Detecting defects on a sample

Abstract

Methods and systems for detecting defects on a sample are provided. One system computes different candidate reference images from different combinations of images of the sample generated by an inspection subsystem and combines different portions of the candidate reference images without modification to thereby generate a final reference image. The final reference image is then used for defect detection, which can be single or dual detection. Embodiments are particularly useful for defect detection of regions of a sample that contain only indistinguishable, repeating device patterns, such as in a cell area, but can also be used for inspection of other types of regions.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to methods and systems for detecting defects on a sample in semiconductor technology. BACKGROUND

[0002] The following description and examples will not be considered as prior art merely because they are included in this section.

[0003] Fabricating semiconductor devices, such as logic and memory devices, typically includes processing a sample, e.g., a semiconductor wafer, using a number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that typically involves transferring patterns to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices can be fabricated in an arrangement on a semiconductor wafer and then separated into individual semiconductor devices.

[0004] In the semiconductor industry, inspection using either optical or e-beam imaging is an important technique for debugging semiconductor fabrication processes, monitoring process variations, and improving production yield. As the scale of modern integrated circuits (ICs) continues to shrink and the complexity of the fabrication process increases, inspection becomes more and more difficult.

[0005] At each processing step performed on a semiconductor wafer, the same circuit pattern is printed on each die. Most wafer inspection systems take advantage of this fact and use a relatively simple inter-die comparison to detect defects on the wafer. However, the printed circuit in each die can include many regions of patterned features that repeat in the x or y direction, such as DRAM, SRAM, or FLASH regions. This type of region is often referred to as an "array region" (the remaining regions are referred to as "random" or "logic regions"). To achieve better sensitivity, advanced inspection systems employ different strategies to inspect array regions and random or logic regions.

[0006] Array detection algorithms are designed to achieve relatively high sensitivity for DRAM cell regions by exploiting the repeatability of the cell regions. For example, an inspection system configured for array region inspection typically performs an inter-cell comparison, where images of different cells in an array region in the same die are subtracted from each other and the differences are examined for defects. This array inspection strategy can achieve much higher sensitivity in array regions than random inspection, which is typically performed by subtracting an image of one die from an image of another die, because it avoids noise caused by inter-die variations.

[0007] Many currently used array defect detection methods divide the entire area of interest into several units (referred to as cells). All cells are placed into independent groups, and each group is used to generate a single reference cell that is used as the basis for the final reference image. Currently used array defect detection methods generally assume that all cells in each group are identical except for some random noise. The grayscale of the same location of all cells only fluctuates around the noise-free baseline true value. However, due to highly non-uniform reflectivity from different wafer areas, such uniform grayscale assumption can not always be correct. For example, relatively strong leakage from the edge area of a cell can introduce relatively low frequency grayscale variation, which can eventually cause relatively low inspection sensitivity. Furthermore, currently used array detection methods treat each area of interest as a single detection area. By bundling different locations together, a good balance between sensitivity and nuisance control cannot be achieved. With the settings under which the best sensitivity can be achieved from the inner area, there can be a lot of nuisance from the edge / corner area.

[0008] In random defect detection algorithms, arbitrating the results of two detections has shown better performance than relying on a single detection only. This double detection capability would also benefit array defect detection methods in nuisance reduction, but it is not used in currently used array defect detection methods.

[0009] Therefore, it would be advantageous to develop systems and methods for inspection of a sample that do not have one or more of the drawbacks described above. SUMMARY

[0010] The following description of various embodiments should in no way be interpreted as limiting the subject matter of the claims attached hereto.

[0011] One embodiment relates to a system configured for detecting defects on a specimen. The system includes an inspection subsystem configured for generating images of a specimen, the images including a test image and two or more other images corresponding to the test image. The system also includes a computer subsystem configured for computing first and second candidate reference images from different combinations of at least two of the test image and the two or more other images. The computer subsystem is also configured for selecting at least a portion of the first candidate reference image corresponding to a first portion of the test image and a portion of the second candidate reference image corresponding to a second portion of the test image. In addition, the computer subsystem is configured for combining the selected portions of the first and second candidate reference images without modifying the selected portions of the first and second candidate reference images to thereby generate a final reference image. The computer subsystem is further configured for generating a difference image by comparing the test image to the final reference image and detecting defects in the test image by applying a defect detection method to the difference image. The system can be further configured as described herein.

[0012] Another embodiment relates to a computer-implemented method for detecting defects on a specimen. The method includes acquiring images of a specimen generated by an inspection subsystem, the images including a test image and two or more other images corresponding to the test image. The method also includes the computing, selecting, combining, generating, and detecting steps described above. The acquiring, computing, selecting, combining, generating, and detecting are performed by a computer subsystem coupled to the inspection subsystem. Each of the steps of the method described above can be performed as further described herein. In addition, embodiments of the method described above can include any other step(s) of any other method(s) described herein. The method described above can be performed by any of the systems described herein.

[0013] 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 specimen. The computer-implemented method includes the steps of the method described above. The computer-readable medium can be further configured as described herein. The steps of the computer-implemented method can be performed as further described herein. In addition, the computer-implemented method for which the program instructions are executable can include any other step(s) of any other method(s) described herein. BRIEF DESCRIPTION OF DRAWINGS

[0014] Further advantages of the present invention will become apparent to those skilled in the art upon consideration of the following detailed description of preferred embodiments with reference to the accompanying drawings, wherein:

[0015] Figure 1 and 1a is a schematic illustration of a plan view of an embodiment illustrating one example of a test image, a corresponding reference image and a difference image generated therefrom using a currently used inspection method and a final reference image generated as described herein and a difference image generated therefrom;

[0016] Figure 2 is a schematic illustration of a plan view of an embodiment illustrating one example of a test image, a corresponding reference image and a difference image generated therefrom using a currently used inspection method and a final reference image generated as described herein and a difference image generated therefrom;

[0017] Figure 3 is a schematic illustration of a plan view of an embodiment illustrating one example of an image generated for a sample and different embodiments of dividing the image into a test cell and two or more other cells adjacent to the test cell;

[0018] Figure 4 is a flowchart illustrating one embodiment of steps that can be performed for detecting defects on a sample;

[0019] Figure 5 is a schematic illustration of a plan view of one example of a job comprising image frames of different cell areas on a sample;

[0020] Figure 6 is a schematic illustration of a plan view of one embodiment of different candidate reference images, wherein different portions corresponding to different portions of a test image are selected and combined without modification to thereby generate a final reference image;

[0021] Figure 7 is a schematic illustration of a plan view of an embodiment of a region of interest divided into different segments and a chart showing one embodiment of detecting defects in each of the different segments separately; and

[0022] Figure 8 is a block diagram illustrating one embodiment of a non-transitory computer- readable medium storing program instructions for causing a computer system to perform the computer-implemented methods described herein.

[0023] While the application is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are herein described in detail. The drawings can not be to scale. It should be understood that the drawings and detailed description thereto are not intended to limit the application to the particular form disclosed but rather the application is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the application as defined by the appended claims. DETAILED DESCRIPTION

[0024] Turning now to the drawings, it is noted that the figures are not drawn to scale. In particular, the scale of some of the elements of the figures can be greatly exaggerated to emphasize features of the elements. It is also noted that the figures are not drawn to the same scale. Like reference numerals can be used to denote like, but not necessarily identical, elements among the figures. Unless otherwise stated herein, any of the elements described and shown can comprise any suitable commercially available element.

[0025] In general, the embodiments described herein are configured for detecting defects on a sample. The embodiments described herein provide new reference image generation and detection methods that are particularly useful for non-pattern detection areas. Current usage methods for defect detection in non-pattern array (NPA) areas assume that the entire inspection area has uniform gray scale and that only wafer / imaging noise causes gray scale fluctuations. But the presence of color variation due to different material and structure reflectivity breaks this assumption. Failure to properly handle color variation can lead to additional noise that is sometimes even larger than the defect of interest (DOI) signal, and reduces NPA defect detection sensitivity. Based on these observations, steps described further herein are proposed to improve defect detection performance at non-pattern areas.

[0026] The term "nuisance" as used herein, which can sometimes be used interchangeably with "nuisance defect," is generally defined as an event that is detected on a sample but is not in fact a real defect on the sample. Due to non-defect noise sources on the sample (e.g., particles in metal lines on the sample, signals from underlying layers or materials on the sample, line edge roughness (LER), relatively small critical dimension (CD) variations in patterned features, thickness variations, etc.) and / or due to edge-of-field nature of the inspection system itself and its configuration for inspection, a nuisance that is not in fact a defect can be detected as an event.

[0027] The terms "first" and "second" are used herein merely for ease of reference to different things, but those terms are not meant to imply any other meaning to the embodiments described herein.

[0028] In some embodiments, the sample is a wafer. The wafer can include any wafer known in semiconductor technology. Although some embodiments can be described herein with respect to one or several wafers, the embodiments are not limited to the samples for which they can be used. For example, the embodiments described herein can be used for samples such as reticles, flat panels, personal computer (PC) boards, and other semiconductor samples.

[0029] Figure 1 One embodiment of a system configured for detecting defects on a sample is shown in FIG. 1. The system includes an inspection subsystem 100. In Figure 1In the embodiments shown in the Figures, the inspection subsystem is configured as a light-based inspection subsystem. However, in other embodiments described herein, the inspection subsystem is configured as an electron beam or charged particle beam-based inspection subsystem. In general, the inspection subsystems described herein include at least an energy source and a detector. The energy source is configured to generate energy that is directed to the sample. The detector is configured to detect energy from the sample and generate an output in response to the detected energy.

[0030] In a light-based inspection subsystem, the energy directed to the sample includes light, and the energy detected from the sample includes light. For example, in a light-based inspection subsystem, the energy source can include a light source, such as a laser, and the detector can include a photodetector. Figure 1 In embodiments of the systems shown in the Figures, the inspection subsystem includes an illumination subsystem configured to direct light to the sample 14. The illumination subsystem includes at least one light source. For example, as shown in Figure 1 As shown in the Figures, the illumination subsystem includes a light source 16. The illumination subsystem is configured to direct light to the sample at one or more angles of incidence that can include one or more oblique angles and / or one or more normal angles. For example, as shown in Figure 1 As shown in the Figures, light from the light source 16 is directed through an optical element 18 at an oblique angle of incidence and then through a lens 20 to the sample 14. The oblique angle of incidence can include any suitable oblique angle of incidence that can vary depending on, for example, characteristics of the sample and defects to be detected on the sample.

[0031] The illumination subsystem can be configured to direct light to the sample at different angles of incidence at different times. For example, the inspection subsystem can be configured to alter one or more characteristics of one or more elements of the illumination subsystem so that light can be directed to the sample at different angles of incidence than Figure 1 As shown in the Figures, the illumination subsystem includes a light source 16. The illumination subsystem is configured to direct light to the sample at one or more angles of incidence that can include one or more oblique angles and / or one or more normal angles. For example, as shown in

[0032] The inspection subsystem can be configured to direct light to the sample at more than one angle of incidence at the same time. For example, the illumination subsystem can include more than one illumination channel, one of which can include a light source 16, an optical element 18, and a lens 20 as shown in Figure 1 As shown in the Figures, the illumination subsystem includes a light source 16. The illumination subsystem is configured to direct light to the sample at one or more angles of incidence that can include one or more oblique angles and / or one or more normal angles. For example, as shown in

[0033] The lighting subsystem may contain only one light source (e.g., Figure 1 The light from source 16 shown can be split into different optical paths (e.g., based on wavelength, polarization, etc.) by several optical elements (not shown) of the illumination subsystem. The light from each of these different optical paths can then be directed to the sample. Multiple illumination channels can be configured to direct light to the sample at the same time or at different times (e.g., when different illumination channels are used to sequentially illuminate the sample). In another example, the same illumination channel can be configured to direct light with different characteristics to the sample at different times. For example, optical element 18 can be configured as a spectral filter and its properties can be varied in various ways (e.g., by replacing one spectral filter with another) to direct light of different wavelengths to the sample 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 sample at different or the same angles of incidence.

[0034] Light source 16 may comprise a broadband plasma (BBP) source. In this way, the light generated by the source and directed to the sample may comprise broadband light. However, the source may comprise any other suitable 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 known in the art. Additionally, the laser may be configured to produce monochromatic or near-monochromatic light. For example, the laser may be a narrowband laser. The source may also comprise a multicolor source that produces light of multiple discrete wavelengths or bands.

[0035] Light from optical element 18 can be focused onto sample 14 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 sample. Figure 1 The illumination subsystem shown and described herein may include any other suitable optical elements (not shown). Examples of such optical elements include, but are not limited to, several polarizing components, several spectral filters, several spatial filters, several reflective optics, several apodizers, several beam splitters, several apertures, and the like, which may include any such suitable optical elements known in the art. Furthermore, the system may be configured to change one or more of the elements of the illumination subsystem based on the type of illumination to be tested.

[0036] The inspection subsystem may also include a scanning subsystem configured to change the position on the sample (directing light to and detecting light from said position) and potentially scan the sample. For example, the inspection subsystem may include a stage 22 on which the sample 14 is placed during inspection. The scanning subsystem may include any suitable mechanical and / or robotic assembly (including the stage 22) configured to move the sample such that light can be directed to and detected from different positions on the sample. Alternatively, the inspection subsystem may be configured such that one or more optical elements of the inspection subsystem perform some scans of the sample, such that light can be directed to and detected from different positions on the sample. The scan of the sample may be performed in any suitable manner (e.g., in a serpentine or helical path).

[0037] The inspection subsystem further includes one or more detection channels. At least one of the detection channels includes a detector configured to detect light originating from the sample due to system illumination of the sample and to generate an output in response to the detected light. For example, Figure 1 The inspection subsystem shown includes two detection channels: one formed by a light collector 24, element 26, and detector 28, and the other formed by a light collector 30, element 32, and detector 34. Figure 1 As shown, two detection channels are configured to collect and detect light at different collection angles. In some examples, the two detection channels are configured to detect scattered light, and the detection channels are configured to detect light scattered from the sample at different angles. However, one or more of the detection channels may be configured to detect another type of light from the sample (e.g., reflected light).

[0038] like Figure 1 The diagram further illustrates that two detection channels are positioned in the plane of the paper, and the illumination subsystem is also positioned in the plane of the paper. Therefore, the two detection channels are positioned (e.g., centered) in the plane of incidence. However, one or more of the detection channels may be positioned outside the plane of incidence. For example, the detection channel formed by the light collector 30, element 32, and detector 34 can be configured to collect and detect light scattered out of the plane of incidence. Therefore, this detection channel may generally be referred to as a "side" channel, and this side channel may be centered in a plane substantially perpendicular to the plane of incidence.

[0039] The inspection subsystem may include and Figure 1The number of detection channels shown in FIG. 1 is illustrative only (e.g., only one detection channel or two or more detection channels can be included). In one such example, the detection channel formed by collector 30, element 32, and detector 34 can form one side channel as described above, and the inspection subsystem can include an additional detection channel (not shown) formed as another side channel positioned on the opposite side of the plane of incidence. Thus, the inspection subsystem can include a detection channel including collector 24, element 26, and detector 28 and centered in the plane of incidence and configured to collect and detect light at or near the scattering angle(s) normal to the sample surface. Thus, this detection channel can be referred to generally as a "top" channel, and the inspection subsystem can also include two or more side channels configured as described above. As such, the inspection subsystem can include at least three channels (i.e., one top channel and two side channels), and each of the at least three channels has its own collector, each of which is configured to collect light at a different scattering angle than each of the other collectors.

[0040] As described further above, each of the detection channels included in the inspection subsystem can be configured to detect scattered light. Thus, Figure 1 The inspection subsystem shown in FIG. 1 can be configured for dark-field (DF) inspection of a sample. However, the inspection subsystem can also or instead include detection channel(s) configured for bright-field (BF) inspection of a sample. In other words, the inspection subsystem can include at least one detection channel configured to detect light specularly reflected from a sample. Thus, the inspection subsystem described herein can be configured for DF only, BF only, or both DF and BF inspection. Although each of the collectors is shown in FIG. 1 as a single refractive optical element, it should be understood that each of the collectors can include one or more refractive optical elements and / or one or more reflective optical elements. Figure 1

[0041] ​The one or more detection channels can include any suitable detectors known in the art. For example, the detectors can include photomultiplier tubes (PMTs), charge-coupled devices (CCDs), and time delay integration (TDI) cameras. The detectors can also include non-imaging detectors or imaging detectors. If the detectors are non-imaging detectors, each of the detectors can be configured to detect certain properties of the scattered light (e.g., intensity), but can not be configured to detect such properties as a function of position within the imaging plane. As such, the output generated by each of the detectors included in each of the detection channels can be a signal or data, rather than an image signal or image data. In such examples, a computer subsystem (e.g., computer subsystem 36 forming part of an inspection system having the inspection subsystem) can be configured to generate an image of the sample from the non-imaging output of the detectors. However, in other examples, the detectors can be configured as imaging detectors configured to generate imaging signals or image data. Thus, the inspection subsystem can be configured to generate images in a number of ways.

[0042] It should be noted that the configurations of the inspection subsystems provided herein Figure 1 The configurations of the inspection subsystems that can be included in the system embodiments described herein are generally illustrated. It is apparent that the inspection subsystem configurations described herein can be altered to optimize the performance of the inspection subsystems as is typically performed when designing a commercial inspection system. In addition, the systems described herein can be implemented using existing inspection systems, e.g., by adding the functionality described herein to an existing inspection system, such as the 29xx / 39xx series tools commercially available from KLA Corp., Milpitas, Calif. For some such systems, the methods described herein can be provided as optional functionality of the inspection system (e.g., in addition to other functionality of the inspection system). Alternatively, the inspection systems described herein can be designed "from scratch" to provide a completely new inspection system.

[0043] Computer subsystem 36 can be coupled to the detectors of the inspection subsystem in any suitable manner (e.g., via one or more transmission media, which can include "wired" and / or "wireless" transmission media) such that the computer subsystem can receive the output generated by the detectors. Computer subsystem 36 can be configured to perform a number of functions using the output of the detectors, as described further herein. The computer subsystem coupled to the inspection subsystem can be further configured as described herein.

[0044] A computer subsystem coupled to the inspection subsystem (as well as other computer subsystems described herein) can also be referred to herein as computer system(s). Each of the computer subsystem(s) or system(s) described herein can take various forms, including a personal computer system, image computer, mainframe computer system, workstation, network appliance, Internet appliance, or other device. In general, the term "computer system" can be broadly defined to encompass any device having one or more processors, which executes instructions from a memory medium. The computer subsystem(s) or system(s) can also include any suitable processor known in the art, such as a parallel processor. Additionally, the computer subsystem(s) or system(s) can include a computer platform with high speed processing and software, either as a stand-alone tool or as a networked tool.

[0045] If the system includes more than one computer subsystem, the different computer subsystems can be coupled to one another such that images, data, information, instructions, etc. can be sent between the computer subsystems. For example, computer subsystem 36 can be coupled to computer system(s) 102 (as shown by the dashed line in Figure 1a ). Two or more such computer subsystems can also be effectively coupled by sharing a computer-readable storage medium (not shown).

[0046] Although the inspection subsystem is described above as an optical or light-based inspection subsystem, in another embodiment the inspection subsystem is configured as an electron beam-based inspection subsystem. In an electron beam type inspection subsystem, the energy directed to the sample includes electrons, and the energy detected from the sample includes electrons. In one such embodiment shown in Figure 1 , the inspection subsystem includes an electron column 122, and the system includes a computer subsystem 124 coupled to the inspection subsystem. Computer subsystem 124 can be configured as described above. Additionally, this inspection subsystem can be coupled to another or more computer subsystems in the same manner as described above and shown in Figure 1a .

[0047] As also shown in Figure 1a , the electron column includes an electron beam source 126 configured to generate electrons that are focused by one or more elements 130 to the sample 128. The electron beam source can include, for example, a cathode source or a tip, and the one or more elements 130 can 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 can include any such suitable elements known in the art.

[0048] Electrons (e.g., secondary electrons) returned from the sample can be focused by one or more elements 132 to a detector 134. The one or more elements 132 can include, for example, a scanning subsystem, which can be the same scanning subsystem included in the element(s) 130.

[0049] The electron column can include any other suitable elements known in the art. Additionally, the electron column can be further configured as described in U.S. Patent Nos. 8,664,594, issued April 4, 2014 to Jiang et al., 8,692,204, issued April 8, 2014 to Kojima et al., 8,698,093, issued April 15, 2014 to Gubbens et al., and 8,716,662, issued May 6, 2014 to MacDonald et al., which are incorporated by reference as if fully set forth herein.

[0050] Although the electron column is shown in Figure 1a as being configured such that electrons are directed to the sample at a tilted angle of incidence and scattered from the sample at another tilted angle, the electron beam can be directed to the sample and scattered from the sample at any suitable angles. Additionally, as further described herein, the electron beam inspection subsystem can be configured to use multiple modes to produce an output of the sample (e.g., with different angles of illumination, collection angles, etc.). The multiple modes of the electron beam inspection subsystem can differ in any output production parameter of the inspection subsystem.

[0051] The computer subsystem 124 can be coupled to the detector 134, as described above. The detector can detect electrons returned from the surface of the sample, thereby forming an electron beam image (or other output) of the sample. The electron beam image can include any suitable electron beam image. The computer subsystem 124 can be configured to perform any step(s) described herein. Included Figure 1a The system of the inspection subsystem shown in

[0052] Provided herein Figure 1aTo generally illustrate a configuration of an electron beam inspection subsystem that can be included in the embodiments described herein. As with the optical inspection subsystems described above, the electron beam inspection subsystem configurations described herein can be altered to optimize the performance of the inspection subsystem as is typically performed when designing a commercial inspection system. In addition, the systems described herein can be implemented using existing inspection systems, e.g., tools commercially available from KLA, e.g., by adding the functionality described herein to the existing inspection systems. For some such systems, the methods described herein can be provided as optional functionality of the system (e.g., in addition to other functionality of the system). Alternatively, the systems described herein can be designed "from scratch" to provide a new system.

[0053] Although the inspection subsystems are described above as optical or electron beam inspection subsystems, the inspection subsystems can be ion beam inspection subsystems. Such inspection subsystems can be configured as shown in Figure 1 U.S. Patent No. 6,888,945, the disclosure of which is incorporated herein by reference in its entirety. In addition, the inspection subsystems can include any other suitable ion beam systems, e.g., systems included in commercially available focused ion beam (FIB) systems, helium ion microscopy (HIM) systems, and secondary ion mass spectrometers (SIMS) systems.

[0054] The inspection subsystems described herein can be configured to generate outputs, e.g., images, of a sample using multiple modes. Generally, a "mode" is defined by the values of the parameters of the inspection subsystem used to generate the outputs and / or images of the sample (or the outputs used to generate the images of the sample). Thus, modes can differ in the values of at least one of the parameters of the inspection subsystem (other than the location on the sample where the output is generated). For example, in an optical subsystem, different modes can use different wavelengths of light for illumination. As described further herein, modes can differ in the illumination wavelength(s) (e.g., by using different light sources, different spectral filters, etc. for different modes). In another example, different modes can use different illumination channels of the optical subsystem. For example, as described above, the optical subsystem can include more than one illumination channel. Thus, different illumination channels can be used for different modes. Modes can also or instead differ in one or more collection / detection parameters of the optical subsystem. Modes can differ in any one or more alterable parameters of the inspection subsystem (e.g., illumination polarization(s), angle(s), wavelength(s), etc., detection polarization(s), angle(s), wavelength(s), etc.). The inspection subsystem can be configured to scan the sample using different modes in the same scan or different scans, e.g., depending on the ability to simultaneously scan the sample using multiple modes.

[0055] In a similar manner, the output generated by the electron beam subsystem can include outputs (e.g., images) generated by the electron beam subsystem using two or more different values of a parameter of the electron beam subsystem. The multiple modes of the electron beam subsystem can be defined by the values of the parameter of the electron beam subsystem used to generate the output and / or images of the specimen. Thus, the modes can differ in the value of at least one of the electron beam parameters of the electron beam subsystem. For example, different modes can use different angles of incidence for illumination.

[0056] As described above, the inspection subsystem is configured for enabling energy (e.g., light, electrons, etc.) to scan across the physical version of the specimen, thereby generating an output of the physical version of the specimen. In this way, the inspection subsystem can be configured as a "real" subsystem rather than a "virtual" subsystem. However, the storage medium (not shown) and Figure 2 The computer subsystem(s) 102 shown in FIG. 1 can be configured as a "virtual" system. In particular, the storage medium and computer subsystem(s) can be configured as a "virtual" inspection system as described in commonly assigned U.S. Patents 8,126,255 to Bhaskar et al., issued February 28, 2012, and 9,222,895 to Duffy et al., issued December 29, 2015, both of which are incorporated by reference as if fully set forth herein. Embodiments described herein can be further configured as described in these patents.

[0057] Non-patterned array (NPA) reference generation is an important part of array inspection. NPA inspection methods are used to perform array inspection on highly repetitive but indiscernible pattern regions. Thus, for NPA inspection, there is no pattern in the optical (or other) image. One issue that affects NPA inspection sensitivity is color variation (CV). CV can be manifested in relatively low frequency gray scale variations in the input NPA image, especially near the corner or edge regions. CV poses a significant challenge to NPA inspection. A relatively high number of nuisance is another significant challenge to NPA inspection. Embodiments described herein provide new methods for addressing both of the above two challenges via a new reference generation method that handles CV and a new detection strategy that suppresses nuisance.

[0058] Figure 3These challenges are illustrated and how the embodiments described herein are designed to overcome them. In this figure, image 200 is a test image generated by an optical inspection subsystem, such as the optical inspection subsystems described herein, on a sample. Reference image 202 is a reference image generated by current methods of use for NPA defect detection, and difference image 204 is generated by subtracting reference image 202 from test image 200. As can be seen by comparing reference image 202 to test image 200, reference image 202 is much quieter near the left and right edges of the cell regions in the reference image (the lighter areas in the reference image) than near the same edges in the test image. These differences between the test and reference images can arise when, for example, the test image contains noise in some areas such as near the edges, and the reference image generation fails to account for this noise.

[0059] These differences between the reference and test images manifest in the difference image as darker areas located near the left and right edges of the cell regions in the difference image. Then, when a defect detection method is applied to this difference image, these noise differences between the reference and test images can be falsely detected as defects. If the defect detection threshold is increased to avoid the detection of this noise, then DOIs present in the test image can not be detected. Both of these scenarios are disadvantageous for several obvious reasons.

[0060] In contrast, reference image 206 illustrates an embodiment of a reference image that can be generated by the embodiments described herein. Difference image 208 is generated by subtracting reference image 206 from test image 200. As can be seen by comparing reference image 206 to test image 200, reference image 206 and test image 200 contain similar noise near the left and right edges of the cell regions in the test image (the lighter areas). These similarities in noise characteristics become possible because performing the reference generation described herein is such that noise in the test image can be much better replicated in the reference image, which in turn results in a much quieter difference image, as can be seen by comparing difference image 208 to difference image 204. Thus, defect detection performed on difference image 204 and difference image 208 with the same parameters (e.g., threshold) will not detect as many nuisance in difference image 208 as compared to difference image 204. In addition, due to the relatively quieter nature of difference image 208, more sensitive inspection can be performed on difference image 208 without detecting an unacceptably high number of nuisance, while also detecting more DOIs, including DOIs with relatively weak signals.

[0061] One embodiment of a system configured for detecting defects on a sample includes an inspection subsystem configured for generating images of the sample, which can include any of the inspection subsystems described herein. The images include, but are not limited to, a test image and two or more other images corresponding to the test image. The test image and the two or more other images can be generated in an area on the sample that includes only indistinguishable, repeating device patterns. In other words, the images used in the embodiments described herein do not contain images of device patterns formed in the area of the sample in which the images are generated. In one such example, test image 200 is an image of two cell areas on a sample containing patterned features formed therein, but none of the patterned features are resolved in this image. Although the embodiments described herein are particularly suitable and advantageous for NPA inspection, the embodiments can also be used for inspection performed with images in which the patterned features are resolved.

[0062] The terms "image" and "image frame" can be used interchangeably herein. In general, an "image frame" is defined as a collection of pixels in an image that are processed together for purposes of, for example, defect detection. Thus, the size of an image or image frame can vary depending on certain characteristics of the inspection subsystem or computer subsystem. In some inspection use cases, an image can actually be composed of multiple image frames, but the embodiments described herein are not inherently limited in the size of the image that can be processed.

[0063] The term "job" as used herein is defined as a number of image frames processed together by a computer subsystem to detect defects on a sample. In general, all of the images generated during an inspection process cannot all be processed together (e.g., even if possible, it is often not time or cost efficient). Thus, all of the images are divided into jobs of image frames that can be processed together more cheaply and timely for defect detection.

[0064] Acquiring an image can include generating the image with an inspection subsystem configured as described herein. This image acquisition can be done when the computer subsystem and the inspection subsystem are coupled in one tool and possibly when the defect detection described herein is performed on the tool and / or when the image is generated. In other examples, the computer subsystem can acquire the image from another method, system, or storage medium. For example, the computer subsystem and the inspection subsystem can or can not be coupled into a single tool, and the inspection subsystem, the computer subsystem, or another computer subsystem can store the image generated by the inspection subsystem. Then, the computer subsystem can acquire the image from the storage medium in which the image is stored. This image acquisition can be done when the computer subsystem performs the steps described herein outside of a tool and / or after all (or at least some) of the images have been generated. Each of the elements described above can be configured as described and shown further herein.

[0065] In one embodiment, the computer subsystem is configured to divide an image corresponding to a region of interest on a sample into a test image corresponding to a test cell and two or more other images corresponding to two or more other cells adjacent to the test cell. Each region of interest can be divided into many cells, as with the currently used NPA defect detection method. But instead of placing all cells into some non-overlapping groups, as with the currently used method and system, the computer subsystem can form a group containing several adjacent cells for each cell. The CV compensation method can be performed to provide NPA reference generation by grouping cells in this way. By reorganizing the cell grouping, a clearer difference image can be achieved.

[0066] Figure 3 An example of an image of a region of interest and different embodiments of dividing the region of interest image into a test image of a test cell and other images corresponding to other cells adjacent to the test cell is illustrated. In particular, image 300 is a hypothetical image of a region of interest. While this image is only a single grayscale value image and thus does not truly represent an image that can be generated by an inspection subsystem configured as described herein, it accurately represents an NPA image in that none of the patterned device features formed in the region of interest on the sample are resolved in the image. There can be multiple instances of the same region of interest on a sample, and image 300 represents an image of only one such instance.

[0067] Each region of interest can be divided into several cells along only a horizontal or vertical direction, etc. As Figure 3 As shown in FIG. 3, if the region of interest is divided along the horizontal direction, then the test cell image can be image portion 302 and the other cell images adjacent to the test cell can be image portions 304, 306, 308, and 310. If the region of interest is divided along the vertical direction, then the test cell image can be image portion 312 and the other cell images adjacent to the test cell can be image portions 314, 316, 318, and 320. In this way, the adjacent cells are the cells to the left and / or right of one target cell (if the region of interest is divided along the horizontal direction) or the top and / or bottom of a target cell (if the region of interest is divided along the vertical direction). The computer subsystem can form adjacent cell groups for each test cell target. In other words, if an inspection is performed for a test cell corresponding to image portion 304, then a different group of image portions than described above can be selected as the cells adjacent to that test cell.

[0068] Thus, as Figure 3As shown, cells adjacent to the test cell do not necessarily need to be adjacent to the test cell and can be separated from the test cell by one or more other cells. However, cells adjacent to the test cell can be selected as adjacent cells. Additionally, cells adjacent to the test cell can be included in the cells on both sides of the test cell, but this is not required. Furthermore, although the number of cells adjacent to the test cell is... Figure 4 The image is presented as four units, but the number of adjacent units can vary considerably and depends on several factors, such as the degree of color change between units and the amount of actual image data that can be processed simultaneously by the computer subsystem. Different numbers of adjacent units can also be selected for units in the horizontal and vertical directions. The same region of interest can be divided along the horizontal and vertical directions, and the resulting test units can be processed separately. For example, reference generation and defect detection can be performed on test unit 302 and its adjacent units 304, 306, 308, and 310, and reference generation and defect detection can be performed separately on test unit 312 and its adjacent units 314, 316, 318, and 320.

[0069] In some embodiments, before calculating the first and second candidate reference images as further described herein, the computer subsystem is configured to apply CV compensation to the test image and two or more other images. For example, a first step may be to add CV compensation to the reference generation process. A CV compensation may be applied to all cells in a group before creating the reference cells. For example, CV compensation may be applied to the cells before using cells 304, 306, 308, and 310 to generate the reference cell image. After this step, the CV is inherently integrated into the final reference image subtracted from the test image, thereby producing a sharper final difference image. In this way, the embodiments described herein provide a CV compensation method for NPA reference generation. A sharper difference image is achieved by reorganizing cell groupings and compensating CV.

[0070] CV allows each cell to have a different gray level, which limits cell-based noise removal capabilities unless compensation is applied. A particularly suitable way to perform CV compensation is to set the gray level in a cell as a variable x. i Next, several unknown parameters p can be used. n Create a special function f(x) i |p1, p2, ...). This function can be applied to each pixel position. It can be achieved by minimizing the target cell and operating the function ||x. t -f(x iThe parameters for each unit are calculated based on the differences between units following |p1, p2, ...)||. Applying this function to the corresponding unit compensates for the CV. In this way, the computer subsystem can fit the grayscale changes within each unit. The basic fitting function can be (for example) piecewise linear. After fitting is complete, the fitting result f(x) is calculated. i |p1, p2, ...) become the local reference images within each unit. Since the fitted reference images are essentially very similar to the test images, the CVs embedded in the original test images are removed or significantly reduced.

[0071] As mentioned above, CV is integrated into the reference unit image. One reason for this is the CV compensation described above. Specifically, the goal of better reference generation described herein is to obtain a clearer difference image. In the past, CV compensation was not performed or was not carried out, meaning that CV would remain in the difference image and could make the difference image too noisy for defect detection. However, by performing CV compensation on the image used to generate the reference image, the CV compensation is integrated into any reference image generated from it, and then said reference image can be used to generate a clearer difference image. In other words, CV is inherently handled during the reference image generation process. In this way, the CV in the reference image will compensate for the CV in the test image. CV compensation can be inherently integrated into reference generation for all reference images generated as described herein.

[0072] The embodiments described herein generate multiple candidate reference images for each test image and blend them together as further described herein to further improve CV processing capabilities. A computer subsystem is configured to compute first and second candidate reference images from different combinations of the test image and at least two of two or more other images. For example, as... Figure 5 As shown in step 400, the computer subsystem can compute a candidate reference image. The computation of any of the candidate reference images described herein can be performed by combining two or more images, for example, using linear combination, median calculation, averaging, or another currently used method for generating computational references (CRs). In this way, computing a candidate reference image from two or more images can produce an image with characteristics different from one or more of the input images. In other words, pixels in the computed candidate reference image may have image characteristics, such as grayscale values, different from corresponding pixels in each of the images used to generate the computed candidate reference image. Therefore, the generation of a candidate reference image is fundamentally different from the generation of a final reference image, as further described herein.

[0073] In most cases, two or more candidate reference images can be computed for each test image. In other words, candidate reference images may not be used for more than one test image in a job. Instead, after grouping the images as further described herein, the computer subsystem will create at least two candidate reference images for each test image within each job. However, in other cases, candidate reference images may be reused for more than one test image.

[0074] In one embodiment, the first of a different combination of a test image and at least two of two or more other images used to calculate a first candidate reference image includes a portion of an image generated across the sample only in the x-direction. In another embodiment, the second of a different combination of a test image and at least two of two or more other images used to calculate a second candidate reference image includes an additional portion of an image generated across the sample only in the y-direction. Currently used NPA defect detection methods always generate references in a row-independent manner (i.e., using an image generated along the x-direction to generate a reference). The embodiments described herein can also employ the same strategy to form a candidate reference image. Alternatively, a similar operation can be applied in a column-independent manner to generate another candidate reference image (i.e., using an image generated along the y-direction to generate a candidate reference image). Generating two such candidate reference images provides several significant advantages further described herein.

[0075] In a further embodiment, one of the different combinations of the test image and at least two of two or more other images used to calculate the first and second candidate reference images includes all images generated on two or more dies on the sample in the image job. For example, additional references can be generated by considering all dies. In this context, "all dies" means all dies included in a job, which is the smallest unit of image processing. A job may contain at most all dies in a dies row or at least three dies. Figure 4 This illustration describes an example of a job comprising multiple cell regions for testing. Specifically, job 500 includes cell regions 502, 504, 506, and 508, each cell region potentially contained within a different die on a sample. In this embodiment, candidate reference images can be generated from all images of all cell regions included in job 500.

[0076] In this way, several candidate reference images (e.g., row-independent and column-independent references) can be generated from a single shrapnel image, while one or more other candidate reference images (e.g., candidate reference images generated from "all shrapnel images") can be generated from two or more shrapnel images. Generally, in the embodiments described herein, at least two candidate reference images are generated such that a final candidate reference image can be created from those two images. However, in many use cases, it would be advantageous to generate all three types of candidate reference images described herein (i.e., row-independent, column-independent, and multiple shrapnel candidate reference images). Two or more of these candidate reference images can be used to generate the final reference image. In examples where dual detection is performed, one or more of these candidate reference images can also be used as a second or additional final reference image. Such embodiments are further described herein.

[0077] In the currently used NPA defect detection, reference generation occurs within each individual frame. In other words, the reference generation for frame 3 does not require any input from frame 2 or frame 4. In contrast, in the embodiments described herein, both frame 3 and all other frames can be used to generate the reference image for frame 3. Using these images to generate candidate reference images can be advantageous, for example, when a cell corner region has a unique grayscale value relative to the rest of the cell region. Such unique grayscale values ​​can make it difficult to find a good matching reference image for every corner within each cell region. However, finding a good match from other corners of other cell regions can be significantly easier. By using all other frames in the job, better candidate reference images can be created, and these can be used to create a better final reference image.

[0078] The computer subsystem is also configured to select at least a portion of a first candidate reference image corresponding to a first portion of the test image and a portion of a second candidate reference image corresponding to a second portion of the test image. For example, such as Figure 6 As shown in step 402, the computer subsystem can be configured to select different portions of candidate reference images corresponding to different parts of the test image. As further described herein, embodiments are configured to provide a better reference image, which can then be used to generate a better difference image, thereby achieving less interference detection and / or more sensitive testing. To do this, the better reference will then be a reference that better matches the test image, particularly regarding noise in the test image. For example, if the noise in the reference image substantially matches the noise in the test image, then when the reference image is subtracted from the test image, that noise in the test image will be canceled out, thereby preventing it from being detected as interference and / or interfering with the detection of the DOI. Therefore, the objective of the selection step is to find the best-matching reference image from a set of candidate reference images on a portion-by-portion basis of the test image, which can be performed in several ways further described herein.

[0079] In one embodiment, selecting includes identifying which portions of the first and second candidate reference images best match different portions of the test image. Figure 2 This illustrates one embodiment of how this selection can be performed. In this embodiment, test image 600 is an example of a test image that can be generated for a region of interest on a sample. As... Figure 6 The test image shown herein contains a central portion 606, which is relatively quieter compared to portions 602 near the left edge and 604 near the right edge. These portions are relatively noisier compared to the central portion due to color variations that can occur near the edges of the region of interest image (for example). As further described above, if the reference image used for defect detection along with the test image does not contain similar noise, it will cause problems in defect detection. Therefore, a new method for generating the reference image described herein is created.

[0080] The selection step may include dividing the test image into an array of blocks, as shown in Figure 608. Each of these blocks may have the same predetermined characteristics, such as size, and is used only to divide the image into smaller portions that can be evaluated on an individual basis, as further described herein. The number of blocks into which the image is divided can vary (potentially very large) and can be determined based on several factors, such as the initial image size and the rate at which noise changes across the image (when it changes relatively rapidly, smaller blocks may be better suited to capturing different values ​​of noise on the same or similar scale of its change).

[0081] As mentioned above, the computer subsystem can identify portions of the candidate reference image that best match different parts of the test image. This means that even if each portion of the test image is processed individually, the correspondence between the test image portion and the reference image portion can be relaxed. In other words, the portion of the candidate reference image that best matches a part of the test image may not necessarily be limited to those portions of the candidate reference image that only have the same intra-image location as the portion of the test image.

[0082] To illustrate this concept, consider two instance candidate reference images, 614 and 616, which can be either of the candidate reference images generated in any of the manner described herein. Each of these candidate reference images can be divided into blocks in the same way as the test image, as shown by black lines superimposed on these images. The identification process can begin with the test image block located in the upper row of column 610 of the block. This test image block can be compared with each block in candidate reference image 614 to determine which blocks in this candidate reference image best match the test image block. The block of the candidate reference image that best matches the test image block can be saved, and then the same comparison process can be performed on the block of candidate reference image 616 to determine whether that candidate reference image contains even a better matching block. Then, the best matching block in candidate reference images 614 or 616 can be identified and saved to be included in the final reference image at the location of the test image block.

[0083] In this way, for each test image patch, all patches in all (or at least some) candidate reference images can be considered to find the best-matching image patch. This same process can then be performed on the next patch in the test image. In some cases, the same patch in one of the candidate reference images can be identified as the best match for more than one test image patch. By allowing any patch from any of the candidate reference images to be used as the best-matching patch for the test image patch, it is possible to create a better reference image than if only patches at the same location within the same image were considered. This method provides a better final reference image when each of the test image and candidate reference images exhibits noise with substantially different spatial or other characteristics.

[0084] In another embodiment, selection involves identifying which portions of the first and second candidate reference images best match the corresponding portions of the test image. In this way, unlike the embodiment described above, for each test image block, only candidate reference image blocks having the same intra-image location can be considered. This embodiment may be faster than the embodiment described above because the number of blocks considered for any given test image block will be limited to the number of generated candidate reference images. However, unlike the embodiment described above, this embodiment will be less flexible or exhaustive in the blocks considered, and is therefore more suitable for examples where at least one of the known or anticipated candidate reference images contains noise with similar spatial and possibly other characteristics to the test image.

[0085] To illustrate this concept, image 608, as a block-divided test image 600, can be used again with candidate reference images 614 and 616. The identification step can begin with the test image block located in the upper row of column 610 of the block. This block can be compared with the image blocks located in the upper row of column 618 of candidate reference image 614 and the image blocks located in the upper row of column 626 of candidate reference image 616 to determine which of these blocks best matches the test image block. If no other candidate reference images have been generated for this test image, then other candidate reference image blocks may not be considered for this test image block. As shown by images 608, 614, and 616, the image block located in the upper row of column 618 of candidate reference image 614 is a much better match than the image block located in the upper row of column 610 of the test image block, compared to the image block at the same position in candidate reference image 616. Next, the best-matching candidate reference image block identified through this step can be saved to generate the final reference image, as further described herein.

[0086] Next, the identification step can continue with the test block located in the second row from the top of column 610 of the block. This block can be compared with the image block located in the second row from the top of column 618 of candidate reference image 614 and the image block located in the second row from the top of column 626 of candidate reference image 616 to determine which of these two blocks best matches the test image block. Moreover, if no other candidate reference images have been generated for this test image, then other candidate reference image blocks may not be considered for this test image block. As shown by images 608, 614, and 616, the image block located in the second row from the top of column 618 of candidate reference image 614 is a better match for the test image block located in the second row from the top of column 610 compared to the image block at the same position in candidate reference image 616. The best matching candidate reference image block identified by this step can also be saved to generate the final reference image, as further described herein. Next, the identification step can continue for all remaining test blocks in image 608.

[0087] Regardless of the method used for Figure 4The images shown are compared, and each candidate reference image contains noise different from the test image. Specifically, the noise at the left and right edges of candidate reference image 614 (displayed by the darker parts of the image) extends further into the candidate reference image than the noise at the left and right edges of the test image (also displayed by the darker parts of the test image). Specifically, columns 618, 620, 622, and 624 of the blocks in candidate reference image 614 contain noise, while only columns 610 and 612 of the blocks in test image 608 contain noise. In contrast, candidate reference image 616 has almost no noise at its left and right edges (as shown by the absence of any relatively dark areas in the candidate reference image). Specifically, columns 626 and 628 of candidate reference image 616 have no or almost no noise, while the corresponding columns 610 and 612 of image 608 clearly contain noise. Therefore, if either candidate reference image is used together with the test image for defect detection, either reference image can lead to a large number of irritating detections or other problems in defect detection. However, by selectively identifying portions of the candidate reference image to be included in the final reference image based on the degree of matching between the candidate reference image and the test image, a final reference image 634 that is obviously noisy and matches the test image 600 better can be produced.

[0088] The computer subsystem is further configured to combine selected portions of the first and second candidate reference images without modifying selected portions of the first and second candidate reference images to generate a final reference image. For example... Figure 6 As shown in step 404, the computer subsystem can combine selected portions to generate a first final reference image, which may be a unique final reference image in the case of single detection or one of two final reference images in the case of dual detection. For example, the first final reference image can be generated by combining selected portions of those images without modifying selected portions of the first candidate reference image 614 and the second candidate reference image 616. Figure 6 The final reference image 634 shown in the document.

[0089] Specifically, such as Figure 6 As shown, for the image patch in column 610 of image 608, the image patch in column 618 of candidate reference image 614 is a much better match than the image patch in column 626 of candidate reference image 616. Therefore, the image patch in column 636 of the final reference image 634 can be the image patch in column 618. Similarly, for the image patch in column 612 of image 608, the image patch in column 620 of candidate reference image 614 is a much better match than the image patch in column 628 of candidate reference image 616. Therefore, the image patch in column 642 of the final reference image 634 can be the image patch in column 620.

[0090] In contrast, the image blocks in columns 622 and 624 of candidate reference image 614 do not match the corresponding columns of the image blocks in image 608 very well. However, the image blocks in columns 630 and 632 of candidate reference image 616 are much better matches of the corresponding columns of the image blocks in image 608. Therefore, these image blocks can be included in the final reference image 634 as columns 638 and 640, respectively. The remaining four center columns of the blocks in image 608 match very well with the four center columns of the blocks in both candidate reference images. Therefore, the four center columns of the blocks in either of the candidate reference images can be used as the four center columns of the blocks in the final reference image 634.

[0091] In this way, multiple candidate reference images are blended together to create a final reference image that can be block-based. The image is cut into relatively small blocks. Each block is used, and a test image is compared with each individual reference image. The reference image block that best matches the test image block is selected to be included in the final reference image.

[0092] As further described above, the combination step is performed to generate the final reference image without modifying selected portions of the first and second candidate reference images. Therefore, this reference image generation differs significantly from other computational reference (CR) generation methods. Specifically, the final reference image generation described herein can be considered a "pick-and-place" operation, where, once different portions of the candidate reference images are identified and selected as described above, they are placed in the positions of the portions of the test image for which they are selected. Then, the selected portions of the different candidate reference images can be "stitched together" to some extent to form the final reference image. However, these steps are performed without modifying the images themselves. In other words, the selected image portions are not modified during the actual combination of the images.

[0093] In contrast, in current CR generation methods, combining two or more images to produce a CR involves combining the images such that the resulting image data differs from the image data of the original images. For example, a linear combination operation can be used to generate a CR, and thus one or more pixels in the resulting CR may differ from the same one or more pixels in all the images used to generate the CR. This modification of at least some pixels of the image is essentially the essence of current CR methods. Specifically, the goal of current CR methods is typically to generate a reference image that is as still as possible, making it essentially a "defect-free" image.

[0094] That objective is fundamentally different from the objective of the combination step described in this paper, which aims to produce a final reference image that is as noisily similar as possible to the test image. This is due to... Figure 6The test image 600 is illustrated herein, which, as further described herein, contains relatively significant noise near its left and right edges. If a significantly noise-free reference image, such as candidate reference image 616, is used to generate a difference image of the test image, then the noise at the left and right edges of test image 600 can be detected as a defect. In contrast, the final reference image 634 can be generated by the embodiments described herein and may contain relatively significant noise similar to that of the test image having the same or substantially the same characteristics. In this way, this final reference image is by no means noise-free, but rather, when subtracted from the test image to generate the difference image, results in a substantially silent difference image that can be used to detect DOIs containing DOIs with relatively low signal strength without detecting many disturbances. Therefore, the embodiments described herein produce a final reference image that is fundamentally different from the CRs currently generated and used, which provides significant advantages to the embodiments described herein.

[0095] Instead of mixing multiple candidate reference images to produce the different final reference images described above, the comparison steps described above can be performed on a portion-by-portion basis of the test image to determine which of the candidate reference images best matches the test image. In this way, after multiple candidate reference images have been generated for each test image, the best reference image can be selected from the multiple reference images. For example, the best reference image is selected where a portion of the test image that is not a good match for the corresponding portion of the test image is different from two of the candidate reference images. Figure 4 In some of the examples shown, there may be cases where one of the candidate reference images generated in one of the various methods described herein has noise characteristics substantially similar to those of the test image. Specifically, if image 634 is the third candidate reference image rather than the final reference image as described above, then it is clear that image 634 matches the noise characteristics of the test image 600 much better than both the other candidate reference images 614 and 616. Therefore, it may not be necessary to combine different portions of the different candidate reference images, since a suitable final candidate reference image already exists in candidate reference image 634.

[0096] In some embodiments, the computer subsystem is configured to compute a third candidate reference image from different combinations and to select at least a portion of the third candidate reference image corresponding to a third portion of the test image, and the combination step includes combining selected portions of the first, second, and third candidate reference images without modifying the selected portions of the first, second, and third candidate reference images to thereby generate a final reference image. For example, a final reference image can be constructed from three candidate reference images for each test image. One of the references can be generated in a row-independent manner, another in a column-independent manner, and a third in a reference can be generated using more than one die, thereby giving the resulting final reference image a three-dimensional (3D) nature. In this way, the embodiments described herein may be referred to as 3DNPA. The three candidate reference image generation and merging method described herein provides advantages for the embodiments described herein. For example, better noise reduction can be obtained by generating three candidate reference images from row-independent, column-independent, and multi-die methods and then merging all three reference images. These steps can be performed as described above, except that more candidate reference images are used.

[0097] In one embodiment, following the combination step, the computer subsystem is configured to apply color variation compensation to the result of the combination. For example, even if combining different portions of different candidate reference images into a final reference image does not itself involve modifying selected portions of the different candidate reference images, one or more additional image processing steps may be performed on the final reference image before it is used for defect detection. As described above, CV compensation may be applied to the candidate reference images before the step of generating the final reference image, meaning that the resulting final reference image will inherently have the same CV compensation. Therefore, it may not be necessary to perform an additional CV step on the final reference image, and whether to perform this step can be evaluated on a case-by-case basis. In any case, any CV compensation applied to the resulting final reference image may be performed as further described above.

[0098] The computer subsystem is further configured to generate a difference image by comparing the test image with a final reference image. For example, such as Figure 4 As shown in step 408, the computer subsystem can be configured to generate a difference image 1 by subtracting the final reference image 1 from the test image. Generating the difference image can be performed in any suitable manner known in the art. Specifically, subtracting the final reference image from the test image can be performed in any suitable manner.

[0099] The computer subsystem is further configured to detect defects in the test image by applying a defect detection method to the difference image. For example, such as Figure 4As shown in step 412, the computer subsystem can be configured to perform detection using the difference image 1 as input. In perhaps the simplest implementation, detecting a defect in step 412 may involve comparing the difference image pixels or signals with a threshold and determining that any pixel or signal with a value higher than the threshold is a defect and any pixel or signal without a value higher than the threshold is not a defect. However, many more sophisticated defect detection methods have been developed in the art, and such methods can be used in step 412. In other words, the difference image generated by the embodiments described herein can be used for defect detection in the same manner as any other difference image used for defect detection. Furthermore, the defect detection method may include any defect detection method known in the art, such as the MCAT defect detection method, which is a defect detection algorithm used by some inspection systems commercially available from KLA-Tex, or another suitable commercially available defect detection method and / or algorithm. In the case of a single detection method, the output of this step may be the final defect result 418.

[0100] The embodiments described herein can be configured for both single-detection and dual-detection modes. Both detection modes can be based on multiple reference generation performed as described herein. In single-detection mode, only one final reference image is required and used to generate a single difference image for each test image. Defect detection is then performed using the single difference image. More than one final reference image can be generated for any test image and used for dual-detection. For example, the embodiments described herein implement dual-detection of NPA defect detection, which is not supported in currently used NPA defect detection methods. To achieve dual-detection, two final reference images can be generated from candidate reference images instead of one final reference image.

[0101] Several embodiments can be configured and used for this defect detection. In one embodiment, a computer subsystem is configured to repeatedly select and combine images to generate additional final reference images. For example, using the steps described herein, two equally clear but distinct reference images can be constructed based on three candidate reference images. In this way, the embodiments described herein advantageously provide a dual-reference generation method for interference reduction.

[0102] In one of these embodiments, Figure 4 Step 402 shown can be performed twice: once to select different portions of the candidate reference image for the first final reference image and again to select other different portions of the candidate reference image for the second final reference image. The two selection steps can be performed in one or more of the manner described above. Furthermore, the selection steps performed for each final reference image can be performed in the same or different manners. For example... Figure 5As shown in step 406, the computer subsystem can combine selected portions to generate a second final reference image. Specifically, the selection step can generate a first set of selected portions for generating the first final reference image and a second set of selected portions for generating the second final reference image. The first and second sets of selected portions should include at least some different portions of either portion of the test image; otherwise, the second final reference image is redundant. The first set of selected portions can be used to generate the first final reference image in step 404, and the second set of selected portions can be used to generate the second final reference image in step 406. The combination step can be performed in other ways, as further described herein.

[0103] In another embodiment, the computer subsystem is configured to select one of the first and second candidate reference images as an additional final reference image. For example, one final reference image may be generated in the manner described above, while the other final reference image may be simply one of the candidate reference images, rather than two final reference images that are combinations of different portions of different candidate reference images. The best candidate reference image used as the second final reference image may be selected as further described herein.

[0104] In a further embodiment, the computer subsystem is configured to select additional candidate reference images as additional final reference images. For example, the additional candidate reference images may be available and / or specifically generated for consideration as a second final reference image. In one such example, the computer subsystem may be configured to acquire images of adjacent regions of interest in the same die or adjacent dies. In this way, this image may not be a CR (Crew Detection) image, as it may not be generated by combining two or more images. Instead, this image may simply be an image generated by the inspection subsystem, possibly applying one or more image processing steps (e.g., CV compensation, high-pass filtering, and the like). This image can then be compared with one or more of the test image and / or other candidate reference images to determine which one or both of the images are best suited for defect detection. In any case, multiple candidate reference images may be generated for each test image, and the computer subsystem may select the two best reference images, rather than the single best reference image, for defect detection.

[0105] In another instance, the two final reference images can be derived from a selection process performed on three generated candidate reference images. Specifically, the two best candidate reference images can be selected for dual detection. Special attention can be paid to ensure that both selected reference images have good quality. For example, in cell regions (e.g., Figure 4In the corner regions of the cell area shown, the best reference image is typically derived from a third reference image (e.g., an inter-wafer operation type reference). A second-best reference image may have significantly degraded quality in the corner regions (i.e., it may be very different from the same region in the test image). In such cases, the computer subsystem can force the two selected reference images used for dual detection to be of the same type, meaning they come from the best multi-wafer candidate reference images. In this way, the embodiments described herein ensure relatively high sensitivity in dual detection.

[0106] Dual final reference image generation can activate dual detection in NPA defect detection. In any of the embodiments described above, the computer subsystem can be configured to generate an additional difference image by comparing a test image with an additional final reference image and to detect defects in the test image by applying a defect detection method to the additional difference image. Regardless of which additional final reference image is generated and selected as used in the embodiments described herein, the computer subsystem can also subtract the final reference image 2 (e.g., from the test image) from the final reference image. Figure 4 The steps (as shown in step 410) and performing detection (as shown in step 414 using difference image 2 as input) produce difference image 2. These steps can be performed as further described herein.

[0107] A defect detection method determines that a defect exists at a location in the test image only if it detects a defect at the corresponding location in both the difference image and additional difference images. For example, such as Figure 4 As shown in step 416, defects detected by both detection steps 412 and 414 can be combined into a set of defects detected in the two comparisons. This set of combined defects can then be analyzed to generate... Figure 7 The final defect result 418 shown herein contains only defects detected at corresponding locations in the first and second difference images. Specifically, the computer subsystem can compare defects detected in the difference images, and any commonality among the defects will be designated as a real defect, while any difference between the defects will be designated as an artifact or spurious object. The computer subsystem can compare the results of different defect detection steps performed using different difference images in any suitable manner, such that the results at corresponding locations in the different difference images can be compared to determine whether the detected defect is a real defect or an artifact or spurious object. In this way, the defect detection method is preferably configured such that a defect is reported only if it is detected at the same location or the same pixel in the two difference images. Thus, the embodiments described herein can perform double detection that helps suppress spurious object detection. In other words, CR double detection can minimize the impact of a single spurious object or artifact in the reference image on the defect detection result through arbitration.

[0108] The embodiments described herein also advantageously provide a novel fragment-based detection strategy for suppressing interference detection. In one embodiment, a computer subsystem is configured to segment an image of a region of interest corresponding to a cell region on a sample into a test image corresponding to a test cell and two or more other images corresponding to two or more other cells adjacent to the test cell, and the defect detection method divides the region of interest into different fragments of the cell region and detects defects in each of the different fragments separately. In this way, the embodiments described herein can add image segmentation to aggregate different locations into a separate detection flow. The computer subsystem can divide the entire image into different regions and can determine the size of each region based on interference differences. During the detection phase, each region can have independent parameters to accommodate different interference control requirements.

[0109] In one embodiment, the different segments include a first segment for the top and bottom edges of the cell area, a second segment for the left and right edges of the cell area, a third segment for the center of the cell area, and a fourth segment for the corners of the cell area. Figure 7 This embodiment illustrates a segment-based detection method for improving harassment reduction. In this embodiment, the entire image of the region of interest 700 is divided into different segments. The segments include a center segment 700a, left / right edge segments 700b, top / bottom edge segments 700c, and corner segments 700d. Each example of this region of interest on the sample can be divided into these four segments.

[0110] Each of the steps described herein can be performed jointly or independently for each of the segments. For example, for a test image corresponding to the entire region of interest, a final reference image can be generated as described herein, and a difference image for the entire region of interest can be generated by subtracting the final reference image from the test image. The difference image can then be divided into corresponding portions of each of the different segments, and defect detection can be performed separately for each portion of the difference image.

[0111] In one of these examples, Figure 7 In the diagram 702 shown, different noise clouds can be generated separately and independently for each different segment within the region of interest. Specifically, noise cloud 702a can be generated only for the central segment 700a, noise cloud 702b only for the left / right edge segments 700b, noise cloud 702c only for the top / bottom edge segments 700c, and noise cloud 702d only for the corner segments 700d. Defect detection can then be performed separately for each noise cloud. During the detection phase, each area can have independent parameters to accommodate different nuisance control requirements. In this way, individual parameters can be used to form separate noise clouds for each segment to achieve an optimal balance between DOI detection and nuisance suppression.

[0112] It can generate multi-noise cloud charts for each test image, for example Figure 4 The chart shown in Figure 702 illustrates this. In other words, the noise cloud shown in Figure 702 is only for a single test image, and similar charts can be generated for other test images. Furthermore, the noise cloud shown in Figure 702 is based on a difference image generated for a single test image. A separate noise cloud chart can be generated for another difference image generated for the same test image (e.g., in dual detection, where multiple difference images are generated for a single test image).

[0113] Any of the computer subsystems described herein can produce inspection results, which may include the results of any of the steps described herein. The inspection results may include information about the detected defects, such as the defect ID, location, etc., size, detection score, information about defect classification (e.g., category label or ID), or any such suitable information known in the field. The computer subsystem may produce the defect results in any suitable manner. The defect results may have any suitable form or format, such as a standard file type. The computer subsystem may produce and store the results, such that the results can be used by the computer subsystem and / or another system or method to perform one or more functions of a sample or another sample of the same type.

[0114] The computer subsystem can be configured to store information about detected defects in any suitable computer-readable storage medium. The information may be stored together with 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 information has been stored, it 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.

[0115] The results and information generated by performing inspections on samples can be used in various ways through the embodiments and / or other systems and methods described herein. Such functionality includes, but is not limited to, modifying processes, such as manufacturing processes or steps, that have been or will be performed on the inspected sample or another sample in a feedback or feedforward manner. For example, a computer subsystem can be configured to determine one or more modifications to the process that has been or will be performed on a sample inspected as described herein, based on several detected defects. Process modifications may include any suitable changes to one or more parameters of the process. The computer subsystem preferably determines those modifications such that defects on other samples to which a corrective process is performed can be reduced or prevented, defects on the sample can be corrected or eliminated in another process performed on the sample, defects can be compensated for in another process performed on the sample, etc. The computer subsystem can determine such modifications in any suitable manner known in the art.

[0116] Next, those changes can be sent to a semiconductor manufacturing system (not shown) or to a storage medium (not shown) that can access the computer subsystem and the semiconductor manufacturing system. The semiconductor manufacturing system may or may not be part of the system embodiments described herein. For example, the computer subsystem and / or inspection subsystem described herein may be coupled to the semiconductor manufacturing system via one or more common elements (e.g., housing, power supply, sample handling device or mechanism, etc.). The semiconductor manufacturing system may include any semiconductor manufacturing system known in the art, such as lithography tools, etching tools, chemical mechanical polishing (CMP) tools, deposition tools, and the like.

[0117] The embodiments described herein offer several advantages over other methods and systems used for detecting defects on samples. For example, the embodiments described herein provide better reference generation to reduce inspection noise in the presence of color variations and offer new detection strategies to further suppress interference. Additionally, the embodiments described herein provide new reference generation methods and detection strategies in non-patterned areas, which provide better difference image generation with less noise and therefore offer higher sensitivity and stronger interference suppression capabilities in the presence of color variations.

[0118] The advantages described above are achieved through several important new features of the embodiments described herein. For example, the embodiments described herein enable the new possibility of simultaneously generating two or even three candidate reference images for any given test image. This capability is novel for both indistinguishable patterned features and distinguishable features. For distinguishable repeating cell regions, unlike the embodiments described herein, currently used inspection systems and methods typically generate reference images only along the repeating direction. The multi-candidate reference image mixing process described herein is also useful for inspecting indistinguishable patterned regions as well as other types of regions. Furthermore, the embodiments described herein support single detection using a single, optimal final reference image and dual detection using two optimal reference images generated in any manner described herein. The embodiments described herein are not specific to any particular defect detection method and can be used to generate inputs suitable for any defect detection method known in the art.

[0119] Each of the embodiments of the system described above can be combined together to form a single embodiment. In other words, unless otherwise stated herein, no system embodiment is mutually exclusive with any other system embodiment.

[0120] Another embodiment relates to a computer-implemented method for detecting defects on a sample. The method includes acquiring images of a sample generated by an inspection subsystem, the images including a test image and two or more other images corresponding to the test image. The method further includes calculating first and second reference images (e.g., in...) from different combinations of at least two of the test image and the two or more other images. Figure 4 In step 400 shown in the figure). The method also includes selecting at least a portion of a first candidate reference image corresponding to a first part of the test image and a portion of a second candidate reference image corresponding to a second part of the test image (as shown in the figure). Figure 4 In step 402). Additionally, the method includes combining selected portions of the first and second candidate reference images without modifying selected portions of the first and second candidate reference images to generate a final reference image (as in...). Figure 4 In step 404). The method also includes generating a difference image by comparing the test image with a final reference image (as in...). Figure 4 In step 408). Additionally, the method includes detecting defects in the test image (such as in...) by applying a defect detection method to the difference image. Figure 8 (as shown in step 412). The acquisition, calculation, selection, combination, generation, and detection steps are performed by a computer subsystem coupled to an inspection subsystem that can be configured according to any of the embodiments described herein.

[0121] Each of the steps in the method may be performed as further described herein. The method may also include any other steps that may be performed by the verification subsystem and / or computer subsystem described herein. Furthermore, the method described above may be performed by any of the system embodiments described herein.

[0122] Additional embodiments relate to a non-transitory computer-readable medium storing program instructions executable on a computer system to perform a computer-implemented method for detecting defects on a sample. Figure 8 An example of this embodiment is shown below. Specifically, as... ​ As shown herein, the non-transitory computer-readable medium 800 contains program instructions 802 executable on a computer system 804. A computer-implemented method may include any steps of any of the methods described herein.

[0123] Program instructions 802 for implementing the methods described herein may be stored on a computer-readable medium 800. 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.

[0124] Program instructions can be implemented in any of a variety of ways, including procedural, component-based, and / or object-oriented techniques. For example, program instructions can be implemented using ActiveX controls, C++ objects, JavaBeans, Microsoft Foundation Classes (“MFC”), SSE (Streaming SIMD Extensions), or other technologies or methodologies, depending on the desired approach.

[0125] Computer system 804 may be configured according to any of the embodiments described herein.

[0126] In view of this description, further modifications and alternative embodiments of various aspects of the invention will be apparent to those skilled in the art. For example, methods and systems for detecting defects on samples are provided. Therefore, this description should be construed as illustrative only and for the purpose of teaching those skilled in the art the general manner in which the invention will be practiced. It should be understood that the forms of the invention shown and described herein should be considered as the presently preferred embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed, and certain properties of the invention may be utilized independently, all of which will be apparent to those skilled in the art upon benefiting from the description of the invention. Changes may be made to the elements described herein without departing from the spirit and scope of the invention as set forth in the appended claims.

Claims

1. A system configured for detecting defects on a sample, comprising: an inspection subsystem configured for generating images of a sample, the images including a test image and two or more other images corresponding to the test image; and a computer subsystem configured for: computing first and second candidate reference images from different combinations of at least two of the test image and the two or more other images; selecting at least a portion of the first candidate reference image corresponding to a first portion of the test image and a portion of the second candidate reference image corresponding to a second portion of the test image; combining the selected portions of the first and second candidate reference images without modifying the selected portions to thereby generate a final reference image; generating a difference image by comparing the test image to the final reference image; and detecting defects in the test image by applying a defect detection method to the difference image.

2. The system of claim 1, wherein the test image and the two or more other images are generated in an area on the sample that includes only an indistinguishable, repeating device pattern.

3. The system of claim 1, wherein the computer subsystem is further configured for dividing the images corresponding to an area of interest on the sample into the test image corresponding to a test cell and the two or more other images corresponding to two or more other cells adjacent to the test cell.

4. The system of claim 1, wherein prior to the computing, the computer subsystem is further configured for applying color variation compensation to the test image and the two or more other images.

5. The system of claim 1, wherein subsequent to the combining, the computer subsystem is further configured for applying color variation compensation to a result of the combining.

6. The system of claim 1, wherein a first of the different combinations of the at least two of the test image and the two or more other images used to compute the first candidate reference image includes a portion of the images generated across the sample only in an x-direction.

7. The system of claim 6, wherein a second of the different combinations of the at least two of the test image and the two or more other images used to compute the second candidate reference image includes an additional portion of the images generated across the sample only in a y-direction.

8. The system of claim 1, wherein one of the different combinations of the at least two of the test image and the two or more other images used to compute one of the first and second candidate reference images includes all of the images generated in two or more dies on the sample in a job of the images.

9. The system of claim 1, wherein the computer subsystem is further configured for computing a third candidate reference image from the different combination and selecting at least a portion of the third candidate reference image corresponding to a third portion of the test image, and wherein the combining comprises combining the selected portions of the first, second, and third candidate reference images without modifying the selected portions of the first, second, and third candidate reference images to thereby generate the final reference image.

10. The system of claim 1, wherein the selecting comprises identifying which portions of the first and second candidate reference images best match different portions of the test image.

11. The system of claim 1, wherein the selecting comprises identifying which portions of the first and second candidate reference images best match corresponding portions of the test image.

12. The system of claim 1, wherein the computer subsystem is further configured for repeating the selecting and combining to thereby generate an additional final reference image, generating an additional difference image by comparing the test image to the additional final reference image, and detecting a defect in the test image by applying the defect detection method to the additional difference image, and wherein the defect detection method determines that the defect is present at a location in the test image only if the defect detection method detects a defect at corresponding locations in the difference image and the additional difference image.

13. The system of claim 1, wherein the computer subsystem is further configured for selecting one of the first and second candidate reference images as an additional final reference image, generating an additional difference image by comparing the test image to the additional final reference image, and detecting a defect in the test image by applying the defect detection method to the additional difference image, and wherein the defect detection method determines that the defect is present at a location in the test image only if the defect detection method detects a defect at corresponding locations in the difference image and the additional difference image.

14. The system of claim 1, wherein the computer subsystem is further configured for selecting an additional candidate reference image as an additional final reference image, generating an additional difference image by comparing the test image to the additional final reference image, and detecting a defect in the test image by applying the defect detection method to the additional difference image, and wherein the defect detection method determines that the defect is present at a location in the test image only if the defect detection method detects a defect at corresponding locations in the difference image and the additional difference image.

15. The system of claim 1, wherein the computer subsystem is further configured for dividing the image corresponding to a region of interest of a cell area on the sample into the test image corresponding to a test cell and the two or more other images corresponding to two or more other cells adjacent to the test cell, and wherein the defect detection method divides the region of interest into different segments of the cell area and separately detects defects in each of the different segments.

16. The system of claim 15, wherein the different segments include a first segment for top and bottom edges of the cell area, a second segment for left and right edges of the cell area, a third segment for a center of the cell area, and a fourth segment for corners of the cell area.

17. The system of claim 1, wherein the inspection subsystem is further configured as a light-based inspection subsystem.

18. The system of claim 1, wherein the inspection subsystem is further configured as an electron beam-based inspection subsystem.

19. 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 sample, wherein the computer-implemented method comprises: obtaining an image of a sample generated by an inspection subsystem, the image including a test image and two or more other images corresponding to the test image; computing first and second candidate reference images from different combinations of at least two of the test image and the two or more other images; selecting at least a portion of the first candidate reference image corresponding to a first portion of the test image and a portion of the second candidate reference image corresponding to a second portion of the test image; combining the selected portions of the first and second candidate reference images without modifying the selected portions to thereby generate a final reference image; generating a difference image by comparing the test image to the final reference image; and detecting defects in the test image by applying a defect detection method to the difference image.

20. A computer-implemented method for detecting defects on a sample, comprising: obtaining an image of a sample generated by an inspection subsystem, the image including a test image and two or more other images corresponding to the test image; computing first and second candidate reference images from different combinations of at least two of the test image and the two or more other images; selecting at least a portion of the first candidate reference image corresponding to a first portion of the test image and a portion of the second candidate reference image corresponding to a second portion of the test image; combining the selected portions of the first and second candidate reference images without modifying the selected portions to thereby generate a final reference image; generating a difference image by comparing the test image to the final reference image; and detecting defects in the test image by applying a defect detection method to the difference image. defects in the test image are detected by applying a defect detection method to the difference image, wherein the acquiring, calculating, selecting, combining, generating, and detecting are performed by a computer subsystem coupled to the inspection subsystem.

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