Mask inspection for semiconductor sample fabrication

Abstract

A system and method of inspecting a mask usable to manufacture a semiconductor sample is provided. The method includes obtaining an original defect image including defect pixels representing a candidate defect and a location of the candidate defect, and a library of defect images and a library of reference images of the candidate defect acquired at a plurality of focus levels; determining a best focus among the plurality of focus levels, and generating a synthetic defect image at the best focus; aligning the original defect image with the synthetic defect image to identify a region of target pixels in the synthetic defect image corresponding to the defect pixels; and providing, for each focus level, a measurement indicative of a displacement between the set of defect images and at least one reference image, resulting in a plurality of measurements corresponding to the plurality of focus levels.

Description

TECHNICAL FIELD

[0001] The presently disclosed subject matter relates generally to the field of mask inspection, and more particularly, to defect detection and measurement with respect to photomasks. BACKGROUND

[0002] Current demands for high density and high performance associated with very large scale integration of microelectronic devices require sub-micron features, increased transistor speed and circuit speed, and increased reliability. As semiconductor processes advance, pattern dimensions, such as line widths, and other types of critical dimensions continue to shrink. These demands require that device features be formed with high accuracy and high uniformity, which in turn requires careful monitoring of the manufacturing process, including automated inspection of the devices while they are still in the form of semiconductor wafers.

[0003] Semiconductor devices are typically manufactured using photolithography masks (also referred to as photomasks, masks, or reticles) in a photolithography process. The photolithography process is one of the main processes in semiconductor device manufacturing and includes patterning a wafer surface according to a circuit design of a semiconductor device to be produced. Such a circuit design is first patterned on a mask. Thus, in order to obtain a working semiconductor device, the mask must be defect-free. Masks are manufactured by complex processes and can suffer from various defects and variations.

[0004] Furthermore, a mask typically creates many dies on a wafer in a repetitive manner. Thus, any defect on the mask will be repeated multiple times on the wafer and will result in a defect in multiple devices. Establishing a process suitable for production requires strict control of the entire photolithography process, especially considering large scale circuit integration and the constant reduction in size of semiconductor devices.

[0005] Various mask inspection methods have been developed and used. According to certain conventional mask design and evaluation techniques, a mask is created and used to expose a wafer through the mask, and then an inspection is performed to determine whether the features / patterns of the mask have been transferred to the wafer according to the design. Any variation of the final printed features from the intended design can require modification of the design, repair of the mask, creation of a new mask, and / or exposure of a new wafer.

[0006] Alternatively, various mask inspection tools can be used to directly inspect the mask. The inspection process can include multiple inspection steps. The inspection steps can be performed multiple times during the manufacturing process of the mask, e.g., after the manufacturing or processing of certain layers, etc. Additionally or alternatively, each inspection step can be repeated multiple times, e.g., multiple times for different mask locations, or multiple times for the same mask location with different inspection settings.

[0007] Mask inspection generally involves generating certain inspection output (e.g., images, signals, etc.) for a mask by directing light or electrons to the mask and detecting light or electrons from the mask. Once the output is generated, defect detection is typically performed by applying defect detection methods and / or algorithms to the output. Generally, the goal of the inspection is to provide high sensitivity and accuracy for defect detection and / or related measurements on the mask. SUMMARY

[0008] According to certain aspects of the presently disclosed subject matter, there is provided a computerized system for inspecting a mask that can be used to manufacture a semiconductor sample, the system comprising: an inspection tool configured to: provide an original defect image comprising one or more defect pixels representing a candidate defect, and a location of the candidate defect on the mask; and based on the location, acquire a library of defect images and a library of reference images of the candidate defect at a plurality of focus levels in a focus process window, the library of defect images comprising a set of defect images acquired at each focus level, and the library of reference images comprising a set of reference images acquired at each focus level; and processing and memory circuitry (PMC) operably connected to the inspection tool and configured to: determine a best focus among the plurality of focus levels, and generate a synthetic defect image based on the set of defect images at the best focus; align the original defect image with the synthetic defect image to identify a region of one or more target pixels in the synthetic defect image corresponding to the one or more defect pixels; and for each focus level, provide a measurement indicative of a displacement between the set of defect images at the focus level and at least one reference image derived from the set of reference images based on the region, thereby yielding a plurality of measurements corresponding to the plurality of focus levels.

[0009] In addition to the features described above, the system according to this aspect of the presently disclosed subject matter can include one or more of the following listed features (i) to (xvi) in any desired combination or arrangement technically possible:

[0010] (i) The candidate defect is from a list of candidate defects selected from a defect map indicative of a distribution of candidate defects on the mask or a portion thereof.

[0011] (ii) The inspection tool is further configured to calibrate a print threshold (PT). Providing the measurement comprises applying the PT to the set of defect images and the set of reference images at the focus level, thereby yielding a set of binary defect images and a set of binary reference images, and performing the measurement based on the set of binary defect images and the set of binary reference images.

[0012] (iii) The library of defect images and the library of reference images are acquired by placing the candidate defect at an optimal position in a field of view (FOV) of the inspection tool, wherein the optimal position is selected to at least reduce noise caused by FOV distortion.

[0013] (iv) the plurality of focus levels are predefined based on focus steps according to accuracy and throughput requirements.

[0014] (v) the plurality of focus levels further include one or more focus levels that extend the focus process window.

[0015] (vi) the best focus is determined by applying a focus measurement on at least one of the defect images in the set of defect images at each focus level.

[0016] (vii) the aligning further includes verifying registrability of the pattern included in the synthetic defect image, and determining the region in the synthetic defect image based on the verifying.

[0017] (viii) the registrability verifying includes shifting the pattern in a set of directions with respective offsets to obtain a set of shifted images, performing image registration between the synthetic defect image and the set of shifted images, and determining the registrability based on results of the image registration.

[0018] (ix) the PMC is further configured to determine the best focus of the plurality of focus levels for the library of reference images, and in response to an offset between the best focus of the reference images and the best focus of the defect image, correlate the reference images and the respective focus levels of the defect image based on the offset.

[0019] (x) the at least one reference image is a synthetic reference image generated by combining a set of reference images.

[0020] (xi) the set of defect images consists of one defect image, and the synthetic defect image is the defect image.

[0021] (xii) the providing the measurement includes measuring displacements of difference images derived between each defect image of the set of defect images and the at least one reference image in the region, thereby producing a set of displacements corresponding to the set of defect images, and generating the measurement result based on the set of displacements.

[0022] (xiii) the mask is a multi-die mask, the library of defect images is captured for candidate defects located in an inspection die, and the library of reference images is captured from corresponding locations in a reference die.

[0023] (xiv) the mask is a single-die mask, and the library of defect images and the library of reference images are taken from different regions in the same die sharing similar design patterns.

[0024] (xv) the steps of providing the original defect image, taking, determining, aligning, and providing the measurement are repeated for one or more additional candidate defects from a list of candidate defects selected from the defect map indicating a distribution of candidate defects on the mask or portion thereof.

[0025] (xvi) the inspection tool is an actinic inspection tool configured to mimic an optical configuration of a lithography tool that can be used to manufacture the semiconductor sample.

[0026] According to other aspects of the presently disclosed subject matter, there is provided a method of inspecting a mask that can be used to manufacture a semiconductor sample, the method being performed by processing and memory circuitry (PMC), and the method comprising: obtaining, from an inspection tool: an original defect image comprising one or more defect pixels representing a candidate defect, and a location of the candidate defect on the mask; and based on the location, a library of defect images of the candidate defect obtained at a plurality of focus levels in a focus process window, and a library of reference images, the library of defect images comprising a set of defect images obtained at each focus level, and the library of reference images comprising a set of reference images obtained at each focus level; and determining a best focus among the plurality of focus levels, and generating a synthetic defect image based on the set of defect images at the best focus; aligning the original defect image with the synthetic defect image to identify a region of one or more target pixels in the synthetic defect image corresponding to the one or more defect pixels; and for each focus level, providing, based on the region, a measure indicative of a displacement between the set of defect images at the focus level and at least one reference image derived from the set of reference images, thereby yielding a plurality of measures corresponding to the plurality of focus levels.

[0027] This aspect of the disclosed subject matter can include one or more of the features(i) through (xvi) listed above with respect to the system, in any desired combination or arrangement technically possible, with the necessary modifications.

[0028] According to other aspects of the presently disclosed subject matter, there is provided a non-transitory computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform a method of inspecting a mask usable for manufacturing a semiconductor sample, the method comprising: obtaining, from an inspection tool: an original defect image comprising one or more defect pixels representing a candidate defect, and a location of the candidate defect on the mask; and based on the location, a library of defect images of the candidate defect obtained at a plurality of focus levels in a focus process window, and a library of reference images, the library of defect images comprising a set of defect images obtained at each focus level, and the library of reference images comprising a set of reference images obtained at each focus level; and determining a best focus among the plurality of focus levels, and generating a synthetic defect image based on the set of defect images at the best focus; aligning the original defect image with the synthetic defect image to identify a region of one or more target pixels in the synthetic defect image corresponding to the one or more defect pixels; and for each focus level, providing a measure indicative of a displacement between the set of defect images at the focus level and at least one reference image derived from the set of reference images based on the region, thereby producing a plurality of measurements corresponding to the plurality of focus levels.

[0029] This aspect of the disclosed subject matter can include one or more of features (i) through (xvi) listed above with respect to the system, in any desired combination or arrangement technically possible, with the necessary modifications. BRIEF DESCRIPTION OF DRAWINGS

[0030] For the purpose of understanding this disclosure and to acquaint those skilled in the art with the manner of executing this disclosure, embodiments will now be described, by way of non-limiting examples only, with reference to the accompanying drawings in which:

[0031] Figure 1 A functional block diagram of a mask inspection system according to certain embodiments of the presently disclosed subject matter is shown.

[0032] Figure 2 A general flowchart of mask inspection of a mask usable for manufacturing a semiconductor sample according to certain embodiments of the presently disclosed subject matter is shown.

[0033] Figure 3 A preliminary processing prior to the present mask inspection and EPD estimation process according to certain embodiments of the presently disclosed subject matter is shown.

[0034] Figure 4 A general flowchart of alignment between an original defect image and a synthetic defect image according to certain embodiments of the presently disclosed subject matter is shown.

[0035] Figure 5 A schematic diagram of an actinic inspection tool and a lithography tool according to certain embodiments of the presently disclosed subject matter is shown.

[0036] Figure 6 is a schematic illustration of exemplary defect images and reference images for a given candidate defect on a mask, in accordance with certain embodiments of the presently disclosed subject matter.

[0037] Figure 7 shows a defect image library and a reference image library acquired for a given candidate defect on a mask, in accordance with certain embodiments of the presently disclosed subject matter.

[0038] Figure 8 shows a defect image set at the best focus, in accordance with certain embodiments of the presently disclosed subject matter.

[0039] Figure 9 is a schematic illustration of a registrability verification of an exemplary pattern, in accordance with certain embodiments of the presently disclosed subject matter.

[0040] Figure 10 shows an example of an original defect image, a defect image in the defect image library, and a target region identified in the defect image, in accordance with certain embodiments of the presently disclosed subject matter.

[0041] Figure 11 shows an example of a binary defect image, a binary reference image, and a difference image thereof, in accordance with certain embodiments of the presently disclosed subject matter.

[0042] Figure 12 is a schematic illustration of a general lithography and pattern transfer process based on a print threshold, in accordance with certain embodiments of the presently disclosed subject matter.

[0043] Figure 13 shows an example of an EPD measurement on a binary difference image, in accordance with certain embodiments of the presently disclosed subject matter.

[0044] Figure 14 shows an exemplary case where the best focus 1102 of the defect image library is offset from the best focus 1104 of the reference image library, in accordance with certain embodiments of the presently disclosed subject matter.

[0045] Figure 15 shows an exemplary graphical representation of a plurality of EPD measurements corresponding to a plurality of focus levels, in accordance with certain embodiments of the presently disclosed subject matter. DETAILED DESCRIPTION

[0046] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be understood by those skilled in the art that the presently disclosed subject matter can be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the presently disclosed subject matter.

[0047] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as "checking", "providing", "acquiring", "determining", "aligning", "calibrating", "applying", "performing", "placing", "verifying", "shifting", "correlating", "measuring", "repeating", "obtaining", "simulating", or the like, refer to the action and / or processes of a computer that manipulates and / or transforms data represented as physical, such as electronic, quantities. The term "computer" should be expansively construed to cover any kind of hardware-based electronic device with data processing capabilities, including, by way of non-limiting examples, a mask inspection system, an inspection tool, an EPD estimation system, and respective portions thereof as disclosed in the present application.

[0048] The term "mask" used in the present specification is also referred to as "photolithographic mask" or "photomask" or "reticle". Such terms should be construed equivalently and expansively to encompass a template holding a circuit design (e.g., defining a layout of a particular layer of an integrated circuit) to be patterned on a semiconductor wafer in a photolithographic process. For example, a mask can be implemented as a fused quartz plate covered with a pattern of opaque, transparent, and phase-shifting areas that are projected onto a wafer in a photolithographic process. For example, a mask can be an extreme ultraviolet (EUV) mask or an argon fluoride (ArF) mask. As another example, a mask can be a memory mask (usable for manufacturing memory devices) or a logic mask (usable for manufacturing logic devices).

[0049] The term "inspection" or "mask inspection" used in the present specification should be construed expansively to encompass any operation for assessing the accuracy and integrity of a photomask manufactured with respect to a circuit design and its ability to produce an accurate representation of the circuit design on a wafer. Inspection can include any kind of operation related to various types of defect detection, defect review, and / or defect classification, and / or metrology operations during and / or after the mask manufacturing process, and / or during the mask is used for semiconductor sample manufacturing. Inspection can be provided using a non-destructive inspection tool after the mask is manufactured. By way of non-limiting example, an inspection process can include one or more of the following operations: scanning (in a single or multiple scans), imaging, sampling, detecting, measuring, classifying, and / or other operations provided with respect to a mask or portions thereof using an inspection tool. Likewise, mask inspection can also be construed to include, for example, generation of inspection recipe(s) and / or other setup operations prior to actual inspection of the mask. It is noted that the term "inspection" or its synonyms used in the present specification are not limited to resolution or size of an inspected area unless specifically stated otherwise. Various non-destructive inspection tools include, by way of non-limiting examples, optical inspection tools, scanning electron microscopes, atomic force microscopes, etc.

[0050] The term "metrology operation" as used in the present specification should be broadly interpreted to encompass any metrology operation procedure for extracting metrology information related to one or more structural elements on a semiconductor sample, such as a mask. In some embodiments, the metrology operation can include a measurement operation, such as, for example, a critical dimension (CD) measurement performed for certain structural elements on a sample, including but not limited to the following: dimensions (e.g., line width, line spacing, contact diameter, element size, edge roughness, grayscale statistics, etc.), element shape, distance within or between elements, related angles, overlay information associated with elements corresponding to different design levels, etc. The measurement results, such as measurement images, are analyzed, for example, by employing image processing techniques. It is noted that the term "metrology" or its synonyms as used in the present specification are not limited to measurement techniques, measurement resolution, or size of the inspection area, unless specifically stated otherwise.

[0051] The term "sample" as used in the present specification should be broadly interpreted to encompass any type of wafer, related structures, combinations, and / or components thereof, used for manufacturing semiconductor integrated circuits, magnetic heads, flat panel displays, and other semiconductor manufacturing articles.

[0052] The term "defect" as used in the present specification should be broadly interpreted to encompass any type of abnormality or unwanted feature / functionality formed on a sample. In some cases, the defect can be a defect of interest (DOI), which is a real defect that has some impact on the functionality of the manufactured device when printed on a wafer, and thus is detected in the interest of the customer. For example, any "killer" defect that can cause yield loss can be represented as a DOI. In some other cases, the defect can be a nuisance (also referred to as a "false positive" defect), which can be disregarded as the defect has no impact on the functionality of the completed device.

[0053] The term "candidate defect" as used in the present specification should be broadly interpreted to encompass a suspicious defect location on a mask that is detected to have a relatively high probability of being a defect of interest (DOI). Thus, upon review, the candidate defect can actually be a DOI, or in some other cases, the candidate defect can be a nuisance or random noise caused by different variations during inspection (e.g., process variations, color variations, mechanical and electrical variations, etc.).

[0054] The terms "non-transitory storage" and "non-transitory storage media" as used herein, should be taken to be broad terms and should be interpreted to cover any volatile or non-volatile computer-readable media suitable for storing the instructions for execution by the computer. These terms should be construed to cover a single medium or multiple media (e.g., centralized or distributed databases, and / or associated caches and servers) that store the instructions in a manner such that they are not readily accessible until loaded into the computer. The terms should therefore be taken to include, but not be limited to, memories, including both volatile and non-volatile memories, such as read-only memory ("ROM"), random access memory ("RAM"), magnetic storage media, optical storage media, flash memory devices, and the like.

[0055] It should be appreciated that certain features of the presently disclosed subject matter, which are described in the context of separate embodiments, can also be provided in combination in a single embodiment, unless otherwise specifically noted, Conversely, various features of the presently disclosed subject matter, which are described in the context of a single embodiment, can also be provided separately or in any appropriate subcombination. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the methods and devices. However, it will be apparent to one skilled in the art that the methods and devices can be practiced without these specific details.

[0056] In view of this, attention is turned to Figure 1 , Figure 1 A functional block diagram of a mask inspection system in accordance with certain embodiments of the presently disclosed subject matter is shown.

[0057] Figure 1 The inspection system 100 shown in FIG. 1 can be used to inspect a mask during or after a mask manufacturing process, and / or during a semiconductor sample manufacturing process that uses the mask. As noted above, inspection as referred to herein can be interpreted to cover any type of operation related to defect inspection / detection, various types of defect classification, and / or metrology operations such as, for example, critical dimension (CD) measurements, on a mask or portions thereof. In accordance with certain embodiments of the presently disclosed subject matter, the illustrated inspection system 100 includes a computer-based system 101 that is capable of automatically inspecting and detecting defects on a mask.

[0058] As noted above, the defects to be detected herein can refer to any type of anomaly or unwanted feature / function formed on the mask relative to the original design. The defects to be detected on the mask can include various defects such as, for example, bridging, protrusions, breaks, critical dimension (CD) related defects, contact anomalies (such as contact missing, contact merging, contact shrinkage, etc.), or any other type of defect. For example, in some cases, the defects to be detected can be related to edge placement displacement (EPD), which indicates a deviation between the actual position of one or more edges / profiles of a printed feature on the mask from its planned / expected position (also referred to herein as EPD defects). In some embodiments, the system 101 can be configured to perform CD measurements for detected EPD defects and provide its EPD estimates. In such cases, the system 101 is also referred to as an EPD estimation system, which is a subsystem of the inspection system 100.

[0059] The system 101 can be operatively connected to a mask inspection tool 120 configured to scan the mask and capture one or more images of the mask for inspection thereof. The term “mask inspection tool” as used in this specification is to be broadly construed to encompass any type of inspection tool that can be used in mask inspection related processes, including, by way of non-limiting examples, scanning (in single or multiple scans), imaging, sampling, detecting, measuring, classifying, and / or other processes of a mask or portions thereof.

[0060] Without limiting the scope of the disclosure in any way, it is also noted that the mask inspection tool 120 can be implemented as various types of inspection machines, such as optical inspection tools, electron beam tools, etc. In some cases, the mask inspection tool 120 can be a relatively low resolution inspection tool (e.g., optical inspection tool, low resolution scanning electron microscope (SEM), etc.). In some cases, the mask inspection tool 120 can be a relatively high resolution inspection tool (e.g., high resolution SEM, atomic force microscope (AFM), transmission electron microscope (TEM), etc.). In some cases, the inspection tool can provide both low resolution image data and high resolution image data. In some embodiments, the mask inspection tool 120 has metrology capabilities and can be configured to perform metrology operations on the captured images. The resulting image data (low resolution image data and / or high resolution image data) can be transmitted to the system 101 directly or via one or more intermediate systems. The present disclosure is not limited to any particular type of mask inspection tool and / or resolution of the image data produced by the inspection tool.

[0061] According to certain embodiments, the mask inspection tool can be implemented as an actinic inspection tool configured to emulate / optically mimic the optical configuration of a lithography tool (e.g., a scanner or stepper) that can be used to manufacture the semiconductor sample, for example, by projecting the pattern formed on the mask onto a wafer, as described in further detail below with respect to Figure 5

[0062] Turning now to Figure 5 wherein a schematic diagram of an actinic inspection tool and a lithography tool according to certain embodiments of the presently disclosed subject matter is shown.

[0063] Similar to the lithography tool 520, the actinic inspection tool 500 can include an illumination source 502 configured to produce light (e.g., laser light) at an exposure wavelength, illumination optics 504, a mask holder 506, and projection optics 508. The illumination optics 504 and the projection optics 508 can include one or more optical elements such as, for example, lenses, apertures, spatial filters, etc.

[0064] In the lithography tool 520, a mask is positioned at the mask holder 506 and optically aligned to project an image of the circuit pattern to be replicated onto a wafer placed on the wafer holder 512 (e.g., to produce or replicate the pattern on the wafer by employing various stepping, scanning, and / or imaging techniques). Unlike the lithography tool 520, instead of placing the wafer holder 512, the actinic inspection tool 500 places a detector 510 such as, for example, a charge-coupled device (CCD) in the location of the wafer holder, where the detector 510 is configured to detect the light projected through the mask and generate an image of the mask.

[0065] As can be seen, the actinic inspection tool 500 is configured to emulate the optical configuration of the lithography tool 520, including but not limited to, for example, illumination / exposure conditions such as, for example, wavelength, partial coherence of the exposure light, pupil shape, illumination aperture, numerical aperture (NA), etc., which are used to expose the photoresist during the actual lithography process during semiconductor device manufacturing. Thus, the mask image 514 acquired by the detector 510 is expected to be similar to the image 516 of a wafer manufactured using the mask via the lithography tool. The mask image acquired using such an actinic inspection tool is also referred to as an aerial image. The aerial image is provided to the system 101 for further processing, as described below.

[0066] According to certain embodiments, in some cases, the mask inspection tool 120 can be implemented as a non-actinic inspection tool such as, for example, a conventional optical inspection tool, an electron beam tool (e.g., SEM), etc. In such cases, the detector of the inspection tool is capable of interfacing with the particular type of microscope used and digitizing the image information from the microscope, thereby acquiring an image of the mask. ​

[0067] Simulation of the acquired images can be performed to simulate the optical configuration of the lithography tool, thereby generating an aerial image. In some cases, the image simulation can be performed by the system 101 (e.g., by including an image simulation model in the PMC 102, the functionality of the simulation can be integrated into the PMC 102 of the system 101), while in some other cases, the image simulation can be performed by the processing module of the mask inspection tool 120, or by a separate simulation engine / unit operably connected to the mask inspection tool 120 and the system 101.

[0068] For illustration purposes only, certain embodiments described below provide for images acquired by a photochemical mask inspection tool. Those skilled in the art will readily appreciate that the teachings of the presently disclosed subject matter are equally applicable to images acquired by any other suitable technique and inspection tool, and further converted to aerial images using appropriate simulation models. The term “aerial image” is to be interpreted broadly as encompassing images acquired by photochemical mask inspection tools as well as aerial images simulated from images captured by non-photochemical inspection tool(s).

[0069] The system 101 comprises a processor and memory circuitry (PMC) 102 operably connected to a hardware-based I / O interface 126. The PMC 102 is configured to provide processing required for the operation of the system, as further detailed with reference to Figure 2 、 Figure 3 and Figure 4 described in further detail, and comprises a processor (not shown separately) and a memory (not shown separately). The processor of the PMC 102 can be configured to execute several functional modules in accordance with computer-readable instructions implemented on a non-transitory computer-readable memory comprised in the PMC. Such functional modules are referred to hereinafter as being comprised in the PMC.

[0070] The processor referred to herein can represent one or more general- purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processor can be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, processor implementing other instruction sets, or processors implementing a combination of instruction sets. The processor can also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processor is configured to execute instructions for performing the operations and steps discussed herein.

[0071] The memory, as referred to herein, can include a main memory (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or blue

[0072] As previously mentioned, in some embodiments, the system 101 can be configured to detect defects on the mask, such as EPD defects. EPD defects can be caused by various factors, such as physical effect(s) during the mask manufacturing process, and / or other factors, e.g., oxidation (which can occur gradually during mask usage), particles, scratches, crystal growth, electrostatic discharge (ESD), etc. Such defects, if not detected prior to wafer mass production, will be repeated multiple times on the produced wafers, potentially resulting in defective semiconductor devices (e.g., affecting the functionality of the devices and failing to meet the expected performance), and adversely affecting the yield, especially considering the reduction in the size of semiconductor devices in large-scale circuit integration and advanced photomask processes.

[0073] To perform CD measurements on EPD defects (or candidate defects) detected by the mask inspection tool, it is typically required to move the mask to a separate metrology tool, which is used to capture additional images of the mask at the location of the candidate defects, and perform measurements on the additional images. The use of two tools (the inspection tool and the metrology tool) and the switching between them is both time-consuming (thereby affecting the inspection throughput) and cost-inefficient. Moreover, since the two tools typically have different coordinate systems, it is difficult to ensure that the additional images captured by the metrology tool are from the exact locations reported by the inspection tool, not to mention that these tools can also be associated with navigation errors with respect to a given coordinate. Thus, in some cases, it can happen that the metrology tool unknowingly acquires additional images at wrong locations, and the measurements obtained at such locations are invalid / nonsensical, and in some cases even misleading information is provided to the user.

[0074] Moreover, as mentioned above, lithography tools aim to print the circuit pattern of the mask onto a semiconductor sample within a process window (which can be defined with respect to various lithography parameters) in order to manufacture integrated circuits with high yield. For example, one of the main impacts on the yield is related to the focus process window (also referred to as the defocus process window), which refers to the range of focus of the lithography tool in which the semiconductor sample should be printed in order to meet the desired yield. It is desirable to check how the circuit pattern on the mask can respond to changes in various lithography parameters within the process window, such as, for example, different focus levels within the focus process window, for example, by estimating printing defects / errors, such as EPDs associated with different parameters within the process window. Such estimation can provide the user with an indication of how different parameters can affect the chip yield in the entire process window.

[0075] According to certain embodiments of the presently disclosed subject matter, improved mask inspection systems and methods are presented that are configured to perform CD measurements for detected EPD defects and provide a range of EPD estimates across a process window with higher accuracy and improved yield.

[0076] According to certain embodiments, the mask inspection system 100 comprises a mask inspection tool 120 and a subsystem 101 operably connected to the inspection tool and configured for EPD estimation as described above. The mask inspection tool 120 can be configured to provide an original defect image comprising one or more defect pixels representing a candidate defect, and a location of the candidate defect on the mask. The mask inspection tool 120 can be further configured to acquire, based on the location, a library of defect images and a library of reference images of the candidate defect at a plurality of focus levels across a focus process window. The library of defect images comprises a set of defect images acquired at each focus level, and the library of reference images comprises a set of reference images acquired at each focus level.

[0077] The functional modules included in the PMC 102 of the system 101 can include an image processing module 104, an alignment 106, and a measurement module 108. The image processing module 104 can be configured to determine a best focus among the plurality of focus levels, and generate a composite defect image based on the set of defect images at the best focus. The alignment 106 can be configured to align the original defect image with the composite defect image to identify a region of one or more target pixels in the composite defect image corresponding to the one or more defect pixels. The measurement module 108 can be configured to provide, for each focus level, a measurement (e.g., an EPD measurement) indicative of a displacement between the set of defect images and at least one reference image derived from the set of reference images of the focus level, thereby resulting in a plurality of measurements corresponding to the plurality of focus levels.

[0078] The operation of the system 100, the system 101, the PMC 102, and the functional modules therein will be described in further detail with reference to Figure 2 , Figure 3 and Figure 4 .

[0079] According to certain embodiments, system 100 can include a storage unit 122. Storage unit 122 can be configured to store any data required for operation of system 100 and system 101, such as data related to inputs and outputs of system 100 and system 101, and intermediate processing results produced by system 101. For example, storage unit 122 can be configured to store raw defect images, defect image libraries and reference image libraries produced by mask inspection tool 120 and / or derivatives thereof (e.g., pre-processed images). Accordingly, images can be retrieved from storage unit 122 and provided to PMC 102 for further processing.

[0080] In some embodiments, system 100 can optionally include a computer-based graphical user interface (GUI) 124 configured to enable user-specified inputs related to system 101. For example, a user can be presented with a visual representation of a mask (e.g., via a display forming part of GUI 124), such as an image of the mask or a portion thereof. The user can be provided with options via the GUI to define certain operational parameters, such as, for example, process window parameters, such as, for example, a range of the process window, a focus step size, etc., a number of defect images and reference images to be acquired at a given focus level, a print threshold, etc. In some cases, the user can also view operational results on the GUI, such as acquired image libraries, a plurality of measurement results corresponding to different focus levels, and / or further inspection results.

[0081] As noted above, system 101 is configured to receive, via I / O interface 126, defect image libraries and reference image libraries of candidate defects at a plurality of focus levels throughout a focus process window. These images can include images (and / or derivatives thereof) captured by mask inspection tool 120 and / or pre-processed images derived from the captured images obtained via various pre-processing stages, etc. Note that in some cases, the images can include associated digital data (e.g., metadata, handcrafted attributes, etc.). It is also noted that in some embodiments, the image data relates to a target layer of a semiconductor device to be printed on a wafer.

[0082] System 101 is further configured to process the received images and send operational results (e.g., a plurality of measurement results corresponding to different focus levels, etc.) to storage unit 122, and / or GUI 124 for rendering, and / or mask inspection tool 120, via I / O interface 126.

[0083] According to some embodiments, in addition to system 101, mask inspection system 100 may also include one or more inspection modules, such as, for example, additional defect detection modules and / or automated defect review modules (ADR) and / or automated defect classification modules (ADC) and / or metrology-related modules and / or other inspection modules that can be used to perform additional inspections of the mask. One or more inspection modules may be implemented as stand-alone computers, or their functionality (or at least some of them) may be integrated with mask inspection tool 120. In some embodiments, the output obtained from system 101 may be used by mask inspection tool 120 and / or one or more inspection modules (or portions thereof) for further inspection of the mask.

[0084] Those skilled in the art will readily understand that the teachings of the currently disclosed subject matter are not subject to Figure 1 The system shown is subject to constraints; equivalent and / or modified functionality may be combined or divided in another manner and may be implemented in any suitable combination of software, firmware and / or hardware.

[0085] Note that in some cases... Figure 1 The mask inspection system shown can be implemented in a distributed computing environment. For example, the mask inspection tool 120 and subsystem 101 can be distributed across different devices (local and / or remote devices) and linked via a communication network. For example, the mask inspection tool 120 can be located within a manufacturing plant, while the subsystem 101 can be a processing server remotely connected to the inspection tool. Additionally or alternatively, the aforementioned functional modules included in PMC 102 can also be distributed across several local and / or remote devices and linked via a communication network. It should also be noted that in other embodiments, one or more of the mask inspection tool 120, storage unit 122, and / or GUI 124 can be external to system 100 and communicate with system 101 via I / O interface 126. System 101 can be implemented as one or more independent computers for use with the mask inspection tool. Optionally, the various functions of system 101 can be at least partially integrated with the mask inspection tool 120, thereby facilitating and enhancing the functionality of the mask inspection tool 120 in inspection-related processes.

[0086] Although this is not necessarily the case, the operating procedures of system 101 and system 100 can correspond to the following: Figures 2 to 4 Some or all of the stages of the described method. Similarly, regarding... Figures 2 to 4 The described method and its possible implementations can be implemented by system 101 and system 100. Therefore, it should be noted that regarding Figures 2 to 4 The methods described and the embodiments discussed can also be implemented as various embodiments of system 101 and system 100 with the necessary modifications, and vice versa.

[0087] Reference is now made to the following descriptions and the drawings wherein Figure 2 wherein a general flowchart of mask inspection useful for manufacturing a mask for a semiconductor sample is shown in accordance with certain embodiments of the presently disclosed subject matter.

[0088] An original defect image including one or more defect pixels representing a candidate defect can be provided (202), e.g., by the mask inspection tool 120. The location of the candidate defect on the mask (e.g., in mask coordinates) is also obtained. According to certain embodiments, the candidate defect is from a list of candidate defects selected from a defect map indicative of a distribution of candidate defects on the mask (or at least a portion of the mask).

[0089] According to some embodiments, the defect map is a result from a preliminary inspection of the mask (e.g., by the mask inspection tool 120). Figure 3 A preliminary process prior to the present mask inspection and EPD estimation process in accordance with certain embodiments of the presently disclosed subject matter is shown.

[0090] A list of candidate defects can be selected (302) from a defect map resulting from a preliminary inspection of the mask, e.g., by the mask inspection tool or a separate defect detection module, as exemplified in more detail below.

[0091] For example, a plurality of inspection images of the mask can be sequentially obtained during scanning of the mask, each inspection image representing a respective portion of the mask. A plurality of defect maps corresponding to the plurality of inspection images can be generated, e.g., by a defect detection module of the mask inspection tool. Each defect map can be generated using at least one reference image and can be indicative of a distribution of candidate defects on the respective inspection image. For example, at least one difference image can be generated based on a difference between pixel values of the inspection image and pixel values of the at least one reference image. The defect map can be generated by determining locations of suspicious defects (i.e., candidate defects) based on the at least one difference image using a detection threshold. In some cases, the plurality of defect maps can be combined to obtain a defect map of the mask.

[0092] The term defect map used herein can be interpreted to refer to a defect map corresponding to a portion of the mask, or a defect map of the entire mask. In some embodiments, the generated defect map can further be indicative of one or more defect features of the candidate defect, such as, for example, a location of the candidate defect, an intensity (of a defect signal), and a size, etc. Based on the location of the candidate defect revealed by the defect map, the candidate defect can be located in the respective inspection image.

[0093] In some embodiments, the list of candidate defects can be selected from the defect map based on the intensity of the candidate defects. In some other cases, a preliminary EPD measurement of each candidate defect can be estimated, and the list of candidate defects can be selected based on the ranking of the EPD measurements of the candidate defects in the map and a predefined EPD threshold. For example, the candidate defects in the defect map can be ranked in ascending order according to their preliminary EPD measurements, and the N (N can be a predetermined number) candidate defects with EPD measurements greater than the predefined EPD threshold can be selected to constitute the list of candidate defects.

[0094] For each given candidate defect in the selected list, an image patch can be extracted from the inspection image at the location of the given candidate defect. The image patch includes one or more defect pixels representing the given candidate defect, and is referred to as an original defect image in the following (the term original is used in contrast to a later acquired defect image as described below with reference to block 204).

[0095] The original defect image can be stored (e.g., in storage unit 122) along with the location of the given candidate defect on the mask (e.g., in mask coordinates) for further processing. As part of the preliminary processing, a print threshold can be calibrated (304) before being applied to the later acquired images, which will be described in further detail below.

[0096] Some embodiments of the presently disclosed subject matter propose to use the same mask inspection tool to directly capture new images based on the locations of the candidate defects, instead of sending the locations of the candidate defects to a separate metrology tool to capture the new images and perform EPD measurements on the new images. Specifically, the mask inspection tool is used to acquire (204) a library of defect images and a library of reference images of the candidate defects at a plurality of focus levels in the entire focus process window based on the locations of the candidate defects. The library of defect images includes a set of defect images acquired at each focus level, and the library of reference images includes a set of reference images acquired at each focus level. As described above, the defect images and the reference images are top-down images.

[0097] Using one tool instead of two separate tools to capture both the original defect image and the library of new images can avoid the difference in coordinate systems in different tools and minimize the navigation errors associated with the tools, thereby improving the accuracy of the later obtained EPD measurements. In addition, this can also significantly reduce the inspection cost and improve the yield.

[0098] For each defect image, one or more reference images can be acquired and used as a reference for comparison (such as, for example, in a D2D inspection). For example, in a case where the mask to be inspected is a multi-die mask (where the mask field includes multiple dies with the same / similar design pattern) and the candidate defect is located in a die under inspection on the mask, one or more reference images can be acquired from one or more reference dies (e.g., neighboring dies of the die under inspection) of the die under inspection on the mask (at a location corresponding to the candidate defect).

[0099] As another example, in a case where the mask is a single-die mask (where the mask field includes only one die), the defect image and one or more reference images can be acquired from different regions in the same die of the mask, where the different regions share the same / similar design pattern. For example, the regions sharing the similar design pattern can be identified based on design data of the mask using any suitable algorithm, which will be described in further detail below. Reference is made to Figure 6 and Figure 7 Acquisition of defect image library and reference image library is described.

[0100] Reference is made to Figure 6 , which is a schematic illustration of exemplary defect images and reference images for a given candidate defect on a mask according to certain embodiments of the presently disclosed subject matter.

[0101] As shown, the multi-die mask 600 has a mask field including nine dies sharing the same design pattern. Many candidate defects detected in a preliminary process are illustrated (indicated with asterisks) on the mask. For a given candidate defect 602 in a die under inspection of the mask 600, a defect image 604 around the candidate defect 602 can be acquired by the mask inspection tool. Similarly, a reference image 606 of the defect image 604 can be acquired at a corresponding location of a reference die (e.g., a neighboring die) of the die under inspection.

[0102] A mask inspection tool images only a portion / part of a mask (also referred to as a field of view (FOV) of the tool) at a time. The size and dimension of the FOV can vary depending on certain factors, such as different tool configurations. In one example, an inspection image corresponding to a rectangular FOV can be approximately 1000 pixels long and 1000 pixels wide. In another example, an inspection image corresponding to a rectangular FOV can be approximately 800 pixels by 1600 pixels in size. An inspection image 608 is illustrated in Figure 6 In some cases, when capturing an inspection image containing a candidate defect, the candidate defect can be placed at the center of the FOV. A defect image can then be extracted from the center of the inspection image.

[0103] In some embodiments, when capturing an inspection image containing a candidate defect, the candidate defect can be placed at an optimal location of the FOV. As exemplified in the inspection image 608, the candidate defect 602 is placed at an optimal location near the center of the FOV. In some cases, the optimal location can be selected to reduce various noises, including but not limited to, for example, noises caused by FOV distortion. The term FOV distortion as used herein refers to variations and non-uniformities of image intensity at different locations within the FOV of an image. This can be caused by certain optical system aberrations, including but not limited to, for example, astigmatism, non-uniform illumination in the field, distortion due to lens shape, and speckle, etc. For example, the optimal location can be calculated by overlaying one or more maps that provide information of potentially problematic locations in the FOV (problematic locations can represent aberrated pixels in the FOV). These maps can come from a calibration process performed previously for a particular toolset. The calibration process is based on statistical and theoretical knowledge of the hardware behavior of these tools. The overall process minimizes interaction with potentially problematic sensor regions, and navigates the FOV of the tool to be centered at the optimal location so that the region of interest utilizes the best sensor region.

[0104] A defect image can be extracted from the inspection image in a predetermined size (e.g., 32*32 pixels, 64*64 pixels, 100*100 pixels, etc.). The defect image includes one or more defect pixels representing the candidate defect. The one or more defect pixels are also referred to herein as defect spots, which can be in a size such as 4*4 pixels or 2*2 pixels, etc.

[0105] Thus, in some embodiments, as mentioned above, the defect image library can each be acquired by placing a candidate defect at an optimal location of the FOV of an inspection tool (e.g., the FOV of an image sensor of the tool), and the reference image library is acquired at a corresponding location in a reference die.

[0106] Reference is now made to Figure 7 which shows a defect image library and a reference image library acquired for a given candidate defect on a mask, in accordance with certain embodiments of the presently disclosed subject matter.

[0107] Continuing with the example of Figure 6 for the candidate defect 602, a defect image library 702 and a reference image library 704 are acquired at multiple focus levels throughout a focus process window 700. The defect image library 702 includes a set of defect images acquired at each focus level (e.g., four defect images as exemplified in Figure 7 ). Similarly, the reference image library 704 includes a set of reference images acquired at each focus level (e.g., four reference images corresponding to the four defect images).

[0108] As mentioned earlier, the focusing process window refers to the focusing range of the photolithography tool within which semiconductor samples should be printed to achieve the desired yield. In some cases, the range of the process window can be predefined by the semiconductor sample manufacturer. Figure 7 As shown, the focusing process window 700 includes multiple focus levels, which can be predefined based on a focus step size 706. The focus step size can be determined, for example, based on the manufacturer's process accuracy and yield requirements. For instance, the focus range of the process window could be [-500nm, +500nm], and the focus step size could be 100nm. In this case, there would be a total of 11 focus levels (including the upper boundary (represented as +PW in the figure) and the lower boundary (represented as -PW) of the focus range).

[0109] In some embodiments, there may be a fill range that extends the original range of the focusing process window (e.g., ...). Figure 7 The fill range (708) can be defined according to the manufacturer's process accuracy requirements. In such cases, multiple focus levels may also include one or more focus levels within the fill range. For example, the total number of focus levels (or steps) can be calculated as: (process window + fill range * 2) / focus step size.

[0110] At each focus level, a set of defect images (e.g., such as...) can be continuously acquired by a mask inspection tool (e.g., with a relatively short time interval between every two images). Figure 7 (The example shows four defect images) without changing any tool configuration. The focus level of the inspection tool can then be adjusted to the next of several focus levels in the process window, and another set of defect images can be obtained in a similar manner. Once multiple focus levels have been traversed, multiple sets of defect images are obtained, thus forming a defect image library 702.

[0111] Similarly, a reference image library 704 can be obtained in a similar manner, comprising multiple sets of reference images acquired at multiple focus levels for corresponding locations of reference dies. It should be noted that in this example, although one reference location is used for each candidate defect, and one reference image library 704 is acquired at that reference location, in some other cases multiple references can be identified (e.g., multiple reference locations from multiple reference dies in a mask), and multiple reference image libraries can be acquired at multiple reference locations and used as references to the defect image library, rather than just a single reference image library.

[0112] It should also be noted that, although... Figure 7The set of images (defect images or reference images) acquired at a given focus level is exemplified to include multiple images (e.g., four images), but this is for illustration and exemplification purposes only and is not intended to limit the present disclosure in any way. In some embodiments, the set of images acquired at a given focus level can include a single image.

[0113] The option of having multiple defect / reference images in a set can effectively reduce false positives caused by random noise present in the defect / reference images, thereby improving detection sensitivity and measurement accuracy. For example, in some cases, multiple reference images in a set can be filtered and combined, thereby creating a best reference image that can be used to suppress random noise and reveal true differences between defect images and reference images.

[0114] In some embodiments, the mask inspection tools mentioned herein for capturing original defect images, as well as the defect image library and reference images, such as, for example, mask inspection tool 120, are actinic inspection tools, such as, for example, the Aera mask inspection tool by Applied Materials Inc. Actinic inspection tools are configured to simulate the optical configuration of a lithography tool (e.g., a scanner or stepper) that can be used to manufacture semiconductor wafers from a mask, as mentioned above with reference to Figure 5 The.

[0115] Images (i.e., top-down images) acquired by such actinic inspection tools are expected to resemble images of wafers manufactured via a lithography tool using a mask. In other words, actinic mask inspection tools are configured to capture mask images that can mimic how design patterns in a mask would actually appear in a physical wafer after the manufacturing process.

[0116] In some cases, actinic inspection tools can not be available for inspecting a mask. In such cases, non-actinic inspection tools, such as, for example, conventional optical inspection tools, e-beam tools, etc., can be used to acquire non-top-down images of the mask. Simulations can be performed on the acquired non-top-down images to simulate the optical configuration of a lithography tool, thereby generating top-down images of the mask. Thus, in some embodiments, the described mask inspection method can further include the preliminary step of obtaining a library of images acquired by non-actinic inspection tools, and performing simulations on the images to simulate the optical configuration of a lithography tool (e.g., by image processing module 104 of PMC 102, or by a processing module of mask inspection tool 120, etc.), thereby producing a library of defect images (i.e., top-down images). Figure 2 The described mask inspection method can further include the preliminary step of obtaining a library of images acquired by non-actinic inspection tools, and performing simulations on the images to simulate the optical configuration of a lithography tool (e.g., by image processing module 104 of PMC 102, or by a processing module of mask inspection tool 120, etc.), thereby producing a library of defect images (i.e., top-down images).

[0117] In some embodiments, the obtained library of defect images (and / or reference images) can be pre-processed prior to further processing. Pre-processing can include one or more of the following operations: interpolation (e.g., in case the images have relatively low resolution), noise filtering, focus correction, aberration compensation, and image format transformation, etc.

[0118] It should be noted that the present disclosure is not limited to the specific modalities of the mask inspection tool and / or the types of images acquired thereby and / or the pre-processing operations required to process the images.

[0119] Continuing Figure 2 The description of the foregoing, once the mask inspection tool obtains the library of defect images and the library of reference images of the candidate defects at the plurality of focus levels, the images can be transmitted to the system 101 operatively connected to the mask inspection tool and further processed. In some embodiments, the optimal focus among the plurality of focus levels can be determined and a synthetic defect image can be generated (208) based on the set of defect images at the optimal focus (e.g., by the image processing module 104 in the PMC 102).

[0120] According to certain embodiments, the optimal focus can be determined by applying a focus measure to at least one of the set of defect images at each focus level to identify the image that produces the highest contrast of the image pattern (i.e., the pattern of the sample being imaged). For example, a focus score can be computed based on the focus measure of at least one image at each focus level and the image with the largest score is identified.

[0121] According to certain embodiments, the focus score of an image can be computed using different focus measures that evaluate the degree of focus (e.g., sharpness / contrast) of the image and the present disclosure is not limited to a particular focus score computation. For example, a gradient-based focus measure based on the gradient or approximation of the first derivative of the image can be used to compute the focus score. Such a focus measure follows the assumption that a focused image presents sharper edges than a blurred image. Thus, the energy of the gradient can be exploited to estimate the degree of focus. Similarly, a Laplacian-based focus measure based on the second derivative of the image can also be used. As another example, a statistics-based focus measure based on textual descriptors of the image can be used. Such a focus measure follows the assumption that a defocused image can be interpreted as a texture whose smoothness increases with the defocus level.

[0122] It should be noted that the focus measures as described above are shown for illustrative purposes only and should not be considered as limiting the present disclosure in any way. Other suitable focus measures, such as for example wavelet-based focus measures, or focus measures based on image contrast, can be used in addition or instead of the measures described above.

[0123] Once the optimal focus among multiple focus levels is determined, a synthetic defect image can be generated based on the defect image set at the optimal focus point. (Reference) Figure 8 The image shows a set of defective images at the optimal focus point according to certain embodiments of the subject matter currently disclosed.

[0124] Figure 8 The diagram illustrates a defect image library comprising multiple sets of defect images obtained at multiple focus levels. In this example, each set of defect images comprises five images. In other words, the defect image library comprises five image packages, each containing multiple images corresponding to multiple focus levels. Based on the focus measurements illustrated above, a focus score can be calculated for at least one image in each set of defect images. For example, in some cases, an image can be selected from each set, and a focus score can be calculated for the selected image. For example, the selected image could be a corresponding image from an image package. In some other cases, focus scores can be calculated for all images in each set, and a normalized focus score for each set can be generated by combining the scores of all images. Multiple focus scores (or normalized focus scores) can be sorted, and the optimal focus score can be selected. The focus level from which the optimal focus score is derived is selected as the optimal focus.

[0125] like Figure 8 As shown, assume an optimal focus 800 is selected, and image group 802 is the image acquired at the optimal focus 800. A synthetic defect image can be generated based on defect image group 802, for example, by combining / averaging the defect image group. Since the images in a group are acquired sequentially without changing any tool configuration, it should be recognized that there may be little or no offset between the images, or in some cases only minute offsets (e.g., subpixel offsets). In some embodiments, the image group can be registered before combining / averaging. For example, as shown, the four side images can be registered with the middle image, for example, using the Lucas-Kanade registration algorithm. The registered images can be summed and then averaged to derive the synthetic defect image.

[0126] Image registration mentioned in the present disclosure can include measuring the offset between two images, and moving one image relative to the other in order to correct for the offset. The offset can be caused by various factors, such as, for example, navigation errors caused by tool drift (e.g., scanner and / or stage drift), etc. Registration can be implemented according to any suitable registration algorithm known in the art. For example, registration can be performed using one or more of the following algorithms: region-based algorithms, feature-based registration, or phase correlation registration. An example of a region-based approach is registration using optical flow, such as the Lucas-Kanade (LK) algorithm described above. A feature-based approach is based on finding different points of information (“features”) in the two images, and computing the required transformation between each pair from the correspondence of the features. This allows for elastic registration (i.e., non-rigid registration), where different regions are moved separately. Phase correlation registration is done using frequency domain analysis (where the phase difference in the Fourier domain is converted to registration in the image domain).

[0127] The synthetic defect image generated as described above is believed to suppress random noise, and thus has higher accuracy compared to the individual defect images of the image set. In cases where the image set consists of a single defect image, there is no need to generate a synthetic defect image, or in other words, the single image can be considered as the synthetic defect image.

[0128] Once the synthetic defect image is generated, the original defect images can be aligned (210) with the synthetic defect image (e.g., by the alignment module 106 in the PMC 102) to identify a region of one or more target pixels in the synthetic defect image that correspond to one or more defect pixels in the original defect images. In some cases, in order to align the images, it is necessary to first check whether the pattern contained in the synthetic defect image / original defect images is registrable.

[0129] Reference is made to Figure 4 FIG. 4 shows a general flowchart of the alignment between the original defect images and the synthetic defect image, in accordance with certain embodiments of the presently disclosed subject matter.

[0130] In some embodiments, the alignment can include verifying (400) the registrability of the pattern contained in the synthetic defect image, and determining (408) a region of one or more target pixels in the synthetic defect image (also referred to herein as a target region) based on the verification. In some cases, the registrability of the pattern can be determined based on the periodicity of the pattern relative to the image size.

[0131] In some embodiments, the verifiable registrability can be performed as follows: offsetting the pattern in a set of directions by respective offsets (402) to obtain a set of offset images, performing (404) image registration between the synthetic defect image and the set of offset images, and determining (406) the registrability based on a result of the image registration.

[0132] Figure 9 is a schematic illustration of verification of registrability of an exemplary pattern according to certain embodiments of the presently disclosed subject matter.

[0133] A defect image 902 having a particular line pattern is shown. To determine the registrability of the pattern, the pattern is offset in a set of eight different directions, resulting in eight offset images, as shown. An image registration algorithm, such as any of the algorithms exemplified above, can be used to register between the un-offset image 902 and each of the eight offset images. If all (or a substantial portion of) the offset images can be properly registered with the un-offset image, then the pattern is deemed registrable. Otherwise, the pattern is deemed non-registrable.

[0134] A region of one or more target pixels in the synthetic defect image can be determined based on the verified registrability. For example, where the pattern is deemed registrable, it is relatively easy to identify the target pixels corresponding to the defect pixels in the original defect image, e.g., by performing image registration between two images and finding the corresponding target pixels based on the offset of the registration. In this case, the target region can be determined to include the identified target pixels. In some cases, the target region can additionally include a relatively small pixel dilation that extends the target pixels, to tolerate minor registration errors.

[0135] Where the pattern is deemed non-registrable, it is typically difficult to identify the target pixels corresponding to the defect pixels in the original defect image, since a non-registrable pattern is typically repetitive in the image, and thus it is not possible to separate one repetitive feature from another. To not miss the target pixels, the target region can be a relatively large region. For example, the size of the target region can be determined according to the size of the defect spots of the defect pixels in the original defect image, with a large pixel dilation.

[0136] The target region thus determined can be used as an indication of a location in the defect image library to perform EPD measurements, as described below with reference to block 212.

[0137] In particular, for each focus level, a measurement can be provided (212) based on the area of one or more target pixels, indicating a displacement between the set of defect images at the focus level and at least one reference image derived from the set of reference images, e.g., by the measurement module 108 in the PMC 102. Once the measurement is obtained for each of the plurality of focus levels, a plurality of measurements corresponding to the plurality of focus levels can be provided. For example, the measurement is an EPD measurement as described above.

[0138] Once the target area is identified in the synthetic defect image, as described above, a corresponding area in each image of the defect image library can be identified based on the location of the target area in the synthetic defect image (where it is assumed that there can be little or no offset between the images within the image library).

[0139] Figure 10 Examples of an original defect image, a defect image from a defect image library, and a target area identified in the defect image are shown in accordance with certain embodiments of the presently disclosed subject matter.

[0140] As shown, image 1002 is an original defect image as described above with reference to block 202, and image 1004 is a defect image from a defect image library taken as described above with reference to block 204. As exemplified, both images include an unregistrable pattern with repeating lines and spaces. As described above, in this case, it is difficult to identify the exact target pixel 1006 in the defect image 1004 that corresponds to the defect pixel 1001 in the original defect image 1002. As described above, a target area 1008 can be determined that is large enough to be believed to cover the target pixel.

[0141] As shown, the location of the target pixel 1006 in the defect image 1004 and the location of the defect pixel 1001 in the original defect image 1002 are offset, even though the two images were captured using the same inspection tool. If an EPD measurement is performed directly from the location of the defect pixel 1001 in the original defect image 1002 without performing the alignment process and determining the target area 1008, an invalid measurement will occur because the measurement is taken from the wrong location that is different from the actual defect location that is offset. Using the determined target area, an EPD estimation can be performed within the target area so as not to miss the EPD defect at the actual location, as described in detail below.

[0142] According to certain embodiments, to perform the measurements at the location of the identified target region, a print threshold (PT) can be applied to the defect image set and the reference image set at a given focus level, thereby producing a binary defect image set and a binary reference image set, and the measurements can be performed based on the binary defect image set and the binary reference image set. The binary images provide information of the structural elements / features of the respective portion of the mask that is printable on the semiconductor sample (e.g., wafer).

[0143] In a lithography process, a wafer is covered with a chemical photoresist that responds to a total amount of absorbed energy. If a mask is illuminated above (or in some cases below) a certain intensity (which causes a chemical change in the resist), the pattern is printed on the wafer, this intensity level is hereinafter referred to as the print threshold (PT).

[0144] Reference is now made to Figure 12 wherein a schematic illustration of a general lithography and pattern transfer process based on a print threshold according to certain embodiments of the presently disclosed subject matter.

[0145] As shown, illustration 1200 exhibits an exemplary mask that includes transparent regions 1202 (e.g., made of quartz) that transmit light when illuminated and opaque regions 1204 (e.g., made of chrome) that block light. The plurality of images (overhead images) obtained as described above refer to images captured by a detector that collects transmitted light through the mask.

[0146] In fact, the actual wafer fabrication process of a manufacturing tool (e.g., a scanner or stepper) includes a resist process and an etch process after the lithography process. The wafer is coated with a photoresist, which is a light-sensitive material. Depending on the process, exposure to light hardens or softens portions of the resist. After exposure, the wafer is developed, dissolving the photoresist in certain areas according to the amount of transmitted light (i.e., light intensity) the areas received during exposure.

[0147] For example, a waveform 1205 representing the intensity of the transmitted light is illustrated. If the photoresist of a given area is exposed to transmitted light below a certain intensity, the pattern will be printed on the wafer. These photoresist areas and the areas without photoresist reproduce the design pattern on the mask. Thus, the certain intensity is referred to as the print threshold 1205, as Figure 12 The developed wafer is then exposed to a solvent, which etches away the silicon in the parts of the wafer that are no longer protected by the photoresist coating, thereby producing a printed wafer 1208 (for a given layer).

[0148] Accordingly, in a lithography inspection tool that mimics the optical configuration of a wafer fabrication tool, the waveform 1205 represents the transmitted light that would be captured by the detector of the lithography inspection instrument to form a first image. Since in the lithography inspection tool the detector replaces the wafer, and there is no actual resist and etching process, in order to obtain an image that resembles a printed wafer, the print threshold 1205 needs to be applied to the overhead image to mimic the effect of the resist and etching process, resulting in a binary image that resembles the printed pattern on the wafer 1208. Specifically, the binary image provides information of the plurality of structural elements of the mask that can be printed on the wafer.

[0149] It is noted that although in the present example, the pattern below the print threshold is shown as printable on the wafer (i.e., positive resist), this need not necessarily be the case. In some other cases, the situation can be reversed, i.e., the pattern above the print threshold is printable on the wafer (i.e., negative resist). The present disclosure is not limited to a particular application of a particular resist process or print threshold for presenting printable features.

[0150] According to certain embodiments, the print threshold can be initially calibrated (304) prior to being applied to the image library, as shown in Figure 3 .

[0151] In some embodiments, the PT can be calculated from a representative pattern having known dimensions (e.g., a pattern selected from a "design intent" CAD clip). In some cases, the representative pattern should be long enough to enable the PT to be calculated and averaged over multiple locations therein, thereby reducing tool noise and providing a high accuracy PT calculation.

[0152] For example, after obtaining an overhead image of a representative pattern, the mask inspection tool can simulate wafer resist in order to convert the overhead image to a binary image having widths and lengths corresponding to the "design intent" in the corresponding CAD data. A gray level (GL) threshold can be calculated for all pixels along the length of the representative pattern, while the PT can be calculated as the average (e.g., minimum, maximum, mean, median, or other statistics-based) GL threshold. Optionally, the accuracy of the PT can be improved by further calibration based on exposure conditions and / or calibration based on location in the frame.

[0153] Reference is now made to Figure 11 wherein an example of a binary defect image, a binary reference image, and a difference image thereof, in accordance with certain embodiments of the presently disclosed subject matter, is shown.

[0154] Reference is made to Figure 10 The image 1004 described is a defect image from the defect image library taken at the reference frame 204. The image 1103 is a reference image of the defect image 1004. For example, as shown in Figure 7As illustrated, the image 1004 and the image 1103 can be a pair of images 710 from the defect image library 702 and the reference image library 704 at the same focus level.

[0155] The image 1102 is a binary defect image obtained by applying a print threshold to the defect image 1004. The image 1104 is a binary reference image obtained by applying a print threshold to the reference image 1103. The two binary images can be compared (e.g., by subtracting one from the other) at the identified target region 1008, resulting in a binary difference image 1106 that represents the differences within the target region. As shown, the differences / discrepancies in the binary difference image 1106 indicate edge position displacement (EPD) between edges / profiles of line structures in the two images.

[0156] In some cases, the differences obtained from the binary images can be quite subtle and can be mixed in with random edge roughness. It should be noted that the EPD defects differ from edge roughness (which can be caused by different variations in the manufacturing process) at least in that: i) the EPD is local (present at local locations of the profile), whereas the edge roughness is present along all edges; and ii) the magnitude of the EPD is relatively more significant (i.e., stronger / larger) compared to the magnitude of the subtle roughness along the edges.

[0157] To verify the validity of the identified differences, a GL difference image 1108 can be derived by comparing the defect image 1004 and the reference image 1103. The GL difference image 1108 can be used to verify that the differences indicated in the binary difference image are indeed associated with the EPD defects shown in the GL difference image.

[0158] It should be noted that in some embodiments, the defect image and the corresponding reference image should be registered (e.g., using any of the image registration algorithms described above) prior to comparison. Alternatively, in some embodiments, registration can be skipped. For example, in cases where it can be estimated that there is no substantial shift between the defect image and the reference image, registration can be omitted.

[0159] Figure 13 An example of EPD measurement on a binary difference image is shown, in accordance with certain embodiments of the presently disclosed subject matter. As shown, the maximum distance 1302 between the two edges / profiles in the binary difference image is measured as a measure of edge displacement.

[0160] Reference Figure 11 and Figure 13The above-described measurement process can be repeated for each defect image in the defect image library. Specifically, for each focus level in the plurality of focus levels, the displacement in the difference image derived in the target region between each defect image in the defect image set and the at least one reference image can be measured, resulting in a set of displacements corresponding to the defect image set. The EPD measurement can be generated based on the set of displacements (e.g., by averaging the set of displacements).

[0161] In some embodiments, at a given focus level, each defect image can be compared to a corresponding reference image, as exemplified in the image pair 710. In some cases, a composite reference image can be generated by combining the reference image set at a given focus level, thereby reducing various random noises. The composite reference image can be used as the reference image for each defect image in the defect image set at the given focus level, thereby deriving a difference image with improved SNR. Figure 7

[0162] According to certain embodiments, the optimal focus for the reference image library in the plurality of focus levels can be determined in a similar manner as described for the reference block 208. In response to a shift between the optimal focus of the reference image and the optimal focus of the defect image, the respective focus levels of the reference image and the defect image can be associated based on the shift. Figure 14 An exemplary case is shown in which the optimal focus 1102 of the defect image library is shifted from the optimal focus 1104 of the reference image library, in accordance with certain embodiments of the presently disclosed subject matter. In this case, starting from the optimal focus level, the respective focus levels from the defect image library and the reference image library can be associated. For a defect image at a given focus level, the reference image used for comparison is taken from the associated focus level.

[0163] Once the EPD measurements are obtained for each focus level, a plurality of EPD measurements can be provided, corresponding to the plurality of focus levels in the focus process window. Figure 15 An exemplary plot representation of a plurality of EPD measurements corresponding to a plurality of focus levels, in accordance with certain embodiments of the presently disclosed subject matter, is shown. As previously mentioned, it can be desirable to examine how a circuit pattern on a mask can respond to changes in different focus levels within a process window. The EPD measurements can provide the user with information about how different focus levels in the process window can affect chip yield. In some cases, such a plot can be presented on the GUI 124. Optionally, in cases in which there is a relatively large difference (relative to a change threshold) between the EPD measurements of different focus levels in the process window, the EPD defects can be flagged as defects for further review by the user. Optionally, in cases in which the EPD measurements of different focus levels exceed a respective predefined EPD threshold, the EPD defects can be flagged as defects for further review by the user.​

[0164] The EPD measurements can be used by the mask inspection tool 120 and / or one or more inspection modules included in the mask inspection system 100 for further inspection of the mask, such as, for example, additional defect detection, defect review, defect classification, metrology related operations (e.g., CD measurements), and / or any other inspection operations.

[0165] As described above with reference to block 302, the reference Figure 2 The described process. In some embodiments, a region of interest (ROI) on the mask to be inspected can be predefined, and the above-described process can be used to inspect one or more candidate defects in the ROI. In some cases, the ROI can be defined as the entire mask, while in other cases, the ROI can be defined as a portion of the mask.

[0166] It should be noted that while reference Figure 2 The described mask inspection process is exemplified using the example of a multi-die mask as shown in Figure 6 It should be noted that while reference

[0167] For example, for a candidate defect located in an inspection region in a single die, one or more reference regions from the same die that shares the same design pattern as the inspection region can be used as references for comparison. The reference regions in a single-die mask can be identified in a variety of ways. The design data of the die (or portion(s) thereof) can include various design patterns with specific geometrical structures and arrangements.

[0168] In some embodiments, design data of a single-die mask can be received, and a plurality of design groups can be retrieved, each design group corresponding to one or more die regions with the same design pattern. Thus, regions in the die that correspond to the same design pattern can be identified. It is noted that design patterns can be considered to be “the same” when they are identical, or are highly correlated, or are similar to each other. Various similarity measures and algorithms can be applied to match and cluster similar design patterns, and the present disclosure should not be construed as being limited to any particular measure for deriving the design groups. The clustering of the design groups (i.e., the partitioning of the CAD data into a plurality of design groups) can be performed in advance, or by the PMC 102 as a preliminary step of the current inspection process.

[0169] Optionally, in some embodiments, in response to the EPD measurements, it can be further determined how to respond to the EPD defects, e.g., whether to accept the mask, repair the mask, or reject the mask. This can be done, for example, by evaluating whether the EPD defects, if printed, would affect the functionality of a semiconductor sample manufactured using the mask. In some cases, possible handling operations in response to the presence of EPD defects can include one or more of: repairing the mask, defining the mask as a defective mask, defining the mask as functional, generating a repair indication for the mask, etc. For example, if the EPD defects are not acceptable, the mask can be sent to a mask shop for repair or rejected.

[0170] Optionally, in some embodiments, at least one or any combination of the following outputs / indications can be provided: (i) provide a pass / fail criteria for a mask to be shipped from a mask shop; (ii) provide input to a mask generation process; (iii) provide input to a semiconductor sample manufacturing process; (iv) provide input to a simulation model used in a lithography process; (v) provide a correction map to a lithography tool; and (vi) identify areas on a mask characterized by greater than expected CD variation.

[0171] It should be noted that the masks suitable for the presently disclosed inspection processes can be any kind of mask, including but not limited to memory masks and / or logic masks, and / or ArF masks and / or EUV masks, etc. The present disclosure is not limited to a particular type or function of mask to be inspected.

[0172] According to certain embodiments, the mask inspection processes described above with reference to Figure 2 , Figure 3 and Figure 4 may be included as part of an inspection recipe that can be used by system 101 and / or inspection tool 120 for run-time online mask inspection. Accordingly, the presently disclosed subject matter also includes systems and methods for generating an inspection recipe at a recipe setup stage, where the recipe includes the steps described with reference to Figure 2 , Figure 3 and Figure 4 (and various embodiments thereof). It should be noted that the term “inspection recipe” should be broadly construed to encompass any recipe that can be used by an inspection tool to perform operations related to any type of mask inspection, including embodiments as described above.

[0173] It should be noted that the examples shown in the present disclosure, such as, for example, mask inspection tool architecture and configuration, mask types and / or layouts, example image libraries, process windows and focus levels, and image patterns described above, etc., are shown for illustrative purposes and should not be considered limiting in any way. Other suitable examples / embodiments can be used to supplement or replace the above.

[0174] An advantage of certain embodiments of the mask inspection process described herein is that, for a given candidate defect, the image used for EPD measurement (e.g., the defect image library) is acquired by the same inspection tool that acquired the original defect image, thereby avoiding differences in coordinate systems in different tools and minimizing navigation errors associated with the tools, thereby improving the accuracy of the measurement results. Moreover, using one inspection tool instead of two tools (e.g., one inspection tool to capture the original defect image and one metrology tool to re-capture a new image for measurement) can significantly reduce inspection costs and improve throughput.

[0175] Furthermore, in the EPD estimation process, the original defect image is available and used to align with the newly captured defect image library in order to identify the exact target area corresponding to the original defect pixels, which further ensures the accuracy of the location of the EPD measurement.

[0176] Additional advantages of certain embodiments of the mask inspection process include acquiring the image libraries across different focus levels throughout the process window, enabling estimation of how the circuit pattern on the mask can respond to changes in different focus levels (e.g., by estimating EPD measurement results indicative of print errors associated with different focus levels) and providing indications as to how different parameters can affect chip yield throughout the process window.

[0177] Furthermore, the option of capturing a set of images (particularly multiple images in the defect image set or the reference image set) at each given focus level can effectively suppress random noise in the acquired images and reduce false positives in the resulting difference images, thereby improving the detection sensitivity and accuracy of the EPD measurement.

[0178] Moreover, determining the best focus among the multiple focus levels can identify the actual best focus for each image library (which can vary with respect to various factors, such as different image patterns) to recalibrate the range of the process window. The images from the best focus level are used to align with the original defect image, which further ensures the accuracy of the registration and identification of the target area to perform the EPD measurement.

[0179] It should be understood that the application of the present disclosure is not limited to the details of the description included herein or illustrated in the drawings.

[0180] It should also be understood that the systems according to the present disclosure can be implemented, at least in part, on a suitably programmed computer. Likewise, the present disclosure contemplates a computer program that is readable by a computer for executing the methods of the present disclosure. The present disclosure also contemplates a non-transitory computer-readable memory tangibly embodying a program of instructions executable by a computer to perform the methods of the present disclosure.

[0181] The present disclosure is capable of other embodiments and of being practiced or being carried out in various ways. As such, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. Therefore, those skilled in the art will appreciate that the conception, upon which this disclosure is based, can readily be utilized as a basis for the designing of other structures, methods and systems for carrying out the several purposes of the presently disclosed subject matter.

[0182] Those skilled in the art will readily appreciate that various modifications and changes can be applied to the embodiments of the present disclosure described above without departing from the scope of the present disclosure as defined by the appended claims.

Claims

1. A computerized system of inspecting a mask usable for manufacturing a semiconductor sample, the system comprising: an inspection tool configured to: provide an original defect image comprising one or more defect pixels representing a candidate defect resulting from a preliminary inspection of the mask, and a location of the candidate defect on the mask; and acquire, based on the location, a defect image library and a reference image library of the candidate defect at a plurality of focus levels in an overall focus process window, the defect image library comprising a set of defect images acquired at each focus level, and the reference image library comprising a set of reference images acquired at each focus level; and a processing and memory circuitry (PMC) operably connected to the inspection tool and configured to: determine a best focus among the plurality of focus levels, and generate a synthetic defect image based on the set of defect images at the best focus; align the original defect image with the synthetic defect image to identify a region of one or more target pixels in the synthetic defect image corresponding to the one or more defect pixels; and provide, for each focus level, a measure indicative of a displacement between the set of defect images at the focus level and at least one reference image derived from the set of reference images based on the identified region, thereby resulting in a plurality of measures corresponding to the plurality of focus levels.

2. The computerized system of claim 1, wherein the candidate defect is from a list of candidate defects selected from a defect map indicative of a distribution of candidate defects on the mask or a portion thereof.

3. The computerized system of claim 1 or 2, wherein the inspection tool is further configured to calibrate a print threshold (PT), and the providing measure comprises applying the PT to the set of defect images and the set of reference images at the focus level, thereby resulting in a set of binary defect images and a set of binary reference images, and performing the measure based on the set of binary defect images and the set of binary reference images.

4. The computerized system of claim 1 or 2, wherein the defect image library and the reference image library are acquired by placing the candidate defect at an optimal position in a field of view (FOV) of the inspection tool, wherein the optimal position is selected to at least reduce noise caused by FOV distortions.

5. The computerized system of claim 1 or 2, wherein the plurality of focus levels are predefined based on focus steps according to accuracy and yield requirements.

6. The computerized system of claim 1 or 2, wherein the plurality of focus levels further comprises one or more focus levels extending the focus process window.

7. The computerized system of claim 1 or 2, wherein the best focus is determined by applying a focus measure to at least one defect image in the set of defect images at each focus level.

8. The computerized system of claim 1 or 2, wherein the aligning further comprises verifying registrability of a pattern included in the synthetic defect image, and determining the region in the synthetic defect image based on the verifying.

9. The computerized system of claim 8, wherein the registrability verifying comprises shifting the pattern by respective offsets towards a set of directions to obtain a set of shifted images, performing image registration between the synthetic defect image and the set of shifted images, and determining the registrability based on results of the image registration.

10. The computerized system of claim 1 or 2, wherein the PMC is further configured to determine a best focus among the plurality of focus levels for the reference image library, and in response to a shift between the best focus of the reference image and the best focus of the defect image, correlate respective focus levels of the reference image and the defect image based on the shift.

11. The computerized system of claim 1 or 2, wherein the at least one reference image is a synthetic reference image generated by combining the set of reference images.

12. The computerized system of claim 1 or 2, wherein the set of defect images consists of one defect image, and the synthetic defect image is the defect image.

13. The computerized system of claim 1 or 2, wherein the providing measurements comprises measuring displacements of difference images derived in the region between each defect image of the set of defect images and the at least one reference image, thereby producing a set of displacements corresponding to the set of defect images, and generating the measurements based on the set of displacements.

14. The computerized system of claim 1 or 2, wherein the mask is a multi-die mask, the set of defect images is captured for the candidate defects located in an inspected die, and the set of reference images is captured from corresponding locations in a reference die.

15. The computerized system of any one of claims 1 to 13, wherein the mask is a single-die mask, and the set of defect images and the set of reference images are taken from different regions in the same die sharing similar design patterns.

16. The computerized system of claim 1 or 2, wherein the steps of providing an original defect image, taking a set of defect images of a candidate defect and a set of reference images, determining a best focus, aligning the original defect image with a synthetic defect image, and providing measurements are repeated for one or more additional candidate defects from a list of candidate defects selected from a defect map indicative of a distribution of candidate defects on the mask or a portion thereof.

17. The computerized system of claim 1 or 2, wherein the inspection tool is a light-based inspection tool configured to emulate an optical configuration of a lithography tool usable for manufacturing the semiconductor sample.

18. A computerized method of inspecting a mask usable for manufacturing a semiconductor sample, the method performed by processing and memory circuitry (PMC), and the method comprising: obtained from the inspection tool: an original defect image of the candidate defect and a location of the candidate defect on the mask resulting from a preliminary inspection of the mask, the original defect image comprising one or more defect pixels representing the candidate defect; and a library of defect images of the candidate defect and a library of reference images obtained at a plurality of focus levels throughout a focus process window based on the location, the library of defect images comprising a set of defect images obtained at each focus level and the library of reference images comprising a set of reference images obtained at each focus level; and determining a best focus among the plurality of focus levels and generating a synthetic defect image based on the set of defect images at the best focus; aligning the original defect image with the synthetic defect image to identify a region of one or more target pixels in the synthetic defect image corresponding to the one or more defect pixels; and providing, for each focus level, a measure indicative of a displacement between the set of defect images at the focus level and at least one reference image derived from the set of reference images based on the identified region, thereby resulting in a plurality of measures corresponding to the plurality of focus levels.

19. The computerized method of claim 18, wherein the candidate defect is from a list of candidate defects selected from a defect map indicative of a distribution of candidate defects on the mask or a portion thereof.

20. The computerized method of claim 18 or 19, further comprising obtaining a print threshold (PT) from the inspection tool, and wherein the providing a measure comprises applying the PT to the set of defect images and the set of reference images at the focus level, thereby resulting in a set of binary defect images and a set of binary reference images, and performing the measure based on the set of binary defect images and the set of binary reference images.

21. The computerized method of any one of claims 18 to 20, wherein the library of defect images and the library of reference images are obtained by placing the candidate defect at an optimal position in a field of view (FOV) of the inspection tool, wherein the optimal position is selected to at least reduce noise caused by FOV distortions.

22. The computerized method of any one of claims 18 to 21, wherein the plurality of focus levels is predefined based on focus steps according to accuracy and throughput requirements.

23. The computerized method of any one of claims 18 to 22, wherein the plurality of focus levels further comprises one or more focus levels extending the focus process window.

24. The computerized method of any one of claims 18 to 23, wherein the best focus is determined by applying a focus measure to at least one defect image in the set of defect images at each focus level.

25. The computerized method of any one of claims 18 to 24, wherein the aligning further comprises verifying registrability of a pattern included in the synthetic defect image, and determining the region in the synthetic defect image based on the verifying.

26. The computerized method of claim 25, wherein the registrability verification comprises shifting the pattern by respective offsets in a set of directions to obtain a set of shifted images, performing image registration between the synthetic defect image and the set of shifted images, and determining the registrability based on results of the image registration.

27. The computerized method of any one of claims 18-26, further comprising determining a best focus among the plurality of focus levels for the reference image library, and in response to a shift between the best focus of the reference image and the best focus of the defect image, correlating respective focus levels of the reference image and the defect image based on the shift.

28. The computerized method of any one of claims 18-27, wherein the at least one reference image is a synthetic reference image generated by combining the set of reference images.

29. The computerized method of any one of claims 18-28, wherein the set of defect images consists of one defect image, and the synthetic defect image is the defect image.

30. The computerized method of any one of claims 18-29, wherein the providing measurements comprises measuring displacements of difference images derived between each defect image of the set of defect images and the at least one reference image in the region, thereby producing a set of displacements corresponding to the set of defect images, and generating the measurements based on the set of displacements.

31. The computerized method of any one of claims 18-30, wherein the mask is a multi-die mask, the library of defect images is captured for the candidate defects located in an inspection die, and the library of reference images is captured from corresponding locations in a reference die.

32. The computerized method of any one of claims 18-30, wherein the mask is a single-die mask, and the library of defect images and the library of reference images are acquired from different regions in the same die sharing similar design patterns.

33. The computerized method of any one of claims 18-32, wherein the steps of acquiring an original defect image, acquiring a library of defect images and a library of reference images for the candidate defects, determining a best focus, aligning the original defect image with a synthetic defect image, and providing measurements are repeated for one or more additional candidate defects from a list of candidate defects selected from defect images indicative of a distribution of candidate defects on the mask or a portion thereof.

34. The computerized method of any one of claims 18-33, wherein the inspection tool is an actinic inspection tool configured to emulate an optical configuration of a lithography tool usable to manufacture the semiconductor sample.

35. A non-transitory computer-readable storage medium tangibly embodying a program of instructions, the program of instructions, when executed by a computer, causing the computer to perform the method of any one of claims 18-34.

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