Device feature specific edge placement error (EPE)

Abstract

A system and method for generating metrology measurements using a second subsystem, such as an optical subsystem, is disclosed. The method can include performing training and run-time operations. The training can include receiving first metrology data from a first metrology subsystem (e.g., optical) for device features, generating first metrology measurements (e.g., critical dimensions, etc.), classifying the device features into two or more device bin groups based on the first metrology measurements, and identifying representative metrology targets for the two or more device bin groups based on a distribution of the first metrology measurements. The run-time operations can include receiving run-time metrology data (e.g., optical) for the representative metrology targets, and generating run-time metrology measurements based on the run-time metrology data.

Description

[0001] Cross Reference to Related Applications

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

[0003] The present disclosure relates generally to metrology, and more particularly to determining device feature dependent metrology. BACKGROUND

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

[0005] Specialized overlay / metrology targets can be utilized to help improve alignment. For example, a specialized overlay target can be a diffractive grating printed on multiple layers in a region separate from the device features being formed, such as in a scribe lane of the sample. The scribe lane can define a location at which a wafer is to be cut in a subsequent process. For example, a semiconductor wafer can include an array of dies, each of which can include many device features and is separated from other dies by a scribe lane.

[0006] Not all device feature layouts are suitable for direct overlay measurement. Furthermore, overlay measurement can damage or otherwise affect the performance of device features. Thus, overlay measurement is often performed on specialized overlay targets having features designed for sensitive overlay measurement, rather than directly on device features. However, differences in the size, orientation, density, and / or location on the sample of the overlay targets relative to device features can introduce a mismatch between the measured overlay at the target and the actual overlay of the device features. Thus, ensuring device feature dependent overlay measurement of overlay targets remains a persistent challenge in overlay metrology.

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

[0008] The effort to obtain overlay or CD measurements of device features can often be balanced with throughput requirements. For example, overlay targets with device feature level features can provide device feature related overlay. However, device feature level features of device features can often be directly resolved using particle beam metrology tools, such as but not limited to scanning electron microscopes (SEMs), which can be relatively slow and limit throughput in a production environment. In contrast, optical overlay metrology can provide higher throughput, but has relatively lower resolution and is not capable of directly measuring device feature overlay, and thus is more susceptible to errors.

[0009] Further, error margin budgets, such as acceptable amounts of 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 non-zero offsets (NZOs), such as adjusting the difference in measured overlay between after development inspection (ADI) and after etch inspection (AEI), can not be sufficient to meet more stringent error budgets.

[0010] Actual / true overlay of device features can vary depending on the type of device feature. In current and future nodes, the range of distribution of actual overlay values for various device features can hinder the achievement of error budgets.

[0011] Accordingly, there is a need for a system and method to provide accurate measurements that meet error budgets while still allowing for large scale high throughput operations. SUMMARY

[0012] In another illustrative embodiment, the metrology system can also include one or more controllers communicatively coupled to the first metrology sub-system and the second metrology sub-system. In another illustrative embodiment, the one or more controllers can include one or more processors configured to execute program instructions configured to perform training and run-time operations. In another illustrative embodiment, the training can include receiving first metrology data for a plurality of device features from the first metrology sub-system, generating first metrology measurements for the device features based on the first metrology data, classifying the device features into two or more device bin groups based on the first metrology measurements, and identifying a representative metrology target for the two or more device bin groups based on a distribution of the first metrology measurements. In another illustrative embodiment, the run-time operations can include receiving run-time metrology data for the representative metrology target on one or more run-time samples from the second metrology sub-system, and generating run-time metrology measurements for the representative metrology target on the one or more run-time samples based on the run-time metrology data.

[0013] In another aspect, the metrology system can also include determining a threshold violation based on monitoring of run-time metrology measurements and directing a transmission indicating the threshold violation. In another aspect, the identifying of a representative metrology target can 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 classifying of device features can involve identifying one or more groups of device features based on one or more ranges of values of first metrology measurements and classifying the one or more groups of device features into one or more outlier device bin groups.

[0014] In another aspect, the training and run-time operations can be performed during after-etch inspection (AEI) or after-development inspection (ADI). In another aspect, the second metrology sub-system can comprise a spectroscopic ellipsometer (SE) sub-system or any other suitable sub-system. In another aspect, the first metrology sub-system can comprise a scanning electron microscope (SEM) sub-system. In another aspect, the classifying of device features can involve determining one or more device feature property distributions for device features based on first metrology measurements and classifying device features into two or more device bin groups based on the one or more device feature property distributions.

[0015] In another aspect, the training can further include receiving an optical calibration measurement of a representative metrology target via the second metrology sub-system, verifying a correlation between the optical calibration measurement of the representative metrology target and a measurement of a device block group associated with the representative metrology target, and determining a calibration between the optical calibration measurement and the measurement of the device block group associated with the representative metrology target. In another aspect, the metrology system can be configured to provide a correctable term to a lithography tool to adjust a sample manufacturing process based on a run-time metrology measurement corresponding to the representative metrology target.

[0016] In another aspect, the design of the representative metrology target can 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 can include an outlier set of values based on an outlier range of values of the first metrology measurement.

[0018] In another aspect, the outlier range of values can include an outlier range of CD values.

[0019] In another aspect, the outlier range of values can include an outlier range of OVL values.

[0020] In another aspect, a range of the one or more ranges can be based on a peak in a distribution of values of the first metrology measurement.

[0021] In another aspect, two or more of the one or more ranges can each be based on a respective peak in a distribution of values of the first metrology measurement.

[0022] In another aspect, the monitoring of the run-time metrology measurement for a threshold violation can involve monitoring a run-time edge placement error (EPE) distribution determined based on the run-time metrology measurement.

[0023] In another aspect, the second metrology sub-system can include at least one of several designated sub-systems, including a SE sub-system configured for multiple angles of illumination, a SE sub-system configured for measuring Mueller matrix elements, a single-wavelength ellipsometer sub-system, a beam profile ellipsometer sub-system, a beam profile reflectometer sub-system, a broadband reflectance spectrometer sub-system, a single-wavelength reflectometer sub-system, an angle-resolved reflectometer sub-system, an imaging sub-system, or a scatterometer sub-system.

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

[0025] According to one or more illustrative embodiments of the disclosure, a metrology system is disclosed. In one illustrative embodiment, the metrology system can include one or more controllers. In another illustrative embodiment, the controller can include one or more processors configured to execute program instructions. In another illustrative embodiment, the program instructions can be configured to perform training and run-time operations. In another illustrative embodiment, the training can include receiving first metrology data for a plurality of device features from a first metrology sub-system, generating first metrology measurements of the device features based on the first metrology data, classifying the device features into two or more device bin groups based on the first metrology measurements, and identifying representative metrology targets for the two or more device bin groups based on distributions of the first metrology measurements. In another illustrative embodiment, each of the representative metrology targets can be selected to specify that a second metrology measurement based on second metrology data for the representative metrology target from a second metrology sub-system represents the first metrology measurement of the device features in the corresponding two or more device bin groups. In another illustrative embodiment, the run-time operations can include receiving run-time metrology data for the representative metrology targets on one or more run-time samples from the second metrology sub-system, and generating run-time metrology measurements of the representative metrology targets on the one or more run-time samples based on the run-time metrology data.

[0026] According to one or more illustrative embodiments of the disclosure, a method for metrology is disclosed. In one illustrative embodiment, the method can include performing training and performing run-time operations. In another illustrative embodiment, the training can include receiving first metrology data for a plurality of device features from a first metrology sub-system, generating first metrology measurements of the device features based on the first metrology data, classifying the device features into two or more device bin groups based on the first metrology measurements, and identifying representative metrology targets for the two or more device bin groups based on distributions of the first metrology measurements. In another illustrative embodiment, the run-time operations can include receiving run-time metrology data for the representative metrology targets on one or more run-time samples from a second metrology sub-system, and generating run-time metrology measurements of the representative metrology targets on the one or more run-time samples based on the run-time metrology data.

[0027] In another aspect, the method can include providing correctable terms to a lithography tool to adjust a sample manufacturing process based on run-time metrology measurements corresponding to representative metrology targets.

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

[0029] The many advantages of the present disclosure can be better understood by referring to the drawings, in which:

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

[0034] Figure 2B is a conceptual diagram of a device bin group in the context of a plot of overlay measurements of device features according to one or more embodiments of the present disclosure.

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

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

[0037] Figure 4 is a flowchart illustrating steps performed in a method for run-time operation 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, examples of which are illustrated in the accompanying drawings. The present disclosure has been particularly shown and described with the understanding that the disclosed subject matter is to be considered an exemplification of the principles of the disclosure rather than a limitation thereof. It is to be understood that the drawings are not to scale and are merely intended for use in illustrating the principles of the disclosure.

[0039] Embodiments of this 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, such that measurements of targets having these 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 metrological targets. For example, instead of metrological targets used to approximate wafer-level / chip-level / region-level measurements (e.g., region superposition), device feature-related metrological target designs can be identified and paired with one or more device features to act as proxies to more accurately determine measurements of such device features. Using targets corresponding to specific device feature groups allows for consideration of the differences between actual / true measurements of those device features, thereby enabling more accurate measurements and more specific process control adjustments during sample manufacturing to be kept within a tighter error budget. This differs from targets that only consider superposition measurements as a whole without considering specific device feature types. For example, high-throughput ADI optical metrology can be associated with high-resolution AEI metrology to measure the superposition of those block groups / groups using corresponding representative targets specific to device features. In this regard, optical measurements of the target using a high-throughput second metrology subsystem can be used at the post-development inspection (ADI) step during high-volume manufacturing (HVM) to predict device feature overlay (OVL), critical dimension (CD), and / or edge placement error (EPE) measurements at the post-etch inspection (AEI) step.

[0040] This paper considers, for example, that if device features are grouped together and target designs associated with such grouped device features are identified, the actual measurements of OVL, CD, and / or EPE can be determined more accurately. For example, some target designs may share the same (or similar) measurements and thus 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 can be used to group device features. For example, device features can be grouped 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.), sample tilt, location (e.g., distance from the edge of the device, cutaway, etc.), type of feature (e.g., type of transistor, type of logic gate), shape of feature, size of feature, whether feature repeats periodically, in which type of layer the feature appears, or any other attribute. For example, a specific shape located within a range of the edge of the device (e.g., within 100 nanometers) and having a higher (average) SEM overlay measurement at AEI than other device features (e.g., an L-shape near two vertical lines, such as through...). Figure 2AFeatures exhibited by feature #4 204d in FIG. 2B can be ranked together in an effort to identify a representative metrology target design that is relevant to such device features. In this regard, a subset of ranked device features can be relevant to a representative metrology target design. One or more attributes can be used to define a device bin group, such that any other device features (e.g., samples manufactured in the future, device features of subsequent layers, device features that are not measured) can be determined to be part of the device bin group, provided that they have such attributes.

[0041] Measurement of device features (e.g., overlay, CD, and EPE) can be useful in sample manufacturing 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). Instead, targets (e.g., dedicated targets rather than electrically functional devices) can be used to determine measurements.

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

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

[0044] Multiple metrology subsystems (e.g., optical and / or SEM metrology subsystems) can be used at various stages of training and / or run-time. For example, the terms “first” and “second” can be used for purposes of differentiation only. For example, a first metrology subsystem (e.g., first metrology subsystem 102 of Figure 1C ) and a second metrology subsystem (e.g., second metrology subsystem 104 of Figure 1B ) can be used. 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 for generating overlay, CD, and / or EPE. 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 made during training steps, while the second metrology subsystem can be used for high-throughput processing of samples during run-time operations. In this way, different resolution and throughput subsystems can be used to take advantage of respective strengths during training and during run-time operations.

[0045] The second metrology sub-system can provide high throughput measurements suitable for in-line process control. For example, the second metrology sub-system can utilize imaging-based optical overlay methods 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 use model-based methods in which light scattered and / or diffracted from a sample is compared to an expected pattern based on known sample features, such as, but not limited to, grating structures in overlapping layers. In another example, CD targets can be imaged by imaging-based optical CD methods. Features of a target suitable for optical characterization can be segmented to provide multiple measurement points within a single field of view, which can facilitate highly accurate measurements using low-noise based statistical averaging of repeating structures. Furthermore, the second metrology sub-system can provide, but is not necessarily required to provide, measurements of approximately 0.2 to 1 seconds per site, and thus can provide substantial flexibility in measurement frequency per sample and per lot in a production line.

[0046] For purposes of the present disclosure, the term "optically resolvable" indicates that at least portions of a feature can be resolved within a selected tolerance with a selected second metrology sub-system. Examples of optically resolvable features can include, but are not limited to, grating patterns of dedicated overlay targets positioned in a street of a wafer, which can be resolved / measured by image-based overlay (IBO) metrology methods. For example, the size of an optically resolvable target can be approximately 10 microns or more (e.g., 30 microns).

[0047] Furthermore, "device feature level" features can include one or more characteristics similar to device features to be incorporated into a manufactured device feature (e.g., line width, separation distance between features, etc.). Examples of "device features" can include, but are not limited to, functional features of a die (e.g., transistors, logic gates, etc.) of a plurality of dies of a wafer. Device features are typically smaller than optically resolvable targets and are typically not directly optically resolvable. For example, the size of at least some characteristics of device feature level features can be approximately less than 3 microns and the first metrology sub-system can include a tool defined to have a resolution of, for example, less than 1 micron, less than 10 nanometers, between 0.5 and 4 nanometers, etc. It is recognized herein that a particular device feature can be at least partially resolved by a selected second metrology sub-system, but can further contain characteristics below the resolution of the selected second metrology sub-system. It is 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, can permit both optically resolvable measurements of the hybrid overlay targets and device feature level measurements. In embodiments, the plurality of target designs is a plurality of hybrid overlay target designs.

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

[0049] It should be noted that for purposes of the present disclosure, edge placement error (EPE) is defined as a function of overlay and CD. For example, EPE can be defined as the average of overlay and CD (i.e., ½ x (overlay + CD)).

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

[0051] Figure 1A A conceptual diagram of a metrology system 100 in accordance with one or more embodiments of the present disclosure is described. The metrology system 100 can include, but is not limited to, a second metrology sub-system 104 and / or a first metrology sub-system 102. The metrology system 100 can additionally include, but is not limited to, a controller 108. In embodiments, 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 can perform any of the various process steps described throughout the present disclosure. For example, the controller 108 can receive data from either of the second metrology sub-system 104 or the first metrology sub-system 102 and can further generate run-time metrology measurements. The metrology system 100 can be configured to measure one or more samples (e.g., samples 118 of Figure 1B , but need not include the samples.

[0052] In embodiments, the first metrology sub-system 102 can be configured for a first resolution and a first throughput. The second metrology sub-system 104 can 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 can be different than the first resolution and the first throughput. For example, the first resolution can be higher (e.g., more accurate, higher spatial resolution) than the second resolution and the first throughput can be lower than the second throughput. For example, the second resolution can be configured (e.g., capable of) resolving at least measurements of targets (e.g., optically resolvable targets) and the second resolution can be configured (e.g., capable of) resolving at least device features for overlay and / or CD purposes.

[0053] In embodiments, the second metrology sub-system 104 includes Figure 1B an optical metrology sub-system 172 and the first metrology sub-system 102 includes a device feature resolvable sub-system. For example, the device feature resolvable sub-system can include Figure 1C a particle beam metrology sub-system 174. It is noted that while the first metrology sub-system 102 can be described with respect to Figure 1C a particle beam metrology sub-system 174, such as a scanning electron microscope (SEM) metrology sub-system, the first metrology sub-system 102 can alternatively or additionally include a soft x-ray (SXR) metrology sub-system configured to measure a sample using electromagnetic radiation in the soft x-ray spectral range. For example, the SXR metrology sub-system can be used to measure a plurality of AEI device feature measurements of a plurality of device features.

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

[0055] Figure 1B is a conceptual diagram of an optical metrology tool 178 in accordance with one or more embodiments of the present disclosure. In embodiments, the optical metrology tool 178 includes an optical metrology sub-system 172. The optical metrology sub-system 172 can be any sub-system used in the art suitable for generating measurements based on optically resolvable features. For example, the optical metrology sub-system 172 can be an example of the second metrology sub-system 104 and can include any type of optical overlay metrology sub-system 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 sub-system or a scatterometry-based optical metrology sub-system.

[0056] The second metrology sub-system 104 can include an optical sub-system for optically (e.g., using light wavelengths) receiving / measuring metrology data of the sample 118, rather than a scanning electron microscope. For example, the second metrology sub-system 104 can include a spectroscopic ellipsometer (SE) sub-system.

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

[0058] Figure 1C 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 sub-system 174. The particle beam metrology sub-system 174 can be an instance of the first metrology sub-system 102 and can 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] Referring back to Figure 1A , the second metrology sub-system 104 can provide measurements (e.g., optical measurements) of one or more targets and the first metrology sub-system 102 can provide measurements suitable for determining measurement calibration adjustments to the optical measurements provided by the second metrology sub-system 104. Further, the metrology system 100 can utilize any components (e.g., the second metrology sub-system 104, the first metrology sub-system 102) at any selected frequency (at run-time) in a production line to balance measurement accuracy and throughput requirements. For example, the second metrology sub-system 104 can be used for in-line measurement monitoring, while the first metrology sub-system 102 can be selectively used at a lower frequency to determine measurement calibration adjustments to the optical measurements provided by the second metrology sub-system 104.

[0060] Figure 2A A conceptual diagram illustrating utilization of various sub-systems of the metrology system 100 for measuring device features 204 and targets 210 according to one or more embodiments of the present disclosure is illustrated. For example, the first metrology sub-system 102 can be configured (e.g., via properties such as resolution (e.g., spatial resolution), wavelength, field of view, program instructions stored on a memory, etc.) to measure both device features 204 and targets 210 as illustrated and the second metrology sub-system 104 can be configured to image targets 210.

[0061] In an embodiment, device features 204 are classified (e.g., prior to the classifying step 306) based on location (e.g., location in a die; proximity to other components, devices, etc.; and / or the like).

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

[0063] In embodiments, device features are ranked based on measurements (e.g., overlay, CD, and / or EPE).

[0064] In embodiments, device features are ranked based on device type (e.g., transistor type, logic gate type such as NOR gate, etc.).

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

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

[0067] In step 304, first metrology measurements of device features 204 are generated based on the first metrology data.

[0068] In step 306, device features 204 are ranked into two or more device bin groups 212 based on the first metrology measurements.

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

[0070] Ranking can include determining a device feature attribute distribution of device features based on the first metrology measurements, and ranking device features into device bin groups based on the one or more device feature attribute distributions.

[0071] For example, a device feature attribute distribution can include a sample tilt (i.e., angular tilt of sample 118) distribution. For example, consider that tilt can allow for improved (e.g., more accurate) device ranking and / or identification of representative metrology targets.

[0072] For example, a device feature attribute distribution can include a distribution of OVL distribution, CD distribution, and / or edge placement error (EPE) distribution. For example, several device features having similar CD values (e.g., values above an 80thpercentile threshold value) can be ranked together.

[0073] For example, device features can belong to feature type groups. Different feature type groups can be denoted 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, e.g., feature #N 204e), where each feature type group can include many (e.g., tens, thousands, billions) of device features having a shape associated with the feature type group. For example, a particular logic gate structure used in a wafer die can be associated with a particular feature type group.

[0074] The ranking can include identifying one or more groups of device features 204 based on one or more ranges of values of the first metrology measurements; and ranking the one or more groups of device features 204 into one or more outlier device bin groups 212. For example, Figure 2C An outlier device bin group 212d is illustrated for device features #1 204a.

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

[0076] The outlier value range can include an outlier value range of CD values. The outlier value range can include an outlier value range of OVL values. The range can be based on a peak in a distribution of values of the first metrology measurements. For example, instead of a simple bell curve, the distribution of values can appear with multiple peaks, like a sinusoidal curve. Each peak / crest of the curve indicates a cluster of device features having similar values and thus can benefit from using a representative metrology target as a proxy to measure their equivalent values. Two or more ranges can each be based on a respective peak in the distribution of values.

[0077] In step 308, a representative metrology target of the two or more device bin groups 212 is identified based on a distribution of the first metrology measurements. For example, the identifying can include designing a representative metrology target or selecting a representative metrology target from candidate representative metrology targets.

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

[0079] In embodiments, 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 can be configured to be selected from using the controller 108. For example, a candidate representative metrology target can be selected to specify that a second metrology measurement based on second metrology data from the second metrology sub-system for a representative metrology target represents a first metrology measurement of device features in a corresponding two or more device tile groups. For example, candidate target metrology data can be received and used to generate candidate target metrology measurements. For example, in this particular context, receiving can mean receiving from the first metrology sub-system 102 and / or the second metrology sub-system 104. In another example, historical data for a candidate representative metrology target can be used (e.g., extracted from memory and known a priori).

[0080] Figure 4 is a flowchart illustrating steps performed in a method 400 for performing run-time operations in accordance with one or more embodiments of the present disclosure. For example, after a representative metrology target is identified in the training of the method 300, the method 400 can facilitate high throughput measurement of the sample 118 during the manufacturing process of the sample 118.

[0081] In step 402, run-time metrology data for the representative metrology target on one or more run-time samples from the second metrology sub-system is received.

[0082] Run-time operations can be configured to be performed during ADI. For example, both training and run-time operations can be performed in ADI. In another example, training can be configured to be performed during AEI, and run-time operations are performed during ADI. For example, training can be configured to be performed during AEI and run-time operations can be configured to be performed during ADI using the optical second metrology system 104.

[0083] Run-time operations can be configured to be performed during after-etch inspection (AEI). For example, both training and run-time operations can be performed in AEI. For example, training can be configured to be performed during after-development inspection (ADI), and run-time operations are performed during AEI.

[0084] In step 404, run-time metrology measurements of representative metrology targets on one or more run-time samples 118 are generated based on the run-time metrology data.

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

[0086] Training can include calibration, which can include receiving information (e.g., calibration measurements), verifying a correlation of representative metrology targets to device block groups, and using the received information to determine a calibration function to be used during run-time.

[0087] For example, training can include receiving optical calibration measurements of representative metrology targets via the second metrology system 104. Run-time metrology measurements of representative metrology targets can be related to, but not exactly match, actual / real measurements of actual device features. If the correlation is strong enough and predictable (as can be determined via verification), then it is safe to use the verified representative metrology targets.

[0088] For example, the training can include verifying a correlation between the optical calibration measurements of the representative metrology targets and the measurements of the device block group 212 associated with the representative metrology targets. For example, the optical calibration measurements can be compared to the measurements of the device features (e.g., first metrology measurements) to determine if they are correlated. The correlation can be verified over a range of values. For example, measured values of 10.00, 11.00, and 12.00 can be correlated to 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 would closely match the second actual values. In an example, the correlation is confirmed / verified if the difference is within a verification threshold. If not, a different representative metrology target can be identified. This correlation / verification can be important.

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

[0090] In an embodiment, the correlation includes an ADI to AEI correlation between the optical calibration measurements and the measurements of the device block group 212. The optical calibration measurements can be configured as after development inspection (ADI) measurements and the measurements of the device block group can be configured as after etch inspection (AEI) measurements. For example, if a representative metrology target is identified based on SEM second metrology subsystem measurements in AEI, but a user wants to monitor EPE distribution in ADI, the controller 108 can be configured to calibrate and verify a correlation between the optical ADI measurements of the representative metrology target and the SEM measurements 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) can be used to predict the AEI (i.e., after etching) device feature distribution.

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

[0092] Figure 2B A conceptual diagram of device bin groups 212 in the context of a plot 206 of overlay measurements 222 of device features 204 is illustrated in accordance with one or more embodiments of the present disclosure. For example, device feature measurements 222 can include CD, EPE, and / or overlay measurements.

[0093] Device feature measurements 222, 224 can vary for different structural properties of device features 204 and target design measurements 214, 216 (e.g., measurements of representative metrology targets) vary for different representative metrology target designs. For example, device feature measurements can vary depending on properties (e.g., shape, size, location, etc.) of device features and representative metrology target measurements can vary depending on properties (e.g., shape, size, etc.) of representative metrology targets. For example, overlay measurements 222 associated with feature #2 204b of Figure 2B may vary due to, for example, location relative to a die edge. Figure 2A

[0094] In embodiments, device features can be ranked into device bin groups 212 based on ranges. For example, a range of measurements can mean average maximum, minimum, etc. For example, features # associated with the top 40% of overlay measurements can be ranked together as shown by device bin group 212b. Device features with the lowest average of measurements 222 (e.g., the lowest average, the two lowest averages as shown, and / or the three lowest averages) can be ranked together as shown by ranking features numbered 1 and 2 in device bin group 212c.

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

[0096] Each device bin group 212 can be related to a target 210. For example, overlay device bin group 212b can be associated with target 210b of Figure 2A . For example, as shown, close to Figure 2B ​Target measurements 214 of measurements 222 in FIG. 2B can be obtained from target 210b. In this regard, target 210b can be identified as a representative metrology target of superposition device bin 212b. Similarly, target 210c (e.g., a different target) of target measurements 216 can be identified as a representative metrology target of superposition device bin 212a due to the proximity / similarity to measurements 222 of device bin 212a.

[0097] As explained in Figure 2B As explained in Figure 2B In the case of device bin 212a in FIG. 2B, a target 210 of multiple targets 210a having similar target design properties can be the target used to generate target design measurements 216 (which can include multiple target design measurements 216 or be an average of multiple target design measurements). Target 210a can have one or more target design properties that can be selected for defining a representative metrology target design. For example, such target design properties can be (but are not limited to) size; various grating characteristics such as pitch, shape, etc. of each grating; thickness; type (e.g., frame-in-frame style superposition target, AIM style superposition target, etc.); and any other properties. For example, after measuring many (e.g., tens, hundreds, thousands) of target designs, it can become apparent in an identification step that the pitch of the gratings of the measured target designs within a particular range are related to a particular device bin 212. For example, target designs having this pitch can be most closely related to measurements 222 of device bin 212. In this example, the identified "representative metrology target," "representative metrology target design," etc. can be any target having the described pitch.

[0098] Figure 2C A conceptual diagram of device bins 212 in the context of a plot 208 of critical dimension (CD) measurements 224 of device features is illustrated in accordance with one or more embodiments of the present disclosure. For example, CD measurements 224 can be generated (e.g., measured, calculated based on data) using first metrology subsystem 102.

[0099] As shown, device bin 212d can be an outlier device bin 212d selected to contain device features of feature number 1 associated with measurements 224 shown within CD device bin 212d. For example, a representative metrology target of optical measurements 220 having CD can be identified based on a correlation to CD device bin 212d.

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

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

[0102] Referring again to Figure 1A and 1C embodiments of various components are described in additional detail.

[0103] The controller 108 can be communicatively coupled to one or more external manufacturing tools (e.g., but not limited to, photolithography tools). In this regard, the controller 108 can operate as an advanced process controller (APC) suitable to control inputs of the external manufacturing tools to maintain the overlay within a selected overlay tolerance.

[0104] In an optional step, an overlay correctable term (based on the run-time metrology measurements) for a device feature region (e.g., a portion of a functional / active region of a die including device features) is provided to the photolithography tool to adjust a sample manufacturing process based on the run-time metrology measurements, e.g., modify exposure conditions of at least one subsequent exposure of the manufacturing process. For example, the run-time metrology measurements can include run-time metrology measurements corresponding to a plurality of representative metrology targets corresponding to respective device bin groups 212. For example, the overlay correctable term can be an adjustment of any parameter, e.g., if the overlay is misaligned by a certain distance, the photolithography process can be adjusted in an opposite direction to correct for the misalignment.

[0105] It should be understood that Figure 1A the metrology system 100 described in connection with the associated description is provided for illustrative purposes only and should not be construed as limiting. For example, the metrology system 100 can include any combination of the elements described in Figure 1A . In one example, the metrology system 100 can include the second metrology sub-system 104, the first metrology sub-system 102, and the controller 108. Further, any of the components of the metrology system 100 can be positioned proximate to one another or can be positioned remotely from one another. In embodiments, multiple components of the metrology system 100 can be integrated into a single physical device feature.

[0106] Figure 1Bis a conceptual diagram of an optical metrology tool 178 (e.g., including a first metrology sub-system 104) in accordance with one or more embodiments of the present disclosure.

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

[0108] The optical illumination source 114 can be any type of illumination source known in the art suitable for generating the optical illumination beam 116.

[0109] The optical illumination source 114 can include any type of illumination source suitable for providing the optical illumination beam 116. In embodiments, the optical illumination source 114 is a laser source. For example, the optical illumination source 114 can 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 can provide the optical illumination beam 116 with high coherence (e.g., high spatial and / or temporal coherence). In embodiments, the optical illumination source 114 includes a laser-sustained plasma (LSP) source. For example, the optical illumination source 114 can include, but is not limited to, an LSP lamp, LSP bulb, or 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 embodiments, the optical illumination source 114 includes a lamp source. For example, the optical illumination source 114 can 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 can provide the optical illumination beam 116 with low coherence (e.g., low spatial and / or temporal coherence).

[0110] In embodiments, the optical illumination source 114 directs the optical illumination beam 116 to the sample 118 via an illumination path 120. The illumination path 120 can include one or more illumination path lenses 122 or additional optical components 124 suitable for modifying and / or conditioning the optical illumination beam 116. For example, the one or more optical components 124 can 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 can further include an objective lens 126 configured to direct the optical illumination beam 116 to the sample 118.

[0111] In embodiments, the sample 118 is disposed on a sample stage 128. The sample stage 128 can 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 can include any combination of a linear translation stage, a rotation stage, a flip / tilt stage, etc.

[0112] In embodiments, the optical metrology subsystem 172 includes a detector 130 configured to capture light emanating from the sample 118 through a collection path 132. The collection path 132 can 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 can 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 way of another example, the detector 130 can receive light generated by the sample 118 (e.g., luminescence associated with absorption of the optical illumination beam 116, etc.). By way of another example, the detector 130 can receive light from one or more diffraction orders of the sample 118 (e.g., 0thorder diffraction, ±1storder diffraction, ±2ndorder diffraction, etc.).

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

[0114] The collection path 132 can further include any number of optical elements to direct and / or modify 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 embodiments, the detector 130 is positioned approximately normal to a surface of the sample 118. In embodiments, the optical metrology subsystem 172 includes a beamsplitter 136 oriented such that the objective lens 126 can simultaneously direct the optical illumination beam 116 to the sample 118 and collect light emanating from the sample 118. Further, the illumination path 120 and the collection path 132 can share one or more additional elements (e.g., the objective lens 126, an aperture, a filter, etc.).

[0116] The optical metrology subsystem 172 can measure overlay based on any technique known in the art, such as but not limited to, image-based techniques or scatterometry-based techniques. For example, the optical metrology subsystem 172 operating in an imaging mode can illuminate a portion of the sample 118 and capture an image of the illuminated portion of the sample 118 on a detector 130. The captured image can 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, etc. Further, the captured images can be stitched together (e.g., by the optical metrology subsystem 172, by the controller 108, etc.) to form a composite image of the sample 118. By way of another example, the optical metrology subsystem 172 can scan a focused optical illumination beam 116 across the sample 118 and capture light and / or particles emanating from the sample 118 at one or more measurement angles on one or more detectors 130 to generate 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, a piezoelectric mirror, etc.) and / or by translating the sample 118 through the focal volume of the focused beam. Thus, overlay 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 can operate as a scatterometry-based metrology subsystem by determining overlay based on a pattern 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 can capture (e.g., with a detector 130) one or more pupil plane images including an angular distribution of light emanating from the sample, such as different regions of an overlay target. Thus, overlay between two or more sample layers can be determined from the pupil plane images based on modeled scattering and / or diffraction of overlay target features having known sizes and distributions from each layer.

[0118] Further, the optical metrology subsystem 172 can measure overlay at any fabrication step by propagating the optical illumination beam 116 through a current layer to interact with features on one or more previously fabricated layers such that signals received by the detector 130 (e.g., images of the sample 118, images of a pupil plane, etc.) are indicative of overlay between at least two layers. For example, the optical metrology subsystem 172 can measure overlay between a previously fabricated layer and a current layer as an after-development inspection (ADI) step following exposure of the current layer. In this regard, overlay measurements of the current layer and any previous layers can be generated based on differences in refractive index of exposed features relative to unexposed features. By way of another example, the optical metrology subsystem 172 can measure overlay between a previously fabricated layer and a current layer as an after-etch inspection (AEI) step following etching of a developed pattern into the current layer as a standing 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 the first metrology subsystem 102 and can include, for example, a scanning electron microscope (SEM) metrology subsystem 102.

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

[0121] In embodiments, 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 can include any particle source known in the art suitable for generating a particle beam 140. For example, the particle source 138 can include, but is not limited to, an electron gun or an ion gun. In embodiments, 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 can provide, but is not limited to, an acceleration voltage in a range of 0.1 kV to 30 kV. By way of another example, a particle source 138 including an ion source can provide, but need not provide, an ion beam with an energy in a range of 1 to 50 keV.

[0122] In embodiments, the particle beam metrology subsystem 174 includes one or more particle focusing elements 142. For example, the one or more particle focusing elements 142 can include, but are not limited to, a single particle focusing element or one or more particle focusing elements forming a compound system. In embodiments, the one or more particle focusing elements 142 include a particle objective 144 configured to direct the particle beam 140 to a sample 118 positioned on a sample stage 146. Further, the one or more particle sources 138 can 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 embodiments, the particle beam metrology subsystem 174 includes at least one particle detector 148 that images or otherwise detects particles emanating from the sample 118. In embodiments, the particle detector 148 includes an electron collector (e.g., a secondary electron collector, a backscattered electron detector, etc.). In embodiments, 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 appreciated that the description of the particle beam metrology tool 176 and the above associated description are provided for illustrative purposes only and should not be construed as limiting. For example, the particle beam metrology tool 176 can include a multi-beam and / or multi-column system adapted to interrogate the sample 118 simultaneously. In further embodiments, the particle beam metrology tool 176 can 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. Figure 1C

[0125] It should be appreciated herein that the penetration depth of the particle beam 140 into the sample 118 can depend on the particle energy, such that higher energy beams typically penetrate more deeply into the sample 118. In embodiments, 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 can utilize a relatively lower energy electron beam (e.g., approximately 1 keV or less) and can utilize a higher energy beam (e.g., approximately 10 keV or more) to characterize previously fabricated layers. It should be appreciated herein that the penetration depth as a function of particle energy can vary for different materials, such that the selection of particle energy for a particular layer can vary for different materials.

[0126] ​The optically resolvable features and device feature level features of the hybrid overlay target can have any orientation or distribution in the hybrid overlay target suitable to provide both optical overlay and device feature level overlay in the same direction or multiple directions. In embodiments, the optically resolvable features and device feature level features are physically separated. For example, optical metrology targets with embedded device level features are generally described in U.S. Patent No. 9,093,458, issued July 28, 2015, entitled DEVICE CORRELATED METROLOGY (DCM) FOR OVL WITH EMBEDDED SEM STRUCTURE OVERLAY TARGETS, which is incorporated by reference herein in its entirety. In embodiments, 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 can be performed in the same physical location, which can provide increased accuracy of overlay tool error. For example, segmented targets with optically resolvable features and device level features are generally described in U.S. Patent Publication No. US 2014 / 0307256, entitled PROCESS COMPATIBLE SEGMENTED TARGETS AND DESIGN METHODS, published October 16, 2014, which is incorporated by reference herein in its entirety.

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

[0128] It is recognized herein that overlay errors can be introduced at nearly every stage of fabrication and can vary spatially across a sample or temporally from sample to sample or from batch to batch in a production run. For example, lithography tools (e.g., steppers, scanners, etc.) can typically have a field of view that is less than a full sample and thus can divide a sample into a series (e.g., grid) of exposure fields that can be exposed separately. Grid errors associated with misalignment of the reticle to the sample during the exposure step of one or more exposure fields can contribute to overlay errors that vary spatially across a sample. Additionally, aberrations in the lithography tool during exposure (e.g., lens aberrations, turbulence associated with heat, etc.) can result in spatially varying pattern placement errors within a single exposure field. By way of further example, overlay errors can include process errors associated with fabricating three-dimensional structures on a sample based on the exposed patterns. Process errors can include, but are not limited to, distortion of the exposed patterns during lithography, etch-induced errors, polishing errors, or errors associated with variations in the sample. Thus, overlay measured at an overlay target can be subject to spatially varying target-to-device feature errors based on the displacement between the overlay target and the device features.

[0129] According to the metrology recipe, overlay targets can typically be placed at any location on a sample. However, the size, orientation, and / or density of the features in the target can affect target placement. For example, overlay targets with optically resolvable features are typically placed in the scribe lanes between dies of a sample in order to reserve space within the dies for device features, and / or because the optically resolvable features can not comply with process design rules. By way of further example, overlay targets with features that comply with process design rules can typically be placed near device features of interest within a sample die or placed within a scribe lane.

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

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

[0132] In embodiments, the one or more processors 110 can include any processing element(s) known in the art. In this sense, the one or more processors 110 can include any microprocessor-type device configured to execute software algorithms and / or instructions. In embodiments, the one or more processors 110 can be comprised of a desktop computer, a mainframe computer system, a workstation, a graphics computer, a parallel processor, or other computer system (e.g., networked linked computers) configured to execute processes configured to operate the metrology system 100, as described throughout this disclosure. It should be recognized that the steps described throughout this disclosure can be carried out by a single computer system or, alternatively, multiple computer systems. Further, it should be recognized that the steps described throughout this disclosure can be carried out on any one or more of the one or more processors 110. Generally, the term “processor” can be broadly defined to encompass any device having one or more processing elements that execute program instructions from memory 112. Further, different subsystems of the metrology system 100 (e.g., optical metrology subsystem 172, device-level metrology subsystem 174, controller 108, user interface 170, etc.) can include processors or logic elements suitable for carrying out at least portions of the steps described throughout this disclosure. Accordingly, the above description should not be interpreted as a limitation on the present disclosure but merely an illustration.

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

[0134] In embodiments, the user interface 170 is communicatively coupled to the controller 108. The user interface 170 can include, but is not limited to, one or more desktop computers, tablets, smartphones, smartwatches, and the like. In embodiments, 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 can include any display known in the art. For example, the display can 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 integration with the user interface 170 is suitable for implementation in the present disclosure. In embodiments, a user can 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 of the methods described herein can include storing results of one or more steps of the method embodiments in a memory. The results can include any results described herein and can be stored in any manner known in the art. The memory can include any memory described herein or any other suitable storage medium known in the art. After the results have been stored, the results can be accessed in the memory and used by any of the method or system embodiments described herein, formatted for display to a user, used by another software module, method, or system, and the like. Further, the results can be stored "permanently," "semi-permanently," "temporarily," or for some period of time. For example, the memory can be random access memory (RAM) and the results can not necessarily remain in the memory indefinitely.

[0136] It should further be appreciated that each of the embodiments of the methods described above can include any other step(s) of any other method(s) described herein. Additionally, each of the embodiments of the methods described above can be performed by any of the systems described herein.

[0137] Those skilled in the art will recognize that, for clarity and the sake of pedagogy, the component operations, apparatus, objects and their accompanying discussion described herein are presented as examples and that various configuration modifications are contemplated. Thus, as used herein, the specific exemplars set forth and the accompanying discussion are intended to be representative of their more general classes. In general, use of any specific exemplar is intended to be representative of its class, and the specific components, operations, apparatus and objects should not be construed as limiting.

[0138] As used herein, directional terms such as "top," "bottom," "upper," "lower," "up," "down," "over," "under," "above," and "below" are intended to provide relative positions for purposes of description, and are not intended to designate absolute reference frames. Various modifications to the implementations described will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other implementations without departing from the scope of the disclosure. Accordingly, the description is intended to be illustrative, but not limiting, of the scope of the disclosure.

[0139] With respect to the use of substantially any plural and / or singular terms herein, those having skill in the art can translate the mutability to singular and / or plural terms and / or to the mutability where appropriate. For clarity, various singular / plural arrangements are not explicitly set forth herein.

[0140] The subject matter described herein is sometimes illustrated using different components contained within, or connected with, other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermediate components. Likewise, any two components so associated can also be viewed as being "connected", or "coupled", to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being "couplable", to each other to achieve the desired functionality. Specific examples of couplable include but are not limited to physically mateable and / or physically interacting components and / or wirelessly interactable and / or wirelessly interacting components and / or logically interacting and / or logically interactable components.

[0141] Further, it is to be understood that the invention is defined by the appended claims. Those skilled in the art will appreciate that, in general, the terms used in the description herein, and particularly in the appended claims (for example, the body of the appended claims) are intended to be interpreted broadly. For example, the terms "including" and "having" are intended to be interpreted broadly so as to encompass the terms "consisting of, "consisting essentially of, "including at least, and / or the like. Those skilled in the art will further appreciate that if a particular number of introduced claim recitations is intended, that number will be explicitly recited in the claims. But, if no particular number of introduced claim recitations is intended, that intention will be inferred as a recitation of "at least one" of the introduced claim recitations. For example, the following phrases are all intended to be interpreted to mean "at least one" of the recitations listed: "at least one of A and / or B", "at least one of A or B", "at least one of A, B, and / or C", "at least one of A, B, and C", "at least one of A, B, C, and / or D", "at least one of A, B, C, and D", "at least one of A, B, C, D, and / or E", "at least one of A, B, C, D, and E", and "at least one of A, B, C, D, E, and / or F". In addition, the use of a conjunction in the body of claims to refer to a list of items should not be interpreted as requiring that there be at least one of each item for the conjunctive phrase to be true. For example, referring to a "system having A, B, and C" will be interpreted to allow for "a system having at least one of A, at least one of B, and at least one of C."

[0142] It is believed that, by virtue of the foregoing description, the present disclosure and many of its attendant advantages will be understood by the reader, and that changes in or substitutions of the form and arrangements of parts and components can be made by those skilled in the art, without departing from the scope of the disclosed subject matter or sacrificing all of its material advantages. The foregoing description is thus to be understood as being illustrative only, and not as limiting the appended claims, which, by the way, are to be interpreted in the broadest sense allowable.

Claims

1. A metrology system, comprising: a first metrology sub-system configured to have a first resolution; a second metrology sub-system configured to have a second resolution lower than the first resolution; and one or more controllers communicatively coupled to the first metrology sub-system and the second metrology sub-system and comprising one or more processors configured to execute program instructions configured to: perform a training, the training comprising: receiving first metrology data for a plurality of device features from the first metrology sub-system; generating first metrology measurements of the device features based on the first metrology data; classifying the device features into two or more device bin groups based on the first metrology measurements; and identifying representative metrology targets for the two or more device bin groups based on a distribution of the first metrology measurements, wherein each of the representative metrology targets is selected to specify that a second metrology measurement for the representative metrology target based on second metrology data from the second metrology sub-system represents the first metrology measurements for the device features in the two or more device bin groups; and performing a run-time operation, the run-time operation comprising: receiving run-time metrology data for the representative metrology targets on one or more run-time samples from the second metrology sub-system; and generating run-time metrology measurements for the representative metrology targets on the one or more run-time samples based on the run-time metrology data.

2. The metrology system of claim 1, further comprising: determining a threshold violation based on monitoring of the run-time metrology measurements; and directing a transmission indicating the threshold violation.

3. The metrology system of claim 1, wherein the identifying the representative metrology targets associated with each device bin group based on the distribution of the first metrology measurements comprises: designing the representative metrology targets.

4. The metrology system of claim 3, wherein the designing of the representative metrology targets comprises: designing a particular representative metrology target to include an array of repeated device features.

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

6. The metrology system of claim 1, wherein the classifying the device features into the two or more device bin groups based on the first metrology measurements comprises: identifying one or more groups of the device features based on one or more ranges of values of the first metrology measurements; and classifying the one or more groups of the device features into one or more outlier device bin groups.

7. The metrology system of claim 6, wherein the one or more groups of the device features comprise an outlier value set of outlier values based on the one or more ranges of values of the first metrology measurements.

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

9. The metrology 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 of 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 an after-etch inspection (AEI).

13. The metrology system of claim 1, wherein the run-time operation is configured to be performed during an after-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 run-time 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 an after-development inspection (ADI), and wherein the run-time operation is configured to be performed during an after-etch inspection (AEI).

17. The metrology system of claim 1, wherein the training is configured to be performed during an after-etch inspection (AEI), and wherein the run-time operation is configured to be performed during an after-development inspection (ADI).

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

19. The metrology system of claim 1, wherein the second metrology sub-system comprises a spectroscopic ellipsometer (SE) sub-system.

20. The metrology system of claim 1, wherein the second metrology sub-system comprises at least one of: an SE sub-system configured for multiple angles of illumination; an SE sub-system configured for measuring Mueller matrix elements; a single-wavelength ellipsometer sub-system; a beam profile ellipsometer sub-system; a beam profile reflectometer sub-system; a broadband reflectance spectrometer sub-system; a single-wavelength reflectometer sub-system; an angle-resolved reflectometer sub-system; an imaging sub-system; or a scatterometer sub-system.

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

22. The metrology system of claim 1, wherein the classifying the device feature into the two or more device bin groups based on the first metrology measurement comprises: determining one or more device feature property distributions of the device feature based on the first metrology measurement; and classifying the device feature into the two or more device bin groups based on the one or more device feature property distributions.

23. The metrology system of claim 22, wherein the one or more device feature property distributions comprise an OVL distribution.

24. The metrology system of claim 22, wherein the one or more device feature property distributions comprise a CD distribution.

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

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

27. The metrology system of claim 1, the training further comprising: receiving an optical calibration measurement of the representative metrology target via the second metrology sub-system; verifying a correlation between the optical calibration measurement of the representative metrology target and a measurement associated with a device bin group of the representative metrology target; and determining a calibration between the optical calibration measurement and the measurement associated with the device bin group of the representative metrology target.

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 bin group, wherein the optical calibration measurement is configured as a post-develop inspection (ADI) measurement and the measurement of the device bin group is configured as an etch post-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 manufacturing process based on the run-time metrology measurements corresponding to the representative metrology target.

30. A metrology system comprising: one or more controllers comprising one or more processors configured to execute program instructions, the program instructions configured to: perform a training, the training comprising: receiving first metrology data for a plurality of device features from a first metrology sub-system; generating first metrology measurements of the device features based on the first metrology data; classifying the device features into two or more device bin groups based on the first metrology measurements; and identifying representative metrology targets of the two or more device bin groups based on distributions 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 for the representative metrology targets from a second metrology sub-system represents the first metrology measurements of the device features in the two or more device bin groups; and performing a run-time operation, the run-time operation comprising: receiving run-time metrology data of the representative metrology targets on one or more run-time samples from the second metrology sub-system; and generating run-time metrology measurements of the representative metrology targets on the one or more run-time samples based on the run-time metrology data.

31. A method for metrology comprising: performing a training, the training comprising: receiving first metrology data for a plurality of device features from a first metrology sub-system; generating first metrology measurements of the device features based on the first metrology data; classifying the device features into two or more device bin groups based on the first metrology measurements; and identifying representative metrology targets of the two or more device bin groups based on distributions 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 for the representative metrology targets from a second metrology sub-system represents the first metrology measurements of the device features in the two or more device bin groups; and performing a run-time operation, the run-time operation comprising: receiving run-time metrology data of the representative metrology targets on one or more run-time samples from the second metrology sub-system; and generating run-time metrology measurements of the representative metrology targets on the one or more run-time samples based on the run-time metrology data. generating first metrology measurements of the device features based on the first metrology data; classifying the device features into two or more device bin groups based on the first metrology measurements; and identifying representative metrology targets of the two or more device bin groups based on a 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 measurements of the device features in the two or more device bin groups; and performing run-time operations, the run-time operations comprising: receiving run-time metrology data from the second metrology subsystem for the representative metrology targets on one or more run-time samples; and generating run-time metrology measurements of the representative metrology targets on the one or more run-time samples based on the run-time metrology data.

32. The method of claim 31, further comprising providing a correctable term to a lithography tool to adjust a sample manufacturing process based on the run-time metrology measurements corresponding to the representative metrology targets.

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