Projection-based die-to-database alignment for image-based inspection

Abstract

A non-transitory computer-readable medium stores a set of instructions that is executable by at least one processor of an apparatus to cause the apparatus to perform operations. The operations include extracting boundaries of a formed pattern disposed on a substrate from image information and boundaries of a design pattern from design information. The design information includes a design pattern. The image information is a two-dimensional representation of the formed pattern acquired by an image acquisition system. The operations also include generating one-dimensional projections of the boundaries of the formed pattern and the design pattern. The operations also include cross-correlating the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.

Description

PROJECTION-BASED DIE-TQ-DATABASE ALIGNMENT FOR IMAGE-BASED INSPECTIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority of US application 63 / 723,523 which was filed on 21 November 2024 and which is incorporated herein in its entirety by reference.FIELD

[0002] The description herein relates to inspection systems, and more particularly, to computational image analysis techniques used in connection with charged particle inspection systems.BACKGROUND

[0003] In manufacturing processes of integrated circuits (ICs), unfinished or finished circuit components are inspected to ensure that they are manufactured according to design and are free of defects. Inspection can be performed using systems such as optical microscopes or charged particle beam microscopes (e.g., a scanning electron microscope (SEM)). As the sizes of IC components continue to shrink, accuracy and speed of defect detection become more and more important for yield and throughput. However, imaging resolution and throughput of inspection tools struggle to keep pace with the ever-decreasing feature size of IC components.SUMMARY

[0004] Embodiments of the present disclosure provide a system and method for reliable and fast overlay metrology using a projection-based die-to-database alignment.

[0005] In some embodiments, a non-transitory computer-readable medium that stores a set of instructions for a projection-based die-to-database alignment is provided. The instructions are executable by at least one processor of an apparatus to cause the apparatus to perform operations. The operations can comprise extracting edges from an inspection image of a formed pattern on a substrate and edges of a design pattern from design information. The operations can also comprise projecting the edges of the formed pattern and the design pattern to reduce dimensionality of information of the edges of the formed pattern and the edges of the design pattern. The operations can also comprise cross-correlating projections of the edges of the formed pattern and the edges of the design pattern to generate an alignment result.

[0006] In some embodiments, a non-transitory computer-readable medium that stores a set of instructions for a projection-based die-to-database alignment is provided. The instructions are executable by at least one processor of an apparatus to cause the apparatus to perform operations. The operations can comprise extracting boundaries of a formed pattern disposed on a substrate from image information and boundaries of a design pattern from design information. The design information can comprise a design pattern. The image information can be a two-dimensional representation of theformed pattern acquired by an image acquisition system. The operations can also comprise generating one-dimensional projections of the boundaries of the formed pattern and the design pattern. The operations can also comprise cross-correlating the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.

[0007] In some embodiments, a system for performing a projection-based die-to-database alignment is provided. The system can comprise a charged particle beam source configured to generate a beam of primary charged particles. The system can also comprise a charged particle optical system configured to direct the beam of primary charged particles at a surface of a substrate comprising a formed pattern. The system can also comprise a charged particle detector configured to generate image information based on detection of secondary charged particles associated with interaction of the primary charged particles with the surface. The system can also comprise a controller comprising circuity. The controller can be configured to identify boundary positions of the formed pattern from the image information and boundary positions of a design pattern from design information, wherein the design information comprises a design pattern. The image information can be a two-dimensional representation of the formed pattern. The controller can also be configured to generate onedimensional projections of the boundaries of the formed pattern and the design pattern. The controller can also be configured to cross-correlate the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.

[0008] In some embodiments, an apparatus for performing projection-based die-to-database alignment is provided. The apparatus can comprise one or more processors. The apparatus can also comprise one or more storage devices configured to store a set of instructions. The set of instructions is executable by the one or more processors to cause the apparatus to perform operations. The operations can comprise extracting boundaries of a formed pattern disposed on a substrate from image information and boundaries of a design pattern from design information. The design information can comprise a design pattern. The image information can be a two-dimensional representation of the formed pattern acquired by an image acquisition system. The operations can also comprise generating one-dimensional projections of the boundaries of the formed pattern and the design pattern. The operations can also comprise cross-correlating the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.BRIEF DESCRIPTION OF FIGURES

[0009] The above and other aspects of the present disclosure will become more apparent from the description of example embodiments, taken in conjunction with the accompanying drawings.

[0010] FIG. 1 is a schematic diagram illustrating an example charged-particle beam inspection system, consistent with embodiments of the present disclosure.

[0011] FIG. 2A is a schematic diagram illustrating an example single beam inspection apparatus, consistent with embodiments of the present disclosure that can be a part of the example charged- particle beam inspection system of FIG. 1.

[0012] FIG. 2B is a schematic diagram illustrating an example multi-beam inspection apparatus, consistent with embodiments of the present disclosure that can be a part of the example charged- particle beam inspection system of FIG. 1.

[0013] FIG. 3 is a schematic diagram illustrating an example die-to-database alignment, consistent with embodiments of the present disclosure.

[0014] FIG. 4 is a schematic diagram illustrating an example die-to-database alignment, consistent with embodiments of the present disclosure.

[0015] FIG. 5A is a schematic diagram illustrating an example projection-based die-to-database alignment, consistent with embodiments of the present disclosure.

[0016] FIG. 5B is a schematic diagram illustrating an example projection-based die-to-database alignment, consistent with embodiments of the present disclosure.

[0017] FIG. 6A is a schematic diagram illustrating an example projection-based die-to-database alignment, consistent with embodiments of the present disclosure.

[0018] FIG. 6B is a schematic diagram illustrating an example projection-based die-to-database alignment, consistent with embodiments of the present disclosure

[0019] FIG. 7 is a flowchart of an example method for performing a projection-based die-to-database alignment, consistent with embodiments of the present disclosure.DETAILED DESCRIPTION

[0020] Reference will now be made in detail to example embodiments, examples of which are illustrated in the drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise represented. The implementations set forth in the following description of example embodiments do not represent all implementations consistent with the invention. Instead, they are merely examples of apparatuses, systems, and methods consistent with aspects related to subject matter that may be recited in the appended claims. For example, although some embodiments are described in the context of utilizing charged-particle beams (e.g., electron beams), the disclosure is not so limited. Unless infeasible, embodiments described herein can be implemented in any other type of charged particle device (e.g., proton beams). Furthermore, other imaging systems may be used, such as optical imaging, photodetection, x-ray detection, or the like.

[0021] Electronic devices are constructed of circuits formed on a piece of silicon called a substrate. Many circuits may be formed together on the same piece of silicon and are called integrated circuits or ICs. With advancements in technology, the size of these circuits has decreased dramatically so that many more of them can fit on the substrate. For example, an IC chip in a smart phone can be as smallas a fingernail and yet may include over 2 billion transistors, the size of each transistor being less than 1 / 1, 000th the width of a human hair.

[0022] Making these ICs with extremely small structures or components is a complex, timeconsuming, and expensive process, often involving hundreds of individual steps. Errors in even one step have the potential to result in defects in the finished IC, rendering it useless. Thus, one goal of the manufacturing process is to avoid such defects to maximize the number of functional ICs made in the process, that is, to improve the overall yield of the process.

[0023] Yield is a metric that characterizes failure rate in device fabrication, which relates to cost and efficiency. Yield can be defined as a ratio of all the wafers that are produced by a fab to the number of wafers that were introduced to the fab. Or yield can be the number of working chips that survive the device fabrication process performed on a wafer to the number of potential chips that can be fabricated from that wafer in the ideal case of zero failure. As some wafers or chips fail during fabrication, the overall yield is less than 100%. For example, to obtain a 75% yield for a 50-step process (where a step can be indicative of the number of layers formed on a wafer), each individual step should have a yield greater than 99.4%. In contrast, if individual steps have a yield of 95%, the compounding errors at each step result in an overall process yield as low as 7-8%. Every wafer or chip lost during fabrication is a sunk cost and lost time for the fab.

[0024] Integral to the making of these ICs with extremely small structures are highly accurate inspection processes, performed in between one or more fabrication steps, to ascertain whether fabrication steps are performing at expected tolerances. Errors in even one step have the potential to result in defects in the finished IC, rendering it useless. A goal of the manufacturing process is to avoid such defects to maximize the number / yield of functional ICs made in the process.

[0025] Inspection can be carried out using a scanning charged-particle microscope (e.g., a scanning electron microscope (SEM)). A scanning charged-particle microscope can be used to image extremely small structures of ICs, by capturing an image of the structures on the wafer. The image can be used to determine if the structure was formed properly (e.g., having the expected dimensions and being properly located on the wafer). If the structure is defective, then the process can be adjusted, so the defect is less likely to recur.

[0026] The working principle of a SEM is analogous to that of a camera. A camera takes a picture by receiving and recording intensity of light reflected or emitted from people or objects. SEMs capture images by receiving and recording energies or quantities of electrons reflected or emitted from the structures of the wafer. Similar to how a camera uses a light source (e.g., ambient, sunlight, or a flash), SEMs use an electron source to send a beam(s) of electron to a surface of a wafer that has structures of interest for imaging. The electron beam(s) can be deflected and the wafer can be moved (on a movement stage) so that a plurality of regions of the wafer can be irradiated by the electrons. When the electrons are reflected or emitted (“exiting”) from the structures (e.g., from the wafer surface, from the structures underneath the wafer surface, or both), a detector of the SEM can receiveand record the energies or quantities of those electrons to generate an inspection image of the regions of the wafer that were irradiated by electrons.

[0027] Speed, or throughput, has been a traditionally important metric alongside yield. Throughput is a measurable quantity that characterizes the manufacture speed of a fab (e.g., number of IC units produced per unit time). Generating and processing these images to determine whether any defects exist (sometimes as small as the nanometer scale) are computationally intensive and affect throughput. And as the physical sizes of IC components continue to shrink, accuracy and efficiency in defect detection become more important. To inspect a single wafer, it is not uncommon for an inspection system to generate and process a substantial number of images. For example, if each image taken corresponds to 6 pm x 6 pm portion of a wafer, for a 200mm wafer, it would take over 872 million images to image the entire wafer. If these images are not processed and evaluated efficiently, not surprisingly, speed will be dramatically impacted, thereby affecting the wafer throughput.

[0028] Throughput has become even more important in view of recent global chip shortages. As there are multiple steps in the fabrication of a chip device (e.g., multiple steps for multiple layers), each step can have a characteristic throughput. For an inspection operation among the fabrication steps, throughput can characterize how quickly an inspection process can clear a wafer before moving on to the next wafer. Innovation of inspection tools can increase throughput, or at least resolve problems in another aspect while mitigating adverse impact to throughput.

[0029] State-of-the-art inspection tools are focused on maximizing yield and throughput. Inspection tools each have their limitations and temperaments / unique variations, and no one measurement technique is without flaw. For example, analysis of an SEM image can be performed via an automated computer algorithm, as opposed to a manual analysis by a person. The algorithm can be programmed to search for certain structural patterns that were fabricated on a wafer, the structural patterns corresponding to a digitized design pattern (e.g., provided in a computer-aided design (CAD) file or a graphics design system (GDS) file, or the like). If the quality of the image or fabrication process are below tolerances, or if the overlayed structured layers are numerous and complex, a consequence can be that the algorithm spends an inordinate amount of time searching the image for a given structure (e.g., stuck in a loop), which adversely impacts throughput. Yield can also be affected if the algorithm fails to find the expected structures or misidentifies structures. An example process that is susceptible to this type of drawback is a die-to-database (D2DB) alignment, a process that aligns an image representation of a fabricated structure (on a die) to its design pattern counterpart (in a database, such as a design file).

[0030] A D2DB alignment process can fail to align the die structures to the database pattern if the fabricated structures on a die / substrate has been damaged or does not track well with its design pattern counterpart.

[0031] Embodiments of the present disclosure provide devices and operations for performing faster and more accurate D2DB alignments while working with fabricated structures that do not track wellwith their design pattern counterpart. For example, the two-dimensional pixel field of an SEM image is analyzed. Edges of the fabricated structure can be identified. The edge data can be projected to a lower number of dimensions (e.g., from two dimensions to one dimension) while the design pattern counterpart is also projected down in dimensions in the same manner, allowing for a less error-prone comparison of the SEM image and the design information.

[0032] Objects and advantages of the disclosure can be realized by the elements and combinations as set forth in embodiments described herein. However, embodiments of the present disclosure are not necessarily required to achieve such example objects or advantages. Some embodiments can achieve a different feature or enhancement without necessarily achieving any expressly stated object or advantage.

[0033] As used herein, unless specifically stated otherwise, the term “or” encompasses all possible combinations, except where infeasible. For example, if it is stated that a component can comprise A or B, then, unless specifically stated otherwise or infeasible, the component can comprise A, or B, or A and B. As a second example, if it is stated that a component can comprise A, B, or C, then, unless specifically stated otherwise or infeasible, the component can comprise A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0034] Relative dimensions of components in drawings may be exaggerated for clarity. Within the following description of drawings, the same or like reference numbers refer to the same or like components or entities, and only the differences with respect to the individual embodiments are described.

[0035] FIG. 1 illustrates a schematic diagram of an example electron beam inspection (EBI) system 100, consistent with embodiments of the present disclosure. EBI system 100 can be used for imaging. EBI system 100 can comprise a main chamber 101, a load / lock chamber 102, a beam tool 104, and an equipment front end module (EFEM) 106. Beam tool 104 can be located within main chamber 101. EFEM 106 can comprise a first loading port 106a and a second loading port 106b. EFEM 106 can comprise additional loading port(s). First loading port 106a and second loading port 106b can receive wafer front opening unified pods (FOUPs) that contain wafers (e.g., semiconductor wafers or wafers made of other material(s)) or samples to be inspected (wafers and samples may be used interchangeably). The term “lot” can refer to a plurality of wafers that can be loaded for processing as a batch.

[0036] One or more robotic arms (not shown) in EFEM 106 can transport the wafers to load / lock chamber 102. Load / lock chamber 102 can be connected to a load / lock vacuum pump system (not shown) that can evacuate gas molecules in load / lock chamber 102 to reach a first pressure below atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) can transport the wafer from load / lock chamber 102 to main chamber 101. Main chamber 101 can be connected to a main chamber vacuum pump system (not shown) that can evacuate gas molecules in main chamber 101 to reach a second pressure below the first pressure. After reaching the secondpressure, the wafer can be subject to inspection by beam tool 104. Beam tool 104 can be a singlebeam system or a multibeam system.

[0037] A controller 109 can be electronically connected to beam tool 104. Controller 109 can be a computer configured to execute various controls of EBI system 100. While controller 109 is shown in FIG. 1 as being outside of the structure that includes main chamber 101, load / lock chamber 102, and EFEM 106, in some embodiments, controller 109 can be part of the structure.

[0038] In some embodiments, controller 109 comprises one or more processors (not shown). A processor may be a generic or specific electronic device capable of manipulating or processing information. For example, the processor can comprise a central processing unit (or “CPU”), a graphics processing unit (or “GPU”), an optical processor, a programmable logic controllers, a microcontroller, a microprocessor, a digital signal processor, an intellectual property (IP) core, a Programmable Logic Array (PL A), a Programmable Array Logic (PAL), a Generic Array Logic (GAL), a Complex Programmable Logic Device (CPLD), a Field-Programmable Gate Array (FPGA), a System On Chip (SoC), an Application-Specific Integrated Circuit (ASIC), any type circuitry capable of data processing, or any combination of any number thereof. The processor can be a virtual processor. The virtual processor can include one or more processors distributed across multiple machines or devices coupled via a network.

[0039] In some embodiments, controller 109 further comprises one or more memories (not shown). A memory can be a generic or specific electronic device capable of storing instructions, code, or data accessible by the processor (e.g., via a bus). For example, the memory can comprise a random-access memory (RAM), a read-only memory (ROM), an optical disc, a magnetic disk, a hard drive, a solid- state drive, a flash drive, a security digital (SD) card, a memory stick, a compact flash (CF) card, any type of storage device, or any combination of any number thereof. The instructions, code, or data can include an operating system (OS) and one or more application programs (or “apps”) for specific tasks. The memory can be a virtual memory. The virtual memory can include one or more memories distributed across multiple machines or devices coupled via a network.

[0040] FIG. 2A illustrates a schematic diagram of an example beam tool 104 A and an image processing system 199 that can be configured for use with EBI system 100 (FIG. 1), consistent with embodiments of the present disclosure. Beam tool 104A can be provided as beam tool 104 of FIG. 1. In some embodiments, beam tool 104A is a single-beam tool that uses one primary electon beam to scan locations on a wafer in series (one location after the other).

[0041] Beam tool 104A can comprise a wafer holder 136 supported by motorized stage 134 to hold a wafer 150 to be inspected. Beam tool 104A can also comprise an electron emitter comprising several elements, such as a cathode 103, an anode 121, and a gun aperture 122. Beam tool 104A can also comprise a beam limit aperture 125, a condenser lens 126, a column aperture 135, an objective lens assembly 132, and a detector 144. Objective lens assembly 132 can be a swing objective retarding immersion lens (SORIL) or a modified version thereof. Objective lens assembly 132 can comprise apole piece 132a, a control electrode 132b, a deflector unit 132c, and an exciting coil 132d. In a detection or imaging process, an electron beam 161 emanating from the tip of cathode 103 can be accelerated by anode 121 voltage, can pass through gun aperture 122, beam limit aperture 125, condenser lens 126, and can be focused into a probe spot 170 by objective assembly 132. Electron beam 161 can impinge onto the surface of wafer 150. A deflector can be used to scan probe spot 170 across the surface of wafer 150 (e.g., deflector unit 132c or other deflector(s) in the SORIL lens). Secondary or scattered particles, such as secondary electrons or scattered primary electrons emanated from the wafer surface can be collected by detector 144 to determine intensity of the beam and so that an image of an area of interest on wafer 150 can be reconstructed.

[0042] In some embodiments, image processing system 199 comprises an image acquirer 120, a storage 130, and controller 109. Image acquirer 120 can comprise one or more processors. For example, image acquirer 120 can comprise a computer, server, mainframe host, terminals, personal computer, any kind of mobile computing devices, or the like. Image acquirer 120 can be connected with detector 144 of beam tool 104A through a communication medium, such as an electrical conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, or the like. Image acquirer 120 can receive a signal from detector 144. Image acquirer 120 can construct an image based on the signal from detector 144. Image acquirer 120 can thus acquire images of wafer 150. Image acquirer 120 can also perform various post-processing functions, such as image averaging, generating contours, superimposing indicators on an acquired image, or the like. Image acquirer 120 can perform adjustments of brightness and contrast, etc. of acquired images. Storage 130 can be a storage medium, such as a hard disk, random access memory (RAM), cloud storage, other types of computer readable memory, or the like. Storage 130 can be coupled with image acquirer 120 and can be used for saving scanned raw image data as original images, as well as postprocessed images. Image acquirer 120 and storage 130 can be connected to controller 109. In some embodiments, image acquirer 120, storage 130, and controller 109 can be integrated together as one electronic control unit (e.g., on the same chip).

[0043] In some embodiments, image acquirer 120 acquires one or more images of a sample based on an imaging signal received from detector 144. An imaging signal can correspond to a scanning operation that is executed for the purposes of charged particle imaging. An acquired image can be a single image comprising a plurality of imaging areas that can contain various features of wafer 150. The single image can be stored in storage 130. Imaging can be performed on the basis of imaging frames.

[0044] The condenser and illumination optics of the electron beam tool can comprise, or be supplemented by, electromagnetic quadrupole electron lenses. In the example of FIG. 2A, electron beam tool 104A comprises a first quadrupole lens 148 and a second quadrupole lens 158. The quadrupole lenses can be used for controlling the electron beam. First quadrupole lens 148 can becontrolled to adjust the beam current and second quadrupole lens 158 can be controlled to adjust the beam spot size and beam shape.

[0045] The single primary beam of beam tool 104A can be used to generate secondary electrons by interacting with wafer 150. Detector 144 can be placed proximal to or aligned with optical axis 105. The primary electron beam can be configured to travel along optical axis 105. Accordingly, detector 144 can comprise a hole at its center so that the primary electon beam can pass through to reach wafer 150. In some embodiments, a detector can be placed off-axis relative to the optical axis along which the primary electron beam travels. In such instances a beam separator can be provided to divert secondary electron beams toward a detector placed off-axis.

[0046] The images generated by scanning charged-particle microscope (e.g., an SEM) can be used for defect inspection. A generated image capturing a test device region of a wafer can be compared with a reference image of the same test device region. The reference image can be predetermined (e.g., by simulation) and include no known defect. If a difference between the generated image and the reference image exceeds a tolerance level, a potential defect can be identified. For another example, an SEM is used to scan multiple regions of a wafer, each region including a test device region designed to be constant from region to region. The SEM can generate multiple images capturing those test device regions as fabricated. The multiple images can be compared with each other. If a difference between the multiple images exceeds a tolerance level, a potential defect can be identified.

[0047] Another example of a charged particle beam apparatus will now be described with reference to FIG. 2B, which illustrates a schematic diagram of an example beam tool 104B and an image processing system 290 that can be configured for use in EBI system 100 (FIG. 1), consistent with embodiments of the present disclosure. Beam tool 104B can be provided as beam tool 104 of FIG. 1. In some embodiments, beam tool 104B is a multibeam tool that uses multiple beams of primary electrons to scan multiple locations on a wafer. It is appreciated that hardware and functions described in reference to an element in one of FIGS. 2A and 2B can be applicable to a corresponding element in the other of FIGS. 2A and 2B (e.g., structure and function of a condenser lens, defect analysis of a sample, or the like), unless infeasible.

[0048] Beam tool 104B can comprise a charged-particle source 202 configured to emit a primary charged-particle beam 210, a gun aperture 204, a condenser lens 206, a source conversion unit 212, a primary projection optical system 220, a motorized wafer stage 280, a wafer holder 282, a secondary optical system 242, and a charged-particle detection device 244. Primary charged-particle beam 210 can comprise a plurality of beamlets 214, 216, and 218 of primary charged-particle beam 210. Primary projection optical system 220 can comprise a beam separator 222, a deflection scanning unit 226, and an objective lens 228. Charged-particle detection device 244 can comprise detection subregions 246, 248, and 250.

[0049] Charged-particle source 202, gun aperture 204, condenser lens 206, source conversion unit 212, beam separator 222, deflection scanning unit 226, and objective lens 228 can be aligned with a primary optical axis 260 of beam tool 104B. Secondary optical system 242 and charged-particle detection device 244 can be aligned with a secondary optical axis 252 of beam tool 104B.

[0050] Charged-particle source 202 can emit one or more charged particles, such as electrons, protons, ions, muons, or other particle carrying electric charges. In some embodiments, charged- particle source 202 can be an electron source. For example, charged-particle source 202 can comprise a cathode, an extractor, or an anode. Primary electrons can be emitted from the cathode and extracted or accelerated to form primary charged-particle beam 210 (in this case, a primary electron beam) with a crossover 208 (virtual or real crossover). For simplicity, some embodiments will be described in the context of electrons as the charged particles. However, it is appreciated that any charged particle can be used in any embodiment of this disclosure, not limited to electrons. Primary charged-particle beam 210 can be visualized as being emitted from crossover 208. Gun aperture 204 can block off peripheral charged particles of primary charged-particle beam 210 to reduce Coulomb interactions (or Coulomb effect). The Coulomb effect can cause the size of a probe spot (or beam spot) of an electron beam to increase and blur, thereby reducing resolution.

[0051] Source conversion unit 212 can comprise an array of image-forming elements and an array of beam-limit apertures. The array of image-forming elements can comprise an array of micro-deflectors or micro-lenses. The array of image-forming elements can form a plurality of parallel images (virtual or real) of crossover 208 with a plurality of beamlets 214, 216, and 218 of primary charged-particle beam 210. The array of beam-limit apertures can limit the plurality of beamlets 214, 216, and 218. While three beamlets 214, 216, and 218 are shown in FIG. 2B, embodiments of the present disclosure are not so limited. In some embodiments, beam tool 104B can be configured to generate a first number of beamlets. For example, the first number of beamlets can be in a range from 1 to 1000 or from 200-500. The first number of beamlets can be, for example, 400 beamlets.

[0052] Condenser lens 206 can focus primary charged-particle beam 210. The electric currents of beamlets 214, 216, and 218 downstream of source conversion unit 212 can be varied by adjusting the focusing power of condenser lens 206 or by changing the radial sizes of the corresponding beam- limit apertures within the array of beam-limit apertures. Condenser lens 206 may be an adjustable condenser lens that may be configured so that the position of its first principal plane is movable. The adjustable condenser lens may be configured to be magnetic, which may result in off-axis beamlets 216 and 218 landing on the beamlet-limit apertures with rotation angles. The rotation angles change with the focusing power and the position of the first principal plane of the adjustable condenser lens. In some embodiments, the adjustable condenser lens may be an adjustable anti-rotation condenser lens, which involves an anti-rotation lens with a movable first principal plane. An example of an adjustable condenser lens is further described in U.S. Publication No. 2017 / 0025241, which is incorporated by reference herein in its entirety.

[0053] Objective lens 228 can focus beamlets 214, 216, and 218 onto a wafer 230 for imaging, and can form a plurality of probe spots 270, 272, and 274 on a surface of wafer 230.

[0054] Beam separator 222 can be a beam separator of Wien filter type, generating an electrostatic dipole field and a magnetic dipole field. The force exerted by the electrostatic dipole field on a charged particle (e.g., an electron) of beamlets 214, 216, and 218 can be substantially equal in magnitude and opposite in a direction to the force exerted on the charged particle by magnetic dipole field. Beamlets 214, 216, and 218 can, therefore, pass straight through beam separator 222 with zero or minimal deflection angle. However, the total dispersion of beamlets 214, 216, and 218 generated by beam separator 222 can also be non- zero. Beam separator 222 can separate secondary charged- particle beams 236, 238, and 240 from beamlets 214, 216, and 218 and direct secondary charged- particle beams 236, 238, and 240 towards secondary optical system 242.

[0055] Deflection scanning unit 226 can deflect beamlets 214, 216, and 218 to scan probe spots 270, 272, and 274 over a surface area of wafer 230. In response to the incidence of beamlets 214, 216, and 218 at probe spots 270, 272, and 274, secondary charged-particle beams 236, 238, and 240 can be emitted from wafer 230. Secondary charged-particle beams 236, 238, and 240 can comprise charged particles (e.g., electrons) with a distribution of energies. For example, secondary charged-particle beams 236, 238, and 240 can be secondary electron beams including secondary electrons (energies < 50 eV) and backscattered electrons (energies between 50 eV and landing energies of beamlets 214, 216, and 218). Secondary optical system 242 can focus secondary charged-particle beams 236, 238, and 240 onto detection sub-regions 246, 248, and 250 of charged-particle detection device 244. Detection sub-regions 246, 248, and 250 can be configured to detect corresponding secondary charged-particle beams 236, 238, and 240 and generate corresponding signals (e.g., voltage, current, or the like) used to reconstruct a scanning charged-particle microscope image (e.g., SEM image) of structures on or underneath the probed surface of wafer 230.

[0056] The generated signals can represent intensities of secondary charged-particle beams 236, 238, and 240 and can be provided to image processing system 290 that is in communication with charged- particle detection device 244, primary projection optical system 220, and motorized wafer stage 280. The movement speed of motorized wafer stage 280 can be synchronized and coordinated with the beam deflections controlled by deflection scanning unit 226, such that the movement of the scanning of probe spots (e.g., probe spots 270, 272, and 274) can orderly cover regions of interests on the wafer 230. The movement of the scanning can be, for example, a raster movement. The parameters of such synchronization and coordination can be adjusted to adapt to different materials of wafer 230. For example, different materials of wafer 230 can have different resistance-capacitance characteristics that can cause different signal sensitivities to the movement of the scanning of the probe spots.

[0057] The intensity of secondary charged-particle beams 236, 238, and 240 can vary according to the external or internal structure of wafer 230, and thus can indicate whether wafer 230 includes defects. Moreover, as discussed above, beamlets 214, 216, and 218 can be projected onto differentlocations of the top surface of wafer 230, or different sides of local structures of wafer 230, to generate secondary charged-particle beams 236, 238, and 240 that can have different intensities. Therefore, by mapping the intensity of secondary charged-particle beams 236, 238, and 240 with the areas of wafer 230, image processing system 290 can reconstruct an image that reflects the characteristics of internal or external structures of wafer 230.

[0058] In some embodiments, image processing system 290 can include an image acquirer 292, a storage 294, and a controller 296. Image acquirer 292 can comprise one or more processors. For example, image acquirer 292 can comprise a computer, server, mainframe host, terminals, personal computer, any kind of mobile computing devices, or the like, or a combination thereof. Image acquirer 292 can be communicatively coupled to charged-particle detection device 244 of beam tool 104B through a medium such as an electric conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, or a combination thereof. Image acquirer 292 can receive a signal from charged-particle detection device 244. Image acquirer 292 can construct an image (e.g., as a digital representation, an image data file) based on one or more signals from charged- particle detection device 244. Image acquirer 292 can thus acquire scanning charged-particle microscope images of probed regions of wafer 230. Image acquirer 292 can perform various postprocessing functions, such as generating contours that are representative of structures in the image, superimposing indicators on an acquired image, or the like. Image acquirer 292 can perform adjustments of brightness and contrast of acquired images.

[0059] In some embodiments, storage 294 is a storage medium such as a hard disk, flash drive, cloud storage, random access memory (RAM), other types of computer-readable memory, or the like. Storage 294 can be coupled with image acquirer 292. Storage 294 can be used for saving scanned raw image data as original images, as well as post-processed images. Image acquirer 292 and storage 294 can be connected to controller 296. Image acquirer 292, storage 294, and controller 296 can be integrated together as one control unit.

[0060] In some embodiments, image acquirer 292 acquires one or more scanning charged-particle microscope images of a wafer based on one or more imaging signals received from charged-particle detection device 244. An imaging signal can correspond to a scanning operation for conducting charged-particle imaging. An acquired image can be a single image comprising a plurality of imaging areas or an image of an imaging area. The single image can be stored in storage 294. The single image can be an original image that is divided into a plurality of regions. Each of the regions can comprise one imaging area containing a feature of wafer 230. The acquired images can comprise multiple images of a single imaging area of wafer 230 sampled multiple times over a time sequence. The multiple images can be stored in storage 294. In some embodiments, image processing system 290 performs image processing steps with the multiple images of the same location of wafer 230.

[0061] In some embodiments, image processing system 290 comprises measurement circuits (e.g., analog-to-digital converters) to obtain a distribution of the detected secondary charged particles (e.g.,secondary electrons). The charged-particle distribution data collected during a detection time window, in combination with corresponding scan path data of beamlets 214, 216, and 218 incident on the wafer surface, can be used to reconstruct images of the wafer structures under inspection. The reconstructed images can be used to reveal various features of the internal or external structures of wafer 230, and thereby can be used to reveal defects in the wafer.

[0062] In some embodiments, when electrons of primary charged-particle beam 210 are projected onto a surface of wafer 230 (e.g., probe spots 270, 272, and 274), the electrons of primary charged- particle beam 210 can penetrate the surface of wafer 230 for a certain depth to interact with deeper matter in wafer 230. Some electrons of primary charged-particle beam 210 can elastically interact with (e.g., in the form of elastic scattering or collision) the materials of wafer 230. Electrons can be reflected or recoiled out of the surface of wafer 230. An elastic interaction conserves the total kinetic energies of the bodies (e.g., electrons of primary charged-particle beam 210) of the interaction, in which the kinetic energy of the interacting bodies does not convert to other forms of energy (e.g., heat, electromagnetic energy, or the like). Such reflected electrons generated from elastic interaction may be referred to as backscattered electrons (BSEs).

[0063] In some embodiments, some electrons of primary charged-particle beam 210 inelastically interact with (e.g., in the form of inelastic scattering or collision) the materials of wafer 230. An inelastic interaction does not conserve the total kinetic energies of the bodies of the interaction, in which some or all of the kinetic energy of the interacting bodies convert to other forms of energy. For example, through the inelastic interaction, the kinetic energy of some electrons of primary charged- particle beam 210 can cause electron excitation and transition of atoms of the materials. Such inelastic interaction can also generate electrons that exit the surface of wafer 230, which may be referred to as secondary electrons (SEs). Yield or emission rates of BSEs and SEs can depend on, e.g., the material under inspection and the landing energy of the electrons of primary charged-particle beam 210 landing on the surface of the material, among others. The energy of the electrons of primary charged- particle beam 210 can be imparted in part by its acceleration voltage (e.g., the acceleration voltage between the anode and cathode of charged-particle source 202 in FIG. 2B). The quantity of BSEs and SEs can be more or fewer (or even the same) than the injected electrons of primary charged-particle beam 210.

[0064] An image can be in the form of an analog signal (e.g., detector signals from an SEM). Alternatively, or additionally, an image can have a digital form, which can be stored as a digital file, reside in volatile memory, be transmitted as a digital signal, or the like. When describing image analysis, image manipulation, and other image processes, it is to be appreciated that such processes can be executed on any form or representation of the image. For example, cropping an image can correspond to truncating the corresponding pixels in a digital image file.

[0065] The images generated by SEM can be used for defect inspection. For example, a generated image of a test device region of a wafer can be compared with a reference image that corresponds tothe same test device region. The reference image can be predetermined (e.g., by simulation) and include no known defect. If a difference between the generated image and the reference image exceeds a tolerance level, a potential defect can be identified. For another example, the SEM can scan multiple regions of the wafer, each region including a test device region designed as the same, and generate multiple images capturing those test device regions as manufactured. The multiple images can be compared with each other. If a difference between the multiple images exceeds a tolerance level, a potential defect can be identified.

[0066] In some embodiments, charged particle implementations described herein can be modified to use photons instead, for example, such as light in the visible, UV, DUV, EUV, x-ray, or any other wavelength range. For example, in a photon embodiment, a secondary beam spot can refer to reflected, refracted, diffracted or scattered light from a sample upon which a primary light beam is incident. Therefore, while detectors in the present disclosure may be described in the context of electron detection (e.g., SEM), embodiments of the present disclosure can be implemented with other charged particles (e.g., ions) or photons.

[0067] FIG. 3 illustrates a schematic diagram of an example D2DB alignment 300, consistent with embodiments of the present disclosure. In some embodiments, D2DB alignment 300 is performed using a design pattern in a database (e.g., CAD, GDS, or the like) and an image acquired using a charged-particle -based inspection system (e.g., an SEM consistent with the description of FIGS. 1- 2B).

[0068] A database can comprise design information for a layout 302 comprising a design pattern 304. Design pattern 304 can comprise one or more stripes 306. It will be appreciated that, in general, pattern layouts can comprise other shapes and patterns (more than just one or more stripes 306) and that the other shapes and patterns are not expressly shown in FIG. 3. Layout 302 can be associated with a layout coordinate system 308. Layout coordinate system 308 can provide an arbitrary reference frame via which the positioning of different patterns, shapes, and boundaries (e.g., stripe edges) can be understood relative to one another. Embodiments described herein are not limited to the use of layout coordinate system 308 (e.g., coordinate transformations are allowed).

[0069] When the design information (e.g., layout 302) is used for fabricating device structures on a substrate (e.g., forming device patterns on a wafer), non-conformities in fabrication processes can cause the formed patterns to differ substantially from the intended structure represented by design pattern 304 see FIG. 4 for examples of fabrication errors). These errors in lithographic printing can reduce yield and decrease throughput (e.g., instigate reprocessing of a wafer or discarding the wafer altogether).

[0070] In some embodiments, image -based inspection (e.g., using an SEM) help with identifying fabrication errors and allow for subsequent error mitigation or compensation processes. D2DB alignment 300 can use an SEM image 310. SEM image 310 can comprise an image representation of a formed pattern 312 at a region of a substrate 314. Formed pattern 312 can comprise one or moreformed stripes 316. In the example scenario of FIG. 3, it is to be understood that design pattern 304 was used to fabricate formed pattern 312. Hence, one or more formed stripes 316 in SEM image 310 can correspond one or more stripes 306 of layout 302.

[0071] It is to be appreciated that image information can be represented in several forms. For example, the graphical representations of layout 302 and SEM image 310 can also be represented as digital data (e.g., saved in memory as binary data). The graphical representations shown in FIG. 3 can be what is shown at a computer display based on an inspection measurement using the systems described in FIGS. 1-2B or based on opening a digital file with image information. An analog representation can be in the form of electrical signals from a detector (e.g., charged-particle detection device 244 (FIG. 2B)) along with positional information of the scanned electron beam (can also be a digital representation if using an analog-to-digital converter). The present disclosure will describe the analyses and processing of image information with reference to graphical representation of FIG. 5, for simplicity and clarity. However, it is to be appreciated that operations for image analyses and processing can be performed with respect to any representation of an image (e.g., graphical, electrical signal, binary data, or the like). For example, from the perspective of a computing device, the generating of SEM image 310 via an inspection measurement comprises one or more digital functions that calculate pixel intensities of SEM image 310 based on post-processing of detection signals from charged-particle detection device 244 (FIG. 2B).

[0072] In some embodiments, D2DB alignments are an important preparatory process that allows for subsequent metrology operations (e.g., measuring overlay shift). For example, let design pattern 304 represent a first layer design pattern of an intended device. The first layer design pattern can be fabricated on a wafer via a first fabrication step, which can then be followed by fabrication of a second layer design pattern via a second fabrication step. The second layer can be applied over the first layer (hence the term overlay). As explained above, non-conformities in fabrication processes can cause the formed patterns of different layers to become misaligned. This type of misalignment is referred to as overlay error or overlay shift. Overlay can be measured via image -based inspection, such as SEM. An SEM can acquire images of a region of a substrate with sub-nanometer resolution.

[0073] After the SEM image is acquired, a computer-based algorithmic D2DB alignment can analyze the SEM image and identify the boundaries of the formed patterns. With the boundaries of the formed patterns identified, D2DB alignment 300 can attempt to align formed pattern 312 (e.g., on a die) to design pattern 304 (e.g., in a database) (hence die-to-database alignment). Fourier analysis of the two- dimensional image information can be performed using both layout 302 and SEM image 310 to ascertain a closest alignment match between the design geometry and the fabricated geometry. The results are shown to the right of FIG. 3, which shows the positions design pattern 304 (as dotted-line contours) and formed pattern 312 are closely matched. The result of D2DB alignment 300 is the knowledge of the positioning of the structures present in SEM image 310 relative to layout 302. The positioning knowledge can be understood as knowing the positioning of one or more formed patterns316 relative to layout coordinate system 308 (or any other coordinate system with a suitable coordinate transformation).

[0074] In some embodiments, D2DB alignment 300 as shown in FIG. 3 is a successful D2DB alignment (e.g., success can mean that the alignment matches the layout to within a tolerance and that, if the mismatch exceeds the tolerance (failure), allowing lithography to continue without corrective action would result in significant yield reduction).

[0075] FIG. 4 illustrates a schematic diagram of example D2DB alignments 400 and 402, consistent with embodiments of the present disclosure. In some embodiments, D2DB alignments 400 and 402 are performed as described above in reference to FIG. 3. D2DB alignments 400 and 402 can represent failed D2DB alignments.

[0076] Similar to the example in FIG. 3, a layout having a design pattern 414 can be used to fabricate formed pattern 406 on a substrate. Formed pattern 406 can comprise one or more stripes 410. Formed pattern 406 can comprise different shapes, patterns, sizes, and boundaries other than those shown in FIG. 4. One or more fabrication process errors can cause a stripe to be formed as a fragmented dummy line 412. Using a charged-particle-based inspection system as described in reference to FIGS. 1-2B, an SEM image 404 of formed pattern 406 (including “good” stripes and dummy line 412) can be acquired. An issue here is whether the computer-based algorithm of a D2DB alignment is capable of coping with a defective structure present in SEM image 404.

[0077] In some embodiments, D2DB alignment 400 is performed to align design pattern 414 onto the formed pattern 406 using the D2DB alignment processes described above in reference to FIG. 3. Due to the formation of dummy line 412, D2DB alignment 400 can fail to align design pattern 414 to formed pattern 406. The irregularity of the dummy line 412 can cause D2DB alignment 400 to disregard dummy line 412, even though dummy line 412 corresponds to one of stripes 410. With dummy line 412 disregarded by the computer-based algorithm, design pattern 414 can become misaligned (shown as a misalignment 416). Misalignment 416 illustrates a relative shift or offset between design pattern 414 and formed pattern 406 along a y-direction. X- and Y-axes are provided not to limit, but to provide a consistent reference for discussions on projections. The extent of the offset can be a full stripe pitch, which is commensurate with the identification failure of dummy line 412.

[0078] In some embodiments, D2DB alignment 402 illustrates an example misalignment shift or offset in the x-direction. As before, design pattern 414 can be used to fabricate formed pattern 418 on a substrate. Formed pattern 418 can comprise one or more stripes 420. One or more fabrication process errors can cause one or more stripes 410 to form with random variations of length (whereas design pattern 414 depicted equal lengths and matching left and right boundaries). The computer- based algorithmic D2DB alignment 402 can analyze SEM image 408 to align design pattern 414 relative to formed pattern 418. However, the varying lengths of the stripes can result in a misalignment 422, which represents a relative shift or offset between design pattern 414 and formedpattern 418 along an x-direction. The extent of the offset can depend on the irregularities in the length of stripes of formed pattern 418.

[0079] As alluded above, the analysis and subsequent alignment of a design pattern and a formed pattern in an SEM image can rely on computations that take into account the two-dimensional nature of the design pattern and the SEM image (e.g., SEM image is an MxN array of pixels, with M representing a number of rows (height) and N representing a number of columns (width)). This holds true even for a single axis alignment. For example, even for a specific x-directional D2DB alignment, the algorithm performs calculations based on the complete information of the full MxN array as the algorithm maintains “awareness” of the shapes and structures in the SEM image. This can result in a computational complexity of O(MxN) (e.g., the nomenclature “O(X)”, which can be used herein to represent a computational complexity as a function of X). D2DB alignment algorithms can be enhanced by the use of a fast Fourier transform (FFT). Even so, the resulting reduction of computational complexity can be limited, approximately in the order of O(MNxlog(MN)). Embodiments described herein can implement projection of dimensionally defined information to reduce dimensionality in alignment calculations, thereby reducing computational complexity to approximately 0(M+N), or closer to O(M-i-N) than to O(MNxlog(MN)).

[0080] Embodiments of the present disclosure provide projection-based D2DB alignment that can address and overcome the issues described above.

[0081] FIG. 5A illustrates a schematic diagram of an example projection-based D2DB alignment 500, consistent with embodiments of the present disclosure. In some embodiments, projection-based D2DB alignment 500 is performed using a design pattern in a database (e.g., CAD, GDS, or the like) and an image acquired using a charged-particle-based inspection system (e.g., an SEM consistent with the description of FIGS. 1-2B).

[0082] A database can comprise design information for a layout 502 comprising a design pattern 504. Design pattern 504 can comprise one or more stripes 506. SEM image 508 can comprise an image representation of a formed pattern 510 at a region of a substrate. Formed pattern 510 can comprise one or more formed stripes 512. In the example scenario of FIG. 5A, design pattern 504 was used to fabricate formed pattern 510. Hence, one or more formed stripes 512 in SEM image 508 can correspond to one or more stripes 506 of layout 502. Furthermore, due to a deviation in the fabrication process, one of the stripes formed can have a defective shape. The defective shape is illustrated as dummy line 514.

[0083] Design pattern 504 and SEM image 508 can be analyzed computationally for identification of patterns, shapes, and boundaries. To simplify the computational complexity, specific conditions can be imposed on the algorithm when determining the pattern boundaries. For example, the condition can be a projection of the formed patterns in the SEM image onto a one-dimensional line (e.g., projected onto a directional axis). X- and Y-axes are provided not to limit, but to provide clarity for the projection analysis. Edges of a pattern that face the y axis can be identified and flagged.

[0084] The terms “face” or “facing” can be used to describe geometric elements that have a side that is exposed or otherwise projectable onto a reference element (e.g., the y axis can be the reference element onto which geometric elements are projected). In FIG. 5A, the identified pattern boundaries can be the left extrema of the stripes (e.g., boundaries 516 in layout 502). Terms such as “edge,” “boundary,” or the like can be used herein interchangeably. Since layout 502 is essentially positioning information (e.g., in a design file), identification of the left-exposed edges can be performed by simply analyzing the positioning information of the design pattern. Subsequently, boundaries 516 can be projected onto the y axis to generate a y-projection histogram 518 for design pattern 504.

[0085] In a similar manner, SEM image 508 can be analyzed by searching for edges of a formed pattern that face the y axis. In the sheet-orientation of FIG. 5A, the identified pattern boundaries would be the left extrema (e.g., boundaries 520 in SEM image 508). Since SEM image 508 comprises pixel gray level information for a grid of pixels, identification of the left-exposed edges can be performed by analyzing sharp gradients of the pixel gray level data and identifying pattern boundaries that face the y axis. It is noted that dummy line 514 is not exempted and that its y-axis-facing boundaries 522 can also be captured in the boundary identification process. Subsequently, boundaries 520 and 522 can be projected onto the y axis to generate a y-projection histogram 524 for formed pattern 510.

[0086] In some embodiments, y-projection histograms 518 and 524 can have the form of space domain signals (e.g., a one-dimensional waveforms). A cross-correlation operation can then be performed to determine a maximum cross-correlation value between y-projection histograms 518 and 524. The maximum cross-correlation can correspond to a “best” or optimum D2DB alignment. Since the cross-correlation is performed with y-projection information (corresponds to rows M of the MxN pixel array of SEM image 502), the reduced dimensionality from MxN to just M reduces computational complexity to O(M). This feature can provide for faster and more efficient metrology, thereby increasing metrology throughput and therefore overall fabrication throughput.

[0087] Furthermore, projection-based D2DB alignments can be more robust and error-resistant compared the D2DB alignment described in reference to FIG. 4. The robust and error-resistant nature of projection-based D2DB alignment 500 can be better appreciated by considering peak 526. In y- projection histogram 524 for formed pattern 510, peak 526 corresponds to boundaries 522 of dummy line 514. Even if the fragments of dummy line 514 are shaped substantially different from an originally intended single stripe shape, the projection of the boundary information preserves the important y-positioning information while concealing or obscuring unhelpful fab-error information. From the point of view of projection-based D2DB alignment 500, the position of peak 526 tracks well with the corresponding peak 528 in y-projection histogram 518. Hence, projection-based D2DB alignment 500 can overcome the challenges presented by dummy line 514 where other D2DB techniques fail.

[0088] It is noted that FIG. 5 A is an example in which the projection direction (shown with an arrow) is substantially parallel to a length dimension of the stripes. Projection-based D2DB alignments can also be used with the projection direction being substantially perpendicular to a length dimension of the stripes, as will be described in reference to FIG. 5B.

[0089] FIG. 5B illustrates a schematic diagram of an example projection-based D2DB alignment 530, consistent with embodiments of the present disclosure. In some embodiments, projection-based D2DB alignment 530 is performed using the same layout and techniques described in reference to FIG. 5A.

[0090] SEM image 532 can comprise an image representation of a formed pattern 534 at a region of a substrate. Formed pattern 534 can comprise one or more formed stripes 536. In the example scenario of FIG. 5B, design pattern 504 was used to fabricate formed pattern 534. Hence, one or more formed stripes 536 in SEM image 532 can correspond to one or more stripes 506 of layout 502. Furthermore, due to one or more fabrication process errors, one of the stripes formed with a defective shape. The defective shape is illustrated as dummy line 538 (e.g., an unintended shortening of the stripe). Furthermore, many of formed stripes 536 can have length fluctuations that are inconsistent with the original intent of design pattern 504.

[0091] Design pattern 504 and formed pattern 534 can be analyzed computationally for identification of patterns, shapes and boundaries. In FIG 5B, the identified pattern boundaries can be the left extrema of the stripes (e.g., boundaries 516 in layout 502). Boundaries 516 can be projected onto the x-axis to generate an x-projection histogram 544. Because boundaries 516 are aligned in the projection direction, the projection can produce a single peak 546 in x-projection histogram 544.

[0092] SEM image 532 can be analyzed as described in reference to FIG. 5A (e.g., pixel gray level gradients). The formed pattern 534 can be searched and the left extrema of the stripes can be identified for subsequent projection onto the x-axis. The determined boundaries can be boundaries 540, as well as boundary 542 for dummy line 538. The projection of the boundary data can be used to generate x-projection histogram 548.

[0093] The effects of fabrication process errors can be seen in x-projection histogram 548. a main histogram peak 550 can correspond to the approximately aligned boundaries 540. Because the aligning of boundaries 540 has variability, main histogram peak 550 can reflect such variability by exhibiting broadening and increased noise as compared to histogram peak 546 (the ideal case). Furthermore, an extraneous histogram peak 552 can correspond to boundary 542 of dummy line 538.

[0094] In some embodiments, x-projection histograms 544 and 548 can have the form of space domain signals (e.g., a one-dimensional waveforms). A cross-correlation operation can then be performed to determine a maximum cross-correlation value between y-projection histograms 544 and 548. Since cross-correlation is a measure of how well two independent signals resemble each other as the two signals are shifted with respect to one another (higher cross-correlation values can indicate more resemblance), the cross-correlation can be at a maximum when main histogram peak 550 iscoincident with the single main histogram peak 546 in x-projection histogram 544. An alignment determination can correspond to a maximum cross-correlation value.

[0095] The robustness of projection-based D2DB alignment 530 can be appreciated by considering the non-impact of extraneous histogram peak 552. Even though extraneous histogram peak 552 can cause the cross-correlation value to be reduced by some amount, extraneous histogram peak 552 does not appreciably affect the relative shift (alignment) at which maximum cross-correlation occurs.

[0096] FIG. A illustrates a schematic diagram of an example projection-based D2DB alignment 600, consistent with embodiments of the present disclosure. In some embodiments, projection-based D2DB alignment 600 can illustrate a practical implementation of the projection-based D2DB alignment processes described in reference to FIG. 5A (e.g., y-direction projection-based D2DB alignment).

[0097] In some embodiments, SEM image 602 and layout 604 with a design pattern are related to one another as described above in reference to FIGS. 3-5B. Boundary extraction can be performed on both SEM image 602 and layout 604. The pixel gray level data of SEM image 602 can be analyzed to determine boundaries 606 of a formed pattern (e.g., lines / stripes of a grating structure). The formed pattern can include a dummy structure 608. Comparing dummy structure 608 to layout 604, it can be surmised that dummy structure 608 is a defect resulting from one or more fabrication process errors. Furthermore, the varied lengths of the formed structures in SEM image 602 can also be attributed to one or more fabrication process errors.

[0098] The determination of which pixels correspond to “edge” pixels can be based on a gradient of the gray level data (e.g., where the pixel gray level data experiences an abrupt change when transitioning from the background substrate to the formed pattern). The determination of relevant edge pixels (e.g., relevant to the direction of projection) can be performed via constraints that exclude boundaries that add little to no information to the alignment process (e.g., edge lines that are substantially parallel to the direction of projection, where substantially parallel can refer to being closer to parallel than perpendicular). With boundaries 606 identified, a y-proj ection histogram 610 can be generated based on the identified boundaries. A histogram peak 612 of y-projection histogram 610 can correspond to dummy structure 608. Here, it can be seen that the image analysis algorithm has included both a left-facing boundary 614 and a right-facing boundary 616 of dummy structure 608. The inclusion of right-facing boundary 616 can be a failure of the determination of relevant edge pixels. Nonetheless, the algorithm succeeds in making sure that y-projection histogram 610 preserves the general y-directional characteristics (e.g., y-direction periodicity) that is eventually used at the cross-correlation step.

[0099] In this manner, projection-based D2DB alignment 600 can provide accurate D2DB alignment results even when performed on SEM images with defective structures (e.g., malformed structures with varied lengths / dimensions), thereby contributing to higher yield. The computational complexityis also reduced to 0(M+N) for an MxN pixel array (e.g., as opposed to O(MNxlog(MN)), thereby increasing metrology throughput and overall fabrication throughput.

[0100] In a similar manner, the coordinate data of layout 604 can be analyzed to determine boundaries 618 of a design pattern (e.g., lines / stripes of a grating structure). With boundaries 618 identified, a y-projection histogram 620 can be generated based on the identified boundaries. Then, projection-based D2DB alignment 600 can proceed to determining a cross-correlation between y- projection histogram 610 and y-projection histogram 620. An alignment determination can correspond to a maximum cross-correlation value. The alignment result along the y-direction is shown in aligned image 622 (the additional x-directional alignment depicted here is due to a similar process described in reference to FIG. 6B).

[0101] FIG. 6B illustrates a schematic diagram of an example projection-based D2DB alignment 624, consistent with embodiments of the present disclosure. In some embodiments, projection-based D2DB alignment 624 can illustrate a practical implementation of the projection-based D2DB alignment processes described in reference to FIG. 5B (e.g., x-direction projection-based D2DB alignment). The example of FIG. 6B will refer to the same SEM image 602 and layout 604 as in FIG. 6A, including all of the physical features and boundaries (e.g., dummy structure 608, boundaries 606, 614, 616, and 618).

[0102] In some embodiments, an x-proj ection histogram 626 (for SEM image 602) and an x- proj ection histogram 626 (for layout 604) are generated based on the identified boundary positions. In contrast to the projections in FIG. 6A, x-projection histogram 628 (for layout 604) indicates that an ideally formed pattern (no defects) would likely have a single peak to represent boundaries 606 aligned along the projection direction, as is the case in x-projection histogram 628. However, fabrication process errors can cause the actual formed pattern to generate extraneous histogram peaks 630. The extraneous histogram peaks can correspond to the fluctuation of the identified boundaries 606 that were produced by one or more fabrication process errors. Some extraneous histogram peaks 630 can correspond to undesirable length variability of the formed stripes. Some extraneous histogram peaks 630 can correspond to left-facing boundary 614 and right-facing boundary 616 of dummy structure 608. A highest histogram peak 632 can correspond to the aligned ones of boundaries 606.

[0103] To execute projection-based D2DB alignment 624, the computer-based algorithm can determine a cross-correlation between x-projection histogram 626 and x-projection histogram 628. Since cross-correlation is a measure of how well two independent signals resemble each other as the two signals are shifted with respect to one another, the cross-correlation can be at a maximum when highest histogram peak 632 is coincident with the single histogram peak in x-projection histogram 628. An alignment determination can correspond to a maximum cross-correlation value. The alignment result along the x-direction is shown in aligned image 622 (the additional y-directional alignment depicted here is due to a similar process described in reference to FIG. 6A).

[0104] FIG. 7 illustrates an example method 700 for performing a projection-based D2DB alignment, consistent with embodiments of the present disclosure. Method 700 can be executed using devices and functions described in reference to FIGS. 1-7, such as controller 109, image processing system 199, or image processing system 290 (FIGS. 1-2B).

[0105] In some embodiments, at operation 702, boundaries of a formed pattern and a design pattern can be extracted from image information and from design information in a database, respectively (e.g., positions of boundaries 516, 520, 522, 540, 542, 606, 614, or 616 (FIGS. 5A-6B) can be identified). The inspection image can be an SEM image of a formed pattern on a substrate (e.g., SEM images 508, 532, or 602 having an image representation of formed patterns 510 or 534 on a substrate (FIGS. 5A and 5B)). The image information can be a two-dimensional representation of the formed pattern acquired by an inspection system (e.g., EBI 100, beam tool 104, 104A, or 104B (FIGS. 1- 2B)). The design information in the database can be a file that comprises coordinate information for layouts and design patterns (e.g., layouts 502 or 604 can include design patterns (FIGS. 5A-5B)).

[0106] At operation 704, one-dimensional projections of the boundaries of the formed pattern and the design pattern can be generated (e.g., generate y-projection histograms 518, 524, 610, or 620 or x- projection histograms 544, 548, 626, or 628 (FIGS. 5A-6B)).

[0107] At operation 706, the one-dimensional projections of the formed pattern and the design pattern can be cross-correlated to generate an alignment result (e.g., y-projection histograms 518 and 524 are cross-correlated, y-projection histograms 610 and 620 are cross-correlated, x-projection histograms 544 and 548 are cross-correlated, or x-projection histograms 626 and 628 are crosscorrelated (FIGS. 5A-6B)).

[0108] In some embodiments, method 700 includes further operations that correspond to devices and functions described above with reference to FIGS. 1-6 (e.g., histogram generation, maximum crosscorrelation determinations, obscuring defect information, reduction of computational complexity, extraction of boundaries via exclusion of irrelevant structures, or the like).

[0109] A non-transitory computer-readable medium may be provided that stores instructions for a processor of a controller (e.g., controller 109, image processing system 199, or image processing system 290 (FIGS. 1-2B)) for performing a projection-based D2DB alignment according to method 700, consistent with embodiments in the present disclosure. For example, the instructions stored in the non-transitory computer-readable medium may be executed by the circuitry of the controller for performing method 700 in part or entirely. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a Compact Disc Read-Only Memory (CD-ROM), any other optical data storage medium, any physical medium with patterns of holes, a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), and Erasable Programmable Read-Only Memory (EPROM), a FLASH-EPROM or any other flash memory, Non-Volatile Random AccessMemory (NVRAM), a cache, a register, any other memory chip or cartridge, and networked versions of the same.

[0110] Some embodiments may further be described using the following clauses:1. A non-transitory computer-readable medium that stores a set of instructions that is executable by at least one processor of an apparatus to cause the apparatus to perform operations, the operations comprising: extracting edges from an inspection image of a formed pattern on a substrate and edges of a design pattern from design information; projecting the edges of the formed pattern and the design pattern to reduce dimensionality of information of the edges of the formed pattern and the edges of the design pattern; and cross-correlating projections of the edges of the formed pattern and the edges of the design pattern to generate an alignment result.2. A non-transitory computer-readable medium that stores a set of instructions that is executable by at least one processor of an apparatus to cause the apparatus to perform operations, the operations comprising: extracting boundaries of a formed pattern disposed on a substrate from image information and boundaries of a design pattern from design information, wherein the design information comprises a design pattern, and wherein the image information is a two-dimensional representation of the formed pattern acquired by an image acquisition system; generating one-dimensional projections of the boundaries of the formed pattern and the design pattern; and cross-correlating the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.3. The non-transitory computer-readable medium of clause 2, wherein generating the onedimensional projections comprises: generating x-proj ections of the formed pattern and the design pattern; and generating y-projections of the formed pattern and the design pattern.4. The non-transitory computer-readable medium of clause 3, wherein cross-correlating the onedimensional projections of the formed pattern and the design pattern comprises: determining a maximum of a cross-correlation between the x-projection of the formed pattern and the x-projection of the design pattern; and determining a maximum of a cross-correlation between the y-projection of the formed pattern and the y-projection of the design pattern.5. The non-transitory computer-readable medium of clause 3 or 4, wherein generating the x- projections or the y-projections comprises: obscuring fabrication defect information of the formed pattern.6. The non-transitory computer-readable medium of any one of clauses 3 to 5, wherein generating the x-proj ections or the y-proj ections comprises: generating histograms based on the extracted boundaries of the formed pattern and the design pattern.7. The non-transitory computer-readable medium of any one of clauses 2 to 6, wherein a computational complexity of the cross-correlating is approximately 0(M+N), wherein the image information comprises an MxN array of pixels, M corresponds to a number of rows the array, and N corresponds to a number of columns of the array.8. The non-transitory computer-readable medium of any one of clauses 2 to 7, wherein extracting the boundaries of the formed pattern is based on constraints that exclude pattern boundaries that are substantially parallel to a direction of projection.9. A system, comprising: a charged particle beam source configured to generate a beam of primary charged particles; a charged particle optical system configured to direct the beam of primary charged particles at a surface of a substrate comprising a formed pattern; a charged particle detector configured to generate image information based on detection of secondary charged particles associated with interaction of the primary charged particles with the surface; and a controller comprising circuitry and configured to: identify boundary positions of the formed pattern from the image information and boundary positions of a design pattern from design information, wherein the design information comprises a design pattern, and wherein the image information is a two-dimensional representation of the formed pattern; generate one-dimensional projections of the boundaries of the formed pattern and the design pattern; and cross-correlate the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.10. The system of clause 9, wherein the controller is further configured to generate the onedimensional projections based on: generating x-proj ections of the formed pattern and the design pattern; and generating y-projections of the formed pattern and the design pattern.11. The system of clause 10, wherein the controller is further configured to cross-correlate the onedimensional projections of the formed pattern and the design pattern based on: determining a maximum of a cross-correlation between the x-projection of the formed pattern and the x-projection of the design pattern; and determining a maximum of a cross-correlation between the y-projection of the formed pattern and the y-projection of the design pattern.12. The system of clause 10 or 11, wherein the controller is further configured to generate the x- proj ections or the y-proj ections based on obscuring fabrication defect information of the formed pattern.13. The system of any one of clauses 10 to 12, wherein the controller is further configured to generate the x-proj ections or the y-proj ections based on generating a histogram based on the extracted boundaries of the formed pattern and the design pattern.14. The system of any one of clauses 9 to 13, wherein a computational complexity of the crosscorrelating is approximately 0(M+N), wherein the image information comprises an MxN array of pixels, M corresponds to a number of rows the array, and N corresponds to a number of columns of the array.15. The system of any one of clauses 9 to 14, wherein the controller is further configured to identify the boundary positions of the formed pattern based on constraints that exclude pattern boundaries that are substantially parallel to a direction of projection.16. An apparatus comprising: one or more processors; and one or more storage devices configured to store a set of instructions that is executable by the one or more processors to cause the apparatus to perform operations, the operations comprising: extracting boundaries of a formed pattern disposed on a substrate from image information and boundaries of a design pattern from design information, wherein the design information comprises a design pattern, and wherein the image information is a two-dimensional representation of the formed pattern acquired by an image acquisition system; generating one-dimensional projections of the boundaries of the formed pattern and the design pattern; and cross-correlating the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.17. The apparatus of clause 16 wherein generating the one-dimensional projections comprises: generating x-projections of the formed pattern and the design pattern; and generating y-projections of the formed pattern and the design pattern.18. The apparatus of clause 17, wherein cross-correlating the one-dimensional projections of the formed pattern and the design pattern comprises: determining a maximum of a cross-correlation between the x-projection of the formed pattern and the x-projection of the design pattern; and determining a maximum of a cross-correlation between the y-projection of the formed pattern and the y-projection of the design pattern.19. The apparatus of clause 17 or 18, wherein the generating x-projections or the y-projections comprises: obscuring fabrication defect information of the formed pattern.20. The apparatus of any one of clauses 17 to 19, wherein generating the x-projections or the y- proj ections comprises: generating a histogram based on the extracted boundaries of the formed pattern and the design pattern. 21. The apparatus of any one of clauses 16 to 20, wherein a computational complexity of the crosscorrelating is approximately 0(M+N), wherein the image information comprises an MxN array of pixels, M corresponds to a number of rows the array, and N corresponds to a number of columns of the array.22. The apparatus of any one of clauses 16 to 21, wherein extracting the boundaries of the formed pattern is based on constraints that exclude pattern boundaries that are substantially parallel to a direction of projection.

[0111] It will be appreciated that the embodiments of the present disclosure are not limited to the exact construction that has been described above and illustrated in the accompanying drawings and that various modifications and changes may be made without departing from the scope thereof.

Claims

CLAIMS1. A non-transitory computer-readable medium that stores a set of instructions that is executable by at least one processor of an apparatus to cause the apparatus to perform operations, the operations comprising: extracting boundaries of a formed pattern disposed on a substrate from image information and boundaries of a design pattern from design information, wherein the design information comprises a design pattern, and wherein the image information is a two-dimensional representation of the formed pattern acquired by an image acquisition system; generating one-dimensional projections of the boundaries of the formed pattern and the design pattern; and cross-correlating the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.

2. The non-transitory computer-readable medium of claim 1 , wherein generating the one-dimensional projections comprises: generating x-proj ections of the formed pattern and the design pattern; and generating y-projections of the formed pattern and the design pattern.

3. The non-transitory computer-readable medium of claim 2, wherein cross-correlating the onedimensional projections of the formed pattern and the design pattern comprises: determining a maximum of a cross-correlation between the x-projection of the formed pattern and the x-projection of the design pattern; and determining a maximum of a cross-correlation between the y-projection of the formed pattern and the y-projection of the design pattern.

4. The non-transitory computer-readable medium of claim 2, wherein generating the x-projections or the y-projections comprises: obscuring fabrication defect information of the formed pattern.

5. The non-transitory computer-readable medium of claim 2, wherein generating the x-projections or the y-projections comprises: generating histograms based on the extracted boundaries of the formed pattern and the design pattern.

6. The non-transitory computer-readable medium of claim 1 , wherein a computational complexity of the cross-correlating is approximately 0(M+N), wherein the image information comprises an MxNarray of pixels, M corresponds to a number of rows the array, and N corresponds to a number of columns of the array.

7. The non-transitory computer-readable medium of claim 1, wherein extracting the boundaries of the formed pattern is based on constraints that exclude pattern boundaries that are substantially parallel to a direction of projection.

8. A system, comprising: a charged particle beam source configured to generate a beam of primary charged particles; a charged particle optical system configured to direct the beam of primary charged particles at a surface of a substrate comprising a formed pattern; a charged particle detector configured to generate image information based on detection of secondary charged particles associated with interaction of the primary charged particles with the surface; and a controller comprising circuitry and configured to: identify boundary positions of the formed pattern from the image information and boundary positions of a design pattern from design information, wherein the design information comprises a design pattern, and wherein the image information is a two-dimensional representation of the formed pattern; generate one-dimensional projections of the boundary positions of the formed pattern and the design pattern; and cross-correlate the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.

9. The system of claim 8, wherein the controller is further configured to generate the one-dimensional projections based on: generating x-proj ections of the formed pattern and the design pattern; and generating y-projections of the formed pattern and the design pattern.

10. The system of claim 9, wherein the controller is further configured to cross-correlate the onedimensional projections of the formed pattern and the design pattern based on: determining a maximum of a cross-correlation between the x-projection of the formed pattern and the x-projection of the design pattern; and determining a maximum of a cross-correlation between the y-projection of the formed pattern and the y-projection of the design pattern.

11. The system of claim 9, wherein the controller is further configured to generate the x-proj ections the y-projections based on obscuring fabrication defect information of the formed pattern.

12. The system of claim 9, wherein the controller is further configured to generate the x-projections or the y-projections based on generating histograms based on the identified boundary positions of the formed pattern and the design pattern.

13. The system of claim 8, wherein a computational complexity of an operation that cross-correlates the one-dimensional projections of the formed pattern and the design pattern is approximately O(M+N), wherein the image information comprises an MxN array of pixels, M corresponds to a number of rows the array, and N corresponds to a number of columns of the array.

14. The system of claim 8, wherein the controller is further configured to identify the boundary positions of the formed pattern based on constraints that exclude pattern boundaries that are substantially parallel to a direction of projection.

15. An apparatus comprising: one or more processors; and one or more storage devices configured to store a set of instructions that is executable by the one or more processors to cause the apparatus to perform operations, the operations comprising; extracting boundaries of a formed pattern disposed on a substrate from image information and boundaries of a design pattern from design information, wherein the design information comprises a design pattern, and wherein the image information is a two-dimensional representation of the formed pattern acquired by an image acquisition system; generating one-dimensional projections of the boundaries of the formed pattern and the design pattern; and cross-correlating the one-dimensional projections of the formed pattern and the design pattern to generate an alignment result.

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