Mask inspection for semiconductor specimen fabrication

Abstract

To provide a system and a method of mask inspection.SOLUTION: There are provided a system and a method of mask inspection, comprising the steps of: obtaining a first image representative of at least part of a mask; applying a printing threshold on the first image to obtain a second image; estimating a contour for each structural element of interest (SEI) of a group of SEIs, and extracting a set of attributes characterizing the contour, giving rise to a group of contours corresponding to the group of SEIs and respective sets of attributes associated with SEIs; for each given contour, identifying, among the remaining contours in the group of contours, one or more reference contours similar to the given contour, by comparing between the respective sets of attributes associated with the contour; and measuring a deviation between the given contour and each reference contour thereof, giving rise to one or more measured deviations indicative of whether a defect is present.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The subject matter of the present disclosure generally relates to the field of mask inspection, and more particularly to defect detection for photomasks.

Background Art

[0002] Current requirements associated with the high density and high performance related to the mass production of manufactured microelectronic devices require sub-micron features, improved transistor and circuit speeds, and improved reliability. As semiconductor processes progress, pattern dimensions such as line widths, and other types of critical dimensions are constantly being reduced. Such requirements necessitate the formation of device features with high precision and uniformity, which requires careful monitoring of the manufacturing process, including automatically inspecting the devices while they are still in the form of semiconductor wafers.

[0003] Semiconductor devices are often manufactured using a photolithography mask (also referred to as a photomask or mask or reticle) in a photolithography process. The photolithography process is one of the main processes in the manufacture of semiconductor devices and involves patterning the surface of the wafer according to the circuit design of the semiconductor device to be manufactured. Such a circuit design is first patterned on a mask. Therefore, in order to obtain a functioning semiconductor device, the mask must be free of defects. Masks are manufactured by complex processes and can be subject to various defects and variations.

[0004] In addition, masks are often used repeatedly to create multiple dies on a wafer. Therefore, any defect on the mask will be repeated multiple times on the wafer, resulting in defects in multiple devices. To establish a suitable manufacturing process, it is necessary to strictly control the entire lithography process. Within this process, critical dimension (CD) control is a determining factor for device performance and yield.

[0005] Various mask inspection methods have been developed and are commercially available. According to certain prior art for designing and evaluating masks, a mask is created and used to expose a wafer through the mask, and then an inspection is performed to determine whether the mask's features / patterns have been transferred to the wafer according to the design. Any variation in the final printed features from the intended design may require modifying the design, repairing the mask, creating a new mask, and / or exposing a new wafer.

[0006] In this regard, verifying the accuracy and quality of printed features allows for an indirect method of verifying the mask. However, because the final printed pattern on the wafer or die is formed after the printing process, such as resist development and substrate processing (material etching or deposition, etc.), it can be difficult to attribute, distinguish, or isolate errors in the final printed pattern to problems associated with the mask and / or resist deposition and / or development process. Furthermore, inspecting the final printed pattern on the wafer or die tends to limit the number of usable samples provided to detect, determine, and resolve any processing problems. This process is labor-intensive and can result in enormous inspection and analysis times.

[0007] Alternatively, masks can be directly inspected using various mask testing tools. [Overview of the Initiative]

[0008] According to certain aspects of the subject matter of the present disclosure, a computerized system is provided for inspecting a mask usable for the manufacture of a semiconductor sample, the system comprising a processing and memory circuit (PMC) configured to obtain a first image representing at least a portion of the mask, the first image being obtained by emulating the optical configuration of a lithography tool usable for the manufacture of a semiconductor sample, a print threshold being applied to the first image to produce a second image providing information on a plurality of structural elements of the mask printable on a semiconductor sample, a contour for each SEI of a group of structural elements of interest (SEIs) from the plurality of structural elements, extract a set of attributes characterizing the contour to produce a group of contours corresponding to the group of SEIs and a set of attributes associated with each SEI, for each given contour in the group of contours to identify one or more reference contours similar to a given contour from among the remaining contours of the group of contours by comparing the set of attributes associated with each contour for each given contour in the group of contours, a deviation being measured between the given contour and each of the one or more reference contours, and a deviation being produced to indicate whether a defect exists with respect to the SEI associated with the given contour.

[0009] In addition to the features described above, the systems according to these embodiments of the subject matter of this disclosure may include one or more of the features listed below (i) to (xiii) in any desired combination or permutation that is technically possible. (i) The mask includes a mask field and a scribe area of ​​a single die, and at least a portion of the mask includes at least a portion of the single die and / or at least a portion of the scribe area. (ii) The mask includes mask fields and scribe regions of multiple dies, and at least a portion of the mask includes at least a portion of the scribe regions. (iii) The first image is acquired by a chemical beam inspection tool configured to emulate the optical configuration of the lithography tool. (iv) The first image is obtained by acquiring an image using a non-chemical beam inspection tool and performing a simulation on this image to simulate the optical configuration of the lithography tool, thereby producing the first image. (v) A defect is an edge displacement in which a given contour exhibits a substantial deviation from its expected position. (vi) The contour is estimated using edge detection. (vii) The set of attributes is selected from groups that include the region formed by the contour, the width of the region, the height of the region, the number of pixels along the contour, the chain code, and the centroid. (viii) The PMC is configured to measure deviation by aligning a given contour with one or more reference contours to produce one or more pairs of aligned contours, measuring the distance between corresponding points in each pair of aligned contours, and selecting the maximum distance from the measured distances as the measured deviation between the given contour and each of the one or more reference contours. (ix) The PMC is further configured to derive a composite deviation based on one or more measured deviations, apply a deviation threshold to the composite deviation, and report the presence of a defect if the composite deviation exceeds the deviation threshold. (x) The PMC is further configured to provide a defect map that indicates the presence and location of defects on at least a portion of the mask, corresponding to the first image. (xi) The PMC is configured to obtain a plurality of first images, each representing a different part of the mask, and the plurality of first images are acquired sequentially at a predetermined 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 independently apply, estimate, identify, and measure to each of the multiple first images, providing a defect map that indicates the presence of defects on each portion of the mask corresponding to each of the multiple first images, thereby generating multiple defect maps corresponding to the multiple first images. (xii) The PMC is further configured to identify one or more SEIs associated with the defects presented in each of the multiple defect maps, compare one or more contours of the one or more SEIs across the multiple 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 obtain a plurality of first images, each representing at least a given portion of the mask, and the plurality of first images are acquired sequentially at a predetermined step size such that the plurality of fields of view (FOV) of the plurality of first images overlap by at least a given portion. The PMC is configured to apply, estimate, identify, and measure to each of a plurality of first images, provide a defect map corresponding to each of the plurality of first images that indicates the presence of defects in at least a given portion, thereby generating a plurality of defect maps corresponding to the plurality of first images, and compare the defects presented in the plurality of defect maps to determine whether a defect is a defect of interest or a false alarm.

[0010] In other aspects of the subject matter of this disclosure, a method is provided for inspecting a mask usable for the manufacture of a semiconductor sample, the method being performed by a processing and memory circuit (PMC) and comprising the steps of: obtaining a first image representing at least a portion of the mask, the first image being obtained by emulating the optical configuration of a lithography tool usable for the manufacture of a semiconductor sample; applying a print threshold to the first image to produce a second image, the second image providing information on a plurality of structural elements of the mask that can be printed on a semiconductor sample; estimating a contour for each SEI of a group of structural elements of interest (SEIs) from the plurality of structural elements, extracting a set of attributes characterizing the contour to produce a group of contours corresponding to the group of SEIs and a set of attributes associated with each SEI; identifying one or more reference contours from the rest of the contour group that are similar to a given contour by comparing the set of attributes associated with each contour for each given contour in the group of contours; and measuring a deviation between a given contour and each of the one or more reference contours to produce one or more measured deviations indicating whether a defect exists with respect to the SEI associated with the given contour.

[0011] These embodiments of the disclosed subject matter may, with necessary modifications, include one or more of the features (i) to (xiii) listed above with respect to the System in any desired combination or permutation that is technically possible.

[0012] According to other aspects of the subject matter of this disclosure, a non-transient computer-readable medium is provided which, when executed by a computer, causes the computer to perform a method for inspecting a mask usable for the manufacture of a semiconductor sample, the method comprising: obtaining a first image representing at least a portion of the mask, the first image being obtained by emulating the optical configuration of a lithography tool usable for the manufacture of a semiconductor sample; applying a print threshold to the first image to produce a second image, the second image providing information on a plurality of structural elements of the mask that can be printed on a semiconductor sample; and a group of structural elements of interest (SEI) from the plurality of structural elements. The method includes the steps of: estimating a contour for each SEI and extracting a set of attributes that characterize the contour to produce a group of contours corresponding to the group of SEIs and a set of attributes associated with each SEI; identifying one or more reference contours from the remaining contours of the group that are similar to the given contour by comparing each given contour in the group of contours with the set of attributes associated with the contour; and measuring the deviation between the given contour and each of the one or more reference contours to produce one or more measured deviations indicating whether a defect exists with respect to the SEI associated with the given contour.

[0013] These embodiments of the disclosed subject matter may, with necessary modifications, include one or more of the features (i) to (xiii) listed above with respect to the System in any desired combination or permutation that is technically possible.

[0014] To understand this disclosure and how it may actually be put into practice, embodiments are described hereby with reference to the accompanying drawings, but only as non-limiting examples. [Brief explanation of the drawing]

[0015] [Figure 1] This is a functional block diagram of a mask inspection system according to a specific embodiment of the subject matter of this disclosure. [Figure 2]The generalized flowchart of mask inspection according to a particular embodiment of the subject matter of the present disclosure. [Figure 3] The generalized flowchart of measuring the deviation between a given contour and its reference contour according to a particular embodiment of the subject matter of the present disclosure. [Figure 4] The generalized flowchart of obtaining and utilizing overlapping images for false alarm removal according to a particular embodiment of the subject matter of the present disclosure. [Figure 5] The schematic diagram of a chemical ray inspection tool and a lithography tool according to a particular embodiment of the subject matter of the present disclosure. [Figure 6] The figure schematically showing an exemplary layout of a single die mask and an exemplary layout of a multi-die mask according to a particular embodiment of the subject matter of the present disclosure. [Figure 7] The schematic diagram of a process of applying a printing threshold, and examples of a first image and a second image according to a particular embodiment of the subject matter of the present disclosure. [Figure 8A] Some examples of contours (shown by dashed lines) extracted for several structural elements having different shapes according to a particular embodiment of the subject matter of the present disclosure. [Figure 8B] An example of a reference contour for a given contour according to a particular embodiment of the subject matter of the present disclosure. [Figure 9] Two examples of measured deviations indicating the presence of defects according to a particular embodiment of the subject matter of the present disclosure. [Figure 10A] The figure schematically showing an example of a series of non-overlapping images captured for a part of a mask according to a particular embodiment of the subject matter of the present disclosure. [Figure 10B] The figure schematically showing an example of a series of overlapping images captured for a part of a mask according to a particular embodiment of the subject matter of the present disclosure. [Figure 11] The figure showing examples of a target defect and a false alarm determined using overlapping images according to a particular embodiment of the subject matter of the present disclosure. [[ID='35']]

Embodiments for Carrying Out the Invention

[0016] The following detailed description includes numerous specific details to provide a complete understanding of the disclosure. However, those skilled in the art will understand that the invention can be carried out without these specific details. In other instances, well-known methods, procedures, components, and circuits are not described in detail so as not to obscure the subject matter of the disclosure.

[0017] Unless otherwise specified, as is evident from the following descriptions, any description using terms such as “inspect,” “obtain,” “emulate,” “apply,” “estimate,” “extract,” “identify,” “compare,” “measure,” “get,” “execute,” “align,” “select,” “derive,” “report,” “provide,” and “determine” throughout this specification refers to the operations and / or processes of a computer that manipulate and / or convert data into other data, wherein such data is expressed as a physical quantity such as an electronic quantity, and / or such data represents a physical object. The term “computer” should be interpreted more broadly to include, in non-limiting examples, any kind of hardware-based electronic device having data processing capabilities, including the mask inspection system, mask defect detection system, and each of their respective components disclosed in this application.

[0018] As used herein, the term “mask” is also referred to as “photolithography mask,” “photomask,” or “reticle.” Such terms should be interpreted equivalently and broadly to encompass a template that holds a circuit design (e.g., defining the layout of a particular layer of an integrated circuit) to be patterned on a semiconductor wafer in a photolithography process. For example, a mask can be implemented as a quartz glass plate covered with a pattern of opaque, transparent, and phase-shifted regions to be projected onto the wafer in 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 may be a memory mask (usable for manufacturing memory devices) or a logic mask (usable for manufacturing logic devices).

[0019] As used herein, the terms “inspection” or “mask inspection” should be interpreted broadly to encompass any operations for evaluating 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 may include any kind of operation relating to defect detection, defect review, and / or classification of various types of defects, as well as / or metrological operations during and / or after the mask manufacturing process, and / or while the mask is in use for semiconductor sample manufacturing. Inspection can be performed by using non-destructive testing tools after the mask has been manufactured. As a non-limiting example, the inspection process may include one or more of the following operations, namely, scanning (one or more scans), imaging, sampling, detection, measurement, classification, and / or other operations provided with respect to the mask or a portion thereof using inspection tools. Similarly, mask inspection may also be interpreted to include, for example, generating inspection strategies and / or other setting operations prior to the actual inspection of the mask. Unless otherwise noted, the term “inspection” or its derivatives as used herein is not limited with respect to the resolution or size of the inspection area. Various non-destructive testing tools include, but are not limited to, optical inspection tools, scanning electron microscopes, and atomic force microscopes.

[0020] As used herein, the term “mechanical operation” should be interpreted broadly to encompass any mechanical operation procedure used to extract mechanical information about one or more structural elements on a mask. In some embodiments, a mechanical operation may include, for example, a measurement operation such as limit dimension (CD) measurement performed on a particular structural element on a sample, which may include, but are not limited to, dimensions (e.g., line width, line spacing, contact diameter, element size, edge roughness, gray level statistics), element shape, distances within or between elements, associated angles, and overlay information associated with elements corresponding to different design levels. Measurement results, such as measurement images, are analyzed, for example, by using image processing techniques. Unless otherwise specified, the term “mechanics” or its derivatives as used herein should not be limited in terms of measurement techniques, measurement resolution, or the size of the inspection area.

[0021] As used herein, the term “sample” should be interpreted more broadly to include any type of wafer, associated structure, combination thereof, and / or portion thereof used to manufacture semiconductor integrated circuits, magnetic heads, flat panel displays, and other semiconductor manufactured articles.

[0022] As used herein, the term “defect” should be interpreted broadly to encompass any kind of abnormal or undesirable feature formed on the mask. In some cases, a defect may refer to a true defect or defect of interest (DOI) that, when printed on the wafer, has a specific effect on the functionality of the manufactured device. In some other cases, a defect may refer to a potentially detrimental, nuisance, or “false alarm” defect that can be ignored because it does not affect the functionality of the finished device.

[0023] As used herein, the terms “non-transient memory” and “non-transient storage medium” should be interpreted more broadly to encompass any volatile or non-volatile computer memory suitable for the subject matter of this disclosure. These terms should be interpreted to include a single or multiple mediums that store one or more instruction sets (e.g., a centralized or distributed database, and / or associated caches and servers). These terms should also be interpreted to include any medium that stores or encodes instruction sets for computer execution, causing a computer to execute one or more of the methodologies of this disclosure. Accordingly, these terms should be interpreted to include, but are not limited to, read-only memory ("ROM"), random-access memory ("RAM"), magnetic disk storage mediums, optical storage mediums, flash memory devices, and the like.

[0024] Unless otherwise specified, certain features of the subject matter of this disclosure described in the context of separate embodiments may also be provided in combination in a single embodiment. Conversely, various features of the subject matter of this disclosure described in the context of a single embodiment may also be provided separately or in any suitable subcombination. The following detailed description includes numerous specific details to provide a complete understanding of the methods and apparatus.

[0025] With this in mind, we will now focus on Figure 1, which shows a functional block diagram of a mask inspection system according to a particular embodiment of the subject matter of this disclosure.

[0026] The inspection system 100 shown in Figure 1 can be used to inspect masks during or after the mask manufacturing process. As stated above, the inspections referred to herein can be interpreted as encompassing any kind of operation relating to defect detection and / or classification of various types of defects and / or metrological operations such as limit dimension (CD) measurement relating to a mask or a part thereof. According to certain embodiments of the subject matter of this disclosure, the illustrated inspection system 100 comprises a computer-based system 101 that can automatically detect defects relating to structural elements on a mask. Specifically, in some embodiments, the defects detected in this disclosure relate to edge displacements of structural elements on a mask. Thus, system 101, also known as the mask defect detection system, is a subsystem of the inspection system 100.

[0027] System 101 can be operably connected to a mask inspection tool 120, which is configured to scan a mask and capture one or more images of the mask for inspection. The term “mask inspection tool” as used herein should be interpreted broadly to include, in non-limiting examples, any type of inspection tool that can be used in mask inspection-related processes, including scanning (one or more scans), imaging, sampling, detection, measurement, classification, and / or other processes provided with respect to a mask or part thereof.

[0028] Without limiting the scope of this disclosure, it should also be noted that the mask inspection tool 120 may be implemented as various types of inspection machines, such as optical inspection tools and electron beam tools. In some cases, the mask inspection tool 120 may be a relatively low-resolution inspection tool (e.g., an optical inspection tool, a low-resolution scanning electron microscope (SEM), etc.). In some cases, the mask inspection tool 120 may be a relatively high-resolution inspection tool (e.g., a high-resolution SEM, an atomic force microscope (AFM), a transmission electron microscope (TEM), etc.). In some cases, the inspection tool may provide both low-resolution and high-resolution image data. In some embodiments, the mask inspection tool 120 may have metrological capabilities and be configured to perform metrological operations on the captured image. The obtained image data (low-resolution image data and / or high-resolution image data) may be transmitted to system 101 directly or via one or more intermediate systems.

[0029] According to certain embodiments, the mask inspection tool can be implemented as a chemical beam inspection tool configured to emulate / mimic the optical configuration of a lithography tool (e.g., a scanner or stepper) usable for the manufacture of semiconductor samples, for example, by projecting a pattern formed on a mask onto a wafer.

[0030] Now, looking at Figure 5, a schematic diagram of a chemical beam inspection tool and a lithography tool according to a specific embodiment of the subject matter of this disclosure is shown.

[0031] Similar to the lithography tool 520, the chemical beam inspection tool 500 may include an illumination source 502 configured to generate light of an exposure wavelength (e.g., a laser), an illumination optical system 504, a mask holder 506, and a projection optical system 508. The illumination optical system 504 and the projection optical system 508 may include one or more optical elements (e.g., lenses, apertures, spatial filters, etc.).

[0032] In the lithography tool 520, the mask is placed in the mask holder 506 and optically aligned to project an image of the circuit pattern to be replicated onto the wafer placed on the wafer holder 512 (for example, by using various stepping, scanning, and / or imaging techniques to generate or replicate the pattern on the wafer). Unlike the lithography tool 520, the chemical beam inspection tool 500 places a detector 510 (for example, a charge-coupled device (CCD)) in the position of the wafer holder instead of the wafer holder 512, and the detector 510 is configured to detect the light projected through the mask and generate an image of the mask.

[0033] As can be seen from the figure, the chemical beam inspection tool 500 is configured to emulate the optical configuration of the lithography tool 520, including but not limited to illumination / exposure conditions such as wavelength, pupil shape, and numerical aperture (NA). Thus, the mask image 514 acquired by the detector 510 is expected to be similar to the image 516 of the wafer manufactured using the mask via the lithography tool. The mask image acquired using such a chemical beam inspection tool is also referred to as a spatial image or first image, as described in this disclosure. The first image is provided to the system 101 for further processing, as described below.

[0034] In certain embodiments, the mask inspection tool 120 may be implemented as a non-chemical beam inspection tool, such as a conventional optical inspection tool or an electron beam tool. In such cases, the non-chemical beam inspection tool may be configured to acquire an image of the mask. To simulate the optical configuration of the lithography tool, a simulation may be performed on the acquired image, thereby generating a spatial image (i.e., a first image). In some cases, the simulation may be performed by system 101 (for example, the simulation function may be integrated into the PMC 102 of system 101), and in some other cases, the simulation may be performed by a processing module of the mask inspection tool 120, or by a separate simulation unit operably connected to the mask inspection tool 120 and system 101.

[0035] System 101 includes a processor and memory circuit (PMC) 102 operably connected to a hardware-based I / O interface 126. The PMC 102 is configured to provide the processing necessary to operate the system, as further detailed with reference to Figures 2, 3, and 4, and comprises a processor (not shown separately) and memory (not shown separately). The processor of the PMC 102 can be configured to execute several functional modules according to computer-readable instructions implemented on non-transient computer-readable memory contained within the PMC. Such functional modules are hereafter referred to as being contained within the PMC.

[0036] The processors referred to herein may represent one or more general-purpose processing devices, such as microprocessors and central processing units. More specifically, a processor may be a composite instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing another instruction set, or a processor implementing a combination of instruction sets. A processor may also be one or more dedicated processing devices, such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), or a network processor. A processor is configured to execute instructions for performing the operations and steps discussed herein.

[0037] The memories referred to herein 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)).

[0038] As described above, in some embodiments, the system 101 can be configured to detect defects related to the edge displacement of one or more structural elements on the mask. As used herein, the term “edge displacement” refers to the substantial deviation of the edge / contour of a structural element from its expected position.

[0039] As used herein, structural elements or structural features may refer to any original object on a mask having a contoured geometric shape or geometric structure, which may be combined / superimposed with other objects (and thus form a pattern). Examples of structural elements may include, for example, common shape features such as junctions and lines, and / or features having complex structures / shapes, and / or features combined with one or more other features. Structural elements may be 2D or 3D features, and images capturing structural elements may reflect a 2D representation of the structural elements.

[0040] The edge displacement defects referred to herein can be caused by a variety of factors, including physical influences and / or other factors during the mask manufacturing process, such as oxidation (which may occur gradually during mask use), particles, scratches, crystal growth, and electrostatic discharge (ESD). If such mask defects are not detected before mass production of wafers, they can be repeated multiple times on the wafer, rendering multiple semiconductor devices unusable (e.g., affecting the functionality of the devices) and thus significantly reducing yield.

[0041] A mask contains a mask field that is converted into a wafer. In some cases, a mask field can contain multiple dies having the same design pattern (such a mask is called a multi-die mask). In some other cases, a mask field can contain a single die (such a mask is called a single-die mask). To detect whether defects exist associated with the structural elements of a die, a reference structural element from another die is usually required for comparison in 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, a reference for defect detection must be obtained for the structural elements of a single die.

[0042] Now, looking at Figure 6, exemplary layouts of a single die mask and an exemplary layout of a multi-die mask according to a particular embodiment of the subject matter of this disclosure are schematically shown.

[0043] As shown in the figure, the multi-die mask 604 includes a mask field of nine dies having the same design pattern. For any die structural element in the mask field, one or more reference structural elements can always be found in one or more adjacent dies. However, in the case of the single-die mask 602, which includes a mask field of a single die 606, there is no reference die on the mask that can be used for defect detection with respect to the structural elements of the single die.

[0044] In addition, the single die mask 602 further includes a scribe region 608 between the die region 606 and the peripheral region 610 of the mask. The scribe region 608 includes auxiliary features such as alignment features and calibration features. Such auxiliary features / structures may be printed onto the wafer in the lithography process along with the die region pattern. Therefore, in addition to, or instead of, defect detection related to the structural elements of the die, it is also desirable to detect defects related to these auxiliary features (if any). This also applies to auxiliary features of the scribe region 612 of the multi-die mask 604 between the die region and the peripheral region, as well as between dies. However, there are no reference features for such auxiliary features on the mask used for inspection.

[0045] Certain prior art techniques for chemical beam inspection tools can acquire two images each from the tool's transmission modality and reflection modality, and analyze the difference between the two images to estimate the presence of any defect. However, utilizing two imaging modalities is time-consuming in both the image acquisition and image processing processes, and therefore can affect the inspection throughput (TpT).

[0046] Alternatively, certain inspection tools may generate simulated images based on the mask design data and attempt to use these simulated images as reference images for defect detection on the mask image. However, such methods often require obtaining the mask design data, which may not be available. In addition, the simulated images may be inaccurate due to uncertainties in process variability during the mask manufacturing process, which can inevitably affect the accuracy of the inspection results.

[0047] Therefore, particularly due to the development of advanced processes and complex features related to photomasks, current inspection methods are insufficient to provide the desired process control of mask features / elements. Thus, there is a need for improved defect detection methods that address the above problems, enabling more sensitive and accurate detection of defects with respect to structural elements on the mask (e.g., die regions in single-die masks, as well as structural elements in scribe regions in both single-die and multi-die masks) without affecting inspection throughput.

[0048] According to certain embodiments of the subject matter of this disclosure, a novel mask inspection system and method are proposed for detecting defects related to edge displacement of structural elements on a mask. The proposed method has been shown to have improved accuracy and detection sensitivity for advanced process control of mask features.

[0049] According to a particular embodiment, the functional modules included in the PMC102 of system 101 may include an image processing module 104, a measurement module 106, and optionally a defect processing module 108. The PMC102 can be configured to obtain a first image representing at least a portion (e.g., a portion) of the mask via an I / O interface 126. The first image can be obtained by emulating an optical configuration lithography tool usable for the fabrication of semiconductor samples. For example, the image can be obtained by a mask inspection tool 120, such as a chemical beam inspection tool.

[0050] The image processing module 104 can be configured to apply a print threshold to a first image to produce a second image. The second image provides information on multiple structural elements of a mask printable on a semiconductor sample. The image processing module 104 can be further configured to estimate the contours for each SEI of a group of structural elements of interest (SEIs) from the multiple structural elements, extract a set of attributes characterizing the contours, and produce a group of contours corresponding to the group of SEIs and a set of attributes associated with each SEI.

[0051] The image processing module 104 can be further configured to identify one or more reference contours similar to a given contour from the rest of the contour group by comparing each given contour with its respective set of attributes associated with the contour. The measurement module 106 can be configured to measure the deviation between a given contour and each of its one or more reference contours, and to produce one or more measured deviations indicating whether a defect exists with respect to the SEI associated with the given contour. Optionally, the defect handling module 108 can be configured to report the presence of a defect when the deviation exceeds a deviation threshold and / or to determine how to respond to the detected defect.

[0052] The operation of systems 100, 101, PMC102, and their functional modules will be described in further detail with reference to Figures 2, 3, and 4.

[0053] According to a particular embodiment, system 100 may include a storage unit 122. The storage unit 122 can be configured to store any data necessary for the operating systems 100 and 101, such as data related to the inputs and outputs of systems 100 and 101, as well as intermediate processing results generated by system 101. For example, the storage unit 122 can be configured to store images and / or derivatives thereof generated by the mask inspection tool 120 (e.g., pre-processed images). Thus, images can be retrieved from the storage unit 122 and provided to the PMC 102 for further processing.

[0054] In some embodiments, system 100 may optionally include a computer-based graphical user interface (GUI) 124 configured to enable user-specified inputs related to system 101. For example, a visual representation of the sample, including an image of the sample and / or an image representation of its structural elements, can be presented to the user (e.g., by a display forming part of the GUI 124). The user may be provided with options via the GUI to define specific operating parameters, such as print thresholds and deviation thresholds. In some cases, the user may also view operational results on the GUI, such as measured deviations, detected defects, and / or further inspection results.

[0055] As described above, system 101 is configured to receive one or more images of a mask (e.g., a first image) via the I / O interface 126. The images may include image data (and / or derivatives thereof) generated by the mask inspection tool 120, as well as / or image data stored in the storage unit 122 or one or more data storage locations. In some cases, the image data may refer to images captured by the mask inspection tool during or after the mask manufacturing process, and / or pre-processed images derived from captured images obtained by various pre-processing steps. It should be noted that in some cases, the images may include associated numerical data (e.g., metadata, handcrafted attributes, etc.). It should be further noted that the image data is related to the target layer of the semiconductor device to be printed on the wafer.

[0056] The system 101 is further configured to process the received image and send the results (e.g., measured deviations, detected defects) via the I / O interface 126 to the storage unit 122 and / or the GUI 124 for rendering and / or the mask inspection tool 120.

[0057] In some embodiments, in addition to system 101, the mask inspection system 100 may further comprise one or more inspection modules, such as an additional defect detection module and / or an automated defect review module (ADR) and / or an automated defect classification module (ADC) and / or a metrology-related module 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 standalone computers, or the functions of the inspection modules (or at least a portion thereof) may be integrated with the mask inspection tool 120. In some embodiments, the output obtained from system 101 may be used by the mask inspection tool 120 and / or one or more inspection modules (or a portion thereof) for further inspection of the mask.

[0058] Those skilled in the art will readily understand that the teachings of the subject matter of this disclosure are not constrained by the system shown in Figure 1, and that equivalent and / or modified functions can be integrated or separated in other ways and implemented in any suitable combination of software and firmware and / or hardware.

[0059] It should be noted that the inspection system shown in Figure 1 can be implemented in a distributed computing environment in which the aforementioned functional modules included in the PMC 102 can be distributed across several local and / or remote devices and linked via a communication network. Furthermore, it should be noted that in other embodiments, at least some of the test tool 120, storage unit 122, and / or GUI 124 may be external to the inspection system 100 and can operate by communicating data with system 101 via the I / O interface 126. System 101 can be implemented as a standalone computer used in conjunction with the test tool. Alternatively, each function of system 101 can be integrated, at least partially, with the mask inspection tool 120, thereby facilitating and enhancing the functionality of the mask inspection tool 120 in inspection-related processes.

[0060] While not always the case, the operational processes of systems 101 and 100 can correspond to some or all of the steps of the methods described with respect to Figures 2 to 4. Similarly, the methods described with respect to Figures 2 to 4 and their possible embodiments can be implemented by systems 101 and 100. Therefore, it should be noted that embodiments discussed in relation to the methods described with respect to Figures 2 to 4 can be implemented as various embodiments of systems 101 and 100 with necessary modifications, and vice versa.

[0061] Referring now to Figure 2, a generalized flowchart of mask inspection according to a specific embodiment of the subject matter of this disclosure is shown.

[0062] A first image representing at least a portion of the mask (the mask being inspected) can be obtained (for example, by the PMC 102 via the I / O interface 126, from the mask inspection tool 120 or from the storage unit 122) (202). The first image can be obtained by emulating the optical configuration of a lithography tool usable for the manufacture of semiconductor samples.

[0063] In some embodiments, the mask to be inspected is a single-die mask. As illustrated in Figure 6, 602, the mask field of a single-die mask, which includes the die region and scribe region of the single die (including auxiliary features such as alignment features and calibration features), contains printable features / structures that are transferred onto the wafer during the lithography process. Therefore, the inspection methods currently proposed are applicable to detect defects with respect to any of these regions / areas. As an example, the obtained first image may represent at least a portion of the single-die region and / or at least a portion of the scribe region.

[0064] In some other embodiments, the mask to be inspected may be a multi-die mask, as illustrated in Figure 6, 604. In such cases, the currently proposed inspection method is applicable to detecting defects with respect to at least a portion of the scribed area of ​​the multi-die mask.

[0065] In some embodiments, the first image is acquired by a chemical beam mask inspection tool, such as the Aera Mask Inspection tool from Applied Materials Inc. As described above with reference to Figure 5, the chemical beam mask inspection tool is configured, in particular, to emulate the optical configuration of a lithography tool (e.g., a scanner or stepper) used in the manufacture of semiconductor wafers according to the mask. The emulated optical configuration may include one or more of the illumination / exposure conditions, such as wavelength, pupil shape, and numerical aperture (NA).

[0066] The mask image acquired by such a chemical beam inspection tool (e.g., the first image) is expected to resemble the image of the wafer manufactured using the mask via a lithography tool, and is therefore also called a spatial image. In other words, the chemical beam mask inspection tool is configured to capture a mask image that can mimic how the mask's design pattern will actually appear on the physical wafer after the manufacturing process.

[0067] In some cases, a chemical beam inspection tool may not be available to inspect the mask. In such cases, an image of the mask (non-spatial image) can be obtained using a non-chemical beam inspection tool, such as a conventional optical inspection tool or an electron beam tool. To simulate the optical configuration of the lithography tool, a simulation can be performed on the acquired non-spatial image, thereby generating a spatial image / first image of the mask. Therefore, in some embodiments, the mask inspection method described with reference to Figure 2 may further include a preliminary step of obtaining an image acquired by a non-chemical beam inspection tool, and a preliminary step of performing a simulation on the image to simulate the optical configuration of the lithography tool (for example, by the image processing module 104 of the PMC 102, or by the processing module of the mask inspection tool 120, etc.) to produce a first image.

[0068] In some embodiments, the obtained first image may be preprocessed before further processing, as described with reference to Figure 2. Preprocessing may include one or more operations such as interpolation (for example, if the first image has a relatively low resolution), noise filtering, focus correction, aberration compensation, and image format conversion.

[0069] Please note that this disclosure is not limited to the specific modality of the mask inspection tool, and / or the type of image obtained thereby, and / or the preprocessing operations required to process the image.

[0070] A second image can be generated by applying a printing threshold (204) to the first image (for example, by the image processing module 104 of the PMC102). The second image provides information on multiple structural elements of a mask that can be printed on a semiconductor sample.

[0071] Referring now to Figure 7, a schematic diagram of the process of applying a print threshold is shown, as well as examples of first and second images according to a particular embodiment of the subject matter of this disclosure.

[0072] As illustrated, Figure 700 shows an exemplary (and simplified) mask comprising a transparent region 702 (e.g., made of quartz) that transmits light when illuminated and an opaque region 704 (e.g., made of chromium) that blocks light. The first image (spatial image) obtained as described above refers to the image captured by a detector that focuses the light transmitted through the mask, as illustrated in Image 710.

[0073] In practice, the actual wafer manufacturing process using manufacturing tools (e.g., scanners or steppers) includes a lithography process followed by a resist process and an etching process. The wafer is coated with a photoresist, which is a photosensitive material. Exposure to light hardens or softens parts of the resist, depending on the process. After exposure, the wafer is developed, dissolving the photoresist in specific areas depending on the amount of transmitted light (i.e., light intensity) received by those areas during exposure.

[0074] As an example, a waveform 705 representing the intensity of transmitted light is shown. When a given region of photoresist is exposed to a transmitted light intensity below a certain level, a pattern is printed on the wafer. These regions of photoresist and regions without photoresist reproduce the design pattern on the mask. Thus, the specific intensity is known as the printing threshold 706, as illustrated in Figure 7. The developed wafer is then exposed to a solvent that etches away the silicon in the portions of the wafer that are no longer protected by the photoresist coating, resulting in a printed wafer 708 (for a given layer).

[0075] Therefore, in a chemical beam inspection tool that mimics the optical configuration of a wafer manufacturing tool, the waveform 705 represents transmitted light captured by the detector of the chemical beam inspection tool to form a first image. In the chemical beam inspection tool, since the detector replaces the wafer and there is no actual resist and etching process, in order to obtain an image similar to a printed wafer, it is necessary to apply a printing threshold 706 to the first image to mimic the effects of the resist and etching process and produce a second image containing features that can be printed on the wafer. Specifically, the second image is a binary image that provides information on multiple structural elements of a mask that can be printed on the wafer.

[0076] Figure 7 shows an example of a first image 710 and a corresponding second image 720 generated after applying a print threshold to the first image. As shown, the second image 720 is a binary image similar to the printed pattern on wafer 708. In this example, patterns below the print threshold are shown as printable on the wafer (i.e., positive resist), but it should be noted that this is not necessarily the case. In some other cases, the opposite may be true, i.e., patterns above the print threshold are printable on the wafer (i.e., negative resist). This disclosure is not limited to any particular resist process for rendering printable features or any particular application of a print threshold.

[0077] Continuing the explanation of Figure 2, the contour of each SEI in a group of structural elements of interest (SEI) can be estimated from multiple structural elements, and a set of attributes characterizing the contour can be extracted (206) (for example, by the image processing module 104 of PMC102), resulting in a group of contours corresponding to the group of SEIs and a set of attributes associated with each SEI.

[0078] According to a particular embodiment, a group of structural elements of interest (SEIs) can be selected from multiple structural elements, and contour estimation can be performed for 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 elements on the second image, the type and / or shape of the structural elements, structural elements detected in previous inspections as defect candidates, and customer input / feedback regarding the importance of the particular structural elements being inspected. In some cases, selection can be skipped, and a group of SEIs can actually include the entire population of multiple structural elements on the second image.

[0079] The term contour can refer to the external shape or boundary of an object or element. Contour estimation can generally be used to detect various objects / elements in an image. In some embodiments of this disclosure, the contours of structural elements 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 applicable to the 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 has been assigned to the assignee of this patent application and is incorporated herein by reference in its entirety. Figure 8A shows some examples of contours (indicated by dashed lines) extracted for several structural elements having different shapes according to a particular embodiment of the subject matter of this disclosure.

[0080] For each contour, a set of attributes that characterize the contour can be extracted. For example, the set of attributes can be selected from groups including the area formed by the contour, the width of the area, the height of the area, the number of pixels along the contour, the chain code, and the centroid. For instance, a chain code is used to represent a contour by connecting a series of straight line segments of a specific length and direction. Typically, this representation is based on four or eight connections between the line segments. The direction of each line segment is encoded using a numbering scheme. The contour code formed as a series of such directional numbers is called the chain code and indicates the shape of the contour.

[0081] For example, the set of attributes for each contour in a group of contours can be represented in different data formats, such as tables, vectors, or lists. For instance, a table representation might include N rows, each representing a specific contour in a group of N contours, and K columns, each representing a set of K features associated with the contour.

[0082] For each given contour in a group of contours, one or more reference contours similar to the given contour can be identified (for example, by the image processing module 104 of the PMC102) from among the remaining contours in the group (i.e., candidate contours) (208). In some embodiments, one or more reference contours can be identified by comparing the set of attributes associated with a given contour with the respective sets of attributes associated with at least some of the remaining contours. The comparison can be based on a similarity scale, and one or more contours that satisfy the similarity criterion can be identified as reference contours similar to the given contour.

[0083] For example, similarity measures can be distance-based metrics such as Euclidean distance, Manhattan distance, cosine distance, Pearson correlation distance, or Spearman correlation distance. The similarity criterion can be a predetermined distance. In some cases, the number of criterion contours to be identified can also be predetermined.

[0084] For example, the comparison can be performed by, for instance, starting with a first candidate contour in the table, and calculating the distance between each pair of the attribute set of a given contour and the candidate contours from the remaining contours. The process ends when the number of reference contours whose distances satisfy the similarity criterion is met, and the identified reference contours are provided.

[0085] Alternatively, in another example, the comparison can be performed by calculating the distance for each candidate contour in the remaining contours to a given contour, and then selecting a predetermined number of reference contours by ranking the calculated distances. In some cases, the number of reference contours to be identified is not predetermined. All candidate contours whose distances satisfy the similarity criteria can be identified as reference contours.

[0086] Referring to Figure 8B, an example of a reference contour is shown for a given contour according to a particular embodiment of the subject matter of this disclosure.

[0087] Assume that for each given contour, the number of reference contours identified is 1. For example, for contour 804, the reference contour identified as satisfying the similarity criterion is contour 802. For contour 806, the reference contour identified as satisfying the similarity criterion is either contour 804 or 802. If the number of reference contours is predetermined to be 2, both contours 802 and 804 can be used as reference contours for contour 806. For contour 810, its reference contour is contour 808.

[0088] Continuing the explanation of Figure 2, the deviation between a given contour and each of one or more reference contours can be measured (for example, by the measurement module 106 of the PMC 102) (210) to produce one or more measured deviations indicating whether a defect exists with respect to the SEI associated with the given contour.

[0089] The defects detected here refer to edge displacement, which represents a relatively substantial deviation from the expected location of a given contour (e.g., the expected location of the contour according to the original design data or according to an identified reference contour). Edge displacement differs from edge roughness (which can be caused by various variations in the manufacturing process) in that i) edge displacement is local (existing at local locations on the contour), whereas edge roughness is present along the entire edge, and ii) the amplitude of the deviation in edge displacement is relatively more substantial (i.e., stronger / larger) compared to the amplitude of fine roughness along the edge.

[0090] Such edge displacements can sometimes be caused by specific physical influences and / or other factors during the mask manufacturing process, such as oxidation, particles, scratches, crystal growth, and electrostatic discharge (ESD). When these are printed onto the wafer, they can affect the electrical measurements of the manufactured device, potentially leading to reduced yield and poor device performance. Therefore, it is necessary to detect such displacement defects and measure their amplitude.

[0091] Now, looking at Figure 3, we see a generalized flowchart illustrating how to measure the deviation between a given contour and a reference contour according to a particular embodiment of the subject matter of this disclosure.

[0092] Specifically, a given contour can be aligned with one or more reference contours (302) to produce one or more pairs of aligned contours. For example, alignment can be performed by aligning the centroids and / or specific anchor points of a given contour with those of the reference contours. The distance between corresponding points in each pair of aligned contours can be measured (304). For example, the distance can be measured using the Hausdorff distance metric. The maximum distance can be selected from the measured distances (306), and this maximum distance is used as the measured deviation between a given contour and each of the reference contours (of the one or more reference contours).

[0093] In some embodiments, a composite deviation can be derived based on one or more measured deviations between a given contour and one or more reference contours, and a deviation threshold can be applied to the composite deviation. If the composite deviation exceeds the deviation threshold, the presence of a defect can be reported (for example, by the defect processing module 108 of the PMC 102). For example, the composite deviation can be derived by averaging (or weighted averaging) one or more measured deviations, such as the mean or median of the deviations or any other type of averaging calculation (with or without weights).

[0094] For example, three reference contours can be identified for a given contour, and three deviations are measured between the given contour and the three reference contours. The three deviations can be averaged to generate a composite deviation that is compared to a deviation threshold. In some cases, the deviation threshold can be predetermined according to, for example, a specific inspection application, type of structural element, technical node and / or specifications used by the customer, etc.

[0095] Referring now to Figure 9, two examples of measured deviations indicating the presence of a defect are shown according to a particular embodiment of the subject matter of this disclosure.

[0096] Graph 900 shows the estimated contour 904 of a structural element having an elliptical shape, and a reference contour 902 identified relative to contour 904, representing the expected location of the contour of a defect-free structural element. As shown in the figure, the two contours are aligned, and the maximum distance 906 is measured as the deviation between the given contour 904 and the reference contour 902. Since this deviation is greater than a predetermined deviation threshold, an edge displacement defect is identified at the measurement location.

[0097] Graph 910 shows another example where an edge displacement defect is identified at location 912, which is relatively more substantial compared to the edge roughness 914 as illustrated, and therefore exceeds the deviation threshold.

[0098] According to a particular embodiment, when defect detection is performed for each SEI in a group of SEIs, a defect map can be provided that indicates the presence of defects in at least a portion of the mask and at its location, corresponding to the first image.

[0099] In some embodiments, optionally, if one or more defects are present, it may be determined (e.g., by the defect handling module 108 of the PMC 102) how to respond to the detected defects, for example by evaluating the printability of these defects or by evaluating whether, when printed, these defects would affect the functionality of a semiconductor sample manufactured using the mask (212). For example, the evaluation may include estimating the variability of printable structural elements / features associated with the defects when printed on a semiconductor sample. For example, possible processing operations in response to the presence of defects may include repairing the mask, defining the mask as a defective mask, defining the mask as functional, generating instructions to repair the mask, etc. For example, if these estimated variability are unacceptable, the mask may be sent to a mask factory for repair or rejected.

[0100] Furthermore, in some embodiments, at least one of the following outputs / instructions, or any combination thereof, can be provided (e.g., by the defect processing module 108 of the PMC102): (i) providing qualification criteria for masks shipped from a mask factory; (ii) providing input to a mask production process; (iii) providing input to a semiconductor sample manufacturing process; (iv) providing input to a simulation model used in a lithography process; (v) providing a correction map for a lithography tool; and (vi) identifying areas on the mask characterized by greater-than-expected feature parameter variability.

[0101] According to a particular embodiment, 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 step by step by the mask inspection tool, which images only a portion of the mask at a time. Thus, multiple first images of the mask can be obtained sequentially, each representing a different part of the mask. According to a particular embodiment, the multiple first images can be acquired sequentially in predetermined step sizes such that the multiple fields of view (FOV) of the multiple first images do not overlap.

[0102] Figure 10A schematically shows an example of a series of non-overlapping images captured over a portion of a mask according to a particular embodiment of the subject matter of this disclosure. As shown, a series of four first images 1002 are acquired over a portion 1004 of the mask 1000. The four first images are captured with a step size such that their fields of view (FOV) do not overlap, but rather together constitute the FOV of the entire portion 1004.

[0103] In such cases, the series of operations described with respect to blocks 204-210 in Figure 2 can be repeatedly performed on each of the multiple first images, thereby generating a defect map corresponding to each of the multiple first images, indicating the presence of defects on each part of the mask. Multiple defect maps can be obtained corresponding to multiple first images.

[0104] In some cases, false alarms may exist for detected defects due to the presence of structural elements unique to the first image. For example, the first image may contain a structural element with a uniquely shaped contour, for which there is no reference contour with a similar shape. In such cases, the inspection process may report it as a defect because it cannot find a reference for this structural element, or it may use a reference contour that is not actually similar to this element for comparison, and therefore also report it as a defect. In such cases, these reported defects are false alarms because they do not represent true defects but actually represent the presence of a unique structural element. In some cases, another instance of such a unique structural element may appear in subsequent images. Therefore, by comparing detected defects across multiple first images, such false alarms can be identified and removed from the detected defects.

[0105] Specifically, according to certain embodiments, one or more SEIs associated with a presented defect can be identified in each of a plurality of defect maps, and one or more contours of one or more SEIs can be compared across a plurality of first images. This comparison can be performed, for example, by comparing a set of attributes associated with the SEI, as described above with reference to block 208. Then, based on the comparison results, it can be determined 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, if the comparison shows that a structural element reported to be associated with a defect has similar instances in subsequent images, then this defect is most likely to be a false alarm indicating the presence of a unique structural element.

[0106] Furthermore, in some cases, false alarms may exist for detected defects caused by tool noise (e.g., shot noise from the inspection tool). To eliminate such false alarms from defects and thus improve detection sensitivity, a method is proposed for acquiring and utilizing duplicate images for false alarm elimination, as described with reference to Figure 4, according to a particular embodiment of the subject matter of this disclosure.

[0107] Specifically, in some embodiments, multiple first images can be obtained for a given portion of the mask (402), each first image representing at least the given portion. The multiple first images can be acquired sequentially with a predetermined step size such that the multiple fields of view (FOV) of the multiple first images overlap by at least a given portion.

[0108] Figure 10B schematically shows an example of a series of overlapping images captured on a portion of a mask according to a particular embodiment of the subject matter of this disclosure. As shown, a series of first images 1010 are acquired on the same portion 1004 of the mask 1000. The series of first images are acquired in a specific step size such that their FOVs overlap by, for example, one-third of the FOV of the images. For example, a given portion 1012 of the mask is acquired three times in three consecutive images. Thus, an actual defect 1014 (i.e., the defect of interest) present in a given portion 1012 of the mask will naturally appear three times in the three images, but false alarms resulting from random noise will not be repeated in consecutive images.

[0109] Therefore, the series of operations described with respect to blocks 204-210 in Figure 2 can be performed on each of the multiple first images, and a defect map can be provided that indicates the presence of defects on at least a given portion of each of the multiple first images (404), thereby generating multiple defect maps corresponding to the multiple first images. By comparing the defects presented in the multiple defect maps (406), it can be determined whether a defect is a defect of interest or a false alarm.

[0110] Now, looking at Figure 11, we see examples of focus defects and false alarms determined using overlapping images, according to a particular embodiment of the subject matter of this disclosure.

[0111] As shown in the upper series of three consecutive images capturing the same structural element, in each comparison of detected defects, defect 1102 is present in all three images and appears in the same position relative to the structural element in each image. Therefore, defect 1101 can be determined to be the defect of interest. Conversely, as shown in the lower series of three consecutive images capturing the structural element, defect 1104 is present only in the second image, but not in the first and third images. Therefore, defect 1104 is most likely a false alarm, which can be caused by random noise (e.g., shot noise). Such false alarms should be removed from the defect map.

[0112] It should be noted that the masks applicable to the inspection methods of this disclosure may be any type of mask that may be susceptible to defects of the edge displacement types described herein, including but not limited to memory masks and / or logic masks, and / or ArF masks and / or EUV masks. This disclosure is not limited to any particular type or function of the mask being inspected.

[0113] For illustrative and illustrative purposes, specific embodiments and / or examples of the subject matter disclosed herein are described in relation to structural elements having a particular type / shape and / or specific edge displacement. This is not intended to limit the disclosure in any way. It will be understood that the proposed methods and systems can be applied to structural elements of other types / shapes having various kinds of edge displacement.

[0114] According to certain embodiments, the mask inspection process described above with reference to Figures 2, 3, and 4 can be included as part of an inspection strategy available to the system 101 and / or inspection tool 120 for online mask inspection at runtime. Thus, the subject matter of this disclosure also includes a system and method for generating an inspection strategy during the strategy setting stage, the strategy including the steps described with reference to Figures 2, 3, and 4 (and their various embodiments). It should be noted that the term “inspection strategy” should be interpreted broadly to include any strategy that can be used by an inspection tool for performing operations related to any type of mask inspection, including the embodiments described above.

[0115] For example, the examples shown in this disclosure, such as the mask inspection tool architecture and configuration, mask layout, exemplified structural elements, and the specific methods of comparing and measuring deviations as described above, are provided for illustrative purposes only and should not be considered as limiting the disclosure. Other suitable examples / embodiments may be used in addition to or instead of the above.

[0116] Among the advantages of the specific embodiments of the mask inspection process described herein is the ability to detect certain types of defects (i.e., edge displacements) relating to structural elements on the mask. The proposed process is designed in particular for single-die masks where there is no reference die available for inter-die comparison. Furthermore, the proposed process is also applicable to inspecting scribed areas in both single-die and multi-die masks.

[0117] Among the advantages of the specific embodiments of the mask inspection process described herein is that the proposed inspection process does not require image acquisition from different modalities to provide a reference image, which can be time-consuming. Nor does it require acquisition of mask design data (often unavailable) or simulations based on design data that tend to be inaccurate. The proposed process utilizes specific processing of the spatial image to provide a reference within the image itself, which has been shown to have improved accuracy and sensitivity for defect detection in advanced process control of mask features without affecting throughput (TpT).

[0118] Among 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 eliminated, and thus detection sensitivity can be further improved without the need to adjust the deviation threshold.

[0119] This disclosure should be understood to be limited in its application to the details contained herein or described in the drawings.

[0120] It will also be understood that the system described herein may be implemented, at least in part, on a properly programmed computer. Similarly, this disclosure envisions a computer program that is readable by a computer to perform the method described herein. This disclosure further envisions a non-transient computer-readable memory that explicitly embodies a program of computer-executable instructions for performing the method described herein.

[0121] This disclosure is capable of other embodiments and can be implemented and carried out in various ways. Therefore, it should be understood that the terms and technical descriptions used herein are for illustrative purposes only and should not be considered limiting. Accordingly, those skilled in the art will understand that the underlying concepts of this disclosure can be readily used as a basis for designing other structures, methods, and systems to accomplish some of the objectives of the subject matter of this disclosure.

[0122] Those skilled in the art will readily understand that, as described above, various modifications and changes can be applied to embodiments of this disclosure without departing from the scope of the invention as defined by the appended claims. [Explanation of Symbols]

[0123] 100 Mask Inspection System 101 System 102 Processor and memory circuits 104 Image Processing Module 106 Measurement Module 108 Defect Handling Module 120 Mask Testing Tools 122 storage units 124 Graphical User Interface 126 I / O interfaces 500 Chemical Ray Inspection Tools 502 illumination source 504 Illumination optical system 506 Mask Holder 508 Projection optical system 510 detector 512 Wafer Holder 514 Masked Images Image of wafer 516 520 Lithography Tools 602 Single Die Mask 604 Multi-Die Mask 606 Die area 608 Scribe area 610 Peripheral area 612 Scribe area 702 Transparent area 704 Opaque area 705 Waveform 706 Print threshold 708 wafer 710 Image 1 720 Second image 802 Outline 804 Outline 806 Outline 808 Outline 810 Outline 900 graphs 902 Reference contour 904 Estimated contour 906 Maximum distance 910 Graph 914 Edge roughness 1000 masks 1002 Image 1 1004 parts 1010 Image 1 1012 parts 1014 Defect 1101 Defect 1102 Defect 1104 Defect

Claims

1. 1. A computerized system for inspecting a mask usable in the manufacture of a semiconductor sample, comprising: obtaining a first image representing at least a portion of the mask, the first image being obtained by emulating an optical configuration of a lithography tool that can be used to fabricate the semiconductor sample; applying a printing threshold to the first image to generate a second image providing information about a plurality of features of the mask that can be printed on the semiconductor sample; estimating a contour for each structural element of interest (SEI) of a group of SEIs from the plurality of structural elements and extracting a set of attributes characterizing the contours to generate a group of contours corresponding to the group of SEIs and a respective set of attributes associated with the SEIs; for each given contour of the group of contours, identifying one or more reference contours from among the remaining contours of the group of contours that are similar to the given contour by comparing the respective sets of attributes associated with the contour; measuring a deviation between the given contour and each of the one or more reference contours, resulting in one or more measured deviations indicative of whether a defect exists with respect to an SEI associated with the given contour, the defect being indicative of an edge displacement; a processing and memory circuit (PMC) configured to Computerized systems.

2. 2. The computerized system of claim 1, wherein the mask includes a mask field and a scribe area of ​​a single die, and the at least a portion of the mask includes at least a portion of the single die and / or at least a portion of the scribe area.

3. 2. The computerized system of claim 1, wherein the mask includes mask fields and scribe areas for multiple dies, and the at least a portion of the mask includes at least a portion of the scribe area.

4. The computerized system of claim 1 , wherein the first image is acquired by an actinic inspection tool configured to emulate the optical configuration of the lithography tool.

5. 10. The computerized system of claim 1, wherein the first image is obtained by acquiring an image using a non-actinic inspection tool and performing a simulation on the image to simulate the optical configuration of the lithography tool to produce the first image.

6. The computerized system of claim 1 , wherein the edge displacement indicates a relatively substantial deviation of the given contour from an expected position of the given contour with respect to a deviation threshold.

7. 2. The computerized system of claim 1, wherein the set of attributes is selected from a group including an area formed by the contour, a width of the area, a height of the area, a number of pixels along the contour, a chain code, and a centroid.

8. The PMC is aligning the given contour with the one or more reference contours, respectively, to produce one or more aligned contour pairs; measuring the distance between corresponding points of each aligned contour pair; selecting a maximum distance from among the measured distances as the measured deviation between the given contour and each of the one or more reference contours; and measuring the deviation by The computerized system of claim 1 .

9. 2. The computerized system of claim 1, wherein the PMC is further configured to: derive a composite deviation based on the one or more measured deviations; apply a deviation threshold to the composite deviation; and report the presence of a defect when the composite deviation exceeds the deviation threshold.

10. 10. The computerized system of claim 9, wherein the PMC is further configured to provide a defect map indicating the presence and locations of one or more defects on at least a portion of the mask corresponding to the first image.

11. the PMC is configured to obtain a plurality of first images, each representing a respective portion of the mask, the plurality of first images being acquired successively with a predetermined step size such that a plurality of fields of view (FOVs) of the plurality of first images do not overlap; the PMC is configured to perform the applying, estimating, identifying, and measuring for each of the plurality of first images to provide a defect map indicative of the presence of one or more defects on a respective portion of the mask corresponding to each of the plurality of first images, thereby producing a plurality of defect maps corresponding to the plurality of first images. The computerized system of claim 1 .

12. 12. The computerized system of claim 11, wherein the PMC is further configured to identify one or more SEIs associated with defects presented in each of the plurality of defect maps, compare one or more contours of the one or more SEIs between 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.

13. the PMC is configured to obtain, for a given portion of the mask, a plurality of first images, each representing at least the given portion of the mask, the plurality of first images being acquired successively with a predetermined step size such that a plurality of fields of view (FOVs) of the plurality of first images overlap by at least the given portion of the mask; the PMC is configured to perform the applying, estimating, identifying, and measuring on each of the plurality of first images to provide a defect map indicating the presence of one or more defects on at least a given portion of the mask corresponding to each of the plurality of first images, thereby generating a plurality of defect maps corresponding to the plurality of first images; and compare the defects presented in the plurality of defect maps to determine whether the defects are defects of interest or false alarms. The computerized system of claim 1 .

14. 1. A computerized method for inspecting a mask usable in the manufacture of a semiconductor sample, said method being performed by a processing and memory circuit (PMC): obtaining a first image representing at least a portion of the mask, the first image being obtained by emulating an optical configuration of a lithography tool that can be used to fabricate the semiconductor sample; applying a printing threshold to the first image to produce a second image, the second image providing information about a plurality of features of the mask that can be printed on the semiconductor sample; estimating a contour for each structural element of interest (SEI) of a group of SEIs from the plurality of structural elements and extracting a set of attributes characterizing the contours to generate a group of contours corresponding to the group of SEIs and a respective set of attributes associated with the SEIs; for each given contour of the group of contours, identifying one or more reference contours from among the remaining contours of the group of contours that are similar to the given contour by comparing the respective sets of attributes associated with the contour; measuring a deviation between the given contour and each of the one or more reference contours to produce one or more measured deviations indicative of whether a defect exists with respect to an SEI associated with the given contour; Including, The computerized method, wherein the defect exhibits an edge displacement.

15. 15. The computerized method of claim 14, wherein the mask includes a mask field and a scribe area of ​​a single die, and the at least a portion of the mask includes at least a portion of the single die and / or at least a portion of the scribe area.

16. the measuring step aligning the given contour with the one or more reference contours, respectively, to produce one or more aligned contour pairs; measuring the distance between corresponding points of each aligned contour pair; selecting a maximum distance from among the measured distances as the measured deviation between the given contour and each of the one or more reference contours; Including, 15. The computerized method of claim 14.

17. 15. The computerized method of claim 14, further comprising: deriving a composite deviation based on the one or more measured deviations; applying a deviation threshold to the composite deviation; and reporting the presence of a defect when the composite deviation exceeds the deviation threshold.

18. the obtaining step includes, for a given portion of the mask, obtaining a plurality of first images, each representing at least the given portion of the mask, the plurality of first images being acquired successively with a predetermined step size such that a plurality of fields of view (FOVs) of the plurality of first images overlap by at least the given portion of the mask; performing the applying, estimating, identifying, and measuring for each of the plurality of first images; providing a defect map indicating the presence of one or more defects on at least a given portion of the mask corresponding to each of the plurality of first images, thereby producing a plurality of defect maps corresponding to the plurality of first images; and comparing the defects presented in the plurality of defect maps to determine whether the defects are defects of interest or false alarms.

15. The computerized method of claim 14.

19. 1. A non-transitory computer-readable storage medium tangibly embodying a program of instructions that, when executed by a computer, causes the computer to perform a method of inspecting a mask usable in the manufacture of a semiconductor sample, the method comprising: obtaining a first image representing at least a portion of the mask, the first image being obtained by emulating an optical configuration of a lithography tool that can be used to fabricate the semiconductor sample; applying a printing threshold to the first image to produce a second image, the second image providing information about a plurality of features of the mask that can be printed on the semiconductor sample; estimating a contour for each structural element of interest (SEI) of a group of SEIs from the plurality of structural elements and extracting a set of attributes characterizing the contours to generate a group of contours corresponding to the group of SEIs and a respective set of attributes associated with the SEIs; for each given contour of the group of contours, identifying one or more reference contours from among the remaining contours of the group of contours that are similar to the given contour by comparing the respective sets of attributes associated with the contour; measuring a deviation between the given contour and each of the one or more reference contours to produce one or more measured deviations indicative of whether a defect exists with respect to an SEI associated with the given contour; Including, The non-transitory computer-readable storage medium, wherein the defects exhibit edge displacement.

20. The computerized system of claim 1, wherein the contour is estimated using an edge detection method.

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