Device Feature Specific Edge Placement Error (EPE)

Abstract

A system and method for generating metrology measurements with a second subsystem, e.g., an optical subsystem, is disclosed. Training and runtime operations can be performed according to the method. The training can include receiving first metrology data for device features from a first metrology subsystem (e.g., optical), generating first metrology measurements (e.g., critical dimensions, etc.), binning the device features into two or more device bins based on the first metrology measurements, and identifying representative metrology targets for the two or more device bins based on a distribution of the first metrology measurements. The runtime operations can include receiving runtime metrology data (e.g., optical) for the representative metrology targets and generating runtime metrology measurements based on the runtime metrology data.

Description

[Technical Field]

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application No. 63 / 427,518, filed November 23, 2022, in the names of Amnon Manassen, Nadav Gutman, Frank Laske, and Andrei Shchegrov, entitled "DEVICE FEATURE SPECIFIC EDGE PLACEMENT ERROR (EPE)," the entire contents of which are incorporated herein by reference.

[0002] FIELD OF THE DISCLOSURE This disclosure relates generally to metrics, and more particularly to determining device feature-related metrics. [Background technology]

[0003] Semiconductor manufacturing typically requires the creation of multiple layers on a specimen and the placement of device features (e.g., features, transistors) on some or all of those layers. Overlay metrology is the measurement of the relative positions of structures on various layers of the specimen, which are critical to the performance of the device features and typically must be controlled within tight tolerances. The greater the overlay, the greater the misalignment.

[0004] Dedicated overlay / metrology targets can be used to help improve alignment. For example, diffraction gratings, which can be dedicated overlay targets, can be printed on multiple layers in areas separate from where device features will be formed, such as within scribe lines on the specimen. The scribe lines can define where the wafer will be cut in later processing. For example, a semiconductor wafer can have an array of dies, each with multiple device features and separated from the other dies by scribe lines.

[0005] Not all device feature layouts are amenable to overlay metrology. Furthermore, overlay metrology can affect, e.g., impair, the performance of device features. Therefore, overlay metrology is typically performed on dedicated overlay targets with features designed for sensitive overlay metrology, rather than directly on device features. However, differences in the size, orientation, density, and / or location on the specimen of the overlay target and that of the device features can introduce inconsistencies between the overlay measured on the target and the actual overlay of the device features. Therefore, establishing device-feature-related overlay metrology for overlay targets remains a continuing challenge in overlay metrology.

[0006] Additionally, a critical dimension (CD) can be, for example, the lateral dimension of a feature in cross section, such as the width of a gate or line or the diameter of a hole. CD can also be defined as an angle, such as, but not limited to, a sidewall angle.

[0007] The effort required to obtain device feature metrology values ​​for overlay and CD can usually be balanced against throughput requirements. For example, overlay targets with device-feature-scale features can be used to determine device-feature-related overlay. However, device-feature-scale features inherent in device features are typically directly resolvable using particle-beam metrology tools, such as, but not limited to, scanning electron microscopes (SEMs), whose relatively slow speed can limit throughput in a production environment. In contrast, optical overlay metrology offers higher throughput but is more prone to error due to its relatively low resolution and inability to directly measure device feature overlay.

[0008] Furthermore, error tolerance budgets (e.g., overlay, CD, etc.) will become increasingly smaller and more difficult to achieve, especially for optical tools used in high-throughput runtime operations. Adjustments for non-zero offsets (NZO) (e.g., adjustments for overlay measurement differences between after-develop inspection (ADI) and after-etch inspection (AEI)) will not be sufficient to meet increasingly tight error budgets.

[0009] The actual / realistic overlay of device features can vary depending on the type of device feature. At current and future nodes, the range of actual overlay values ​​for various device features will prevent the achievement of error budgets. [Prior art documents] [Patent documents]

[0010] [Patent Document 1] U.S. Patent No. 10,533,848 [Patent Document 2] U.S. Patent No. 9,093,458 [Patent Document 3] US Patent Application Publication No. 2014 / 0307256 Summary of the Invention [Problem to be solved by the invention]

[0011] Therefore, a need exists for a system and method that can provide accurate measurements that meet error budgets, yet is capable of large-scale, high-throughput operation. [Means for solving the problem]

[0012] In some example embodiments, the metrology system may also include one or more controllers communicatively coupled to the first and second metrology subsystems. In some example embodiments, the one or more controllers may include one or more processors configured to execute program instructions configured to perform training and runtime operations. In some example embodiments, the training may include receiving first metrology data for a plurality of device features from the first metrology subsystem, generating first metrology measurements for the device features based on the first metrology data, binning the device features into two or more device bins based on the first metrology measurements, and identifying representative metrology targets for the two or more device bins based on a distribution of the first metrology measurements. Additionally, in one illustrative embodiment, runtime operation may receive runtime metrology data for representative metrology targets on one or more runtime samples from the second metrology subsystem, and generate runtime metrology measurements for the representative metrology targets on the one or more runtime samples based on the runtime metrology data.

[0013] In a further aspect, the metrology system may monitor runtime metric measurements to determine whether a threshold has been breached and may provide a notification indicating the breach. In another aspect, the representative metric target identification may involve designing a representative metric target or selecting the representative metric target from among a plurality of candidate representative metric targets based on candidate target metric data. In another aspect, the device feature binning may involve identifying one or more sets of device features based on one or more ranges of first metric measurements, and binning the device features comprising the one or more sets into one or more outlier device bins.

[0014] In another aspect, the training and runtime operations can be performed during an after etch inspection (AEI) or an after develop inspection (ADI). In another aspect, the second metrology subsystem can include a spectroscopic ellipsometer (SE) subsystem or any other suitable subsystem. In another aspect, the first metrology subsystem can include a scanning electron microscope (SEM) subsystem. In another aspect, the device feature binning can include determining one or more device feature attribute distributions for the device features based on the first metrology measurements, and binning the device features into the two or more device bins based on the one or more device feature attribute distributions.

[0015] In another aspect, the above training can further receive optical calibration measurements for representative metrology targets via a second metrology subsystem, verify correlations between the optical calibration measurements for the representative metrology targets and measurements associated with device bins for the representative metrology targets, and determine calibration quantities between the optical calibration measurements and measurements associated with device bins for the representative metrology targets. In another aspect, the metrology system can be configured to provide corrective quantities to a lithography tool, and thereby adjust a specimen fabrication process based on runtime metrology measurements corresponding to the representative metrology targets.

[0016] In another aspect, in designing representative metrology targets as described above, a particular representative metrology target can be designed to comprise an array of repetitive device features.

[0017] In another aspect, the one or more sets of device features may include an outlier set based on an outlier range within the one or more ranges of values ​​of the first metric measurement.

[0018] In another embodiment, the outlier range can include an outlier range for CD values.

[0019] In another embodiment, the outlier range may include an outlier range for OVL values.

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

[0021] In another aspect, two or more of the one or more ranges of values ​​may each be based on a distinct peak in the distribution of values ​​of the first metric measurement.

[0022] In another aspect, the runtime metric measurements described above may be monitored to determine whether a threshold has been breached, and a runtime edge placement error (EPE) distribution determined based on the runtime metric measurements may be monitored.

[0023] In another aspect, the second metrology subsystem can include at least one of several specialized subsystems, including an SE subsystem configured for multiple illumination angles, an SE subsystem configured for Mueller matrix element measurements, a single wavelength ellipsometer subsystem, a beam profile ellipsometer subsystem, a beam profile reflectometer subsystem, a broadband reflective spectrometer subsystem, a single wavelength reflectometer subsystem, an angle resolved reflectometer subsystem, an imaging subsystem, and a scatterometer subsystem.

[0024] In another aspect, the correlations in the training process described above can include ADI to AEI correlations between optical calibration measurements and device bin-related measurements, and between optical calibration measurements configured to be ADI measurements and device bin-related measurements configured to be AEI measurements.

[0025] A metrology system is disclosed in accordance with one or more exemplary embodiments of the present disclosure. In some exemplary embodiments, the metrology system can include one or more controllers. In some exemplary embodiments, the controller can include one or more processors configured to execute program instructions. In some exemplary embodiments, the program instructions can be configured to perform training and runtime operations. In some exemplary embodiments, the training can include receiving first metrology data for a plurality of device features from a first metrology subsystem, generating first metrology measurements for the device features based on the first metrology data, binning the device features into two or more device bins based on the first metrology measurements, and identifying representative metrology targets for the two or more device bins based on a distribution of the first metrology measurements. In some example embodiments, each representative metrology target may be selected such that a second metrology measurement based on second metrology data from the second metrology subsystem and associated with the representative metrology target is representative of a first metrology measurement for device features in the corresponding two or more device bins. In some example embodiments, the runtime operations may receive runtime metrology data for the representative metrology targets on one or more runtime samples from the second metrology subsystem and generate runtime metrology measurements for the representative metrology targets on the one or more runtime samples based on the runtime metrology data.

[0026] A metrology method is disclosed in accordance with one or more example embodiments of the present disclosure. The method of an example embodiment can perform training and runtime operations. In an example embodiment, the training can include receiving first metric data for a plurality of device features from a first metrology subsystem, generating first metric measurements for the device features based on the first metric data, binning the device features into two or more device bins based on the first metric measurements, and identifying representative metrology targets for the two or more device bins based on a distribution of the first metric measurements. In an example embodiment, the runtime operations can include receiving runtime metric data for representative metrology targets on one or more runtime samples from a second metrology subsystem, and generating runtime metric measurements for the representative metrology targets on the one or more runtime samples based on the runtime metric data.

[0027] In a further aspect, a method can provide corrections to a lithography tool and thus adjust a specimen fabrication process based on run-time metrology measurements corresponding to a representative metrology target.

[0028] Both the foregoing general description and the following detailed description are exemplary and explanatory only and do not necessarily limit the invention as claimed. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the general description, serve to explain the principles of the invention.

[0029] Those skilled in the art will be able to better appreciate the many advantages of the present disclosure by reviewing the accompanying drawings, in which: [Brief explanation of the drawings]

[0030] [Figure 1A] FIG. 1 is a conceptual diagram of a weighing system according to one or more embodiments of the present disclosure. [Figure 1B]FIG. 1 is a conceptual diagram of an optical metrology tool according to one or more embodiments of the present disclosure. [Figure 1C] FIG. 1 is a conceptual diagram of a particle beam metrology tool according to one or more embodiments of the present disclosure. [Figure 2A] FIG. 1 is a conceptual diagram of the use of various tools of a metrology system for measurement according to one or more embodiments of the present disclosure. [Figure 2B] FIG. 1 is a conceptual diagram of a device bin in the form of a device feature overlay metrology plot in accordance with one or more embodiments of the present disclosure. [Figure 2C] FIG. 1 is a conceptual diagram of a device bin in the form of a device feature critical dimension metrology plot in accordance with one or more embodiments of the present disclosure. [Figure 3] FIG. 1 is a flow diagram depicting steps performed in a training method according to one or more embodiments of the present disclosure. [Figure 4] FIG. 1 is a flow diagram depicting steps performed in a runtime operation method according to one or more embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0031] Reference will now be made in detail to the disclosed subject matter, which is illustrated in the accompanying drawings. The present disclosure has been particularly shown and described with reference to certain embodiments and particular features thereof. The embodiments described herein are to be understood as illustrative and not limiting. It will be readily apparent to those skilled in the art that various changes and modifications in form and detail can be made therein without departing from the spirit and scope of the present disclosure.

[0032] Embodiments of the present disclosure are directed to systems and methods for binning device features (e.g., features comprising transistors, logic gates, etc.) into groups (e.g., device bins), identifying which target designs are representative of which device bins so that measurements for targets having those target designs can be used to predict measurements for the binned device features, and generating measurements for the representative metrology targets to generate measurements for the binned device features. For example, rather than using metrology targets to approximately determine wafer-scale / chip-scale / area-scale measurements (e.g., area overlay), a metrology target design associated with a device feature can be identified and paired with one or more device features and used as a surrogate to more accurately determine measurements for those device features. Using targets corresponding to specific groups of device features allows for accounting for differences in actual / real-world measurements for those device features, resulting in more accurate measurements and more specific process control adjustments, thereby allowing for tighter error budgets when preparing samples. This differs from targets where overlay measurements are considered overall and not specific device feature types. For example, high-throughput ADI optical metrology measurements can be correlated with high-resolution AEI metrology measurements, and the overlay of device features in specific bins / groups can be measured using corresponding representative targets unique to those bins / groups. Thus, optical measurements obtained for targets using a high-throughput second metrology subsystem can be used in the after-develop inspection (ADI) step within a high-volume manufacturing (HVM) process to predict overlay (OVL), critical dimension (CD), and / or edge placement error (EPE) measurements for device features in the after-etch inspection (AEI) step.

[0033] As contemplated herein, for example, actual measurements of OVL, CD, and / or EPE can be more accurately determined if multiple device features are binned together and the target designs associated with the binned device features are identified. For example, some target designs may share the same (or similar) measurements and thus be used to predict measurements for a particular device feature, while other target designs may not necessarily be consistently and / or accurately predictive for that device feature. It should be noted that various attributes can be used to bin device features. For example, device features can be binned based on one or more attributes, including, but not limited to, measurements (e.g., SEM measurements of device features in AEI, e.g., overlay, critical dimensions, etc.), sample tilt, location (e.g., distance from the edge of the device, scribe line, etc.), feature type (e.g., transistor type, logic gate type), feature shape, feature size, whether the feature is periodically repeated, the type of layer in which the feature appears, or some other attribute. For example, features of a certain shape (e.g., an L-shape next to two vertical lines, as shown by the fourth feature 204d in FIG. 2A ), located within a certain range (e.g., within 100 nanometers) of the edge of the device, and having higher (on average) SEM overlay measurements in AEI than other device features can be binned together in an effort to identify a representative metrology target design for those device features. In this way, a subset of the binned device features can be correlated with the representative metrology target design. By defining a device bin using one or more attributes, any other device features (e.g., device features from future fabricated samples, subsequent layers, or device features not being measured) can be identified as part of that device bin if they share those attributes.

[0034] Measurements of device features, such as overlay, CD, and EPE, can be useful in sample fabrication and / or metrology processes. However, directly measuring these device features to within the desired accuracy may damage them and / or require slow and expensive metrology techniques, such as those using a scanning electron microscope (SEM). Alternatively, measurements can be determined using targets (e.g., dedicated targets that are not electrically functioning devices).

[0035] Correlations may exist between measurements on device features and measurements on targets, at least for some samples, so that the target measurements can be used to predict measurements related to device features. For example, a system and method for generating device-related overlay measurements by adjusting optical overlay measurements on targets with device-related corrections is disclosed in U.S. Patent Application Publication No. 2018 / 0129999, entitled "METROLOGY AND CONTROL OF OVERLAY AND EDGE PLACEMENT ERRORS," filed August 7, 2018, the entire contents of which are incorporated herein by reference.

[0036] Further embodiments of the present disclosure are directed to generating runtime metrology measurements. For example, runtime metrology measurements can include metrology measurements acquired at runtime, e.g., during a sample fabrication process, such as an HVM process. In this regard, a second metrology subsystem can capture data directly on a sample being fabricated, close to the development step, without damaging the sample. Furthermore, identifying potential problems at this stage can facilitate sample rework in the current or future lot, thereby correcting the problem prior to a time-consuming, irreversible etching step. In certain examples, target overlay measurements from a current process step, measured optically on the sample, can be used (e.g., as an overlay correction) to compensate for drift and maintain overlay within a selected tolerance for process steps on subsequent samples in the same or subsequent lot. In another example, overlay measurements from a current process step can be fed forward to adjust subsequent process steps to compensate for any measured overlay errors. Systems and methods for generating device-related overlay measurements by adjusting optical overlay measurements with device-related corrections and overlay corrections are disclosed in U.S. Patent Application Publication No. 2018 / 0122999, entitled "METROLOGY AND CONTROL OF OVERLAY AND EDGE PLACEMENT ERRORS," filed August 7, 2018, the entire contents of which are incorporated herein by reference.

[0037] Multiple metrology subsystems may be used at various stages of training and / or runtime (e.g., optical and / or SEM metrology subsystems). For example, the terms “first” and “second” may be used solely for distinguishing purposes. For example, a first metrology subsystem (e.g., first metrology subsystem 102 in FIG. 1C ) and a second metrology subsystem (e.g., second metrology subsystem 104 in FIG. 1B ) may be used. For example, the first metrology subsystem may be, but is not required to be, a higher-resolution, lower-capacity (e.g., slower) subsystem, such as an SEM, to accurately determine data used to generate overlay, CD, and / or EPE. Conversely, the second metrology subsystem may be, but is not required to be, a lower-resolution, higher-capacity subsystem, such as an optical subsystem. The first metrology subsystem may perform measurements during the training step, while the second metrology subsystem may be used during runtime to facilitate high-volume sample processing. In this manner, subsystems with different resolutions and throughputs may be used to leverage individual strengths during training and runtime operations.

[0038] The second metrology subsystem can provide high-throughput metrology suitable for in-line process control. For example, the second metrology subsystem can utilize an imaging-based optical overlay method, which simultaneously images features on multiple sample layers and determines overlay based on the relative offset between those features. In another example, overlay can be determined using a scatterometry-based optical metrology method, using a model-based approach that compares light scattered and / or diffracted from the sample to a desired pattern based on known sample features, such as, but not limited to, grating structures within overlapping layers. In another example, a CD target can be imaged using an imaging-based optical CD method. Target features suitable for optical characterization can be segmented to provide multiple measurement points within a single field of view, thereby enabling highly accurate measurements with low noise due to statistical averaging of repetitive structures. Furthermore, the second metrology subsystem can perform measurements on the order of 0.2 to 1 second per site, although this is not required, providing significant flexibility in terms of measurement frequency per lot and per sample on a production line.

[0039] For purposes of this disclosure, the term "optically resolvable" refers to at least a portion of the features being resolvable within a specified tolerance by a selected second metrology subsystem. Examples of optically resolvable features may include, but are not limited to, a grid pattern of dedicated overlay targets located within the scribe lines of a wafer that are resolvable / measurable by image-based overlay (IBO) metrology. For example, optically resolvable targets may be on the order of greater than 10 micrometers (e.g., 30 micrometers) in size.

[0040] Furthermore, a "device feature-scale" feature can have one or more characteristics (e.g., linewidth, feature-to-feature separation, etc.) similar to the device feature incorporated into the device feature being fabricated. Examples of a "device feature" can include, but are not limited to, a functional feature of a die of multiple dies comprising a wafer, such as a transistor, logic gate, etc. Device features are typically smaller than optically resolvable targets and generally not directly optically resolvable. For example, at least some of the characteristics of the device feature-scale features can be on the order of less than 3 micrometers in size, and the first metrology subsystem can be equipped with tools that typically have a resolution of less than 1 micrometer, less than 10 nanometers, 0.5 to 4 nanometers, etc. It is recognized herein that certain types of device features, while at least partially resolvable by a selected second metrology subsystem, can also contain characteristics that are less than the resolution of the selected second metrology subsystem. It is understood that the terms "optically resolvable" features and "device feature-scale" features, etc., are illustrative and are not intended to limit the size, orientation, or distribution of any patterned features on the sample. Certain targets, such as hybrid overlay targets, may allow both optically resolvable and device feature-scale metrology of the hybrid overlay target. In embodiments, the multiple target designs are multiple hybrid overlay target designs.

[0041] Direct measurement of device features can be damaging to the device features themselves, at least in some particle metrology tools. Embodiments of the present disclosure provide the benefit of using relatively fast optical methods to measure targets and make adjustments to the optical measurements, thereby estimating measurements for device features without directly measuring the device features themselves. These embodiments provide the benefit of optically measuring targets instead of measuring device features in potentially damaging ways, thereby improving the reliability of device features.

[0042] It should be noted that edge placement error (EPE) is defined for purposes of this disclosure as a function of overlay and CD. For example, EPE can be defined as the average of overlay and CD (i.e., (1 / 2) x (overlay + CD)).

[0043] 1A-1C and 3-4 generally depict systems and methods for providing run-time metrology measurements according to one or more embodiments of the present disclosure. In embodiments, the systems and methods can be used to augment existing sample metering methods by providing cost-effective device feature metrology monitoring.

[0044] 1A illustrates a conceptual diagram of a metering system 100 according to one or more embodiments of the present disclosure. The metering system 100 may include, but is not limited to, a second metering subsystem 104 and / or a first metering subsystem 102. The metering system 100 may additionally include, but is not limited to, a controller 108. In various embodiments, the controller 108 includes one or more processors 110 configured to execute program instructions stored on a memory 112. In this regard, the one or more processors 110 included in the controller 108 may perform any of the various process steps described throughout this disclosure. For example, the controller 108 may receive data from either the second metering subsystem 104 or the first metering subsystem 102 and may generate runtime metering measurements. The metering system 100 need only be configured to measure one or more samples (eg, sample 118 in FIG. 1B), but need not contain the samples.

[0045] In various embodiments, the first metrology subsystem 102 may be configured for a first resolution and a first throughput. The second metrology subsystem 104 may be configured for a second resolution (e.g., spatial resolution) and a second throughput (e.g., measurements per second). For example, the second resolution and the second throughput may be different from the first resolution and the first throughput. For example, the first resolution may be higher (e.g., more precisely, higher spatial resolution) than the second resolution, and the first throughput may be lower than the second throughput. For example, the second resolution may be configured to resolve (e.g., be resolvable) at least one measurement per target (e.g., an optically resolvable target), and the second resolution may be configured to resolve (e.g., be resolvable) at least one device feature for overlay and / or CD purposes.

[0046] In various embodiments, the second metrology subsystem 104 includes the optical metrology subsystem 172 of FIG. 1B, and the first metrology subsystem 102 includes a device feature resolvable subsystem. For example, the device feature resolvable subsystem may include the particle beam metrology subsystem 174 of FIG. 1C. It should be noted that while the first metrology subsystem 102 may be described in the context of the particle beam metrology subsystem 174 of FIG. 1C, e.g., a scanning electron microscope (SEM) metrology subsystem, the first metrology subsystem 102 may alternatively or additionally include a soft x-ray (SXR) metrology subsystem configured to measure the sample using electromagnetic radiation in the soft x-ray spectral range. For example, the SXR metrology subsystem may be used to obtain multiple AEI device feature measurements for multiple device features.

[0047] A "tool" (e.g., a metrology tool, a first tool, e.g., particle beam metrology tool 176, a second tool, e.g., optical metrology tool 178, etc.) can be a subsystem coupled to controller 108. For example, controller 108 in FIG. 1B can be the same controller as controller 108 in FIG. 1A or 1C, or can be a separate controller. For example, optical metrology tool 178 can include optical metrology subsystem 172 and controller 108. Also, for example, particle beam metrology tool 176 can include particle beam metrology subsystem 174 and controller 108.

[0048] 1B is a conceptual diagram of an optical metrology tool 178 according to one or more embodiments of the present disclosure. In various embodiments, the optical metrology tool 178 includes an optical metrology subsystem 172. The optical metrology subsystem 172 can be any subsystem suitable for generating measurements based on optically resolvable features and used in the art. For example, the optical metrology subsystem 172 can be an example of the second metrology subsystem 104, which can include various optical overlay metrology subsystems known in the art suitable for generating optical overlay data for two or more layers of the sample 118, such as, but not limited to, an imaging-based optical metrology subsystem or a scatterometry-based optical metrology subsystem.

[0049] The second metrology subsystem 104 may not include a scanning electron microscope, but may include an optical subsystem used to optically (e.g., using wavelengths of light) receive / measure metrology data about the sample 118. For example, the second metrology subsystem 104 may include a spectroscopic ellipsometer (SE) subsystem.

[0050] In various embodiments, the second metrology subsystem 104 may include at least one of an SE subsystem configured for multiple illumination angles, an SE subsystem configured for Mueller matrix element measurements, a single wavelength ellipsometer subsystem, a beam profile ellipsometer subsystem, a beam profile reflectometer subsystem, a broadband reflective spectrometer subsystem, a single wavelength reflectometer subsystem, an angle resolved reflectometer subsystem, an imaging subsystem, and a scatterometer subsystem.

[0051] 1C is a conceptual diagram of a particle beam metrology tool 176 according to one or more embodiments of the present disclosure. In various embodiments, particle beam metrology tool 176 includes a particle beam metrology subsystem 174. Particle beam metrology subsystem 174 may be an example of first metrology subsystem 102, which may include, for example, various metrology tools suitable for resolving device feature-scale features of a target using particle beam technology, such as, but not limited to, a scanning electron microscope (SEM) metrology tool.

[0052] 1A , the second metrology subsystem 104 can provide measurements (e.g., optical measurements) of one or more targets, and the first metrology subsystem 102 can provide measurements suitable for determining metrology calibration adjustments to those optical measurements. Furthermore, the metrology system 100 can enable any component (e.g., the second metrology subsystem 104 or the first metrology subsystem 102) to be utilized at any specified frequency within the production line (at runtime) to balance metrology accuracy and throughput requirements. For example, the second metrology subsystem 104 can be used for in-line metrology monitoring, while the first metrology subsystem 102 can be selectively utilized less frequently to determine metrology calibration adjustments to the optical measurements provided by the second metrology subsystem 104.

[0053] 2A illustrates a conceptual diagram of the use of various subsystems of metrology system 100 to measure device features 204 and targets 210 in accordance with one or more embodiments of the present disclosure. For example, a first metrology subsystem 102 can be configured to measure both device features 204 and targets 210 as shown (e.g., via various characteristics such as resolution (e.g., spatial resolution), wavelength, field of view, program instructions stored in memory, etc.), and a second metrology subsystem 104 can be configured to image targets 210.

[0054] In embodiments, device features 204 are binned (eg, prior to binning steps 306, 604) based on location (eg, location within a die, proximity to other components, devices, etc., and / or the like).

[0055] In embodiments, device features are binned based on shape (eg, feature number, L-shape, square-shape, etc.).

[0056] In embodiments, device features are binned based on metrology (eg, overlay, CD, and / or EPE).

[0057] In embodiments, device features are binned based on device type (eg, transistor type, logic gate type, eg, NOR gate, etc.).

[0058] 3 is a flow diagram illustrating steps performed in a method 300 for performing training including representative metrology target identification, according to one or more embodiments of the present disclosure. For example, training may identify which targets 210 are potential representative metrology targets for which device bins 212. It should be noted that embodiments and enabling technologies described herein in the context of metrology system 100 should be understood to extend to method 300 and other methods herein (e.g., methods 400 and 500). However, it should be further noted that methods 300, 400, and 500 are not limited by the architecture of metrology system 100.

[0059] In step 302, first metrology data is received for a plurality of device features 204 from a first metrology subsystem.

[0060] In step 304, first metric measurements are generated for the device features 204 based on the first metric data.

[0061] In step 306, the device features 204 are binned into two or more device bins 212 based on the first metrology measurements.

[0062] In embodiments, device features may be binned using one or more attributes (eg, measurement distribution, location, shape, etc.).

[0063] The binning may involve determining a device feature attribute distribution for the device features based on the first metric measurement, and binning the device features into device bins based on the one or more device feature attribute distributions.

[0064] For example, the device feature attribute distribution may include a distribution of sample tilt (i.e., angular tilt of the sample 118). For example, accounting for tilt may allow for improved (e.g., more accurate) device binning and / or representative metrology target qualification.

[0065] For example, the device feature attribute distributions may include distributions such as OVL distribution, CD distribution, and / or edge placement error (EPE) distribution, etc. For example, several device features with similar CD values ​​(e.g., values ​​above an 80th percentile threshold) may be binned together.

[0066] For example, device features may be grouped into feature type groups. Different feature type groups may be represented by feature numbers (e.g., first feature 204a, second feature 204b, third feature 204c, fourth feature 204d, and so on up to any numbered feature type group, e.g., Nth feature 204e), and each feature type group may contain many (e.g., tens, thousands, or billions) device features having shapes associated with that feature type group. For example, a particular logic gate structure used in a wafer die may be associated with a particular feature type group.

[0067] The binning may involve identifying one or more sets of device features 204 based on one or more ranges of the first metrology measurements, and binning the one or more sets of device features 204 into one or more outlier device bins 212. For example, Figure 2C depicts outlier device bin 212d for first device feature 204a.

[0068] The one or more sets of device features may include an outlier set based on an outlier range within one or more ranges of the first metrology measurement. For example, a threshold may be used to determine the outlier set. For example, a value above an outlier threshold (e.g., a 90th percentile threshold) may be used as a cutoff such that all features above (or below) the threshold are binned together. In this example, the range is determined by the range above the 90th percentile. The range may be defined as a range between certain percentiles, multiples of standard deviations, and / or the like.

[0069] The outlier bins may include outlier bins for CD values. The outlier bins may include outlier bins for OVL values. The bins may be based on peaks in the distribution of first metric measurements. For example, instead of a simple bell curve, the value distribution may have multiple peaks, such as a sinusoidal curve. Each peak / apex of the curve indicates a group of device features with similar values, and individual targets can be usefully used as proxies to measure those values. Two or more bins may each be based on a distinct peak in the distribution of values.

[0070] In step 308, representative metric targets are identified for the two or more device bins 212 based on the distribution of the first metric measurements. For example, the identification may involve designing the representative metric targets or setting the representative metric targets from among candidate representative metric targets.

[0071] In various embodiments, the representative metrology targets are designed. For example, the representative metrology targets may be designed using software that employs one or more design methodologies. For example, any methodology may be used. For example, a particular representative metrology target may be designed as an array of repeating device features. For example, the device features may be repeated as a grid of rows and columns. For example, the number of rows and / or columns may be greater than 10. For example, the number of rows and / or columns may be greater than 100. For example, the number of rows and / or columns may be greater than 1000. In this manner, the representative metrology targets may be designed using the device features within the device bins themselves as a basis. According to another example, the design may involve performing analysis to predict and / or collect attributes of representative metrology targets that are representative of the binned device features. For example, the prediction may involve simulation, machine learning prediction, selection from attributes of historical metrology targets stored in memory, and / or some other manner to predict or generate measurements for the (designed) representative metrology targets.

[0072] In various embodiments, the representative metric target is identified by receiving candidate target metric data for a plurality of candidate representative metric targets and selecting a representative metric target from among the plurality of candidate representative metric targets based on the candidate target metric data. For example, one or more candidate representative metric targets may be configured for selection using the controller 108. For example, the representative metric targets may be selected such that second metric measurements based on second metric data from the second metric subsystem for the representative metric targets are representative of first metric measurements for device features in two or more corresponding device bins. For example, candidate target metric data may be received and used to generate candidate target metric measurements. For example, this specific receipt may refer to receipt from the first metric subsystem 102 and / or the second metric subsystem 104. According to another example, historical data (e.g., fetched from memory or known a priori) for the candidate representative metric targets may be used.

[0073] 4 is a flow diagram depicting steps performed in a method 400 for performing runtime operations in accordance with one or more embodiments of the present disclosure. For example, after representative metrology targets have been identified through training in method 300, method 400 may facilitate high-throughput metrology of specimens 118 during their manufacturing process.

[0074] In step 402, runtime metrology data is received for representative metrology targets on one or more runtime samples from a second metrology subsystem.

[0075] Runtime operations may be configured to be performed during ADI. For example, both training and runtime may be performed during ADI. According to another example, training may be configured to be performed during AEI and runtime operations may be configured to be performed during ADI. For example, training may be configured to be performed during AEI and runtime operations may be configured to be performed during ADI using the optical second metrology subsystem 104.

[0076] Runtime operations may be configured to be performed during an after etch inspection (AEI). For example, both training and runtime may be performed during AEI. For example, training may be configured to be performed during an after develop inspection (ADI) and runtime operations may be configured to be performed during AEI.

[0077] In step 404, runtime metrics measurements are generated for representative metrics targets on the one or more runtime samples 118 based on the runtime metrics data.

[0078] In another step, a threshold breach is determined based on the monitored runtime metrology measurements, and a notification indicating the threshold breach is initiated (sent by controller 108). For example, controller 108 may be configured to initiate (send or command) a notification when a distribution of runtime metrology measurements exceeds a threshold (e.g., a known threshold, such as a user-selected threshold for tolerance). For example, the notification may include an alert configured for user observation or a command configured to stop the runtime operation. In this manner, the optical runtime metrology measurements may be used to monitor the manufacturing process of specimen 118 to keep the process within threshold limits. For example, a runtime edge placement error (EPE) distribution may be monitored; a runtime CD distribution may be monitored; or a runtime overlay distribution may be monitored.

[0079] During training, calibration may be performed, for example, including receiving information (e.g., calibration measurements) to verify the correlation of representative metric targets to device bins, and then using the received information to determine the calibration function to be used during runtime.

[0080] For example, during training, optical calibration measurements may be received for a representative metrology target via the second metrology subsystem 104. The optical calibration measurements for the representative metrology target may exhibit a correlation, though not a perfect match, to actual / real-world measurements for actual device features. If the correlation is sufficiently strong and predictable, it can be determined through validation, and the validated representative metrology target may be safe to use.

[0081] For example, during training, correlations between optical calibration measurements for representative metrology targets and measurements belonging to the device bins 212 associated with those representative metrology targets may be verified. For example, the optical calibration measurements may be compared to measurements for device features (e.g., first metrology measurements) to determine whether they correlate. The correlation may be verified across a range of values. For example, measurements of 10.00, 11.00, and 12.00 may correlate with second actual measurements of 10.11, 11.14, and 12.09. As shown, the second values ​​are approximately 10% larger, so multiplying the measurements by 1.10 results in the measurements closely matching the second actual values. In some examples, the correlation is confirmed / validated if the difference is within a validation threshold. If not, a different representative metrology target may be identified. This correlation / validation may be significant.

[0082] For example, during training, calibration quantities (e.g., calibration functions, offsets, and / or the like) can be determined between optical calibration measurements and measurements in device bins 212 associated with representative metrology targets, so that applying the calibration quantities produces more accurate runtime metrology measurements. For example, the calibration quantities can be determined based on the difference between the optical calibration measurements for representative metrology targets and the measurements in device bins 212 associated with those representative metrology targets. For example, the calibration quantities can include an offset (e.g., a value N equal to N + offset) or a linear mismatch (e.g., N = 1.10N). The calibration quantities can be determined using any known method for calibration. For example, the difference between regression fit lines for each set of values ​​can be used to determine the calibration quantities.

[0083] In various embodiments, the correlations include ADI-to-AEI correlations between optical calibration measurements and measurements belonging to device bins 212. The optical calibration measurements may be configured to be ADI measurements, and the device bin measurements may be configured to be AEI measurements. For example, if representative metrology targets are qualified based on SEM measurements from the second metrology subsystem at AEI, but a user desires to monitor EPE distribution at ADI, the controller 108 may be configured to calibrate and verify correlations between optical ADI measurements for those representative metrology targets and SEM measurements for corresponding binned device features at AEI. In this way, high-throughput optical imaging (e.g., imaging by the second metrology subsystem 104) at ADI (i.e., before etch) can be used to predict device feature distribution at AEI (i.e., after etch).

[0084] As shown in Figures 2B and 2C, one or more groups of device features (e.g., features 1 through 5 on the horizontal axis) can be binned into one or more device bins 212 (e.g., device bins 212a, 212b, 212c, 212d, and 212e) based on the similarity between the device feature measurements 222 and 224 and the target design measurements 214, 216, and 218.

[0085] 2B illustrates a conceptual diagram of a device bin 212 in the form of a plot 206 of overlay metrology values ​​222 for device features 204, in accordance with one or more embodiments of the present disclosure. For example, the device feature metrology values ​​222 may include CD, EPE, and / or overlay metrology values.

[0086] The device feature measurements 222, 224 may vary due to differences in the structural characteristics of the device feature 204, and the target design measurements 214, 216 (e.g., measurements on a representative metrology target) may vary due to differences in the representative metrology target design. For example, the device feature measurements may vary based on their attributes (e.g., shape, size, location, etc.), and the representative metrology target measurements may vary based on their attributes (e.g., shape, size, etc.). For example, the overlay measurement 222 associated with the second feature 204b in FIG. 2A, i.e., plotted above the "2" on the horizontal axis in FIG. 2B, may vary due to its location relative to the die edge, for example.

[0087] In various embodiments, device features may be binned into device bins 212 based on a range of values. For example, a range of measurements may refer to measurements that are, on average, largest, smallest, or the like. For example, features numbered in the top 40% of overlay measurements may be binned together as shown by device bin 212b. Device features with the lowest average values ​​of measurements 222 (e.g., the lowest average value, the second lowest average value, and / or the third lowest average value as shown) may be binned together as shown by the binning of first and second features in device bin 212c.

[0088] In embodiments, device features may be binned based on the average of the measurements for those device features. For example, measurements 222 for feature type number 3 in FIG. 2B may be binned into average distribution device bin 212a. Average may refer to, for example, the average of all measurements taken or a subset of measurements taken.

[0089] Each device bin 212 can be correlated with a target 210. For example, overlay device bin 212b can be associated with target 210b in FIG. 2A. For example, target measurement 214, shown in FIG. 2B near measurement 222, can be obtained from target 210b. In this case, target 210b can be identified as the representative metric target for overlay device bin 212b. Similarly, target 210c (e.g., another target) in target measurement 216 can be identified as the representative metric target for overlay device bin 212a due to its proximity / similarity to measurement 222 for device bin 212a.

[0090] As depicted in FIG. 2B , a representative metrology target design can sometimes be identified based on correlations (e.g., similarities) between target design measurements 216 and device feature measurements (e.g., overlay measurements 222). In certain instances, a representative metrology target can be defined by one or more target design attributes, such that all targets meeting those attribute criteria are considered to be the target design. For example, in the case of device bin 212a in FIG. 2B , target 210, e.g., including multiple targets 210a with similar target design attributes, can be the target used to generate target design measurements 216 (e.g., including multiple target design measurements 216 or being an average of multiple target design measurements). Target 210a can have one or more target design attributes that can be selected for use in defining the representative metrology target design. For example, such target design attributes may include, but are not limited to, size, various grating characteristics such as pitch, shape of each grating, thickness, type (e.g., box-in-box overlay target, AIM overlay target, etc.), or any other attribute. For example, after measuring a large number of target designs (e.g., tens, hundreds, or thousands), the qualification step may reveal that grating pitches of the measured target designs within a certain range correlate with a particular device bin 212. For example, a target design having such a pitch may be the one that most closely correlates with measurements 222 for device bin 212. In this example, the qualified "representative metrology target," "representative metrology target design," etc., may be any target having that pitch.

[0091] 2C illustrates a conceptual diagram of a device bin 212 in the form of a plot 208 of critical dimension (CD) measurements 224 for device features, in accordance with one or more embodiments of the present disclosure. For example, the CD measurements 224 can be generated (e.g., measured, calculated based on data) using the first metrology subsystem 102.

[0092] As shown, device bin 212d may be an outlier device bin 212d selected because it contains device feature number 1 associated with measurement 224 shown within CD device bin 212d. For example, a representative metrology target associated with CD optical measurement 220 may be identified based on correlation with CD device bin 212d.

[0093] As shown, CD device bin 212e may be selected because it contains device features numbered 2-4 associated with measurement 224 shown within CD device bin 212e. For example, a representative metrology target may be identified based on correlation with CD device bin 212e. For example, target 210 of target measurement 218 may be identified as the representative metrology target associated with CD device bin 212e.

[0094] In various embodiments, system 100 may include one or more controllers 108. For example, the steps may be performed individually and / or in combination on one or more controllers. For example, the steps may be performed using separate software applications stored on separate computers (example controllers), and / or the like.

[0095] 1A and 1C, various component embodiments will be described in further detail.

[0096] The controller 108 may be communicatively coupled to one or more external manufacturing tools, such as, but not limited to, lithography tools, and may operate as an advanced process controller (APC) suitable for controlling inputs of the external manufacturing tools to maintain overlay within specified overlay tolerances.

[0097] In an optional step, the sample fabrication process is adjusted by providing an overlay correction (based on the run-time metrology measurements) to a lithography tool for a device feature area (e.g., a functional / active area of ​​a die containing device features); for example, at least one subsequent exposure during the fabrication process is modified based on the run-time metrology measurements. For example, the run-time metrology measurements may include run-time metrology measurements corresponding to a plurality of representative metrology targets corresponding to individual device bins 212. For example, the overlay correction may be an adjustment to any parameter, such as if the overlay is misaligned by a certain distance, the lithography process may be adjusted backward to correct the misalignment.

[0098] The metering system 100 depicted in FIG. 1A and the related description are presented for illustrative purposes only and should not be construed as limiting. For example, any combination of the elements depicted in FIG. 1A may be present within the metering system 100. In one example, the metering system 100 may include a second metering subsystem 104, a first metering subsystem 102, and a controller 108. Furthermore, any of the components of the metering system 100 may be located proximate to or remote from one another. In various embodiments, multiple components of the metering system 100 may be integrated into a single physical device feature.

[0099] FIG. 1B is a conceptual diagram of an optical metrology tool 178 (eg, having a first metrology subsystem 104) according to one or more embodiments of the present disclosure.

[0100] In various embodiments, the optical metrology tool 178 and the first metrology subsystem 104 include an optical illumination source 114 that generates an optical illumination beam 116. The optical illumination beam 116 may include light at one or more specified wavelengths, including, but not limited to, ultraviolet (UV), visible, or infrared (IR) light.

[0101] Optical illumination source 114 may be any type of illumination source known in the art suitable for producing optical illumination beam 116 .

[0102] The optical illumination source 114 may include any type of illumination source suitable for providing the optical illumination beam 116. In some embodiments, the optical illumination source 114 is a laser light source. For example, the optical illumination source 114 may include, but is not limited to, one or more narrowband laser sources, broadband laser sources, supercontinuum (ultra-broadband) laser sources, white light laser sources, etc., which may provide the optical illumination beam 116 with high coherence (e.g., high spatial and / or temporal coherence). In some embodiments, the optical illumination source 114 may include a laser-sustained plasma (LSP) light source. For example, the optical illumination source 114 may include, but is not limited to, an LSP lamp, an LSP bulb, or an LSP chamber suitable for containing one or more elements and capable of emitting broadband illumination when excited into a plasma state by a laser light source. In some embodiments, the optical illumination source 114 includes a lamp light source. For example, optical illumination source 114 may include, but is not limited to, an arc lamp, a discharge lamp, an electrodeless lamp, etc. In this case, optical illumination source 114 may provide an optical illumination beam 116 having low coherence (e.g., low spatial coherence and / or low temporal coherence).

[0103] In various embodiments, an optical illumination source 114 directs an optical illumination beam 116 via an illumination path 120 to a sample 118. The illumination path 120 may include one or more illumination path lenses 122 or additional optical elements 124 suitable for modifying and / or modulating the optical illumination beam 116. For example, the one or more optical elements 124 may include, but are not limited to, one or more polarizers, one or more filters, one or more beam splitters, one or more diffusers, one or more homogenizers, one or more apodizers, and one or more beam shapers. The illumination path 120 may further include an objective lens 126 configured to direct the optical illumination beam 116 to the sample 118.

[0104] In various embodiments, the sample 118 is placed on a sample stage 128. The sample stage 128 may include any suitable device features for positioning and / or scanning the sample 118 within the optical metrology subsystem 172. For example, the sample stage 128 may include any combination of a linear translation stage, a rotation stage, a tip / tilt stage, etc.

[0105] In various embodiments, the optical metrology subsystem 172 includes a detector 130 configured to capture light emanating from the sample 118 via a collection path 132. The collection path 132 may include, but is not limited to, one or more collection path lenses 134 that collect light from the sample 118. For example, the detector 130 may receive light reflected or scattered from the sample 118 (e.g., due to specular reflection, diffuse reflection, etc.) via the one or more collection path lenses 134. In another example, the detector 130 may receive light generated by the sample 118 (e.g., luminescence associated with absorption of the optical illumination beam 116, etc.). In another example, the detector 130 may receive one or more diffraction orders from the sample 118 (e.g., 0th diffraction order, ±1st diffraction orders, ±2nd diffraction orders, etc.).

[0106] Detector 130 may include any type of detector known in the art suitable for measuring illumination received from sample 118. For example, detector 130 may include, but is not limited to, a multi-pixel detector, such as a CCD detector, a CMOS detector, etc. In various embodiments, detector 130 may include a spectroscopic detector suitable for distinguishing wavelengths of light emitted by sample 118.

[0107] The collection path 132 may further include a number of optical elements that direct and / or modify the illumination collected 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 blocks.

[0108] In some embodiments, the detector 130 is positioned substantially orthogonal to the surface of the sample 118. In some embodiments, the optical metrology subsystem 172 includes a beam splitter 136 oriented to simultaneously direct the optical illumination beam 116 onto the sample 118 and collect light emanating from the sample 118 at the objective 126. Additionally, the illumination path 120 and the collection path 132 may share one or more additional elements (e.g., the objective 126, an aperture, a filter, etc.).

[0109] The optical metrology subsystem 172 can measure overlay based on any technique known in the art, such as, but not limited to, imaging or scatterometry. 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 the detector 130. The captured image can be any type of image known in the art, such as, but not limited to, a bright-field image, a dark-field image, or a phase-contrast image. The captured images can then be stitched together (e.g., by the optical metrology subsystem 172, the controller 108, etc.) to form a composite image of the sample 118. According to another example, the optical metrology subsystem 172 can scan the focused optical illumination beam 116 over the sample 118 and capture light and / or particles emanating from the sample 118 on one or more detectors 130 at one or more measurement angles to generate an image pixel by pixel. The focused optical illumination beam 116 can be scanned over the sample 118 by modifying the beam path (e.g., using a galvanometer mirror, a piezoelectric mirror, etc.) and / or by translating the sample 118 through the focused beam's focal space. Thus, the overlay of two or more sample layers can be determined based on the relative positions of features on the two or more sample layers.

[0110] According to another example, optical metrology subsystem 172 can operate as a scatterometric metrology subsystem by determining overlay based on patterns of light scattered and / or diffracted from sample 118 in response to optical illumination beam 116. For example, optical metrology subsystem 172 can capture (e.g., with detector 130) one or more pupil plane images (e.g., of different regions of an overlay target) containing the angular distribution of light emanating from the sample. Overlay between two or more sample layers can then be determined from the pupil plane images based on modeled scattering and / or diffraction from overlay target features of known size and distribution for each layer.

[0111] Additionally, the optical metrology subsystem 172 can measure overlay in any manufacturing step by propagating the optical illumination beam 116 within the current layer to interact with features on one or more previously fabricated layers, thereby causing a signal received by the detector 130 (e.g., an image of the sample 118, an image of the pupil plane, etc.) to indicate overlay between at least two layers. For example, the optical metrology subsystem 172 can perform overlay measurement between the previously fabricated layer and the current layer after the current layer is exposed as an after-development inspection (ADI) step. In this way, overlay measurements for the current layer and any previous layers can be generated based on the refractive index difference of the exposed features relative to the unexposed features. In another example, the optical metrology subsystem 172 can perform overlay measurement between the previously fabricated layer and the current layer as an after-etch inspection (AEI) step after the developed pattern is etched into the current layer as a relief structure.

[0112] 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 tool 176 includes a particle beam metrology subsystem 174, which is an example of a first metrology subsystem 102, which may include, for example, a scanning electron microscope (SEM) metrology subsystem 102.

[0113] Measurements by the first metrology subsystem 102 may be performed at any stage of the manufacturing process. For example, the first metrology subsystem 102 may perform measurements between a previously fabricated layer and the current layer as an after-development inspection (ADI) step after exposing and / or developing the current layer. In another example, the first metrology subsystem 102 may perform measurements between a previously fabricated layer and the current layer as an after-etch inspection (AEI) step after the developed pattern is etched into the current layer as a relief structure.

[0114] In various embodiments, the particle beam metrology subsystem 174 includes a particle source 138 (e.g., an electron beam source, an ion beam source, etc.) that generates the particle beam 140 (e.g., an electron beam, an ion beam, etc.). The particle source 138 may include any particle source known in the art suitable for generating the particle beam 140. For example, the particle source 138 may include, but is not limited to, an electron gun or an ion gun. In various embodiments, the particle source 138 is configured to adjust the energy of the particle beam 140 it provides. For example, a particle source 138 including an electron source may provide an acceleration voltage in the range of, but not limited to, 0.1 kV to 30 kV. Alternatively, a particle source 138 including an ion source may provide an ion beam having an energy in the range of, but not limited to, 1 keV to 50 keV.

[0115] In embodiments, the particle beam metering subsystem 174 includes one or more particle focusing elements 142. For example, the one or more particle focusing elements 142 may include, but are not limited to, a single particle focusing element or one or more particle focusing elements forming a composite system. In embodiments, the one or more particle focusing elements 142 may include a particle objective lens 144 configured to direct the particle beam 140 toward the sample 118 located on the sample stage 146. Additionally, the one or more particle sources 138 may include any type of electron lens known in the art, such as, but not limited to, an electrostatic lens, a magnetic lens, a monopotential lens, or a bipotential lens.

[0116] In some embodiments, the particle beam metrology subsystem 174 includes at least one particle detector 148 that detects, e.g., images, particles emanating from the sample 118. In some embodiments, the particle detector 148 includes an electron collector (e.g., a secondary electron collector, a backscattered electron detector, etc.). In some embodiments, the particle detector 148 includes a photon detector (e.g., a photodetector, an x-ray detector, a scintillating element coupled to a photomultiplier tube (PMT)-type detector, etc.) that detects electrons and / or photons from the sample surface.

[0117] 1C , and the related description above, are provided for illustrative purposes only and should not be construed as limiting. For example, particle beam metrology tool 176 can include a multi-beam and / or multi-column system suitable for concurrently interrogating sample 118. In further embodiments, particle beam metrology tool 176 can include one or more elements (e.g., one or more electrodes) configured to apply one or more voltages to one or more locations on sample 118. In this case, voltage contrast imaging data can be generated by particle beam metrology tool 176.

[0118] It is recognized herein that the penetration depth of the particle beam 140 in the sample 118 can depend on the particle energy, with more energetic beams typically penetrating deeper into the sample 118. In various embodiments, different particle energies are utilized in the particle beam metrology subsystem 174 to interrogate different layers of a device feature based on the penetration depth of the particle beam 140 into the sample 118. For example, a relatively low energy electron beam (e.g., about 1 keV or less) may be utilized in the particle beam metrology subsystem 174, while a higher energy electron beam (e.g., about 10 keV or greater) may be utilized to characterize a previously fabricated layer. It is recognized herein that the penetration depth is a function of particle energy and may vary for different materials, and therefore the selection of particle energies for a particular layer may vary for different materials.

[0119] The optically resolvable features and device-scale features of a hybrid overlay target can have any orientation or distribution within the hybrid overlay target suitable for determining both optical and device-scale overlay along the same direction or directions. In various embodiments, the optically resolvable features and device-scale features are physically separated. For example, optical metrology targets with embedded device-scale features are outlined in U.S. Patent Application Publication No. 2015 / 0129999, entitled "DEVICE CORRELATED METROLOGY (DCM) FOR OVL WITH EMBEDDED SEM STRUCTURE OVERLAY TARGETS," issued July 28, 2015, and incorporated herein by reference in its entirety. In various embodiments, at least some of the optically resolvable features of a hybrid overlay target are segmented at a device-scale pitch. In this case, optical overlay metrology and device-scale overlay metrology can be performed at the same physical location, thereby increasing the accuracy of the overlay tool error. For example, segmented targets having optically resolvable features and device-scale features are generally described in U.S. Patent Application Publication No. 2014 / 0122999, entitled "Process Compatible Segmented Targets and Design Methods," published October 16, 2014, the entire contents of which are incorporated herein by reference.

[0120] According to another example, the target 210 can have a periodic structure (e.g., features periodically distributed along 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 based on any of the periodic elements. In this case, a target having periodic elements in one or more layers can enhance the accuracy and / or throughput of overlay metrology (e.g., device-feature-scale overlay metrology or optical overlay metrology for device-feature-scale features). For example, for a given dose of overlay-metrology-relevant illumination (e.g., deposited energy per area on the sample), an overlay measurement for a periodic feature that relies on multiple measurement locations can have higher accuracy than an overlay measurement that relies on a single measurement location (e.g., a single feature). In another example, a given overlay measurement accuracy can be obtained using a lower illumination dose based on multiple measurement locations compared to overlay measurements based on a single measurement location. It is further recognized herein that reducing the illumination dose required to perform overlay measurements can reduce damage to the sample 118 and / or increase measurement throughput.

[0121] This application recognizes that overlay error can be introduced at nearly every manufacturing stage and can vary spatially across the sample and over time from one sample to the next or from one sample lot to the next during a production run. For example, a lithography tool (e.g., a stepper, scanner, etc.) typically has a field of view smaller than the entire sample, thereby dividing the sample into a series (e.g., a grid) of exposure fields that are then exposed separately. Grid errors, i.e., errors related to sample-to-reticle misalignment during the exposure process for one or more exposure fields, can contribute to the spatial variation of overlay error across the sample. In addition, deviations in the lithography tool during exposure (e.g., lens aberrations, thermally related disturbances, etc.) can result in spatially varying pattern placement errors within a single exposure field. In another example, overlay error can include process errors related to the creation of three-dimensional structures on the sample based on the exposure pattern. Process errors can include, but are not limited to, distortion of the exposed pattern during lithography, etching-induced errors, polishing errors, or errors related to variations in the sample. As a result, overlay measured with an overlay target can suffer from spatially varying target-to-device feature errors due to misalignment between the overlay target and the device features.

[0122] Overlay targets can generally be placed anywhere on the specimen while complying with the metrology recipe. However, the size, orientation, and / or density of features in the target can affect target placement. For example, overlay targets with optically resolvable features are typically placed within the scribe lines between specimen dies to preserve intra-die space for device features and / or because optically resolvable features may not be bound by process design rules. According to another example, overlay targets with features bound by process design rules can typically be placed within the specimen die near the device features of interest or within the scribe lines.

[0123] As previously described herein, one or more processors 110 included in the controller 108 may be communicatively coupled to a memory 112 and may be configured to execute a set of program instructions stored in the memory 112, which may be configured to cause the one or more processors 110 to perform various functions and steps of the present disclosure.

[0124] It is noted that one or more components of the metering system 100 may be communicatively coupled to various other components of the metering system 100 in any manner known in the art. For example, one or more processors 110 may be communicatively coupled to each other and to other components via wired connections (e.g., copper wire, fiber optic cable, etc.) or wireless connections (e.g., RF coupling, IR coupling, WiMax, Bluetooth, 3G, 4G, 4G LTE, 5G, etc.). According to another example, the controller 108 may be communicatively coupled to one or more components of the metering system 100 via any wired or wireless connection known in the art.

[0125] In various embodiments, the one or more processors 110 may include one or more of any processing elements known in the art. In this sense, the one or more processors 110 may include any microprocessor-based device configured to execute software algorithms and / or instructions. In various embodiments, the one or more processors 110 may be a desktop computer, mainframe computer system, workstation, image computer, parallel processor, or other computer system (e.g., networked computer) configured to execute programs configured to operate the metering system 100 as described elsewhere in this disclosure. It should be appreciated that steps described elsewhere in this disclosure may be performed by a single computer system or, alternatively, by multiple computer systems. It should also be appreciated that steps described elsewhere in this disclosure may be performed by any one or more of the one or more processors 110. In general, the term "processor" may be broadly defined to encompass any device having one or more processing elements that execute program instructions obtained from memory 112. Additionally, processors or logic elements suitable for performing at least a portion of the steps described elsewhere in this disclosure may be provided within the various subsystems of metrology system 100 (e.g., optical metrology subsystem 172, device-scale metrology subsystem 174, controller 108, user interface 170, etc.). Accordingly, the above description should be taken as merely illustrative and not as a limitation on the present disclosure.

[0126] The memory 112 may include any storage medium known in the art suitable for storing program instructions executable by the associated one or more processors 110, as well as data received from the metering system 100. For example, the memory 112 may include a non-transitory storage medium. For example, the memory 112 may include, but is not limited to, read-only memory (ROM), random access memory (RAM), magnetic or optical memory (e.g., disk), magnetic tape, solid-state drives, etc. It is further noted that the memory 112 may be housed within a common controller housing with the one or more processors 110. In an alternative embodiment, the memory 112 may be located remotely from the physical locations of the processors 110, the controller 108, etc. In various embodiments, the program instructions retained by the memory 112 may cause the one or more processors 110 to perform the steps described elsewhere in this disclosure.

[0127] In various embodiments, a user interface 170 is communicatively coupled to the controller 108. The user interface 170 may include, but is not limited to, one or more desktops, tablets, smartphones, smartwatches, etc. In various embodiments, the user interface 170 includes a display used to display data from the metering system 100 to a user. The display of the user interface 170 may include any display known in the art. For example, the display may include, but is not limited to, a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a CRT display, etc. Those skilled in the art should recognize that any display device capable of being integrated with the user interface 170 is suitable for implementation in the present disclosure. In various embodiments, the user interface 170 includes a user input device that allows a user to input selections and / or commands in response to data displayed to the user.

[0128] All methods described herein may include storing results of one or more steps of a method embodiment in a memory. These results may include any of the results described herein and may be stored in any manner known in the art. The memory may include any of the memories described herein, as well as any other suitable storage medium known in the art. After the results are stored, they may be accessed in the memory and used in any of the method or system embodiments described herein, formatted for display to a user, used in another software module, method, or system, etc. Furthermore, the storage of the results may be "permanent," "semi-permanent," "transient," or for some period of time. For example, the memory may be random access memory (RAM), and the results may not necessarily reside in the memory permanently.

[0129] It is further contemplated that each of the method embodiments described above can include any other step(s) of any other method(s) described herein. Additionally, each of the method embodiments described above can be performed by any of the systems described herein.

[0130] The components, acts, devices, objects, and the accompanying discussion described herein are used as examples for conceptual clarity, and various structural modifications are contemplated. Thus, as used herein, the specific exemplars described above and the accompanying discussion are intended to be representative of their more general class. In general, the use of any specific exemplar is intended to be representative of that class, and the absence of specific components, acts, devices, and objects should not be construed as a limitation.

[0131] Directional terms used herein, such as "top," "bottom," "up," "down," "upward," "upward," "downward," and "downward," are intended to indicate relative positions for descriptive purposes and are not intended to specify an absolute reference frame. Various modifications to the described embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments.

[0132] With respect to the use of substantially all plural and / or singular terms herein, those skilled in the art will be able to translate from the plural to the singular and / or from the singular to the plural as appropriate to the context and / or use. In the interest of clarity, the various singular / plural permutations have not been explicitly set forth herein.

[0133] The subject matter described herein is sometimes depicted as various components embedded within or connected or coupled to other components. Such illustrated architectures are merely exemplary, and in fact, many other configurations that achieve the same functionality can be implemented. Conceptually, any arrangement of components that achieves the same functionality is effectively "integrated" to achieve the desired functionality. Thus, any two components herein that are combined to achieve a particular function can be considered to be "integrated" with each other to achieve the desired functionality, regardless of the architecture or intervening components. Similarly, any two components so integrated can be considered to be "connected" or "coupled" to each other to achieve the desired functionality, and any two components that can be so integrated can be considered to be "combinable" with each other to achieve the desired functionality. Examples of what is connectable include, but are not limited to, physically interlockable and / or physically interacting elements, and / or wirelessly interlockable and / or wirelessly interacting elements, and / or logically interlocking and / or logically interacting elements.

[0134] The present invention is further defined by the appended claims. Generally, the terms used in this application, particularly in the appended claims (e.g., the body of the appended claims), are generally intended to be "open" (e.g., the term "comprising" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "including" should be interpreted as "including but not limited to," etc.). If a specific number of claim-introducing features is intended, that intention will be clearly stated in the claim, and the absence of such features indicates no intention. For example, as an aid to understanding, some of the appended claims below incorporate claim-introducing features through the use of the introductory phrases "at least one" and "one or more." However, the use of the indefinite article "a" or "an" to introduce a claim feature should not be interpreted as implying that all individual claims containing that feature are limited to inventions containing only one of that feature, nor should such interpretation be interpreted when the introductory phrase "one or more" or "at least one" coexists with an indefinite article, such as "a" or "an," in the same claim (e.g., "a" and / or "an" should generally be interpreted as meaning "at least one" or "one or more"). The same is true for the introduction of claim features with the use of a definite article. Additionally, even when a specific number of a claim feature is specified, that number should generally be interpreted to mean at least that specified number, as would be recognized by a person skilled in the art (e.g., the bare phrase "two features" without any other modifier generally means at least two features or more than two features). Furthermore, where a convention similar to "at least one of A, B, and C, etc." is used, the syntax is generally intended to conform to the sense in which one of ordinary skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, a system having only A, only B, only C, both A and B, both A and C, both B and C, and / or all three of A, B, and C, etc.).In instances where a convention similar to "at least one of A, B, or C, etc." is used, the syntax is generally intended to conform to the sense in which one of ordinary skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include, but is not limited to, systems having A only, B only, C only, both A and B, both A and C, both B and C, and / or A, B, and C, etc.). As one of ordinary skill in the art will also understand, nearly all presentations of two or more alternative terms by disjunctive conjunctions and / or disjunctive phrases, whether appearing in the specification, claims, or drawings, should be understood to contemplate the inclusion of either, either, or both terms. For example, the phrase "A or B" would be understood to encompass the possibilities of "A" or "B" or "A and B."

[0135] The present disclosure and many of its attendant advantages will be understood from the foregoing description, and it will also be apparent that various changes can be made in the form, construction and arrangement of the parts without departing from the disclosed subject matter or diminishing all of its essential advantages. The described form is illustrative only, and it is the intent of the following claims to encompass and embrace all such modifications. It is the appended claims which further define the invention.

Claims

1. 1. A weighing system comprising: a first metrology subsystem configured to have a first resolution; a second metrology subsystem configured to have a second resolution lower than the first resolution; one or more controllers communicatively coupled to the first metering subsystem and the second metering subsystem, the one or more controllers comprising one or more processors configured to execute program instructions; wherein the program instructions It is training, receiving first metrology data for a plurality of device features from the first metrology subsystem; generating a first metrology measurement for the device feature based on the first metrology data; binning the device features into two or more device bins based on the first metrology measurements; and identifying representative metrology targets for the two or more device bins based on the distribution of the first metrology measurements, wherein each representative metrology target is selected such that a second metrology measurement based on second metrology data from the second metrology subsystem and associated with the representative metrology target is representative of the first metrology measurements for the device features in a corresponding one of the two or more device bins; To carry out training, and It is a runtime behavior, receiving runtime metrology data for the representative metrology targets on one or more runtime samples from the second metrology subsystem; and generating a runtime metrology measurement for the representative metrology target on the one or more runtime samples based on the runtime metrology data; To perform runtime operations, The configured metering system.

2. 10. The metering system of claim 1, further comprising: determining a threshold breach based on monitoring the runtime metric measurements; and directing a notification indicating the threshold breach.

3. 2. The metrology system of claim 1, wherein the identification of the representative metrology target for each device bin based on the distribution of the first metrology measurements comprises: A metrology system for designing said representative metrology target.

4. 4. The metrology system of claim 3, wherein the design of the representative metrology target comprises: A metrology system in which a particular representative metrology target is designed to comprise an array of repetitive device features.

5. 2. The metrology system of claim 1, wherein the identification of the representative metrology target for each device bin based on the distribution of the first metrology measurements comprises: receiving candidate target metric data for a plurality of candidate representative metric targets; and selecting the representative measurement target from among the plurality of candidate representative measurement targets based on the candidate target measurement data; Weighing system.

6. 2. The metrology system of claim 1, wherein the binning of the device features into the two or more device bins based on the first metrology measurement comprises: identifying one or more sets of the device features based on one or more ranges of the first metrology measurements; and binning the one or more sets of device features into one or more outlier device bins; Weighing system.

7. 7. A metrology system as described in claim 6, wherein the one or more sets of device features include an outlier set based on an outlier range among the one or more ranges of values ​​of the first metrology measurement value.

8. The metrology system of claim 7 , wherein the outlier ranges include outlier ranges for CD values.

9. The weighing system according to claim 7 , wherein the outlier range includes an outlier range for OVL values.

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

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

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

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

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

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

16. 2. The metrology system of claim 1, wherein the training is configured to be performed during an after develop inspection (ADI) and the runtime operation is configured to be performed during an after etch inspection (AEI).

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

18. 10. The weighing system of claim 1, further comprising: a weighing system that monitors the runtime metric measurements to determine whether a threshold has been breached and directs notification of the breach; and a weighing system that monitors a runtime edge placement error (EPE) distribution determined based on the runtime metric measurements during the monitoring.

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

20. 2. The weighing system of claim 1, wherein the second weighing subsystem comprises: an SE subsystem configured for multiple illumination angles; an SE subsystem configured for Mueller matrix element measurements; Single wavelength ellipsometer subsystem, Beam Profile Ellipsometer Subsystem, Beam profile reflectometer subsystem, a broadband reflective spectrometer subsystem; Single wavelength reflectometer subsystem, Angle-resolved reflectometer subsystem, an imaging subsystem; and Scatterometer subsystem, A weighing system comprising at least one of the following:

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

22. 2. The metrology system of claim 1, wherein the binning of the device features into the two or more device bins based on the first metrology measurement comprises: determining one or more device feature attribute distributions for the device feature based on the first metrology measurement; and binning the device features into the two or more device bins based on the one or more device feature attribute distributions; Weighing system.

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

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

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

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

27. 2. The weighing system according to claim 1, wherein the training further comprises: receiving optical calibration measurements for the representative metrology target via the second metrology subsystem; verifying a correlation between the optical calibration measurements for the representative metrology target and measurements belonging to device bins associated with the representative metrology target; and determining a calibration quantity between the optical calibration measurement value and the measurement value belonging to the device bin of the representative metrology target; Weighing system.

28. 28. The metrology system of claim 27, wherein the correlation comprises an ADI to AEI correlation between the optical calibration measurement value and the measurement value belonging to the device bin, wherein the optical calibration measurement value is configured to be an after development inspection (ADI) measurement value and the measurement value belonging to the device bin is configured to be an after etch inspection (AEI) measurement value.

29. 10. The metrology system of claim 1, configured to provide correction amounts to a lithography tool to adjust a specimen fabrication process based on the run-time metrology measurements corresponding to the representative metrology target.

30. 1. A weighing system comprising: one or more controllers comprising one or more processors configured to execute program instructions, the program instructions comprising: It is training, receiving first metrology data for a plurality of device features from a first metrology subsystem; generating a first metrology measurement for the device feature based on the first metrology data; binning the device features into two or more device bins based on the first metrology measurements; and identifying representative metrology targets for the two or more device bins based on the distribution of the first metrology measurements, wherein each representative metrology target is selected such that a second metrology measurement based on second metrology data from the second metrology subsystem and associated with the representative metrology target is representative of the first metrology measurements for the device features in a corresponding one of the two or more device bins; To carry out training, and It is a runtime behavior, receiving runtime metrology data for the representative metrology targets on one or more runtime samples from the second metrology subsystem; and generating a runtime metrology measurement for the representative metrology target on the one or more runtime samples based on the runtime metrology data; To perform runtime operations, The configured metering system.

31. A weighing method comprising: It is training, receiving first metrology data for a plurality of device features from a first metrology subsystem; generating a first metrology measurement for the device feature based on the first metrology data; binning the device features into two or more device bins based on the first metrology measurements; and identifying representative metrology targets for the two or more device bins based on the distribution of the first metrology measurements, wherein each representative metrology target is selected such that a second metrology measurement based on second metrology data from the second metrology subsystem and associated with the representative metrology target is representative of the first metrology measurements for the device features in a corresponding one of the two or more device bins; Conduct training, and It is a runtime behavior, receiving runtime metrology data for the representative metrology targets on one or more runtime samples from the second metrology subsystem; and generating a runtime metrology measurement for the representative metrology target on the one or more runtime samples based on the runtime metrology data; perform runtime operations, method.

32. 32. The method of claim 31 , further comprising providing a correction to a lithography tool to adjust a specimen fabrication process based on the run-time metrology measurements corresponding to the representative metrology target.

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