Spectral delta measurement

Abstract

The inspection system may use an inline measurement subsystem to receive first measurement data of a training sample after a first process step, which is before the creation of test features on one or more training samples, and to receive second measurement data of a test feature after a second process step, which is after the creation of the test features. The inspection system may determine a delta index associated with the first and second measurement data for the test feature. The inspection system may generate a measurement model for determining the measurement values ​​of the test feature based on the second measurement data and at least one of the delta index. The inspection system may determine the values ​​of the measurement values ​​for additional instances of the test feature based on the second measurement data and at least one of the delta index.

Description

TECHNICAL FIELD

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63 / 544,448, filed on October 17, 2023, entitled "SPECTRA DELTA METROLOGY" and naming Houssam Chouaib, Zhengquan Tan, HaoMiao Chang, Valeria Dimastrodonato, Anderson Chou, and Boxue Chen as inventors, which application is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to metrology and / or defect inspection, and more particularly to metrology and / or defect inspection based on differences in metrology data between target process steps.

BACKGROUND ART

[0003] Many current optical metrology applications for integrated circuit manufacturing require complex spectroscopic analysis. Furthermore, while the number of critical process steps that require highly sensitive metrology continues to increase, the tolerance ranges and accuracy limits of inspection techniques have become significantly more stringent.

[0004] In some applications, it is desirable to characterize a sample after a process step that produces relatively small physical changes to the sample. However, such process steps may produce only corresponding slight changes in the optical metrology data. As a result, optical characterization techniques used to identify defects in such processes may suffer from noise and / or insufficient sensitivity.

PRIOR ART DOCUMENTS

PATENT DOCUMENTS

[0005]

PATENT DOCUMENT 1

[0006] Therefore, it is necessary to develop systems and methods to solve the aforementioned shortcomings. [Means for solving the problem]

[0007] In embodiments, the technology described herein provides one or more processors, using an inline measurement subsystem, to receive first measurement data of one or more training samples after a first process step (wherein the first process step is before the creation of multiple instances of test features on the one or more training samples), to receive second measurement data of the multiple instances of the test features after a second process step (wherein the second process step is after the creation of the multiple instances of the test features, where the multiple instances of the test features provide a distribution of intentional variations of one or more aspects of the test features), and the multiple instances of the test features The present invention relates to a testing system comprising a controller including one or more processors configured to execute program instructions for determining one or more delta indices associated with the first measurement data and the second measurement data for a chest of drawers; generating a measurement model for determining the measurement values ​​of the test feature based on at least one of the second measurement data and the one or more delta indices using the second measurement data and the one or more delta indices; and controlling the determination of the measurement values ​​for the one or more additional test feature instances based on at least one of the second measurement data and the one or more delta indices associated with the one or more additional test feature instances generated using the inline measurement subsystem.

[0008] In embodiments, the technology described herein relates to an inspection system in which the one or more delta indices are based on at least one of the mean squared error (RMSE), difference, absolute difference, sum, or absolute sum of the first measurement data and the second measurement data.

[0009] In embodiments, the technology described herein relates to an inspection system in which the program instructions cause one or more processors to receive ground truth measurements of the multiple instances of the test feature after the second process step using a reference measurement subsystem different from the inline measurement subsystem, generate reference data based on the ground truth measurements and the one or more delta indices, generate the measurement model for determining the measurement values ​​of the multiple instances of the test feature based on at least the second measurement data using the second measurement data and the reference data.

[0010] In embodiments, the technology described herein relates to an inspection system in which generating the reference data based on the ground truth measurement and the one or more delta indicators includes determining a fitting function that associates the ground truth measurement with the one or more delta indicators, and generating the reference data based on the fitting function.

[0011] In embodiments, the technology described herein relates to an inspection system in which the inline measurement subsystem includes an optical measurement subsystem.

[0012] In embodiments, the technology described herein relates to an inspection system in which the reference measurement subsystem includes at least one of an X-ray measurement subsystem or a particle beam measurement subsystem.

[0013] In embodiments, the technology described herein relates to an inspection system in which the inline measurement subsystem includes a length-measuring scanning electron microscope system.

[0014] In embodiments, the technology described herein relates to an inspection system in which the reference measurement subsystem includes at least one of an X-ray photoelectron spectroscopy system or a transmission electron microscope system.

[0015] In embodiments, the technology described herein relates to an inspection system in which the measurement model includes the second measurement data and a machine learning model trained on at least one of the one or more delta indices or the reference data.

[0016] In embodiments, the technology described herein relates to an inspection system in which the inline measurement subsystem includes an optical measurement subsystem and the measurement model includes an electromagnetic model.

[0017] In embodiments, the technology described herein relates to an inspection system in which the electromagnetic model includes an exact coupled-wave analysis (RCWA) model.

[0018] In embodiments, the technology described herein relates to an inspection system in which the measurement model includes a machine learning model trained on the second measurement data and the one or more delta indices.

[0019] In embodiments, the technology described herein relates to an inspection system in which the inline measurement subsystem includes an optical measurement subsystem and the measurement model includes an electromagnetic model.

[0020] In embodiments, the technology described herein relates to an inspection system in which the electromagnetic model includes an exact coupled-wave analysis (RCWA) model.

[0021] In embodiments, the technology described herein relates to an inspection system in which the inline measurement subsystem includes an optical measurement subsystem.

[0022] In an embodiment, the technology described in the present specification relates to an inspection system, wherein the optical metrology subsystem is configured to determine at least some Mueller matrix elements associated with the plurality of instances of the test feature.

[0023] In an embodiment, the technology described in the present specification relates to an inspection system, wherein the in-line metrology subsystem comprises a particle beam metrology subsystem.

[0024] In an embodiment, the technology described in the present specification relates to an inspection system, wherein the in-line metrology subsystem comprises an X-ray metrology subsystem.

[0025] In embodiments, the technique described herein uses an inline measurement subsystem to generate first measurement data of one or more training samples after a first process step (wherein the first process step is before the creation of multiple instances of test features on the one or more training samples), and uses the inline measurement subsystem to generate second measurement data of the multiple instances of the test features after a second process step (wherein the second process step is after the creation of the test features, and the multiple instances of the test features provide a distribution of intentional variations of one or more aspects of the test features), and the testf The present invention relates to an inspection method comprising: determining one or more delta indices associated with the first measurement data and the second measurement data for the plurality of instances of the examiner; generating a measurement model for determining the measurement values ​​of the test features based on at least one of the second measurement data and the one or more delta indices using the second measurement data and the one or more delta indices; and determining the measurement values ​​for the one or more additional test feature instances based on at least one of the second measurement data and the one or more delta indices associated with the one or more additional test feature instances generated using the inline measurement subsystem.

[0026] In embodiments, the technology described herein comprises an inline measurement subsystem, a reference measurement subsystem, and one or more processors, which use the inline measurement subsystem to receive first measurement data of one or more training samples after a first process step (wherein the first process step is before the creation of multiple instances of test features on the one or more training samples), use the inline measurement subsystem to receive second measurement data of the multiple instances of the test features after a second process step (wherein the second process step is after the creation of the multiple instances of the test features, where the multiple instances of the test features provide a distribution of intentional variations of one or more aspects of the test features), and for the multiple instances of the test features, one or more associated with the first measurement data and the second measurement data. The present invention relates to a testing system comprising a controller including one or more processors configured to execute program instructions for determining a number of delta indices, receiving ground truth measurements of the multiple instances of the test feature after the one or more second process steps using the reference measurement subsystem, generating reference data based on the ground truth measurements and the one or more delta indices, generating a measurement model using the second measurement data and the reference data to determine the measurement values ​​of the multiple instances of the test feature based on at least the second measurement data, and controlling the determination of the measurement values ​​for the one or more additional instances of the test feature based on at least one of the second measurement data and the one or more delta indices associated with the one or more additional instances of the test feature generated using the inline measurement subsystem.

[0027] In embodiments, the technology described herein relates to an inspection system in which generating the reference data based on the ground truth measurement and the one or more delta indicators includes determining a fitting function that associates the ground truth measurement with the one or more delta indicators, and generating the reference data based on the fitting function.

[0028] In embodiments, the technology described herein relates to an inspection system in which the inline measurement subsystem includes an optical measurement subsystem.

[0029] In embodiments, the technology described herein relates to an inspection system in which the optical measurement subsystem is configured to determine at least some Müller matrix elements associated with the plurality of instances of the test feature.

[0030] In embodiments, the technology described herein relates to an inspection system in which the reference measurement subsystem includes at least one of an X-ray measurement subsystem or a particle beam measurement subsystem.

[0031] In embodiments, the technology described herein relates to an inspection system in which the inline measurement subsystem includes a particle beam measurement subsystem.

[0032] In embodiments, the technology described herein relates to an inspection system in which the inline measurement subsystem includes a length-measuring scanning electron microscope system.

[0033] In embodiments, the technology described herein relates to an inspection system in which the reference measurement subsystem includes at least one of an X-ray photoelectron spectroscopy system or a transmission electron microscope system.

[0034] In embodiments, the technology described herein relates to an inspection system in which the inline measurement subsystem includes an X-ray measurement subsystem.

[0035] It should be understood that the above general description and the following detailed description are illustrative and descriptive only and do not necessarily limit the claimed invention. The accompanying drawings incorporated herein and forming part thereof illustrate embodiments of the invention and, together with the general description, are useful in illustrating the principles of the invention. [Brief explanation of the drawing]

[0036] Many of the advantages of this disclosure may be better understood by those skilled in the art by referring to the accompanying drawings.

[0037] [Figure 1A] This is a block diagram of a measurement system according to one or more embodiments of the present disclosure. [Figure 1B] This is a simplified schematic diagram of an optical measurement subsystem according to one or more embodiments of the present disclosure. [Figure 1C] This is a simplified schematic diagram of an X-ray measurement subsystem according to one or more embodiments of the present disclosure. [Figure 1D] This is a simplified schematic diagram of a particle beam measurement subsystem 102 according to one or more embodiments of the present disclosure. [Figure 2] This is a flowchart illustrating the steps performed in a measurement method according to one or more embodiments of the present disclosure. [Figure 3A] This is a cross-sectional view of a VNAND device after a first process step prior to fabricating a bottom open recess test feature, according to one or more embodiments of the present disclosure. [Figure 3B] Figure 3A is a cross-sectional view of the VNAND device after a second process step of fabricating a bottom open recess test feature according to one or more embodiments of the present disclosure. [Figure 4A] This is a cross-sectional view of a VNAND device after a first process step prior to fabricating recess test features, according to one or more embodiments of the present disclosure. [Figure 4B]This is an enlarged view of a portion of Figure 4A following a first process step, according to one or more embodiments of the present disclosure. [Figure 4C] This is an enlarged view of a portion of Figure 4A after a second process step in forming a recess test feature, according to one or more embodiments of the present disclosure. [Figure 5A] This is a perspective view of a device after a first process step associated with an HKMG loop, according to one or more embodiments of the present disclosure. [Figure 5B] This is a perspective view of the device after a second process step associated with an HKMG loop, according to one or more embodiments of the present disclosure. [Figure 5C] This is a perspective view of the device after a third process step associated with the HKMG loop, according to one or more embodiments of the present disclosure. [Figure 6] This is a plot of XPS ground truth measurement data as a function of the delta index for multiple instances of a first test feature associated with a design of experiment, according to one or more embodiments of the present disclosure. [Figure 7] This is a plot of XPS ground truth measurement data as a function of the delta index for multiple instances of a second test feature associated with a design of experiment, according to one or more embodiments of the present disclosure. [Figure 8] This is a plot of XPS ground truth measurement data as a function of the delta index for multiple instances of test features associated with a design of experiments, according to one or more embodiments of the present disclosure. [Figure 9A] This plot shows the measured values ​​generated via a measurement model generated without noise filtering, using a delta index as a function of ground truth measurements, according to one or more embodiments of the present disclosure. [Figure 9B]This plot shows the measured values ​​generated via a noise-filtered measurement model that uses a delta index as a function of ground truth measurements, according to one or more embodiments of the present disclosure. [Modes for carrying out the invention]

[0038] The subject matter disclosed, as shown in the accompanying drawings, will be described in detail below. This disclosure is specifically shown and described with respect to particular embodiments and their particular components. The embodiments described herein should be considered illustrative and not limiting. It will be readily apparent to those skilled in the art that various changes and modifications can be made in form and detail without departing from the spirit and scope of this disclosure.

[0039] Embodiments of the present disclosure relate to a system and method for providing a measurement of one or more features (referred to herein as critical parameters (CPs)) on a sample for measurement and / or inspection applications, based on one or more delta indices, wherein the delta indices are generated based on a first measurement data before (e.g., a pre-process step) the creation of the one or more features and a second measurement data after (e.g., a post-process step) the creation of the one or more features. For example, the delta indices may be associated with the mean squared error (RMSE), difference, absolute difference, sum, or absolute sum of the measurement data before and after the process step associated with the creation of the one or more features, but are not limited to these.

[0040] This specification assumes that the use of delta indices can significantly improve the sensitivity and / or robustness of measurements, particularly when the feature of interest is associated with relatively small changes in the structure of the sample. Specifically, delta indices can strongly correlate with structural changes associated with the feature of interest. As a result, delta indices may be used directly as reference data, or they may be used to filter (e.g., reduce noise) ground truth data generated using reference measurement tools (e.g., X-ray measurement tools, particle beam measurement tools, etc.).

[0041] For example, a measurement model for determining one or more measurement values ​​associated with one or more target features may be generated using second measurement data (e.g., associated with a post-process step) and one or more delta indices. For example, the measurement model may be generated using delta indices as reference data, reference data generated based on delta indices, or a combination thereof. Furthermore, any type of measurement model may be developed, including but not limited to machine learning models, physics-based models (e.g., electromagnetic solution models), or a combination thereof.

[0042] The runtime measurement subsystem may be an optical measurement subsystem with relatively high throughput, including but not limited to an ellipsometer, reflectometer, scatellometer, Raman spectroscopy system, spectrophotometric reflectance tool, spectrophotometric luminescence tool, or LDSR system. In this case, the reference measurement subsystem may provide relatively high accuracy but have relatively low throughput and may include, but not limited to, a particle beam measurement system or an X-ray measurement system. In this configuration, a reference dataset may be generated using a fit between ground truth data and a delta index associated with the parameter of interest.

[0043] Referring here to Figures 1A to 9B, a system and method for providing measurements based on the delta index will be described in more detail according to one or more embodiments of this disclosure.

[0044] Figure 1A is a block diagram of a measurement system 100 according to one or more embodiments of the present disclosure.

[0045] In some embodiments, the measurement system 100 includes one or more measurement subsystems 102 for generating a measurement dataset of test features 104 on a sample 106, and further includes a controller 108 for generating one or more associated measurement values ​​based on the measurement data. The controller 108 may include one or more processors 110 configured to execute a set of program instructions held in memory 112 or a storage device, the program instructions causing the processors 110 to perform various actions.

[0046] The measurement subsystem 102 may include any components or combinations of components suitable for generating measurement data of the test feature 104. For example, the measurement subsystem 102 may direct illumination 114 onto the test feature 104 and capture collected signals 116 from the test feature 104 in response to the illumination 114. Measurement tools and techniques for measurement are generally as follows: U.S. Patent No. 10,458,912 issued on October 29, 2019; U.S. Patent No. 11,573,077 issued on February 7, 2023; U.S. Patent No. 11,036,898 issued on June 15, 2021; U.S. Patent No. 10,101,670 issued on October 16, 2018; and U.S. Patent No. 10,101,670 issued on March 2, 2021. U.S. Patent No. 10,935,893, issued on November 27, 2018; U.S. Patent No. 10,139,352, issued on December 11, 2018; U.S. Patent No. 10,152,678, issued on December 10, 2019; U.S. Patent No. 10,502,549, issued on December 10, 2019; U.S. Patent No. 9,875,946, issued on January 23, 2018; U.S. Patent Publication No. 10,935,893, issued on November 27, 2018; U.S. Patent No. 10,139,352, issued on December 11, 2018; U.S. Patent No. 10,152,67810,502,549, issued on December 10, 2019; U.S. Patent No. 9,875,946, issued on January 23, 2018; U.S. Patent Publication No. 10,935,893, issued on May 19, 2016. Application Publication No. 2016 / 0139032, U.S. Patent No. 7,478,019 issued on January 13, 2009, U.S. Patent No. 7,933,026 issued on May 26, 2011, U.S. Patent No. 5,608,526 issued on March 4, 1997, U.S. Patent No. 5,859,424 issued on January 12, 1999, U.S. Patent No. 6,429,9 issued on August 6, 2002 These are described in U.S. Patent No. 43, No. 9,405,290 issued on 2 August 2016, No. 9,915,522 issued on 18 February 2015, No. 9,291,554 issued on 22 March 2016, and No. 10,769,320 issued on 8 September 2020, all of which are incorporated herein by reference in their entirety. In some embodiments, a particular measurement subsystem 102 provides multiple types of measurements. Furthermore, multiple measurement subsystems 102 may be provided as a single tool or as multiple tools.A single tool providing multiple measurement configurations is generally described in U.S. Patent No. 7,933,026, issued April 26, 2011, which is incorporated herein by reference in its entirety. Analysis of multiple tools and structures is generally described in U.S. Patent No. 7,478,019, issued January 13, 2009, which is incorporated herein by reference in its entirety.

[0047] In some embodiments, the measurement subsystem 102 includes an optical measurement subsystem 102 for generating measurement data based on the interaction between the sample 106 and illumination 114, which includes light of any suitable wavelength or combination of wavelengths, including but not limited to ultraviolet (UV), visible, or infrared (IR) wavelengths. For example, the optical measurement subsystem 102 may include, but is not limited to, a spectroscopic ellipsometer (SE), an SE with multiple illumination angles, an SE that measures Müller matrix elements (e.g., using a rotational compensator), a single-wavelength ellipsometer, a beam profile ellipsometer (angle-resolved ellipsometer), a beam profile reflectometer (angle-resolved reflectometer), a broadband reflectometer (spectroscopic reflectometer), a single-wavelength reflectometer, an angle-resolved reflectometer, an imaging system, a scatellometer (e.g., a speckle analyzer), a Raman measurement tool, a laser-driven spectroscopic reflectance measurement (LDSR) system, or any combination thereof.

[0048] In some embodiments, the measurement subsystem 102 includes an X-ray measurement subsystem 102 for generating measurement data based on the interaction between the X-ray-containing illumination 114 and the sample 106. For example, the measurement subsystem 102 may include, but is not limited to, a small-angle X-ray scattermeter (SAXR), a soft X-ray reflectometer (SXR), or an X-ray photoelectron spectroscopy (XPS) system.

[0049] In some embodiments, the measurement subsystem 102 includes a particle beam measurement subsystem 102 for generating measurement data based on the interaction between the sample 106 and illumination 114, which includes, but is not limited to, an electron beam (e-beam), an ion beam, or a neutral particle beam. For example, the e-beam measurement subsystem may include a scanning electron microscope (SEM), a CD-SEM (CD-SEM), a Gray SEM (e.g., a model-based SEM similar to a CD-SEM, which provides, but is not limited to, additional information regarding measurement structures such as depth, height, and bottom CD), or a transmission electron microscope (TEM).

[0050] Regardless of the configuration of the measurement subsystem 102, any type of acquired signal 116, including but not limited to light, X-rays, or particles, emitted from the test feature 104 in response to the illumination 114 may be captured to generate measurement data.

[0051] In some embodiments, the measurement system 100 includes at least one inline measurement subsystem 102 (e.g., an inline measurement tool) and at least one reference measurement subsystem 102 (e.g., a reference measurement tool). In a general sense, the inline measurement subsystem 102 and / or the reference measurement subsystem may include any type of system known in the art. In some applications, the reference measurement subsystem may have relatively higher accuracy than the inline measurement subsystem 102, but with lower throughput. For example, the inline measurement subsystem 102 may include an optical measurement subsystem 102 configured to capture at least several Müller matrix elements associated with a test feature 104, while the reference measurement subsystem 102 may include an X-ray or particle beam measurement subsystem 102. Another example is that the inline measurement subsystem 102 may include a particle beam measurement subsystem 102 (e.g., a CD-SEM, Gray SEM, etc.), while the reference measurement subsystem 102 may include a TEM or XPS system. These examples are provided for illustrative purposes only and should not be construed as limiting the disclosure. Rather, the measurement system 100 may include any type of inline measurement system 102 and any type of reference measurement subsystem 102.

[0052] Furthermore, multiple measurement subsystems 102 may be provided as a single tool or as multiple tools. A single tool providing multiple measurement configurations is generally described in U.S. Patent No. 7,933,026, issued April 26, 2011, which is incorporated herein by reference in its entirety. Analysis of multiple tools and structures is generally described in U.S. Patent No. 7,478,019, issued January 13, 2009, which is incorporated herein by reference in its entirety.

[0053] The measurement system 100 may be suitable for generating any type of measurement on any type of test feature 104. Furthermore, the measurement system 100 may generate multiple measurement values ​​associated with various sub-features (e.g., critical parameters) associated with the test feature 104, and / or measurement values ​​associated with multiple test features 104.

[0054] For example, test feature 104 may be associated with any stage in the fabrication process, including but not limited to etching, lithography, or deposition processes. Non-limiting examples of test feature 104 include, but are not limited to, patterned multilayer structures, patterned monolayer structures (e.g., lattice structures), film stacks (e.g., unpatterned film stacks), or combinations thereof. Furthermore, non-limiting examples of measured values ​​include, but are not limited to, CD measurements, height measurements (e.g., height of patterned features, height of multilayer features), overlay measurements, tilt measurements, electrical test measurements (e.g., Vt, work function, mobility, Ion / Ioff), stress or strain measurements, film thickness, or material property measurements (e.g., refractive index measurements, spectral measurements).

[0055] The controller 108 may be communicatively coupled to any component of the measurement system 100, including but not limited to one or more measurement subsystems 102. In this way, the controller 108 may receive communications (e.g., data, commands, etc.) from any connected component and / or instruct the connected component to perform a selected action (e.g., via a control signal). Thus, the controller 108 may directly or indirectly perform any desired action.

[0056] Figure 2 is a flowchart illustrating the steps performed in the measurement method 200 according to one or more embodiments of the present disclosure. Embodiments and enabling techniques described herein in the context of the measurement system 100 should be interpreted as extending to method 200. For example, one or more processors 110 of the controller 108 may implement program instructions (e.g., stored in memory 112) that cause the processors 110 to directly or indirectly perform any of the steps of method 200. However, method 200 is not limited to the architecture of the measurement system 100. Thus, the embodiments of method 200 described herein are merely illustrative and should not be interpreted as limiting the scope of the present disclosure.

[0057] In some embodiments, Method 200 includes a step 202 to generate first measurement data for one or more training samples after a first process step, using an inline measurement subsystem 102 (or more generally, a first measurement subsystem 102), wherein the first process step is before the creation of various instances of the test feature 104 on the one or more training samples. In some embodiments, Method 200 includes a step 204 to generate second measurement data for multiple instances of the test feature 104 after a second process step, using an inline measurement subsystem 102, wherein the second process step is after the creation of the measurement subsystem 102. Furthermore, the various instances of the test feature 104 may provide a distribution of intentional variations of one or more aspects of the test feature 104. In other words, the second measurement data from step 204 may be associated with a Design of Experiment (DOE) in which the test feature 104 is created with intentional process variations resulting in intentional deviations of various aspects of the test feature. In this way, the second measurement data may provide training data associated with known variations of the aspect of the test feature 104 related to the inline measurement.

[0058] In some embodiments, Method 200 includes step 206 of determining one or more delta indices associated with first and second measurement data. Any particular delta indice may be associated with any combination of first and second measurement data representing a test feature 104. For example, the delta indice may be associated with the RMSE, difference, absolute difference, sum, or absolute sum of the first and second measurement data. Furthermore, step 206 may include generating any number of delta indices (e.g., one or more delta indices) associated with different mathematical combinations of the first and second measurement data. Furthermore, step 206 is not limited to determining a delta indice associated with a single test feature 104. In some embodiments, step 206 includes determining delta indices associated with two or more test features 104.

[0059] Figures 3A–5C illustrate various non-limiting examples of test features 104 that may benefit from characterization using a delta index, as shown in Method 200. It is assumed herein that delta indexes based on measurement data generated before and after the creation of test features 104 may correlate highly with test features 104. Furthermore, delta indexes may be particularly valuable when test features 104 correspond to relatively small changes in sample 106. In these applications, measurement data from any type of measurement subsystem 102 (e.g., an inline measurement subsystem 102, or even a reference measurement subsystem 102) may be weak and / or noisy.

[0060] Figures 3A and 3B show examples in which a test feature 104 is associated with a bottom open recess. In particular, Figure 3A is a cross-sectional view of the VNAND device 302 after a first process step (e.g., a pre-process step) prior to fabricating the bottom open recess test feature 104, according to one or more embodiments of the present disclosure. Figure 3B is a cross-sectional view of the VNAND device 302 of Figure 3A after a second process step (e.g., a post-process step) for fabricating the bottom open recess test feature 104, according to one or more embodiments of the present disclosure. As shown in Figures 3A and 3B, the structural differences in the VNAND device 302 before and after fabricating the bottom open recess are relatively small and relate to the removal of material 304 at the bottom of the cavity structure 306. Therefore, the differences between the first and second measurement data associated with these first and second process steps are also expected to be relatively small. As a result, typical techniques for developing measurement models to generate measurement values ​​for this bottom-open recess test feature 104 are expected to suffer from low correlation and high noise. However, it is assumed herein that measurement models incorporating delta metrics may provide high correlation and relatively low noise.

[0061] Figures 4A to 4C show examples in which the test feature 104 is a recess test feature 104 associated with a pre-polyliner of a VNAND device 402, according to one or more embodiments of the present disclosure. Figure 4A is a cross-sectional view of the VNAND device 402 after a first process step (e.g., a pre-process step) prior to fabricating the recess test feature 104, according to one or more embodiments of the present disclosure. Figure 4B is an enlarged view of a portion 404 of Figure 4A after the first process step, according to one or more embodiments of the present disclosure. Figure 4C is an enlarged view of the portion 404 of Figure 4A after a second process step (e.g., a post-process step) forming the recess test feature 104, according to one or more embodiments of the present disclosure. In particular, the recess test feature 104 may correspond to partial etching of the SiO2 layer 406. Similar to the bottom-open recess test feature 104 shown in Figures 3A-3B, it is assumed herein that using a delta index to generate a measurement model for characterizing the recess test feature 104 shown in Figures 4A-4C may provide higher correlation and lower noise than existing techniques.

[0062] Figures 5A–5C show examples of various test features 104 associated with key steps in the fabrication of high-dielectric-constant metal gates (HKMGs). The HKMG process is located at the end of the front-end-of-the-line (FEOL) loop of a CMOS device. After the removal of the dummy gate and the stripping and cleaning of the oxide film, a series of ultrathin materials are deposited on the gate before metal deposition. These materials have comparable optical dispersion properties and are often very thin. Typically, an 8–10 angstrom SiO2 interface layer (IL) is deposited first on the gate (and fins). Then, hafnium dioxide (e.g., 14 angstroms thick) is used as the high-dielectric-constant material. This is followed by a barrier metal, TaN (e.g., 5–10 angstroms), then another TiN layer, then TiAlC, and finally a 10 angstrom TiN work function material. Furthermore, different HKMG process flows are required for PMOS and NMOS devices, which complicates the issue and increases the number of critical layers. Atomic layer deposition (ALD) is a typical process used to form deposits.

[0063] It should be noted that typical techniques for characterizing such devices have focused on measuring planar film stacks (e.g., using X-ray-based techniques). However, these typical techniques often struggle to achieve sufficient accuracy and / or sensitivity.

[0064] Figure 5A is a perspective view of device 500 after a first process step associated with the HKMG loop, according to one or more embodiments of the present disclosure. In particular, Figure 5A shows the nanosheet release step in which the nanosheet 502 is formed. Figure 5B is a perspective view of device 500 after a second process step associated with the HKMG loop, according to one or more embodiments of the present disclosure. In particular, Figure 5B shows the interface layer deposition step in which the interface layer 504 is deposited on the nanosheet 502. Figure 5C is a perspective view of device 500 after a third process step associated with the HKMG loop, according to one or more embodiments of the present disclosure. In particular, Figure 5C shows the high dielectric constant layer deposition step in which the high dielectric constant layer 506 is deposited on the interface layer 504.

[0065] In this specification, it is assumed that Method 200 may be performed to generate measurement values ​​for any of the process steps shown in Figures 5A to 5C. For example, measurement values ​​of the interface layer 504 as a test feature 104 may be provided based on the delta index between the steps shown in Figures 5A and 5B. As another example, measurement values ​​of the high dielectric constant layer 506 as a test feature 104 may be provided based on the delta index between the steps shown in Figures 5B and 5C.

[0066] Referring here to Figures 3A–5C in general, it should be understood that Figures 3A–5C are provided solely for illustrative purposes and should not be construed as limiting the scope of this disclosure. Rather, Method 200 may be performed to provide measurement values ​​for any number or type of test features 104.

[0067] Referring again to Figure 2, in some embodiments, Method 200 includes a step 208 to generate a measurement model for determining the measurement values ​​of the test feature 104 based on at least one of the second measurement data and the delta index, using the second measurement data and one or more delta indexes. In some embodiments, Method 200 includes a step 210 to determine the measurement values ​​for one or more additional instances of the test feature 104 based on at least one of the second measurement data and the delta indexes associated with one or more additional instances of the test feature 104, generated using the inline measurement subsystem 102.

[0068] Steps 208 and 210 may utilize measurement models of any type or combination of types.

[0069] In some embodiments, the measurement model is a physics-based measurement model. For example, if the inline measurement subsystem 102 is an optical measurement subsystem 102, the measurement model may include, but is not limited to, an electromagnetic solver based on algorithms such as exact coupled-wave analysis (RCWA) techniques, finite element method (FEM) techniques, method of moments techniques, surface integration techniques, volume integration techniques, and time-domain difference (FDTD) techniques.

[0070] In some embodiments, the measurement model is a machine learning model.

[0071] A machine learning model may incorporate any type or combination of machine learning techniques, including supervised, semi-supervised, reinforcement, or unsupervised machine learning techniques. For example, a machine learning model may include, but is not limited to, linear models, neural network models, polynomial models, decision tree models, or random forest models. In some applications, training data may include a combination of experimental and simulation data.

[0072] The machine learning model may accept any type of input data suitable for determining the metric values ​​of an instance of test feature 104 based on at least one of the second metric data or delta index associated with that instance.

[0073] For example, a machine learning model may accept measurement data (e.g., raw data) associated with a particular measurement configuration. For instance, the spectroscopic-based optical measurement subsystem 102 may generate signals associated with 15 Müller matrix elements, and by having approximately 670 wavelength pixels per signal, it may provide approximately 10,000 distinct signals for a particular measurement configuration. However, this is merely illustrative and should not be construed as limiting the scope of the disclosure. For example, such a system may generate signals associated with any number (e.g., up to 16) Müller matrix elements, and may provide any number of data points for any number of wavelengths. Furthermore, as stated above, Method 200 is not limited to the optical measurement subsystem 102.

[0074] As another example, a particular machine learning model may accept principal components (PCs) associated with a subset of measurement datasets or transformations associated with a particular measurement configuration. In some embodiments, Method 200 includes a step (not shown) of extracting a set of principal components (e.g., features) from at least one of a second set of measurement data or delta indices (e.g., generally input data to a machine learning model), and the machine learning model generates measurement values ​​based on the set of principal components. In this way, the step of extracting a set of principal components from the input data may provide dimensionality reduction of the relevant measurement datasets. The set of principal components may correspond to a subset of the input data or a transformation of the input data. The step of extracting a set of principal components from the measurement datasets may be carried out using any suitable technique, including but not limited to principal component analysis (PCA) (e.g., linear or nonlinear) or Fast Fourier Transform (FFT) analysis. In a general sense, the set of principal components may correspond to aspects of the relevant measurement data that correlate with the measurement values.

[0075] More generally, it is assumed herein that the delta index can be used in different ways to develop the measurement model in step 208.

[0076] As previously stated herein, the delta index can correlate highly with test feature 104 even when test feature 104 is associated with relatively small structural changes in sample 106. In this way, the delta index can be used directly or indirectly as reference data for measurement models.

[0077] In some embodiments, the delta index is used to filter ground truth data associated with ground truth measurements of training instances of the test features 104 characterized in steps 202 and 204. For example, method 200 may include an additional step of receiving ground truth measurements of the one or more features after the one or more second process steps using a reference measurement subsystem 102 different from the inline measurement subsystem 102, and generating reference data based on the ground truth measurements and the delta index. Thus, the measurement model generated in step 208 may be based on both the second measurement index and this reference data.

[0078] For example, the reference measurement subsystem 102 may be an X-ray measurement subsystem 102, a particle beam measurement subsystem 102, or the like, which is suitable for generating ground truth measurements with a higher accuracy than that achievable by the inline measurement subsystem 102. However, this specification assumes that, in the case of certain test features 104, particularly when test features 104 are associated with minute structural changes in the sample 106, even ground truth measurements may be noisy or have relatively low sensitivity. In this case, noise may be reduced and a more robust set of reference data for the measurement model may be generated by filtering the ground truth data using a delta index. In some embodiments, method 200 includes determining a fitting function that associates ground truth measurements with a delta index, and generating reference data based on the fitting function.

[0079] Figures 6–9B illustrate the use of a delta index to generate reference data by filtering ground truth measurements, according to one or more embodiments of this disclosure. Furthermore, Figures 6–9B are based on the HKMG loop shown in Figures 5A–5C.

[0080] Figure 6 is a plot of XPS ground truth measurement data as a function of a delta index for multiple instances of a first test feature 104 associated with a design of experiment according to one or more embodiments of the present disclosure. In Figure 6, the first measurement data is associated with post-fabrication steps of the HKMG TiN layer, while the second measurement data is associated with post-fabrication steps of the HKMG TiAlC layer. The delta index is generated as the RMSE between the first and second measurement data (e.g., associated with steps 202 and 204 of Method 200). In Figure 6, the ground truth XPS data is fitted to the RMSE delta index using a polynomial function. Thus, new reference data (e.g., used for developing measurement models) may be generated using the fitting function rather than the ground truth measurement values ​​themselves.

[0081] Figure 7 is a plot of XPS ground truth measurement data as a function of the delta index for multiple instances of a second test feature 104 associated with a design of experiment, according to one or more embodiments of the present disclosure. In Figure 7, the first measurement data is associated with a post-fabrication step of the HKMG high dielectric constant layer, while the second measurement data is associated with a post-fabrication step of the HKMG TiN layer. The delta index is generated as the RMSE between the first and second measurement data (e.g., those associated with steps 202 and 204 of Method 200). In Figure 7, the ground truth XPS data is fitted to the RMSE delta index using a polynomial function (here, a linear function). In this way, new reference data (e.g., used for developing a measurement model) may be generated using the fitting function rather than the ground truth measurements themselves. Furthermore, Figure 7 includes data for instances of the second test feature 104 associated with experimental design data (e.g., instances of the second test feature 104 with intentional variations), as well as data for instances of the second test feature 104 from inline samples that are not part of the training dataset.

[0082] Figure 8 plots XPS ground truth measurement data as a function of the delta index for multiple instances of test feature 104 associated with a design of experiment, according to one or more embodiments of the present disclosure. Figure 8 is substantially similar to Figure 7, except that it includes only instances of test feature 104 associated with the design of experiment data. Comparing Figure 7 and Figure 8, it is clear that the polynomial fitting function accurately describes not only the design of experiment data but also the inline data.

[0083] Figures 9A and 9B show blind test data demonstrating the effectiveness of the measurement model (e.g., from step 208) in determining measurement values ​​for blind data (e.g., instances of test feature 104 where ground truth measurements are obtained but not used to train the measurement model). Figure 9A is a plot showing measurement values ​​(on the Y axis) generated via a measurement model generated without noise filtering using a delta index, as a function of ground truth measurements (on the X axis), according to one or more embodiments of this disclosure. Figure 9B is a plot showing measurement values ​​(on the Y axis) generated via a measurement model generated with noise filtering using a delta index (e.g., based on fitting ground truth measurements to a delta index, as described herein), as a function of ground truth measurements (on the X axis), according to one or more embodiments of this disclosure. As shown in Figures 9A and 9B, using a delta index to generate filtered reference data for the measurement model provides an accurate and robust solution without requiring a large number of reference measurements. In particular, by using a delta index for filtering the reference measurement, the coefficient of determination (R² value) increased from 0.74 to 0.95. For example, while typical current techniques may require hundreds of reference data points, the systems and methods disclosed herein can converge with substantially fewer reference data points when using a delta index.

[0084] Referring again to Figure 2 in general, in some embodiments, the delta index is used directly as reference data for generating a measurement model (e.g., step 208 of method 200). For example, step 208 for generating a measurement model may include training a machine learning model with the delta index. For example, such a model may be trained (e.g., in step 208) with the delta index and second measurement data (e.g., data after post-process steps associated with test feature 104) for various instances of test feature 104 having various properties in design of experiments. The model may then be used (e.g., in step 210) to generate measurement values ​​using second measurement data and / or the delta index generated using inline samples.

[0085] Herein, with reference to Figures 1A to 1D, additional aspects of the measurement system 100 according to one or more embodiments of this disclosure will be described in more detail.

[0086] One or more processors 110 of the controller 108 may include any processor or processing element known in the art. For the purposes of this disclosure, the terms “processor” or “processing element” may be broadly defined to include any device having one or more processing or logic elements (e.g., one or more microprocessor devices, one or more application-specific integrated circuit (ASIC) devices, one or more field-programmable gate arrays (FPGAs), or one or more digital signal processors (DSPs)). In this sense, one or more processors 110 may include any device configured to execute algorithms and / or instructions (e.g., program instructions stored in memory). In some embodiments, one or more processors 110 may be embodied as a desktop computer, mainframe computer system, workstation, image computer, parallel processor, network computer, or any other computer system configured to operate in conjunction with or control the operation of the measurement subsystem 102, as described throughout this disclosure. Furthermore, different subsystems of the measurement system 100 may include processors or logic elements suitable for performing at least some of the steps described in this disclosure. Therefore, the above description should not be construed as a limitation on embodiments of the present disclosure, but merely as an example. Furthermore, the steps described throughout the present disclosure may be performed by a single controller or, alternatively, by multiple controllers. In addition, controller 108 may include one or more controllers housed in a common housing or in multiple housings. Thus, any controller or combination of controllers may be individually packaged as modules suitable for integration into the measurement system 100.

[0087] Memory 112 may include any storage medium known in the art that is suitable for storing program instructions executable by the associated one or more processors 110. For example, memory 112 may include a non-temporary storage medium. Another example of memory 112 may include, but is not limited to, read-only memory (ROM), random access memory (RAM), magnetic or optical memory devices (e.g., disks), magnetic tape, solid-state drives, etc. Furthermore, it should be noted that memory 112 may be housed in a common controller housing together with one or more processors 110. In some embodiments, memory 112 may be located remotely from the physical locations of one or more processors 110 and controller 108. For example, one or more processors 110 of controller 108 may access remote memory (e.g., a server) accessible via a network (e.g., the Internet, an intranet, etc.).

[0088] Figures 1B to 1D show variations of the measurement subsystem 102 according to one or more embodiments of the present disclosure.

[0089] Figure 1B is a simplified schematic diagram of an optical measurement subsystem 102 according to one or more embodiments of the present disclosure. For example, the measurement subsystem 102 may include, but is not limited to, a spectroscopic ellipsometer (SE), an SE having multiple illumination angles, an SE measuring Müller matrix elements (e.g., using a rotational compensator), a single-wavelength ellipsometer, a beam profile ellipsometer (angle-resolved ellipsometer), a beam profile reflectometer (angle-resolved reflectometer), a broadband reflectometer (spectroscopic reflectometer), a single-wavelength reflectometer, an angle-resolved reflectometer, an imaging system, a scatellometer (e.g., a speckle analyzer), or any combination thereof.

[0090] In some embodiments, the measurement subsystem 102 includes an illumination source 118 configured to produce illumination 114 in the form of at least one illumination beam. The illumination 114 from the illumination source 118 may include one or more selected wavelengths of light, including but not limited to ultraviolet (UV) radiation, visible radiation, or infrared (IR) radiation. Furthermore, the spatial profile of the illumination 114 on the sample 106 may be controlled by a field plane aperture to have any selected spatial profile.

[0091] The illumination source 118 may include any type of illumination source suitable for providing illumination 114 formed from light. In some embodiments, the illumination source 118 is a laser light source. For example, the illumination source 118 may include, but is not limited to, one or more narrowband laser light sources, broadband laser light sources, supercontinuum laser light sources, white light laser light sources, etc. In some embodiments, the illumination source 118 includes a laser-sustained plasma (LSP) source. For example, the illumination source 118 may include, but is not limited to, an LSP lamp, an LSP bulb, or an LSP chamber suitable for encapsulating one or more elements that can emit broadband illumination when excited to a plasma state by a laser light source. In some embodiments, the illumination source 118 includes a lamp light source. In some embodiments, the illumination source 118 may include, but is not limited to, an arc lamp, a discharge lamp, an electrodeless lamp, etc.

[0092] The lighting source 118 may provide lighting 114 using free-space technology and / or optical fibers.

[0093] In some embodiments, the measurement subsystem 102 directs illumination 114 to the sample 106 through an illumination path 122 via at least one illumination lens 120 (e.g., an objective lens). The illumination path 122 may include one or more optical components suitable for correcting and / or adjusting the illumination 114 and directing the illumination 114 towards the sample 106. In some embodiments, the illumination path 122 includes one or more illumination path optics 124 for shaping or controlling the illumination 114. For example, the illumination path optics 124 may include, but are not limited to, one or more field diaphragms, one or more pupil diaphragms, one or more polarizers, one or more filters, one or more beam splitters, one or more diffusers, one or more homogenizers, one or more apodizers, one or more beam shapers, or one or more mirrors (e.g., fixed mirrors, movable mirrors, scanning mirrors, etc.).

[0094] The measurement subsystem 102 may position the sample 106 for measurement using any suitable technique. In some embodiments, as shown in Figure 1B, the measurement subsystem 102 includes a sample stage 126 which includes one or more actuators (e.g., linear actuators, tip / tilt actuators, rotary actuators, etc.) for positioning the sample 106 relative to the illumination beam. In some embodiments, although not explicitly shown, the measurement subsystem 102 includes a beam scanning optical system (e.g., galvanometer mirrors, scanning prisms, etc.) for adjusting and / or scanning the position of one or more beams of illumination 114.

[0095] In some embodiments, the measurement subsystem 102 includes at least one collection lens 128 for capturing an acquired signal 116 (e.g., light) and directing this acquired signal 116 through a collection path 132 to one or more detectors 130. The collection path 132 may include one or more optical elements suitable for correcting and / or adjusting the acquired signal 116 from the sample 106. In some embodiments, the collection path 132 includes one or more collection path optics 134 for shaping or controlling the acquired signal 116. For example, the collection path optics 134 may include, but are not limited to, one or more field diaphragms, one or more pupil diaphragms, one or more polarizers, one or more filters, one or more beam splitters, one or more diffusers, one or more homogenizers, one or more apodizers, one or more beam shapers, or one or more mirrors (e.g., fixed mirrors, movable mirrors, scanning mirrors, etc.).

[0096] The measurement subsystem 102 may generally include any number or type of detectors 130. For example, the measurement subsystem 102 may include at least one single-pixel detector 130, including but not limited to a photodiode, avalanche photodiode, or single-photon detector. As another example, the measurement subsystem 102 may include at least one multi-pixel detector 130, including but not limited to a charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) device, a line detector, or a time-delay integral (TDI) detector.

[0097] The detector 130 may be positioned at any selected location within the collection path 132. In some embodiments, the measurement subsystem 102 includes the detector 130 in the field of view plane (e.g., the plane conjugate to the sample 106) to generate an image of the sample 106. In some embodiments, the measurement subsystem 102 includes the detector 130 in the pupil plane (e.g., the diffraction plane) to generate a pupil image. In this regard, the pupil image may correspond to the angular distribution of light from the sample 106 to the detector 130. For example, the diffraction order associated with the diffraction of illumination 114 from the sample 106 may be imaged or observed in the pupil plane. In a general sense, the detector 130 may capture any combination of reflected (or transmitted), scattered, or diffracted light from the sample 106.

[0098] The illumination path 122 and the acquisition path 132 of the measurement subsystem 102 may be oriented in a wide range of configurations. For example, as shown in Figure 1B, the illumination path 122 and the acquisition path 132 may include non-overlapping optical paths. In some embodiments, although not explicitly shown, the measurement subsystem 102 may include a beam splitter oriented so that a common objective lens can direct illumination 114 onto the sample 106 while simultaneously capturing the acquisition signal 116.

[0099] Figure 1C is a simplified schematic diagram of an X-ray measurement subsystem 102 according to one or more embodiments of the present disclosure. Such a measurement subsystem 102 may, but is not limited to, a SAXR, SXR, or XPS system. X-ray characterization systems and related measurement techniques are generally referred to as follows: U.S. Patent No. 7,929,667 issued on April 19, 2011; U.S. Patent No. 9,885,962 issued on February 6, 2018; U.S. Patent No. 10,013,518 issued on July 3, 2018; U.S. Patent No. 10,324,050 issued on June 18, 2019; U.S. Patent No. 10,352,695 issued on July 16, 2019; U.S. Patent No. 10,775,323 issued on September 15, 2020; "Intercomparison between optical and x-ray scatterometry measurements of FinFET structures" by Germer et al., Proc. SPIE, v.8681, p. 86810Q (2013); and "X-ray scattering critical dimensional metrology using a compact x-ray source for next generation semiconductor devices" by Kline et al., Journal of This is described in Micro / Nanolithography, MEMS, and MOEMS 16.1 (2017), U.S. Patent No. 11,333,621 issued on 17 May 2022, and U.S. Patent Application Publication No. 2021 / 0207956 published on 8 July 2021, all of which are incorporated herein by reference in their entirety.

[0100] In some embodiments, the illumination source 118 is an X-ray source configured to produce X-ray illumination 114 having any particle energy (e.g., soft X-rays, hard X-rays, etc.). In this case, the measurement subsystem 102 may include any combination of components suitable for capturing the associated acquired signal 116, including but not limited to X-ray emission, optical emission, or particle emission.

[0101] For example, the measurement subsystem 102 may include at least one X-ray illumination lens 120 and / or illumination path optics 124 suitable for collimating or focusing the X-ray illumination 114. Although not shown, the measurement subsystem 102 may further include at least one X-ray acquisition path lens and / or acquisition path optics suitable for acquiring, collimating, and / or focusing the acquired signal 116 from the sample 106. Furthermore, the measurement subsystem 102 may include various illumination path optics 124 and / or acquisition path optics 134, such as X-ray collimating mirrors, specular X-ray optics such as oblique incidence elliptical mirrors, polycapillary optics such as hollow capillary X-ray waveguides, multilayer optics, or systems, or any combination thereof. In an embodiment, the measurement subsystem 102 includes an X-ray detector 130, which includes, but is not limited to, an X-ray monochromator (e.g., a crystalline monochromator such as a Loxley-Tanner-Bowen monochromator), an X-ray aperture, an X-ray beam stop, or a diffraction optical system (e.g., a zone plate).

[0102] Figure 1D is a simplified schematic diagram of a particle beam measurement subsystem 102 according to one or more embodiments of the present disclosure. Such a measurement subsystem 102 may be configured as, but is not limited to, a SEM, CD-SEM, Gray SEM, or TEM.

[0103] In some embodiments, the illumination source 118 includes a particle source (e.g., an electron beam source, an ion beam source, etc.) such that the illumination 114 includes a particle beam (e.g., an electron beam, a particle beam, etc.). The illumination source 118 may include any particle source known in the art that is suitable for generating particle illumination 114. For example, the illumination source 118 may include, but is not limited to, an electron gun or an ion gun. In some embodiments, the illumination source 118 is configured to provide a particle beam having an adjustable energy. For example, the illumination source 118 including an electron source may provide, but is not limited to, an acceleration voltage in the range of 0.1 kV to 30 kV. As another example, the illumination source 118 including an ion source may provide, but is not limited to, an ion beam having an energy in the range of 1 to 50 keV.

[0104] In some embodiments, the measurement subsystem 102 includes one or more particle focusing elements. For example, the one or more particle focusing elements may include, but are not limited to, a single particle focusing element or one or more particle focusing elements forming a composite system. In some embodiments, the one or more particle focusing elements include an illumination lens 120 configured to direct a particle illumination beam onto the sample 106. Furthermore, the one or more particle focusing elements may include, but are not limited to, electrostatic lenses, magnetic lenses, uni-potential lenses, or double-potential lenses, as well as any type of electron lens known in the art.

[0105] In some embodiments, the measurement subsystem 102 includes one or more particle detectors 130 for imaging or detecting particles emitted from the sample 106. For example, the detector 130 may include an electron collector (e.g., a secondary electron collector, a backscatter electron detector, etc.). In another example, the detector 130 may include a photon detector (e.g., a photodetector, an X-ray detector, a scintillator element coupled to a photomultiplier tube (PMT) detector, etc.) for detecting electrons and / or photons from the sample surface.

[0106] The subjects described herein may illustrate different components that are contained within or connected to other components. Such illustrated architectures are merely illustrative, and it should be understood that many other architectures can be implemented to achieve the same function. Conceptually, any arrangement of components to achieve the same function is effectively “associated” in such a way that the desired function is achieved. Therefore, any two components combined herein to achieve a particular function, regardless of architecture or intermediate components, can be considered “associated” with each other in such a way that the desired function is achieved. Similarly, any two such associated components can be considered “connected” or “joined” with each other to achieve the desired function, and any two components that can be associated in such a way can be considered “joinable” with each other to achieve the desired function. Specific examples of joinability include, but are not limited to, components that are physically interactable and / or interacting physically, and / or interacting wirelessly, and / or interacting wirelessly, and / or interacting logically.

[0107] Many of the present disclosure and its associated advantages are to be understood from the above description, and it will be clear that various modifications can be made to the form, configuration, and arrangement of the components without departing from the disclosed subject matter or sacrificing all of its important advantages. The described forms are for illustrative purposes only, and the following claims are intended to encompass and include such modifications. Furthermore, it should be understood that the present invention is defined by the appended claims.

Claims

1. A testing system comprising a controller including one or more processors, wherein the one or more processors are The process involves receiving first measurement data of one or more training samples after a first process step using an inline measurement subsystem, wherein the first process step is before the creation of multiple instances of test features on the one or more training samples. The inline measurement subsystem is used to receive second measurement data of the plurality of instances of the test feature after the second process step, the second process step being after the creation of the plurality of instances of the test feature, the plurality of instances of the test feature providing a distribution of intentional variations of one or more aspects of the test feature. For the multiple instances of the test feature, determine one or more delta indices associated with the first measurement data and the second measurement data. Using the second measurement data and the one or more delta indices, generate a measurement model for determining the measurement values ​​of the test feature based on at least one of the second measurement data and the one or more delta indices, Determining the value of the measurement for the instance of the one or more additional test features based on the second measurement data associated with the instance of one or more additional test features generated using the inline measurement subsystem and at least one of the one or more delta indices, A testing system configured to execute program instructions that cause one or more processors to execute the aforementioned program.

2. The inspection system according to claim 1, wherein the one or more delta indices are based on at least one of the mean squared error (RMSE), difference, absolute difference, sum, or absolute sum of the first measurement data and the second measurement data.

3. The program instructions are given to the one or more processors: Using a reference measurement subsystem different from the inline measurement subsystem, ground truth measurements of the multiple instances of the test feature after the second process step are received. The system is further configured to generate reference data based on the ground truth measurement and the one or more delta indices. Using the second measurement data and the one or more delta indices, a measurement model is generated for determining the measurement values ​​of the multiple instances of the test feature based on at least the second measurement data. The inspection system according to claim 1, comprising using the second measurement data and the reference data to generate the measurement model for determining the measurement values ​​of the plurality of instances of the test feature based on at least the second measurement data.

4. The reference data is generated based on the ground truth measurement and the one or more delta indices. A fitting function is determined that associates the ground truth measurement with the one or more delta indices. The inspection system according to claim 3, further comprising generating the reference data based on the fitting function.

5. The inspection system according to claim 3, wherein the inline measurement subsystem comprises an optical measurement subsystem.

6. The inspection system according to claim 5, wherein the reference measurement subsystem comprises at least one of an X-ray measurement subsystem or a particle beam measurement subsystem.

7. The inspection system according to claim 3, wherein the inline measurement subsystem comprises a particle beam measurement subsystem.

8. The inspection system according to claim 7, wherein the reference measurement subsystem comprises at least one of an X-ray photoelectron spectroscopy system or a transmission electron microscope system.

9. The inspection system according to claim 3, wherein the measurement model comprises the second measurement data and a machine learning model trained on at least one of the one or more delta indices or the reference data.

10. The inspection system according to claim 3, wherein the inline measurement subsystem comprises an optical measurement subsystem and the measurement model comprises an electromagnetic model.

11. The inspection system according to claim 10, wherein the electromagnetic model comprises an exact coupled-wave analysis (RCWA) model.

12. The inspection system according to claim 1, wherein the measurement model comprises a machine learning model trained on the second measurement data and the one or more delta indices.

13. The inspection system according to claim 1, wherein the inline measurement subsystem comprises an optical measurement subsystem, and the measurement model comprises an electromagnetic model.

14. The inspection system according to claim 13, wherein the electromagnetic model comprises an exact coupled-wave analysis (RCWA) model.

15. The inspection system according to claim 1, wherein the inline measurement subsystem comprises an optical measurement subsystem.

16. The inspection system according to claim 15, wherein the optical measurement subsystem is configured to determine at least some Mueller matrix elements associated with the plurality of instances of the test feature.

17. The inspection system according to claim 1, wherein the inline measurement subsystem comprises an optical measurement subsystem.

18. The inspection system according to claim 1, wherein the inline measurement subsystem comprises an X-ray measurement subsystem.

19. The first process step involves generating first measurement data for one or more training samples after a first process step using an inline measurement subsystem, wherein the first process step is before the creation of multiple instances of test features on the one or more training samples. Using the inline measurement subsystem, second measurement data is generated for the plurality of instances of the test feature after the second process step, the second process step being after the creation of the plurality of instances of the test feature, the plurality of instances of the test feature providing a distribution of intentional variations of one or more aspects of the test feature. For the multiple instances of the test feature, determine one or more delta indices associated with the first measurement data and the second measurement data. Using the second measurement data and the one or more delta indices, generate a measurement model for determining the measurement values ​​of the test feature based on at least one of the second measurement data and the one or more delta indices, Determining the value of the measurement for the instance of the one or more additional test features based on the second measurement data associated with the instance of one or more additional test features generated using the inline measurement subsystem and at least one of the one or more delta indices, Testing methods including those mentioned.

20. Inline measurement subsystem, Reference measurement subsystem and A testing system comprising a controller including one or more processors, wherein the one or more processors are The inline measurement subsystem is used to receive first measurement data of one or more training samples after the first process step, wherein the first process step is before the creation of multiple instances of test features on the one or more training samples. The inline measurement subsystem is used to receive second measurement data of the plurality of instances of the test feature after the second process step, the second process step being after the creation of the plurality of instances of the test feature, the plurality of instances of the test feature providing a distribution of intentional variations of one or more aspects of the test feature. For the multiple instances of the test feature, determine one or more delta indices associated with the first measurement data and the second measurement data. Using the reference measurement subsystem, receive ground truth measurements of the multiple instances of the test feature after the one or more second process steps. To generate reference data based on the ground truth measurement and the one or more delta indices, Using the second measurement data and the reference data, generate a measurement model for determining the measurement values ​​of the multiple instances of the test feature based on at least the second measurement data, and, Determining the value of the measurement for the instance of the one or more additional test features based on the second measurement data associated with the instance of one or more additional test features generated using the inline measurement subsystem and at least one of the one or more delta indices, A testing system configured to execute program instructions that cause one or more processors to execute the aforementioned program.

21. The reference data is generated based on the ground truth measurement and the one or more delta indices. A fitting function is determined that associates the ground truth measurement with the one or more delta indices. The inspection system according to claim 20, further comprising generating the reference data based on the fitting function.

22. The inspection system according to claim 20, wherein the inline measurement subsystem comprises an optical measurement subsystem.

23. The inspection system according to claim 22, wherein the optical measurement subsystem is configured to determine at least some Müller matrix elements associated with the plurality of instances of the test feature.

24. The inspection system according to claim 22, wherein the reference measurement subsystem comprises at least one of an X-ray measurement subsystem or a particle beam measurement subsystem.

25. The inspection system according to claim 20, wherein the inline measurement subsystem comprises a particle beam measurement subsystem.

26. The inspection system according to claim 25, wherein the inline measurement subsystem comprises a length-measuring scanning electron microscope system.

27. The inspection system according to claim 26, wherein the reference measurement subsystem comprises at least one of an X-ray photoelectron spectroscopy system or a transmission electron microscope system.

28. The inspection system according to claim 20, wherein the inline measurement subsystem comprises an X-ray measurement subsystem.

Images