Computerized system, method and computer readable storage medium of mask inspection for semiconductor specimen fabrication

Abstract

A system and method are provided for mask inspection, comprising: obtaining a first image representing at least a portion of the mask; applying a printing threshold to the first image to obtain a second image; estimating the contour of each of a set of structural elements of interest (SEIs) and extracting a set of attributes characterizing the contours to generate a set of contours corresponding to the set of SEIs and a corresponding set of attributes associated with the set of contours; for each given contour, identifying one or more reference contours similar to the given contour among the remaining contours of the set of contours by comparing the corresponding sets of attributes associated with the set of contours; and measuring the deviation between the given contour and each of the reference contours of the given contour to generate one or more measurement deviations, the one or more measurement deviations indicating the presence of a defect.

Description

Technical Field

[0001] The topics currently being discussed generally relate to the field of mask inspection, and more specifically, to the detection of defects in light masks. Prior Technology

[0002] Currently, the high density and high performance requirements associated with the ultra-large-scale integration of manufactured microelectronic devices demand submicron features, increased transistor and circuit speeds, and improved reliability. As semiconductor manufacturing processes advance, pattern dimensions, such as substrate width, and other types of critical dimensions continue to shrink. This demand necessitates the formation of device features with high precision and uniformity, which in turn requires careful monitoring of the manufacturing process, including automated inspection of the device while it is still in semiconductor wafer form.

[0003] Semiconductor devices are typically manufactured using photolithography processes using photolithography masks (also known as photomasks, masks, or crosshairs). Photolithography is one of the key processes in manufacturing semiconductor devices and involves patterning the wafer surface according to the circuit design of the semiconductor device to be produced. This circuit design is first patterned on the mask. Therefore, in order to obtain a functional semiconductor device, the mask must be defect-free. Masks are manufactured using complex processes and can contain various defects and variations.

[0004] Furthermore, masks are typically used repeatedly to create multiple dies on a wafer. Therefore, any defects in the mask will be repeated multiple times on the wafer, leading to defects in multiple devices. Establishing a productive process requires strict control over the entire lithography process. In this process, critical dimension (CD) control is a determining factor regarding device performance and yield.

[0005] Various mask inspection methods have been developed and are commercially available. Based on certain general techniques for designing and evaluating masks, a mask is created and used to expose the wafer through the mask. An inspection is then performed to determine whether the mask's features / patterns have been transferred to the wafer according to the design. Any discrepancies between the final printed features and the intended design may necessitate design modifications, mask repairs, the creation of new masks, and / or the exposure of new wafers.

[0006] Therefore, verifying the accuracy and quality of printed features allows for indirect methods of mask verification. However, since the final printed pattern on the wafer or die is formed after the printing process, such as resist development, substrate treatment (such as material etching or deposition), it may be difficult to attribute, distinguish, or exclude errors in the final printed pattern from problems related to the mask and / or resist deposition and / or development processes. Furthermore, inspecting the final printed pattern on the wafer or die often provides a limited number of samples that can be used to detect, determine, and resolve any processing problems. This process can also be labor-intensive and involves significant inspection and analysis time.

[0007] In addition, various mask inspection tools can be used to directly inspect masks. Summary of the Invention

[0008] Based on certain aspects of the currently disclosed subject matter, a computerized system for examining a mask that can be used to manufacture a semiconductor sample is provided. The system includes a processing and memory circuit (PMC) configured to: acquire a first image representing at least a portion of the mask, wherein the first image is acquired by analogy to the optical configuration of a lithographic printing tool that can be used to manufacture the semiconductor sample; apply a printing threshold to the first image to generate a second image, wherein the second image provides information on a plurality of structural elements of the mask that can be printed on the semiconductor sample; and estimate the SE of each of a set of structural elements of interest (SEIs) from the plurality of structural elements. The contour of SEI is obtained, and a set of attributes characterizing the contour is extracted to generate a set of contours corresponding to the set of SEIs and a corresponding set of attributes associated with the set of contours; for each of the set of contours, given a contour, one or more reference contours similar to the given contour are identified among the remaining contours in the set of contours by comparing the corresponding set of attributes associated with the set of contours; and the deviation between the given contour and each of the one or more reference contours of the given contour is measured to generate one or more measurement deviations indicating whether there is a defect with respect to the SEI associated with the given contour.

[0009] In addition to the features described above, a system of this kind according to the subject matter currently disclosed may include one or more of the features listed below (i) to (xiii) in any technically possible combination or arrangement: (i). The mask includes a mask field and a scribing region for a single grain, and the at least portion of the mask includes at least a portion of the single grain and / or at least a portion of the scribing region. (ii). The mask includes a mask field and a scribing region for a plurality of grains, and the at least portion of the mask includes at least a portion of the scribing region. (iii). The first image was acquired by a photochemical inspection tool configured to analog with the optical configuration of the lithographic printing tool. (iv) The first image is obtained by acquiring an image using a non-optical inspection tool and performing an analogy on the image to analogize the optical configuration of the lithographic printing tool, thereby producing the first image. (v). The defect is an edge displacement that indicates a significant relative deviation of the given contour from the expected position of the given contour. (vi) The contour was estimated using an edge detection method. (vii). The attribute set is selected from the group consisting of: the region formed by the contour, the width of the region, the height of the region, the number of primitives along the contour, chain code, and centroid. (viii). The PMC is configured to measure the deviation by: registering the given profile with one or more reference profiles respectively, thereby generating one or more pairs of registered profiles; measuring the distance between corresponding points of each pair of registered profiles; and selecting the maximum distance from the measured distances as the measurement deviation between the given profile and the corresponding reference profile among the one or more reference profiles. (ix). The PMC is further configured to output a combined deviation based on the one or more measurement deviations, apply a deviation threshold to the combined deviation, and report the presence of a defect when the combined deviation exceeds the deviation threshold. (x). The PMC is further configured to provide a defect map corresponding to the first image and indicating the presence and location of defects on at least a portion of the mask. (xi). The PMC is configured to acquire a plurality of first images, each first image representing a corresponding portion of the mask, the plurality of first images being acquired sequentially with a predefined step size, such that the plurality of fields of view (FOV) of the plurality of first images do not overlap. The PMC is configured to perform the application, estimation, identification, and measurement on each of the plurality of first images, and to provide a defect map corresponding to each of the plurality of first images and indicating the presence of defects on the corresponding portion of the mask, thereby generating a plurality of defect maps corresponding to the plurality of first images. (xii). The PMC is further configured to identify one or more SEIs associated with a defect present in each of the plurality of defect images, compare one or more contours of the one or more SEIs among the plurality of first images, and determine whether at least one defect is a false alarm indicating the presence of a unique structural element in at least one of the first images. (xiii) The PMC is configured to acquire a plurality of first images for a given portion of the mask, each first image representing at least the given portion, the plurality of first images being acquired sequentially with a predefined step size, such that a plurality of fields of view (FOV) of the plurality of first images overlap at least the given portion. The PMC is configured to perform the application, estimation, identification, and measurement on each of the plurality of first images, provide a defect map corresponding to each of the plurality of first images and indicating the presence of defects on at least a given portion, thereby generating a plurality of defect maps corresponding to the plurality of first images, and comparing the defects present in the plurality of defect maps to determine whether a defect is a defect of interest or a false alarm.

[0010] Based on other aspects of the currently disclosed subject matter, a method for examining a mask that can be used to manufacture a semiconductor sample is provided, the method being performed by a processing and memory circuit (PMC), and the method comprising: obtaining a first image representing at least a portion of the mask, wherein the first image is acquired by analogy to the optical configuration of a lithographic printing tool that can be used to manufacture the semiconductor sample; applying a printing threshold to the first image to produce a second image, wherein the second image provides information on a plurality of structural elements of the mask that can be printed on the semiconductor sample; and estimating the SEI of each of a set of structural elements of interest (SEI) from the plurality of structural elements. The contours are obtained, and attribute sets characterizing the contours are extracted to generate a set of contours corresponding to the set of SEIs and a corresponding set of attribute sets associated with the set of contours; for each given contour in the set of contours, one or more reference contours similar to the given contour are identified among the remaining contours in the set of contours by comparing the corresponding set of attribute sets associated with the set of contours; and the deviation between the given contour and each of the one or more reference contours of the given contour is measured to generate one or more measurement deviations indicating whether there is a defect with respect to the SEI associated with the given contour.

[0011] With necessary modifications, the present subject matter may include one or more of the features (i) to (xiii) listed above with respect to the system in any technically possible combination or arrangement.

[0012] According to other variations of the currently disclosed subject matter, a non-transitory computer-readable medium is provided, comprising instructions which, when executed by a computer, cause the computer to perform a method of inspecting a mask that can be used to manufacture a semiconductor sample. The method includes: obtaining a first image representing at least a portion of the mask, wherein the first image is acquired by analogy to the optical configuration of a lithographic printing tool that can be used to manufacture the semiconductor sample; applying a printing threshold to the first image to produce a second image, wherein the second image provides information on a plurality of structural elements of the mask that can be printed on the semiconductor sample; and estimating a set of structural elements of interest (SEIs) from the plurality of structural elements. For each SEI, a contour is obtained, and a set of attributes characterizing the contour is extracted, thereby generating a set of contours corresponding to the set of SEIs and a corresponding set of attributes associated with the set of contours; for each given contour in the set of contours, one or more reference contours similar to the given contour are identified among the remaining contours in the set of contours by comparing the corresponding set of attributes associated with the set of contours; and the deviation between the given contour and each of the one or more reference contours of the given contour is measured, thereby generating one or more measurement deviations indicating whether there is a defect with respect to the SEI associated with the given contour.

[0013] With necessary modifications, the present subject matter may include one or more of the features (i) to (xiii) listed above with respect to the system in any technically possible combination or arrangement. Simple Explanation of the Diagram

[0014] To understand the content of this case and how to implement it in practice, embodiments will now be described only by non-limiting examples, with reference to the accompanying drawings, wherein:

[0015] [picture] [1] The figure shows a functional block diagram of a mask inspection system according to certain embodiments of the subject matter currently disclosed.

[0016] [picture] [2] The figure shows a general flowchart of mask inspection according to certain embodiments of the subject matter currently disclosed.

[0017] [picture] [3] The figure shows a general flowchart of measuring the deviation between a given profile and a reference profile according to certain embodiments of the subject matter currently disclosed.

[0018] [picture] [4] The figure shows a general flowchart of acquiring and utilizing overlapping images to remove false alarms according to certain embodiments of the subject matter disclosed herein.

[0019] [picture] [5] The figure shows a schematic diagram of a photochemical inspection tool and a lithographic printing tool according to certain embodiments of the subject matter currently disclosed.

[0020] [picture] [6] Schematic illustrations of exemplary layouts of single-grain masks and multi-grain masks according to certain embodiments of the subject matter currently disclosed.

[0021] [picture] [7] The illustrations are schematic diagrams of a process for applying printing thresholds according to certain embodiments of the subject matter currently disclosed, and examples of a first image and a second image.

[0022] [picture] [8A] The illustration shows several examples of extracted outlines (marked by dashed lines) of several structural elements with different shapes according to certain embodiments of the subject matter currently disclosed.

[0023] [picture] [8B] The illustration shows an example of a reference profile of a given profile according to certain embodiments of the subject matter currently disclosed.

[0024] [picture] [9] The illustration shows two examples of measurement biases that indicate defects in certain embodiments of the subject matter currently disclosed.

[0025] [picture] [10A] This illustration schematically depicts an example of a non-overlapping image sequence captured for a portion of a mask, according to certain embodiments of the subject matter currently disclosed.

[0026] [picture] [10B] This illustration schematically shows an example of an overlapping image sequence captured for a portion of a mask, according to certain embodiments of the subject matter currently disclosed.

[0027] [picture]

[11] The illustration shows examples of defects and false alarms of interest determined by using overlapping images according to certain embodiments of the subject matter currently disclosed. Implementation

[0028] In the following detailed description, numerous specific details are set forth to provide a comprehensive understanding of the subject matter. However, those skilled in the art will understand that the subject matter disclosed herein can be practiced without these specific details. In other instances, well-known methods, processes, components, and circuits have not been described in detail to avoid obscuring the subject matter disclosed herein.

[0029] Unless otherwise specified, as will be apparent from the following discussion, it should be understood that throughout the discussion of this specification, the use of terms such as “inspect,” “obtain,” “simulate,” “apply,” “estimate,” “extract,” “identify,” “compare,” “measure,” “acquire,” “execute,” “register,” “select,” “export,” “report,” “provide,” and “determine” refers to the operation of a computer and / or the actions and / or processes (multiples) of converting data into other data, said data being represented as physical (such as electronic) quantities and / or said data representing physical objects. The term “computer” should be broadly interpreted to encompass any type of hardware-based electronic device with data processing capabilities, including, by way of non-limiting example, the mask inspection system, mask defect detection system, and corresponding parts thereof disclosed in this case.

[0030] The term "mask" as used in this specification is also known as "lithographic mask," "photomask," or "crosshair." These terms should be interpreted equivalently and broadly to encompass patterns that will be patterned on a semiconductor wafer during a photolithography process to maintain a circuit design (e.g., define the layout of specific layers of an integrated circuit). For example, a mask can be implemented as a fused silica plate covered with a pattern of opaque, transparent, and phase-shifted regions projected onto the wafer during a lithography 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 (used in the manufacture of memory devices) or a logic mask (used in the manufacture of logic devices).

[0031] The term "inspection" or "mask inspection" as used in this specification should be broadly interpreted to encompass any operation used to evaluate the accuracy and integrity of a manufactured photomask in relation to a circuit design and its ability to produce an accurate representation of the circuit design on a wafer. Inspection may include any type of operation related to various types of defect detection, defect review, and / or defect classification, and / or metrological operations during and / or after the mask manufacturing process and / or during semiconductor sampling using the mask. Inspection may be provided using non-destructive testing tools after mask manufacturing. As a non-limiting example, the inspection process may include one or more of the following operations: scanning the mask or portions thereof using inspection tools (in a single or multiple scans), imaging, sampling, inspection, measurement, classification, and / or providing other operations. Similarly, mask inspection may also be interpreted to include, for example, generating inspection recipes and / or other setup operations prior to the actual inspection of the mask. It should be noted that, unless otherwise specified, the term "inspection" or its derivatives as used in this specification are not limited in terms of the resolution and size of the inspection area. Various non-destructive testing tools include optical inspection tools, scanning electron microscopes, atomic force microscopes, etc.

[0032] The term "metrology operation" as used in this specification should be broadly interpreted to encompass any metrology operation program used to extract metrology information relating to one or more structural elements on a mask. In some embodiments, metrology operations may include measurement operations, such as, for example, critical dimension (CD) measurements of certain structural elements on a sample, including but not limited to: dimensions (e.g., liner width, liner spacing, contact diameter, element size, edge roughness, grayscale statistics, etc.), element shape, distances within or between elements, relevant angles, overlay information associated with elements corresponding to different design levels, etc. For example, analyzing measurement results such as measured images using image processing techniques. Note that unless otherwise specified, the term "metrology" or its derivatives as used in this specification are not limited in terms of measurement techniques, measurement resolution, or inspection area size.

[0033] The term "sampling" as used in this specification should be broadly interpreted to encompass any type of wafer, related structure, combination, and / or component thereof used in the manufacture of semiconductor integrated circuits, magnetic heads, flat panel displays, and other semiconductor components.

[0034] The term "defect" as used in this specification should be broadly interpreted to encompass any type of anomaly or undesirable feature formed on the mask. In some cases, a defect may refer to a genuine defect or defect of interest (DOI) that, when printed on a wafer, has some effect on the function of the manufactured device. In other cases, a defect may refer to a nuisance or "false alarm" defect, which is a negligible, questionable defect because it has no effect on the function of the completed device.

[0035] The terms “non-transitory memory” and “non-transitory storage medium” as used herein should be broadly interpreted to encompass any volatile or non-volatile computer memory applicable to the subject matter currently disclosed. These terms should be considered to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated cache and server) that stores one or more instruction sets. These terms should also be considered to include any medium capable of storing or encoding instruction sets for execution by a computer and causing the computer to perform any or more of the methods described in this case. Therefore, these terms should include, but are not limited to, read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices, etc.

[0036] It should be understood that, unless otherwise specifically stated, certain features of the currently disclosed subject matter described in the context of separate embodiments may also be provided in combination in a single embodiment. Conversely, various features of the currently disclosed subject matter may be described in the context of a single embodiment, or may be provided individually or in any suitable combination of subgroups. Numerous specific details are set forth in the following detailed description to provide a thorough understanding of the methods and apparatus.

[0037] With this in mind, please note [picture] [1], [picture] [1] The figure shows a functional block diagram of a mask inspection system according to certain embodiments of the subject matter currently disclosed.

[0038] [picture] The inspection system shown in [1]

[0100] This can be used to inspect masks during or after the mask manufacturing process. As mentioned above, the inspection referred to herein can be interpreted as encompassing any type of operation related to defect detection and / or various types of defect classification, and / or metrological operations (such as, for example, critical dimension (CD) measurements of the mask or portions thereof). According to certain embodiments of the subject matter currently disclosed, the inspection system shown...

[0100] Includes computer-based systems

[0101] ,system

[0101] The system is capable of automatically detecting defects related to structural elements on the mask. Specifically, in some embodiments, the defects to be detected are related to edge displacement of structural elements on the mask. Therefore, the system...

[0101] Also known as a mask defect detection system, it is an inspection system

[0100] subsystem.

[0039] system

[0101] Operable connection to mask inspection tool

[0120] Masking inspection tool

[0120] It is configured to scan a mask and capture one or more images of it to inspect the mask. The term "mask inspection tool" as used herein should be broadly interpreted to cover any type of inspection tool that can be used in processes related to mask inspection, including, as a non-limiting example, scanning (in a single or multiple scans), imaging, sampling, detecting, measuring, classifying and / or providing other operations on a mask or portions thereof.

[0040] Without limiting the scope of this case in any way, it should also be noted that masking inspection tools

[0120] It can be implemented as various types of inspection machines, such as optical inspection tools, electron beam tools, etc. In some cases, mask inspection tools...

[0120] This can be a relatively low-resolution inspection tool (e.g., an optical inspection tool, a low-resolution scanning electron microscope (SEM), etc.). In some cases, masked inspection tools...

[0120] This can be a relatively high-resolution inspection tool (e.g., high-resolution SEM, atomic force microscopy (AFM), transmission electron microscopy (TEM), etc.). In some cases, the inspection tool can simultaneously provide both low-resolution and high-resolution image data. In some embodiments, the inspection tool is masked.

[0120] It has metrological capabilities and can be configured to perform metrological operations on the captured images. The obtained image data (low-resolution image data and / or high-resolution image data) can be transmitted directly or via one or more intermediate systems to the system.

[0101] .

[0041] According to some embodiments, the mask inspection tool can be implemented as a photochemical inspection tool configured to analog / mimic the optical configuration of a lithographic printing tool (such as, for example, a scanner or stepper) that can be used to manufacture semiconductor sampling, for example, by projecting a pattern formed in the mask onto the wafer.

[0042] Now go to [picture] [5] The figure shows a schematic diagram of a photochemical inspection tool and a lithographic printing tool according to certain embodiments of the subject matter currently disclosed.

[0043] With lithographic printing tools

[0520] Similarly, photochemical inspection tools

[0500] It may include an illumination source configured to generate light at the exposure wavelength (e.g., a laser).

[0502] Illumination optical devices

[0504] Mask retainer

[0506] and projection optics

[0508] Illumination optics

[0504] and projection optical device

[0508] It may include one or more optical elements (such as, for example, lenses, apertures, spatial filters, etc.).

[0044] In lithographic printing tools

[0520] In this case, the mask is positioned on the mask holder.

[0506] and optical alignment is performed to project an image of the circuit pattern to be copied onto a wafer holder.

[0512] On a wafer (e.g., by creating or replicating a pattern on the wafer using various stepping, scanning, and / or imaging techniques). With offset printing tools

[0520] Different, instead of placing a wafer holder

[0512] , Photochemical inspection tool

[0500] Place the detector at the location of the wafer holder.

[0510] (such as, for example, a charge-coupled device (CCD)), in which the detector

[0510] is configured to detect light projected through the mask and generate an image of the mask.

[0045] It can be seen that the photochemical inspection tool

[0500] Configured as an analog lithographic printing tool

[0520] The optical configuration includes, but is not limited to, illumination / exposure conditions such as wavelength, pupil shape, numerical aperture (NA), etc. Therefore, the detector...

[0510] Acquired masking image

[0514] The image is expected to resemble a chip manufactured using a mask via lithography.

[0516] The masked image obtained using this optical inspection tool is also called an aerial image, or the first image as described in this case. As described below, the first image is provided to the system.

[0101] For further processing.

[0046] According to some embodiments, in some cases, the mask inspection tool

[0120] This can be implemented as a non-optical inspection tool, such as, for example, a general optical inspection tool, an electron beam tool, etc. In this case, the non-optical inspection tool can be configured to acquire an image of the mask. An analogy can be performed on the acquired image to analogize the optical configuration of the lithographic printing tool, thereby generating an aerial image (i.e., a first image). In some cases, the analogy can be performed by the system.

[0101] The execution (e.g., simulation) function can be integrated into its PMC.

[0102] (in the middle), while in some other cases, analogies can be made using masking inspection tools.

[0120] is executed by the processing module, or is operatively connected to the mask inspection tool.

[0120] and system

[0101] is executed by a separate analog unit.

[0047] system

[0101] Includes an operatively connected hardware-based I / O interface.

[0126] Processor and memory circuitry (PMC)

[0102] . See reference. [picture] [2]、 [picture] [3] and [picture] [4] As further described in detail, PMC

[0102] Configured to provide the processing required by the operating system, PMC

[0102] Includes a processor (not shown separately) and memory (not shown separately). PMC

[0102] The processor can be configured to execute several functional modules according to computer-readable instructions implemented on non-transitory computer-readable memory included in the PMC. These functional modules are referred to below as included in the PMC.

[0048] The processor referred to in this article can mean one or more general-purpose processing devices, such as microprocessors, central processing units, etc. More specifically, a processor can be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing combinations of instruction sets. A processor can also be one or more special-purpose processing devices, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), network processors, etc. The processor is configured to execute instructions to perform the operations and steps discussed in this article.

[0049] The memory referred to in this article may include main memory (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) (such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM)) and static memory (e.g., flash memory, static random access memory (SRAM)).

[0050] As mentioned above, in some embodiments, the system

[0101] It can be configured to detect defects related to edge displacement of one or more structural elements on the mask. As used herein, the term "edge displacement" refers to a relatively significant deviation of the edge / profile of a structural element from its intended position.

[0051] The structural elements or features used herein can refer to any original object on a mask that has a geometric shape or structure with an outline, which in some cases is combined / overlaid with other objects (thus forming a pattern). Examples of structural elements can include general shape features such as contacts, linings, etc., and / or features with complex structures / shapes, and / or features combined with one or more other features. Structural elements can be 2D or 3D features, and capturing an image of a structural element can reflect its 2D representation.

[0052] The edge displacement defects mentioned in this article can be caused by a variety of factors, such as multiple physical effects during the mask manufacturing process, and / or other factors such as oxidation, particles, scratches, crystal growth, electrostatic discharge (ESD), etc. (which may occur gradually during masking). If such masking defects are not detected before mass production of wafers, they will be repeated multiple times on the wafer and will result in defects in multiple semiconductor devices (e.g., affecting device functionality), thus significantly reducing yield.

[0053] A mask comprises a mask field that will be transferred to the wafer. In some cases, the mask field may include multiple dies with the same design pattern (this type of mask is called a multi-die mask). In other cases, the mask field may include a single die (this type of mask is called a single-die mask). To detect the presence of defects associated with structural elements within a die, a reference structural element from another die is typically required for comparison during inter-die inspection. However, in the case of a single-die mask, there is no reference die on the mask that can be used for comparison. Therefore, for structural elements in a single die, a reference is required for defect detection.

[0054] Now go to [picture] [6], schematically illustrating exemplary layouts of single-grain masks and multi-grain masks according to certain embodiments of the subject matter currently disclosed.

[0055] As shown in the figure, polycrystalline masking

[0604] This includes a masking field comprising nine grains with the same design pattern. For any structural element in any grain of the masking field, one or more reference structural elements can always be found in one or more adjacent grains. However, for a single grain...

[0606] Single-crystal masking of the masking field

[0602] There is no reference grain on the mask that can be used for defect detection of structural elements in a single grain.

[0056] In addition, single-crystal masking

[0602] Further includes the grain region located in the mask.

[0606] and surrounding areas

[0610] The engraved area between

[0608] Inscribed area

[0608] This includes auxiliary features, such as alignment features, calibration features, etc. These auxiliary features / structures can be printed on the wafer along with patterns in the die region during a lithography process. Therefore, it is also desirable to perform defect detection on these auxiliary features (if any), and additionally or alternatively, on structural elements within the die. This also applies to multi-die masks.

[0604] engraved area

[0612] Auxiliary features in the characterization region

[0612] Located between the grain region and the surrounding region, and between grains. However, there are no reference features for such auxiliary features on the mask used for inspection.

[0057] Some general techniques related to photochemical inspection tools involve acquiring two images, one from the transmission mode and the other from the reflection mode of the tool, and analyzing the differences between the two images to estimate the presence of any defects. However, using two imaging modes can be time-consuming in both the image acquisition process and the image processing process, thus affecting the throughput (TpT) of the inspection.

[0058] Alternatively, some inspection tools may attempt to generate analog images based on the mask's design data and use these analog images as reference images for defect detection on the mask. However, this method requires access to the mask's design data, which is often unavailable. Furthermore, due to the indeterminacy of process variations during mask manufacturing, the analog images may be inaccurate, which can inevitably affect the accuracy of the inspection results.

[0059] Therefore, especially due to the advancements in the manufacturing processes and complex features of photomasks, current inspection methods are insufficient to provide the desired process control for mask features / components. Thus, there is a need for improved defect detection methods that address these issues, enabling more sensitive and accurate detection of defects in structural components on the mask (e.g., structural components in the grain region of a single-crystal mask, and structural components in the scribed regions of both single-crystal and multi-crystal masks) without affecting the inspection throughput.

[0060] Based on certain embodiments of the currently disclosed subject matter, novel mask inspection systems and methods are proposed for detecting defects related to edge displacement of structural elements on a mask. The proposed methods have been demonstrated to offer higher accuracy and detection sensitivity for advanced process control of mask features.

[0061] According to some embodiments, the system

[0101] PMC

[0102] The functional modules included may include an image processing module.

[0104] Measurement module

[0106] And optional defect handling module

[0108] PMC

[0102] It can be configured to be via I / O interface

[0126] A first image representing at least a portion (e.g., a fraction) of the mask is obtained. The first image can be obtained by analogy to an optically configured lithographic printing tool used for manufacturing semiconductor sampling. For example, it can be obtained using a mask inspection tool.

[0120] (such as, for example, a photochemical inspection tool) to acquire images.

[0062] Image processing module

[0104] It can be configured to apply a printing threshold to a first image to generate a second image. The second image provides information on multiple structural elements of a mask that can be printed on a semiconductor sample. Image processing module

[0104] It can be further configured to estimate the profile of each of a set of structural elements of interest (SEI) from a plurality of structural elements, and extract a set of attributes characterizing the profile, thereby generating a set of profiles corresponding to the set of SEIs and a corresponding set of attributes associated with the set of profiles.

[0063] Image processing module

[0104] It can be further configured such that, for each given contour, one or more reference contours similar to the given contour are identified among the remaining contours in the set of contours by comparing them among the corresponding sets of attributes associated with that set of contours. Measurement module

[0106] It can be configured to measure the deviation between a given profile and each of one or more reference profiles of the given profile, thereby generating one or more measurement deviations indicating whether a defect exists with respect to the SEI associated with the given profile. Optionally, a defect handling module

[0108] It can be configured to report the presence of a defect when the deviation exceeds a deviation threshold, and / or determine how to respond to a detected defect.

[0064] Reference [picture] [2]、 [picture] [3] and [picture] [4] Regarding the system

[0100] 、

[0101] , PMC

[0102] The operation of the functional modules therein will be described in further detail.

[0065] According to some embodiments, the system

[0100] May include a storage unit

[0122] Storage unit

[0122] Can be configured as a storage operation system

[0100] and

[0101] Any required information, for example, with the system

[0100] and

[0101] The data related to the input and output of the system, and the data related to the system.

[0101] The intermediate processing results generated. For example, storage unit.

[0122] It can be configured to store multiple images and / or their derivatives (e.g., preprocessed images) generated by the mask inspection tool 120. Therefore, the images can be retrieved from the storage unit.

[0122] Detect (multiple) images and provide them to the PMC

[0102] For further processing.

[0066] In some embodiments, the system

[0100] Optionally, a computer-based graphical user interface (GUI) may be included.

[0124] It is configured to enable with the system

[0101] Related user-specified input. For example, a sampled visual representation can be presented to the user (e.g., by constructing a GUI).

[0124] A portion of the display includes sampled images and / or image representations of structural elements. The GUI may provide the user with options to define certain command parameters, such as, for example, printing thresholds, deviation thresholds, etc. In some cases, the user may also view operation results on the GUI, such as measured deviations, detected defects, and / or further inspection results.

[0067] As mentioned above, the system

[0101] Configured to be accessible via I / O interface

[0126] Receive one or more images of the mask (e.g., a first image). The images may include those obtained by the mask inspection tool.

[0120] The generated image data (and / or its derivatives) and / or stored in the storage unit

[0122] Image data in one or more data stores. In some cases, image data may refer to images captured by a mask inspection tool during or after the mask manufacturing process, and / or preprocessed images exported from captured images obtained through various preprocessing stages, etc. It should be noted that in some cases, images may include associated digital data (e.g., relay data, manual attributes, etc.). It should also be noted that image data relates to a target layer of a semiconductor device to be printed on a wafer.

[0068] system

[0101] is further configured to process the received image, and via I / O interface

[0126] The results (e.g., measurement deviation, detected defects) are sent to the storage unit.

[0122] , and / or GUI

[0124] To perform rendering and / or mask inspection tools

[0120] .

[0069] In some embodiments, except the system

[0101] External, mask inspection system

[0100] It may also include one or more inspection modules, such as, for example, multiple additional defect detection modules and / or automatic defect review modules (ADR) and / or automatic defect classification modules (ADC) and / or metrology-related modules and / or other inspection modules that can be used to perform additional inspections on the mask. One or more inspection modules may be implemented as stand-alone computers, or their functions (or at least some of their functions) may be compatible with the mask inspection tool.

[0120] Integration. In some embodiments, via a mask inspection tool.

[0120] and / or one or more inspection modules (or portions thereof) can transmit data from the system.

[0101] The obtained output is used for further inspection of the mask.

[0070] Those skilled in the art will readily understand that the teachings of the subject matter currently presented are not subject to... [picture] [1] The constraints of the system shown; equivalent and / or modified functions may be combined or divided in another way and may be implemented in any suitable combination of software and firmware and / or hardware.

[0071] It should be noted that [picture] The verification system shown in [1] can be implemented in a distributed computing environment, including PMC. The functional modules described above in

[0102] can be distributed across multiple local and / or remote devices and can be connected via a communication network. It should also be noted that in other embodiments, (multiple) verification tools...

[0120] Storage unit

[0122] and / or GUI At least some of

[0124] can be located in the verification system

[0100] is external and can be accessed via I / O interface.

[0126] In relation to the system

[0101] Operation in data communication. System

[0101] This can be implemented as multiple independent computers used in conjunction with the verification tool. Alternatively, the system The various functions of

[0101] can be at least partially related to the mask inspection tool.

[0120] Integration, thereby facilitating and enhancing mask inspection tools

[0120] Functions in processes related to inspection.

[0072] Although this may not be the case, the system

[0101] and

[0100] The operating process can correspond to the reference. [picture] [2] to [picture] [4] is part or all of the stages of the method. Similarly, regarding [picture] [2] to [picture] [4] The method and its possible implementation can be derived from the system.

[0101] and

[0100] Implementation. Therefore, it should be noted that regarding the reference [picture] [2] to [picture] [4] The embodiments of the methods described can also be implemented as a system with necessary modifications.

[0101] and The various embodiments of

[0100] are, and vice versa.

[0073] Now for reference [picture] [2] The figure shows a general flowchart of mask inspection according to certain embodiments of the subject matter currently disclosed.

[0074] It is possible (e.g., through PMC)

[0102] Via I / O interface

[0126] From the mask inspection tool

[0120] Or from the storage unit

[0122] ) obtained (

[0202] The image represents at least a portion of the mask (the mask to be inspected). The first image can be obtained by analogy to the optical configuration of a lithographic printing tool used for semiconductor sampling.

[0075] In some embodiments, the mask to be inspected is a single-grain mask. For example... [picture] [6]

[0602] As illustrated, the mask field of the single-crystal mask (including the grain region of the single grain and the scribing region (containing auxiliary features, such as alignment features, calibration features, etc.)) includes printable features / structures that will be transferred onto the wafer in a lithographic printing process. Therefore, the currently proposed inspection method is suitable for detecting defects with respect to any of these regions / areas. For example, the obtained first image may represent at least a portion of the single-crystal region and / or at least a portion of the scribing region.

[0076] In some other embodiments, such as [picture] [6]

[0604] As illustrated, the mask to be inspected may be a polycrystalline mask. In this case, the currently proposed inspection method is suitable for detecting defects with respect to at least a portion of the scribed region in the polycrystalline mask.

[0077] In some embodiments, the first image is acquired using a photochemical masking inspection tool, such as, for example, Aera Masking Inspection Tool from Applied Materials Inc. (See above reference) [picture] [5] The photochemical mask inspection tool is specifically configured to mimic the optical configuration of a lithographic printing tool (e.g., a scanner or stepper) used to manufacture semiconductor wafers according to a mask. The optical configuration to be simulated may include one or more of the following illumination / exposure conditions: such as, for example, wavelength, pupil shape, numerical aperture (NA), etc.

[0078] The mask image (e.g., a first image) obtained by this photochemical inspection tool is expected to resemble an image of a wafer manufactured using a mask via a lithographic printing tool; therefore, the mask image is also referred to as an aerial image. In other words, the photochemical mask inspection tool is configured to capture a mask image that can mimic how a design pattern in the mask would actually appear on a physical wafer after the manufacturing process.

[0079] In some cases, photochemical inspection tools may not be usable for inspecting masks. In such cases, non-photochemical inspection tools (such as, for example, general optical inspection tools, electron beam tools, etc.) can be used to obtain an image of the mask (a non-aerial image). An analogy can be performed on the acquired non-aerial image to analogize the optical configuration of the lithographic printing tool, thereby producing an aerial image / first image of the mask. Therefore, in some embodiments, as referenced... [picture] [2] The mask inspection method described may also include a preliminary step of obtaining an image acquired by a non-photochemical inspection tool, and performing an analogy on the image (e.g., by PMC).

[0102] Image processing module

[0104] , or by using a masking inspection tool

[0120] The processing module, etc., is analogous to the optical configuration of a lithographic printing tool, thereby generating a first image.

[0080] In some embodiments, such as referring to [picture] [2] As described, the obtained first image may be preprocessed before further processing. Preprocessing may include one or more of the following operations: interpolation (e.g., in the case where the first image has a relatively low resolution), noise filtering, focus correction, aberration compensation, image format conversion, etc.

[0081] It should be noted that the content of this case is not limited to the specific mode of the mask inspection tool, and / or the type of image obtained therefrom, and / or the preprocessing operations required to process the image.

[0082] It is possible (e.g., through PMC)

[0102] Image processing module

[0104] ) Apply (to the first image)

[0204] The printing threshold is determined to produce a second image. The second image provides information about multiple structural elements of the mask that can be printed on the semiconductor sample.

[0083] Now for reference [picture] [7], illustrating a process for applying printing thresholds, and examples of first and second images according to certain embodiments of the subject matter currently disclosed.

[0084] As shown in the figure, the image

[0700] An exemplary (and simplified) mask is shown, including a transparent area that transmits light when illuminated.

[0702] (For example, made of quartz) and opaque areas that block light.

[0704] (For example, made of chromium). The first image obtained as described above (aerial image) refers to an image captured by a detector that collects transmitted light through a mask, such as an image.

[0710] exemplified.

[0085] In fact, the actual wafer manufacturing process, performed by manufacturing tools (e.g., scanners or steppers), includes a resist process and an etching process following a lithography process. The wafer is coated with a photoresist, which is a photosensitive material. Exposure to light causes the resist to partially harden or soften, depending on the process. After exposure, the wafer is developed, causing the photoresist to dissolve in certain areas, depending on the amount of transmitted light (i.e., light intensity) received by those areas during exposure.

[0086] For example, the diagram shows the waveform representing the intensity of transmitted light.

[0705] If a given area of ​​photoresist is exposed to a specific intensity of transmitted light, a pattern will be printed onto the wafer. These areas, with and without photoresist, will have their designed patterns reproduced on a mask. Therefore, as... [picture] As illustrated in [7], the specific intensity is referred to as the printing threshold.

[0705] . The developed wafer is then exposed to a solvent that etches away silicon in portions of the wafer no longer protected by the photoresist coating, thereby producing a printed wafer.

[0708] (For a given layer)

[0087] Therefore, in photochemical inspection tools that mimic the optical configuration of wafer manufacturing tools, waveforms

[0705] This refers to the transmitted light that will be captured by the detector of the photochemical inspection tool to form the first image. Since the detector in the photochemical inspection tool is replaced by a wafer, and there is no actual resist and etching process, a printing threshold needs to be applied to the first image to obtain an image similar to that of a printed wafer.

[0705] To mimic the effects of resist and etching processes, a second image is generated, which includes printable features on the wafer. Specifically, the second image is a binary image providing information on a plurality of structural elements of a mask that can be printed on the wafer.

[0088] [picture] [7] Illustration of the first image

[0710] and the corresponding second image generated after applying a printing threshold to the first image.

[0720] Example. As shown in the figure, the second image.

[0720] is similar to a chip.

[0708] A binary image of the printed pattern. It should be noted that although in this example, patterns below the printing threshold are shown as printable on the wafer (i.e., positive resist), this is not necessarily the case. In some other cases, the opposite may be true, i.e., patterns above the printing threshold can be printed on the wafer (i.e., negative resist). The scope of this application is not limited to a specific resist process for presenting printable features, nor is it limited to a specific application of the printing threshold.

[0089] continue [picture] [2] The description allows for the estimation of the profile of each of a set of structural elements of interest (SEIs) from multiple structural elements, and can be (e.g., via PMC)

[0102] Image processing module

[0104] ) Extract (

[0206] The set of attributes that characterizes the contours is used to generate a set of contours corresponding to the set of SEIs and a corresponding set of attributes associated with the set of contours.

[0090] According to some embodiments, a group of structural elements of interest (SEIs) can be selected from a plurality of structural elements, and contour estimation can be performed on each SEI in the group. For example, a group of SEIs can be selected based on one or more of the following factors: the location of the structural element on the second image, the type and / or shape of the structural element, structural elements detected as defect candidates in prior inspection, customer input / feedback regarding the importance of certain structural elements to be inspected, etc. In some cases, selection can be skipped, and a group of SEIs may actually include all elements from the plurality of structural elements on the second image.

[0091] The term contour can refer to the outline or boundary of an object or element. Contour estimation is commonly used to detect various objects / elements in an image. In some embodiments of the present invention, the contour of a structural element can be estimated by using edge detection methods. For example, edge detection methods can be implemented using Canny or Sobel edge detection algorithms. Another example of an edge detection algorithm suitable for this subject matter is described in U.S. Patent No. 9,165,376 entitled "System, method and computer readable medium for detecting edges of a pattern," which is assigned to the assignee of this patent application and is incorporated herein by reference in its entirety. [picture] [8A] The figure shows several examples of extracted outlines (marked by dashed lines) of several structural elements with different shapes according to certain embodiments of the subject matter currently disclosed.

[0092] For each contour, a set of attributes characterizing the contour can be extracted. For example, the attribute set can be selected from a group including: the region formed by the contour, the width of the region, the height of the region, the number of primitives along the contour, chain code, and centroid, etc. For example, a chain code is used to represent a contour by a sequence of connected straight segments of specified length and direction. Typically, this representation is based on four or eight connections of segments. The direction of each segment is encoded using a numbering scheme. The contour code formed as this sequence of directional values ​​is called a chain code, which indicates the shape of the contour.

[0093] For example, the attribute set of each of a set of contours can be represented in different data formats, such as tables, vectors, lists, etc. For example, a table representation can include N rows and K columns, where each row represents a specific contour in a set of N contours, and each column represents a set of K features associated with the contour.

[0094] Given a contour in each of a set of contours, the remaining contours in the set (i.e., candidate contours) can be selected (e.g., via PMC).

[0102] Image processing module

[0104] ) Identification (

[0208] One or more reference contours that are similar to a given contour. In some embodiments, one or more reference contours can be identified by comparing a set of attributes associated with a given contour with corresponding sets of attributes associated with at least some of the remaining contours. The comparison can be based on a similarity metric, and one or more contours that meet the similarity criteria can be identified as reference contours that are similar to the given contour.

[0095] For example, similarity measures can be distance-based, such as Euclidean distance, Manhattan distance, cosine distance, Pearson correlation distance, Spearman correlation distance, etc. Similarity criteria can be predetermined distances. In some cases, the number of reference contours to be identified can also be predetermined.

[0096] For example, the comparison can be performed by, for example, starting with the first candidate contour in the table, calculating the distance between each pair of given contours and the set of attributes of candidate contours from the remaining contours, and the process ends when the number of reference contours whose distance reaches the similarity criterion is satisfied, and the identified reference contours are provided.

[0097] Alternatively, in another example, the comparison can be performed by calculating the distance of each of the remaining candidate contours relative to the given contour, and selecting a predetermined number of reference contours by sorting the calculated distances. In some cases, the number of reference contours to be identified is not predetermined. All candidate contours whose distances meet the similarity criteria can be identified as reference contours.

[0098] refer to [picture] [8B], illustrating an example of a reference profile of a given profile according to certain embodiments of the subject matter currently disclosed.

[0099] Assume that for each given contour, the number of reference contours to be identified is one. For example, for contour...

[0804] The reference contour identified as meeting the similarity standard is the contour.

[0802] For the outline

[0806] The reference contour identified as meeting the similarity standard is the contour.

[0804] or

[0802] . If the number of reference contours is predetermined to be two, then the contours

[0802] and

[0804] can all be used as outlines

[0806] Reference profile. For the profile...

[0810] Its reference profile is the profile.

[0808] .

[0100] continue [picture] The description in [2] can be (e.g., via PMC)

[0102] Measurement module

[0106] ) Measurement (

[0210] The deviation between a given profile and one or more reference profiles of the given profile generates one or more measurement deviations, which indicate whether there is a defect with respect to the SEI associated with the given profile.

[0101] In this paper, the defect to be detected refers to edge displacement, which indicates a relatively significant deviation of a given profile from its expected position (e.g., the expected position of the profile based on the original design data of the given profile or based on an identified reference profile). Edge displacement differs from edge roughness (which may be caused by different variations in the manufacturing process) at least in that: i) edge displacement is local (exists at a local location on the profile), while edge roughness exists at all locations along the edge; and ii) the amplitude of edge displacement deviation is relatively more significant (i.e., stronger / larger) compared to the amplitude of minor roughness along the edge.

[0102] In some cases, this edge displacement may be caused by certain physical effects and / or other factors during the mask manufacturing process (such as oxidation, particles, scratches, crystal growth, electrostatic discharge (ESD), etc.). This edge displacement, when printed on the wafer, may affect the electrical measurement results of the manufactured device, potentially leading to decreased yield and device performance failure. Therefore, it is necessary to detect such displacement defects and measure their amplitude.

[0103] Now go to [picture] [3] The figure shows a general flowchart of measuring the deviation between a given profile and a reference profile according to certain embodiments of the subject matter currently disclosed.

[0104] Specifically, a given contour can be registered with one or more reference contours respectively.

[0302] This generates one or more pairs of registered profiles. For example, this registration can be performed by aligning the centroids and / or certain anchor points of a given profile and a reference profile. Measurements can be taken...

[0304] The distance between corresponding points of each pair of registered profiles. For example, the Hausdorff distance metric can be used to measure the distance. (The distance can be selected from the measured distance...)

[0306] The maximum distance is used as the measurement deviation between a given profile and the corresponding reference profile (among one or more reference profiles).

[0105] In some embodiments, a combined deviation can be derived based on one or more measurement deviations between a given profile and one or more reference profiles, and a deviation threshold can be applied to the combined deviation. When the combined deviation exceeds the deviation threshold, it can be (e.g., via PMC)

[0102] Defect handling module

[0108] ) Report the existence of defects. For example, a combined deviation can be derived by averaging (or weighted averaging) one or more measurement deviations, such as, for example, the mean or median of the deviations or any other type of (weighted or unweighted) average of the deviations.

[0106] For example, three reference profiles for a given profile can be identified, and three deviations between the given profile and these three reference profiles can be measured respectively. These three deviations can be averaged to produce a combined deviation, which is then compared to a deviation threshold. In some cases, the deviation threshold can be predetermined based on, for example, the specific inspection application, the type of structural element, the technical node, and / or the customer's instruction manual.

[0107] Now for reference [picture] [9], illustrating two examples of measurement biases that indicate defects in certain embodiments of the subject matter currently disclosed.

[0108] Curve graph

[0900] The figure shows the estimated profile of a structural element with an elliptical shape.

[0904] and for contours

[0904] Identified reference contour

[0902] Reference outline

[0902] This indicates the expected location of the defect-free structural element profile. As shown in the figure, the two profiles are registered, and the maximum distance is...

[0906] Measured as a given contour

[0904] and reference profile The deviation between

[0902] is greater than a predetermined deviation threshold, thus identifying edge displacement defects at the measurement location.

[0109] Curve graph

[0910] Another example is illustrated, where in

[0912] The edge displacement defect was identified at the location shown, along with the edge roughness.

[0914] In comparison, the edge displacement defect is relatively more significant, thus exceeding the deviation threshold.

[0110] According to some embodiments, once defect detection has been performed on each of a set of SEIs, a defect map corresponding to a first image can be provided, indicating the presence of defects on at least a portion of the mask and the location of the defects.

[0111] In some embodiments, optionally, when one or more defects are present, it can be (e.g., through PMC)

[0102] Defect handling module

[0108] ) Decision (

[0212] How to respond to detected defects, for example, by assessing the printability of these defects, or by assessing whether these defects would affect the functionality of a semiconductor sample manufactured using a mask during printing. For example, the assessment might include estimating changes in printable structural elements / features associated with the defect when printed onto the semiconductor sample. Possible handling actions in response to the presence of a defect might include: repairing the mask, defining the mask as a faulty mask, defining the mask as functional, generating a repair instruction for the mask, etc. For example, if these estimated changes are unacceptable, the mask could be sent to a mask shop for repair or rejected.

[0112] Furthermore, in some embodiments, it is possible (e.g., via PMC)

[0102] Defect handling module

[0108] ) Provide at least one or any combination of the following outputs / indications: (i) provide qualification criteria for masks to be shipped out of the mask workshop; (ii) provide input to the mask production process; (iii) provide input to the semiconductor sampling manufacturing process; (iv) provide input to the simulation model used in the lithography process; (v) provide a calibration map for the lithography tool; and (vi) identify areas on the mask characterized by greater-than-expected changes in feature parameters.

[0113] According to some embodiments, during inspection, the mask holder and detector of the mask inspection tool can be moved in opposite directions to each other during exposure, and the mask can be scanned progressively by the mask inspection tool, which images only a portion of the mask at a time. Therefore, multiple first images of the mask can be obtained sequentially, each first image representing a corresponding portion of the mask. According to some embodiments, multiple first images can be acquired continuously using a predefined step size, such that the multiple fields of view (FOV) of the multiple first images do not overlap.

[0114] [picture] [10A] This illustration schematically depicts an example of a non-overlapping image sequence captured for a portion of a mask, according to certain embodiments of the currently disclosed subject matter. As shown, for a mask...

[1000] Part

[1004] Get four first images

[1002] The sequence. Four first images are captured with such a step size, so that the FOV of the four first images do not overlap, but together constitute the FOV of the entire part 1004.

[0115] In this case, the process of recursively executing the procedure for each of the multiple first images can be performed. [picture] [2] square

[0204] To the square

[0210] The described sequence of operations generates a defect map corresponding to each of a plurality of first images and indicating the presence of defects on the corresponding portions of the mask. Multiple defect maps corresponding to the plurality of first images can be obtained.

[0116] In some cases, detected defects may be false positives due to the presence of a unique structural element in the first image. For example, a structural element with a unique contour shape may exist in the first image, and this structural element does not have a reference contour with a similar shape. In this case, the inspection process may not find a reference for this structural element and therefore report it as a defect. Alternatively, it may use a reference contour that is not actually very similar to the element for comparison and thus also report it as a defect. In these cases, these reported defects are false positives because they actually represent the presence of a unique structural element, not a real defect. In some cases, another instance of such a unique structural element may appear in subsequent images. Therefore, by comparing defects detected in multiple first images, such false positives can be identified and removed from the list of detected defects.

[0117] Specifically, according to certain embodiments, one or more SEIs associated with a defect (or multiple defects) can be identified (as present in each of a plurality of defect images), and one or more contours of one or more SEIs can be compared among a plurality of first images. This can be described with reference to the above-mentioned blocks.

[0208] Comparisons are performed in a similar manner as described, for example, by comparing a set of attributes associated with the SEI. The comparison results can then be used to determine whether at least one defect is a false alarm indicating the presence of a unique structural element in at least one of the first images. For example, in each comparison, if a structural element reported as associated with a defect has similar instances in subsequent images, the defect is likely a false alarm that actually indicates the presence of a unique structural element.

[0118] In some other cases, detected defects may contain false alarms caused by tool noise (e.g., shot noise from inspection tools). To remove such false alarms from defects and thus improve detection sensitivity, methods for acquiring and utilizing overlapping images to remove false alarms have been proposed according to certain embodiments of the currently disclosed subject matter, such as those described in reference [reference]. [picture] [4] As stated.

[0119] Specifically, in some embodiments, it is possible to obtain (for a given portion of the mask)

[0402] Multiple first images, each representing at least a given portion. Multiple first images can be acquired consecutively using a predefined step size, such that multiple fields of view (FOV) of the multiple first images overlap at least a given portion.

[0120] [picture] [10B] This illustration schematically depicts an example of an overlapping image sequence partially captured for a mask, according to certain embodiments of the currently disclosed subject matter. As shown, for a mask... The same part of

[1000]

[1004] Obtain the first image

[1010] The sequence. A first image sequence is captured with a specific step size, such that the field of view (FOV) of the first image sequence overlaps by, for example, one-third of the image FOV. For example, a given portion of the mask.

[1012] It is captured three times in three consecutive images. Therefore, in a given part of the mask... The real defects present in

[1012]

[1014] (That is, the defect of interest) will naturally appear three times in three images, while false alarms caused by random noise will not repeat in consecutive images.

[0121] Therefore, it is possible to perform operations on each of the multiple first images regarding the first image. [picture] [2] square

[0204] To the square

[0210] The described operation sequence, and can provide (

[0404] A defect map, corresponding to each of a plurality of first images and indicating the presence of a defect at least in a given portion, thereby generating a plurality of defect maps corresponding to the plurality of first images. Comparisons are possible.

[0406] The defects present in multiple defect maps are used to determine whether a defect is a defect of interest or a false alarm.

[0122] Now go to [picture]

[11] The illustration shows examples of defects and false alarms of interest determined by using overlapping images according to certain embodiments of the subject matter currently disclosed.

[0123] As shown in the upper sequence of three consecutive images of the same structural element, based on the comparison of detected defects, the defects...

[1102] It exists in all three images and appears in the same position with respect to the structural element in each image. Therefore, the defect can be determined.

[1101] The defects are of interest. Conversely, as shown in the lower sequence of three consecutive images capturing structural elements, the defects...

[1104] It exists only in the second image, but not in the first and third images. Therefore, the defect...

[1104] These are likely false alarms caused by random noise (e.g., shot noise). Such false alarms should be removed from the defect map.

[0124] It should be noted that the mask applicable to the currently disclosed inspection method can be any type of mask that may suffer from edge displacement type defects as described herein, including but not limited to memory masks and / or logic masks, and / or Arf masks and / or EUV masks, etc. The scope of this application is not limited to any particular type or function of the mask to be inspected.

[0125] For illustrative and exemplary purposes, certain embodiments and / or examples of the subject matter currently disclosed are described with respect to structural elements having a particular type / shape and / or a particular edge displacement. This is in no way intended to limit the scope of this application. It should be understood that the proposed methods and systems can be applied to other types / shapes of structural elements having various edge displacements.

[0126] According to certain embodiments, as referred to above [picture] [2]、 [picture] [3] and [picture] [4] The mask inspection process described can be included as a system that can be used for mask inspection.

[0101] and / or inspection tools

[0120] This is part of the inspection recipe used for online masking inspection at runtime. Therefore, the currently disclosed subject matter also includes systems and methods for generating inspection recipes during the recipe setup phase, wherein the recipe comprises as referenced... [picture] [2]、 [picture] [3] and [picture] The steps described in [4] (and its various embodiments) should be noted. It should be noted that the term "inspection formula" should be broadly interpreted to cover any formula that can be used by inspection tools to perform operations related to any type of mask inspection, including the embodiments described above.

[0127] It should be noted that the examples shown in this document, such as the architecture and configuration of the mask inspection tool, the mask layout, the illustrated structural elements, and the specific methods of comparing and measuring deviations as described above, are for illustrative purposes only and should not be construed as limiting the scope of this document in any way. Other appropriate examples / implementations may be used to supplement or replace the foregoing.

[0128] One of the advantages of certain embodiments of the mask inspection process described herein is the ability to detect specific types of defects (i.e., edge displacement) concerning structural elements on the mask. The proposed process is specifically designed for single-die mask designs where there is no reference die available for inter-die comparison. Furthermore, the proposed process is also applicable to inspecting etched areas in both single-die and multi-die masks.

[0129] One of the advantages of certain embodiments of the mask inspection process described herein is that the proposed inspection process does not require acquiring images from different modes to provide a reference image (which can be time-consuming). The mask inspection process also does not require acquiring mask design data (which is often unavailable) or making analogies based on design data (which are often inaccurate). The proposed process utilizes specific processing of aerial images to provide references within the image itself, which has proven to improve the accuracy and sensitivity of defect detection in advanced process control of mask features without affecting delivery time (TpT).

[0130] One of the advantages of certain embodiments of the mask inspection process described herein is that by acquiring and utilizing overlapping images of the mask, false alarms caused by random noise can be effectively removed, thereby further improving detection sensitivity without adjusting the deviation threshold.

[0131] It should be understood that the application of the content of this case is not limited to the details set forth in the descriptions included herein or shown in the accompanying drawings.

[0132] It should also be understood that the system according to the contents of this case can be implemented, at least in part, on a computer with appropriate programming. Similarly, the contents of this case envision a computer-readable computer program for performing the methods described herein. The contents of this case also envision a non-transitory computer-readable memory that tangibly embodies an instruction program executable by a computer for performing the methods described herein.

[0133] This invention can have other embodiments and can be practiced or implemented in various ways. Therefore, it should be understood that the expressions and terminology used herein are for descriptive purposes and should not be considered limiting. Consequently, those skilled in the art will understand that the concepts upon which this invention is based can be readily used as the basis for designing other structures, methods, and systems to achieve the various objectives of the currently disclosed subject matter.

[0134] Those skilled in the art will readily understand that various modifications and alterations may be applied to the embodiments of the present invention as described above, without departing from the scope of the present invention, the scope of which is defined in and by the appended claims.

[0135] 100: Inspection System 101: System 102:PMC 104: Image Processing Module 106: Measurement Module 108: Defect Handling Module 120: Inspection tools 122: Storage Unit 124:GUI 126:I / O interface 202: Square 204: Square 206: Square 208: Square 210: Square 212: Square 302: Square 304: Square 306: Square 402: Square 404: Square 406: Square 500: Photochemical Inspection Tool 502: Lighting source 504: Illumination Optical Devices 506: Mask Holder 508: Projection Optical Device 510: Detector 512: Chip Holder 514: Masked Image 516: Image 520: Lithographic Printing Tools 602: Single-crystal mask 604: Polycrystalline mask 606: Single crystal grain 608: Inscribed area 610: Surrounding Area 612: Inscribed area 700: Image 702: Transparent Area 704: Opaque Area 705: Waveform 708: Chip 710: Image 720: Second Image 802: Outline 804: Outline 806: Outline 808: Outline 810: Outline 900: Curve Graph 902: Reference Profile 904: Estimate the profile 906: Maximum distance 910: Curve Graph 914: Edge Roughness 1000: Mask 1002: First Image 1004: Part 1010: First image 1012: Given part 1014: Real Defects 1102: Defect 1104: Defect

[0136] Domestic storage information (please note in order of storage institution, date, and number) none Overseas storage information (please note in the order of storage country, institution, date, and number) none

Claims

1. A computerized system for inspecting a mask that can be used to manufacture a semiconductor sample, the system comprising a processing and memory circuit (PMC) configured to: acquire a first image representing at least a portion of the mask, wherein the first image is acquired by analogy to the optical configuration of a lithographic printing tool that can be used to manufacture the semiconductor sample; apply a printing threshold to the first image to generate a second image, wherein the second image provides information on a plurality of structural elements of the mask that can be printed on the semiconductor sample; estimate a profile of each of a set of structural elements of interest (SEIs) from the plurality of structural elements, and extract a set of attributes characterizing the profile to generate a set of profiles corresponding to the set of SEIs and a corresponding set of attributes associated with the set of profiles; For each of the given contours in the set, one or more reference contours similar to the given contour are identified among the remaining contours in the set by comparing the corresponding attribute sets associated with the given contours; and a deviation between the given contour and each of the one or more reference contours of the given contour is measured, thereby generating one or more measurement deviations that indicate the presence of a defect with respect to the SEI associated with the given contour, wherein the defect indicates an edge displacement.

2. The computerized system as claimed in claim 1, wherein the mask includes a mask field of a single die and a scribing region, and the at least portion of the mask includes at least a portion of the single die and / or at least a portion of the scribing region.

3. The computerized system as claimed in claim 1, wherein the mask includes a mask field and a scribing region of a plurality of grains, and the at least portion of the mask includes at least a portion of the scribing region.

4. The computerized system as described in claim 1, wherein the first image is acquired by a photochemical inspection tool configured to analog with the optical configuration of the lithographic printing tool.

5. The computerized system as claimed in claim 1, wherein the first image is obtained by acquiring an image using a non-optical inspection tool and performing an analogy on the image to analogize the optical configuration of the lithographic printing tool, thereby producing the first image.

6. The computerized system as claimed in claim 1, wherein the edge displacement indicates a relatively significant deviation of the given contour from a desired position of the given contour relative to a deviation threshold.

7. The computerized system as described in claim 1, wherein the contour is estimated using an edge detection method.

8. The computerized system as described in claim 1, wherein the set of attributes is selected from the group consisting of: the region formed by the contour, the width of the region, the height of the region, the number of primitives along the contour, chain code, and centroid.

9. The computerized system as claimed in claim 1, wherein the PMC is configured to measure a deviation by: registering the given profile with one or more reference profiles respectively, thereby generating one or more pairs of registered profiles; measuring the distance between corresponding points of each pair of registered profiles; and selecting the maximum distance from the measured distances as the measurement deviation between the given profile and the corresponding reference profile among the one or more reference profiles.

10. The computerized system as described in claim 1, wherein the PMC is further configured to output a combined deviation based on the one or more measurement deviations, apply a deviation threshold to the combined deviation, and report the presence of a defect when the combined deviation exceeds the deviation threshold.

11. The computerized system as claimed in claim 10, wherein the PMC is further configured to provide a defect map corresponding to the first image and indicating the presence of one or more defects on at least a portion of the mask and the location of the one or more defects.

12. The computerized system of claim 1, wherein the PMC is configured to acquire a plurality of first images, each first image representing a corresponding portion of the mask, the plurality of first images being acquired sequentially at a predefined step size such that a plurality of fields of view (FOV) of the plurality of first images do not overlap, and wherein the PMC is configured to perform the application, estimation, identification, and measurement on each of the plurality of first images, and provide a defect map corresponding to each of the plurality of first images and indicating the presence of one or more defects on the corresponding portion of the mask, thereby generating a plurality of defect maps corresponding to the plurality of first images.

13. The computerized system as claimed in claim 12, wherein the PMC is further configured to identify one or more SEIs associated with a defect present in each of the plurality of defect images, compare one or more contours of the one or more SEIs among the plurality of first images, and determine whether at least one defect is a false alarm indicating the presence of a unique structural element in at least one of the first images.

14. The computerized system of claim 1, wherein the PMC is configured to acquire a plurality of first images for a given portion of the mask, each first image representing at least the given portion of the mask, the plurality of first images being acquired in a sequential manner at a predefined step size such that a plurality of fields of view (FOV) of the plurality of first images overlap with at least the given portion of the mask, and wherein the PMC is configured to perform the application, estimation, identification, and measurement on each of the plurality of first images, provide a defect map corresponding to each of the plurality of first images and indicating the presence of one or more defects on the at least given portion of the mask, thereby generating a plurality of defect maps corresponding to the plurality of first images, and comparing the defects present in the plurality of defect maps to determine whether a defect is a defect of interest or a false alarm.

15. A computerized method for inspecting a mask that can be used to manufacture a semiconductor sample, the method being performed by a processing and memory circuit (PMC), and the method comprising the steps of: obtaining a first image representing at least a portion of the mask, wherein the first image is acquired by analogy to the optical configuration of a lithographic printing tool that can be used to manufacture the semiconductor sample; applying a printing threshold to the first image to generate a second image, wherein the second image provides information on a plurality of structural elements of the mask that can be printed on the semiconductor sample; estimating the profile of each of a set of structural elements of interest (SEIs) from the plurality of structural elements, and extracting a set of attributes characterizing the profile to generate a set of profiles corresponding to the set of SEIs and a corresponding set of attributes associated with the set of profiles; For each of the given contours in the set, one or more reference contours similar to the given contour are identified among the remaining contours in the set by comparing the corresponding attribute sets associated with the given contours; and a deviation between the given contour and each of the one or more reference contours of the given contour is measured, thereby generating one or more measurement deviations that indicate the presence of a defect with respect to the SEI associated with the given contour, wherein the defect indicates an edge displacement.

16. The computerized method of claim 15, wherein the mask includes a mask field and a scribing region of a single die, and the at least portion of the mask includes at least a portion of the single die and / or at least a portion of the scribing region.

17. The computerized method as described in claim 15, wherein the measurement includes: The given profile is registered with one or more reference profiles to generate one or more pairs of registered profiles; the distance between corresponding points of each pair of registered profiles is measured; and the maximum distance from the measured distances is selected as the measurement deviation between the given profile and a corresponding reference profile among the one or more reference profiles.

18. The computerized method as described in claim 15 further includes deriving a combined deviation based on the one or more measurement deviations, applying a deviation threshold to the combined deviation, and reporting the presence of a defect when the combined deviation exceeds the deviation threshold.

19. The computerized method as described in claim 15, wherein obtaining includes: The method involves obtaining a plurality of first images for a given portion of the mask, each first image representing at least the given portion of the mask, the plurality of first images being acquired sequentially with a predefined step size such that a plurality of fields of view (FOV) of the plurality of first images overlap with at least the given portion of the mask, and wherein the method includes: performing the application, estimation, identification, and measurement on each of the plurality of first images; providing a defect map corresponding to each of the plurality of first images and indicating the presence of one or more defects on the at least given portion of the mask, thereby generating a plurality of defect maps corresponding to the plurality of first images; and comparing the defects present in the plurality of defect maps to determine whether a defect is a defect of interest or a false alarm.

20. A non-transitory computer-readable storage medium tangibly embodies an instruction program that, when executed by a computer, causes the computer to perform a method of examining a mask that can be used to manufacture a semiconductor sample, the method comprising the steps of: obtaining a first image representing at least a portion of the mask, wherein the first image is acquired by analogy to the optical configuration of a lithographic printing tool that can be used to manufacture the semiconductor sample; applying a printing threshold to the first image to generate a second image, wherein the second image provides information on a plurality of structural elements of the mask that can be printed on the semiconductor sample; estimating a profile of each of a set of structural elements of interest (SEIs) from the plurality of structural elements, and extracting a set of attributes characterizing the profile to generate a set of profiles corresponding to the set of SEIs and a corresponding set of attributes associated with the set of profiles; For each of the given contours in the set, one or more reference contours similar to the given contour are identified among the remaining contours in the set by comparing the corresponding attribute sets associated with the given contours; and a deviation between the given contour and each of the one or more reference contours of the given contour is measured, thereby generating one or more measurement deviations that indicate the presence of a defect with respect to the SEI associated with the given contour, wherein the defect indicates an edge displacement.

Images