Computerized system and method for obtaining information about a region of an object

Abstract

A method, system and computer readable medium for providing information about a region of a sample. The method includes (i) obtaining a plurality of images of the region by an imager; wherein the plurality of images differ from one another by at least one parameter; (ii) receiving or generating a plurality of reference images; (iii) generating a plurality of difference images representing differences between the plurality of images and the plurality of reference images; (iv) calculating a set of regional pixel attributes; (v) calculating a set of noise attributes based on the set of regional pixel attributes of the plurality of regional pixels; and (vi) determining for each regional pixel whether the regional pixel represents a defect based on a relationship between the set of noise attributes and the set of regional pixel attributes of the pixel.

Description

Background Art

[0001] As design rules shrink, wafer inspection tools are needed to detect smaller defects. Previously, defect detection was primarily limited by laser power and detector noise. In today's (and likely future) tools, detection is often limited by light scattered by the roughness of the pattern printed on the wafer, and particularly by line-edge roughness. This irregular scattered light, with its random phase, can combine to produce bright spots (speckles) on the detector, making it nearly indistinguishable from actual defects. Current filtering techniques are ineffective in removing these spots, as their shape closely resembles that of actual defects in standard brightfield or darkfield channels.

[0002] There is a growing demand to provide reliable defect detection systems. Summary of the Invention

[0003] A method for obtaining information about a region of a sample may be provided, the method comprising: (a) obtaining, by an imager, a plurality of images of the region; wherein the plurality of images differ from one another by at least one parameter selected from: an illumination spectrum, a collection spectrum, an illumination polarization, a collection polarization, an illumination angle, a collection angle, and a sensing type; wherein obtaining the plurality of images comprises: illuminating the region and collecting radiation from the region; wherein the region comprises a plurality of region pixels; (b) receiving or generating a plurality of reference images; (c) generating, by an image processor, a plurality of difference images representing differences between the plurality of images and a plurality of reference images; (d) calculating, for each of the plurality of region pixels, a set of region pixel attributes; wherein the calculations are based on pixels of the plurality of difference images; (e) calculating, based on the plurality of sets of region pixel attributes for the plurality of region pixels, a set of noise attributes; and (f) determining, for each of the region pixels, whether the region pixel represents a defect based on a relationship between the set of noise attributes and the set of region pixel attributes for the pixel.

[0004] A computerized system for obtaining information about a region of a sample may be provided, the system comprising: an imager comprising optics and an image processor; wherein the imager is configured to obtain a plurality of images of the region; wherein the plurality of images differ from one another by at least one parameter selected from: an illumination spectrum, a collection spectrum, illumination polarization, collection polarization, an illumination angle, and a collection angle; wherein obtaining the plurality of images comprises: illuminating the region and collecting radiation from the region; wherein the region comprises a plurality of region pixels; wherein the computerized system is configured to receive or generate a plurality of reference images; wherein the image processor is configured to: generate a plurality of difference images representing differences between the plurality of images and a plurality of reference images; calculate a set of region pixel attributes for each of the plurality of region pixels; wherein the set of region pixel attributes is calculated based on pixels of the plurality of difference images; calculate a set of noise attributes based on the plurality of sets of region pixel attributes for the plurality of region pixels; and determine for each region pixel whether the region pixel represents a defect based on a relationship between the set of noise attributes and the set of region pixel attributes for the pixel.

[0005] A non-transitory computer-readable medium may be provided, the non-transitory computer-readable medium storing instructions to cause a computerized system to: obtain, by an imager of the computerized system, a plurality of images of an area of ​​an object; wherein the plurality of images differ from one another by at least one parameter selected from: an illumination spectrum, a collection spectrum, an illumination polarization, a collection polarization, an illumination angle, a collection angle, and a sensing type; wherein obtaining the plurality of images comprises: illuminating an area and collecting radiation from the area; wherein the area comprises a plurality of area pixels; receiving or generating a plurality of reference images; generating, by an image processor of the computerized system, a plurality of difference images representing differences between the plurality of images and a plurality of reference images; calculating a set of area pixel attributes for each of the plurality of area pixels; wherein the calculations are based on pixels of the plurality of difference images; calculating a set of noise attributes based on the plurality of sets of area pixel attributes for the plurality of area pixels; and determining, for each area pixel, whether the area pixel represents a defect based on a relationship between the set of noise attributes and the set of area pixel attributes for the pixel. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The subject matter regarded as the invention is particularly pointed out and separately claimed in the concluding portion of the specification. However, the invention both as to its organization and method of operation, together with objects, features, and advantages thereof, may be understood from the accompanying drawings. Figure 1 It is best understood when read in conjunction with the following detailed description, of which:

[0007] Figure 1 Examples of graphic methods;

[0008] Figure 2 Examples of illustrated systems and samples;

[0009] Figure 3 An example of a scan of a region of an object is shown;

[0010] Figure 4 Examples of illustrated systems and samples;

[0011] Figure 5 Examples of illustrated systems and samples;

[0012] Figure 6 Illustrate examples of various images;

[0013] Figure 7 illustrates examples of various images; and

[0014] Figure 8 Illustrated are examples of various images.

[0015] It should be understood that for simplicity and clarity of illustration, the components shown in the drawings are not necessarily drawn to scale. For example, the dimensions of some components may be exaggerated relative to other components for clarity. Furthermore, when considered appropriate, reference numerals may be repeated between the drawings to indicate corresponding or similar components. DETAILED DESCRIPTION

[0016] In the following detailed description, several specific details are set forth to provide a thorough understanding of the present invention. However, those skilled in the art will appreciate that the present invention can be practiced without these specific details. In other examples, well-known methods, procedures, and components are not described in detail so as not to obscure the present invention.

[0017] The subject matter regarded as the invention is particularly pointed out and separately claimed in the concluding portion of the specification. However, the invention both as to its organization and method of operation, together with objects, features, and advantages thereof, may be understood from the accompanying drawings. Figure 1 It is best understood when reading this article with reference to the following detailed description.

[0018] It should be understood that for simplicity and clarity of illustration, the components shown in the drawings are not necessarily drawn to scale. For example, the dimensions of some components may be exaggerated relative to other components for clarity. Furthermore, when considered appropriate, reference numerals may be repeated between the drawings to indicate corresponding or similar components.

[0019] Since the illustrated embodiments of the present invention can for the most part be implemented using electronic components and circuits known to those skilled in the art, for the sake of understanding and appreciation of the following content of the present invention and so as not to obscure or distract from the teachings of the present invention, details will not be explained to a greater extent than is deemed necessary as described above.

[0020] Any reference in the specification to a method shall apply mutatis mutandis to a system capable of performing the method and shall apply mutatis mutandis to a non-transitory computer readable medium storing instructions that, when executed by a computer, result in the performance of the method.

[0021] Any reference in the specification to a system shall apply mutatis mutandis to the method executable by the system and shall apply mutatis mutandis to a non-transitory computer readable medium storing instructions executable by the system.

[0022] Any reference in the specification to a non-transitory computer readable medium shall apply mutatis mutandis to systems capable of executing instructions stored in the non-transitory computer readable medium and shall apply mutatis mutandis to methods executable by a computer reading the instructions stored in the non-transitory computer readable medium.

[0023] Systems, methods, and computer-readable media are provided for combining the use of multiple detection channels and high-dimensional analysis of data acquired from these channels. The proposed method incorporates more information from the interaction of electromagnetic fields with the wafer, resulting in significant benefits for detection. Using the methods outlined below, improvements of at least 70 percent have been achieved.

[0024] Figure 1 An example of method 300 is shown.

[0025] Method 300 may include steps 310 , 320 , 330 , 340 , 350 , and 360 .

[0026] Method 300 may begin at step 310 by obtaining a plurality of images of a region using an imager. The region may include a plurality of region pixels.

[0027] An imager is a module or unit configured to acquire multiple images.

[0028] The imager may be configured to illuminate the area with radiation, collect radiation from the area, and detect the radiation collected from the sample.The imager may include optics, an image processor, and may include a plurality of detectors.

[0029] Step 310 may include illuminating a region of the sample, collecting radiation from the region, and detecting the radiation collected from the sample.

[0030] The radiation may be ultraviolet (UV) radiation, deep UV radiation, extreme UV radiation, or any other type of radiation.

[0031] It is assumed that a single radiation beam may scan an area - however it should be noted that multiple radiation beams may scan multiple areas simultaneously.

[0032] The plurality of images differ from one another by at least one parameter selected from: (i) illumination spectrum (being a spectral response of an illumination portion of the imager), collection spectrum (being a spectral response of a collection portion of the imager), illumination polarization (being a polarization imposed by an illumination portion of the imager), collection polarization (being a polarization imposed by a collection portion of the imager), illumination angle (being an illumination angle of an area of an illumination portion of the imager), collection angle, and detection type (e.g., - detection amplitude and / or detection phase).

[0033] The imager can include a plurality of detectors for producing the plurality of images. Different detectors can be assigned to detect radiation from different ones of the plurality of pupil segments - one detector per pupil segment. Each of the plurality of detectors can be positioned in a plane that is conjugate to the pupil plane. For example, see detector 70 positioned in a plane that is conjugate to pupil plane 26 of system 10. Figure 1

[0034] Different pupil segments can not overlap, can not overlap at all, or can only partially overlap. Pupil segments can have equal shape and size, but shape of at least two pupil segments can differ from one another, and additionally or alternatively size and / or position on the pupil plane differ from one another.

[0035] There can be more than four pupil segments.

[0036] The imager can include a plurality of detectors for producing the plurality of images. Different detectors can be assigned to detect radiation of (a) different polarizations and (b) different combinations of different pupil segments. For example, see detector 70 assigned to detect radiation of a first polarization from different pupil segments, and detector 70' assigned to detect radiation of a second polarization from different pupil segments.

[0037] It is noted that the pupil can not be segmented, and each of the plurality of detectors can be assigned to the entire pupil.

[0038] Step 310 can include obtaining the plurality of images at the same point in time.

[0039] Alternatively, step 310 can include obtaining two or more of the plurality of images at different points in time.

[0040] Step 320 can include receiving or producing a plurality of reference images. The reference images can be images of areas of the sample that are different from the region of the sample obtained by the imager. The areas and region can be non-overlapping (e.g., when performing die-to-die comparisons), or can partially overlap (e.g., when performing cell-to-cell comparisons). The reference images can be computed in various ways - for example, by processing computer-aided design (CAD) information of the region, by producing a golden reference, and the like.​

[0041] It should be noted that one of the plurality of images can be used as a reference image for another of the plurality of images.

[0042] It can be beneficial to use a reference image that is not another of the plurality of images - as it can provide more information about the region.

[0043] Step 330 can comprise generating, by the image processor, a plurality of difference images representative of differences between the plurality of images and the plurality of reference images.

[0044] Step 340 can comprise calculating, for each of the plurality of region pixels, a set of region pixel attributes. The calculation can be based on pixels of the plurality of difference images.

[0045] The set of pixel attributes of a region pixel can comprise data about the region pixel and neighboring region pixels of the region pixel.

[0046] Step 350 can comprise calculating, based on the sets of region pixel attributes of the plurality of region pixels, a set of noise attributes.

[0047] Step 350 can comprise calculating a covariance matrix.

[0048] Step 350 can comprise:

[0049] a. calculating, for each of the region pixels, a set of covariance values representative of covariances between different attributes of the set of region pixel attributes of the region pixel.

[0050] b. calculating, based on the sets of covariance values of the plurality of region pixels, a covariance matrix.

[0051] Step 360 can comprise determining, for each of the region pixels, whether the region pixel is representative of a defect based on a relationship between the set of noise attributes and the set of region pixel attributes of the pixel.

[0052] Step 360 can also be responsive to a set of attributes of a real defect, or responsive to a set of attributes of an estimated defect. The estimated defect can be a model of a defect.

[0053] Step 360 can comprise:

[0054] comparing a product involving a multiplication of (i) the set of attributes of the region pixel, (ii) the covariance matrix, and (iii) the set of attributes of the real noise or the estimated noise, to a threshold.

[0055] Figure 2 An example of the system 10 and the sample 100 is illustrated.

[0056] Figure 2 An example of assigning nine detectors to nine pupil segments - one detector per pupil segment is illustrated.

[0057] Figure 2 The pupil plane 26 is also shown.

[0058] The system 10 includes a radiation source 30 , optics (eg, a first beam splitter 40 and an objective lens 50 ), a detector 70 , and an image processor 90 .

[0059] The optical device may include any combination of optical elements that can determine one or more optical properties (e.g., shape, size, polarization) of a radiation beam from the radiation source 30, can determine the path of a radiation beam from the radiation source 30, can determine one or more optical properties (e.g., shape, size, polarization) of one or more radiation beams scattered and / or reflected by the sample, and can determine the path of one or more radiation beams - and direct one or more radiation beams toward the detector 70.

[0060] Optical devices may include lenses, grids, telescopes, beam splitters, polarizers, reflectors, refractors, apertures, and the like.

[0061] exist Figure 2 In FIG. 2 , a radiation beam from a radiation source 30 passes through a first beam splitter 40 and is focused by an objective lens 50 onto an area of ​​a sample 22. The radiation beam from the area is collected by the objective lens 50 and reflected by the first beam splitter 40 toward a detector 70.

[0062] Figure 2 A pupil 60 is shown segmented into nine segments—a first pupil segment 61 , a second pupil segment 62 , a third pupil segment 63 , a fourth pupil segment 64 , a fifth pupil segment 65 , a sixth pupil segment 66 , a seventh pupil segment 67 , an eighth pupil segment 68 , and a ninth pupil segment 69 .

[0063] Figure 2 The detector 70 is illustrated as comprising nine detectors arranged in a 3x3 grid - the nine detectors comprising a first detector 71, a second detector 72, a third detector 73, a fourth detector 74, a fifth detector 75, a sixth detector 76, a seventh detector 77, an eighth detector 78 and a ninth detector 79 - one detector for each pupil segment.

[0064] The nine images produce nine images that are different from one another—the images include a first image 91 , a second image 92 , a third image 93 , a fourth image 94 , a fifth image 95 , a sixth image 96 , a seventh image 97 , an eighth image 98 , and a ninth image 99 —one for each detector.

[0065] Figure 3 The diagram shows an area of ​​the object being scanned along the y-axis. It should be noted that scanning can occur along any other axis.

[0066] Figure 4 The illustrated implementation distributes eighteen detectors to nine pupil segments and to two polarizations in the system 12—one detector per pupil segment and polarization combination.

[0067] The first to ninth detectors 71 - 79 (collectively 70 ) are assigned to the first polarization and to nine pupil segments.

[0068] The eighth to eighteenth detectors 71 ′, 72 ′, 73 ′, 74 ′, 75 ′, 76 ′, 77 ′, 78 ′ and 79 ′ (collectively 70 ′) are assigned to the second polarization and to nine pupil segments.

[0069] The difference in polarization for detector 70 and detector 70' can be introduced using a polarization beam splitter and / or by inserting a polarization component before the detector. It should be noted that full characterization of the polarization may require applying at least three different polarizations (and not just two) on each - so additional polarization components and additional detectors should be added.

[0070] Figure 4 The illustrated system 13 includes a first polarization beam splitter 81 , a second polarization beam splitter 82 , and a third polarization beam splitter 83 .

[0071] Figure 5 The system 13 is illustrated in which different detectors receive radiation reflected at different polarizations - due to the lack of a polarizer before the third detector 73, a first polarizer 89 is positioned before the first detector 71, and a second polarizer 88 is positioned before the second detector 73. Each of the first detector 71, the second detector 72, and the third detector 73 receives radiation from the entire pupil.

[0072] To make the explanation simple, Figure 6 The source of radiation is not shown.

[0073] The optics of system 13 include a first beam splitter 40 and additional beam splitters (eg, a second beam splitter 86 and a third beam splitter 87) for splitting the light beam from the object between the first, second, and third detectors.

[0074] In the following text, it is assumed that there are nine different pupil segments, nine difference images are calculated, and the neighborhood of each pixel includes eight pixels—so that the pixel and its neighborhood include nine pixels. The number of pupil segments can be different from nine, the number of difference images can be different from nine, and the number of pixel neighbors can be different from eight.

[0075] Under these assumptions, each region pixel is represented by a vector of eighty-one elements and a covariance matrix comprising 81 x 81 elements. The number of elements in each vector may be different from eighty-one, and the number of elements in the covariance matrix may be different from 81 x 81. For example, the number of pixel neighbors may differ from the number in the difference image by more than one.

[0076] Figure 6 A first difference image 111 , a second difference image 112 , a third difference image 113 , a fourth difference image 114 , a fifth difference image 115 , a sixth difference image 116 , a seventh difference image 117 , an eighth difference image 118 , and a ninth difference image 119 are illustrated.

[0077] Figure 6 Also illustrated are the (m,n)th pixel and its eight neighboring pixels in each of the nine difference images - 111(m-1,n-1) through 111(m+1,n+1), 112(m-1,n-1) through 112(m+1,n+1), 113(m-1,n-1) through 113(m+1,n+1), 114(m-1,n-1) through 114(m+1,n+1 ), 115(m-1,n-1) until 115(m+1,n+1), 116(m-1,n-1) until 116(m+1,n+1), 117(m-1,n-1) until 117(m+1,n+1), 118(m-1,n-1) until 118(m+1,n+1), and 119(m-1,n-1) until 119(m+1,n+1).

[0078] The nine difference images represent the same area of ​​the wafer. Each position on the area (also called an area pixel) is associated with nine pixels of the nine difference images - located at the same position within the respective difference images.

[0079] The (m,n)th region pixel may be represented by a vector (Vdata(m,n)), which includes values ​​(e.g., intensities) associated with the (m,n)th pixel in each of the nine difference images, and values ​​associated with neighboring pixels of the (m,n)th pixel in each of the nine difference images.

[0080] For example, for the (m,n)th region pixel, the vector (V data (m,n)) may include the following eighty-one vector elements - I[111(m-1,n-1)]...I[111(m+1,n+1)], I[112(m-1,n-1)]...I[112(m+1,n+1)], I[113(m-1,n-1)]...I[113(m+1,n+1)], I[114(m-1,n-1)]...I[114(m+1,n +1)], I[115(m-1,n-1)]...I[115(m+1,n+1)], I[116(m-1,n-1)]...I[116(m+1,n+1)], I[117(m-1,n-1)] ...I[117(m+1,n+1)], I[118(m-1,n-1)]...I[118(m+1,n+1)] and I[119(m-1,n-1)]...I[119(m+1,n+1)].

[0081] The noise can be estimated by the covariance matrix. The covariance matrix can be calculated by: (a) calculating all possible multiplications between pairs of vector elements for each region pixel - so there are 81x81 multiplications for the eighty-one vector elements of each vector, (b) adding the corresponding products for all region pixels, and (c) normalizing the sum to provide the covariance matrix.

[0082] Normalization may include averaging the sums.

[0083] For example, the first eighty-one multiplications of step (a) may include multiplying I[111(m-1,n-1)] by all elements of V(m,n), and the last eighty-one multiplications of step (a) may include multiplying I[119(m+1,n+1)] by all elements of V(m,n).

[0084] Assuming there are MxN region pixels (and each difference image has MxN pixels), step (a) includes computing MxNx81x81 products. Step (b) includes performing, generating 81x81 sums—each sum having MxN elements, and step (c) includes normalizing the 81x81 sums—for example, by computing the average—and dividing each sum by nine.

[0085] Assume that the defects are known or estimated—the defects can be represented by a defect vector (Vdefect) of eighty-one elements.

[0086] Determining whether an area pixel includes a defect may include calculating a relationship (e.g., a ratio) between the likelihood that the area pixel (represented by the vector V data) is obtained due to a defect and the likelihood that the area pixel (represented by the vector V data) is not obtained due to a defect.

[0087] The decision whether or not a region pixel is defective can comprise comparing the product involving the V defect T , the covariance matrix and the V data to a threshold TH. If the product exceeds TH, the region pixel is determined to represent a defect - otherwise it is assumed that the region pixel does not include a defect.

[0088] Other decisions can be made, the threshold can be calculated in any way, can be fixed, can change over time, etc. The same threshold can be applied to all region pixels - but this is not necessarily the case, and different thresholds can be calculated for different region pixels. Differences in the threshold can result from e.g. non-uniformities in the optics, aberrations, and similar different parts in a die can also have different characteristics, and need different thresholds.

[0089] Figure 7 The first difference image 111, the second difference image 112, the third difference image 113, the fourth difference image 114, the fifth difference image 115, the sixth difference image 116, the seventh difference image 117, the eighth difference image 118 and the ninth difference image 119 are illustrated.

[0090] Figure 7 The first reference image 101, the second reference image 102, the third reference image 103, the fourth reference image 104, the fifth reference image 105, the sixth reference image 106, the seventh reference image 107, the eighth reference image 108 and the ninth reference image 109 are also illustrated.

[0091] Figure 7 The nine images acquired by the nine detectors - the nine images (also referred to as acquired images) comprise the first image 91, the second image 92, the third image 93, the fourth image 94, the fifth image 95, the sixth image 96, the seventh image 97, the eighth image 98 and the ninth image 99 are further illustrated.

[0092] The first difference image 111 represents the difference between the first image 91 and the first reference image 101.

[0093] The second difference image 112 represents the difference between the second image 92 and the second reference image 102.

[0094] The third difference image 113 represents the difference between the third image 93 and the third reference image 103.

[0095] The fourth difference image 114 represents the difference between the fourth image 94 and the fourth reference image 104.

[0096] The fifth difference image 115 represents the difference between the fifth image 95 and the fifth reference image 105.

[0097] The sixth difference image 116 represents the difference between the sixth image 96 and the sixth reference image 106 .

[0098] The seventh difference image 117 represents the difference between the seventh image 97 and the seventh reference image 107 .

[0099] The eighth difference image 118 represents the difference between the eighth image 98 and the eighth reference image 108 .

[0100] The ninth difference image 119 represents the difference between the ninth image 99 and the ninth reference image 109 .

[0101] Figure 8 These are examples of the first difference image 111, the second difference image 112, the third difference image 113, the fourth difference image 114, the fifth difference image 115, the sixth difference image 116, the seventh difference image 117, the eighth difference image 118, the ninth difference image 119, the first image 91, the second image 92, the third image 93, the fourth image 94, the fifth image 95, the sixth image 96, the seventh image 97, the eighth image 98 and the ninth image 99.

[0102] The present invention may also be implemented in a computer program for running on a computer system, the computer program comprising at least code portions for executing the steps of the method according to the present invention when running on a programmable device such as a computer system, or code portions enabling a programmable device to perform the functions of the apparatus or system according to the present invention.

[0103] A computer program is a listing of instructions, such as a specific application and / or operating system. A computer program may include, for example, one or more of the following: a subroutine, a function, a procedure, a target method, a target implementation, an executable application, an applet, a small service, source code, object code, a shared library / dynamically loaded library, and / or other sequence of instructions designed for execution on a computer system.

[0104] The computer program may be stored internally on a non-transitory computer-readable medium. All or some of the computer programs may be provided on the computer-readable medium permanently, removably, or remotely coupled to an information processing system. For example and not limitation, the computer-readable medium may include any number of the following: magnetic storage media, including magnetic disk and tape storage media; optical storage media, such as compact disc media (e.g., CD-ROM, CD-R, etc.) and digital video disc storage media; non-volatile memory storage media, including semiconductor-based memory cells, such as FLASH memory, EEPROM, EPROM, ROM; ferromagnetic digital memory; MRAM; volatile storage media, including temporary registers, buffers or caches, main memory, RAM, and the like.

[0105] A computer process typically includes an executing (running) program or portion thereof, the current values of the registers and other state information, and the resources used by the operating system to manage the execution of the process. An operating system (OS) is the software that manages the sharing of the resources of a computer for the execution of programs. An operating system processes system data and user input, and responds by allocating and managing tasks and internal system resources, as well as peripherals, in response to user commands and system interrupts.

[0106] For example, a computer system can include at least one processing unit, associated memory and a number of input / output (I / O) devices. When executing a computer program, the computer system processes information according to the computer program and produces result output information via the I / O devices.

[0107] In the above Detailed Description, reference has been made to the illustrative embodiments of the application herein. However, it is to be understood that the application is not to be limited to those precise embodiments, and that various modifications and changes can be made thereto without departing from the broader spirit and scope of the application as set forth in the appended claims.

[0108] Also, in the description and claims of the application, terms such as "front," "back," "top," "bottom," "over," "under," and the like should not be construed as limiting of the positi on of the application, but are intended to encompass different orientations of the application in order to demonstrate that the application can work in other orientations. Additionally, the features discussed herein can be used in a variety of combinations, and each feature disclosed singly can be combined with any and all

[0109] The connections herein discussed can be any type of connection suitable to transfer signals from or to the respective nodes, units, or devices, for example via intermediate devices. Unless specified or implicitly inferred otherwise, connections can be direct or indirect. Connections can be illustrated or described as reference numbers, multiple connections, single directional or bi-directional connections. Different embodiments can vary the implementation of the connections. For example, separate unidirectional connections can be used instead of a bidirectional connection, and bidirectional connections can be used instead of separate unidirectional connections. Also, plurality of signals can be transferred via a single connection, which can be continuous or time-division multiplexed. Similarly, signals carried on a single connection can be separated by various techniques including frequency, time, phase, and code domain techniques. Thus, many options exist for transferring signals.

[0110] Although a particular conductivity type or polarity of potential is illustrated in examples, it is to be understood that the conductivity type and polarity of potential can be reversed.

[0111] Each signal described herein can be designed as either positive or negative logic. In the case of a negative logic signal, the signal is active low when the logically true state corresponds to a logic level zero. In the case of a positive logic signal, the signal is active high when the logically true state corresponds to a logic level one. Note that any signal described herein can be designed as either a negative logic signal or a positive logic signal. Thus, in alternative embodiments, those signals described as positive logic signals can be implemented as negative logic signals, and those signals described as negative logic signals can be implemented as positive logic signals.

[0112] Furthermore, the terms "assert," "set," and "negate" (or "de-assert," or "clear") are used herein when referring to the presentation of a signal, status bit, or the like to its logically true or logically false state, respectively. If the logically true state is a logic level one, the logically false state is a logic level zero. Furthermore, if the logically true state is a logic level zero, the logically false state is a logic level one.

[0113] Those skilled in the art will recognize that the boundaries between logic blocks are merely illustrative, and that alternative embodiments may incorporate logic blocks or circuit components, or impose an alternative decomposition of functionality upon various logic blocks or circuit components. Therefore, it should be understood that the architectures depicted herein are merely exemplary, and that in fact many other architectures may be implemented to achieve the same functionality.

[0114] Any arrangement of components to achieve the same functionality is effectively "associated" so that the desired functionality is achieved. Thus, any two components herein combined to achieve a particular functionality may be considered to be "associated" with each other so that the desired functionality is achieved, regardless of architecture or intermediary components. Similarly, any two components so associated may also be considered to be "operably connected" or "operably coupled" to each other so that the desired functionality is achieved.

[0115] Furthermore, those skilled in the art will recognize that the boundaries between the above-described operations are merely illustrative. Multiple operations may be combined into a single operation, a single operation may be distributed among additional operations, and operations may be performed with at least partial overlap in time. Furthermore, alternative embodiments may include multiple instances of a particular operation, and the order of the operations may be varied in various other embodiments.

[0116] Also for example, in one embodiment, the illustrated examples may be implemented as circuits located on a single integrated circuit or within the same device. Alternatively, the examples may be implemented as any number of separate integrated circuits or separate devices interconnected with each other in a suitable manner.

[0117] Also for example, the examples or portions of the examples may be implemented as software or code representations of physical circuitry, or as logical representations convertible into physical circuitry, such as any appropriate type of hardware description language.

[0118] Furthermore, the present invention is not limited to physical devices or units implemented in non-programmable hardware, but may also be applied in programmable devices or units that are capable of performing the desired device functions by operating according to appropriate program codes, such as mainframes, minicomputers, servers, workstations, personal computers, notepads, personal digital assistants, electronic games, automobiles and other embedded systems, mobile phones and various other wireless devices, collectively referred to as "computer systems" in this application.

[0119] However, other modifications, changes, and substitutions are possible. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.

[0120] In the claims, any reference numerals placed between brackets should not be considered as limiting the claims. The word "comprising" does not exclude the presence of other elements or steps in addition to those listed in the claim. In addition, the terms "a" or "an" as used herein are limited to one or more than one. Moreover, the use of introductory phrases such as "at least one" and "one or more" in the claims should not be considered to imply that any particular claim containing another claim element introduced by the indefinite article "a" or "an" is limited to the invention containing only one such element, even when the same claim includes the introductory phrases "one or more" or "at least one" and the indefinite article such as "a" or "an". The same applies to the use of definite articles. Unless otherwise specified, terms such as "first" and "second" are used to make arbitrary distinctions between the elements described by these terms. Therefore, these terms are not necessarily intended to indicate the temporal or other priority of these elements. The fact that certain measures are recorded only in different claims does not indicate that the combination of these measures cannot be used to obtain advantages.

[0121] While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those skilled in the art. It is therefore to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.

Claims

1. A method of obtaining information about a region of a sample, the method comprising: obtaining, with an imager, a plurality of images of the area; wherein the plurality of images differ from one another by at least one parameter selected from: an illumination spectrum, a collection spectrum, an illumination polarization, a collection polarization, an illumination angle, a collection angle, and a sensing type; wherein obtaining the plurality of images comprises: illuminating the area and collecting radiation from the area; wherein the area comprises a plurality of area pixels; receiving or generating a plurality of reference images; generating, by an image processor, a plurality of difference images representing differences between the plurality of images and the plurality of reference images; calculating a set of region pixel attributes for each region pixel of the plurality of region pixels; wherein said calculating is based on pixels of said plurality of difference images; calculating a set of noise attributes based on the plurality of sets of regional pixel attributes of the plurality of regional pixels; as well as determining, for each region pixel, whether the region pixel represents a defect based on a relationship between the set of noise properties and the set of region pixel properties for the pixel, wherein the set of noise properties is calculated by calculating a covariance matrix; And, wherein calculating the covariance matrix comprises: calculating a set of covariance values ​​for each region pixel, the set of covariance values ​​representing covariances between different attributes of the set of region pixel attributes of the region pixel; And a given covariance matrix is ​​calculated based on the multiple groups of covariance values ​​of the multiple area pixels.

2. The method of claim 1, wherein said determining whether said region of pixels represents a defect is also responsive to a set of attributes of a real defect.

3. The method of claim 1 wherein said determining whether said region of pixels represents a defect is also responsive to a set of attributes of an estimated defect. The method of claim 1 , wherein the set of region pixel attributes for a region pixel comprises data about the region pixel and neighboring region pixels of the region pixel.

5. The method of claim 1 , wherein the imager comprises a plurality of detectors for generating the plurality of images, and wherein the method comprises assigning different detectors to detect radiation from different pupil segments of a plurality of pupil segments. The method of claim 5 , wherein the different pupil segments in the plurality of pupil segments are more than four pupil segments.

7. The method of claim 1 , wherein the imager comprises a plurality of detectors for generating the plurality of images, and wherein the method comprises assigning different detectors to detect radiation from different combinations of: (a) polarization and (b) different pupil segments of a plurality of pupil segments. The method of claim 1 , comprising acquiring the plurality of images at the same point in time.

9. The method of claim 1, comprising obtaining the plurality of images at different points in time.

10. The method of claim 1, comprising classifying the defects.

11. The method of claim 1, comprising determining whether the defect is a defect of interest or not a defect of interest.

12. A computerized system for obtaining information about a region of a sample, the system comprising: an imager, which includes optics and an image processor; wherein the imager is configured to obtain a plurality of images of the area; wherein the plurality of images differ from one another by at least one parameter selected from: an illumination spectrum, a collection spectrum, an illumination polarization, a collection polarization, an illumination angle, and a collection angle; wherein obtaining the plurality of images comprises: illuminating the area and collecting radiation from the area; wherein the area comprises a plurality of area pixels; wherein the computerized system is configured to receive or generate a plurality of reference images; The image processor is configured to: generating a plurality of difference images representing differences between the plurality of images and the plurality of reference images; calculating a set of regional pixel attributes for each of the plurality of regional pixels; wherein the set of regional pixel attributes is calculated based on pixels of the plurality of difference images; calculating a set of noise attributes based on the plurality of sets of regional pixel attributes of the plurality of regional pixels; as well as determining, for each region pixel, whether the region pixel represents a defect based on a relationship between the set of noise properties and the set of region pixel properties for the pixel, wherein the set of noise properties is calculated by calculating a covariance matrix; And, wherein calculating the covariance matrix comprises: calculating a set of covariance values ​​for each region pixel, the set of covariance values ​​representing covariances between different attributes of the set of region pixel attributes of the region pixel; And a given covariance matrix is ​​calculated based on the multiple groups of covariance values ​​of the multiple area pixels.

13. A non-transitory computer-readable medium storing instructions to cause a computerized system to: obtaining, by an imager of the computerized system, a plurality of images of an area of ​​the object; wherein the plurality of images differ from one another by at least one parameter selected from the group consisting of: illumination spectrum, collection spectrum, illumination polarization, collection polarization, illumination angle, collection angle, and sensing type; wherein obtaining the plurality of images comprises: illuminating the region and collecting radiation from the region; wherein the region comprises a plurality of region pixels; receiving or generating a plurality of reference images; generating, by an image processor of the computerized system, a plurality of difference images representing differences between the plurality of images and the plurality of reference images; calculating a set of region pixel attributes for each region pixel of the plurality of region pixels; wherein said calculating is based on pixels of said plurality of difference images; calculating a set of noise attributes based on the plurality of sets of regional pixel attributes of the plurality of regional pixels; as well as determining, for each region pixel, whether the region pixel represents a defect based on a relationship between the set of noise properties and the set of region pixel properties for the pixel, wherein the set of noise properties is calculated by calculating a covariance matrix; And, wherein calculating the covariance matrix comprises: calculating a set of covariance values ​​for each region pixel, the set of covariance values ​​representing covariances between different attributes of the set of region pixel attributes of the region pixel; And a given covariance matrix is ​​calculated based on the multiple groups of covariance values ​​of the multiple area pixels.

Images