System and method for determining target feature focal point in overlay metrology of image base

Abstract

To realize an overlay measurement based on a single image capture feature on a plurality of layers that requires a reference measurement on the basis of an external tool or a full-wafer measurement in order to provide a desired measurement accuracy.SOLUTION: A measurement system contains a through focus imaging measurement sub-system that is communicably coupled to a controller having a processor constructed so as to receive a plurality of training images captured at one or more focal point position. The processor generates a mechanical learning classification device on the basis of a plurality of training images, receives a target feature selection for a target overlay measurement corresponded to a target feature, determines the target focal point position on the basis of the target feature selection by using the mechanical learning classification device, receives a target image captured at the target focal point position, and determines an overlay.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates generally to overlay metrology, and more particularly, to machine learning for target feature focusing. [Background technology]

[0002] Image-based overlay metrology may typically involve determining a relative offset between two or more layers on a sample based on the relative imaged positions of overlay target features on different layers of interest. The accuracy of the overlay metrology may therefore be sensitive to the image quality associated with the imaged features on each sample layer, which may vary based on factors such as the depth of field or the position (e.g., focus position) of the plane relative to the sample. Thus, an overlay metrology procedure typically involves a trade-off between image quality and throughput at a particular sample layer. For example, it may be the case that an overlay measurement based on a separate image of each sample layer may provide the highest quality image of the overlay target features. However, capturing multiple images per target may reduce throughput. As another example, an overlay measurement based on a single image capture feature on multiple layers may provide a relatively high throughput, but may require a reference measurement based on an external tool or full wafer measurement to provide the desired measurement accuracy. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] US Patent Application Publication No. 2016 / 0025650 [Patent Document 2] US Patent Application Publication No. 2018 / 0191948 Summary of the Invention [Problem to be solved by the invention]

[0004] It would therefore be desirable to provide a system and method for curing such defects. [Means for solving the problem]

[0005] A metrology system is disclosed according to one or more embodiments of the present disclosure. In one embodiment, the metrology system includes a controller communicatively coupled to one or more through-focus imaging metrology subsystems, the controller including one or more processors configured to execute a set of program instructions stored in a memory, the set of program instructions configured to cause the one or more processors to: receive a plurality of training images captured at one or more focus positions, the plurality of training images including one or more training features of a training sample, generate a machine learning classifier based on the plurality of training images captured at the one or more focus positions, receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features of a target sample, determine one or more target focus positions based on the one or more target feature selections using the machine learning classifier, receive one or more target images captured at the one or more target focus positions, the one or more target images including one or more target features of the target sample, and determine one or more overlay measurements based on the one or more target images.

[0006] A metrology system is disclosed according to one or more embodiments of the present disclosure. In one embodiment, the metrology system includes one or more through-focus imaging metrology subsystems. In another embodiment, the metrology system includes a controller communicatively coupled to the one or more metrology subsystems, the controller including one or more processors configured to execute a set of program instructions stored in a memory, the set of program instructions configured to cause the one or more processors to: receive a plurality of training images captured at one or more focus positions, the plurality of training images including one or more training features of a training sample; generate a machine learning classifier based on the plurality of training images captured at the one or more focus positions; receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features of a target sample; determine one or more target focus positions based on the one or more target feature selections using the machine learning classifier; receive one or more target images captured at the one or more target focus positions, the one or more target images including one or more target features of the target sample; and determine one or more overlay measurements based on the one or more target images.

[0007] A method of measuring overlay using one or more through-focus imaging metrology subsystems is disclosed in accordance with one or more embodiments of the present disclosure. In one embodiment, the method includes receiving a plurality of training images captured at one or more focus positions, the plurality of training images including one or more training features of a training sample. In another embodiment, the method includes generating a machine learning classifier based on the plurality of training images captured at the one or more focus positions. In another embodiment, the method includes receiving one or more target feature selections for one or more target overlay measurements corresponding to one or more target features of a target sample. In another embodiment, the method includes determining one or more target focus positions based on the one or more target feature selections using the machine learning classifier. In another embodiment, the method includes receiving one or more target images captured at one or more target focus positions, the one or more target images including one or more target features of a target sample. In another embodiment, the method includes determining one or more overlay measurements based on the one or more target images.

[0008] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not necessarily restrictive of the invention as claimed. The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the general description, serve to explain the principles of the invention. [Brief description of the drawings]

[0009] Many advantages of the present disclosure may be better understood by those skilled in the art by reference to the following drawings. [Figure 1] FIG. 1 is a conceptual diagram illustrating a measurement system in accordance with one or more embodiments of the present disclosure. [Diagram 2] FIG. 1 is a simplified schematic diagram illustrating a measurement system in accordance with one or more embodiments of the present disclosure. [Diagram 3]1 is a flow diagram illustrating steps performed in a method for measuring overlay in accordance with one or more embodiments of the present disclosure. [Figure 4] 1 is a flow diagram illustrating steps performed in a method for measuring overlay in accordance with one or more embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Reference will now be made in detail to the disclosed subject matter, as illustrated in the accompanying drawings. The present disclosure has been specifically shown and described with respect to certain embodiments and certain features thereof. The embodiments described herein are to be construed as illustrative and not restrictive. It will be readily apparent to those skilled in the art that various changes and modifications in form and detail may be made therein without departing from the spirit and scope of the present disclosure. Reference will now be made in detail to the disclosed subject matter, as illustrated in the accompanying drawings.

[0011] Embodiments of the present disclosure are directed to a through-focus imaging system and method of an overlay target on a sample to provide self-referencing overlay metrology recipes for additional overlay targets on the sample as well as wafer-to-wafer process monitoring.

[0012] Semiconductor devices are typically formed as multiple patterned layers of patterned material on a substrate. Each patterned layer may be fabricated through a sequence of process steps, such as, but not limited to, one or more material deposition steps, one or more lithography steps, or one or more etching steps. Furthermore, features within each patterned layer must typically be fabricated within selected tolerances in order to properly construct the final device. For example, overlay errors, associated with the relative misalignment of features on different sample layers, must be well characterized and controlled within each layer and with respect to previously fabricated layers.

[0013] Accordingly, overlay targets may be fabricated on one or more sample layers to enable efficient characterization of overlay of features between layers. For example, an overlay target may include features fabricated on multiple layers arranged to facilitate accurate overlay measurements. In this regard, overlay measurements on one or more overlay targets distributed across a sample may be used to determine overlay of corresponding device features associated with a fabricated semiconductor device.

[0014] Image-based overlay metrology tools typically capture one or more images of an overlay target and determine overlay between sample layers based on the relative positions of imaged features of the overlay target on the layer of interest. For example, features of overlay targets suitable for image-based overlay (e.g., box-in-box targets, advanced imaging metrology (AIM) targets, etc.) located on different sample layers may, but need not, be positioned such that features on all layers of interest are simultaneously visible. In this regard, overlay may be determined based on the relative positions of features on the layer of interest in one or more images of the overlay target. Additionally, overlay targets may be designed to facilitate overlay measurements between any number of sample layers in either a single measurement step or multiple measurement steps. For example, features in any number of sample layers may be simultaneously visible for single measurement overlay determination between all sample layers. In another example, an overlay target may have different sections (e.g., cells, etc.) to facilitate overlay measurements between selected layers. In this regard, the overlay between all layers of interest may be determined based on measurements of multiple portions of the overlay target.

[0015] The accuracy of image-based overlay may depend on multiple factors related to image quality, such as, but not limited to, resolution or aberrations. For example, system resolution may affect the accuracy with which a feature's location can be determined (e.g., edge location, center of symmetry, etc.). As another example, aberrations in the imaging system may distort the size, shape, and spacing of features such that image-based location measurements may not accurately represent the physical sample. Furthermore, image quality may vary as a function of focus position. For example, features outside the imaging system's focal volume may appear blurry and / or have lower contrast between the overlay target features and background space than features within the focal volume, which may affect the accuracy of location measurements (e.g., edge measurements, etc.).

[0016] Thus, it may be the case that capturing separate images of features on different layers of a sample (e.g., located at different depths within the sample) may provide accurate overlay metrology measurements. For example, the focus position (e.g., object plane) of an image-based overlay metrology system may be adjusted to correspond to the depth of the imaged feature on each layer of interest. In this regard, features on each layer of interest may be imaged under conditions designed to mitigate focus position dependent effects.

[0017] However, it is recognized herein that capturing multiple images of an overlay target at different depths may adversely affect system throughput, which may offset gains in accuracy associated with multiple images. Embodiments of the present disclosure are directed to overlay measurements at multiple focus positions, where the multiple focus positions may be determined in real time by a metrology system and correspond to one or more varying depths at which one or more portions of one or more metrology targets are located. For example, overlay measurements may be generated for an overlay target based on multiple images captured at multiple focus positions (e.g., including focus depths corresponding to locations of overlay target features), where the metrology system determines the multiple focus positions in real time (e.g., by using a machine learning classifier, etc.).

[0018] Additional embodiments of the present disclosure relate to translating one or more portions of a metrology system along one or more axes of adjustment, such that the optimal coordinate location at which a particular overlay measurement is made may be different from the optimal coordinate location for a subsequent overlay measurement.

[0019] Further embodiments of the present disclosure relate to generating control signals based on overlay measurements across a sample that are provided as feedback and / or feedforward data to a processing tool (e.g., a lithography tool, a metrology tool, etc.).

[0020] 1 is a conceptual diagram illustrating an overlay metrology system 100 in accordance with one or more embodiments of the present disclosure. System 100 may include, but is not limited to, one or more metrology subsystems 102. System 100 may further include, but is not limited to, a controller 104, which may include one or more processors 106, a memory 108, and a user interface 110.

[0021] The one or more metrology subsystems 102 may include any metrology subsystem known in the art, including, but not limited to, an optical metrology subsystem. For example, the metrology subsystem 102 may include, but is not limited to, an optical-based metrology system, a broadband metrology system (e.g., a broadband plasma metrology system), or a narrowband inspection system (e.g., a laser-based metrology system). In another example, the metrology subsystem 102 may include a scatterometry-based metrology system. As another example, the one or more metrology subsystems 102 may include any through-focus imaging metrology subsystem (e.g., an imaging metrology subsystem configured to construct one or more images of a sample, where the one or more images are of a desired focus and are constructed using multiple images of the sample captured at different focus positions).

[0022] In one embodiment, the controller 104 is communicatively coupled to the one or more metrology subsystems 102. In this regard, the one or more processors 106 of the controller 104 may be configured to generate and provide one or more control signals configured to make one or more adjustments to one or more portions of the one or more metrology subsystems 102.

[0023] In another embodiment, the controller 104 is configured to receive a plurality of training images captured at one or more focus positions, the plurality of training images including one or more training features of a training sample. For example, the controller 104 may be configured to receive a plurality of training images from one or more metrology subsystems 102.

[0024] In another embodiment, the controller 104 may be configured to generate the machine learning classifier based on a plurality of training images. For example, the controller 104 may be configured to use a plurality of training images as input to the machine learning classifier.

[0025] In another embodiment, the controller 104 is configured to receive one or more target feature selections for one or more target overlay measurements, where the one or more target feature selections correspond to one or more target features of the target sample. For example, the controller 104 may be configured to receive the one or more target feature selections from a user via the user interface 110.

[0026] In another embodiment, the controller 104 may be configured to determine the one or more target focus positions based on one or more target feature selections. For example, the controller 104 may be configured to determine the one or more target focus positions using a machine learning classifier.

[0027] In another embodiment, the controller 104 may be configured to receive one or more target images captured at one or more target focal positions. For example, the controller 104 may be configured to receive one or more target images including one or more target features at one or more target focal positions.

[0028] In another embodiment, the controller 104 may be configured to determine one or more overlay measurements based on one or more target images. For example, the controller 104 may be configured to determine an overlay between a first layer of the target sample and a second layer of the target sample based on one or more target features formed on each of the first layer and the second layer.

[0029] 2 illustrates a simplified schematic diagram of a system 100 in accordance with one or more embodiments of the present disclosure. In particular, the system 100 illustrated in FIG. 2 includes an optical metrology subsystem 102 such that the system 100 operates as an optical inspection system.

[0030] The optical inspection subsystem 102 may include any optically based inspection known in the art. The metrology subsystem 102 may include, but is not limited to, an illumination source 112, an illumination arm 111, a collection arm 113, and a detector assembly 126.

[0031] In one embodiment, the metrology subsystem 102 is configured to inspect and / or measure the sample 120 disposed on the stage assembly 122. The illumination source 112 may include any illumination source known in the art for generating the illumination 101, including, but not limited to, an illumination source configured to provide a wavelength of light including, but not limited to, vacuum ultraviolet radiation (VUV), deep ultraviolet radiation (DUV), ultraviolet (UV) radiation, visible radiation, or infrared (IR) radiation. In another embodiment, the metrology subsystem 102 may include an illumination arm 111 configured to direct the illumination 101 toward the sample 120. It should be noted that the illumination source 112 of the metrology subsystem 102 may be configured in any orientation known in the art, including, but not limited to, a dark-field orientation, a bright-field orientation, and the like. For example, one or more optical elements 114, 124 may be selectably adjusted to configure the metrology subsystem 102 in a dark-field orientation, a bright-field orientation, and the like.

[0032] Sample 120 may include any sample known in the art, including, but not limited to, a wafer, a reticle, a photomask, etc. Sample 120 may include any sample having one or more overlay metrology targets known in the art to be suitable for image-based overlay metrology. For example, sample 120 may include an overlay metrology target that includes target features of one or more layers printed in one or more lithographically separate exposures. The targets and / or target features may possess various symmetries, such as two-fold or four-fold rotational symmetry, reflection symmetry, etc.

[0033] In one embodiment, the sample 120 is disposed on a stage assembly 122, which is configured to facilitate movement of the sample 120 (e.g., along one or more of an x-direction, a y-direction, or a z-direction). In another embodiment, the stage assembly 122 is an actuatable stage. For example, the stage assembly 122 can include one or more translation stages suitable for selectively translating the sample 120 along one or more linear directions (e.g., being an x-direction, a y-direction, and / or a z-direction). As another example, the stage assembly 122 can include one or more rotation stages suitable for selectively rotating the sample 120 along a rotational direction, but is not limited to such. As another example, the stage assembly 122 can include rotational and translation stages suitable for selectively translating the sample 120 along a linear direction and / or rotating the sample 120 along a rotational direction. It is noted herein that the system 100 can operate in any metrology mode known in the art.

[0034] The illumination arm 111 may include any number and type of optical components known in the art. In one embodiment, the illumination arm 111 includes one or more optical elements 114, a set of one or more optical elements 115, a beam splitter 116, and an objective lens 118. In this regard, the illumination arm 111 may be configured to focus the illumination 101 from the illumination source 112 onto the surface of the sample 120. The one or more optical elements 114 may include any optical element known in the art, including, but not limited to, one or more mirrors, one or more lenses, one or more polarizers, one or more beam splitters, wave plates, etc.

[0035] In another embodiment, the metrology subsystem 102 includes a collection arm 113 configured to collect illumination reflected or scattered from the sample 120. In another embodiment, the collection arm 113 may direct and / or focus the reflected and scattered light via one or more optical elements 124 to one or more sensors of a detector assembly 126. The one or more optical elements 124 may include any optical element known in the art, including, but not limited to, one or more mirrors, one or more lenses, one or more polarizers, one or more beam splitters, wave plates, etc. It should be noted that the detector assembly 126 may include any sensor and detector assembly known in the art for detecting illumination reflected or scattered from the sample 120.

[0036] In another embodiment, the detector assembly 126 of the metrology subsystem 102 is configured to collect inspection data of the sample 120 based on illumination reflected or scattered from the sample 120. In another embodiment, the detector assembly 126 is configured to transmit the collected / acquired images and / or metrology data to the controller 104.

[0037] The metrology system 100 can be configured to image the sample 120 at any selected measurement plane (e.g., at any position along the z-direction). For example, the position of the object plane associated with the image generated on the detector assembly 126 relative to the sample 120 can be adjusted using any combination of components of the metrology system 100. For example, the position of the object plane associated with the image generated on the detector assembly 126 relative to the sample 120 can be adjusted by controlling the position of the stage assembly 122 relative to the objective lens 118. As another example, the position of the object plane associated with the image generated on the detector assembly 126 relative to the sample 120 can be adjusted by controlling the position of the objective lens 118 relative to the sample 120. For example, the objective lens 118 can be mounted on a translation stage configured to adjust the position of the objective lens 118 along one or more adjustment axes (e.g., in the x-direction, the y-direction, or the z-direction). As another example, the position of the object plane associated with the image generated on the detector assembly 126 relative to the sample 120 can be adjusted by controlling the position of the detector assembly 126. For example, the detector assembly 126 may be mounted on a translation stage configured to adjust the position of the detector assembly 126 along one or more adjustment axes. As another example, the position of an object plane associated with an image generated on the detector assembly 126 relative to the sample 120 may be adjusted by controlling the position of the one or more optical elements 124. For example, the one or more optical elements 124 may be mounted on a translation stage configured to adjust the position of the one or more optical elements 124 along one or more adjustment axes. It is specifically noted herein that the controller 104 may be configured to make any of the aforementioned adjustments by providing one or more control signals to one or more portions of the metrology subsystem 102.

[0038] As previously described herein, the controller 104 of the system 100 may include one or more processors 106 and a memory 108. The memory 108 may include program instructions configured to cause the one or more processors 106 to perform various process steps described throughout this disclosure. For example, the program instructions may be configured to cause the one or more processors 106 to adjust one or more characteristics of the metrology subsystem 102 to perform one or more of the process steps of the present disclosure. Additionally, the controller 104 may be configured to receive data from the detector assembly 126, including, but not limited to, image data associated with the sample 120.

[0039] The one or more processors 106 of the controller 104 may include any processor or processing element known in the art. For purposes of this disclosure, the term "processor" or "processing element" may be broadly defined to encompass any device having one or more processing or logic elements (e.g., one or more microprocessor devices, one or more application specific integrated circuit (ASIC) devices, one or more field programmable gate arrays (FPGAs), or one or more digital signal processors (DSPs)). In this sense, the one or more processors 106 may include any device configured to execute algorithms and / or instructions (e.g., program instructions stored in a memory). In an embodiment, the one or more processors 106 may be embodied as a desktop computer, a mainframe computer system, a workstation, an image computer, a parallel processor, a networked computer, or any other computer system configured to execute programs that operate or are configured to operate with the measurement system 100 as described throughout this disclosure.

[0040] Furthermore, different components of the system 100 may include processors or logic elements suitable for performing at least some of the steps described in this disclosure. Thus, the above description should not be construed as a limitation on the embodiments of the present disclosure, but merely as an example. Furthermore, the steps described throughout the present disclosure may be performed by a single controller 104, or alternatively by multiple controllers. Furthermore, the controller 104 may include one or more controllers housed in a common housing or multiple housings. In this manner, any controller or combination of controllers may be packaged separately as a module suitable for integration into the metrology system 100. Furthermore, the controller 104 may analyze data received from the detector assembly 126 and provide data to additional components within or outside the metrology system 100.

[0041] The memory 108 may include any storage medium known in the art suitable for storing program instructions executable by the associated one or more processors 106. For example, the memory 108 may include a non-transitory storage medium. As another example, the memory 108 may include, but is not limited to, a read-only memory (ROM), a random access memory (RAM), a magnetic or optical memory device (e.g., disk), a magnetic tape, a solid-state drive, and the like. Furthermore, it is noted that the memory 108 may be housed in a common controller housing with the one or more processors 106. In one embodiment, the memory 108 may be located remotely relative to the physical location of the one or more processors 106 and the controller 104. For example, the one or more processors 106 of the controller 104 may access a remote memory (e.g., a server) accessible via a network (e.g., the Internet, an intranet, etc.).

[0042] In one embodiment, the user interface 110 is communicatively coupled to the controller 104. The user interface 110 may include, but is not limited to, one or more desktops, laptops, tablets, etc. In another embodiment, the user interface 110 includes a display used to display data of the system 100 to a user. The display of the user interface 110 may include any display known in the art. For example, the display may include, but is not limited to, a liquid crystal display (LCD), an organic light emitting diode (OLED)-based display, or a CRT display. Those skilled in the art should recognize that any display device that can be integrated with the user interface 110 is suitable for implementation in the present disclosure. In another embodiment, a user may input selections and / or commands in response to data displayed to the user via a user input device of the user interface 110.

[0043] In another embodiment, the controller 104 is communicatively coupled to one or more elements of the metrology system 100. In this regard, the controller 104 may transmit and / or receive data from any component of the metrology system 100. Additionally, the controller 104 may direct or otherwise control any component of the metrology system 100 by generating one or more control signals for the associated component. For example, the controller 104 may be communicatively coupled to the detector assembly 126 to receive one or more images from the detector assembly 126.

[0044] FIG. 3 illustrates a method 300 for measuring overlay in accordance with one or more embodiments of the present disclosure.

[0045] In step 302, a plurality of training images captured at one or more focus positions are received. For example, the plurality of training images 125 may be received by the controller 104 from the metrology subsystem 102. In this regard, the plurality of training images 125 may include optical training images. In additional and / or alternative embodiments, the controller 104 may be configured to receive the one or more training images 125 from a source other than the one or more metrology subsystems 102. For example, the controller 104 may be configured to receive the one or more training images 125 of the features of the sample 120 from an external storage device and / or the memory 108. In another embodiment, the controller 104 may be further configured to store the received training images 125 in the memory 108.

[0046] The multiple training images 125 may include one or more training features of the training sample. For example, the multiple training images 125 may include images captured at multiple depths of the training sample. In this regard, the one or more training features of the training sample may include one or more training target features formed in different layers of the training sample. The metrology subsystem 102 may be configured to capture the multiple training images 125 at one or more focus positions corresponding to a depth (e.g., a position along the z-direction) of a particular training feature. In one embodiment, the metrology subsystem 102 may be configured to capture the multiple training images 125 within a focal training range, the focal training range including multiple focus positions corresponding to multiple depths of the one or more training features. The focal training range may be provided by a user via the user interface 110.

[0047] In step 304, a machine learning classifier is generated based on the plurality of training images. For example, the controller 104 may be configured to generate the machine learning classifier based on the plurality of training images. The controller 104 may be configured to generate the machine learning classifier via any one or more techniques known in the art, including, but not limited to, supervised learning, unsupervised learning, etc.

[0048] For example, in the context of supervised learning, the plurality of training images 125 may include various degrees of focus based on the focus position at which each of the plurality of training images was captured (e.g., the plurality of training images may include a plurality of through-focus images of one or more features of a sample). In this regard, the controller 104 may receive one or more optimal focus tolerances such that the controller 104 may determine one or more training images of the plurality of training images that fall within the one or more optimal focus tolerances. Thus, the plurality of training images 125 and the one or more optimal focus tolerances may be used as inputs for training a machine learning classifier. The controller 104 may be further configured to store the plurality of training images 125, the optimal focus tolerances, and the generated machine learning classifier in the memory 108.

[0049] The one or more optimal focus tolerances may be configured such that the machine learning classifier may be configured to determine one or more target focus positions for one or more target overlay measurements. In this regard, the one or more optimal focus tolerances may be configured to ensure that one or more target images that may be subsequently captured at one or more target focus positions are of sufficient quality for overlay measurement by the controller 104. The one or more optimal focus tolerances may be provided by a user via the user interface 110. In another embodiment, the controller 104 may be configured to determine the one or more optimal focus tolerances using any technique known in the art. For example, the controller 104 may be configured to determine the one or more optimal focus tolerances based on a contrast accuracy function (e.g., a function configured to determine a focus with minimal noise based on multiple images captured at multiple focus positions). In an alternative embodiment, the controller 104 may be configured to determine the one or more optimal focus tolerances using a Linnik interferometer integrated into or generated by one or more portions of the metrology subsystem 102. As another example, one or more portions of the metrology subsystem 102 (e.g., one or more portions of the illumination source 112 and / or the illumination arm 111) may be configured to illuminate the sample, and the controller 104 may be configured to generate a Linnik interferometer (e.g., a low coherence interferometer) based on the illumination collected by the detector assembly 126. In this regard, the controller 104 may be configured to determine an interferometer peak (e.g., a point having the greatest contrast among all collected images) and may associate the peak with a through-focus position along the z-axis of the sample.

[0050] It is specifically noted that embodiments of the present disclosure are not limited to the controller 104 generating or referencing a Linnik interferometer to determine one or more optimal focus tolerances. For example, the controller 104 may be configured to generate a machine learning classifier configured to determine best focus and / or best position based on a plurality of training images generated using one or more signals indicative of illumination (e.g., illumination generated by a bright-field and / or dark-field microscope device) emanating from various focus positions and / or positions (e.g., coordinate positions on an x-axis and / or a y-axis, where a plurality of training images may be captured at various coordinate positions along one or both of such axes, such as via a translatable stage). In at least the foregoing embodiments, the controller 104 may be configured to determine best focus and / or best position based on image contrast and / or contrast accuracy of the plurality of training images. In this regard, the machine learning classifier may be configured to determine one or more optimal focus tolerances based on a plurality of training images, where the plurality of training images constitute focus slice images generated at various focus positions along a z-axis of the sample. The controller 104 may be configured to classify and / or label each focused slice image with a corresponding best focus based on the image contrast and / or contrast accuracy of the focused slice image. In this manner, the controller 104 may be configured to determine one or more optimal focus tolerances by interpolation based on multiple training images.

[0051] In another embodiment, the machine learning classifier can be configured to determine one or more optimal focus tolerances based on a plurality of training images, the plurality of training images including images captured at various focus positions along the z-axis of the sample, and the focus of the plurality of training images is altered using a coarse focus mechanism. In particular, it is noted that a coarse focus mechanism (or any other common focus mechanism known in the art to be suitable for the purposes contemplated by the present disclosure) may enable the machine learning classifier to determine one or more optimal focus tolerances in a more accurate manner (e.g., more accurate compared to other methods of focus adjustment described herein or known in the art). For example, as in step 308 (described below), a course focus mechanism may be configured to enable the controller 104 to determine and / or set a coarse focus (e.g., an overall focus position that can be later fine-tuned (see step 308 below)). It should be noted that the coarse focusing system may include any coarse focusing mechanism known in the art to be suitable for the purposes contemplated by the present disclosure, including, but not limited to, a lens triangulation mechanism, a bicell detector arrangement, and / or any ranging system.

[0052] It is noted herein that the machine learning classifier generated in step 304 may include any type of machine learning algorithm / classifier and / or deep learning technique or classifier known in the art, including, but not limited to, a random forest classifier, a support vector machine (SVM) classifier, an ensemble learning classifier, an artificial neural network (ANN), and the like. As another example, the machine learning classifier may include a deep convolutional neural network (CNN). For example, in some embodiments, the machine learning classifier may include ALEXNET and / or GOOGLENET. In this regard, the machine learning classifier may include any algorithm, classifier, or predictive model, including, but not limited to, any algorithm, classifier, or predictive model configured to generate a Linnik interferometer and determine one or more target focal positions for one or more target overlay measurements using the Linnik interferometer. In some embodiments, the machine learning classifier may comprise a neural network having multiple layers and receptors. For example, the machine learning classifier may comprise a neural network having about five layers and about 15 receptors.

[0053] At step 306, one or more target feature selections for one or more target overlay measurements corresponding to one or more target features of the target sample are received. For example, the controller 104 may be configured to receive one or more target feature selections for one or more target overlay measurements from a user via the user interface 110. The one or more target feature selections may include one or more signals configured to instruct the system 100 to capture one or more target images including the one or more target features of the target sample. Upon receiving the one or more target feature selections, the controller 104 may be configured to determine one or more expected depths of the one or more target features in the target sample. For example, the controller 104 may be provided one or more expected depths of the one or more target features for the overlay measurements by a user via the user interface 110. In other embodiments, the controller 104 may determine one or more expected depths of the one or more target features for the overlay measurements by referencing one or more design files or other data corresponding to the target sample stored in the memory 108. In other embodiments, the controller 104 may determine one or more expected depths of the one or more target features for the overlay measurements based on a Linnik interferometer. For example, the controller 104 may determine one or more expected depths by referencing a Linnik interferometer peak associated with a through focus position along the z-axis of the sample.

[0054] In step 308, one or more target focus positions based on the one or more target feature selections are determined using a machine learning classifier. For example, the controller 104 may be configured to determine one or more target focus positions based on the one or more target feature selections using a machine learning classifier. As another example, the controller 104 may provide one or more expected depths of one or more target features of the target sample as inputs to the machine learning classifier. In this regard, the machine learning classifier may be configured to provide one or more target focus positions based on a plurality of training images 125 and an optimal focus tolerance. It should be noted that the machine learning classifier may be configured to determine one or more target focus positions for one or more target overlay measurements by determining one or more focus positions within 1 micron of a focus position provided by a contrast accuracy function, a Linnink interferometer function, or any other method described herein.

[0055] In some embodiments, upon determining the one or more target focal positions based on the one or more target feature selections, the controller 104 may be configured to determine and / or provide one or more control signals to one or more portions of the one or more metrology subsystems 102. The one or more control signals are configured to translate the one or more portions of the one or more metrology subsystems 102 along one or more adjustment axes (e.g., x-direction, y-direction, and / or z-direction). For example, the controller 104 may be configured to provide one or more control signals to the stage assembly 122 and / or the detector assembly 126 to position the target sample at one of the one or more determined target focal positions. In another embodiment, the controller 104 may be configured to provide one or more control signals to at least one of the optical elements 114, 115, the beam splitter 116, the objective lens 118, or the optical element 124 to enable the metrology subsystem 102 to capture one or more target images at the one or more target focal positions.

[0056] In the case of a machine learning classifier configured to determine one or more optimal focus tolerances based on a plurality of training images using a coarse focusing mechanism, the machine learning classifier may be configured to determine one or more control signals to the coarse focusing system (and the controller 104 may be configured to provide one or more control signals to the coarse focusing system), where the one or more control signals may be configured to cause the coarse focusing system to adjust the focus to a focus position within ±2 micrometers of the target focus position. In this regard, the machine learning classifier may be configured to determine the one or more optimal focus tolerances by determining one or more fine focus adjustments to correct the focus position determined by the coarse focusing system.

[0057] In step 310, one or more target images of one or more target features captured at one or more target focus positions are received. For example, the controller 104 may be configured to receive one or more target images 135 from the metrology subsystem 102. As used herein, the term "target image" may refer to an image of one or more target features captured at one or more target focus positions for which one or more overlay measurements are determined. Thus, the term "target image" may be distinguished from "training image," which may be considered an image of a training feature that may be used as an input for training a machine learning classifier.

[0058] It should be noted that any discussion herein regarding acquisition of training images 125 may be deemed to apply to acquisition of target images 135, unless otherwise noted herein. In additional and / or alternative embodiments, controller 104 may be configured to receive one or more target images 135 from a source other than one or more metrology subsystems 102. For example, controller 104 may be configured to receive one or more target images 135 of sample 120 from external storage and / or memory 108.

[0059] In step 312, one or more overlay measurements are determined based on the one or more target images. For example, the controller 104 is configured to determine an overlay between a first layer of the target sample and a second layer of the target sample based on a first overlay measurement corresponding to one or more target features formed on the first layer of the target sample and a second overlay measurement corresponding to one or more target features formed on the second layer of the target sample. In this regard, the controller 104 may be configured to determine an offset (e.g., PPE) between the first layer and the second layer. The one or more overlay measurements may include any overlay measurement known in the art suitable for the purposes contemplated by the present disclosure, including overlay measurements configured for use with a particular target feature of the sample. In this regard, the controller 104 may be configured to utilize one or more overlay algorithms stored in the memory 108 or provided to the controller 104 to determine the one or more overlay measurements.

[0060] In some embodiments, the method 300 may include step 314. In step 314, one or more control signals are provided. For example, one or more control signals are provided to adjust one or more process tools (e.g., lithography tools). As an additional example, the controller 104 may provide one or more control signals (or corrections to the control signals) to one or more portions of the one or more process tools to adjust one or more parameters (e.g., manufacturing settings, configurations, etc.) of the one or more process tools such that one or more parameters of the one or more process tools are adjusted. The controller 104 may determine the one or more control signals based on one or more overlay measurements of the sample. The control signals (or corrections to the control signals) may be provided by the controller 104 as part of a feedback and / or feedforward control loop. The controller 104 may cause the one or more process tools to perform one or more adjustments to one or more parameters of the process tools based on the control signals, or the controller 104 may alert a user to make one or more adjustments to one or more parameters. In this sense, the one or more control signals can compensate for errors in one or more manufacturing processes of one or more process tools, and thus enable the one or more process tools to maintain overlay within a selected tolerance across multiple exposures on subsequent samples in the same or different lots.

[0061] FIG. 4 illustrates a method 400 for measuring overlay in accordance with one or more embodiments of the present disclosure.

[0062] At step 402, one or more target feature selections for one or more target overlay measurements corresponding to one or more target features of the target sample are received. For example, the controller 104 may be configured to receive one or more target feature selections for one or more target overlay measurements from a user via the user interface 110. The one or more target feature selections may include one or more signals configured to instruct the system 100 to capture one or more target images including the one or more target features of the target sample. Upon receiving the one or more target feature selections, the controller 104 may be configured to determine one or more expected positions (e.g., positions along the x-direction, y-direction, and / or z-direction) of the one or more target features within the target sample. For example, the controller 104 may be provided one or more expected positions of the one or more target features for the overlay measurements by a user via the user interface 110. In other embodiments, the controller 104 may determine one or more expected depths of the one or more target features for the overlay measurements by referencing one or more design files or other data corresponding to the target sample stored in the memory 108.

[0063] In step 404, one or more target focus positions are determined based on one or more target feature selections. For example, the controller 104 may be configured to determine one or more target focus positions using a machine learning classifier based on one or more target feature selections, the one or more target feature selections corresponding to one or more target features for one or more overlay measurements. As another example, the controller 104 may provide one or more expected depths of one or more target features of the target sample as inputs to the machine learning classifier. In this regard, the machine learning classifier may be configured to provide one or more target focus positions based on the plurality of training images 125 and an optimal focus tolerance.

[0064] In some embodiments, upon determining the one or more target focal positions based on the one or more target feature selections, as shown in step 406, the controller 104 may be configured to determine and / or provide one or more control signals to one or more portions of the one or more metrology subsystems 102. The one or more control signals may be configured to translate the one or more portions of the one or more metrology subsystems 102 along one or more adjustment axes (e.g., x-direction, y-direction, and / or z-direction). For example, in one embodiment, the controller 104 may be configured to provide one or more control signals to the stage assembly 122 and / or the detector assembly such that one or more target features for overlay measurements are centered within a field of view of one or more components of the metrology subsystem 102. In this regard, the controller 104 may be configured to provide one or more control signals to one or more portions of the metrology subsystem 102 such that the target image 135 includes one or more target features at one or more centers of the target image 135. In another embodiment, the controller 104 may be configured to provide one or more control signals to the stage assembly 122 such that the target sample is located at one of the one or more determined target focal positions. In another embodiment, the controller 104 may be configured to provide one or more control signals to at least one of the optical elements 114, 115, the beam splitter 116, the objective lens 118, or the optical element 124 to enable the metrology subsystem 102 to capture one or more target images at one or more target focal positions.

[0065] It is of particular note that the controller 104 may be configured to simultaneously provide one or more control signals to one or more portions of one or more metrology subsystems 102. For example, the controller 104 may be configured to provide one or more control signals configured to cause simultaneous adjustments to the stage assembly 122 along the x-direction and / or y-direction and one or more other portions of the metrology subsystem 102 along the z-direction (e.g., the detector assembly 126 and / or the objective lens 118).

[0066] It is further noted that the controller 104 may be configured to use a machine learning classifier to determine one or more control signals configured to cause transformation of one or more portions of the one or more metrology subsystems 102. For example, the machine learning classifier may be configured to associate one or more positions (e.g., positions along the x-axis and / or y-axis) with one or more desired target features. In this regard, the machine learning classifier may be configured to determine positions (e.g., coordinate positions on the x-axis and / or y-axis) that automatically center the one or more target features within the field of view.

[0067] At step 408, one or more target images of one or more target features captured at one or more target focus positions are received. For example, the controller 104 may be configured to receive one or more target images 135 from the metrology subsystem 102.

[0068] All of the methods described herein may include storing results of one or more steps of the method embodiments in a memory. The 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 memory described herein, or any other suitable storage medium known in the art. After the results are stored, they can be accessed in the memory, used by any of the method or system embodiments described herein, formatted for display to a user, used by another software module, method, or system, etc. Furthermore, the results may be stored "permanently," "semi-permanently," "temporarily," or for a period of time. For example, the memory may be a random access memory (RAM), and the results may not necessarily persist in the memory indefinitely.

[0069] It is further contemplated that each of the method embodiments described above may include any other step(s) of any other method described herein. In addition, each of the method embodiments described above may be performed by any of the systems described herein.

[0070] Those skilled in the art will recognize that the component operations, devices, objects, and their accompanying discussion described herein are used as examples for conceptual clarity, and that various configuration modifications are contemplated. Thus, as used herein, the specific examples described and the accompanying discussion are intended to be representative of their more general classes. In general, the use of any specific example is intended to represent its class, and non-inclusion of specific components, operations, devices, and objects should not be construed as limiting.

[0071] As used herein, directional terms such as "up", "down", "up", "down", "up", "up", "down", "down" and the like are intended to provide relative positions for purposes of description and are not intended to indicate an absolute frame of reference. 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.

[0072] With respect to the use of virtually any plural and / or singular term herein, those of skill in the art can convert from plural to singular and / or from singular to plural as appropriate to the context and / or application. The various singular / plural permutations are not expressly set forth herein for ease of understanding.

[0073] The subject matter described herein illustrates different components that are sometimes included within or connected to other components. It should be understood that such depicted architectures are merely exemplary, and that in fact many other architectures that achieve the same functionality may be implemented. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Thus, any two components herein that are combined to achieve a particular functionality can be considered to be "associated" with each other such that the desired functionality is achieved, regardless of the architecture or intermediate components. Similarly, any two components so associated can also be considered to be "connected" or "coupled" with each other to achieve the desired functionality, and any two components capable of being so associated can also be considered to be "couplable" with each other to achieve the desired functionality. Specific examples of what can be coupled include, but are not limited to, physically coupleable and / or physically interacting components and / or wirelessly interactable and / or wirelessly interacting components and / or logically interacting and / or logically interacting components.

[0074] It should further be understood that the present invention is defined by the appended claims. In general, those skilled in the art will understand that the terms used in this specification and in particular in the appended claims (e.g., the body of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to", the term "having" should be interpreted as "having at least", the term "includes" should be interpreted as "includes but not limited to", etc.). Those skilled in the art will further understand that if a specific number of claim recitations introduced are intended, such intention will be expressly recited in the claim, and in the absence of such recitation, no such intention exists. For example, as an aid to understanding, the following appended claims may include the use of the introductory phrases "at least one" and "one or more" to introduce the claim recitation. However, the use of such phrases should not be interpreted as meaning that the introduction of a claim recitation with the indefinite article "a" or "an" limits any particular claim that includes such an introduced claim recitation to an invention that includes only one such recitation. The same applies to the use of clear articles used to introduce claim recitations, even when the same claim includes the introductory phrase "one or more" or "at least one" and an indefinite article such as "a" or "an" (e.g., "a" and / or "an" should typically be interpreted to mean "at least one" or "one or more"). Also, those skilled in the art will recognize that even when a specific number of introduced claim recitations is explicitly recited, such recitation should typically be interpreted to mean at least the recited number (e.g., a bare recitation of "two recitations" without other modifiers typically means at least two recitations, or two or more recitations).Furthermore, in instances where a conventional expression similar to "such as at least one of A, B, and C" is used, such a configuration is generally intended in the sense that one of ordinary skill in the art would understand the conventional expression (e.g., "a system having at least one of A, B, and C" includes, but is not limited to, systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together). In instances where a conventional expression similar to "such as at least one of A, B, or C" is used, such a configuration is generally intended in the sense that one of ordinary skill in the art would understand the conventional expression (e.g., "a system having at least one of A, B, or C" includes, but is not limited to, systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together). Those skilled in the art will further appreciate that virtually any disjunctive word and / or phrase presenting two or more alternative terms, wherever it appears in the description, claims, or drawings, should be understood to contemplate the possibility of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B."

[0075] It is believed that the present disclosure and many of its attendant advantages will be understood from the foregoing description, and it will be apparent that various changes can be made in the form, construction and arrangement of the elements without departing from the disclosed subject matter or sacrificing all of its material advantages. The forms described are merely illustrative, and it is the intent of the following claims to embrace and include such modifications. It is to be understood, further, that the invention is defined by the appended claims.

Claims

1. 1. A system comprising: A controller communicatively coupled to one or more through-focus imaging metrology subsystems, the controller including one or more processors configured to execute a set of program instructions stored in a memory, the set of program instructions configuring the one or more processors to: receiving a plurality of training images captured at one or more focus positions, the one or more training images including one or more training features of the training samples; generating a machine learning classifier based on a plurality of training images captured at the one or more focus positions; receiving one or more target feature selections, the target features selected by a user, for one or more target overlay measurements corresponding to one or more target features of the target sample; determining one or more target focus positions based on the one or more target feature selections using the machine learning classifier; receiving one or more target images captured at one or more target focus positions, the one or more target images including one or more target features of a target sample; determining one or more overlays based on the one or more target images; A system comprising a controller configured to:

2. 2. The system of claim 1 , wherein the one or more through-focus imaging metrology subsystems comprise at least one of an optical-based metrology subsystem or a scatterometry-based metrology subsystem.

3. The system of claim 1 , wherein the one or more focus positions comprises a plurality of focus positions within a focus training range.

4. 2. The system of claim 1, wherein the plurality of training images are captured at the one or more focus positions by translating one or more portions of a metrology subsystem along one or more axes of adjustment.

5. The set of program instructions is Providing one or more control signals to one or more portions of one or more of the metrology subsystems, the one or more control signals causing the one or more portions of the metrology subsystems to translate along the one or more adjustment axes.

5. The system of claim 4, further configured to:

6. The system of claim 5 , wherein the one or more control signals are configured to center the one or more portions of the one or more through-focus imaging metrology subsystems.

7. Determining the one or more overlay measurements based on the one or more target images of the one or more target features comprises:

2. The system of claim 1, further comprising determining an overlay between the first layer of the target sample and the second layer of the target sample based on a first overlay measurement corresponding to the one or more target features formed on the first layer of the target sample and a second overlay measurement corresponding to the one or more target features formed on the second layer of the target sample.

8. The system of claim 1 , wherein the one or more target feature selections for the one or more overlay measurements are provided by a user via a user interface.

9. 10. The system of claim 1 , wherein the set of program instructions is further configured to cause the one or more processors to provide one or more control signals to one or more process tools.

10. 2. The system of claim 1, wherein the machine learning classifier comprises at least one of a deep learning classifier, a convolutional neural network, an ensemble learning classifier, a random forest classifier, or an artificial neural network.

11. 1. A system comprising: one or more through-focus imaging metrology subsystems; a controller communicatively coupled to the one or more through-focus imaging metrology subsystems, the controller including one or more processors configured to execute a set of program instructions stored in a memory, the set of program instructions configuring the one or more processors to: receiving a plurality of training images captured at one or more focus positions, the one or more training images including one or more training features of the training samples; generating a machine learning classifier based on a plurality of training images captured at the one or more focus positions; receiving one or more target feature selections, the target features selected by a user, for one or more target overlay measurements corresponding to one or more target features of the target sample; determining one or more target focus positions based on one or more target feature selections using a machine learning classifier; receiving one or more target images captured at one or more target focus positions, the one or more target images including one or more target features of the target sample; determining one or more overlays based on the one or more target images; A system configured to:

12. 1. A method of overlay metrology using one or more through-focus imaging metrology subsystems, comprising: receiving a plurality of training images captured at one or more focus positions, the one or more training images including one or more training features of a training sample; generating a machine learning classifier based on the plurality of training images captured at the one or more focus positions; receiving one or more target feature selections, the target features selected by a user, for one or more target overlay measurements corresponding to one or more target features of the target sample; determining one or more target focus positions based on the one or more target feature selections using the machine learning classifier; receiving one or more target images captured at the one or more target focus positions, the one or more target images including one or more target features of a target sample; determining one or more overlays based on the one or more target images; The method includes:

13. 13. The method of claim 12, wherein the plurality of training images and the one or more target images are captured by the one or more through-focus imaging metrology subsystems including at least one of an optical-based metrology subsystem or a scatterometry-based metrology subsystem.

14. The method of claim 12 , wherein the one or more focus positions comprises a plurality of focus positions within a focus training range.

15. 14. The method of claim 13, wherein the plurality of training images are captured at the one or more focus positions by translating one or more portions of the metrology subsystem along one or more axes of adjustment.

16. 16. The method of claim 15, further comprising providing one or more control signals to one or more portions of the one or more through-focus imaging metrology subsystems, the one or more control signals configured to translate the one or more portions of the one or more through-focus imaging metrology subsystems along one or more adjustment axes.

17. 17. The method of claim 16, wherein the one or more control signals are configured to center the one or more portions of the one or more through-focus imaging metrology subsystems.

18. Determining the one or more overlay measurements based on one or more target images of the one or more target features comprises:

13. The method of claim 12, further comprising determining an overlay between the first layer of the target sample and the second layer of the target sample based on a first overlay measurement corresponding to the one or more target features formed on the first layer of the target sample and a second overlay measurement corresponding to the one or more target features formed on the second layer of the target sample.

19. The method of claim 12 , wherein the one or more target feature selections for the one or more overlay measurements are provided by a user via a user interface.

20. The method of claim 12 , further comprising providing one or more control signals to one or more process tools.

21. 13. The method of claim 12, wherein the machine learning classifier comprises at least one of a deep learning classifier, a convolutional neural network, an ensemble learning classifier, a random forest classifier, or an artificial neural network.

Images