Method and system for overlay measurement using charged particle inspection device

Abstract

Disclosed herein is a system comprising: a charged particle beam inspection device configured to scan a sample comprising a target having a plurality of pattern layers; and a controller, the controller comprising circuitry, the controller configured to: obtain detection data in response to a scan of a target; and determining one or more characteristics of the sample from the obtained detection data and the model; wherein, for each of a plurality of pattern layers of the target, the model includes an item according to a property of the pattern layer.

Description

Technical Field

[0001] The description herein relates to the field of image inspection devices, and more particularly to the measurement of a property of a sample by a charged particle inspection device.The measured property of the sample may include overlap. Background Art

[0002] An image inspection device (e.g., a charged particle beam device or beam device) is capable of producing a two-dimensional (2D) image of a wafer substrate by detecting particles (e.g., photons, secondary electrons, backscattered electrons, specular electrons, or other kinds of electrons) from the surface of the wafer substrate when struck by a beam (e.g., a charged particle beam or light beam) generated by a source associated with the detection device. Various image inspection devices are used on semiconductor wafers in the semiconductor industry for various purposes such as: wafer processing (e.g., electron beam direct write lithography systems), process monitoring (e.g., critical dimension scanning electron microscopes (CD-SEMs)), wafer detection (e.g., electron beam inspection systems), or defect analysis (e.g., defect review SEMs, or such as DR-SEMs, and focused ion beam systems, or such as FIBs).

[0003] In semiconductor manufacturing, integrated circuits can be manufactured as a stack of one or more materials (e.g., silicon, silicon dioxide, metal, etc.) on a wafer. Each material layer may include a design pattern (referred to herein as a "pattern layer") for forming components (e.g., transistors, contacts, etc.) of the integrated circuit. The manufacture of each layer involves transferring the pattern from a mask to the wafer surface through a photolithography process. The position of each pattern layer relative to its previous pattern layer (referred to herein as "alignment") may affect the characteristics or quality of the manufactured integrated circuit.

[0004] Overlap refers to the planar, vector shift, displacement, or misalignment of a pattern layer relative to its adjacent pattern layer. For example, two intra-pattern reference points (e.g., center points) may be selected for two patterns in two adjacent pattern layers, respectively, and the overlap between two adjacent pattern layers may refer to the planar, vector displacement between the two intra-pattern reference points. Large overlap may cause problems or failures in the manufactured integrated circuit. Therefore, high-precision overlay measurement plays an important role in reducing overlap. Summary of the invention

[0005] Embodiments of the present disclosure provide systems and methods for measuring characteristics of a sample under scanning performed by a charged particle beam inspection device. In some embodiments, a system is provided, the system comprising: a charged particle beam inspection device, the charged particle beam inspection device being configured to scan a sample including a target having a plurality of pattern layers; and a controller, the controller comprising a circuit system, the controller being configured to: obtain detection data in response to scanning the target; and determine one or more characteristics of the sample based on the obtained detection data and a model; wherein, for each of the plurality of pattern layers of the target, the model comprises an item according to the property of the pattern layer. In some embodiments, a non-transitory computer-readable medium is provided, the computer-readable medium storing an instruction set, the instruction set being executable by at least one processor of a device to cause the device to perform a method, the method comprising: obtaining detection data in response to scanning a target by a charged particle beam inspection device; determining one or more characteristics of the sample based on the obtained detection data and a model; wherein, the target comprises a plurality of pattern layers, and for each of the plurality of pattern layers, the model comprises an item according to the property of the pattern layer. In some embodiments, a computer-implemented method for measuring characteristics of a sample under scanning performed by a charged particle beam inspection device is provided, the method comprising: obtaining detection data in response to scanning of a target by the charged particle beam inspection device; determining one or more characteristics of the sample based on the obtained detection data and a model; wherein the target comprises a plurality of pattern layers, and for each of the plurality of pattern layers, the model comprises an item based on the property of the pattern layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 is a schematic diagram illustrating an exemplary charged particle beam inspection (CPBI) system consistent with some embodiments of the present disclosure.

[0007] Figure 2 Some embodiments consistent with the present disclosure may be Figure 1 Schematic diagram of an exemplary charged particle beam tool that is a portion of an exemplary charged particle beam inspection system.

[0008] Figure 3 is a schematic diagram illustrating an exemplary target fabricated on a sample consistent with some embodiments of the present disclosure.

[0009] Figure 4A Modeled signals and their components according to some embodiments are shown, consistent with some embodiments of the present disclosure.

[0010] Figure 4BA residual signal or error signal representing the difference between a measured signal and a modeled signal is shown according to some embodiments.

[0011] Figure 5 The modeled signal when fitted to measured data is shown according to some embodiments.

[0012] Figure 6 is a flow chart illustrating an exemplary method of characteristic measurement consistent with some embodiments of the present disclosure. DETAILED DESCRIPTION

[0013] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings, wherein the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments set forth in the following description of the exemplary embodiments do not represent all embodiments that conform to the present disclosure. Instead, they are merely examples of devices and methods that conform to aspects of the subject matter as described in the attached claims. Without limiting the scope of the present disclosure, some embodiments may be described in the context of providing a detection system and a detection method in a system utilizing an electron beam ("electron beam"). However, the present disclosure is not limited thereto. Other types of charged particle beams (e.g., including protons, ions, muons, or any other particles carrying a charge) may be similarly applied. In addition, the systems and methods for detection may be used in other imaging systems, such as optical imaging, photon detection, x-ray detection, ion detection, and the like.

[0014] Electronic devices are made up of circuits formed on a piece of semiconductor material called a substrate. The semiconductor material may include, for example, silicon, gallium arsenide, indium phosphide, or silicon germanium, among others. Many circuits may be formed together on the same piece of silicon and are called integrated circuits or ICs. The size of these circuits has been significantly reduced so that more of the circuits can be mounted on a substrate. For example, an IC chip in a smartphone may be as small as a thumbnail and still include over 2 billion transistors, each less than 1 / 1000 the size of a human hair.

[0015] Manufacturing these ICs with extremely small structures or components is a complex, time-consuming and expensive process that often involves hundreds of independent steps. Even an error in one step may result in a defect in the finished IC that renders the finished IC useless. Therefore, one goal of the manufacturing process is to avoid such defects to maximize the number of functional ICs manufactured in the process; that is, to improve the overall yield of the process.

[0016] One part of improving yield is to monitor the chip manufacturing process to ensure that the chip manufacturing process is producing a sufficient number of functional integrated circuits. One way to monitor the process is to probe the chip circuit structures at various stages of their formation. Inspection can be performed using a scanning charged particle microscope ("SCPM"). For example, the scanning charged particle microscope can be a scanning electron microscope (SEM). The scanning charged particle microscope can be used to actually image these extremely small structures, thereby taking an "image" of the structure of the wafer. The image can be used to determine whether the structure is properly formed in the proper location. If the structure is defective, the process can be adjusted so that the defect is less likely to recur.

[0017] The working principle of a scanning charged particle microscope (e.g., SEM) is similar to that of a camera. A camera takes an image by receiving and recording the intensity of light reflected or emitted from a person or object. A scanning charged particle microscope takes a "picture" by receiving and recording the energy or number of charged particles (e.g., electrons) reflected or emitted from a structure of a wafer. Typically, the structure is fabricated on a substrate (e.g., a silicon substrate) placed on a platform (referred to as a stage) for imaging. Before taking such a "picture," a charged particle beam can be projected onto the structure, and when the charged particles are reflected or emitted ("emitted") from the structure (e.g., from the wafer surface, from a structure below the wafer surface, or both), the detector of the scanning charged particle microscope can receive and record the energy or number of those charged particles to produce an inspection image. To take this "picture," the charged particle beam can be scanned across the wafer (e.g., in a line-by-line or zigzag manner), and the detector can receive the outgoing charged particles from the area (referred to as a "beam spot") below the projection of the charged particle beam. The detector can receive and record the outgoing charged particles from each beam point one at a time and combine the information recorded for all beam points to produce an inspection image. While some scanning charged particle microscopes use a single charged particle beam (referred to as a "single-beam scanning charged particle microscope", such as a single-beam SEM) to take a single "picture" to produce an inspection image, some scanning charged particle microscopes use multiple charged particle beams (referred to as a "multi-beam scanning charged particle microscope", such as a multi-beam SEM) to take multiple "sub-pictures" of the wafer in parallel and stitch the sub-pictures together to produce an inspection image. By using multiple charged particle beams, the SEM can provide more charged particle beams to the structure to obtain these multiple "sub-pictures", resulting in more charged particles emitted from the structure. Therefore, the detector can receive more outgoing charged particles at the same time and produce an inspection image of the structure of the wafer with higher efficiency and faster speed.

[0018] In order to control the quality of the manufactured semiconductor structure, various overlay measurement techniques can be used. Typically, optical tools can be used to measure the overlay. For example, a broadband light beam can be emitted on the surface of the sample. The surface can include a specifically designed and manufactured structure (also referred to as a "target" in this article). The target can include a first layer (e.g., a top layer) and a second layer (e.g., a bottom layer) below the first pattern layer. Optical scattering measurement tools can be used to measure the reflection or diffraction of the broadband light reflected by the target. The reflection or diffraction can have various characteristics, such as different wavelengths, polarizations, angles of incidence, phases, or other optical characteristics, according to which unknown properties of the sample (e.g., overlap) can be determined.

[0019] As an example, an overlap of a target may be determined based on a phase difference between diffraction of a first layer (e.g., a top layer) and diffraction of a second layer (e.g., a layer below the first layer), each of the first layer and the second layer including a particular structure (e.g., a grating). The overlap determined using the target may be referred to as diffraction-based overlap ("DBO"). To measure the diffraction-based overlap, the structures (e.g., gratings) in the first player and the second player may be manufactured using a planned shift. A planned shift between two layers herein may refer to a designed (known) plane vector displacement between the two layers. The planned shift may be used to remove or reduce defects in optical scattering measurements.

[0020] There are several technical challenges in optical-based overlay measurement techniques. The first challenge is that the reflected or diffracted signal becomes weaker as the pitch of the target (e.g., the pitch of the grating) decreases and as the separation between adjacent pattern layers increases. "Pitch" in the present disclosure refers to the minimum center-to-center distance between interconnect lines in the manufactured integrated circuit, which can be used as an indicator of the integration level of the integrated circuit. The second challenge is that the selection of the wavelength of the broadband light beam used for the optical-based overlay measurement technique can be complicated because each wavelength may produce different measurement results. The third challenge is that the measurement results of the optical-based overlay measurement technique may be sensitive to slight tilts in the area between the lines of the target (e.g., the lines of the grating). Those challenges may increase uncertainty and inaccuracy in overlay measurements.

[0021] Embodiments of the present disclosure may provide methods, devices, and systems for non-optical overlay measurement. In some disclosed embodiments, a scanning charged particle microscope (e.g., SEM) may be used for overlay measurement using one or more targets. The scanning charged particle microscope may inject a charged particle beam (e.g., an electron beam) onto the surface of one or more targets, each of which includes a first layer (e.g., a top layer) and a second layer (e.g., below the first layer). Each of the first layer and the second layer may include a similar pattern (e.g., a grating). The incident charged particle beam may interact with a pattern in the first layer and a pattern in the second layer to generate secondary electrons and backscattered electrons. The ejected secondary electrons and backscattered electrons may be detected by a detector to generate a signal. By analyzing the signal, the overlap between the first layer and the second layer may be determined. Compared to optical-based overlay measurement techniques, non-optical overlay measurement may reduce or remove the above challenges and may greatly improve the accuracy of overlay measurement.

[0022] For the sake of clarity, the relative sizes of the components in the drawings may be exaggerated. In the following description of the drawings, the same or similar reference numerals refer to the same or similar components or entities, and only the differences with respect to the various embodiments are described.

[0023] As used herein, unless specifically stated otherwise, the term "or" encompasses all possible combinations unless not feasible. For example, if it is stated that a component may include A or B, then unless specifically stated otherwise or not feasible, the component may include A, or B, or A and B. As a second example, if it is stated that a component may include A, B, or C, then unless specifically stated otherwise or not feasible, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0024] Figure 1 An exemplary charged particle beam inspection (CPBI) system 100 consistent with some embodiments of the present disclosure is illustrated. The CPBI system 100 can be used for imaging. For example, the CPBI system 100 can use an electron beam for imaging. Figure 1As shown in , the CPBI system 100 includes a main chamber 101, a load / lock chamber 102, a beam tool 104, and an equipment front end module (EFEM) 106. The beam tool 104 is located in the main chamber 101. The EFEM 106 includes a first loading port 106a and a second loading port 106b. The EFEM 106 may include (multiple) additional loading ports. The first loading port 106a and the second loading port 106b receive wafer front opening integrated cassettes (FOUPs) containing wafers to be inspected (e.g., semiconductor wafers or wafers made of (multiple) other materials) or samples (wafers and samples can be used interchangeably). A "batch" is a plurality of wafers that can be loaded for processing in batches.

[0025] One or more robotic arms (not shown) in the EFEM 106 may transport the wafer to the load / lock chamber 102. The load / lock chamber 102 is connected to a load / lock vacuum pump system (not shown) that removes gas molecules in the load / lock chamber 102 to reach a first pressure lower than atmospheric pressure. After reaching the first pressure, the one or more robotic arms (not shown) may transport the wafer from the load / lock chamber 102 to the main chamber 101. The main chamber 101 is connected to a main chamber vacuum pump system (not shown) that removes gas molecules in the main chamber 101 to reach a second pressure lower than the first pressure. After reaching the second pressure, the wafer is subjected to inspection by the beam tool 104. The beam tool 104 may be a single beam system or a multi-beam system.

[0026] The controller 109 is electronically connected to the beam tool 104. The controller 109 may be a computer that can perform various controls of the CPBI system 100. Figure 1 106, but it is understood that the controller 109 may be part of the structure.

[0027] In some embodiments, the controller 109 may include one or more processors (not shown). The processor may be a general or special electronic device capable of manipulating or processing information. For example, the processor may include any number of central processing units (or "CPUs"), graphics processing units (or "GPUs"), optical processors, programmable logic controllers, microcontrollers, microprocessors, digital signal processors, intellectual property (IP) cores, programmable logic arrays (PLAs), programmable array logic (PALs), general array logic (GALs), composite programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), systems on chips (SoCs), application specific integrated circuits (ASICs), and any combination of any type of circuit capable of data processing. The processor may also be a virtual processor comprising one or more processors distributed across multiple machines or devices connected via a network.

[0028] In some embodiments, the controller 109 may also include one or more memories (not shown). The memory may be a general or specific electronic device capable of storing code and data that can be accessed by a processor (e.g., via a bus). For example, the memory may include any number of random access memories (RAM), read-only memories (ROM), optical disks, magnetic disks, hard drives, solid-state drives, flash drives, secure digital (SD) cards, memory sticks, compact flash (CF) cards, or any combination of any type of storage device. The code may include an operating system (OS) and one or more application programs (or "apps") for a specific task. The memory may also be a virtual memory that includes one or more memories distributed across multiple machines or devices connected via a network.

[0029] Figure 2 An exemplary imaging system 200 is illustrated in accordance with an embodiment of the present disclosure. Figure 2 The beam tool 104 can be configured for use with the CPBI system 100. The beam tool 104 can be a single beam device or a multi-beam device. Figure 2As shown in , the beam tool 104 includes a motorized sample stage 201, and a wafer holder 202 supported by the motorized sample stage 201 to hold a wafer 203 to be inspected. The beam tool 104 also includes an objective lens assembly 204, a charged particle detector 206 (the charged particle detector 206 includes charged particle sensor surfaces 206a and 206b), an objective lens aperture 208, a condenser lens 210, a beam limiting aperture 212, a gun aperture 214, an anode 216, and a cathode 218. In some embodiments, the objective lens assembly 204 may include a modified swinging objective delayed immersion lens (SORIL), the modified swinging objective delayed immersion lens including a pole piece 204a, a control electrode 204b, a deflector 204c, and an excitation coil 204d. The beam tool 104 may additionally include an energy dispersive X-ray spectrometer (EDS) detector (not shown) to characterize the material on the wafer 203.

[0030] A primary charged particle beam 220 (or simply "primary beam 220"), such as an electron beam, is emitted from the cathode 218 by applying an accelerating voltage between the anode 216 and the cathode 218. The primary beam 220 passes through the gun aperture 214 and the beam limiting aperture 212, both of which can determine the size of the charged particle beam entering the condenser lens 210 residing below the beam limiting aperture 212. The condenser lens 210 focuses the primary beam 220 before the beam enters the objective lens aperture 208 to set the size of the charged particle beam before the charged particle beam enters the objective lens assembly 204. The deflector 204c deflects the primary beam 220 to facilitate scanning of the beam on the wafer. For example, during the scanning process, the deflector 204c can be controlled to sequentially deflect the primary beam 220 to different locations on the top surface of the wafer 203 at different points in time to provide data for image reconstruction of different portions of the wafer 203. In addition, the deflector 204c can also be controlled to deflect the primary beam 220 to different sides of the wafer 203 at a specific location at different time points to provide data for stereoscopic image reconstruction of the wafer structure at the location. In addition, in some embodiments, the anode 216 and the cathode 218 can generate multiple primary beams 220, and the beam tool 104 can include multiple deflectors 204c to simultaneously project multiple primary beams 220 to different parts / sides of the wafer to provide data for image reconstruction of different parts of the wafer 203.

[0031] The excitation coil 204d and the pole piece 204a generate a magnetic field that starts at one end of the pole piece 204a and ends at the other end of the pole piece 204a. A portion of the wafer 203 being scanned by the primary beam 220 may be immersed in the magnetic field and may be charged, which in turn generates an electric field. The electric field reduces the energy of the primary beam 220 impinging near the surface of the wafer 203 before the primary beam 220 collides with the wafer 203. The control electrode 204b, which is electrically isolated from the pole piece 204a, controls the electric field on the wafer 203 to prevent micro-arching of the wafer 203 and ensure proper beam focusing.

[0032] A secondary charged particle beam 222 (or "secondary beam 222"), such as a secondary electron beam, can be emitted from the portion of the wafer 203 when the wafer receives the primary beam 220. The secondary beam 222 can form a beam spot on the sensor surfaces 206a and 206b of the charged particle detector 206. The charged particle detector 206 can generate a signal (e.g., voltage, current, etc.) representing the intensity of the beam spot and provide the signal to the image processing system 250. The intensity of the secondary beam 222 and the resulting beam spot can vary depending on the external or internal structure of the wafer 203. In addition, as discussed above, the primary beam 220 can be projected onto different locations on the top surface of the wafer or on different sides of the wafer at a specific location to produce secondary beams 222 (and resulting beam spots) of different intensities. Therefore, by mapping the intensity of the beam spot using the location of the wafer 203, the processing system can reconstruct an image reflecting the internal or surface structure of the wafer 203.

[0033] The imaging system 200 can be used to inspect a wafer 203 on a motorized sample stage 201 and includes a beam tool 104, as discussed above. The imaging system 200 can also include an image processing system 250, which includes an image acquisition device 260, a memory 270, and a controller 109. The image acquisition device 260 can include one or more processors. For example, the image acquisition device 260 can include a computer, a server, a mainframe computer, a terminal, a personal computer, any kind of mobile computing device, etc., or a combination thereof. The image acquisition device 260 can be connected to the detector 206 of the beam tool 104 via a medium such as an electrical conductor, a fiber optic cable, a portable storage medium, IR, Bluetooth, the Internet, a wireless network, radio, or a combination thereof. The image acquisition device 260 can receive signals from the detector 206 and can construct an image. The image acquisition device 260 can thus acquire an image of the wafer 203. The image acquisition device 260 can also perform various post-processing functions, such as generating contours, superimposing indicators on the acquired image, etc. The image acquirer 260 may perform adjustments to the brightness and contrast of the acquired image, etc. The memory 270 may be a storage medium, such as a hard disk, a cloud storage, a random access memory (RAM), other types of computer-readable memory, etc. The memory 270 may be connected to the image acquirer 260 and may be used to save the scanned raw image data as a raw image, a post-processed image, or other images for auxiliary processing. The image acquirer 260 and the memory 270 may be connected to the controller 109. In some embodiments, the image acquirer 260, the memory 270, and the controller 109 may be integrated together into a control unit.

[0034] In some embodiments, the image acquirer 260 can acquire one or more images of the sample based on the imaging signal received from the detector 206. The imaging signal can correspond to a scanning operation for performing charged particle imaging. The acquired image can be a single image including multiple imaging regions. The single image can be stored in the memory 270. The single image can be an original image that can be divided into multiple regions. Each of the regions can include an imaging region containing a feature of the wafer 203.

[0035] During surface and subsurface structure measurements using charged particle beam tools (e.g., scanning charged particle microscopes), a scanning charged particle microscope (“SCPM”) generates a primary charged particle beam (e.g., Figure 2The primary charged particle beam 220 in the sample is used for inspection. For example, the primary charged particle beam can be a primary electron beam. The electrons of the primary electron beam are projected onto the surface of the sample. The sample may include features formed on the wafer 203. The wafer 203 may alternatively be referred to herein as a substrate 203 or a wafer substrate 203. The sample may have any material, such as a non-conductive resist, a silicon dioxide layer, a metal layer, or any stacked combination of any dielectric or conductive material.

[0036] The electrons of the primary electron beam can penetrate the surface of the sample to a certain depth (e.g., from several nanometers to several microns), thereby interacting with the particles of the sample in the interaction volume. Some electrons of the primary electron beam can interact elastically with the particles in the interaction volume (e.g., in the form of elastic scattering or collision), and can be reflected or bounced off the surface of the sample. The elastic interaction conserves the total kinetic energy of the interacting subject (e.g., the electrons of the primary electron beam and the particles of the sample), wherein the kinetic energy of the interacting subject is not converted into other forms of energy (e.g., thermal energy, electromagnetic energy, etc.). Such reflected electrons generated from the elastic interaction can be referred to as backscattered electrons (BSE). Some electrons of the primary electron beam can interact inelastically with the particles in the interaction volume (e.g., in the form of inelastic scattering or collision). The inelastic interaction does not conserve the total kinetic energy of the interacting subject, wherein some or all of the kinetic energy of the interacting subject can be converted into other forms of energy. For example, through the inelastic interaction, the kinetic energy of some electrons of the primary electron beam can cause electron excitation and cause the generation of electrons ejected from the surface of the sample, which can be referred to as secondary electrons (SE). Some SEMs may have enough energy to eventually eject the surface of the sample and reach the detector, and especially when the surface of the sample is positively charged, some SEs may eventually eject and then re-enter the surface of the sample. The yield or emission rate of BSE and SE may depend on, for example, the energy of the electrons of the primary electron beam and the material being probed, among other factors. The energy of the electrons of the primary electron beam may be determined in part by its accelerating voltage (e.g., Figure 2 The number of BSEs and SEs may be greater or less (or even the same) than the injected electrons of the primary electron beam.

[0037] As an example, the sample may include a first layer (e.g., a resist layer on top of the wafer surface) and a second layer (e.g., a pattern layer below the wafer surface). Each of the first layer and the second layer may include a design pattern (e.g., a target), such as a line, a groove, a corner, an edge, a hole, etc. Those features may be at different heights. The primary electron beam may interact with particles in the first layer to generate SEs, and the SEs generated at different positions of the targets in the first layer may be used to determine geometric information of the targets in the first layer. The primary electron beam may also penetrate the first layer to reach the second layer and interact with particles in the second layer to generate BSEs, and the BSEs generated at different positions of the targets in the second layer may be used to determine geometric information of the targets in the second layer. The landing energy of the electrons of the primary electron beam determines the depth of the bulk in which SEs and BSEs are generated in the sample. When the landing energy is low, a considerable proportion of both SEs and BSEs may reach the detector. When the landing energy is high, SEs are increasingly unable to reach the detector, and thus the proportion of electrons detected as BSEs increases.

[0038] In accordance with some embodiments of the present disclosure, a computer-implemented method for measuring overlap of a sample under scanning performed by a charged particle beam inspection device may include obtaining a detector signal in response to scanning a target of the sample. In some embodiments, the charged particle beam inspection device may include a scanning electron microscope. The sample may include a wafer.

[0039] As an example, a charged particle beam inspection device may be an imaging system (e.g., Figure 2 The sample may be a wafer (e.g., Figure 2 The target may be a wafer 203 in the wafer having a fabricated structure (e.g., a circuit) on a surface of the wafer. In some embodiments, the target may be a specifically designed and fabricated structure. For example, the target may be independent of and have no functional relationship to the fabricated circuit on the wafer. In some embodiments, the target may be fabricated at free space on the wafer that is not occupied by the fabricated circuit.

[0040] The detector signal may be a detector of a charged particle inspection device (e.g. Figure 2 In some embodiments, the target may be scanned by a charged particle beam (e.g., a single beam inspection device) or a charged particle beamlet (e.g., a multi-beam inspection device). During scanning of the sample, the primary beam (e.g., Figure 2 After charged particles (e.g., electrons) in the primary beam 220 in the sample strike the surface of the sample, at least one of secondary charged particles (e.g., SE) or backscattered charged particles (e.g., BSE) may be emitted from the surface of the sample and directed to a detector (e.g., Figure 2In some embodiments, at least one of the secondary electrons or the backscattered electrons may be emitted from the target and directed to the detector to produce a detector signal.

[0041] In some embodiments, the detector signal may be a value representing a sum or count of detected electrons emitted from a target. In some embodiments, the detector signal may be a value representing a sum of charges of detected electrons emitted from a target. In some embodiments, the detector signal may be visualized.

[0042] In some embodiments, the target may include a first pattern layer and a second pattern layer below the first pattern layer. Each of the first pattern layer and the second pattern layer may include a grating. The pitch of the grating in each pattern layer may be the same or different.

[0043] As an example, Figure 3 is a schematic diagram illustrating a sample consistent with some embodiments of the present disclosure, the sample including an exemplary target 300 fabricated on a substrate 305. The substrate 305 may be a silicon wafer substrate 203. In some embodiments, the target 300 may be a diffraction-based overlay target. Figure 3 As illustrated in FIG. 3 , the target 300 includes a first pattern layer 301 and a second pattern layer 304 below the first pattern layer 301. The first pattern layer 301 and the second pattern layer 304 may be of a type of grating (eg, line grating), respectively.

[0044] In some embodiments, the first pattern layer 301 may have a material of polymethyl methacrylate (PMMA). The first pattern layer 301 may be fabricated (e.g., via coating, photolithography, and etching processes) on a support layer 302. The support layer 302 may also be made of PMMA. The grating of the first pattern layer 301 may include a plurality of structures 306, which may be linear lines 306 defining peaks and valleys above the support layer 302. The height of each structure 306 above the support layer 302 may be referred to as the height 311 of the grating in the first pattern layer 301. The first pattern layer 301 may have a pitch, the pitch value 309 of which is the distance between the centers of two adjacent structures 306 of the grating.

[0045] In some embodiments, the second pattern layer 304 may have a copper material. The second pattern layer 304 may be fabricated (e.g., via coating, photolithography, and etching processes) on a substrate 305. The grating of the second pattern layer 304 may include a plurality of structures 307, which may be linear lines 307 defining peaks and valleys above the surface of the substrate 305. The height of each structure 307 above the substrate 305 may be referred to as the height 312 of the grating in the second pattern layer 312. The second pattern layer 304 may have a pitch, the pitch value 310 of which is the distance between the centers of two adjacent structures 307 of the grating. The pitch value 309 of the first pattern layer 301 may be different from or the same as the pitch value 310 of the second pattern layer 304.

[0046] In some embodiments, the silicon dioxide layer 303 may separate the support layer 302 from the second pattern layer 304. The gaps between the structures 307 in the second pattern layer may also be filled with silicon dioxide.

[0047] The first pattern layer 301 and the second pattern layer 304 are separated by a separation distance 308 .

[0048] In accordance with some embodiments of the present disclosure, an electron beam such as primary electron beam 220 may be used to irradiate a target (e.g., Figure 3 The electrons may be detected to thereby generate detection data, which may be referred to as a detector signal. A determination of a characteristic of the sample (such as overlap) may be performed in a computer-implemented method based on the detection data.

[0049] According to an embodiment, a model is constructed based on known physical properties of the target 300 and the expected physical response of the target 300 to irradiation by electrons. The inventors have realized that the detected response of the irradiation of the grating is similar to the waveform produced by the convolution of a block function with a kernel function such as a Gaussian function. Specifically, when the electron beam propagates through the structure, the electron beam will widen. The convolution of the block function with the kernel function is an effective way to model this physical process. An embodiment constructs a model, and then tunes the parameters of the model so that the modeled detection data (i.e., the modeled detector signal) closely resembles the actual measurement signal / data. The tunable parameters of the model may include the position of the block function and the width of the kernel function. The tuned values ​​of the parameters of the model can then be used to determine the characteristics of the sample, such as overlap.

[0050] The model may include multiple terms that combine to provide the modeled detection data. The terms of the model may include one or more of the following:

[0051] a) the offset being modelled;

[0052] b) the modelled contribution from the first pattern layer 301;

[0053] c) the modelled contribution from the second pattern layer 304; and

[0054] d) The interaction signal being modeled.

[0055] The modeled offset may represent the average of the minimum signal levels in the actual measured signal / data. The modeled offset may be electron counts (expressed in arbitrary units). The initial value of the modeled offset may be the minimum average of the actual measured signal / data.

[0056] The modeled contribution from the first pattern layer 301 may be referred to as the top signal. The top signal may be calculated as:

[0057] Top signal = A top ×Block (top pitch, top duty cycle, top shift) top_core(type, width)

[0058] in:

[0059] “A top ” represents the amplitude of the detected signal from the grating in the first pattern layer 301. top Can be electron counts (expressed in arbitrary units). A top The value of may depend on material properties, such as density and atomic number, and geometric properties, such as the height 311 of the grating in the first pattern layer 301;

[0060] "Block(..)" is a function that models the shape of the raster. For example, the raster shape can be a square wave or a rectangular wave. The value of the function can vary between 0 and 1;

[0061] "Top pitch" is the pitch value 309 of the first pattern layer 301. The expected value of the top pitch can be known from the design parameters of the target 300. The expected value of the top pitch can be used initially in the model and then varied as the model is tuned;

[0062] The “top duty cycle” is related to the width and spacing of the structure 306;

[0063] The "top shift" is associated with a position reference point, which, along with the bottom shift described later, can be used to determine overlap. The top shift can be defined in a number of different ways. For example, the expected center of the signal can represent a zero top shift position. Then, locations other than the expected center of the signal will have a top shift value. Alternatively, the expected center of structure 306 can represent a zero top shift position. Then, locations other than the expected center of structure 306 will have a top shift value. Thus, the top shift value can represent the difference between the actual position of a feature (such as structure 306) and the expected position;

[0064] “ " indicates a convolution operation; and

[0065] A "top kernel" is a function that has the property that the integral over its width is equal to 1. The top kernel is defined by the type of function (such as a Gaussian function) and the width of the function.

[0066] The modeled contribution from the second pattern layer 304 may be referred to as the bottom signal. The bottom signal may be calculated as:

[0067] Bottom signal = A bottom ×Block (bottom pitch, bottom duty cycle, bottom shift) bottom_core(type, width)

[0068] in:

[0069] “A bottom ” represents the amplitude of the detected signal from the grating in the second pattern layer 304. bottom Can be electron counts (expressed in arbitrary units). A bottom The value of may depend on material properties such as density and atomic number and geometric properties such as the height 312 of the grating in the second pattern layer 304;

[0070] "Block(..)" is a function that models the shape of the raster. For example, the raster shape can be a square wave or a rectangular wave. The value of the function can vary between 0 and 1;

[0071] The "bottom pitch" is the pitch value 310 of the second pattern layer 304. The expected value of the bottom pitch may be known from the design parameters of the target 300. The expected value of the bottom pitch may be used initially in the model and then varied as the model is tuned;

[0072] The “bottom duty cycle” is related to the width and spacing of the structure 307;

[0073] The "bottom shift" is related to a position reference point, which, along with the top shift described later, can be used to determine overlap. The bottom shift can be defined in a number of different ways. For example, the expected center of the signal can represent a zero bottom shift position. Positions other than the expected center of the signal will then have a bottom shift value. Alternatively, the expected center of structure 307 can represent a zero bottom shift position. Positions other than the expected center of structure 307 will then have a bottom shift value. The bottom shift value can represent the difference between the actual position of a feature (such as structure 307) and the expected position. Overlap can be calculated as the difference between the top shift value and the bottom shift value;

[0074] “ " indicates a convolution operation; and

[0075] The "bottom kernel" is a function that has the property that the integral over its width is equal to 1. The bottom kernel is defined by the type of function (such as a Gaussian function) and the width of the function. The width of the bottom kernel can be much larger than the width of the top kernel. Thus, the bottom kernel can resemble a sine wave.

[0076] The interaction signal is calculated based on the top signal and the bottom signal and the additional term A. interaction The dot product of:

[0077] Interaction signal = A interaction ×Top signal×Bottom signal

[0078] A interaction The term is based on the expected different physical interactions between the first pattern layer 301 and the second pattern layer 304. Specifically, when the electron beam passes through the top (i.e., the peak) of the grating in the first pattern layer 301, the beam broadening and signal attenuation experienced are different than when the electron beam passes through the valley of the grating. The interaction signal includes these different physical effects in the model.

[0079] The model may be constructed by adding together the above modeled offset, top signal, bottom signal, and interaction signal.

[0080] Figure 4A Shown are signals and their components that are modeled according to some embodiments. Figure 4B and Figure 5 Demonstrates the accuracy of the signal being modeled. Figure 4A , Figure 4B and Figure 5 In , the x-axis is the pixel index and is related to the location of the data / signal being modeled and / or measured. Figure 4A , Figure 4B and Figure 5 In FIG. 1 , the y-axis is the gray level. The gray level can be based on the number of charged particles detected by the pixels of the detector in a period of time and / or the intensity of the signal detected by the pixels.

[0081] Figure 4A All modeled top signals, bottom signals and interaction components are shown separately. Figure 4A The "Total Model" line in shows the sum of the contributions to the model, including the modeled offset.

[0082] A process for tuning the parameters of the model so that the model fits well to actual measurements of electrons after target illumination may be performed.

[0083] The parameters of the model that can be tuned to fit the model to the measured data include the offset, A top , A bottom, top shift, bottom shift, top kernel width, bottom kernel width, and A interaction Increasing the number of tuned parameters may improve the accuracy of the model at the expense of increasing the complexity of fitting the model to the measured data. Reducing the number of tuned parameters may reduce the accuracy of the model and reduce the complexity of fitting the model to the measured data.

[0084] The tuning process may be performed by using a cost function (such as the L2 norm of the simulated and modeled signals) and, for example, a global minimum or a combination of solving a set of linear equations and a global minimum. Embodiments also include other techniques for fitting the model to the measured data.

[0085] Figure 5 The modeled signal (ie, the "total model" line) when fitted to the measured data according to an embodiment is shown. The modeled signal is a continuous line. The measured data includes measurement structures at different locations, as shown by discrete points at different pixel indices.

[0086] exist Figure 5 In , the values ​​of the parameters that produce the signal being modeled are:

[0087] Offset = 19.877

[0088] Top kernel = (Gaussian kernel, width = 5.84)

[0089] Bottom Kernel = (Gaussian Kernel, Width = 37.00)

[0090] A top =0.751

[0091] A bottom =1.469

[0092] A interaction =0.648

[0093] Top Shift = -0.135

[0094] Bottom Shift = -18.924

[0095] Figure 4B A residual signal or error signal representing the difference between the measured signal and the modeled signal is shown. The value of the residual signal may be 0.782. Figure 4B The lower absolute error value in shows a good fit of the model to the actual measured data.

[0096] After the model has been fitted to the measured data, the parameters of the model may be used to determine characteristics of target 300. Specifically, the overlap may be determined from the top shift parameters and the bottom shift parameters of the model.

[0097] Models according to embodiments can be applied in a number of different situations. Specifically, the model can be used to process signals received from a high voltage scanning electron microscope HV-SEM to determine the average overlap in the image of the sample 300. Averaging the measured data suppresses noise in the measurement. However, embodiments also include not averaging the measured data so that the model determines local overlap.

[0098] Embodiments include that the top duty cycle and bottom duty cycle of the model are also variable parameters that are tuned when the model is fitted to the measured data. The model can thereby determine the average CD. Averaging of the measured data suppresses noise in the measurement. However, embodiments also include not averaging the measured data so that the model determines the local CD.

[0099] Embodiments are not limited to determining properties of gratings. The lines of a grating are an example of a one-dimensional structure. A two-dimensional structure may have, for example, an L-shape in plan view. Embodiments may be used to determine properties of any feature formed on a substrate, including 2D structures such as contact holes, bricks, and other patterns. The block (..) function of the model may be changed so that the block (..) function corresponds to the shape of the structure being measured.

[0100] Figure 6 A flow chart illustrating a computer-implemented method of measuring a characteristic of a sample under scanning performed by a charged particle beam inspection apparatus according to an embodiment.

[0101] In step 601, the method starts.

[0102] In step 603, detection data is obtained in response to scanning a target on a sample by a charged particle beam inspection device, wherein the target includes a plurality of pattern layers.

[0103] In step 605 , one or more characteristics of the sample are determined based on the obtained inspection data and a model, wherein, for each pattern layer of a plurality of pattern layers of the target, the model includes a term based on a property of the pattern layer.

[0104] In step 607, the method ends.

[0105] Embodiments also include numerous modifications and variations of the above-described techniques.

[0106] Embodiments include models that use any of a plurality of different kernel functions for modeling the top signal and / or the bottom signal. The preferred kernel function is a Gaussian kernel because the Gaussian kernel allows accurate modeling. However, other types of kernel functions that reduce the complexity of the modeling process and thereby allow faster modeling may be used, such as a triangular kernel.

[0107] Embodiments include a model that uses a variable kernel function for modeling the top signal and / or the bottom signal. With a variable kernel, the width of the kernel depends on the location of the structure 307 below the first pattern layer 301. This can improve the accuracy of the model at the expense of increased computational time, because improving the accuracy of the model better represents the physical process.

[0108] Embodiments also include using variable kernel functions for modeling the top signal and / or the bottom signal. The width of each kernel can be based on the relative position of the structure 306 in the first pattern layer 301 relative to the structure 307 in the second pattern layer. This can improve the accuracy of the model at the expense of increased computation time, because improving the accuracy of the model better represents the physical process.

[0109] Embodiments may provide improved determination of properties of sample 300 compared to optical techniques.

[0110] Embodiments may also improve other charged particle based measurement techniques that make determinations based on arbitrary thresholds rather than models of the actual physical processes occurring.

[0111] Embodiments may determine characteristics based on measurements of a single target 300. This is an advantage over techniques that require measurements from more than one target.

[0112] Embodiments have been described with reference to a target comprising a first patterned layer 301 and a second patterned layer 304. Embodiments may also be used with other types of targets, such as targets comprising more than two patterned layers. Models according to embodiments may be modified to include terms representing the detection of electrons from each of more than two layers of a target and the interactions between the layers.

[0113] Embodiments have been described in the context of samples irradiated by electron beams, such as primary beam 220. The electron beams may be from a single beam charged particle device or a multi-beam charged particle device. More generally, embodiments include irradiation of samples by any charged particle beam.

[0114] Embodiments include a sample comprising a plurality of targets and a local characteristic of the sample determined at each target.

[0115] A non-transitory computer-readable medium may be provided that stores a computer program product for use by a processor (e.g., Figure 1The processor of the controller 109 of the embodiment of the present invention may be a processor of the controller 109 of the embodiment of the present invention) to perform overlay measurement, data processing, database management, graphic display, operation of an image inspection device or another imaging device, detection of defects on a sample, etc., such as the method 800 of FIG. 8 or the method 900 of FIG. 9. Common forms of non-transitory media include, for example: a floppy disk, a flexible disk, a hard disk, a solid-state drive, a magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with a pattern of holes, a RAM, a PROM, and an EPROM, a FLASH-EPROM, or any other flash memory, an NVRAM, a cache memory, a register, any other memory chip, or a cartridge, and networked versions thereof.

[0116] Embodiments include the following aspects:

[0117] 1. A system comprising:

[0118] a charged particle beam inspection apparatus configured to scan a sample including a target having a plurality of pattern layers; and

[0119] A controller comprising a circuit system, wherein the controller is configured to:

[0120] obtaining detection data in response to scanning the target; and

[0121] determining one or more characteristics of the sample based on the obtained detection data and the model;

[0122] Wherein, for each of the plurality of pattern layers of the target, the model includes an item according to a property of the pattern layer.

[0123] 2. The system of aspect 1, wherein the controller is configured to:

[0124] Determining parameters of the model so that the modeled detection signal substantially fits the acquired detection data; and

[0125] One or more characteristics of the sample are determined based on the determined parameters.

[0126] 3. The system of clause 1 or 2, wherein the one or more characteristics of the sample include overlap and critical dimension.

[0127] 4. A system as described in any preceding aspect, wherein the target includes a first pattern layer and a second pattern layer; and

[0128] Each pattern layer includes a grating.

[0129] 5. The system of aspect 4, wherein:

[0130] The grating included by the first pattern layer has a first pitch;

[0131] The grating included by the second pattern layer has a second pitch; and

[0132] The first pitch is different from the second pitch.

[0133] 6. A system according to any one of aspects 4 or 5, wherein the terms of the model include:

[0134] a top signal, the top signal being a modeled contribution from the first pattern layer;

[0135] a bottom signal, the bottom signal being a modeled contribution from the second pattern layer; and

[0136] An interaction signal models an interaction between the first pattern layer and the second pattern layer.

[0137] 7. The system of aspect 6, wherein the model further comprises an offset term for modeling an average of minimum values ​​of the obtained detection data.

[0138] 8. A system according to any one of aspects 4 to 7, wherein the top signal is calculated as:

[0139] Top signal = A top ×Block (top pitch, top duty cycle, top shift) top_core(type, width)

[0140] in:

[0141] “A top ” represents the amplitude of the detected signal from the grating in the first pattern layer;

[0142] "Block(..)" is a function that models the shape of the raster;

[0143] "Top pitch" is the pitch value of the first pattern layer;

[0144] The "top duty cycle" is related to the width and spacing of the grating in the first pattern layer;

[0145] The “top shift” is relative to the position reference point;

[0146] “ " indicates a convolution operation; and

[0147] "Top Kernel" is the kernel function.

[0148] 9. A system according to any one of aspects 4 to 8, wherein the bottom signal is calculated as:

[0149] Bottom signal = A bottom ×Block (bottom pitch, bottom duty cycle, bottom shift) bottom_core(type, width)

[0150] in:

[0151] “A bottom ” represents the amplitude of the detected signal from the grating in the second pattern layer;

[0152] "Block(..)" is a function that models the shape of the raster;

[0153] "Bottom pitch" is the pitch value of the second pattern layer;

[0154] The "bottom duty cycle" is related to the width and spacing of the grating in the second pattern layer;

[0155] The “bottom shift” is relative to the position reference point;

[0156] “ " indicates a convolution operation; and

[0157] The "bottom kernel" is the kernel function.

[0158] 10. The system of aspects 8 and 9, when dependent on aspects 2 and 7, wherein the determined parameters of the model include one or more of the following: the offset term, A top , A bottom , top shift, bottom shift, top core width, bottom core width, and the interaction signal.

[0159] 11. The system of any one of aspects 8 to 10, wherein the controller is configured to determine the overlap of the samples based on the determined top shift and the determined bottom shift.

[0160] 12. The system of any preceding aspect, wherein the charged particle beam inspection apparatus comprises a scanning electron microscope and the sample comprises a target formed on a substrate.

[0161] 13. A non-transitory computer-readable medium storing a set of instructions, the set of instructions being executable by at least one processor of a device to cause the device to perform a method comprising:

[0162] acquiring detection data in response to scanning of a target by a charged particle beam inspection device;

[0163] determining one or more characteristics of the sample based on the obtained test data and the model;

[0164] The target includes a plurality of pattern layers, and for each of the plurality of pattern layers, the model includes an item according to a property of the pattern layer.

[0165] 14. The non-transitory computer readable medium of clause 13, wherein the method further comprises:

[0166] Determining parameters of the model so that the modeled detection signal substantially fits the acquired detection data; and

[0167] One or more characteristics of the sample are determined based on the determined parameters.

[0168] 15. The non-transitory computer-readable medium of clause 13 or 14, wherein the one or more characteristics of the sample include overlap and critical dimension.

[0169] 16. The non-transitory computer-readable medium of any one of clauses 13 to 15, wherein the target comprises a first pattern layer and a second pattern layer; and

[0170] Each pattern layer includes a grating.

[0171] 17. The non-transitory computer readable medium of clause 16, wherein:

[0172] The grating included by the first pattern layer has a first pitch;

[0173] The grating included by the second pattern layer has a second pitch; and

[0174] The first pitch is different from the second pitch.

[0175] 18. The non-transitory computer-readable medium of any one of clauses 16 or 17, wherein the terms of the model include:

[0176] a top signal, the top signal being a modeled contribution from the first pattern layer;

[0177] a bottom signal, the bottom signal being a modeled contribution from the second pattern layer; and

[0178] An interaction signal models an interaction between the first pattern layer and the second pattern layer.

[0179] 19. The non-transitory computer-readable medium of clause 18, wherein the model further comprises an offset term for modeling an average of minimum values ​​of the obtained detection data.

[0180] 20. The non-transitory computer-readable medium of any one of clauses 18 or 19, wherein the top signal is calculated as:

[0181] Top signal = A top ×Block (top pitch, top duty cycle, top shift) top_core(type, width)

[0182] in:

[0183] “A top ” represents the amplitude of the detected signal from the grating in the first pattern layer;

[0184] "Block(..)" is a function that models the shape of the raster;

[0185] "Top pitch" is the pitch value of the first pattern layer;

[0186] The "top duty cycle" is related to the width and spacing of the grating in the first pattern layer;

[0187] The “top shift” is relative to the position reference point;

[0188] “ " indicates a convolution operation; and

[0189] "Top Kernel" is the kernel function.

[0190] 21. The non-transitory computer-readable medium of any one of aspects 18 to 20, wherein the bottom signal is calculated as:

[0191] Bottom signal = A bottom ×Block (bottom pitch, bottom duty cycle, bottom shift) bottom_core(type, width)

[0192] in:

[0193] “A bottom ” represents the amplitude of the detected signal from the grating in the second pattern layer;

[0194] "Block(..)" is a function that models the shape of the raster;

[0195] "Bottom pitch" is the pitch value of the second pattern layer;

[0196] The "bottom duty cycle" is related to the width and spacing of the grating in the second pattern layer;

[0197] The “bottom shift” is relative to the position reference point;

[0198] “ " indicates a convolution operation; and

[0199] The "bottom kernel" is the kernel function.

[0200] 22. The non-transitory computer readable medium of aspects 20 and 21, when dependent on aspects 14 and 19, wherein the determined parameters of the model include one or more of the following: the offset term, A top , A bottom , top shift, bottom shift, top core width, bottom core width, and the interaction signal.

[0201] 23. The non-transitory computer-readable medium of any of clauses 20 to 22, wherein the controller is configured to determine the overlap of the samples based on the determined top shift and the determined bottom shift.

[0202] 24. The non-transitory computer-readable medium of any of clauses 13 to 23, wherein the charged particle beam inspection device comprises a scanning electron microscope and the sample comprises a target formed on a substrate.

[0203] 25. A computer-implemented method for measuring a property of a sample under scanning performed by a charged particle beam inspection apparatus, the method comprising:

[0204] acquiring detection data in response to scanning of a target by a charged particle beam inspection device;

[0205] determining one or more characteristics of the sample based on the obtained detection data and the model;

[0206] The target includes a plurality of pattern layers, and for each of the plurality of pattern layers, the model includes an item according to a property of the pattern layer.

[0207] 26. The method according to aspect 25, further comprising:

[0208] Determining parameters of the model so that the modeled detection signal substantially fits the acquired detection data; and

[0209] One or more characteristics of the sample are determined based on the determined parameters.

[0210] 27. The method of clause 25 or 26, wherein the one or more characteristics of the sample include overlay and critical dimension.

[0211] 28. The method of any one of clauses 25 to 27, wherein the target comprises a first pattern layer and a second pattern layer; and

[0212] Each pattern layer includes a grating.

[0213] 29. The method of clause 28, wherein:

[0214] The grating included by the first pattern layer has a first pitch;

[0215] The grating included by the second pattern layer has a second pitch; and

[0216] The first pitch is different from the second pitch.

[0217] 30. A method according to any one of clauses 28 or 29, wherein the terms of the model include:

[0218] a top signal, the top signal being a modeled contribution from the first pattern layer;

[0219] a bottom signal, the bottom signal being a modeled contribution from the second pattern layer; and

[0220] An interaction signal models an interaction between the first pattern layer and the second pattern layer.

[0221] 31. The method of clause 30, wherein the model further comprises an offset term for modeling the average of minimum values ​​of the obtained detection data.

[0222] 32. A method as described in any of clauses 30 or 31, wherein the top signal is calculated as:

[0223] Top signal = A top ×Block (top pitch, top duty cycle, top shift) top_core(type, width)

[0224] in:

[0225] “A top ” represents the amplitude of the detected signal from the grating in the first pattern layer;

[0226] "Block(..)" is a function that models the shape of the raster;

[0227] "Top pitch" is the pitch value of the first pattern layer;

[0228] The "top duty cycle" is related to the width and spacing of the grating in the first pattern layer;

[0229] The “top shift” is relative to the position reference point;

[0230] “ " indicates a convolution operation; and

[0231] "Top Kernel" is the kernel function.

[0232] 33. A method as described in any one of aspects 30 to 32, wherein the bottom signal is calculated as:

[0233] Bottom signal = A bottom ×Block (bottom pitch, bottom duty cycle, bottom shift) bottom_core(type, width)

[0234] in:

[0235] “A bottom ” represents the amplitude of the detected signal from the grating in the second pattern layer;

[0236] "Block(..)" is a function that models the shape of the raster;

[0237] "Bottom pitch" is the pitch value of the second pattern layer;

[0238] The "bottom duty cycle" is related to the width and spacing of the grating in the second pattern layer;

[0239] The “bottom shift” is relative to the position reference point;

[0240] “ " indicates a convolution operation; and

[0241] The "bottom kernel" is the kernel function.

[0242] 34. The method of aspects 32 and 33, when dependent on aspects 26 and 31, wherein the parameters of the model include one or more of the following: the offset term, A top , A bottom , top shift, bottom shift, top core width, bottom core width, and the interaction signal.

[0243] 35. The method of any of clauses 32 to 34, wherein the controller is configured to determine the overlap of the samples based on the determined top shift and the determined bottom shift.

[0244] 36. The method of any of clauses 25 to 35, wherein the charged particle beam inspection apparatus comprises a scanning electron microscope and the sample comprises a target formed on a substrate.

[0245] The block diagram in the figure illustrates the possible implementation architecture, function and operation of the system, method and computer hardware or software product according to various embodiments of the present invention.In this regard, each block in the flow chart or block diagram can represent a part of a module, section or code, and a part of the module, section or code includes one or more executable instructions for implementing a specified logical function.It should be understood that in some alternative embodiments, the function indicated in the block may not appear in the order of the sum mentioned in the figure.For example, according to the function of the sum involved, the two blocks shown continuously can be basically performed or implemented at the same time, or the two blocks can sometimes be performed in reverse order.Some blocks can also be omitted.It should also be understood that the combination of each block of the block diagram and the block can be implemented by a system based on special hardware that performs a specified function or action, or by a combination of special hardware and computer instructions.

[0246] It will be understood that the embodiments of the present disclosure are not limited to the exact constructions that have been described above and illustrated in the accompanying drawings, and that various modifications and changes may be made without departing from the scope of the present disclosure.

Claims

1. A system comprising: a charged particle beam inspection apparatus configured to scan a sample including a target having a plurality of pattern layers; as well as A controller comprising a circuit system, wherein the controller is configured to: obtaining detection data in response to scanning the target; as well as determining one or more characteristics of the sample based on the obtained detection data and the model; Wherein, for each of the plurality of pattern layers of the target, the model includes an item according to a property of the pattern layer.

2. The system of claim 1, wherein: The controller is configured to: Determining parameters of the model so that the modeled detection signal substantially fits the acquired detection data; and One or more characteristics of the sample are determined based on the determined parameters.

3. The system of claim 1, wherein: The one or more characteristics of the sample include overlay and critical dimension.

4. The system of claim 1, wherein: The target includes a first pattern layer and a second pattern layer; and Each pattern layer includes a grating.

5. The system of claim 1, wherein: The target includes a first pattern layer and a second pattern layer; Each pattern layer includes a grating; The grating included by the first pattern layer has a first pitch; The grating included by the second pattern layer has a second pitch; and The first pitch is different from the second pitch.

6. The system of claim 1, wherein: The terms of the model include: a top signal, the top signal being a modeled contribution from the first pattern layer; a bottom signal, the bottom signal being the modeled contribution from the second pattern layer; and An interaction signal models an interaction between the first pattern layer and the second pattern layer.

7. The system of claim 1, wherein: The terms of the model include: a top signal, the top signal being a modeled contribution from the first pattern layer; a bottom signal, the bottom signal being a modeled contribution from a second pattern layer; an interaction signal that models an interaction between the first pattern layer and the second pattern layer; and An offset term is used to model the average value of the minimum value of the obtained detection data.

8. The system of claim 1, wherein: The terms of the model include: a top signal, the top signal being a modeled contribution from the first pattern layer; a bottom signal, the bottom signal being a modeled contribution from a second pattern layer; an interaction signal that models an interaction between the first pattern layer and the second pattern layer; and an offset term for modeling an average value of a minimum value of the obtained detection data; and Wherein, the top signal is calculated as: Top signal = A top ×Block (top pitch, top duty cycle, top shift) top_core(type, width) in: "A top ” represents the amplitude of the detected signal from the grating in the first pattern layer; "Block(..)" is a function that models the shape of the grating; "Top pitch" is the pitch value of the first pattern layer; "Top duty cycle" is related to the width and spacing of the grating in the first pattern layer; "Top shift" is relative to the position reference point; " " indicates a convolution operation; and "Top kernel" is the kernel function.

9. The system of claim 1, wherein: The terms of the model include: a top signal, the top signal being a modeled contribution from the first pattern layer; a bottom signal, the bottom signal being a modeled contribution from a second pattern layer; an interaction signal that models an interaction between the first pattern layer and the second pattern layer; and an offset term for modeling an average value of a minimum value of the obtained detection data; and Wherein, the bottom signal is calculated as: Bottom signal = A bottom ×Block (bottom pitch, bottom duty cycle, bottom shift) bottom_core(type, width) in: "A bottom ” represents the amplitude of the detected signal from the grating in the second pattern layer; "Block(..)" is a function that models the shape of the grating; "Bottom pitch" is the pitch value of the second pattern layer; "Bottom duty cycle" is related to the width and spacing of the grating in the second pattern layer; "Bottom shift" is relative to the position reference point; " " indicates a convolution operation; and "Bottom kernel" is the kernel function.

10. The system of claim 1, wherein: The terms of the model include: a top signal, the top signal being a modeled contribution from the first pattern layer; a bottom signal, the bottom signal being a modeled contribution from a second pattern layer; an interaction signal that models an interaction between the first pattern layer and the second pattern layer; and an offset term for modeling an average value of a minimum value of the obtained detection data; and Wherein, the top signal is calculated as: Top signal = A top ×Block (top pitch, top duty cycle, top shift) top_core(type, width) in: "A top ” represents the amplitude of the detected signal from the grating in the first pattern layer; "Block(..)" is a function that models the shape of the grating; "Top pitch" is the pitch value of the first pattern layer; "Top duty cycle" is related to the width and spacing of the grating in the first pattern layer; "Top shift" is relative to the position reference point; " " indicates a convolution operation; and "Top kernel" is the kernel function; Wherein, the bottom signal is calculated as: Bottom signal = A bottom ×Block (bottom pitch, bottom duty cycle, bottom shift) bottom_core(type, width) in: "A bottom ” represents the amplitude of the detected signal from the grating in the second pattern layer; "Block(..)" is a function that models the shape of the grating; "Bottom pitch" is the pitch value of the second pattern layer; "Bottom duty cycle" is related to the width and spacing of the grating in the second pattern layer; "Bottom shift" is relative to the position reference point; " " indicates a convolution operation; and "bottomkernel" is the kernel function; and The determined parameters of the model include one or more of the following: the offset term, A top , A bottom , top shift, bottom shift, top core width, bottom core width, and the interaction signal.

11. The system of claim 1, wherein: The terms of the model include: a top signal, the top signal being a modeled contribution from the first pattern layer; a bottom signal, the bottom signal being a modeled contribution from a second pattern layer; an interaction signal that models an interaction between the first pattern layer and the second pattern layer; and an offset term for modeling an average value of a minimum value of the obtained detection data; and Wherein, the top signal is calculated as: Top signal = A top ×Block (top pitch, top duty cycle, top shift) top_core(type, width) in: "A top ” represents the amplitude of the detected signal from the grating in the first pattern layer; "Block(..)" is a function that models the shape of the grating; "Top pitch" is the pitch value of the first pattern layer; "Top duty cycle" is related to the width and spacing of the grating in the first pattern layer; "Top shift" is relative to the position reference point; " " indicates a convolution operation; and "Top kernel" is the kernel function; Wherein, the bottom signal is calculated as: Bottom signal = A bottom ×Block (bottom pitch, bottom duty cycle, bottom shift) bottom_core(type, width) in: "A bottom ” represents the amplitude of the detected signal from the grating in the second pattern layer; "Block(..)" is a function that models the shape of the grating; "Bottom pitch" is the pitch value of the second pattern layer; "Bottom duty cycle" is related to the width and spacing of the grating in the second pattern layer; "Bottom shift" is relative to the position reference point; " " indicates a convolution operation; and "bottomkernel" is the kernel function; and The controller is configured to determine an overlap of the samples based on the determined top shift and the determined bottom shift.

12. The system of claim 1, wherein: The charged particle beam inspection apparatus includes a scanning electron microscope, and the sample includes a target formed on a substrate.

13. A non-transitory computer-readable medium storing a set of instructions, the set of instructions being executable by at least one processor of a device to cause the device to perform a method comprising: acquiring detection data in response to scanning of a target by a charged particle beam inspection device; determining one or more characteristics of the sample based on the obtained test data and the model; The target includes a plurality of pattern layers, and for each of the plurality of pattern layers, the model includes an item according to a property of the pattern layer.

14. The non-transitory computer readable medium of claim 13, the method further comprising: Determining parameters of the model so that the modeled detection signal substantially fits the acquired detection data; as well as One or more characteristics of the sample are determined based on the determined parameters.

15. The non-transitory computer readable medium of claim 13, wherein: The one or more characteristics of the sample include overlay and critical dimension.

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