Optical metrology using influence maps of unknown sections

Abstract

An optical measurement of a sample including a structure of interest (SOI) optically coupled to a section having an unknown structure is optically measured using an influence map of the deviatoric contribution from the unknown structure. The influence map is generated by obtaining metrology data for a plurality of locations including the SOI and the unknown structure, and determining the deviatoric contribution at each location by separating the deviatoric contribution from the fundamental contribution from the SOI and the unknown structure. During measurement of a location, the deviatoric contribution associated with that location can be obtained from the influence map and removed from the measured data. The processed data can be fitted with models, including an exact model for the SOI and a valid model for the fundamental contribution of the unknown structure, to determine one or more parameters of the SOI.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims priority under 35 U.S.C. § 119 to U.S. Provisional Patent Application No. 63 / 283,201, entitled “OPTICAL METROLOGY WITH INFLUENCE MAP OF UNKNOWN SECTION,” filed November 24, 2021, the contents of which are incorporated by reference in their entirety herein.

[0002] FIELD OF THEINVENTION The subject matter described herein relates generally to optical metrology, and more particularly to modeling and measuring structures that include unknown sections. [Background technology]

[0003] The semiconductor and other similar industries often use optical metrology instruments to provide non-contact evaluation of a specimen during processing. In optical metrology, the specimen under test is illuminated with light, for example at a single wavelength or multiple wavelengths. After interacting with the specimen, the resulting light is detected and analyzed to determine one or more properties of the specimen.

[0004] The analysis typically includes a model of the structure under test. The model may be generated based on the materials and nominal parameters of the structure, such as film thickness, line width, and space width. One or more parameters of the model may be varied, and predicted data may be calculated for each parameter variation based on the model, for example, using Rigorous Coupled Wave Analysis (RCWA) or other similar techniques. Measured data may be compared to the predicted data for each parameter variation, for example, in a nonlinear regression process, until a good fit is achieved between the predicted data and the measured data, at which point the fitted parameters are determined to be an accurate representation of the parameters of the structure under test.

[0005] Modeling techniques are particularly useful when a sample has a periodic structure. Unfortunately, when a sample includes sections that are non-periodic, it can be difficult to analytically model the sample. Furthermore, when a sample includes one or more sections with unknown design and / or properties, i.e., when preliminary structural information about one or more sections is unknown or unavailable, it is not possible to build a rigorous model for the sample. Therefore, improved optical metrology processes that can be used to measure sample structures that include unknown and / or non-periodic sections are desirable. Summary of the Invention

[0006] An optical measurement of a sample including a structure-of-interest (SOI) optically coupled to a section having an unknown structure is optically measured using an influence map of the deviatoric contribution from the unknown structure. The influence map is generated by acquiring metrology data from a plurality of locations including the SOI and the unknown structure, and determining the deviatoric contribution at each location by separating the deviatoric contribution from the fundamental contribution from the SOI and the unknown structure. During measurement of a location, the deviatoric contribution associated with that location may be removed from the influence map and removed from the measured data. The processed data may be fitted with models including an exact model for the SOI and a valid model for the fundamental contribution of the unknown structure to determine one or more parameters of the SOI.

[0007] In one implementation, a method for generating an influence map for optical measurements of a sample includes acquiring metrology data from a plurality of locations, the acquired metrology data from each location being a combination of a first fundamental contribution from a structure of interest (SOI) having a known structure, a second fundamental contribution from a section having an unknown structure, and a deviation contribution from the section having the unknown structure. The method includes, for each location, determining a deviation contribution from the section having the unknown structure based on the metrology data from the plurality of locations. The method further includes storing, for each location of the plurality of locations, the deviation contribution and the associated location to generate an influence map of the section having the unknown structure.

[0008] In one implementation, a system for generating an influence map for optical measurements of a sample includes means for acquiring metrology data from a plurality of locations, the metrology data acquired from each location being a combination of a first fundamental contribution from a structure of interest (SOI) having a known structure, a second fundamental contribution from a section having an unknown structure, and a deviation contribution from the section having the unknown structure. The system may further include means for determining, for each location, a deviation contribution from the section having the unknown structure based on the metrology data from the plurality of locations. The system may further include means for storing in a memory the deviation contribution for each location of the plurality of locations and the associated location to generate an influence map of the section having the unknown structure.

[0009] In one implementation, a system for generating an influence map for optical measurements of a sample includes one or more processors and a memory coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations. The system is configured to, for example, acquire metrology data from a plurality of locations, the acquired metrology data from each location being a combination of a first fundamental contribution from a structure of interest (SOI) having a known structure, a second fundamental contribution from a section having an unknown structure, and a deviation contribution from the section having the unknown structure. For each location, the system is further configured to determine a deviation contribution from the section having the unknown structure based on the metrology data from the plurality of locations. The system is further configured to store in the memory the deviation contribution for each location of the plurality of locations and the associated location to generate an influence map of the section having the unknown structure.

[0010] In one implementation, a method of generating a model for optical measurements of a structure of interest (SOI) on a sample includes acquiring metrology data from different locations on the sample, each of the different locations on the sample including an SOI having a known structure and a section having an unknown structure that varies across the different locations, the metrology data acquired from each of the different locations includes a first fundamental contribution from the SOI having the known structure, a second fundamental contribution from the section having the unknown structure, and a deviation contribution from the section having the unknown structure, the deviation contribution from the section having the unknown structure varies for each of the different locations. The method includes acquiring an influence map for the sample including the deviation contribution associated with each of the different locations. The method includes generating a model for optical measurements of the SOI using the metrology data acquired from the different locations and the influence map, the model further including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section having the unknown structure without the deviation contribution.

[0011] In one implementation, a system for generating a model for optical measurements of a structure of interest (SOI) on a sample includes means for acquiring metrology data from different locations on the sample, each of the different locations on the sample includes an SOI having a known structure and a section having an unknown structure that varies across the different locations, and the metrology data acquired from each of the different locations includes a first fundamental contribution from the SOI having the known structure, a second fundamental contribution from the section having the unknown structure, and a deviation contribution from the section having the unknown structure, the deviation contribution from the section having the unknown structure varies for each of the different locations. The system may further include means for acquiring an influence map for the sample including the deviation contribution associated with each of the different locations. The system may further include means for generating a model for optical measurements of the SOI using the metrology data acquired from the different locations and the influence map, the model including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section having the unknown structure without the deviation contribution.

[0012] In one implementation, a system for generating a model for optical measurement of a structure of interest (SOI) on a sample includes one or more processors and a memory coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations. The system is configured to acquire metrology data from different locations on the sample, for example, each of the different locations on the sample includes an SOI having a known structure and a section having an unknown structure that varies across the different locations, and the metrology data acquired from each of the different locations includes a first fundamental contribution from the SOI having the known structure, a second fundamental contribution from the section having the unknown structure, and a deviation contribution from the section having the unknown structure, and the deviation contribution from the section having the unknown structure varies for each of the different locations. The system is further configured to acquire an influence map for the sample including the deviation contribution associated with each of the different locations. The system is further configured to generate a model for optical measurement of the SOI using the metrology data acquired from the different locations and the influence map, the model including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section having the unknown structure without the deviation contribution.

[0013] In one implementation, a method for optical measurement of a sample includes acquiring metrology data from locations on the sample, the metrology data being a combination of a first fundamental contribution from a structure of interest (SOI) having known parameters, a second fundamental contribution from a section having an unknown structure, and a deviational contribution from the section having the unknown structure. The method includes acquiring an influence map for the sample including the deviational contributions associated with a plurality of locations on the sample. The method further includes removing the deviational contributions associated with the locations from the metrology data acquired from the locations to generate processed metrology data including a combination of the first fundamental contribution from the SOI and the second fundamental contribution from the section having the unknown structure without the deviational contribution, and determining one or more parameters of the SOI using the processed metrology data and a model for optical measurement of the SOI including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section having the unknown structure without the deviational contribution.

[0014] In one implementation, a metrology device configured for optical measurement of a sample includes a light source generating light incident on the sample and a detector detecting light from the sample. The metrology device further includes means for acquiring metrology data from locations on the sample, the metrology data being a combination of a first fundamental contribution from a structure of interest (SOI) having known parameters, a second fundamental contribution from a section having an unknown structure, and a deviation contribution from the section having the unknown structure. The metrology device further includes means for acquiring an influence map for the sample including the deviation contributions associated with a plurality of locations on the sample. The metrology device further includes means for removing the deviation contributions associated with the locations from the metrology data acquired from the locations to generate processed metrology data including a combination of the first fundamental contribution from the SOI and the second fundamental contribution from the section having the unknown structure without the deviation contribution. The metrology device further includes means for determining one or more parameters of the SOI using the processed metrology data and a model for optical measurement of the SOI including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section having the unknown structure without the deviation contribution.

[0015] In one implementation, a metrology device configured for optical measurement of a sample includes a light source generating light incident on the sample, a detector detecting light from the sample, one or more processors coupled to the detector, and a memory coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations. The metrology device is configured to acquire metrology data from locations on the sample, for example, the metrology data being a combination of a first fundamental contribution from a structure of interest (SOI) having known parameters, a second fundamental contribution from a section having an unknown structure, and a deviation contribution from the section having the unknown structure. The metrology device is further configured to acquire an influence map for the sample including the deviation contributions associated with a plurality of locations on the sample. The metrology device is further configured to remove the deviation contributions associated with the locations from the metrology data acquired from the locations to generate processed metrology data including a combination of a first fundamental contribution from the SOI and a second fundamental contribution from the section having the unknown structure without the deviation contribution. The metrology device is further configured to determine one or more parameters of the SOI using the processed metrology data and a model for optical measurements of the SOI, the model including an exact model representing the SOI and a valid model representing a second fundamental contribution from the section having unknown structure without any deviation contribution. [Brief description of the drawings]

[0016] [Figure 1] 1 shows a side view of an example region of interest (ROI) of a sample, the region of interest including an SOI having a known structure with little or no variation across the ROI, and a section having an unknown structure that varies across the ROI. [Diagram 2] 1 shows a top view of a wafer including a die that includes an ROI. [Diagram 3] 3 is a graph showing an example of data that may be collected from multiple locations on an ROI within the die shown in FIG. 2. [Figure 4]1 illustrates a side view of an example region of interest (ROI) of a sample at wafer level and including an SOI with known structure and a section with unknown structure, where both the SOI and the unknown structure may vary across the wafer. [Diagram 5] 5 shows a top view of a wafer including multiple dies corresponding to the ROIs shown in FIG. 4. [Figure 6] FIG. 1 shows a schematic diagram of an optical metrology device that can be used to generate and use an effect map of an unknown section of a sample to measure one or more parameters of the SOI. [Figure 7] A process is shown for measuring a sample that includes an SOI having a known structure and a section having an unknown structure, the process including generating and using an influence map and building and using a validated model for the unknown structure. [Figure 8] A reference region of the sample containing the SOI and unknown structures is shown. [Figure 9] 1 shows multiple locations within an ROI from which measured data is collected, and a graph showing examples of measured data collected from multiple locations and isolating deviation contributions from the measured data for each location. [Figure 10A] An influence map is graphically shown for the deviation contribution associated with each location within the reference region. [Figure 10B] 13 graphically illustrates an influence map for the deviation contribution associated with each location within the reference region, generated by stitching together multiple influence maps from multiple locations within the reference region. [Figure 11] Examples of sample models are presented, including an exact model for SOI and a valid model for the fundamental contribution from the unknown structure. [Figure 12] 1 shows measured data collected from a measurement site on a sample during testing, and processed data after removing the deviation contribution of unknown structures from the measurement site. [Figure 13] 13 shows graphs with examples of fitting modeled data to processed data. [Figure 14] 1 is a flow chart illustrating a method for generating an influence map of an unknown structure for optical metrology of a sample. [Figure 15] 1 is a flowchart illustrating a method for generating a model for optical measurements of a structure of interest (SOI) on a sample. [Figure 16] 1 is a flow chart illustrating a method for optical measurement of a sample. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] During the manufacture of semiconductors and similar devices, it may be necessary to monitor the manufacturing process by measuring the devices non-destructively. Optical metrology can be used for non-contact evaluation of the sample during processing. Optical metrology techniques, such as thin film metrology and Optical Critical Dimension (OCD) metrology, can use modeling of the structure to generate predicted data that is compared to measured data from the sample. Variable parameters in the model, such as layer thickness, line width, space width, sidewall angle, etc., can be varied and predicted data is generated for each variation. The measured data can be compared to the predicted data for each parameter variation, for example in a non-linear regression process, until a good fit is achieved, at which point the value of the fitted parameter is determined to be an accurate representation of the parameter of the sample.

[0018] Traditionally, modeling requires that preliminary structural and material information be known about the sample in order to generate an accurate representative model of the sample, which may include one or more variable parameters. For example, the preliminary structural and material information for the sample may include a physical description of the sample with nominal values ​​for various parameters, such as layer thickness, line width, space width, sidewall angle, etc., along with the ranges within which these parameters may vary. The sample may further include one or more parameters that are not variable, i.e., are not expected to change in a significant amount during fabrication. If the structure and / or material of the SOI is unknown, i.e., if the preliminary structural and material information is unknown or unavailable, an accurate model cannot be generated.

[0019] Furthermore, modeling using techniques such as Rigorous Coupled Wave Analysis (RCWA) requires that the modeled structure is periodic. Using RCWA in a modeling engine is desirable because it is fast and efficient, with the requirement that the structure be periodic. Thus, modeling techniques that require periodicity, such as RCWA, are traditionally inappropriate for non-periodic structures. Other modeling techniques that do not require periodicity, such as Finite-Difference Time-Domain (FDTD) or Finite Element Method (FEM), can be used with non-periodic structures, but these techniques require rigorous modeling of the overall structure as well as nanometer-level positioning during measurement to ensure that the area being measured matches the area being modeled.

[0020] Thus, optical metrology using modeling may not be possible if structural (or material) information for one or more sections of the sample is unknown and / or the structure is non-periodic. As an example, the sample may include a structure of interest (SOI) that can be modeled that is optically coupled to a section of the sample with an unknown structure, e.g., preliminary structural and material information is unknown or unavailable, which may sometimes be referred to herein as an "unknown structure." Furthermore, the unknown structure may be non-periodic or periodic. For example, the SOI may be on an upper layer of the sample, whereas the unknown structure may be on an underlayer that is below the SOI. In other examples, the unknown structure may be above the SOI, or on the side of the SOI, or may be combined with the SOI, e.g., on the same layer, and mixed with the SOI. During optical measurement of the sample, light returning from the unknown structure may be coupled to light returning from the SOI in a complex manner that may be difficult to separate. Furthermore, the unknown structure cannot be rigorously modeled, for example, due to its unknown structure and possibly its non-periodicity, and therefore, a rigorous model for the combined SOI and unknown structure cannot be constructed. As a result, conventional optical metrology techniques for such samples are very challenging, since predicted data for the sample cannot be generated from a rigorous model to fit the measured data.

[0021] As described herein, optical metrology of a sample including an SOI and an unknown section can be performed by minimizing undesired / unknown effects on the measured data due to the unknown section. An effect map of deviation contributions associated with each location from multiple locations on a region of interest (ROI) can be generated using various processing techniques. The deviation contributions of the unknown section associated with the measurement site can then be removed from the measured data. Once the deviation contributions are removed, the processed data will be more dominated by the SOI, which can be modeled. Any remaining invariant contributions from the unknown section can be modeled using a valid model that can be combined with a rigorous model of the SOI.

[0022] 1 illustrates a side view of an example region of interest (ROI) 101 of a sample 100 that includes a SOI 102 having a known structure and a section 104 having an unknown structure, which may sometimes be referred to as an unknown structure 104. Although FIG. 1 illustrates the SOI 102 as being on top of the unknown structure 104, in some implementations the unknown structure 104 may be on top of the SOI 102, or to the side of the SOI 102 (e.g., on the same layer), or may be combined or intermixed with the SOI 102.

[0023] The SOI 102 has a known structure, i.e., preliminary structural and material information about the SOI 102 is available. Therefore, the SOI 102 can be rigorously modeled using RCWA (for periodic structures) or FDTD or FEM (for non-periodic structures). In contrast, the unknown structure 104 has an unknown structure, i.e., preliminary structural and material information is unknown or unavailable. The unknown structure 104 may be non-periodic across the ROI 101. Furthermore, the unknown structure 104 may vary within the ROI 101, as illustrated by the shaded variation of the unknown structure 104. As a result, as explained above, rigorous modeling of the unknown structure 104 is not possible.

[0024] As an example, in some implementations, the unknown structure 104 may be a circuit, such as a complementary metal-oxide-semiconductor (CMOS) circuit, or some other lower (upper) layer circuit, and the SOI 102 may be a repeating structure, such as a Vertical NAND (V-NAND) or Dynamic Random-Access Memory (DRAM) structure. Alternatively, the unknown structure 104 may be a structure including complex regions created in a previous manufacturing process step, whereas the SOI 102 is in a current manufacturing process step. In some implementations, the unknown structure 104 may be periodic or may have the same (or different) periodicity as the SOI 102, but the unknown structure 104 may have a different sensitivity to optical metrology than the SOI 102, e.g., a different sensitivity wavelength region, a different spectral sensitivity signature, etc.

[0025] The unknown structure 104 may vary within the ROI 101, but may be repeatable across corresponding ROIs on the sample. For example, the variation of the unknown structure 104 across different ROIs may be much smaller than the variation within a single ROI 101. As a result, the effect mapping of the unknown structure 104 is repeatable from ROI to ROI, so that a map taken from one ROI can be applied to other ROIs and to the wafer. In contrast, the variation in the SOI 102 across the ROI 101 is small, i.e., the SOI 102 is consistent within the ROI 101 due to the small in process variation within the ROI.

[0026] 2 shows a top view of a wafer 200 including multiple dies 202. The ROI 101 shown in FIG 1 may be at the die level, as illustrated by the shaded die 204 on the wafer 200. In some implementations, the ROI 101 is a region within a die.

[0027] Data collected from multiple locations on the same die 204 (or ROI 101 shown in FIG. 1) may vary significantly due to the influence of unknown structures 104 that vary across the ROI. By way of example, FIG. 2 illustrates that measurements may be collected across a high density scan of points on the die 204 (or ROI 101) to collect data from multiple locations.

[0028] As an example, FIG. 3 is a graph 300 illustrating an example of data that may be collected from multiple locations on the die 204 (or ROI 101) shown in FIG. 2. For example, the data in graph 300 shows spectral data for Mueller matrix (MM) elements from approximately 5,300 nm to 10,500 nm. Each curve shown in FIG. 3 represents MM spectral data for one location in the die 204 (or ROI 101). As can be seen, on the same die 204 (or ROI 101), the collected spectra show a large variation from location to location, especially in the shorter wavelength range, e.g., 5,300 nm to 7,800 nm. As explained above, there is little or no variation in the SOI 102 across the ROI 101, whereas the unknown structure 104 varies across the ROI 101, and thus the variation in the collected data from location to location in the ROI 101 is primarily due to the variation in the unknown structure 104 within the ROI 101. Although a single MM element is illustrated in FIG. 3, it should be understood that other MM elements and other types of metrology data, such as Psi & Delta data, ellipsometric data including Jones matrices, etc., reflectance measured data including reflectance collected at different polarizer angles and / or different angles of incidence (AOI), interferometric data including spectra in the frequency domain, Fourier-Transform Infrared Spectroscopy (FTIR) data, etc., may have similarly large variations across the ROI 101.

[0029] In some implementations, the ROI may be at the wafer level, consisting of single or multiple measurement sites from multiple dies on the wafer.

[0030] For example, Figure 4 shows a side view of an example ROI 401 of a sample 400, where the sample is at wafer level and includes SOIs 402a, 402b, and 402c (which may be collectively referred to as SOI 402) and unknown structures 404a, 404b, and 404c (which may be collectively referred to as unknown structures 404). Rather than a dense scan of the ROI as shown in Figure 2, measurements of ROI 401 at wafer level may be made at locations separate from multiple dies across the wafer, such as locations 406a, 406b, and 406c (which may be collectively referred to as locations 406). Because a scan is not performed across each location 406, each location 406 is shown as being narrower than ROI 101 shown in Figure 1.

[0031] FIG. 5 shows a top view of a wafer 500 including multiple dies 502, where the ROI 401 shown in FIG. 4 is at wafer level, as indicated by the variation of shaded dies 504a, 504b, and 504c, which correspond to locations 406a, 406b, and 406c shown in FIG. 4.

[0032] Both the SOI 402 and the unknown structure 404 may have variations across the wafer-level ROI 401, but those variations may be in different wavelength regions. Thus, in wavelength regions where the unknown structure 404 has a large variation, the SOI may have a small variation, and therefore it is possible to eliminate undesired effects from the unknown structure 404 in the measurement of the SOI 402. In some implementations, the unknown structure 404 may be a non-periodic circuit, such as a CMOS circuit, or some other lower (upper) layer circuit, and the SOI 402 may be a periodic structure, such as a V-NAND or DRAM structure. In other implementations, the unknown structure 404 may be periodic and may have the same (or different) periodicity as the SOI 402. For example, the sample may be a Chemical Mechanical Polishing (CMP) layer or some other back layer, where the interest is to measure parameters of the SOI 402 in the top region and minimize undesired effects from the unknown structure 404 in the bottom region. Alternatively, unknown structure 404 may be a structure containing complex regions created in a previous manufacturing process step, whereas SOI 402 is in a current manufacturing process step.

[0033] Data collected from multiple locations across the wafer 500 (or ROI 401), for example, different dies 504a, 504b, and 504c, may vary significantly due to variations in both the SOI 402 and the unknown structures 404 across the ROI 401.

[0034] 6 shows a schematic diagram of an optical metrology device 600 that can be used to generate and use an influence map of deviation contributions from an unknown structure of a sample to measure one or more parameters of the SOI, as described herein. The optical metrology device 600 can be configured to perform, for example, spectroscopic reflectometry, spectroscopic ellipsometry, interferometry, or FTIR measurements of a sample 601 that includes an SOI having a known structure and a section having an unknown structure optically coupled to the SOI. It should be understood that the optical metrology device 600 is shown as one example of a metrology device that can generate and / or use an influence map as described herein, and that other metrology devices, including normal incidence devices, non-polarized devices, etc., can be used as desired.

[0035] The optical measurement device 600 includes a light source 610 that generates light 602. The light 602 is, for example, ultraviolet-visible light having a wavelength such as 200 nm to 800 nm. The light 602 generated by the light source 610 may include a range of wavelengths, i.e., broadband, or may be monochromatic light. The optical measurement device 600 includes collection optics 620 and 630 that collect and receive the light and direct the light obliquely incident on a top surface of the sample 601. The optics 620, 630 may be refractive, reflective, or a combination thereof, and may be objective lenses.

[0036] The reflected light may be collected by a lens 614 and received by a detector 650. The detector 650 may be a conventional charge coupled device (CCD), photodiode array, CMOS, or similar type detector. The detector 650 may be a spectrometer, for example, if broadband light is used, and the detector 650 may generate a spectral signal as a function of wavelength. A spectrometer may be used to disperse the entire spectrum of polarized light into spectral components across an array of detector pixels. There may be one or more polarizing elements in the beam path of the optical metrology device 600. For example, the optical metrology device 600 may include a polarizing element 604 in the beam path before the sample 601 and / or a polarizing element (analyzer) 612 in the beam path after the sample 601 (or none at all), and may include one or more additional elements such as a compensator or a photoelastic modulator 605.

[0037] The detector 650 may be coupled to at least one processor 660, which may be a workstation, a personal computer, or other suitable computer system, or multiple systems. It should be understood that one processor, multiple separate processors, or multiple coupled processors may be used, all of which may be referred to interchangeably herein as at least one processor 660, one or more processors 660, or simply processor 660. The at least one processor 660 is preferably included in or connected to or otherwise associated with the optical metrology device 600. For example, the at least one processor 660 may control the positioning of the sample 601, for example, by controlling the movement of a stage 609 coupled to a chuck. For example, the stage 609 may allow horizontal movement in either Cartesian (i.e., X and Y) coordinates, or polar (i.e., R and θ) coordinates, or some combination of the two. The stage may also be capable of vertical movement along the Z coordinate. At least one processor 660 may further control the operation of the chuck 608 to further hold or release the specimen 601. At least one processor 660 may also collect and analyze data obtained from the detector 650. At least one processor 660 may analyze the data to generate an influence map for deviation contributions from unknown structures of the specimen, generate a model for the specimen, including an exact model for the SOI and a valid model for the unknown structures, and / or use the influence map and model to measure one or more parameters of the specimen 601 as described herein. For example, at least one processor 660 may collect scanned data from the specimen, process the data to determine and store deviation contributions from unknown structures for each scanned location, and generate an influence map. At least one processor 660 may be used to generate and store a model of the specimen, including an exact model for the SOI and a valid model for the unknown structures, after deviation contributions have been removed.At least one processor 660 can further be used to process measured data from measurement sites on the sample using the influence map to remove deviation contributions from unknown structures at the measurement sites, and compare the processed data to predicted data generated using a model of the sample, including an exact model of the SOI and a valid model for the unknown structure. The predicted data for variations in the model parameters can be compared to the measured data, for example in a nonlinear regression process, until a good fit is achieved between the predicted and measured data, at which point the fitted parameters are determined to accurately describe the parameters of the SOI.

[0038] At least one processor 660 having memory 664 includes at least one processing unit 662, such as a central processing unit, and a user interface including, for example, a display 666 and input devices 668. A non-transitory computer usable storage medium 669 has computer readable program code embodied thereon that, when executed by the at least one processor 660, causes the at least one processor 660 to control the optical metrology device 600 and perform functions including the analysis described herein. Data structures and software code for automatically implementing one or more operations described in this detailed description may be implemented by one of ordinary skill in the art in view of this disclosure and may be stored, for example, on a computer usable storage medium 669, which may be any device or medium capable of storing code and / or data for use by a computer system, such as the processing unit 662. The computer usable storage medium 669 may be, but is not limited to, a disk drive, magnetic tape, compact disk, and flash drive, magnetic storage device, and optical storage device, such as a DVD (digital versatile disk or digital video disk). The communication port 667 may also be used to receive instructions used to program the at least one processor 660 to perform any one or more of the functions described herein, and may represent any type of communication connection, such as with the Internet or any other computer network. The communication port 667 may further export, for example, a signal with the measurement results and / or instructions to another system, such as an external process tool, in a feedforward or feedback process to adjust a process parameter associated with a manufacturing process step of the sample based on the measurement results.Additionally, the functionality described herein may be embodied in whole or in part within the circuitry of an application specific integrated circuit (ASIC) or programmable logic device (PLD), and the functionality may be embodied in a computer understandable description language that may be used to create an ASIC or PLD that operates as described herein. Results from the analysis of the data may be reported, for example, stored in memory 664 associated with the specimen 601, and / or indicated to a user via a display 666, alarm, or other output device. Additionally, the results of the analysis may be reported and fed forward or fed back to process equipment to adjust appropriate manufacturing steps to compensate for any variations detected in the manufacturing process.

[0039] For example, as shown in Figures 1 and 4, a sample including an SOI and an unknown structure can be measured by minimizing the undesired / unknown effect of the unknown structure on the measured data, for example, using an optical metrology device as shown in Figure 6. For example, an effect map of deviation contributions associated with multiple locations within the ROI of the sample can be generated using various data processing techniques. Even with the deviation contributions due to the unknown structure removed from the measured data, the fundamental contributions from the unknown structure will remain in the measured data. The fundamental contributions due to the unknown structure can be modeled using a validated model combined with a rigorous model for the SOI.

[0040] Influence maps for unknown structures that may be useful in measurements of SOI across multiple ROIs on a wafer, and across multiple wafers, may be particularly useful if the unknown structure is repeatable from ROI to ROI, in accordance with the first condition (Condition I). Thus, an influence map extracted from one ROI can be applied to other ROIs on the same wafer and / or other wafers.

[0041] When the ROI is at the die level, the SOI may have little or no intra-ROI process variation during manufacturing, i.e., the SOI is consistent within each ROI, whereas the unknown structure may have large intra-ROI variation, as shown in FIG. 1. After subtracting the deviation contribution taken from the unknown structure's influence map from the measured data, the remaining data should be consistent within the ROI. Therefore, this condition (Condition II) can be used to verify whether the extracted influence map of the unknown structure is accurate. When the ROI is at the wafer level, the SOI and the unknown structure may both have variation across the ROI and generate deviation contributions to the measured data. When the deviation contributions in the measured data for the SOI and the unknown structure are in different wavelength regions, for example, the unknown structure generates a large deviation contribution in a particular wavelength region where the SOI generates little deviation contribution, it is possible to remove the effect of the unknown structure in the measured data by subtracting the deviation contribution from the unknown structure across a particular wavelength, taken from the unknown structure's influence map. Condition II can also be used to verify the accuracy of influence maps extracted from wavelength regions where the unknown structure has large variations while the SOI has little variation.

[0042] FIG. 7 shows a flowchart 700 of a process for measuring a sample that includes an SOI having a known structure and a section having an unknown structure, for example, by generating and using an influence map, as shown in FIGS. 1 and 4, and building and using a valid model for the unknown structure.

[0043] As shown in block 702, data is collected from a scan of the sample at multiple sites within a reference region that represents variations in the unknown structure. By way of example, Figure 8 shows a reference region, e.g., ROI 801, of a sample 800 including an SOI 802 and an unknown structure 804, similar to that shown in Figure 1. Thus, the reference region may be an ROI within a single die, e.g., as shown in Figure 2, or may be an ROI across an entire wafer, e.g., as shown in Figures 4 and 5.

[0044] The data illustrated in FIG. 8 may be collected in a high density scan over the ROI 801, or may be collected over an ROI that includes the entire wafer, as shown in FIGS.

[0045] As an example, FIG. 9 illustrates multiple locations 902 within the ROI 801 from which measured data is collected. As an example, graph 910 is similar to graph 300 illustrated in FIG. 3 and illustrates an example of data that may be collected from locations 902 within the ROI 801. Graph 910 illustrates MM spectral data, where each curve represents spectral data collected from a different location. For ease of reference, the collected data may sometimes be referred to herein as spectral data. However, it should be understood that the collected data may be ellipsometric data, reflectometry data, interferometric data, FTIR data, or any other type of data that can be used to measure parameters of a sample via modeling.

[0046] The optical data collected from each location in the reference region is a complex combination of the response from the SOI and the unknown structure. The signal from the unknown structure is coupled with the signal of the SOI in a complex way that is difficult to resolve. For example, assuming spectral data is collected from each site, the overall spectrum (S) measured from each site is a function of the response from the SOI (S SOI ) and the response from the unknown structure (S UNK), and thus the spectrum (S) can be expressed as the following simple formula, where the symbol "+" indicates combination:

number

[0047] The components of the measured spectrum can be decomposed from the fundamental spectrum into a fundamental contribution (S0) and a deviation contribution (dS). The fundamental contribution S0 is constant within the ROI, whereas the deviation contribution dS varies from site to site within the ROI. Thus, Equation 1 can be written as follows:

number

[0048] As explained above with respect to condition II, when the ROI is at the die level, the ROI variation within the SOI is negligible. Furthermore, the SOI may have variation across the ROI, but when the ROI is at the wafer level, the deviation contribution dS generated by the SOI can be limited to a specific wavelength region that can be excluded from the influence map. Therefore, the deviation contribution dS component to the SOI can be assumed to be zero. Furthermore, as explained above with respect to condition I, the unknown structure varies across the ROI, and therefore the variation within the ROI in the measured data will vary primarily due to the deviation contribution dS component from the unknown structure. The fundamental contribution component (S 0_SOI ) and the fundamental contribution generated by the unknown structure (S 0_UNK ) can be combined, and equation 2 can be rewritten as follows:

number

[0049] As shown in FIG. 8, SOI802 is the fundamental contribution component of the data S 0_SOI, where the fundamental contribution component does not vary across the ROI 801 if the ROI is at die level, and may not vary across a particular wavelength range if the ROI is at wafer level. The unknown structure 804 also contributes to the fundamental contribution component S of the data that does not vary across the ROI. 0_UNK However, due to variations in the unknown structure 804 across the ROI 801, each location (x,y) within the ROI 801 at which data is collected contributes a different deviation contribution component dS UNK_x,y which is contributed by the variation of the unknown structure 804 across the ROI 801 and has a fundamental component S 0_UNK Therefore, the data collected from the sample are mainly composed of the deviation contribution dS UNK_x,y Due to the variation in x,y The data collected every can be written as follows:

number

[0050] At block 704, the deviation contribution dS at each location in the reference region is calculated. UNK_x,y is the collected scan data S x,y For each location, the deviation contribution dS from the unknown structure is determined so that only the fundamental contribution S from the SOI and the unknown structure remains. UNK_x,y Therefore, in block 704, the deviation contribution dS for each location where data is collected is determined and removed. UNK_x,y is calculated by subtracting the deviation contribution dS from the base contribution S at each location (x, y) using at least one of, for example, Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), etc., or a combination thereof, as indicated by arrow 915 in FIG. UNK_x,y This can be determined by separating the

[0051] For example, using PCA with the collected scan data can generate several principal components (PCs), each with an associated score. One or more dominant PCs with corresponding scores for these PCs can be used to calculate the deviation contribution dS, for example based on the sum of the dominant PCs multiplied by their corresponding scores. UNK_x,y For example, the rank of a PC can be chosen to represent the deviation contribution dS UNK_x,y For example, the N highest ranked PCs may be considered to be dominant PCs, where N may be 5, 10, 15, etc. In another implementation, the highest ranked X percent of PCs may be considered to be dominant PCs, where X may be 10%, 20%, 30%, etc. If desired, dominant PCs may be determined in another manner, such as based on PCs having weights above a predetermined threshold, etc. Dominant PCs are determined based on the deviation contribution dS from the unknown structure for each location. UNK_x,y In another example, the deviation contribution dS UNK_x,y One or more of the PCs used to represent dS may be selected based on criteria other than highest rank. For example, a PC that is more highly correlated with the unknown structure and less correlated with the SOI may have a relatively low rank, or a PC with a high rank may be more highly correlated with the SOI and less correlated with the unknown structure. Thus, one or more PCs may be selected based on their correlation to the unknown structure, or other similar criteria, to represent the deviation contribution dS UNK_x,y may be selected to represent

[0052] In some implementations, the deviation contribution dS from the base contribution S at each location (x,y) UNK_x,yThe analysis to separate the deviance contributions may be over a limited data range that is sensitive to the unknown structure, for example. For example, as shown in FIG. 9, the collected data is sensitive to the unknown structure over a limited wavelength range of 5,300 nm to 7,800 nm, and therefore the processing of the collected data to separate the deviance contributions may be limited to that wavelength range. An algorithm other than PCA, such as ICA, PLS, or some combination of algorithms, may be used to separate the deviance contribution dS from the fundamental contribution S at each location (x,y). UNK_x,y It should be understood that the above may also be separated.

[0053] As an example, graph 920 in FIG. 9 shows UNK_x,y The deviance contributions from the unknown structure associated with the dominant PCs for S are shown in Fig. 1. The deviance contributions associated with the dominant PCs are x,y By removing the deviation contribution dS from the ROI, data with little or no variation across the ROI is generated, i.e., the data UNK_x,y With little or no contribution from the SOI 802 and the unknown structure 804 (S0=S 0_SOI +S 0_UNK ).

[0054] In block 706, the deviation contributions dS for the multiple locations are calculated. UNK_x,y The deviation contribution dS associated with each location (x,y) within a reference region, i.e., ROI, is stored in a library or lookup table, sometimes called an influence map. UNK_x,y 10A, where the shading in the impact map 1000 represents different deviation contributions dS UNK In some implementations, the single influence map specifically represents the deviation contribution dS UNK_x,y If dS has large discontinuities or gaps between sub-regions, e.g., locations, within the ROI, then the total deviation contribution dS UNK_x,y10B shows an influence map 1050 that includes a first influence map 1052 for a first location within the ROI and a second influence map 1054 for a second location within the ROI, where the first influence map 1052 and the second influence map 1054 are generated independently and stitched together.

[0055] Further, in some implementations, an impact map, such as the impact map 1000 shown in FIG. 10A, may be generated based on a statistical combination of several independently generated impact maps. For example, separate impact maps may be generated based on data collected from multiple ROIs, e.g., from different dies on a wafer and / or from different wafers. The separate impact maps may then be combined together to generate a single impact map 1000, e.g., by averaging the separate impact maps or summing the separate impact maps with different weights. By generating multiple impact maps that are then statistically combined to generate a single impact map, the resulting impact map may account for manufacturing variations of the unknown structure that may occur across different dies on a wafer and / or across different wafers. In some implementations, the data collected in block 702 may be from corresponding locations in multiple separate reference regions and may be used together to generate the single impact map 1000. In other implementations, process conditions for the unknown structure may vary significantly across different ROIs, e.g., designs for the unknown structure may vary from wafer to wafer. In this case, a separate impact map may be generated based on the data collected from each ROI, and the impact maps of the multiple ROIs may be stored in a library or look-up table, and when measurements are taken, each separate impact map will be used for the measurement site corresponding to the ROI for which the impact map is generated.

[0056] In block 708, a rigorous model for the SOI 802 and the fundamental contribution component S of the unknown structure 804 are 0_UNK A model is generated for the sample, including a valid model for SOI 802 and a valid model for the signal S. A rigorous model for SOI 802 is an accurate representation of the physical structure of SOI 802 having one or more variable parameters. In contrast, a valid model for unknown structure 804 does not attempt to accurately represent the physical structure of unknown structure 804 (which is not known), but instead is an accurate representation of the fundamental component S of the signal. 0_UNK It is possible to generate a deviation contribution dS UNK A valid model may be generated, for example, based on an approximation of the unknown structure 804 with one or more variable parameters. UNK is removed from the measured data S at each location to obtain the total fundamental component, i.e., S 0_SOI +S 0_UNK can be generated, which can be fitted with predicted data from an exact model of the SOI 802 and a validated model of the unknown structure 804. The structure, material, and variable parameters of the validated model can be varied until an acceptable fit can be achieved.

[0057] As an example, FIG. 11 shows an example of a model 1100 of the sample 800 shown in FIG. 8, which includes a rigorous model 1102 for the SOI 802 and a fundamental contribution component S 0_UNK Thus, when predicted data S is generated using the model 1100, the exact model 1102 for SOI 802 includes a valid model 1104 for the fundamental components S of the predicted data. 0_SOI whereas a valid model 1104 for the unknown structure 804 contributes to the fundamental component S 0_UNK The fundamental contribution components do not change across the ROI 801, and therefore the predicted data S is the sum of the combined fundamental components, i.e., S=S0=S 0_SOI +S 0_UNK It is.

[0058] The influence map for the ROI and the model of the sample generated (the exact model for the SOI and the fundamental contribution component S of the unknown structure) 0_UNK Using a method for generating an effect map and model of the sample, one or more locations of the sample within a corresponding ROI, e.g., encompassing the SOI and the unknown structure, can be optically measured. For example, the measured sample can be on the same wafer used to generate the effect map and model of the sample, or can be on a different wafer.

[0059] 7, for example, measured data may be obtained from a measurement site on the specimen under test. The measured data may be obtained, for example, using the same or a similar measurement device as used to collect the data in block 702. Further, the measured data is collected from a measurement site that corresponds to a location within the ROI where the data was collected in block 702 and is included in the influence map.

[0060] For example, Figure 12 shows a graph 1210 of measured data that may be obtained from a measurement site on a sample under test. By way of example, graph 1210 is similar to graph 910 shown in Figure 9 and graph 300 shown in Figure 3, but shows an example of spectral data for Mueller matrix elements that may be collected from a single location (x1, y1) on the sample. As explained above, the data collected at location (x1, y1) represents a response signal (S) from the SOI. 0_SOI ) and the response signal (S 0_UNK +dS UNK_x1,y1 ), and can be written as follows:

number

[0061] While FIG. 12 shows the collected data as a single MM element, it should be understood that the metrology data may be ellipsometric data such as Psi & Delta data, Jones matrices, etc., reflectometry data such as reflectance collected at different polarizer angles and / or different angles of incidence (AOI), interferometric data such as spectra in the frequency domain, FTIR data, etc., and the collected data should be the same as that used to generate the effect map, e.g., blocks 704 and 706 of FIG. 7.

[0062] In block 712 of Figure 7, the deviation contribution associated with the measurement site, i.e., the measurement site at location (x1, y1), is removed from the collected measured data. For example, the deviation contribution associated with location (x1, y1) may be obtained from the influence map generated in blocks 704 and 706 of Figure 7. The deviation contribution may be removed from the collected measured data, for example, by subtracting the deviation contribution from the collected measured data. If desired, other processing techniques can be used to remove the deviation contribution from the collected measured data.

[0063] As an example, FIG. 12 illustrates, using arrow 1215, the deviation contribution dS UNK_x1,y1 12 shows the processing of the collected data to remove the deviation contribution dS UNK_x1,y1 has been removed and it can be written as

number

[0064] In block 714 of FIG. 7, modeled data generated from the rigorous model and the validated model are fitted to the processed data to determine one or more parameters for the sample. For example, the modeled data may include a rigorous model of the SOI 802 and a fundamental contribution component S of the unknown structure 804 obtained from block 708 of FIG. 0_UNK Therefore, the modeled data S modeled is the basic component S for SOI802 0_SOI_Rigorous The modeled data for and the fundamental component S for the unknown structure 804 are 0_UNK_Effective For example, the modeled data S modeled may be generated from a model of the specimen using RCWA, FDTD, FEM, etc.

[0065] As an example, Fig. 13 shows the modeled data S modeled The processed data S processed_x1,y1 13 shows a graph 1310 including an exemplary fit to

[0066] For example, the modeled data S modeled is the fundamental contribution component S 0_UNK A valid model for S is generated for different variable parameter values, which may be stored in a library or calculated in real time. processed_x1,y1 is the modeled data S stored in the library. modeled or may be determined in real time to find the best fit, for example, based on Mean-Squared Error (MSE). Once the best fit is found, the values ​​of the variable parameters of the exact model of the SOI 802 can be assumed to accurately describe the SOI under test.

[0067] If additional measurement sites may be measured in block 716, then the process returns to block 710 for the new measurement locations on the sample, e.g., after moving the sample and / or the optics of the metrology device to measure the new locations, and the process is repeated. If there are no additional measurement sites to be measured, then in block 718, the measurement parameters for the measurement sites may be reported, e.g., as indicated by block 1320 of Figure 13. As discussed above, reporting the measurement parameters may include storing the results, providing an indication of the results to a user, e.g., via a display, alarm, etc., or feeding forward or feeding back the results to adjust the appropriate manufacturing process.

[0068] FIG. 14 is a flowchart 1400 illustrating a method for generating an influence map for optical measurements of a sample, as described herein.

[0069] In block 1402, metrology data is acquired from a plurality of locations, the metrology data acquired from each location being a combination of a first fundamental contribution from a structure of interest (SOI) having known structure, a second fundamental contribution from a section having unknown structure, and a deviation contribution from a section having unknown structure, e.g., as described in block 702 of FIG. 7 and with reference to FIGS. 8 and 9. For example, the metrology data may include ellipsometry data, reflectometry data, interferometry data, FTIR data, or a combination thereof. As an example, in some implementations, the plurality of locations may be within a region of interest (ROI), and the deviation contribution from the section having unknown structure varies across the ROI, e.g., as described with reference to FIGS. 8 and 9. For example, the ROI may be within a die on a wafer, or in another example, the ROI may be one or more measurement sites from multiple dies across a wafer. For example, the section having unknown structure may be in an area under the SOI, on top of the SOI, or outside the SOI, or any combination thereof. A means for acquiring metrology data from a plurality of locations, where the metrology data acquired from each location is a combination of a first fundamental contribution from a structure of interest (SOI) having known structure, a second fundamental contribution from a section having unknown structure, and a deviation contribution from the section having unknown structure, the means may be, for example, an optical metrology device 600 shown in FIG. 6 or other similar device.

[0070] In block 1404, for each location, the deviation contribution from the section having unknown structure is determined based on the measurement data from the multiple locations, e.g., as described in block 704 of FIG. 7 and with reference to FIG. 9. Means for determining the deviation contribution from the section having unknown structure for each location based on the measurement data from the multiple locations may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in FIG. 6. In some implementations, determining the deviation contribution from the section having unknown structure for each location based on the measurement data from the multiple locations may include separating the deviation contribution from a combination of a first fundamental contribution and a second fundamental contribution. For example, separating the deviation contribution from a combination of a first fundamental contribution and a second fundamental contribution may include using at least one of principal component analysis (PCA), independent component analysis (ICA), partial least squares (PLS), or a combination thereof, with the measurement data obtained from the multiple locations. The means for separating the deviation contribution from the combination of the first and second fundamental contributions using at least one of principal component analysis (PCA), independent component analysis (ICA), partial least squares (PLS), or combinations thereof, together with the measurement data obtained from multiple locations, may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer-usable storage medium 669 shown in FIG. 6 .

[0071] In block 1406, the deviation contribution and associated location for each location of the plurality of locations are stored to generate an influence map of the section having unknown structure, e.g., as described in block 706 and with reference to Figures 10A and 10B. Means for storing in memory the deviation contribution and associated location for each location of the plurality of locations to generate an influence map of the section having unknown structure may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in Figure 6.

[0072] In some implementations, the multiple locations may be within multiple regions of interest (ROIs). Further, an impact map may be determined for each ROI, and the impact maps for each ROI may be combined to form an impact map, for example, as described with reference to FIG. 10A. The means for determining an impact map for each ROI and the means for combining the impact maps for each ROI may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in FIG. 6. Alternatively, an impact map may be determined for each ROI, and the impact maps for the multiple ROIs may be stored in a library or look-up table. The means for determining an impact map for each ROI and the means for storing the impact maps for the multiple ROIs in a library or look-up table may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in FIG. 6. For example, each individual impact map may be used for the measurement site corresponding to the ROI for which that impact map is determined. For example, multiple ROIs may be located across multiple dies of the same wafer, multiple wafers, or a combination thereof.

[0073] In some implementations, the multiple locations may be within a same ROI, and an impact map for each location within the multiple locations may be determined, and the impact maps for the multiple locations may be stitched together, for example, as described with reference to Figure 10B. The means for determining an impact map for each location of the multiple locations within the ROI and the means for stitching together the impact maps for the multiple locations may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in Figure 6.

[0074] FIG. 15 is a flowchart 1500 illustrating a method for generating a model for optical measurement of a structure of interest (SOI) on a sample, as described herein.

[0075] In block 1502, metrology data is acquired from different locations on the sample, each of the different locations on the sample including a SOI having a known structure and a section having an unknown structure that varies across the different locations, and the metrology data acquired from each of the different locations includes a first fundamental contribution from the SOI having the known structure, a second fundamental contribution from the section having the unknown structure, and a deviation contribution from the section having the unknown structure, where the deviation contribution from the section having the unknown structure varies for each of the different locations, e.g., as described in blocks 702, 704, and 706 of FIG. 7 and with reference to FIGS. 8, 9, 10A, and 10B. For example, the metrology data may include ellipsometric data, reflectometry data, interferometric data, FTIR data, or a combination thereof. By way of example, in some implementations, the multiple locations may be within a region of interest (ROI), and the deviation contribution from the section having the unknown structure varies across the ROI, e.g., as described with reference to FIGS. 8 and 9. For example, the ROI may be within a die on a wafer, or in another example, the ROI may be a wafer and the multiple locations span the wafer. For example, the section with unknown structure may be in an area under an SOI, on top of an SOI, or outside of an SOI, or any combination thereof. The means for acquiring metrology data from different locations on a sample, each of the different locations on the sample including an SOI with known structure and a section with unknown structure that varies across the different locations, the metrology data acquired from each of the different locations including a first fundamental contribution from the SOI with known structure, a second fundamental contribution from the section with unknown structure, and a deviation contribution from the section with unknown structure, the deviation contribution from the section with unknown structure varying for each of the different locations, may be, for example, an optical metrology device 600 shown in FIG. 6 or other similar device.

[0076] At block 1504, an influence map for the sample is obtained, the influence map including the deviation contribution associated with each of the different locations, e.g., as described in blocks 702, 704, and 706 of Figure 7 and with reference to Figures 8, 9, 10A, 10B. Means for obtaining an influence map for the sample including the deviation contribution associated with each of the different locations may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in Figure 6.

[0077] In block 1506, a model for optical measurement of the SOI is generated using the measurement data acquired from the different locations and the influence map. The model includes, for example, an exact model representing the SOI and a valid model representing the second fundamental contribution from the section with unknown structure without deviation contribution, as described in block 708 and with reference to FIG. 11. The means for generating a model for optical measurement of the SOI using the measurement data acquired from the different locations and the influence map, the model including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section with unknown structure without deviation contribution, may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in FIG. 6.

[0078] In some implementations, a model for optical measurements of the SOI may be generated by removing the deviation contribution from the measurement data from each of the different locations to generate processed measurement data including a combination of the first fundamental contribution of the SOI and the second fundamental contribution from the section with the unknown structure without the deviation contribution. A valid model is developed based on the processed measurement data, for example, as described in block 708 and with reference to FIG. 11. The means for removing the deviation contribution from the measurement data obtained from each of the different locations to generate processed measurement data including a combination of the first fundamental contribution from the SOI and the second fundamental contribution from the section with the unknown structure without the deviation contribution may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in the memory 664 or the non-transitory computer usable storage medium 669 shown in FIG. 6. The means for developing a model based on the processed measurement data may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in the memory 664 or the non-transitory computer usable storage medium 669 shown in FIG. 6.

[0079] FIG. 16 is a flow chart 1600 illustrating a method for optical measurement of a sample, as described herein.

[0080] In block 1602, metrology data from locations on the sample is obtained, the metrology data being a combination of a first fundamental contribution from a structure of interest (SOI) having known parameters, a second fundamental contribution from a section having unknown structure, and a deviation contribution from a section having unknown structure, e.g., as described in block 710 of FIG. 7 and with reference to FIG. 12. For example, the metrology data may include ellipsometry data, reflectometry data, interferometry data, FTIR data, or a combination thereof. As an example, in some implementations, the multiple locations may be within a region of interest (ROI), and the deviation contribution from the section having unknown structure varies across the ROI, e.g., as described with reference to FIG. 8 and FIG. 9. For example, the ROI may be within a die on a wafer, or in another example, the ROI may be a wafer, and the multiple locations may span the wafer. For example, the section having unknown structure may be in an area under the SOI, in an area on top of the SOI, or in an area outside the SOI, or any combination thereof. A means for acquiring metrology data from a location on a sample, the metrology data being a combination of a first fundamental contribution from a structure of interest (SOI) having known parameters, a second fundamental contribution from a section having unknown structure, and a deviation contribution from the section having unknown structure, the means may be, for example, an optical metrology device 600 or other similar device having one or more processors 660 equipped with dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer-usable storage medium 669 shown in FIG. 6 .

[0081] In block 1604, an affect map for the sample is obtained, including deviation contributions associated with a plurality of locations on the sample, e.g., as described in blocks 702, 704, and 706 of Figure 7 and with reference to Figures 8, 9, 10A, 10B. The means for obtaining an affect map for the sample, including deviation contributions associated with a plurality of locations on the sample, may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in Figure 6.

[0082] In block 1606, the process includes removing the deviation contribution associated with the location from the measurement data acquired from the location to generate processed measurement data including a combination of the first elementary contribution from the SOI and the second elementary contribution from the section having unknown structure without the deviation contribution, e.g., as described in block 712 of Figure 7 and with reference to Figure 12. The means for removing the deviation contribution associated with the location from the measurement data acquired from the location to generate processed measurement data including a combination of the first elementary contribution from the SOI and the second elementary contribution from the section having unknown structure without the deviation contribution may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in Figure 6.

[0083] In block 1608, one or more parameters of the SOI are determined using the processed metrology data and a model for optical measurement of the SOI, including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section with unknown structure without the deviation contribution, e.g., as described in block 714 of Fig. 7 and with reference to Fig. 13. The means for determining one or more parameters of the SOI using the processed metrology data and a model for optical measurement of the SOI, including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section with unknown structure without the deviation contribution, may be, for example, one or more processors 660 comprising dedicated hardware or implementing executable code or software instructions in memory 664 or non-transitory computer usable storage medium 669 shown in Fig. 6.

[0084] The above description is intended to be illustrative and not limiting. For example, the above examples (or one or more aspects thereof) may be used in combination with each other. Other implementations may be used, for example, by one of ordinary skill in the art upon reviewing the above description. Also, various features may be grouped together, and fewer than all features of a particular disclosed implementation may be used. Thus, the following aspects are hereby incorporated into the above description as examples or implementations, and it is contemplated that each aspect stands on its own as a separate implementation, and that such implementations may be combined with each other in various combinations or permutations. Thus, the spirit and scope of the appended claims should not be limited to the foregoing description.

Claims

1. 1. A method for optical measurement of a sample, said method comprising: acquiring metrology data from locations on the sample, the metrology data being a combination of a first fundamental contribution from a structure of interest (SOI) having known parameters, a second fundamental contribution from a section having unknown structure, and a deviation contribution from the section having unknown structure; obtaining an influence map for the sample, the influence map including the deviation contributions associated with a plurality of locations on the sample; removing the deviation contribution associated with the location from the metrology data acquired from the location to generate processed metrology data including the combination of the first fundamental contribution from the SOI and the second fundamental contribution from the section having unknown structure without the deviation contribution; determining one or more parameters of the SOI using the processed metrology data and a model for optical measurement of the SOI, the model including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section having unknown structure without the deviation contribution.

2. 2. The method of claim 1, wherein the locations on the sample in the influence map are within a region of interest (ROI), and the deviation contribution from the section having unknown structure varies across the ROI.

3. The method of claim 2 , wherein the ROI is within a die on a wafer.

4. The method of claim 2 , wherein the ROI consists of one or more measurement sites from multiple dies across a wafer.

5. The method of claim 1 , wherein the section having unknown structure is in an area below the SOI, in an area above the SOI, or in an area outside the SOI.

6. The method of claim 1 , wherein the metrology data comprises at least one of ellipsometry data, reflectometry data, interferometry data, Fourier transform infrared spectroscopy (FTIR) data, or a combination thereof.

7. 1. A metrology device configured for optical measurement of a sample, comprising: a light source that generates light for incidence on the sample; a detector for detecting the light from the sample; means for acquiring metrology data from locations on the sample, the metrology data being a combination of a first fundamental contribution from a structure of interest (SOI) having known parameters, a second fundamental contribution from a section having unknown structure, and a deviation contribution from the section having unknown structure; and means for obtaining an influence map for the specimen, the influence map including the deviation contributions associated with a plurality of locations on the specimen; means for removing the deviation contribution associated with the location from the metrology data acquired from the location to generate processed metrology data including the combination of the first fundamental contribution from the SOI and the second fundamental contribution from the section having unknown structure without the deviation contribution; and means for determining one or more parameters of the SOI using the processed metrology data and a model for optical measurement of the SOI, the model including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section having unknown structure without the deviation contribution.

8. 8. The metrology device of claim 7, wherein the locations on the sample in the influence map are within a region of interest (ROI), and the deviation contribution from the section having unknown structure varies across the ROI.

9. The measurement device of claim 7 , wherein the section having unknown structure is in an area below the SOI, in an area above the SOI, or in an area outside the SOI.

10. 1. A method for generating a model for optical metrology of a structure of interest (SOI) on a sample, the method comprising: acquiring metrology data from different locations on the sample, each of the different locations on the sample including the SOI having known structure and a section having unknown structure that varies across the different locations, the metrology data acquired from each of the different locations including a first fundamental contribution from the SOI having known structure, a second fundamental contribution from the section having unknown structure, and a deviation contribution from the section having unknown structure, the deviation contribution from the section having unknown structure varying for each of the different locations; obtaining an influence map for the sample including a deviation contribution associated with each of the different locations; and generating the model for optical measurement of the SOI using the measurement data acquired from the different locations and the influence map, the model including an exact model representing the SOI and a valid model representing the second fundamental contribution from the section having unknown structure without the deviation contribution.

11. Generating the model for optical measurements of the SOI includes: removing the deviation contributions from the metrology data acquired from each of the different locations to generate processed metrology data that includes a combination of the first fundamental contribution from the SOI and the second fundamental contribution from the section having unknown structure without the deviation contribution; and developing the model based on the processed metrology data.

12. The method of claim 10 , wherein the different locations are within a region of interest (ROI), and the deviation contribution from the section with unknown structure varies across the ROI.

13. The method of claim 10 , wherein the section having unknown structure is in an area below the SOI, in an area above the SOI, or in an area outside the SOI.

14. 1. A system for generating a model for optical metrology of a structure of interest (SOI) on a specimen, comprising: means for acquiring metrology data from different locations on the sample, each of the different locations on the sample including the SOI having a known structure and a section having an unknown structure that varies across the different locations, the metrology data acquired from each of the different locations including a first fundamental contribution from the SOI having the known structure, a second fundamental contribution from the section having the unknown structure, and a deviation contribution from the section having the unknown structure, the deviation contribution from the section having the unknown structure varying for each of the different locations; and means for obtaining an influence map for the sample including a deviation contribution associated with each of the different locations; A system comprising: means for generating the model for optical measurement of the SOI using the measurement data acquired from the different locations and the influence map, the model including an exact model representing the SOI and an effective model representing the second fundamental contribution from the section having unknown structure without the deviation contribution.

15. The means for generating the model for optical measurements of the SOI includes: means for removing the deviation contributions from the metrology data acquired from each of the different locations to generate processed metrology data that includes a combination of the first fundamental contribution from the SOI and the second fundamental contribution from the section having unknown structure without the deviation contribution; and means for developing the model based on the processed metrology data.

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