidate can be detected through the dual detection step. In this case, result 416 may contain a label for each detected defect candidate in each test image. Each detected defect candidate can then be evaluated individually or separately to determine which test images actually contain defects at the locations of the detected defect candidates. Furthermore, although the results of the dual detection step... Figure 4 (And other figures described herein) are illustrated graphically, but the results of the dual-detection steps can be reported using any appropriate file and format known in the field.

[0078] Therefore, as further described above, the Type 1 method uses images generated from different magnification mask rows on the wafer in a novel and important way. For example, the embodiments described above use images from different magnification mask rows on the wafer (e.g., CR(B1,B2)) to perform reference image generation. Additionally, the embodiments described herein perform difference image stacking by stacking difference images across two magnification mask rows (e.g., generating a stacked difference 1 image from difference 1 generated from image A1 and difference 1 generated from image A2). Furthermore, dual detection is performed based on a comparison between the test patterned region and two other patterned regions (e.g., AB and AC) in the magnification mask row.

[0079] The Type 1 method described above offers significant advantages in reducing scrambling points. For example, Type 1 will advantageously begin with fewer repeater types than currently used MDR RDD methods. Specifically, scrambling repeater types that do not have a signal in more than one row, detectable by currently used MDR RDD methods, will be suppressed by the Type 1 method described above. When fewer repeater types are detected, fewer scrambling repeaters remain to be filtered. Furthermore, for some use cases, Type 1 may offer the highest noise reduction capability and best original sensitivity of all the different types described herein. Therefore, this embodiment may be most useful for situations where maximum sensitivity is the highest priority.

[0080] The same type 1 method described above for detecting defect candidates in images A1 and A2 can also be used, or alternatively, to detect defect candidates in images B1 and B2 (and / or images C1 and C2). For example, to detect defect candidates in images B1 and B2, the difference image generation step 404 can be performed as described above, except for B1-CR(A1,A2) and B2-CR(A1,A2), and the difference image generation step 408 can be performed as described above, except for B1-CR(C1,C2) and B2-CR(C1,C2). Similarly, to detect defect candidates in images C1 and C2, in addition to C1-CR(A1,A2) and C2-CR(A1,A2), the difference image generation step 404 can be performed as described above, and in addition to C1-CR(B1,B2) and C2-CR(B1,B2), the difference image generation step 408 can be performed as described above. Appropriate images can be used to generate the CR for each of these defect detections, as described above. Instead of being used to detect defects in images A1 and A2, or in addition to being used to detect defects in images A1 and A2, each of the other methods described herein can also be used in a similar manner to detect defects in images B1 and B2 (and / or images C1 and C2).

[0081] If the magnification mask contains more than three blanks in a single row, the Type 1 method described above can also be modified. For example, for the detection of defects in any of the blanks, any two of the other blanks can be used (for the detection of defects in images A1 and A2, images from B1, B2, C1, and C2 or images from C1, C2, D1, and D2 (not shown) can be used). In another instance, for the detection of defects in any of the blanks, multiple detections can be performed using different combinations of blanks (for the detection of defects in images A1 and A2, a first detection can be performed using images from B1, B2, C1, and C2, and a second detection can be performed using images from C1, C2, D1, and D2 (not shown)). In such examples, both of the first and second detections can be performed using dual detection as described above, wherein a defect candidate is identified only at the location where both dual detections detect a defect candidate in images A1 and A2. Each of the other methods described herein can be modified in a similar manner.

[0082] Figure 5 The second method (“Type 2”) shown uses a reference image from the same row as the test image to generate a CR image for each test image and stacks the resulting difference images across two rows. In this embodiment, the difference is achieved by scanning the wafer ( Figure 5 Test images 500 of A1, A2, B1, B2, C1, and C2 are generated from two different rows on the wafer (not shown). In one embodiment, generating the stacked difference 1 includes generating first and second difference images. In one embodiment, generating the first difference image includes subtracting a first CR from an image generated individually for a plurality of examples of a first patterned region in the first of the different rows on the wafer, and generating the second difference image includes subtracting a second CR from an image generated individually for a plurality of examples of a first patterned region in the second of the different rows on the wafer. For example, in Figure 5 In the process, generating a first difference image (e.g., the top row of set 502 of difference 1) involves subtracting a first CR separately from the image generated for A1, and generating a second difference image (e.g., the bottom row of set 502) involves subtracting a second CR separately from the image generated for A2, as shown in step 504.

[0083] In some embodiments, the computer subsystem is configured to generate a first CR from images of multiple examples of a second in a patterned region of a first in different rows on a wafer, and to generate a second CR from images of multiple examples of a second in a patterned region of a second in different rows on a wafer. For example, as Figure 5As shown, the first CR subtracted from A1 in step 504 can be generated as CR(B1), and the second CR subtracted from A2 in step 504 can be generated as CR(B2). In one embodiment, CR(B1) indicates that the CR is calculated from the image generated by scanning each example of B1 in row 1. However, CR(B1) can be generated from all images of less than the patterned area of ​​B1 scanned in row 1. For example, in some examples, a suitable CR can be generated from two B1 images, but the quality of the CR generally increases as the number of reference images used to generate the CR increases. Therefore, it is desirable, but not necessary, to use all available reference images to generate the CR. This also applies to all other CRs described herein.

[0084] In one embodiment, generating the stacked difference 2 includes generating third and fourth difference images. In an additional embodiment, generating the third difference image includes individually subtracting a third CR from multiple examples of images generated for a first patterned region in a first of different rows on the wafer, and generating the fourth difference image includes individually subtracting a fourth CR from images generated for multiple examples of a first patterned region in a second of different rows on the wafer. For example, in Figure 5 In step 508, generating a third difference image (e.g., the top row of set 506 of difference 2) involves subtracting a third CR separately from the image generated for A1, and generating a fourth difference image (e.g., the bottom row of set 506) involves subtracting a fourth CR separately from the image generated for A2, as shown in step 508.

[0085] In another embodiment, the computer subsystem is configured to generate a third CR from images of multiple examples of a third in a patterned region of a first in different rows on the wafer, and to generate a fourth CR from images of multiple examples of a third in a patterned region of a second in different rows on the wafer. For example, as Figure 5 As shown, the third CR subtracted from A1 in step 508 can be generated as CR(C1), and the fourth CR subtracted from A2 in step 508 can be generated as CR(C2).

[0086] like Figure 5 As shown, the computer subsystem can generate stacked difference 1 510 by combining at least a portion of the first and second difference images (i.e., at least some differences 1 in the two rows of set 502) to generate stacked difference 1 and stacked difference 2 512 by combining at least a portion of the third and fourth difference images (i.e., at least some differences 2 in the two rows of set 506).

[0087] The computer subsystem is also configured to detect defect candidates in multiple examples of a first patterned region on the wafer by applying a defect detection method to different stacking difference images. The defect detection method determines that a defect candidate exists at the location in each of the multiple examples of the first patterned region only when the defect detection method detects a defect candidate at a location in the different stacking difference images. For example, the computer subsystem may be configured to perform a dual detection 514 using stacking difference 1 510 and stacking difference 2 512. Dual detection 514 may be performed as further described herein. The output of the dual detection step may be a defect detection result 516, wherein the location of any defect candidate detected by the dual detection is identified in each of the patterned regions having some kind of marker (in this case, an asterisk). In this way, the output of the dual detection step is a complete correlation of the reported defects, without individual frame information. Result 516 may be further configured as described herein.

[0088] Therefore, the Type 2 method also utilizes images generated from different magnification mask rows on the wafer in a novel and important way. For example, although the embodiments described above use images from the same magnification mask row on the wafer as test images (e.g., CR(B1) for A1 and CR(B2) for A2) to perform reference image generation, these embodiments perform difference image stacking by stacking difference images across two magnification mask rows (e.g., generating a stacked difference 1 from a difference 1 image generated from an image from A1 and a difference 1 image generated from an image from A2). Additionally, dual detection is performed based on a comparison between the test patterned region and two other patterned regions (e.g., AB and AC) in the magnification mask row.

[0089] Type 2 methods offer significant advantages in noise reduction. For example, Type 2 methods will have greater noise reduction capabilities than currently used MDR RDD systems and methods.

[0090] Figure 6 The third method (“Type 3”) shown in the diagram uses a reference image from the same row as the test image to generate a CR image for each test image and stacks the resulting difference images in the same row. In this embodiment, the difference can be achieved by scanning the wafer (…). Figure 6Test images 600 are generated from two different rows (not shown) on the wafer to produce A1, A2, B1, B2, C1, and C2. In one embodiment, generating the stacked difference 1 includes generating first and second difference images. In some such embodiments, generating the first difference image includes subtracting a first CR from an image generated individually for a plurality of examples of a first patterned region in the first of the different rows on the wafer, and generating the second difference image includes subtracting a second CR from an image individually for a plurality of examples of a first patterned region in the first of the different rows on the wafer. For example, in Figure 6 In the process, generating a first difference image (e.g., the top row of set 602 of difference 1) involves subtracting a first CR separately from the image generated for A1, and generating a second difference image (e.g., the bottom row of set 602) involves subtracting a second CR separately from the image generated for A1, as shown in step 604.

[0091] In another embodiment, the computer subsystem is configured to generate a first CR from images of multiple examples of a second person in a patterned region of a first person in different rows on the wafer, and to generate a second CR from images of multiple examples of a third person in a patterned region of a first person in different rows on the wafer. For example, as Figure 6 As shown, the first CR subtracted from A1 in step 604 can be generated as CR(B1), and the second CR subtracted from A1 in step 604 can be generated as CR(C1).

[0092] In one embodiment, generating the stacked difference 2 includes generating third and fourth difference images. In an additional embodiment, generating the third difference image includes subtracting a third CR from an image generated individually for multiple examples of the first patterned region in a second of different rows on the wafer, and generating the fourth difference image includes subtracting a fourth CR from an image individually for multiple examples of the first patterned region in a second of different rows on the wafer. For example, in Figure 6 In the process, generating a third difference image (e.g., the top row of set 606 of difference 2) involves subtracting a third CR separately from the image generated for A2, and generating a fourth difference image (e.g., the bottom row of set 606) involves subtracting a fourth CR separately from the image generated for A2, as shown in step 608.

[0093] In a further embodiment, the computer subsystem is configured to generate a third CR from images of multiple examples of the second in patterned regions of different rows on the wafer, and to generate a fourth CR from images of multiple examples of the third in patterned regions of different rows on the wafer. For example, as Figure 6As shown, the third CR subtracted from A2 in step 608 can be generated as CR(B2), and the fourth CR subtracted from A2 in step 608 can be generated as CR(C2).

[0094] like Figure 6 As shown, the computer subsystem can generate stacked difference 1 610 by combining at least a portion of the first and second difference images (i.e., at least some differences 1 in the two rows of set 602) to generate stacked difference 1 and stacked difference 2 612 by combining at least a portion of the third and fourth difference images (i.e., at least some differences 2 in the two rows of set 606).

[0095] The computer subsystem is also configured to detect defect candidates in multiple examples of a first patterned region on the wafer by applying a defect detection method to different stacking difference images. The defect detection method determines that a defect candidate exists at the location in each of the multiple examples of the first patterned region only when the defect detection method detects a defect candidate at a location in the different stacking difference images. For example, the computer subsystem may be configured to perform a dual detection 614 using stacking difference 1 610 and stacking difference 2 612, which may be performed as further described herein. The output of the dual detection step may be a defect detection result 616, wherein the location of any defect candidate detected by the dual detection is identified in each of the patterned regions having some kind of mark (in this case, an asterisk). In this way, the output of the dual detection step is a complete correlation of the reported defects, without individual frame information. Result 616 may be further configured as described herein.

[0096] Therefore, the Type 3 method also uses images generated from different magnification mask rows on the wafer in a new and important way. For example, although the embodiments described above use images from the same magnification mask row on the wafer as test images (e.g., CR(B1) for A1 and CR(B2) for A2) to perform reference image generation, and the embodiments described above perform difference image stacking by stacking difference images in one magnification mask row (e.g., generating stacked difference 1 from difference 1 images generated only from the row 1 image and not the row 2 image), the dual detection is performed across rows (e.g., one detection is performed for one row and another detection is performed for another row).

[0097] Figure 7 The fourth method (“Type 4”) shown uses reference images from two rows to generate CR images for each test image and stacks the resulting difference images in the same row. In this embodiment, the difference can be achieved by scanning the wafer ( Figure 7The test images 700 are generated by using two different rows on (not shown) to produce A1, A2, B1, B2, C1, and C2. In this embodiment, generating stacked difference 1 includes generating a first difference image (difference 1) and combining at least a portion of difference 1 to generate stacked difference, and generating stacked difference 2 includes generating a second difference image (difference 2) and combining at least a portion of difference 2 to generate stacked difference 2.

[0098] In one embodiment, generating difference 1 involves subtracting the first CR from individual images generated for multiple examples of the first patterned region in different rows on the wafer. For example, in Figure 7 In this process, generating difference 1 (e.g., set 702 of differences 1) involves subtracting the first CR individually from the image generated for A1, as shown in step 704. In another embodiment, the computer subsystem is configured to generate the first CR from images of multiple examples of the second in patterned regions of the first and second in different rows on the chip. For example, as Figure 7 As shown, the first CR subtracted from A1 in step 704 can be generated as CR(B1,B2).

[0099] In a further embodiment, generating difference 2 includes subtracting the second CR from an image generated individually from multiple examples of the first patterned region in a second of different rows on the wafer. For example, in Figure 7 In this process, generating difference 2 (e.g., set 706 of difference 2) involves subtracting the second CR separately from the image generated for A2, as shown in step 708. In some such embodiments, the computer subsystem is configured to generate the second CR from images of multiple examples of a third in a patterned region of the first and second in different rows on the chip. For example, as Figure 7 As shown, the second CR subtracted from A2 in step 708 can be generated as CR(C1,C2).

[0100] like Figure 7 As shown, the computer subsystem can generate stacked difference 1 710 by combining at least a portion of difference 1 (i.e., at least some differences 1 in set 702) to generate stacked difference 1 and generate stacked difference 2 712 by combining at least a portion of difference 2 (i.e., at least some differences 2 in set 706) to generate stacked difference 2.

[0101] The computer subsystem is also configured to detect defect candidates in multiple examples of a first patterned region on the wafer by applying a defect detection method to different stacking difference images. The defect detection method determines that a defect candidate exists at the location in each of the multiple examples of the first patterned region only when the defect detection method detects a defect candidate at a location in the different stacking difference images. For example, the computer subsystem may be configured to perform a dual detection 714 using stacking difference 1 710 and stacking difference 2 712. Dual detection 714 may be performed as further described herein. The output of the dual detection step may be a defect detection result 716, wherein the location of any defect candidate detected by the dual detection is identified in each of the patterned regions having some kind of mark (in this case, an asterisk). In this way, the output of the dual detection step is a complete correlation of the reported defects, without individual frame information. Result 716 may be further configured as described herein.

[0102] Therefore, the Type 4 method also uses images generated from different magnification mask rows on the wafer in a novel and important way. For example, the Type 4 method performs reference image generation using images from different magnification mask rows on the wafer (e.g., CR(B1,B2)). Furthermore, although the Type 4 method performs difference image stacking by stacking difference images within a single magnification mask row (e.g., generating stacked difference 1 from difference 1 images generated only from row 1 images and not row 2 images), dual detection is performed across rows (e.g., one detection is performed on image A1 in row 1 and another detection is performed on image A2 in row 2).

[0103] The Type 4 method offers significant advantages over other methods and systems currently in use and described herein. For example, the Type 4 method advantageously starts with fewer repeater types than the other types described herein. Specifically, it suppresses repeater types with scrambling points that do not have a signal in more than one row, which can be detected by currently used MDR RDD methods. Therefore, the Type 4 method can have the highest scrambling point suppression capability and best sensitivity of the embodiments described herein compared to currently used RDD methods and systems. Thus, the Type 4 method may be particularly useful in situations where defect inspection budgets are relatively limited.

[0104] The computer subsystem is further configured to identify defects on the resized mask based on detected defect candidates. As further described herein, when a defect candidate is detected by double detection, the defect candidate is initially identified in each of the test images for which double detection is performed. However, the defect may not actually be located at the defect candidate location in each test image. The computer subsystem may perform one or more steps to first determine whether the defect is located at the defect candidate location in each test image and then determine whether the defect is sufficiently repeated in multiple examples of the patterned region to be considered a resized mask defect. These steps may be performed, as further described herein, at least in part based on some local characteristics of the test image at the location of the detected defect candidate.

[0105] In some embodiments, identifying defects on a scaled-down mask includes determining local image characteristics at a location in each of a plurality of images of a first patterned region, comparing the determined local image characteristics at each location in the plurality of images of the first patterned region with a threshold, determining the number of images of the plurality of images of the first patterned region having determined local image characteristics higher than the threshold, and comparing the determined number with a repeater threshold. For example, a defect detection result generated by any of types 1 to 4 described above can be used as... Figure 8 The results displayed are 800, which are then input into the recognition step.

[0106] Next, the computer subsystem can calculate local image characteristics, such as... Figure 8 As shown in step 802. For example, the computer subsystem may calculate the local signal-to-noise ratio (SNR) for each defect candidate location in result 800. More specifically, the computer subsystem may calculate the local SNR for each defect candidate location in each of the A1 and A2 test images. Although some embodiments may be described herein with respect to local SNR, these embodiments may use any other suitable local image properties or metrics for this step, including (but not limited to) magnitudes, point similarity, local saliency, energy parameters, repeater signals, etc. Any of these local image properties may be determined in any suitable manner known in the art.

[0107] The computer subsystem can compare the determined local image characteristics at each location in multiple examples of images A1 and A2 with a threshold (essentially a defect detection threshold). The result of this step can be result 804, where an asterisk indicates that the defect candidate location in the test image has local image characteristics above the threshold, and a degree sign indicates that the defect candidate location in the test image has local image characteristics below the threshold.

[0108] The computer subsystem may perform scrambling point filtering using local image characteristics, as shown in step 806, to produce defect detection result 808. In one such example, scrambling point filtering may involve eliminating any defect candidates with a local SNR below a threshold (i.e., all defect candidates whose positions are indicated by degree symbols in result 804) and retaining any defect candidates with a local SNR above a threshold (i.e., all defect candidates whose positions are indicated by asterisks in result 804). In this way, result 808 (the output of the scrambling point filtering step) is partially correlated with the reported defect. In this context, partial correlation of the reported defect means that the local SNR test does not need to pass every test image of the defect to be reported. At least some test images must pass the local SNR test to be considered a repeater defect, and how test images must pass to be considered a repeater defect is controlled by the repeater threshold. The repeater threshold can be preset by the user or in another suitable manner and can vary depending on the number of variables (e.g., the level of expected process variations across the wafer). This can cause defects to repeat in fewer than all test images and / or cause noise in the test images that can be falsely detected as defects. Therefore, even if a defect does not repeat at every instance of a test image, it can still be appropriately considered a repeater defect under appropriate circumstances.

[0109] The final repeater defect candidates (i.e., defects that repeat in a number greater than the repeater threshold) can all be identified as defects on the scaled-down mask. However, identifying defects on the scaled-down mask based on the final repeater defect candidates requires some additional analysis. For example, the repeater can be input into any suitable post-processing known in the field, such as defect classification (e.g., via any suitable defect classification method or algorithm). Repeater analysis may also include sampling defects for inspection, which can be performed on a subset of all final repeater defect candidates to verify and / or classify the sampled defects. This inspection sampling can produce an inspection sample containing information of a selected subset of the final repeater defect candidates, which can then be sent to a defect inspection tool (not shown) that performs the defect inspection.

[0110] Generally, the embodiments described herein are configured to detect defects in resize mask repeaters, but some resize mask repeater defects can actually be die repeater defects. For the purposes of the applications described herein, users may wish to retain only the resize mask repeater. If this is the case, then the die repeater in the final report should be removed. Specifically, the embodiments described herein can be used in applications where users are looking for repeater defects caused by problems with the resize mask (mask). In such cases, defects present at the same location in different dies on the same resize mask are most likely caused by wafer processing rather than by the resize mask itself.

[0111] In other words, defects appearing at substantially the same location on multiple dies of a single resize mask on a wafer may not be due to actual resize mask defects (e.g., damaged patterned features, particles, or foreign matter, and the like). Instead, if a defect is repeated on multiple dies of a resize mask on a wafer, then the repeated defects on the wafer may be due to an interaction problem between the resize mask, the wafer, and the process (e.g., a failed edge design due to a design / process interaction problem). Such die repeater defects can be of interest in some use cases, and the embodiments described herein can be used in such cases. However, the embodiments described herein can also be specifically used to detect defects that are repeated from one printed resize mask example to another and therefore can be considered to be caused by a resize mask defect.

[0112] In some cases, the computer subsystem will not report defects in magnification mask coordinates. In other words, when the computer subsystem detects a defect, the coordinates of the defect's location may not have been determined by the computer subsystem relative to the magnification mask. In this way, the coordinates of the defect determined by the computer subsystem can be translated into magnification mask coordinates by the computer subsystem or another system or method. Coordinates can be translated in various ways. For example, a relatively simple approach is to translate the coordinates reported for the defect into a reference common to both the wafer and the magnification mask (e.g., the wafer and magnification mask design). In one such example, the image generated by the inspection subsystem for the defect can be aligned to the wafer design. Once the design coordinates of the defect are determined, those coordinates can be translated into magnification mask coordinates based on the known relationship between the design and the magnification mask.

[0113] The computer subsystem may preferably store information on all detected defects, making such information available for the RDD and any other functions. Once the RDD is executed by the computer subsystem, the stored information can be removed before sending it to another computer subsystem, another method, or back to memory, etc., for defects determined not to be in the magnification mask and / or die repeater.

[0114] A computer subsystem may be configured to store the results of any one or more steps described herein in a computer-readable storage medium. The results may include any of the results described herein and may be stored in any manner known in the art. The storage medium may include any storage medium described herein or any other suitable storage medium known in the art. After the results have been stored, they may be accessed in the storage medium and used by any of the methods or systems described herein, formatted for display to a user, used by another software module, method, or system, etc. For example, defects detected on a photomask can be used to control and / or modify a photomask repair or cleaning process, wherein the objective is to remove or repair one or more photomask defects to prevent such photomask defects from being printed onto other wafers. In one example, information about defects detected on the reduction mask can be used by the computer subsystem described herein, another system (e.g., a reduction mask repair system (not shown), which may or may not be part of or coupled to the system embodiments described herein), or another method (e.g., a reduction mask repair method) to determine where on the reduction mask a repair or cleaning process should be performed. Information about defects detected on the reduction mask can also be used to determine one or more parameters of the process, such as the area on which the process will be performed, the length of time the process will be performed, and which chemicals or gases should be used in the process. In this way, the modified reduction mask can be used to print other wafers during the photolithography process. The modified reduction mask can also be re-inspected at another time after it has been reintroduced into the photolithography process using the embodiments described herein.

[0115] While using a two-row sample plan as described in Types 1 through 4 above is particularly useful for the embodiments described herein (e.g., minimizing system memory requirements and reducing the correlation of scrambling points), more than two rows can be sampled and processed together for detecting defects on the magnified mask. Figure 9 Displayed on the chip ( Figure 9Test images 900 generated from three rows (not shown) can be processed together for defect detection in a reduction photomask. In this embodiment and other embodiments described herein, A1, A2, and A3 refer to patterned areas printed on a wafer using a reduction photomask during photolithography, the patterned areas corresponding to the same areas on the reduction photomask but printed in different rows on the wafer. Similarly, B1, B2, and B3 (and C1, C2, and C3) refer to patterned areas printed on a wafer using a reduction photomask during photolithography, the patterned areas corresponding to the same areas on the reduction photomask but printed in different rows on the wafer. In this way, A1, A2, and A3 can be the same portion of a die printed on a wafer using MDR, B1, B2, and B3 can be the same portion of different dies printed on the wafer using the MDR, and C1, C2, and C3 can be the same portion of another different die printed on a wafer using MDR. Test images 900, A1, A2, A3, B1, B2, B3, C1, C2, and C3, can be generated by scanning three different rows on the chip. For example, in Figure 3 The S1 shown can generate test images A1, B1, and C1, and can generate each set of A1, B1, and C1 for a single MDR example printed on the chip. Figure 3 In S3, test images A2, B2, and C2 can be generated, and each set of A2, B2, and C2 can be generated for a single MDR example printed on the chip. Figure 3 The S5 shown in the image can generate test images of A3, B3, and C3, and can generate each set of A3, B3, and C3 for a single MDR example printed on the chip.

[0116] If the sole objective is to achieve maximum noise reduction, the approach would be to stack difference images across all three rows of the scaled mask in the test sample plan; however, this approach has substantially limited ability to suppress scrambling points. For example, a three-row stacking method could produce only one set of stacked difference images, making scrambling point suppression more challenging. Instead, the embodiments described herein can be configured to test every two rows of the scaled mask by stacking difference images. This can be performed using any of the methods described herein, but... Figure 4 and 10 Type 1 is used as an instance to illustrate this.

[0117] In one embodiment, the computer subsystem is configured to perform generation and detection on the first and second images in different rows on the wafer, to perform generation and detection on the second and third images in different rows on the wafer separately, and to input the results of the two detection steps into the recognition step. In this way, two sets of stacked difference images are independently generated across three magnification mask rows using a 2-row stacking method. Figure 4The method shown in the image is executed for lines 1 and 2, but... Figure 10 The method shown is executed for lines 2 and 3. In this way, line 2 is... Figure 4 and 10 Both have this in common. In other words, for a three-fold reduction mask row sample plan, two rows (row 1, row 2 and row 2, row 3) are tested at once, where row 2 is redundant in both tests.

[0118] like Figure 4 The first method shown in the text, Figure 10 The separately executed Type 1 method shown also uses reference images from two rows to generate CR images for each test image and stacks the resulting difference images across the two rows for maximum noise reduction. In one embodiment, generating a first difference image involves individually subtracting a first CR from images generated for multiple examples of a first patterned region in a first of different rows on the wafer, and generating a second difference image involves individually subtracting the first CR from images generated for multiple examples of a first patterned region in a second of different rows on the wafer. For example, in Figure 10 In the process, generating a first difference image (e.g., the top row of set 1002 of difference 1) involves subtracting a first CR separately from the image generated for A2, and generating a second difference image (e.g., the bottom row of set 1002) involves subtracting a first CR separately from the image generated for A3, as shown in step 1004.

[0119] In some such embodiments, the computer subsystem is configured to generate a first CR from images of multiple examples of a second type of patterned region in different rows on a wafer. For example, as... Figure 10 As shown in the diagram, the first CR subtracted from A2 in step 1004 can be generated as CR(B2,B3). This same CR can be subtracted from A3 separately, as shown in step 1004.

[0120] In an additional embodiment, generating a third difference image includes subtracting a second CR from an image generated individually from multiple examples of a first patterned region in a first of different rows on the wafer, and generating a fourth difference image includes subtracting a second CR from an image individually from an image generated individually from multiple examples of a first patterned region in a second of different rows on the wafer. For example, in Figure 10 In the process, generating a third difference image (e.g., the top row of set 1006 of difference 2) involves subtracting the second CR separately from the image generated for A2, and generating a fourth difference image (e.g., the bottom row of set 1006) involves subtracting the second CR separately from the image generated for A3, as shown in step 1008.

[0121] In another embodiment, the computer subsystem is configured to generate a second CR from images of multiple examples of a third party in patterned regions in different rows on a wafer. For example, as Figure 10 As shown in the diagram, the second CR subtracted from A2 in step 1008 can be generated as CR(C2,C3). This same CR can be subtracted separately from A3, as shown in step 1008.

[0122] like Figure 10 As shown, the computer subsystem can generate stacked difference 1 1010 by combining at least a portion of the first and second difference images (i.e., at least some differences 1 in the two rows of set 1002) to generate stacked difference 1, and generate stacked difference 2 1012 by combining at least a portion of the third and fourth difference images (i.e., at least some differences 2 in the two rows of set 1006).

[0123] The computer subsystem is also configured to detect defect candidates in multiple examples of a first patterned region on the wafer by applying a defect detection method to different stacking difference images. The defect detection method determines that a defect candidate exists at the location in each of the multiple examples of the first patterned region only when the defect detection method detects a defect candidate at a location in the different stacking difference images. For example, the computer subsystem may be configured to perform dual detection 1014 using stacking difference 1 1010 and stacking difference 2 1012. Dual detection 1014 may be performed as further described herein. The output of the dual detection step may be a defect detection result 1016, wherein the location of any defect candidate detected by dual detection is identified in each of the patterned regions having some kind of marker (in this case, an asterisk). In this way, the output of the dual detection step is a full correlation of the reported defect candidates, without individual frame information. Result 1016 may be configured as further described herein.

[0124] The Type 1 and 3 row methods described above offer significant advantages in reducing scrambling points. For example, compared to the currently used MDR RDD method, Type 1 described above will advantageously begin with a lower number of repeater types, regardless of the row to which Type 1 is applied. Specifically, it will suppress scrambling repeater types that do not have a signal in more than one row, as detected by the currently used MDR RDD method, regardless of the row being examined.

[0125] Figure 11 The example shown illustrates an embodiment where the results of two detection steps are input into an identification step. In this embodiment, the defect detection result 416 generated by performing the Type 1 method on rows 1 and 2 is as follows: Figure 4 The display generally produces and is related to, as shown in the example Figure 10 The defect detection results 1016 generated by performing the Type 1 method on rows 2 and 3, as shown in the diagram, are input together into the identification step. Disruptive point filtering can be performed individually for each of the defect detection results. For example, disruptive point filtering by local image characteristics can be performed on defect detection result 416 in step 1100, and also on defect detection result 1016 in step 1102. Disruptive point filtering can be performed on defects by limiting local image characteristics, as described above, resulting in defect detection result 1104. In one example, disruptive point filtering may involve eliminating any defect candidates with a local SNR below a threshold and retaining any defect candidates with a local SNR above a threshold. In this way, result 1104 (the output of the disruptive point filtering step) is partially relevant to the reported defect.

[0126] Therefore, the embodiments described above offer numerous advantages over currently used methods and systems for detecting defects on scale-down masks. For example, if a mask repeater defect is not detected, it causes a yield loss on each printed scale-down mask. As design rules shrink, the size of repeater defects that should not be missed becomes increasingly smaller. Signals for smaller repeater sizes also weaken, necessitating methods to minimize the defect inspection load for optimal noise reduction and optimal suppression of scrambling points. The embodiments described herein provide solutions to meet the needs of different users. Some users seek the maximum raw sensitivity with the minimum possible number of repeaters, while others may prefer to limit their defect inspection budget by requiring a minimum total number of repeaters.

[0127] These and other advantages described herein are achieved through a number of important new features of the embodiments described herein. One such new feature is the use of a staggered scan-band scanning method based on a magnification mask to acquire scan-band images. Another new feature includes the use of an alignment die positioned in the first magnification mask row to align with other magnification mask rows during scan-band scanning. An additional new feature is the use of a reference image spanning multiple magnification mask rows for CR image generation. A further new feature is the stacking of difference images across multiple magnification mask rows for maximum noise reduction. Yet another new feature is the stacking of difference images within the same row as the test image and the double detection across the magnification mask rows for maximum scrambling point suppression.

[0128] Each embodiment of the system described herein may be combined with any other embodiment of the system described herein.

[0129] Another embodiment relates to a computer-implemented method for detecting defects on a reduction photomask. The method includes acquiring an image of a wafer generated by an inspection subsystem. The reduction photomask is used to print patterned regions onto the wafer during a photolithography process. The patterned regions include a first patterned region corresponding to the same region on the reduction photomask and printed in a different row on the wafer. The method further includes the generation, detection, and identification steps described above, performed by a computer subsystem coupled to the inspection subsystem.

[0130] Each step of the method may be performed as further described herein. The method may also include any other steps that can be performed by the verification subsystem and / or computer subsystem described herein, which may be configured according to any of the embodiments described herein. Additionally, the methods described above may be performed by any of the system embodiments described herein.

[0131] Additional embodiments relate to a non-transitory computer-readable medium storing program instructions that execute on a computer system to perform a computer-implemented method for detecting defects on a reduction-size photomask. Figure 12 An example of this embodiment is shown below. Specifically, as... Figure 12 As shown herein, the non-transitory computer-readable medium 1200 contains program instructions 1202 that can be executed on a computer system 1204. A computer-implemented method may include any step of any method described herein.

[0132] Program instructions 1202 for implementing methods such as those described herein may be stored on a computer-readable medium 1200. The computer-readable medium may be a storage medium, such as a disk or optical disc, magnetic tape, or any other suitable non-transitory computer-readable medium known in the art.

[0133] Program instructions can be implemented in any of a variety of ways, including procedural, component-based, and / or object-oriented technologies. For example, program instructions may be implemented using ActiveX controls, C++ objects, JavaBeans, Microsoft Foundation Classes (“MFC”), SSE (Streaming SIMD Extensions), or other technologies or methods as needed.

[0134] Computer system 1204 may be configured according to any of the embodiments described herein.

[0135] In view of this description, those skilled in the art will understand further modifications and alternative embodiments of various aspects of the invention. For example, systems and methods for detecting defects on a magnifying mask are provided. Therefore, this description should be construed as illustrative only and for the purpose of teaching those skilled in the art how to perform the invention in general. It will be understood that the forms of the invention shown and described herein are to be considered the present preferred embodiments. As those skilled in the art will appreciate the benefits of this description of the invention, elements and materials may be substituted for those described herein, parts and processes may be reversed, and certain features of the invention may be utilized alone. Changes may be made to the elements herein without departing from the spirit and scope of the invention as set forth in the appended claims.

Claims

1. A system configured to detect defects on a reduction photomask, comprising: An inspection subsystem configured to generate an image of a wafer, wherein a scale-down mask is used to print patterned regions onto the wafer during photolithography, and wherein the patterned regions include a first patterned region corresponding to the same region on the scale-down mask and printed in a different row on the wafer. and Computer subsystem, configured for: Different stacking difference images of multiple examples of the first patterned region in the different rows are generated based on the images generated by the inspection subsystem for the first patterned region in the different rows; By applying a defect detection method to the different stacking difference images, defect candidates are detected in the plurality of examples of the first patterned region on the wafer, wherein the defect detection method determines that the defect candidate exists at the location in each of the plurality of examples of the first patterned region only when the defect detection method detects the defect candidate at the location in the different stacking difference images. and Defects on the scaled-down mask are identified based on the detected defect candidates.

2. The system of claim 1, wherein the inspection subsystem is further configured to generate the image by interleaving scans of the first patterned region in the different rows on the wafer with scans of a second patterned region in the different rows on the wafer.

3. The system of claim 2, wherein the first patterned region and the second of the patterned region correspond to different portions of the bare sheet on the magnifying mask.

4. The system of claim 2, wherein the first patterned region and the second in the patterned region correspond to different examples of a bare die printed on the reduction mask on the wafer.

5. The system of claim 1, wherein the photomask is a multi-die photomask, and wherein the first patterned region includes at least a portion of only the first die of the multi-die photomask printed on the wafer.

6. The system of claim 1, wherein generating the first of the different stacked difference images comprises generating a first difference image and a second difference image and combining at least a portion of the first difference image and the second difference image to generate the first of the different stacked difference images, and wherein generating the second of the different stacked difference images comprises generating a third difference image and a fourth difference image and combining at least a portion of the third difference image and the fourth difference image to generate the second of the different stacked difference images.

7. The system of claim 6, wherein generating the first difference image comprises subtracting a first computational reference individually from the images generated for the plurality of examples of the first patterned region in the first of the different rows on the wafer, and generating the second difference image comprises subtracting the first computational reference individually from the images generated for the plurality of examples of the first patterned region in the second of the different rows on the wafer.

8. The system of claim 7, wherein the computer subsystem is further configured to generate the first computational reference from the image of a plurality of examples of a second in the patterned regions of the different rows on the wafer.

9. The system of claim 7, wherein generating the third difference image comprises subtracting a second computational reference individually from the image generated for each of the plurality of examples of the first patterned region in the first of the different rows on the wafer, and generating the fourth difference image comprises subtracting the second computational reference individually from the image generated for each of the plurality of examples of the first patterned region in the second of the different rows on the wafer.

10. The system of claim 9, wherein the computer subsystem is further configured to generate the second computational reference from the image of a plurality of examples of a third party in the patterned regions of the different rows on the wafer.

11. The system of claim 6, wherein generating the first difference image comprises subtracting a first computational reference individually from the image generated for each of the plurality of examples of the first patterned region in the first of the different rows on the wafer, and generating the second difference image comprises subtracting a second computational reference individually from the image generated for each of the plurality of examples of the first patterned region in the second of the different rows on the wafer.

12. The system of claim 11, wherein the computer subsystem is further configured to generate the first computational reference from the image of a plurality of examples of the second in the patterned region of the first in the different rows on the wafer, and to generate the second computational reference from the image of a plurality of examples of the second in the patterned region of the second in the different rows on the wafer.

13. The system of claim 11, wherein generating the third difference image comprises individually subtracting a third computational reference from each of the plurality of examples of the image generated for the first patterned region of the first of the different rows on the wafer, and generating the fourth difference image comprises individually subtracting a fourth computational reference from each of the plurality of examples of the image generated for the first patterned region of the second of the different rows on the wafer.

14. The system of claim 13, wherein the computer subsystem is further configured to generate the third computational reference from the image of a plurality of examples of a third party in the patterned region of the first party in the different rows on the wafer, and to generate the fourth computational reference from the image of a plurality of examples of a third party in the patterned region of the second party in the different rows on the wafer.

15. The system of claim 6, wherein generating the first difference image comprises subtracting a first computational reference individually from the images generated for the plurality of examples of the first patterned region in the first of the different rows on the wafer, and generating the second difference image comprises subtracting a second computational reference individually from the images generated for the plurality of examples of the first patterned region in the first of the different rows on the wafer.

16. The system of claim 15, wherein the computer subsystem is further configured to generate the first computational reference from the image of a plurality of examples of a second in the patterned region of the first in the different rows on the wafer, and to generate the second computational reference from the image of a plurality of examples of a third in the patterned region of the first in the different rows on the wafer.

17. The system of claim 15, wherein generating the third difference image comprises subtracting a third computational reference individually from the image generated for each of the plurality of examples of the first patterned region in the second of the different rows on the wafer, and generating the fourth difference image comprises subtracting a fourth computational reference individually from the image generated for each of the plurality of examples of the first patterned region in the second of the different rows on the wafer.

18. The system of claim 17, wherein the computer subsystem is further configured to generate the third computational reference from the image of a plurality of examples of the second in the patterned region of the second in the different rows on the wafer, and to generate the fourth computational reference from the image of a plurality of examples of the third in the patterned region of the second in the different rows on the wafer.

19. The system of claim 1, wherein generating the first of the different stacked difference images comprises generating a first difference image and combining at least a portion of the first difference image to generate the first of the different stacked difference images, and wherein generating the second of the different stacked difference images comprises generating a second difference image and combining at least a portion of the second difference image to generate the second of the different stacked difference images.

20. The system of claim 19, wherein generating the first difference image comprises subtracting a first computational reference individually from the images generated for the plurality of examples of the first patterned region in the first of the first of the different rows on the wafer.

21. The system of claim 20, wherein the computer subsystem is further configured to generate the first computational reference from the image of a plurality of examples of the second in the patterned region of the first and second in the different rows on the wafer.

22. The system of claim 20, wherein generating the second difference image comprises subtracting a second computational reference individually from the images generated for the plurality of examples of the first patterned region in the second of the different rows on the wafer.

23. The system of claim 22, wherein the computer subsystem is further configured to generate the second computational reference from the image of a plurality of examples of a third in a patterned region of the first and second of the different rows on the wafer.

24. The system of claim 1, wherein the computer subsystem is further configured to perform the generation and detection for the first and second of the different rows on the wafer, to perform the generation and detection separately for the second and third of the different rows on the wafer, and to input the results of the two detection steps into the identification step.

25. The system of claim 1, wherein identifying the defect on the magnifying mask comprises determining local image characteristics at the location of each of the plurality of images of the first patterned region, comparing the determined local image characteristics at the location of each of the plurality of images of the first patterned region with a threshold, determining the number of images of the plurality of images of the first patterned region having the determined local image characteristics above the threshold, and comparing the determined number with a repeater threshold.

26. The system of claim 1, wherein the photomask is a single-piece photomask.

27. The system of claim 1, wherein the photomask is a multi-die photomask.

28. The system of claim 1, wherein the magnifying mask is configured for use at extreme ultraviolet wavelengths.

29. A non-transitory computer-readable medium storing program instructions that execute on a computer system to perform a computer-implemented method for detecting defects on a reduction-size photomask, wherein the computer-implemented method includes: Acquire an image of a wafer generated by an inspection subsystem, wherein a scale-down mask is used to print a patterned region onto the wafer during photolithography, and wherein the patterned region includes a first patterned region corresponding to the same region on the scale-down mask and printed in a different row on the wafer. Different stacking difference images of multiple examples of the first patterned region in the different rows are generated based on the images generated by the inspection subsystem for the first patterned region in the different rows; Defect candidates are detected in a plurality of examples of the first patterned region on the wafer by applying a defect detection method to the different stacking difference images, wherein the defect detection method determines that the defect candidate exists at the location in each of the plurality of examples of the first patterned region only when the defect detection method detects the defect candidate at the location in the different stacking difference images; and Defects on the scaled-down mask are identified based on the detected defect candidates, wherein the acquisition, generation, detection, and identification are performed by the computer system coupled to the inspection subsystem.

30. A computer-implemented method for detecting defects on a reduction-size photomask, comprising: Acquire an image of a wafer generated by an inspection subsystem, wherein a scale-down mask is used to print a patterned region onto the wafer during photolithography, and wherein the patterned region includes a first patterned region corresponding to the same region on the scale-down mask and printed in a different row on the wafer. Different stacking difference images of multiple examples of the first patterned region in the different rows are generated based on the images generated by the inspection subsystem for the first patterned region in the different rows; Defect candidates are detected in a plurality of examples of the first patterned region on the wafer by applying a defect detection method to the different stacking difference images, wherein the defect detection method determines that the defect candidate exists at the location in each of the plurality of examples of the first patterned region only when the defect detection method detects the defect candidate at the location in the different stacking difference images; and Defects on the scaled-down mask are identified based on the detected defect candidates, wherein the acquisition, generation, detection, and identification are performed by a computer system coupled to the inspection subsystem.

Images