Device feature specific edge placement error (EPE)

Abstract

A system and method for generating metrology measurements with a second subsystem, such as an optical subsystem, is disclosed. The method may include performing training and run-time operations. The training may include: receiving first metrology data for a device feature from a first metrology subsystem (e.g., optics); generating a first metrology measurement (e.g., critical dimension, etc.); classifying the device features into two or more groups of device blocks based on the first metrology measurements; and identifying a representative metrology target for the two or more device block groups based on the distribution of the first metrology measurements. The run-time operation may include: receiving run-time metrology data (e.g., optics) for the representative metrology target; and generating a run-time metrology measurement based on the run-time metrology data.

Description

[0001] CROSS REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 427,518, filed on November 23, 2022, entitled “DEVICE FEATURE SPECIFIC EDGE PLACEMENT ERROR (EPE)” in the names of Amnon Manassen, Nadav Gutman, Frank Laske and Andrei Shchegrov, under 35 U.S.C. §119(e), the entire text of which is incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to metrology, and more particularly to determining device characteristic related metrology. Background Art

[0004] Semiconductor manufacturing often requires fabrication of multiple layers on a sample, some or all of which contain device features (e.g., features, transistors). Overlay metrology is the measurement of the relative positions of structures on the various layers of a sample, which are critical to the performance of the device features and often must be controlled within tight tolerances. The greater the overlay, the greater the misalignment.

[0005] Special overlay / metrology targets may be utilized to help improve alignment. For example, the special overlay target may be a diffraction grating printed on multiple layers in areas that are separated from the device features being formed, such as in the scribe lines of the sample. The scribe lines may define the locations where the wafer is to be cut in subsequent processes. For example, a semiconductor wafer may include an array of dies, each of which may contain many device features and is separated from other dies by scribe lines.

[0006] Not all device feature layouts are suitable for direct overlay measurements. Furthermore, overlay measurements can damage or otherwise affect the performance of device features. Therefore, overlay measurements are typically performed on dedicated overlay targets with features designed for sensitive overlay measurements, rather than directly on device features. However, differences in the size, orientation, density, and / or location of overlay targets relative to device features on the sample can introduce a mismatch between the measured overlay at the target and the actual overlay of device features. Therefore, ensuring device feature-relevant overlay measurements on overlay targets remains an ongoing challenge in overlay metrology.

[0007] Furthermore, a critical dimension (CD) may be, for example, a lateral dimension of a feature when viewed in cross-section, such as the width of a gate or interconnect or the diameter of a hole. CD may also be defined as, but not limited to, an angle such as a sidewall angle.

[0008] The effort of obtaining device feature measurements of overlay or CD can generally be balanced with throughput requirements. For example, an overlay target with device feature level features can provide device feature related overlays. However, device feature level features of device features can generally be directly resolved using particle beam metrology tools (such as, but not limited to, scanning electron microscopes (SEMs)) that can be relatively slow and limit throughput in a production environment. In comparison, optical overlay metrology can provide higher throughput, but has relatively low resolution and cannot directly measure device feature overlays, and is therefore more susceptible to errors.

[0009] Furthermore, error tolerance budgets (e.g., acceptable overlay, CD, etc.) can or become increasingly smaller and more difficult to achieve, especially for optical tools used in high throughput run-time operations. Even adjusting for non-zero offset (NZO) (e.g., adjusting for the difference in measured overlay between after-develop inspection (ADI) and after-etch inspection (AEI)) may not be sufficient to meet the tighter error budgets.

[0010] The actual / true overlay of device features may vary depending on the type of device features. The distribution range of actual overlay values ​​of various device features in current and future nodes may hinder the achievement of error budgets.

[0011] Therefore, there is a need for a system and method for providing accurate measurements that meet error budgets while still allowing for large-scale, high-throughput operations. Summary of the invention

[0012] In another illustrative embodiment, the metering system may also include one or more controllers communicatively coupled to the first metering subsystem and the second metering subsystem. In another illustrative embodiment, the one or more controllers may include one or more processors configured to execute program instructions, the program instructions configured to perform training and runtime operations. In another illustrative embodiment, the training may include receiving first metering data for a plurality of device features from the first metering subsystem, generating first metering measurements of the device features based on the first metering data, grading the device features into two or more device block groups based on the first metering measurements, and identifying representative metering targets for the two or more device block groups based on a distribution of the first metering measurements. In another illustrative embodiment, the runtime operations may include receiving runtime metering data of the representative metering target over one or more runtime samples from the second metering subsystem, and generating runtime metering measurements of the representative metering target over the one or more runtime samples based on the runtime metering data.

[0013] In another aspect, the metrology system may also include determining a threshold violation based on monitoring of the runtime metrology measurement and directing a transmission indicative of the threshold violation. In another aspect, the identification of a representative metrology target may involve designing a representative metrology target or selecting the representative metrology target from a plurality of candidate representative metrology targets based on candidate target metrology data. In another aspect, the grading of device characteristics may involve identifying one or more groups of device characteristics based on one or more ranges of values ​​of the first metrology measurement, and grading the one or more groups of device characteristics into one or more outlier device block groups.

[0014] In another aspect, the training and run-time operations may be performed during post-etch inspection (AEI) or post-development inspection (ADI). In another aspect, the second metrology subsystem may include a spectroscopic ellipsometry (SE) subsystem or any other suitable subsystem. In another aspect, the first metrology subsystem may include a scanning electron microscope (SEM) subsystem. In another aspect, the binning of the device features may involve determining one or more device feature attribute distributions of the device features based on the first metrology measurements, and binning the device features into two or more device block groups based on the one or more device feature attribute distributions.

[0015] In another aspect, the training may further include receiving, via the second metrology subsystem, optical calibration measurements of a representative metrology target, verifying a correlation between the optical calibration measurements of the representative metrology target and measurements of a set of device blocks associated with the representative metrology target, and determining a calibration between the optical calibration measurements and the measurements of the set of device blocks associated with the representative metrology target. In another aspect, the metrology system may be configured to provide a correctable term to a lithography tool to adjust a sample fabrication process based on a runtime metrology measurement corresponding to the representative metrology target.

[0016] In another aspect, the design of a representative metrology target may involve designing a particular representative metrology target to include an array of repeating device features.

[0017] In another aspect, the one or more sets of device features may include an outlier set based on outlier ranges of the one or more value ranges of the first metrology measurement.

[0018] On the other hand, the outlier range may include an outlier range of CD values.

[0019] On the other hand, the outlier range may include an outlier range of OVL values.

[0020] On the other hand, a range in the one or more ranges may be based on a peak in a distribution of values ​​of the first metrology measurement.

[0021] On the other hand, two or more of the one or more ranges may each be based on a respective peak in the distribution of values ​​of the first metrology measurement.

[0022] In another aspect, monitoring of runtime metrology measurements for determining threshold violations may involve monitoring a runtime edge placement error (EPE) distribution determined based on the runtime metrology measurements.

[0023] On the other hand, the second metrology subsystem may include at least one of a number of designated subsystems, including an SE subsystem configured for multiple illumination angles, an SE subsystem configured for measuring Mueller matrix elements, a single wavelength ellipsometer subsystem, a beam profile ellipsometer subsystem, a beam profile reflectometer subsystem, a broadband reflectance spectrometer subsystem, a single wavelength reflectometer subsystem, an angle-resolved reflectometer subsystem, an imaging subsystem, or a scatterometer subsystem.

[0024] In another aspect, the correlation in the training process may include an ADI to AEI correlation between an optical calibration measurement and a measurement of a device block set, wherein the optical calibration measurement is configured as an after-development inspection (ADI) measurement and the measurement of the device block set is configured as a after-etch inspection (AEI) measurement.

[0025] According to one or more illustrative embodiments of the present disclosure, a metering system is disclosed. In one illustrative embodiment, the metering system may include one or more controllers. In another illustrative embodiment, the controller may include one or more processors configured to execute program instructions. In another illustrative embodiment, the program instructions may be configured to perform training and runtime operations. In another illustrative embodiment, the training may include receiving first metering data for a plurality of device features from a first metering subsystem, generating first metering measurements of the device features based on the first metering data, grading the device features into two or more device block groups based on the first metering measurements, and identifying representative metering targets for the two or more device block groups based on the distribution of the first metering measurements. In another illustrative embodiment, each of the representative metering targets may be selected to specify that a second metering measurement based on second metering data from a second metering subsystem for the representative metering target represents a first metering measurement of the device features in the corresponding two or more device block groups. In another illustrative embodiment, the runtime operation may include receiving runtime metering data for the representative metering target over one or more runtime samples from the second metering subsystem, and generating runtime metering measurements for the representative metering target over the one or more runtime samples based on the runtime metering data.

[0026] According to one or more illustrative embodiments of the present disclosure, a method for metering is disclosed. In one illustrative embodiment, the method may include performing training and performing runtime operations. In another illustrative embodiment, the training may include receiving first metering data for a plurality of device features from a first metering subsystem, generating first metering measurements of the device features based on the first metering data, classifying the device features into two or more device block groups based on the first metering measurements, and identifying representative metering targets for the two or more device block groups based on a distribution of the first metering measurements. In another illustrative embodiment, the runtime operations may include receiving runtime metering data of the representative metering target over one or more runtime samples from a second metering subsystem, and generating runtime metering measurements of the representative metering target over the one or more runtime samples based on the runtime metering data.

[0027] In another aspect, the method may include providing a calibrable term to a lithography tool to adjust a sample fabrication process based on a run-time metrology measurement corresponding to a representative metrology target.

[0028] It should be understood that both the foregoing summary and the following detailed description are exemplary and explanatory only and do not necessarily limit the invention as claimed. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the summary, serve to explain the principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Many advantages of the present disclosure may be better understood by those skilled in the art by referring to the accompanying drawings, in which:

[0030] Figure 1A is a conceptual diagram of a metering system according to one or more embodiments of the present disclosure.

[0031] Figure 1B is a conceptual diagram of an optical metrology tool according to one or more embodiments of the present disclosure.

[0032] Figure 1C is a conceptual diagram of a particle beam metrology tool according to one or more embodiments of the present disclosure.

[0033] Figure 2A is a conceptual diagram of utilization of various tools of a metrology system for measurement in accordance with one or more embodiments of the present disclosure.

[0034] Figure 2B is a conceptual diagram of a group of device blocks in the context of a plot of overlaid measurements of device characteristics in accordance with one or more embodiments of the present disclosure.

[0035] Figure 2C is a conceptual diagram of a group of device blocks in the context of a plot of critical dimension measurements of device features in accordance with one or more embodiments of the present disclosure.

[0036] Figure 3 is a flow chart illustrating steps performed in a method for training according to one or more embodiments of the present disclosure.

[0037] Figure 4 is a flow chart illustrating steps performed in a method for runtime operations according to one or more embodiments of the present disclosure. DETAILED DESCRIPTION

[0038] Reference will now be made in detail to the disclosed subject matter illustrated in the accompanying drawings. The present disclosure has been particularly shown and described with respect to specific embodiments and specific features thereof. The embodiments set forth herein should be regarded as illustrative and not restrictive. It should be readily apparent to one of ordinary skill in the art that various changes and modifications in form and details may be made without departing from the spirit and scope of the present disclosure.

[0039] Embodiments of the present disclosure relate to systems and methods for classifying device features (e.g., features of transistors, logic gates, etc.) into groups (e.g., device block groups), identifying which target designs represent which device block groups, so that measurements of targets having such target designs can be used to predict measurements of the classified device features, and generating measurements of the classified device features by generating measurements of representative metrology targets. For example, instead of metrology targets being used to approximate wafer-level / chip-level / region-level measurements (e.g., overlay of regions), device feature-related metrology target designs can be identified and paired with one or more device features to serve as proxies to more accurately determine measurements of such device features. Using targets corresponding to specific groups of device features allows for consideration of differences in actual / true measurements of those device features, thereby enabling more accurate measurements and more specific process control adjustments to be kept within tighter error budgets when manufacturing samples. This is different from targets that only consider overlay measurements as a whole without considering specific device feature types. For example, high-throughput ADI optical metrology measurements can be correlated with high-resolution AEI metrology measurements to measure overlay of specific block groups / groups of device features using corresponding representative targets specific to those block groups / groups. In this regard, optical measurements of targets using a high throughput second metrology subsystem may be used at an after-development inspection (ADI) step during a high volume manufacturing (HVM) process to predict overlay (OVL), critical dimension (CD), and / or edge placement error (EPE) measurements of device features at a after-etch inspection (AEI) step.

[0040] It is contemplated herein that, for example, actual measurements of OVL, CD, and / or EPE may be more accurately determined if device features are graded together and target designs associated with such graded device features are identified. For example, some target designs may share the same (or similar) measurements and may therefore be used to predict measurements of specific device features, but other target designs may not necessarily be consistent and / or accurately predictive for such device features. It should be noted that various attributes may be used to grade device features. For example, device features may be graded based on one or more attributes, such as (but not limited to) measurements (e.g., SEM measurements of device features at AEI, such as overlay, critical dimensions, etc.), tilt of the sample, location (e.g., distance from the edge of the device, a cut street, etc.), type of feature (e.g., type of transistor, type of logic gate), shape of the feature, size of the feature, whether the feature repeats periodically, in which type of layer the feature appears, or any other attribute. For example, a particular shape (e.g., an L shape close to two vertical lines, as measured by the SEM overlay measurement of the device features at AEI) that is located within a range (e.g., within 100 nanometers) of the edge of the device and has a higher (average) SEM overlay measurement than other device features at AEI. Figure 2AFeatures (such as feature #4 204d in FIG. 1 ) may be binned together in hopes of identifying a representative metrology target design associated with such device features. In this regard, a subset of binned device features may be associated with a representative metrology target design. Device block groups may be defined using one or more attributes, such that any other device features (e.g., future manufactured samples, device features of subsequent layers, unmeasured device features) may be determined to be part of the device block group if they have such attributes.

[0041] Measurements of device features, such as overlay, CD, and EPE, can be useful in sample fabrication and / or metrology processes. However, directly measuring device features within a desired accuracy can damage the device features and / or require slow, expensive metrology techniques, such as using a scanning electron microscope (SEM). Alternatively, a target (e.g., a dedicated target rather than an electrically functional device) can be used to determine the measurements.

[0042] In at least some samples, there may be a correlation between the measurement of the device feature and the measurement of the target, such that the target measurement can be used to predict the measurement related to the device feature. For example, systems and methods for generating device-dependent overlay measurements by adjusting the optical overlay measurements of the target with device-dependent corrections may be disclosed in U.S. Patent No. 10,533,848, filed on August 7, 2018, entitled “METROLOGY AND CONTROL OF OVERLAY AND EDGEPLACEMENT ERRORS,” which is incorporated herein by reference in its entirety.

[0043] Further embodiments of the present disclosure relate to generating run-time metrology measurements. For example, the run-time metrology measurements may be metrology measurements obtained at run-time (which may mean during a sample manufacturing process such as an HVM process). In this regard, the second metrology subsystem may directly capture data on the sample during manufacturing, close in time to the development step, without damaging the sample. In addition, identifying potential problems at this stage may facilitate reworking samples in the current or future batches to correct the problem before the time-consuming and irreversible etching step. In some instances, the target overlay measurement associated with the current process step optically measured on the sample may be used (e.g., used as an overlay correctable term) to compensate for drift and maintain the overlay within a selected tolerance for the process step on subsequent samples in the same or subsequent batches. By way of another example, the overlay measurement associated with the current process step may be fed forward to adjust subsequent process steps to compensate for any measured overlay error. Systems and methods for generating device-dependent overlay measurements by adjusting optical overlay measurements with device-dependent corrections and overlay correctable terms may be disclosed in U.S. Patent No. 10,533,848, filed on August 7, 2018, entitled “METROLOGY AND CONTROL OF OVERLAY AND EDGE PLACEMENT ERRORS,” which is incorporated herein by reference in its entirety.

[0044] Multiple metrology subsystems (e.g., optical and / or SEM metrology subsystems) may be used at various stages of training and / or runtime. For example, the terms "first" and "second" may be used for differentiation purposes only. For example, a first metrology subsystem (e.g., Figure 1C The first metering subsystem 102) and the second metering subsystem (e.g. Figure 1B The second metrology subsystem 104 can be used to measure the sample during the training step. For example, the first metrology subsystem can be (but is not necessarily required to be) a higher resolution and lower throughput (e.g., slower) subsystem (e.g., SEM) to allow accurate determination of data used to generate overlays, CDs, and / or EPEs. In contrast, the second metrology subsystem can be (but is not necessarily required to be) a lower resolution and higher throughput subsystem, such as an optical subsystem. The first metrology subsystem can allow measurements to be taken during the training step, while the second metrology subsystem can be used for bulk processing of samples during run time. In this way, subsystems of different resolutions and throughputs can be used to take advantage of respective strengths during training and during run time operations.

[0045] The second metrology subsystem can provide high-throughput measurements suitable for in-line process control. For example, the second metrology subsystem can utilize an imaging-based optical overlay method to simultaneously image features on multiple sample layers and determine overlay based on the relative displacement between the features. By another example, scatterometry-based optical metrology can determine overlay using a model-based approach in which light scattered and / or diffracted from a sample is compared to an expected pattern based on known sample features (e.g., but not limited to, grating structures in overlapping layers). In another example, a CD target can be imaged by an imaging-based optical CD method. Features of targets suitable for optical characterization can be segmented to provide multiple measurement points within a single field of view, which can facilitate highly accurate measurements based on low-noise statistical averaging using repetitive structures. In addition, the second metrology subsystem can provide (but is not necessarily required to provide) measurements of approximately 0.2 to 1 second per point, and thus can provide substantial flexibility in the measurement frequency per sample and per batch in a production line.

[0046] For purposes of this disclosure, the term "optically resolvable" indicates that at least a portion of a feature is resolvable within a selected tolerance using a selected second metrology subsystem. Examples of optically resolvable features may include, but are not limited to, grating patterns of specialized overlay targets positioned in the scribe lines of a wafer that may be resolved / measured using an image-based overlay (IBO) metrology method. For example, the size of an optically resolvable target may be approximately 10 microns or more (e.g., 30 microns).

[0047] Furthermore, "device feature level" features may include one or more characteristics similar to device features to be incorporated into fabricated device features (e.g., line width, separation distance between features, etc.). Examples of "device features" may include, but are not limited to, functional features of a die of a plurality of dies of a wafer (e.g., transistors, logic gates, etc.). Device features are typically smaller than optically resolvable targets and are typically not directly optically resolvable. For example, the size of at least some of the features of the device feature level features may be approximately less than 3 microns and the first metrology subsystem may include a tool defined to have a resolution of, for example, less than 1 micron, less than 10 nanometers, between 0.5 nanometers and 4 nanometers, etc. It should be recognized herein that a particular device feature may be at least partially resolvable by a selected second metrology subsystem, but may further contain characteristics that are below the resolution of the selected second metrology subsystem. It should be understood that the terms "optically resolvable" features and "device feature level" features, etc., are illustrative and are not intended to limit the size, orientation, or distribution of any patterned features on a sample. Some targets, such as hybrid overlay targets, may allow both optically resolvable measurements of hybrid overlay targets and device feature level measurements. In an embodiment, the plurality of target designs are a plurality of hybrid superposition target designs.

[0048] At least for some particle-based metrology tools, directly measuring device features can cause damage to the device features themselves. Embodiments of the present disclosure allow for the benefit of measuring targets using relatively fast optical-based methods and adjusting those optical measurements to estimate the measurement of device features without directly measuring the device features themselves. The benefit of such embodiments can allow for improved reliability of device features by optically measuring targets rather than measuring device features in a potentially damaging manner.

[0049] It should be noted that for the purposes of this disclosure, edge placement error (EPE) is defined as a function of overlay and CD. For example, EPE may be defined as the average of overlay and CD (ie, 1 / 2×(overlay+CD)).

[0050] Figures 1A to 1C and Figure 3 and 4 Systems and methods for providing runtime metrology measurements in accordance with one or more embodiments of the present disclosure are generally described. In embodiments, the systems and methods can be used to enhance existing methods of sample metrology by providing a cost-effective way to monitor measurements of device characteristics.

[0051] Figure 1A Conceptual diagram illustrating a metrology system 100 according to one or more embodiments of the present disclosure. The metrology system 100 may include, but is not limited to, a second metrology subsystem 104 and / or a first metrology subsystem 102. The metrology system 100 may additionally include, but is not limited to, a controller 108. In an embodiment, the controller 108 includes one or more processors 110 configured to execute program instructions maintained on a memory 112. In this regard, the one or more processors 110 of the controller 108 may perform any of the various process steps described throughout the present disclosure. For example, the controller 108 may receive data from either the second metrology subsystem 104 or the first metrology subsystem 102 and may further generate runtime metrology measurements. The metrology system 100 may be configured to measure one or more samples (e.g., Figure 1B 118), but does not necessarily include the sample.

[0052] In an embodiment, the first metrology subsystem 102 may be configured for a first resolution and a first throughput. The second metrology subsystem 104 may be configured for a second resolution (e.g., spatial resolution) and a second throughput (e.g., measurements per second). For example, the second resolution and the second throughput may be different from the first resolution and the first throughput. For example, the first resolution may be higher (e.g., more accurate, higher spatial resolution) than the second resolution and the first throughput may be lower than the second throughput. For example, the second resolution may be configured (e.g., capable of) resolving at least measurements of an object (e.g., an optically resolvable object) and the second resolution may be configured (e.g., capable of) resolving at least device features for overlay and / or CD purposes.

[0053] In an embodiment, the second metering subsystem 104 includes Figure 1B The optical metrology subsystem 172 and the first metrology subsystem 102 include a device feature resolvable subsystem. For example, the device feature resolvable subsystem may include Figure 1C It should be noted that although the first metrology subsystem 102 may be related to Figure 1C Although the first metrology subsystem 102 is described as a particle beam metrology subsystem 174 (e.g., a scanning electron microscope (SEM) metrology subsystem), the first metrology subsystem 102 may alternatively or additionally include a soft x-ray (SXR) metrology subsystem configured to measure the sample using electromagnetic radiation in the soft x-ray spectral range. For example, the SXR metrology subsystem can be used to measure multiple AEI device feature measurements of multiple device features.

[0054] A “tool” (e.g., a metrology tool, a first tool such as the particle beam metrology tool 176, a second tool such as the optical metrology tool 178, etc.) can be a subsystem coupled to the controller 108. For example, Figure 1B The controller 108 may be Figure 1A or Figure 1C 108. The optical metrology tool 178 may include an optical metrology subsystem 172 and a controller 108. In another example, the particle beam metrology tool 176 may include a particle beam metrology subsystem 174 and a controller 108.

[0055] Figure 1B 1 is a conceptual diagram of an optical metrology tool 178 according to one or more embodiments of the present disclosure. In an embodiment, the optical metrology tool 178 includes an optical metrology subsystem 172. The optical metrology subsystem 172 can be any subsystem used in the art suitable for generating measurements based on optically resolvable features. For example, the optical metrology subsystem 172 can be an example of the second metrology subsystem 104 and can include any type of optical overlay metrology subsystem known in the art suitable for generating optical overlay data associated with two or more layers of the sample 118, such as, but not limited to, an image-based optical metrology subsystem or a scatterometry-based optical metrology subsystem.

[0056] Instead of a scanning electron microscope, the second metrology subsystem 104 may include an optical subsystem for optically (eg, using a wavelength of light) receiving / measuring metrology data of the sample 118. For example, the second metrology subsystem 104 may include a spectroscopic ellipsometry (SE) subsystem.

[0057] In an embodiment, the second metrology subsystem 104 may include at least one of: a SE subsystem that may be configured for multiple illumination angles; a SE subsystem configured to measure Mueller matrix elements; a single wavelength ellipsometer subsystem; a beam profile ellipsometer subsystem; a beam profile reflectometer subsystem; a broadband reflectance spectrometer subsystem; a single wavelength reflectometer subsystem; an angle-resolved reflectometer subsystem; an imaging subsystem; or a scatterometer subsystem.

[0058] Figure 1C 1 is a conceptual diagram of a particle beam metrology tool 176 according to one or more embodiments of the present disclosure. In an embodiment, the particle beam metrology tool 176 includes a particle beam metrology subsystem 174. The particle beam metrology subsystem 174 may be an example of the first metrology subsystem 102 and may include, for example, any type of metrology tool suitable for resolving device features or device feature-level features of a target using particle beam techniques, such as, but not limited to, a scanning electron microscope (SEM) metrology tool.

[0059] Return to reference Figure 1A , the second metrology subsystem 104 can provide measurements of one or more targets (e.g., optical measurements) and the first metrology subsystem 102 can provide measurements suitable for determining measurement calibration adjustments to the optical measurements. In addition, the metrology system 100 can utilize any component (e.g., the second metrology subsystem 104, the first metrology subsystem 102) at any selected frequency in a production line (at run time) to balance measurement accuracy with throughput requirements. For example, the second metrology subsystem 104 can be used for in-line measurement monitoring, while the first metrology subsystem 102 can be selectively used at a lower frequency to determine measurement calibration adjustments to the optical measurements provided by the second metrology subsystem 104.

[0060] Figure 2A Conceptual diagram illustrating utilization of various subsystems of metrology system 100 for measuring device features 204 and target 210 in accordance with one or more embodiments of the present disclosure. For example, first metrology subsystem 102 may be configured (e.g., via properties such as resolution (e.g., spatial resolution), wavelength, field of view, program instructions stored on memory, etc.) to measure both device features 204 and target 210 as shown and second metrology subsystem 104 may be configured to image target 210.

[0061] In an embodiment, the device features 204 are ranked (eg, prior to ranking steps 306, 604) based on location (eg, location in the die; proximity to other components, devices, etc.; and / or the like).

[0062] In an embodiment, device features are ranked based on shape (eg, feature #, L-shape, square shape, etc.).

[0063] In an embodiment, device characteristics are ranked based on measurements such as overlay, CD, and / or EPE.

[0064] In an embodiment, device characteristics are ranked based on device type (eg, transistor type, logic gate type such as a NOR gate, etc.).

[0065] Figure 3 is a flow chart illustrating steps performed in a method 300 for performing training (including identifying representative metrology targets) according to one or more embodiments of the present disclosure. For example, training may include identifying which targets 210 may be representative metrology targets for which device block groups 212. It should be noted that the embodiments and enabling techniques described herein in the context of the metrology system 100 should be interpreted as extending to the method 300 and other methods herein (e.g., the method 400, the method 500). However, it should be further noted that the methods 300, the method 400, and the method 500 are not limited to the architecture of the metrology system 100.

[0066] In step 302 , first metrology data for a plurality of device characteristics 204 is received from the first metrology subsystem 102 .

[0067] In step 304 , a first metrological measurement of the device characteristic 204 is generated based on the first metrological data.

[0068] In step 306 , device features 204 are binned into two or more device block groups 212 based on the first metrology measurement.

[0069] In embodiments, device features may be ranked using one or more attributes (eg, measurement distribution, location, shape, etc.).

[0070] The binning may include: determining a device characteristic attribute distribution of the device characteristic based on the first metrology measurement; and binning the device characteristic into device block groups based on the one or more device characteristic attribute distributions.

[0071] For example, the device characteristic property distribution may include a distribution of sample tilt (ie, the angular tilt of the samples 118). For example, accounting for tilt may allow for improved (eg, more accurate) device grading and / or identification of representative metrology targets.

[0072] For example, the device feature attribute distribution may include a distribution of OVL distribution, CD distribution, and / or edge placement error (EPE) distribution. For example, several device features with similar CD values ​​(eg, values ​​above the 80th percentile threshold) may be binned together.

[0073] For example, device features may belong to feature type groups. Different feature type groups may be described by feature numbers (e.g., feature #1 204a, feature #2 204b, feature #3 204c, feature #4 204d, up to any number of feature type groups, such as feature #N 204e), where each feature type group may include many (e.g., tens, thousands, billions) of device features having shapes associated with the feature type group. For example, a specific logic gate structure used in a wafer die may be associated with a specific feature type group.

[0074] The grading may include identifying one or more groups of device features 204 based on one or more ranges of values ​​of the first metrology measurement; and grading the one or more groups of device features 204 into one or more outlier device block groups 212. For example, Figure 2C The outlier device block group 212d for device feature #1 204a is illustrated.

[0075] One or more groups of device features may include an outlier set based on an outlier range of one or more ranges of values ​​of the first metrology measurement. For example, any threshold value may be used to determine the outlier set. For example, a value above an outlier threshold value (e.g., a 90th percentile threshold value) may be used as a cutoff value so that all features above (or alternatively below) the threshold value may be binned together. In this example, a range is defined by a range of values ​​above the 90th percentile. A range may be defined between any number of percentiles, multiples of a standard deviation, and / or the like.

[0076] The outlier range may include an outlier range for CD values. The outlier range may include an outlier range for OVL values. The ranges may be based on peaks in a distribution of values ​​for the first metrology measurement. For example, instead of a simple bell curve, the distribution of values ​​may appear with multiple peaks, like a sine curve. Each peak / spike of the curve indicates a cluster of device features that have similar values ​​and therefore may benefit from using a representative target as a proxy to measure their equivalent values. Two or more ranges may each be based on a corresponding peak in the distribution of values.

[0077] In step 308, representative metrology targets for the two or more device block groups 212 are identified based on the distribution of the first metrology measurement. For example, identifying may include designing a representative metrology target or selecting a representative metrology target from candidate representative metrology targets.

[0078] In an embodiment, a representative metrology target is designed. For example, software may be used to design a representative metrology target using one or more design methodologies. For example, any methodology may be used. For example, a particular representative metrology target may be designed as an array of repeating device features. For example, the device features may be repeated as a grid of rows and columns. For example, the number of rows and / or columns may exceed 10. For example, the number of rows and / or columns may exceed 100. For example, the number of rows and / or columns may exceed 1000. In this way, the device features of the device block group themselves serve as the basis for designing a representative metrology target. By way of another example, the design may include performing an analysis to predict and / or curate the properties of the representative metrology target to represent the graded device features. For example, the prediction may include simulation, machine learning prediction, selection from the properties of historical metrology targets stored in memory, and / or any other means of predicting or generating a measurement of the (designed) representative metrology target.

[0079] In an embodiment, identifying a representative metrology target includes: receiving candidate target metrology data for a plurality of candidate representative metrology targets; and selecting a representative metrology target from the plurality of candidate representative metrology targets based on the candidate target metrology data. For example, one or more candidate representative metrology targets may be configured to be selected therefrom using the controller 108. For example, the candidate representative metrology target may be selected to specify that a second metrology measurement based on second metrology data from a second metrology subsystem for the representative metrology target represents a first metrology measurement of a device characteristic in the corresponding two or more device block groups. For example, the candidate target metrology data may be received and used to generate the candidate target metrology measurement. For example, in this particular context, receiving may mean receiving from the first metrology subsystem 102 and / or the second metrology subsystem 104. In another example, historical data (e.g., extracted from a memory and known a priori) for the candidate representative metrology target may be used.

[0080] Figure 4 is a flow chart illustrating steps performed in a method 400 for performing runtime operations according to one or more embodiments of the present disclosure. For example, after identifying representative metrology targets in training of method 300, method 400 can facilitate high throughput measurement of sample 118 during its manufacturing process.

[0081] In step 402 , runtime metering data of a representative metering target over one or more runtime samples is received from a second metering subsystem.

[0082] Runtime operations may be configured to be performed during ADI. For example, both training and runtime may be performed in ADI. In another example, training may be configured to be performed during AEI, and runtime operations may be performed during ADI. For example, training may be configured to be performed during AEI and runtime operations may be configured to be performed during ADI using the optical second metrology subsystem 104.

[0083] Runtime operations may be configured to be performed during post-etch inspection (AEI). For example, both training and runtime may be performed in AEI. For example, training may be configured to be performed during post-development inspection (ADI), and runtime operations are performed during AEI.

[0084] In step 404 , runtime metering measurements of representative metering targets on one or more runtime samples 118 are generated based on the runtime metering data.

[0085] In another step, a threshold violation is determined based on monitoring of the runtime metrology measurements; and a transmission is directed (sent by the controller 108) indicating the threshold violation. For example, when the distribution of the runtime metrology measurements is above a threshold (e.g., a known threshold, such as a threshold of acceptable values ​​selected by a user), the controller 108 may be configured to direct a transmission (e.g., a transmission or a command to send a transmission). For example, the transmission may include an alert configured to be viewed by a user or a command configured to stop the runtime operation. In this way, the optical runtime metrology measurements can be used to monitor the sample 118 manufacturing process and keep the process within the threshold. For example, the runtime edge placement error (EPE) distribution can be monitored. For example, the runtime CD distribution can be monitored. For example, the runtime overlay distribution can be monitored.

[0086] Training may include calibration, which may include receiving information (eg, calibration measurements), verifying the correlation of representative metrology targets to the set of device blocks, and using the received information to determine a calibration function to be used during runtime.

[0087] For example, training may include receiving optical calibration measurements of a representative metrology target via the second metrology subsystem 104. The runtime metrology measurements of the representative metrology target may correlate with, but not exactly match, actual / true measurements of actual device characteristics. If the correlation is sufficiently strong and predictable (as may be determined via validation), then the validated representative metrology target may be safely used.

[0088] For example, training may include verifying a correlation between an optical calibration measurement of a representative metrology target and a measurement of a device block group 212 associated with the representative metrology target. For example, the optical calibration measurement may be compared to a measurement of a device feature (e.g., a first metrology measurement) to determine if they are correlated. The correlation may be verified over a range of values. For example, measured values ​​of 10.00, 11.00, and 12.00 may be correlated with second actual values ​​of 10.11, 11.14, and 12.09. As shown, the second values ​​are approximately 10% higher, and if the measured values ​​are multiplied by 1.10, they will closely match the second actual values. In an example, if the difference is within a verification threshold, then the correlation is confirmed / verified. If not, then a different representative metrology target may be identified. This correlation / verification may be significant.

[0089] For example, training may include determining a calibration (e.g., a calibration function, an offset value, and / or the like) between the optical calibration measurements and the measurements of the device block set 212 associated with the representative metrology target, such that the calibration may be applied to produce more accurate runtime metrology measurements. For example, determining the calibration may be based on the difference between the optical calibration measurements of the representative metrology target and the measurements of the device block set 212 associated with the representative metrology target. For example, the calibration may include an offset value (e.g., where the value N should be equal to N+offset) or be linearly mismatched (e.g., such that N should be 1.10N). The calibration may be determined using any method known for calibration. For example, the difference between the linear regression fits of each set of numbers may be used to determine the calibration.

[0090] In an embodiment, the correlation includes an ADI to AEI correlation between the optical calibration measurement and the measurements of the device block group 212. The optical calibration measurement may be configured as a post-development inspection (ADI) measurement and the measurements of the device block group may be configured as a post-etch inspection (AEI) measurement. For example, if a representative metrology target is identified based on a SEM second metrology subsystem measurement in AEI, but a user wishes to monitor the EPE distribution in ADI, the controller 108 may be configured to calibrate and verify the correlation between the optical ADI measurement of the representative metrology target and the SEM measurement of the corresponding graded device features in AEI. In this way, high throughput optical imaging (e.g., second metrology subsystem 104 imaging) in ADI (i.e., before etching) may be used to predict the AEI (i.e., after etching) device feature distribution.

[0091] like Figure 2B and 2CAs shown in , one or more groups of device features (e.g., features #1 to 5 on the horizontal axis) can be ranked into one or more device block groups 212 (e.g., device block groups 212a, 212b, 212c, 212d, 212e) based on similarities between device feature measurements 222, 224 and target design measurements 214, 216, 218.

[0092] Figure 2B A conceptual diagram illustrating device block group 212 in the context of a plot 206 of overlay measurements 222 of device characteristics 204 in accordance with one or more embodiments of the present disclosure. For example, device characteristic measurements 222 may include CD, EPE, and / or overlay measurements.

[0093] The device feature measurements 222, 224 may vary for different structural characteristics of the device feature 204 and the target design measurements 214, 216 (e.g., measurements of representative metrology targets) may vary for different representative metrology target designs. For example, the device feature measurements may vary depending on the properties of the device feature (e.g., shape, size, location, etc.) and the representative metrology target measurements may vary depending on the properties of the representative metrology target (e.g., shape, size, etc.). For example, as shown in FIG. Figure 2B The horizontal axis of the "2" above Figure 2A The overlay measurement 222 associated with feature #2 204b may vary due to, for example, location relative to an edge of the die.

[0094] In an embodiment, device features may be ranked into device block groups 212 based on range. For example, a measurement range may mean an average of a maximum, minimum, etc. measurement. For example, feature #s associated with the top 40% of the stacked measurements may be ranked together, as shown by device block group 212b. Device features with the lowest averages of measurements 222 (e.g., the lowest average, the two lowest averages as shown, and / or the three lowest averages) may be ranked together, as shown by ranking feature numbers 1 and 2 in device block group 212c.

[0095] In an embodiment, device characteristics may be ranked based on the average of the measurements of the device characteristics. Figure 2B The measurements 222 of feature type number 3 may be sorted into the average distribution device block group 212a. For example, the average may be the average of all measurements taken or a subset of the measurements taken.

[0096] Each device block group 212 may be associated with a target 210. For example, an overlay device block group 212b may be associated with Figure 2A For example, as shown close to Figure 2BTarget measurement 214 of measurement 222 in may be obtained from target 210b. In this regard, target 210b may be identified as a representative metrology target for overlay device block group 212b. Similarly, target 210c (e.g., a different target) of target measurement 216 may be identified as a representative metrology target for overlay device block group 212a due to proximity / similarity to measurement 222 of device block group 212a.

[0097] like Figure 2B , a representative metrology target design can sometimes be identified based on a correlation (e.g., similarity) between target design measurements 216 and device characteristic measurements (e.g., overlay measurements 222). In some examples, a representative metrology target can be defined by one or more target design attributes, such that any target that meets such attribute criteria is considered to be such a target design. For example, in Figure 2B In the case of a device block group 212a in FIG. 2 , a target 210a, which may include multiple targets 210a having similar target design attributes, may be a target used to generate a target design measurement 216 (which may include multiple target design measurements 216 or be an average of multiple target design measurements). The target 210a may have one or more target design attributes that may be selected for defining a representative metrology target design. For example, such target design attributes may be, but are not limited to, size; various grating characteristics, such as the pitch, shape, and the like of each grating; thickness; type (e.g., a box-in-box style overlay target, an AIM style overlay target, and the like); and any other attributes. For example, after measuring many (e.g., tens, hundreds, thousands) of target designs, it may become apparent in an identification step that the pitch of the gratings of the measured target designs within a particular range is associated with a particular device block group 212. For example, a target design having such a pitch may be most closely associated with the measurement 222 of the device block group 212. In this example, the identified “representative metrology target,” “representative metrology target design,” and the like may be any target having such a pitch.

[0098] Figure 2C Conceptual diagram illustrating device block group 212 in the context of plot 208 of critical dimension (CD) measurements 224 of device features according to one or more embodiments of the present disclosure. CD measurements 224 may be generated (eg, measured, calculated based on data) using first metrology subsystem 102, for example.

[0099] As shown, device block group 212d may be an outlier device block group 212d selected as containing device features associated with feature number 1 of measurement 224 shown within CD device block group 212d. For example, a representative metrology target having optical measurement 220 of CD may be identified based on correlation with CD device block group 212d.

[0100] As shown, CD device block group 212e may be selected to contain device features of feature numbers 2-4 associated with measurement 224 shown within CD device block group 212e. For example, a representative metrology target may be identified based on correlation with CD device block group 212e. For example, target 210 of target measurement 218 may be identified as a representative metrology target associated with CD device block group 212e.

[0101] In an embodiment, the system 100 may include one or more controllers 108. For example, the steps may be performed individually and / or in combination on one or more controllers. For example, the steps may be performed using different software applications stored on different computers (instances of controllers) and / or the like.

[0102] Reference again Figure 1A and 1C , describing embodiments of various components in additional detail.

[0103] The controller 108 may be communicatively coupled to one or more external fabrication tools (eg, but not limited to, lithography tools). In this regard, the controller 108 may operate as an advanced process controller (APC) adapted to control inputs of the external fabrication tools to maintain overlay within a selected overlay tolerance.

[0104] In an optional step, overlay correctable terms (based on the run-time metrology measurements) for device feature regions (e.g., portions of functional / active regions of a die that include device features) are provided to the lithography tool to adjust the sample fabrication process based on the run-time metrology measurements, such as modifying exposure conditions for at least one subsequent exposure of the fabrication process. For example, the run-time metrology measurements may include run-time metrology measurements corresponding to a plurality of representative metrology targets corresponding to the respective device block groups 212. For example, the overlay correctable terms may be adjustments of any parameters, such as if the overlay is misaligned by a certain distance, the lithography process may be adjusted in the opposite direction to correct the misalignment.

[0105] It should be understood that Figure 1A The metering system 100 illustrated in FIG. 1 and the associated description are provided for illustrative purposes only and should not be construed as limiting. For example, the metering system 100 may include Figure 1A In one example, the metering system 100 may include a second metering subsystem 104, a first metering subsystem 102, and a controller 108. In addition, any components of the metering system 100 may be located close to each other or may be located remotely from each other. In an embodiment, multiple components of the metering system 100 may be integrated into a single physical device feature.

[0106] Figure 1Bis a conceptual diagram of an optical metrology tool 178 (eg, including the first metrology subsystem 104 ) according to one or more embodiments of the present disclosure.

[0107] In an embodiment, the optical metrology tool 178 and the first metrology subsystem 104 include an optical illumination source 114 to generate an optical illumination beam 116. The optical illumination beam 116 may include light of one or more selected wavelengths including, but not limited to, ultraviolet (UV), visible, or infrared (IR).

[0108] Optical illumination source 114 may be any type of illumination source known in the art suitable for producing optical illumination beam 116 .

[0109] The optical illumination source 114 may include any type of illumination source suitable for providing the optical illumination beam 116. In an embodiment, the optical illumination source 114 is a laser source. For example, the optical illumination source 114 may include, but is not limited to, one or more narrowband laser sources, broadband laser sources, supercontinuum laser sources, white light laser sources, and the like. In this regard, the optical illumination source 114 may provide an optical illumination beam 116 having high coherence (e.g., high spatial coherence and / or temporal coherence). In an embodiment, the optical illumination source 114 includes a laser-sustained plasma (LSP) source. For example, the optical illumination source 114 may include, but is not limited to, an LSP lamp, an LSP bulb, or an LSP chamber suitable for housing one or more elements that can emit broadband illumination when excited into a plasma state by a laser source. In an embodiment, the optical illumination source 114 includes a lamp source. For example, the optical illumination source 114 may include, but is not limited to, an arc lamp, a discharge lamp, an electrodeless lamp, and the like. In this regard, the optical illumination source 114 may provide an optical illumination beam 116 having low coherence (eg, low spatial coherence and / or temporal coherence).

[0110] In an embodiment, the optical illumination source 114 directs the optical illumination beam 116 to the sample 118 via an illumination path 120. The illumination path 120 may include one or more illumination path lenses 122 or additional optical components 124 suitable for modifying and / or adjusting the optical illumination beam 116. For example, the one or more optical components 124 may include, but are not limited to, 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, or one or more beam shapers. The illumination path 120 may further include an objective lens 126 configured to direct the optical illumination beam 116 to the sample 118.

[0111] In an embodiment, the sample 118 is disposed on a sample stage 128. The sample stage 128 may include any device features suitable for positioning and / or scanning the sample 118 within the optical metrology subsystem 172. For example, the sample stage 128 may include any combination of a linear translation stage, a rotation stage, a flip / tilt stage, and the like.

[0112] In an embodiment, the optical metrology subsystem 172 includes a detector 130 configured to capture light emitted from the sample 118 through a collection path 132. The collection path 132 may include, but is not limited to, one or more collection path lenses 134 for collecting light from the sample 118. For example, the detector 130 may receive light reflected or scattered (e.g., via specular reflection, diffuse reflection, etc.) from the sample 118 via the one or more collection path lenses 134. By another example, the detector 130 may receive light generated by the sample 118 (e.g., luminescence associated with absorption of the optical illumination beam 116, etc.). By another example, the detector 130 may receive light of one or more diffraction orders (e.g., 0th order diffraction, ±1st order diffraction, ±2nd order diffraction, etc.) from the sample 118.

[0113] The detector 130 may include any type of detector known in the art suitable for measuring the illumination received from the sample 118. For example, the detector 130 may include, but is not limited to, a multi-pixel detector such as a CCD detector, a CMOS detector, etc. In an embodiment, the detector 130 may include a spectral detector suitable for identifying the wavelength of light emitted from the sample 118.

[0114] The collection path 132 may further include any number of optical elements to direct and / or modify the collected illumination from the sample 118, including but not limited to one or more collection path lenses 134, one or more filters, one or more polarizers, or one or more beam stops.

[0115] In an embodiment, the detector 130 is positioned approximately normal to the surface of the sample 118. In an embodiment, the optical metrology subsystem 172 includes a beam splitter 136 oriented so that the objective 126 can simultaneously direct the optical illumination beam 116 to the sample 118 and collect light emitted from the sample 118. Furthermore, the illumination path 120 and the light collection path 132 can share one or more additional elements (e.g., objective 126, apertures, filters, etc.).

[0116] The optical metrology subsystem 172 may measure the overlay based on any technique known in the art, such as, but not limited to, an imaging-based technique or a scatterometry-based technique. For example, the optical metrology subsystem 172 operating in an imaging mode may illuminate a portion of the sample 118 and capture an image of the illuminated portion of the sample 118 on the detector 130. The captured image may be any type of image known in the art, such as, but not limited to, a brightfield image, a darkfield image, a phase contrast image, and the like. Furthermore, the captured images may be stitched together (e.g., by the optical metrology subsystem 172, by the controller 108, and the like) to form a composite image of the sample 118. By way of another example, the optical metrology subsystem 172 may scan the focused optical illumination beam 116 across the sample 118 and capture light and / or particles emitted from the sample 118 at one or more measurement angles on one or more detectors 130 to produce an image pixel by pixel. The focused optical illumination beam 116 can be scanned across the sample 118 by modifying the beam path (e.g., using a galvanometer, piezoelectric mirror, etc.) and / or by translating the sample 118 through a focal volume of the focused beam. Thus, a superposition associated with two or more sample layers can be determined based on the relative positions of features located on the two or more sample layers.

[0117] By way of another example, the optical metrology subsystem 172 may operate as a scatterometry-based metrology subsystem by determining superposition based on patterns of light scattered and / or diffracted from the sample 118 in response to the optical illumination beam 116. For example, the optical metrology subsystem 172 may acquire (e.g., with the detector 130) one or more pupil plane images (e.g., of different regions of a superposition target) that include angular distributions of light emitted from the sample. Thus, superposition between two or more sample layers may be determined from the pupil plane images based on modeled scattering and / or diffraction of superposition target features having known sizes and distributions from each layer.

[0118] Furthermore, the optical metrology subsystem 172 may measure the overlay at any manufacturing step by causing the optical illumination beam 116 to propagate through the current layer to interact with features on one or more previously manufactured layers such that the signal received by the detector 130 (e.g., an image of the sample 118, an image of the pupil plane, etc.) indicates the overlay between at least two layers. For example, the optical metrology subsystem 172 may measure the overlay between the previously manufactured layer and the current layer as a post-development inspection (ADI) step after exposure of the current layer. In this regard, the overlay measurement of the current layer and any previous layers may be generated based on the difference in refractive index of the exposed features relative to the unexposed features. By way of another example, the optical metrology subsystem 172 may measure the overlay between the previously manufactured layer and the current layer as a post-etch inspection (AEI) step after the developed pattern has been etched into the current layer as a relief structure.

[0119] Figure 1C is a conceptual diagram of a particle beam metrology tool 176 according to one or more embodiments of the present disclosure. The particle beam metrology subsystem 174 of the particle beam metrology tool 176 is an example of a first metrology subsystem 102 and may include, for example, a scanning electron microscope (SEM) metrology subsystem 102 .

[0120] The first metrology subsystem 102 can perform measurements at any manufacturing step. For example, the first metrology subsystem 102 can perform measurements between a previously manufactured layer and a current layer as a post-development inspection (ADI) step after exposure and / or development of the current layer. By way of another example, the first metrology subsystem 102 can perform measurements between a previously manufactured layer and a current layer as a post-etch inspection (AEI) step after a developed pattern has been etched into the current layer as a relief structure.

[0121] In an embodiment, the particle beam metrology subsystem 174 includes a particle source 138 (e.g., an electron beam source, an ion beam source, etc.) to generate a particle beam 140 (e.g., an electron beam, an ion beam, etc.). The particle source 138 may include any particle source known in the art suitable for generating a particle beam 140. For example, the particle source 138 may include, but is not limited to, an electron gun or an ion gun. In an embodiment, the particle source 138 is configured to provide a particle beam 140 with a tunable energy. For example, a particle source 138 including an electron source may, but is not limited to, provide an accelerating voltage in the range of 0.1 kV to 30 kV. As another example, a particle source 138 including an ion source may, but need not, provide an ion beam with an energy in the range of 1 to 50 keV.

[0122] In an embodiment, the particle beam metrology subsystem 174 includes one or more particle focusing elements 142. For example, the one or more particle focusing elements 142 may include, but are not limited to, a single particle focusing element or one or more particle focusing elements forming a composite system. In an embodiment, the one or more particle focusing elements 142 include a particle objective lens 144 configured to direct the particle beam 140 to the sample 118 positioned on the sample stage 146. In addition, the one or more particle sources 138 may include any type of electron lens known in the art, including, but not limited to, an electrostatic lens, a magnetic lens, a single potential lens, or a double potential lens.

[0123] In an embodiment, the particle beam metrology subsystem 174 includes at least one particle detector 148 that images or otherwise detects particles emitted from the sample 118. In an embodiment, the particle detector 148 includes an electron collector (e.g., a secondary electron collector, a backscattered electron detector, etc.). In an embodiment, the particle detector 148 includes a photon detector (e.g., a photodetector, an x-ray detector, a scintillation element coupled to a photomultiplier tube (PMT) detector, etc.) for detecting electrons and / or photons from the sample surface.

[0124] It should be understood that Figure 1C The description of the particle beam metrology tool 176 depicted in and the associated description above are provided for illustrative purposes only and should not be construed as limiting. For example, the particle beam metrology tool 176 may include a multi-beam and / or multi-column system suitable for simultaneously interrogating the sample 118. In further embodiments, the particle beam metrology tool 176 may include one or more components (e.g., one or more electrodes) configured to apply one or more voltages to one or more locations of the sample 118. In this regard, the particle beam metrology tool 176 can generate voltage contrast imaging data.

[0125] It is recognized herein that the penetration depth of the particle beam 140 in the sample 118 may depend on the particle energy, such that higher energy beams generally penetrate deeper into the sample 118. In an embodiment, the particle beam metrology subsystem 174 utilizes different particle energies to interrogate different layers of device features based on the penetration depth of the particle beam 140 into the sample 118. For example, the particle beam metrology subsystem 174 may utilize a relatively low energy electron beam (e.g., approximately 1 keV or less) and may utilize a higher energy beam (e.g., approximately 10 keV or more) to characterize a previously manufactured layer. It is recognized herein that the penetration depth that varies depending on the particle energy may vary for different materials, such that the selection of the particle energy for a particular layer may vary for different materials.

[0126] The optically resolvable features and device feature level features of a hybrid overlay target may have any orientation or distribution in the hybrid overlay target suitable for providing both optical overlay and device feature level overlay in the same direction or multiple directions. In an embodiment, the optically resolvable features and device feature level features are physically separated. For example, an optical metrology target with embedded device level features is generally described in U.S. Pat. No. 9,093,458, issued on July 28, 2015, entitled "DEVICE CORRELATED METROLOGY (DCM) FOR OVL WITH EMBEDDED SEM STRUCTURE OVERLAY Targets", which is incorporated herein by reference in its entirety. In an embodiment, at least some of the optically resolvable features of the hybrid overlay target are segmented at a device level pitch. In this regard, optical overlay measurements and device level overlay measurements may be performed in the same physical location, which may provide increased accuracy of overlay tool errors. For example, segmented targets having optically resolvable features and device-level features are generally described in U.S. Patent Publication No. US2014 / 0307256, entitled “Process Compatible Segmented Targets and Design Methods,” published on October 16, 2014, the entire text of which is incorporated herein by reference.

[0127] By way of another example, the target 210 may include a periodic structure (e.g., features that are periodically distributed in one or more directions). It should be recognized herein that a target having periodic features in one or more layers may provide multiple measurement locations. For example, a superposition may be measured relative to any one of the periodic elements. In this regard, a target having periodic elements in one or more layers may thus enhance the accuracy and / or throughput of superposition measurements (e.g., optical superposition measurements or device feature-level superposition measurements of device feature-level features). For example, at a given dose of illumination associated with the superposition measurement (e.g., deposited energy per area on the sample), a superposition measurement of periodic features based on multiple measurement locations may have a higher accuracy than a superposition measurement based on a single measurement location (e.g., a single feature). In another example, a lower dose of illumination may be used to obtain a given superposition measurement accuracy when based on multiple measurement locations compared to a superposition measurement based on a single measurement location. It should be further recognized herein that reducing the illumination dose required to perform a superposition measurement may reduce damage to the sample 118 and / or increase measurement throughput.

[0128] It should be recognized herein that overlay errors may be introduced at nearly every stage of manufacturing and may vary spatially across samples or temporally from one sample to the next or from one batch of samples to the next in a production run. For example, a lithography tool (e.g., a stepper, scanner, etc.) may typically have a field of view that is smaller than the entire sample and may therefore divide the sample into a series (e.g., a grid) of exposure fields that may be exposed separately. Grid errors associated with misalignment of the retracted reticle to the sample during an exposure step of one or more exposure fields may contribute to overlay errors that vary spatially across the sample. Additionally, aberrations in the lithography tool during exposure (e.g., lens aberrations, turbulence associated with heat, etc.) may result in spatially varying pattern placement errors within a single exposure field. By way of another example, overlay errors may include process errors associated with fabricating a three-dimensional structure on a sample based on an exposed pattern. Process errors may include, but are not limited to, distortion of the exposed pattern during lithography, errors induced by etching, polishing errors, or errors associated with variations in the sample. Thus, overlays measured at an overlay target may be subject to spatially varying target-to-device feature errors based on the displacement between the overlay target and the device feature.

[0129] Overlay targets can generally be placed anywhere on the sample, depending on the metrology recipe. However, the size, orientation, and / or density of features in the target can affect target placement. For example, overlay targets with optically resolvable features are generally placed in the scribe lines between die of the sample in order to reserve space for device features within the die, and / or because optically resolvable features may not comply with process design rules. By way of another example, overlay targets with features complying with process design rules may generally be placed within the sample die near device features of interest or within the scribe lines.

[0130] As previously mentioned herein, the one or more processors 110 of the controller 108 may be communicatively coupled to the memory 112, wherein the one or more processors 110 may be configured to execute a set of program instructions maintained in the memory 112, and the set of program instructions may be configured to cause the one or more processors 110 to perform various functions and steps of the present disclosure.

[0131] It should be noted herein that one or more components of the metering system 100 may be communicatively coupled to various other components of the metering system 100 in any manner known in the art. For example, the one or more processors 110 may be communicatively coupled to each other and to other components via a wired (e.g., copper wire, fiber optic cable, etc.) or wireless connection (e.g., RF coupling, IR coupling, WiMax, Bluetooth, 3G, 4G, 4G LTE, 5G, etc.). By way of another example, the controller 108 may be communicatively coupled to one or more components of the metering system 100 via any wired or wireless connection known in the art.

[0132] In an embodiment, the one or more processors 110 may include any one or more processing elements known in the art. In this sense, the one or more processors 110 may include any microprocessor-type device configured to execute software algorithms and / or instructions. In an embodiment, the one or more processors 110 may be composed of: a desktop computer, a large computer system, a workstation, an image computer, a parallel processor, or other computer systems (such as network-linked computers) configured to execute a process configured to operate the metering system 100, as described throughout this disclosure. It should be recognized that the steps described throughout this disclosure can be implemented by a single computer system or alternatively multiple computer systems. In addition, it should be recognized that the steps described throughout this disclosure can be implemented on any one or more of the one or more processors 110. In general, the term "processor" can be broadly defined to cover any device having one or more processing elements that execute program instructions from the memory 112. In addition, the different subsystems of the metrology system 100 (e.g., the optical metrology subsystem 172, the device-level metrology subsystem 174, the controller 108, the user interface 170, etc.) may include processors or logic elements suitable for implementing at least part of the steps described throughout the present disclosure. Therefore, the above description should not be interpreted as limiting the present disclosure, but is merely illustrative.

[0133] The memory 112 may include any storage media known in the art suitable for storing program instructions executable by the associated one or more processors 110 and data received from the metering system 100. For example, the memory 112 may include non-transitory storage media. For example, the memory 112 may include, but is not limited to, read-only memory (ROM), random access memory (RAM), magnetic or optical storage (such as magnetic disk), tape, solid-state drive, etc. It should be further noted that the memory 112 may be housed in a common controller housing with the one or more processors 110. In alternative embodiments, the memory 112 may be remotely located relative to the physical location of the processors 110, controller 108, etc. In an embodiment, the memory 112 maintains program instructions for causing the one or more processors 110 to perform the various steps described through the present disclosure.

[0134] In an embodiment, the user interface 170 is communicatively coupled to the controller 108. The user interface 170 may include, but is not limited to, one or more desktop computers, tablet computers, smart phones, smart watches, and the like. In an embodiment, the user interface 170 includes a display for displaying data of the metering system 100 to a user. The display of the user interface 170 may include any display known in the art. For example, the display may include, but is not limited to, a liquid crystal display (LCD), an organic light emitting diode (OLED)-based display, or a CRT display. Those skilled in the art will recognize that any display device capable of being integrated with the user interface 170 is suitable for implementation in the present disclosure. In an embodiment, a user may input selections and / or instructions in response to data displayed to the user via a user input device of the user interface 170.

[0135] All methods described herein may include storing the results of one or more steps of the method embodiment in a memory. The result may include any result described herein and may be stored in any manner known in the art. The memory may include any memory described herein or any other suitable storage medium known in the art. After the result has been stored, the result may be accessed in the memory and used by any of the method or system embodiments described herein, formatted to display to a user, used by another software module, method or system, etc. In addition, the result may be stored "permanently", "semi-permanently", "temporarily" or stored for a period of time. For example, the memory may be a random access memory (RAM), and the result may not necessarily be retained in the memory indefinitely.

[0136] It should be further considered that each of the embodiments of the method described above may include any other steps of any other method described herein. In addition, each of the embodiments of the method described above may be performed by any one of the systems described herein.

[0137] Those skilled in the art will recognize that for conceptual clarity, the components, operations, devices, objects, and their accompanying discussions described herein are used as examples, and various configuration modifications are contemplated. Therefore, as used herein, the specific examples and accompanying discussions set forth are intended to represent their more general class. In general, the use of any specific example is intended to represent its class, and the non-inclusion of specific components, operations, devices, and objects should not be considered limiting.

[0138] As used herein, directional terms such as "top", "bottom", "above", "below", "up", "upward", "down", "downward", and "downwards" are intended to provide relative positions for descriptive purposes and are not intended to specify an absolute reference system. Various modifications to the described embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments.

[0139] With respect to the use of substantially any plural and / or singular terms herein, those skilled in the art may translate from the plural to the singular and / or from the singular to the plural as appropriate depending on the context and / or application. For clarity, various singular / plural arrangements are not explicitly set forth herein.

[0140] The subject matter described herein sometimes illustrates different components contained in or connected to other components. It should be understood that such depicted architectures are exemplary only, and in fact many other architectures that realize the same functionality can be implemented. In a conceptual sense, any configuration of components that realize the same functionality is effectively "associated" so that the desired functionality is realized. Therefore, any two components combined to realize a specific functionality herein can be considered to be "associated" with each other so that the desired functionality is realized, regardless of the architecture or intermediate components. Similarly, any two components so associated can also be considered to be "connected" or "coupled" to each other to realize the desired functionality, and any two components that can be so associated can also be considered to be "coupleable" to each other to realize the desired functionality. Specific examples that can be coupled include but are not limited to physically mateable and / or physically interactive components and / or wirelessly interactive and / or wirelessly interactive components and / or logically interactive and / or logically interactive components.

[0141] In addition, it should be understood that the present invention is defined by the appended claims. Those skilled in the art will understand that, in general, the terms used herein and in particular in the appended claims (e.g., the body of the appended claims) are generally intended to be "open" terms (e.g., the term "including" should be interpreted as "including but not limited to", the term "having" should be interpreted as "having at least", the term "includes" should be interpreted as "including but not limited to", etc.). Those skilled in the art will further understand that if a specific number of introduced claim recitations is intended, such intent will be explicitly recited in the claims, and in the absence of such a recitation, such intent does not exist. For example, to aid understanding, the appended claims below may contain the use of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be interpreted as implying that the introduction of a claim recitation by the indefinite article "a" or "an" limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even if the same claim contains the introductory phrases "one or more" or "at least one" and an indefinite article such as "a" (e.g., "a" should generally be interpreted as meaning "at least one" or "one or more"); the same is true for the use of definite articles to introduce claim recitations. In addition, even if a specific number of introduced claim recitations is explicitly recited, those skilled in the art will recognize that such recitation should generally be interpreted to mean at least the recited number (e.g., the mere recitation of "two recitations" without other modifiers generally means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention similar to "at least one of A, B, and C, etc." is used, such construction is generally contemplated in the sense that one skilled in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). In those instances where a convention similar to "at least one of A, B, or C, etc." is used, such construction is generally contemplated in the sense that one skilled in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include, but is not limited to, systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). One skilled in the art would further understand that virtually any transitional word and / or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibility of including one of the terms, either of the terms, or both of the terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B."

[0142] It is believed that from the foregoing description, the present disclosure and many of its attendant advantages will be understood, and it will be apparent that various changes may be made to the form, construction, and arrangement of components without departing from the disclosed subject matter or sacrificing all of its significant advantages. The described form is illustrative only, and the appended claims are intended to cover and encompass such changes. Furthermore, it should be understood that the invention is defined by the appended claims.

Claims

1. A metering system, comprising: a first metrology subsystem configured to have a first resolution; a second metrology subsystem configured to have a second resolution lower than the first resolution; and one or more controllers communicatively coupled to the first metering subsystem and the second metering subsystem and comprising one or more processors configured to execute program instructions configured to: Performing training, the training comprising: receiving first metrology data for a plurality of device characteristics from the first metrology subsystem; generating a first metrological measurement of the device characteristic based on the first metrological data; sorting the device characteristics into two or more device block groups based on the first metrology measurement; and identifying representative metrology targets for the two or more device block groups based on the distribution of the first metrology measurement, wherein each of the representative metrology targets is selected to specify that a second metrology measurement based on second metrology data from the second metrology subsystem for the representative metrology target represents the first metrology measurement of the device characteristic in the corresponding two or more device block groups; and Performing a runtime operation, the runtime operation comprising: receiving runtime metering data for the representative metering target over one or more runtime samples from the second metering subsystem; and A runtime metering measurement of the representative metering target over the one or more runtime samples is generated based on the runtime metering data.

2. The metering system according to claim 1, further comprising: determining a threshold violation based on monitoring the runtime metering measurement; and directing a transmission indicating the threshold violation.

3. The metrology system of claim 1 , wherein the identifying the representative metrology target associated with each device block group based on the distribution of the first metrology measurement comprises: Design the representative measurement targets.

4. The metrology system of claim 3, wherein the design of the representative metrology target comprises: A specific representative metrology target is designed to include an array of repeating device features.

5. The metrology system of claim 1 , wherein the identifying the representative metrology target associated with each device block group based on the distribution of the first metrology measurement comprises: receiving candidate target measurement data of a plurality of candidate representative measurement targets; and The representative metrology target is selected from the plurality of candidate representative metrology targets based on the candidate target metrology data.

6. The metrology system of claim 1 , wherein said sorting said device characteristics into said two or more device block groups based on said first metrology measurement comprises: identifying one or more sets of the device characteristics based on one or more ranges of values ​​of the first metrology measurement; and The one or more groups of device features are classified into one or more outlier device block groups. 7 . The metrology system of claim 6 , wherein the one or more groups of the device characteristics include an outlier set of outlier ranges based on the one or more ranges of values ​​of the first metrology measurement.

8. The metering system of claim 7, wherein the outlier range comprises an outlier range of CD values.

9. The metering system of claim 7, wherein the outlier range comprises an outlier range of OVL values.

10. The metrology system of claim 6, wherein a range in the one or more ranges is based on a peak in a distribution of the values ​​of the first metrology measurement.

11. The metrology system of claim 6, wherein two or more of the one or more ranges are each based on a respective peak in a distribution of the values ​​of the first metrology measurement.

12. The metrology system of claim 1, wherein the training is configured to be performed during post-etch inspection (AEI).

13. The metrology system of claim 1, wherein the runtime operation is configured to be performed during post-etch inspection (AEI).

14. The metrology system of claim 1, wherein the training is configured to be performed during an after-development inspection (ADI).

15. The metrology system of claim 1, wherein the runtime operation is configured to be performed during an after-development inspection (ADI).

16. The metrology system of claim 1, wherein the training is configured to be performed during post-development inspection (ADI), and wherein the runtime operation is configured to be performed during post-etch inspection (AEI).

17. The metrology system of claim 1, wherein the training is configured to be performed during post-etch inspection (AEI), and wherein the runtime operation is configured to be performed during post-develop inspection (ADI).

18. The metering system of claim 1, further comprising: determining a threshold violation based on monitoring the runtime metering measurement; and directing a transmission indicative of the threshold violation, wherein the monitoring includes monitoring a runtime edge placement error (EPE) distribution determined based on the runtime metrology measurement.

19. The metrology system of claim 1, wherein the second metrology subsystem comprises a spectroscopic ellipsometry (SE) subsystem.

20. The metering system of claim 1, wherein the second metering subsystem comprises at least one of: SE subsystem configured for multiple lighting angles; an SE subsystem configured to measure a Mueller matrix element; Single wavelength ellipsometer subsystem; beam profiling ellipsometer subsystem; beam profile reflectometer subsystem; Broadband reflectance spectrometer subsystem; Single wavelength reflectometer subsystem; Angle-resolved reflectometer subsystem; Imaging subsystem; or Scatterometer subsystem.

21. The metrology system of claim 1, wherein the first metrology subsystem comprises a scanning electron microscope (SEM) subsystem.

22. The metrology system of claim 1, wherein said sorting said device characteristics into said two or more device block groups based on said first metrology measurement comprises: determining one or more device characteristic attribute distributions of the device characteristic based on the first metrology measurement; and The device features are sorted into the two or more device block groups based on the one or more device feature attribute distributions.

23. The metrology system of claim 22, wherein the one or more device characteristic attribute distributions include an OVL distribution.

24. The metrology system of claim 22, wherein the one or more device characteristic attribute distributions include a CD distribution.

25. The metrology system of claim 22, wherein the one or more device characteristic property distributions include an edge placement error (EPE) distribution.

26. The metrology system of claim 22, wherein the one or more device characteristic property distributions include a sample tilt distribution.

27. The metering system of claim 1, wherein the training further comprises: receiving, via the second metrology subsystem, an optical calibration measurement of the representative metrology target; verifying a correlation between the optical calibration measurements of the representative metrology target and measurements of a set of device blocks associated with the representative metrology target; and A calibration between the optical calibration measurement and the measurement of the set of device blocks associated with the representative metrology target is determined.

28. The metrology system of claim 27, wherein the correlation comprises an ADI to AEI correlation between the optical calibration measurement and the measurement of the device block group, wherein the optical calibration measurement is configured as an after-development inspection (ADI) measurement and the measurement of the device block group is configured as a after-etch inspection (AEI) measurement.

29. The metrology system of claim 1, wherein the metrology system is configured to provide a correctable term to a lithography tool to adjust a sample fabrication process based on the runtime metrology measurement corresponding to the representative metrology target.

30. A metering system comprising: One or more controllers comprising one or more processors configured to execute program instructions configured to: Performing training, the training comprising: receiving first metrology data for a plurality of device characteristics from a first metrology subsystem; generating a first metrological measurement of the device characteristic based on the first metrological data; sorting the device characteristics into two or more device block groups based on the first metrology measurement; and identifying representative metrology targets for the two or more device block groups based on the distribution of the first metrology measurements, wherein each of the representative metrology targets is selected to specify that a second metrology measurement based on second metrology data from a second metrology subsystem for the representative metrology target represents the first metrology measurement of the device characteristic in the corresponding two or more device block groups; and Performing a runtime operation, the runtime operation comprising: receiving runtime metering data for the representative metering target over one or more runtime samples from the second metering subsystem; and A runtime metering measurement of the representative metering target over the one or more runtime samples is generated based on the runtime metering data.

31. A method for metering, comprising: Performing training, the training comprising: receiving first metrology data for a plurality of device characteristics from a first metrology subsystem; generating a first metrological measurement of the device characteristic based on the first metrological data; sorting the device characteristics into two or more device block groups based on the first metrology measurement; and identifying representative metrology targets for the two or more device block groups based on the distribution of the first metrology measurements, wherein each of the representative metrology targets is selected to specify that a second metrology measurement based on second metrology data from a second metrology subsystem for the representative metrology target represents the first metrology measurement of the device characteristic in the corresponding two or more device block groups; and Performing a runtime operation, the runtime operation comprising: receiving runtime metering data for the representative metering target over one or more runtime samples from the second metering subsystem; and A runtime metering measurement of the representative metering target over the one or more runtime samples is generated based on the runtime metering data.

32. The method of claim 31 , further comprising providing a lithography tool with correctable terms to adjust a sample fabrication process based on the runtime metrology measurements corresponding to the representative metrology target.

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