Optimization of metrology algorithms for inspection of semiconductor samples

Abstract

A metrology system and method are provided. The method includes: obtaining a tool parameter set selected from a plurality of tool parameters characterizing an inspection tool; changing a value of each tool parameter from the set a plurality of times, thereby producing a plurality of tool settings corresponding to a plurality of combinations of changed values of the set of tool parameters; configuring the inspection tool with each given tool setting of the plurality of tool settings; and in response to receiving from the inspection tool a plurality of image sets corresponding to the plurality of tool settings and representing expected tool changes over time in a single tool or between different tools, using the plurality of image sets to optimize a metrology algorithm to meet at least one metrology metric including a degree of tool matching.

Description

Technical Field

[0001] The subject matter of the present disclosure relates generally to the field of semiconductor sample inspection, and more particularly to metrology recipe optimization for sample inspection. Background Art

[0002] The current demand for high density and high performance associated with ultra-large scale integration of manufactured devices requires sub-micron feature structures, and requires increasing transistor and circuit speeds, as well as enhancing reliability. As semiconductor processes advance, pattern dimensions such as line widths and other types of critical dimensions continue to shrink. This demand requires the formation of device features with high precision and high uniformity, which in turn requires careful monitoring of the manufacturing process, including automated inspection of the device while it is still in the form of a semiconductor wafer.

[0003] During or after fabrication of the sample to be inspected, inspection may be performed using non-destructive inspection tools. Inspection generally involves generating some output (e.g., image, signal, etc.) of the sample by directing light or electrons to a wafer and detecting the light or electrons from the wafer. As non-limiting examples, various non-destructive inspection tools include scanning electron microscopes, atomic force microscopes, optical inspection tools, and the like.

[0004] The inspection process may include multiple inspection steps. The manufacturing process of the semiconductor device may include various procedures, such as etching, deposition, planarization, growth such as epitaxial growth, implantation, etc. The inspection step may be performed multiple times, for example after certain process procedures and / or after manufacturing certain layers, etc. Additionally or alternatively, each inspection step may be repeated multiple times, for example for different wafer positions, or for the same wafer position with different inspection settings.

[0005] Inspection processes are used at various steps during semiconductor manufacturing to detect and classify defects on samples, as well as to perform metrology-related operations. The effectiveness of inspection can be improved by automating one or more processes such as defect detection, automatic defect classification (ADC), automatic defect review (ADR), image segmentation, automatic metrology-related operations, etc.

[0006] Automated inspection systems ensure that manufactured parts meet expected quality standards and provide useful information regarding adjustments that may be needed to manufacturing tools, equipment and / or components, depending on the type of defect identified. Summary of the invention

[0007] According to certain aspects of the subject matter of the present disclosure, a computerized metrology system is provided, the system comprising an inspection tool operably connected to a processing circuit system, wherein: the processing circuit system is configured to: obtain a tool parameter set selected from a plurality of tool parameters characterizing the inspection tool, change the value of each tool parameter from the set multiple times to produce a plurality of tool settings corresponding to a plurality of combinations of the varied values ​​of the tool parameter set, and configure the inspection tool with each given tool setting from the plurality of tool settings; and the inspection tool is configured to: in response to being configured with each given tool setting, acquire an image set of at least one site on a capture sample with the given tool setting, thereby obtaining a plurality of image sets corresponding to the plurality of tool settings and representing expected tool variations over time in a single tool or between different tools; and wherein the processing circuit system is further configured to use the plurality of image sets to optimize a metrology algorithm to satisfy at least one metrology metric including tool matching.

[0008] In addition to the above features, the system according to this aspect of the subject matter of the present disclosure may include one or more of the features (i) to (ix) listed below in any desired combination or arrangement that is technically possible:

[0009] (i). The metrology algorithm may be optimized with respect to a set of algorithm parameters selected from a plurality of algorithm parameters characterizing the metrology algorithm.

[0010] (ii). The processing circuit system may be further configured to optimize the metrology algorithm by: changing the value of each algorithm parameter from the set multiple times, thereby generating multiple algorithm settings corresponding to multiple combinations of the changed values ​​of the algorithm parameter set; for each given algorithm setting, using the metrology algorithm configured with the given algorithm setting to obtain multiple measurement data sets corresponding to multiple image sets, and evaluating the multiple measurement data sets with respect to at least one metrology metric; and optimizing the metrology algorithm based on the evaluation.

[0011] (iii). The evaluation may include: for each given algorithm setting, calculating a separate value of a separate objective function based on each measurement data set, and calculating an overall value of the overall objective function based on multiple measurement data sets; selecting an algorithm setting from multiple algorithm settings having an optimal overall value that satisfies a specification of at least one metrological metric; and verifying the separate value of each measurement data set in the multiple measurement data sets obtained under the selected algorithm setting with respect to the specification of the at least one metrological metric to determine whether further evaluation is required.

[0012] (iv). The validation may include: in response to individual values ​​of each measurement data set that meet the specification, identifying one or more tool settings that result in one or more measurement data sets having relatively poor performance with respect to the specification; selecting one or more new tool settings with finer resolution from a range of tool settings characterized by the one or more tool settings, and acquiring one or more new image sets under the one or more new tool settings; and obtaining one or more measurement data sets corresponding to the one or more new image sets using a metrology algorithm configured with the selected algorithm settings, and evaluating the one or more new measurement data sets with respect to at least one metrology metric.

[0013] (v). The validation may include, in response to at least some of the plurality of measurement data sets having individual values ​​that do not meet the specification, selecting one or more new algorithm parameters to add to the algorithm parameter set, or tightening tolerances for tool parameter variations.

[0014] (vi). The processing circuit system may be configured to acquire the image set for each given tool setting by: alternately acquiring a first frame sequence and a second frame sequence for a given site in at least one site, wherein the first frame sequence is acquired with a tool setting having optimal conditions for the algorithm parameter set, and the second frame sequence is acquired with a given tool setting; and combining the first frame sequence into a first image and the second frame sequence into a second image, respectively, wherein the image set includes the first image and the second image of the given site.

[0015] (vii). The at least one metrology metric may further include one or more of precision, correlation, and sensitivity.

[0016] (viii). The tool parameter set may include focus, astigmatism and numerical aperture.

[0017] (ix). The algorithm parameter set may include smoothing and derivatives.

[0018] According to other aspects of the subject matter of the present disclosure, a computerized metrology method is provided, the method comprising: obtaining a tool parameter set selected from a plurality of tool parameters characterizing an inspection tool; varying a value of each tool parameter from the set a plurality of times, thereby generating a plurality of tool settings corresponding to a plurality of combinations of varied values ​​of the tool parameter set; configuring the inspection tool with each given tool setting of the plurality of tool settings; and in response to receiving a plurality of image sets from the inspection tool corresponding to the plurality of tool settings and representing expected tool variations over time in a single tool or between different tools, optimizing a metrology algorithm using the plurality of image sets to satisfy at least one metrology metric, the at least one metrology metric comprising tool matching.

[0019] This aspect of the subject matter of the present disclosure may comprise mutatis mutandis in any desired combination or arrangement technically possible one or more of the features (i) to (ix) listed above with respect to the system.

[0020] According to other aspects of the subject matter of the present disclosure, a non-transitory computer-readable medium is provided, the non-transitory computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform a computerized metrology method, the method comprising: obtaining a tool parameter set selected from a plurality of tool parameters characterizing an inspection tool; varying a value of each tool parameter from the set a plurality of times, thereby generating a plurality of tool settings corresponding to a plurality of combinations of varied values ​​of the tool parameter set; configuring the inspection tool with each given tool setting in the plurality of tool settings; and in response to receiving a plurality of image sets from the inspection tool corresponding to the plurality of tool settings and representing expected tool variations over time in a single tool or between different tools, optimizing a metrology algorithm using the plurality of image sets to satisfy at least one metrology metric, the at least one metrology metric comprising tool matching.

[0021] This aspect of the subject matter of the present disclosure may comprise mutatis mutandis in any desired combination or arrangement technically possible one or more of the features (i) to (ix) listed above with respect to the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to understand the present disclosure and to see how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:

[0023] Figure 1 A generalized block diagram of an inspection system according to certain embodiments of the presently disclosed subject matter is depicted.

[0024] Figure 2 A generalized flow diagram of data generation according to certain embodiments of the presently disclosed subject matter is depicted.

[0025] Figure 3 A generalized flow chart of an exemplary process for optimizing a metrology algorithm according to certain embodiments of the presently disclosed subject matter is depicted.

[0026] Figure 4 A generalized flow chart of an exemplary process for evaluating measurement data with respect to at least one metrology metric, in accordance with certain embodiments of the presently disclosed subject matter, is depicted.

[0027] Figure 5 A generalized flow chart of an exemplary validation process for validating, against specifications, individual values ​​in a plurality of measurement data sets obtained under selected algorithm settings, in accordance with certain embodiments of the presently disclosed subject matter is shown.

[0028] Figure 6 The process of data generation and algorithm optimization according to certain embodiments of the subject matter of the present disclosure is schematically depicted.

[0029] Figure 7 A generalized flow chart of a particular image acquisition process for a given site according to certain embodiments of the presently disclosed subject matter is depicted.

[0030] Figure 8 Depicted are examples of image acquisition of a first frame sequence and a second frame sequence according to some embodiments of the presently disclosed subject matter. DETAILED DESCRIPTION

[0031] The process of semiconductor manufacturing generally requires multiple sequential processing steps and / or layers, each of which may produce errors that may result in yield loss. Examples of various processing steps may include lithography, etching, deposition, planarization, growth (e.g., epitaxial growth), and implantation, etc. Various inspection operations such as defect-related inspections and / or metrology-related inspections may be performed at different processing steps / layers during the manufacturing process to monitor and control the process. For example, the inspection operation may be performed multiple times after certain processing steps and / or after manufacturing certain layers, etc.

[0032] As an example, a metrology operation may be performed on a sample to measure one or more characteristics of structural features / elements formed on the sample, such as dimensions (e.g., line width, line spacing, contact diameter, size of elements, edge roughness, grayscale statistics, etc.); shape of elements; distances within or between elements; associated angles; and overlap information associated with elements corresponding to different design levels, etc. Such measurements may be used to evaluate the performance of one or more processing steps during the fabrication of the feature structure. For example, if some of the measurements of the sample are unacceptable (e.g., exceed a predetermined range or threshold), such measurements may be used to change one or more parameters of the one or more processing steps so that subsequent samples fabricated by the same one or more processing steps may have acceptable characteristics.

[0033] Metrology operations are performed by a metrology tool using a metrology recipe. A metrology recipe is typically created during a recipe setup phase and includes one or more metrology algorithms related to different metrology applications. Once a metrology recipe is created, the metrology recipe can be used by a metrology tool to perform metrology operations on semiconductor samples during runtime inspection.

[0034] Metrology algorithms typically include a large set of algorithm parameters that are typically manually adjusted, for example, by application engineers. Such manual adjustments are limited to a small number of parameters and rely on personal experience and proficiency, so even with expert levels of application knowledge, such manual adjustments are typically time-consuming and error-prone. In addition, as semiconductor manufacturing processes continue to advance, semiconductor devices are developed with increasingly complex structures and reduced feature size, which requires more stringent metrology metric specifications, such as accuracy and matching. Application engineers face increasing challenges in adjusting parameters to meet this specification and ensuring the proper balance between various metrology metrics.

[0035] Furthermore, in order to adjust the parameters to meet the specification, a sufficient image dataset is required to be evaluated with respect to the metrology metrics. However, such datasets are often difficult to obtain. As an example, in order to evaluate tool-to-tool matching, which represents measurement deviations between different tools, images should be acquired by different tools. However, in many cases, there are not always a sufficient number of tools available at R&D centers and / or customer sites. Even in the presence of sufficient tools, such image acquisition requires placing multiple tools offline in the semiconductor factory, resulting in excessive tool downtime and affecting system throughput. Furthermore, even if the entire tool cluster is placed offline for image acquisition, it is not always guaranteed that the images acquired from the tool cluster at a given time will necessarily have the expected changes, which are considered to be observed over time.

[0036] Therefore, certain embodiments of the subject matter of the present disclosure propose systems and methods for generating a data set (also referred to as a set of data or an image set) that can be used to optimize a metrology algorithm, which does not have one or more of the above-mentioned disadvantages. The present disclosure proposes changing the value of each tool parameter from a tool parameter set multiple times to provide multiple tool settings corresponding to multiple combinations of the changed values ​​of the tool parameter set, and configuring an inspection tool with each given tool setting in the multiple tool settings to acquire an image set of at least one site on a capture sample with the given tool setting, thereby obtaining multiple image sets corresponding to the multiple tool settings and representing the expected tool changes over time in a single tool or between different tools. The multiple image sets can be used to optimize the metrology algorithm, which will be described in detail below.

[0037] Based on this, note Figure 1 , which depicts a functional block diagram of an inspection system according to certain embodiments of the subject matter of the present disclosure.

[0038] exist Figure 1The inspection system 100 shown in can be used to inspect semiconductor samples (e.g., wafers, chips, and / or portions thereof) as part of a sample manufacturing process. As mentioned above, inspection mentioned in this article can be interpreted as covering any type of operation on the sample, involving defect inspection / detection, defect classification, segmentation and / or metrology operations, such as critical dimension (CD) measurement, roughness, coverage, etc. The system 100 includes one or more inspection tools 120, which are configured to scan the sample and capture its image to be further processed for various inspection applications.

[0039] The term "one or more inspection tools" as used herein should be broadly interpreted to cover any tool that may be used in an inspection-related process, including, by way of non-limiting example, scanning (in words or multiple scans), imaging, sampling, reviewing, measuring, sorting, and / or other processes performed on a sample or portion thereof.

[0040] Without limiting the scope of the present disclosure in any way, it should also be noted that the inspection tool 120 can be implemented as various types of inspection machines, such as an optical inspection machine, an electron beam inspection machine (e.g., a scanning electron microscope (SEM), an atomic force microscope (AFM), or a transmission electron microscope (TEM), etc.), etc. In some cases, the same inspection tool can provide low-resolution image data and high-resolution image data. The resulting image data (low-resolution image data and / or high-resolution image data) can be transmitted to the system 101 directly or via one or more intermediate systems. The present disclosure is not limited to any particular type of inspection tool and / or the resolution of the image data generated by the inspection tool.

[0041] In some embodiments, at least one of the inspection tools 120 has metrology capabilities and can be configured to capture images and perform metrology operations on the captured images. Such inspection tools are also referred to herein as metrology tools.

[0042] According to some embodiments, the metrology tool may be an electron beam tool, such as a scanning electron microscope (SEM). A SEM is an electron microscope that generates an image of a sample by scanning the sample with a focused electron beam. The electrons interact with the atoms in the sample, thereby generating various signals containing information about the surface morphology and / or composition of the sample. The SEM is capable of accurately measuring features during the manufacture of semiconductor wafers. As an example, the metrology tool may be a critical dimension scanning electron microscope (CD-SEM) for measuring the critical dimensions of structural features in an image.

[0043] According to certain embodiments of the subject matter of the present disclosure, inspection system 100 includes a computer-based system 101 that is operably connected to inspection tool 120 and is capable of implementing automated metrology operations on semiconductor samples at runtime based on runtime images obtained during sample fabrication. System 101 is also referred to as a metrology system.

[0044] System 101 includes processing circuitry 102 that is operably connected to hardware-based I / O interface 126 and configured to provide processing required by an operating system, such as reference Figures 2 to 5 and Figure 7 Detailed description is provided below. Processing circuit system 102 may include one or more processors (not shown separately) and one or more memories (not shown separately). One or more processors of processing circuit system 102 may be configured to execute several functional modules individually or in any suitable combination according to computer readable instructions implemented on a non-transitory computer readable memory included in the processing circuit system. Such functional modules are hereinafter referred to as being included in the processing circuit system.

[0045] The one or more processors mentioned herein may represent one or more general-purpose processing devices, such as microprocessors, central processing units, etc. More specifically, a given processor may be one of the following: a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. One or more processors may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. One or more processors are configured to execute instructions for performing the operations and steps described herein.

[0046] The memory mentioned in this document may include one or more of the following: internal memory such as processor registers and cache, main memory such as read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.

[0047] According to certain embodiments of the subject matter of the present disclosure, system 101 may be a metrology system configured to generate a data set that can be used to optimize a metrology application. In this case, one or more functional modules included in processing circuit system 102 of system 101 may include data generator 104 and recipe optimizer 106.

[0048] Specifically, the data generator 104 may be configured to select a tool parameter set from a plurality of tool parameters characterizing the inspection tool; change the value of each tool parameter from the set multiple times, thereby generating a plurality of tool settings corresponding to a plurality of combinations of the varied values ​​of the tool parameter set; and configure the inspection tool with each given tool setting from the plurality of tool settings.

[0049] In response to being configured with each given tool setting, the inspection tool 120 can acquire an image set capturing at least one site on the sample with the given tool setting, thereby obtaining a plurality of image sets corresponding to the plurality of tool settings and representing expected tool changes over time in a single tool or between different tools. The plurality of image sets form a data set that can be used for algorithm optimization.

[0050] The recipe optimizer 106 can be configured to optimize the metrology algorithm using the plurality of image sets to satisfy at least one metrology metric, the at least one metrology metric comprising tool matching. After optimizing the recipe / algorithm, the system 101 can be viewed as a metrology system capable of performing runtime metrology operations using the optimized metrology recipe. Figures 2 to 5 and Figure 7 The details of this processing are described.

[0051] Reference Figures 2 to 5 and Figure 7 The operation of system 100 and system 101 , processing circuitry 102 , and functional modules therein are described in further detail.

[0052] It should be noted that although certain embodiments of the present disclosure relate to a processing circuit system 102 configured to perform the above-described operations, the functionality / operation of the aforementioned functional modules may be performed in various ways by one or more processors in the processing circuit system 102. As an example, the operation of each functional module may be performed by a specific processor or a combination of processors. Therefore, the operations of various functional modules (such as selecting a tool parameter set, changing the value of each tool parameter, configuring the inspection tool, and optimizing the metrology algorithm, etc.) may be performed by a corresponding processor (or combination of processors) in the processing circuit system 102, while optionally, these operations may be performed by the same processor. The present disclosure should not be limited to being interpreted as a single processor always performing all operations.

[0053] In some cases, in addition to the system 101, the inspection system 100 may include one or more inspection modules that can be used to inspect semiconductor samples, such as a metrology operation module, a defect detection module, an automatic defect review module (ADR), an automatic defect classification module (ADC), and / or other inspection modules. One or more inspection modules may be implemented as independent computers, or the functionality of one or more inspection modules (or at least a portion thereof) may be integrated with the inspection tool 120. In some cases, the output of the system 101 (e.g., a generated data set, an optimized metrology algorithm, etc.) may be provided to one or more inspection modules for further processing.

[0054] According to certain embodiments, system 100 may include storage unit 122. Storage unit 122 may be configured to store any data required to operate system 101, such as data relating to inputs and outputs of system 101, and intermediate processing results generated by system 101. As an example, storage unit 122 may be configured to store images of samples generated by inspection tool 120 and / or derivatives thereof. Thus, these input images may be retrieved from storage unit 122 and provided to processing circuit system 102 for further processing. Outputs of system 101, such as generated data sets and optimized metrology algorithms, may be sent to storage unit 122 for storage.

[0055] In some embodiments, the system 100 may optionally include a computer-based graphical user interface (GUI) 124 that is configured to enable user-specified input related to the system 101. For example, a visual representation of a sample, including an image of the sample, etc., may be presented to the user (e.g., via a display forming part of the GUI 124). Options to define certain operating parameters may be provided to the user via the GUI. The user may also view operating results or intermediate processing results, such as acquired image sets, optimized algorithm parameters, etc., on the GUI.

[0056] In some cases, the system 101 may be further configured to send the results of the operation to the inspection tool 120 via the I / O interface 126 for further processing. In some cases, the system 101 may be further configured to send the results to the storage unit 122 and / or an external system (e.g., a yield management system (YMS) of a manufacturing plant (semiconductor plant)). In the context of semiconductor manufacturing, the yield management system (YMS) is a data management, analysis and tool system that collects data from semiconductor plants, especially during manufacturing upgrades, and helps engineers find ways to increase production. YMS helps semiconductor manufacturers and factories manage a large amount of production analysis with fewer engineers. These systems analyze yield data and generate reports. Integrated device manufacturers (IMDs), semiconductor plants, fabless semiconductor companies, and outsourced semiconductor assembly and testing (OSATs) can use YMS.

[0057] Those skilled in the art will readily appreciate that the teachings of the subject matter of the present disclosure are not limited to Figure 1 Constraints of the system shown. Figure 1 Each system component and module in the system may be composed of any combination of software, hardware and / or firmware, and relatedly, any combination of software, hardware and / or firmware is executed on an appropriate device or devices, and the one or more devices perform the functions defined and explained herein. The equivalent and / or modified functionality described with respect to each system component and module may be combined or divided in another manner. Therefore, in some embodiments of the subject matter of the present disclosure, the system may include more Figure 1 The components, modules and functions shown may include fewer, additional, modified and / or different components, modules and functions.

[0058] Figure 1 Each component in the may represent a plurality of specific components that are adapted to operate independently and / or cooperatively to process various data and electrical inputs and to implement operations associated with a computerized inspection system. In some cases, multiple instances of a component may be utilized for performance, redundancy, and / or availability reasons. Similarly, in some cases, multiple instances of a component may be utilized for functionality or application reasons. For example, different portions of a particular functionality may be placed in different instances of a component.

[0059] It should be noted that the present invention can be implemented in a distributed computing environment. Figure 1 The inspection system shown, wherein Figure 1 One or more of the aforementioned components and functional modules shown may be distributed across several local and / or remote devices. As an example, the inspection tool 120 and the system 101 may be located at the same entity (in some cases hosted by the same device), or distributed across different entities, depending on the specific system configuration and implementation needs. In some examples, certain components utilize a cloud implementation, such as implemented in a private or public cloud. In the case where the various components of the inspection system are not completely located in one location or one physical entity, communication between the various components of the inspection system may be achieved through any signaling system or communication component, module, protocol, software language, and drive signal, and the communication may be appropriately wired and / or wireless.

[0060] It should also be noted that in some embodiments, at least some of the inspection tools 120, storage unit 122, and / or GUI 124 may be external to the inspection system 100 and operate in data communication with the systems 100 and 101 via the I / O interface 126. As described above, the system 101 may be implemented as one or more stand-alone computers used in conjunction with the inspection tools and / or additional inspection modules. Alternatively, the corresponding functions of the system 101 may be at least partially integrated with one or more inspection tools 120, thereby improving and enhancing the functionality of the inspection tools 120 in the inspection-related processes.

[0061] Although not necessarily the case, the operational processing of systems 101 and 100 may be similar to that described with respect to Figures 2 to 5 and Figure 7 Some or all of the steps of the method described herein may correspond to the following. Figures 2 to 5 and Figure 7 The described methods and possible implementations thereof may be implemented by the system 101 and the system 100. Therefore, it is noted that Figures 2 to 5 and Figure 7 The embodiments discussed with respect to the described methods may also be implemented mutatis mutandis as various embodiments of the systems 101 and 100 , and vice versa.

[0062] refer to Figure 2 , showing a generalized flow chart of data generation according to certain embodiments of the subject matter of the present disclosure.

[0063] As described above, semiconductor samples are usually made of multiple layers. During the manufacturing process of the sample, for example, after a processing step of a particular layer, the inspection process of the sample may be performed multiple times. In some cases, a sampling set of processing steps may be selected for in-line inspection based on the known effects of multiple processing steps on device characteristics or yield. An image of the sample or a portion thereof may be acquired at the sampling set of processing steps for inspection.

[0064] For purposes of illustration only, certain embodiments described below are described with respect to image acquisition for a given processing step / layer of a sample set of processing steps. Those skilled in the art will readily appreciate that the teachings of the subject matter of the present disclosure, such as data generation and recipe optimization processing, may be performed with respect to any layer and / or processing step of a sample. The present disclosure should not be limited to the number of layers included in a sample and / or the specific layer or layers to be examined.

[0065] An inspection tool is typically configured with a plurality of tool parameters that characterize the tool, such as focus, astigmatism, and numerical aperture (NA), among others. A tool parameter set may be selected (202) from the plurality of tool parameters to configure the tool for image data collection / acquisition. The tool parameter set may be selected in a variety of ways. For example, the parameter set may be predefined, for example, based on known physical effects of the parameter set on measurement variation. As an example, a focus parameter may be selected knowing that after an initial calibration, the tool focus will vary over time. Additionally, two different tools calibrated simultaneously still do not share the same focusing capabilities. Alternatively, the tool parameter set may be automatically selected based on failure analysis of certain metrology metrics.

[0066] The value of each tool parameter from the tool parameter set may be varied (204) a plurality of times, thereby generating a plurality of tool settings corresponding to a plurality of combinations of the varied values ​​of the tool parameter set. As an example, the value of a given parameter (e.g., focus) may be varied within a predefined range according to an interval or step size. The inspection tool (e.g., Figure 1 Inspection tool 120 shown in ).

[0067] In response to being configured with each given tool setting, the inspection tool may acquire (208) a set of images capturing at least one site on the sample with the given tool setting. As an example, the site may refer to a target area of ​​interest containing a target structure (e.g., a structural feature or pattern on a semiconductor sample) to be inspected on the sample. The at least one site may refer to one or more sites on the sample containing the same target structure.

[0068] Once the tool iterates through each of the plurality of tool settings and captures its corresponding image set, the tool may acquire a plurality of image sets corresponding to the plurality of tool settings. The plurality of image sets form a data set representing expected tool variation over time in a single tool or between different tools. The data set so generated may be used to evaluate metrological measures of matching, since measurement deviations between tools result from tool parameter variations.

[0069] like Figure 6As illustrated, 602 depicts a tool parameter space characterized by two tool parameters: focus and numerical aperture (NA) shown in the x-direction and y-direction, respectively. The values ​​of the focus and NA parameters are varied multiple times, respectively, resulting in multiple combinations of varying values ​​of the two parameters represented by the points in 602. Each point represents a particular tool setting configured by a corresponding focus and NA value. As an example, the values ​​of the focus parameters may be varied within a focus window according to a step size. The focus window may be centered approximately around the optimal focus value (e.g., around the center position of the x-axis in 602). The focus window represents the expected focus variation / degradation that may occur over time in a single tool or between different tools. The NA value may be varied in a similar manner.

[0070] For each specific tool setting in 602, such as the tool setting represented by 604, the inspection tool can be configured with setting 604, for example, by adjusting the focus value and NA value of the tool to the specific values ​​of tool setting 604 (e.g., represented by x, y coordinates). The tool configured in this way is used to acquire an image set of one or more sites on the sample. For example, m locations on the sample can be selected, and the tool can continuously acquire n frames of each site under the specific tool setting 604. The n frames of a given site can be combined to form a higher resolution image of the given site. Therefore, the image set acquired under tool setting 604 includes m images. Similarly, multiple tool settings in 602 can be traversed in sequence, and the tool can capture multiple image sets corresponding to multiple tool settings in sequence.

[0071] In some embodiments, certain physical effects may be induced on the sample by a sequential scanning process, such as charging effects established on the sample, physical shocks such as shrinkage, etc. For example, a specific site on the sample is repeatedly scanned by an electron beam tool such as a SEM in order to acquire a sequence of images. During scanning, the surface charge caused by the electron beam continuously accumulates on the site, which may lead to scanning errors and image artifacts, such as gross image distortion and / or image disappearance, etc. Repeated scanning also physically affects the sample, and an example of such an effect is shrinkage (also known as the carbonization effect). Such image artifacts and physical effects may affect the measurement data of the image and lead to an increasing inability to accurately measure critical integrated device dimensions. In order to compensate for such physical effects induced on the sample, specific image acquisition processing may be used to reduce interference with the evaluation of tool changes.

[0072] Figure 7A generalized flow chart of a specific image acquisition process for a given site according to certain embodiments of the subject matter of the present disclosure is depicted. Specifically, for a given tool setting among a plurality of tool settings as depicted in 602, an image set of a given site among at least one site on a sample may be acquired as follows: alternately acquiring (702) a first frame sequence and a second frame sequence, wherein the first frame sequence is acquired with a tool setting having optimal conditions for the algorithm parameter set, and the second frame sequence is acquired with the given tool setting; and combining (704) the first frame sequence into a first image and the second frame sequence into a second image, respectively. Thus, for a given site, two images are obtained: a first image and a second image. For m selected sites on the sample, the image set acquired under the given tool setting includes m*2 images.

[0073] Figure 8 Depicted are examples of image acquisition of a first frame sequence and a second frame sequence according to some embodiments of the presently disclosed subject matter.

[0074] As shown, a frame sequence 800 is acquired for a given location on a sample. Assume that the selected tool parameter in the tool parameter set is focus. The frame sequence 800 includes a first sequence 802 acquired under optimal focus conditions and a second sequence 804 acquired under defocus conditions. The first sequence 802 and the second sequence 804 are acquired in an alternating manner. For example, for a position where the focus is offset from optimal focus (defocused), Figure 6 806. The tool setting 604 shown is first configured with optimal focus, and a focused frame 806 is acquired. Then, the tool is configured with a defocus condition defined by the x-coordinate of the tool setting 604, and a defocused frame 808 is acquired. The tool then returns to the optimal focus setting to acquire another focused frame. This process of focused and defocused image acquisition (also known as ABAB) is repeated until the entire frame sequence 800 is acquired. The first sequence 802 is combined to form a first image, and the second sequence 804 is combined to form a second image. The above process can be repeated for each selected site on the sample, and two images are produced for each site. In this way, physical effects such as carbonization effects can be relatively evenly distributed between the first frame sequence and the second frame sequence. Therefore, the combined first image and second image will be produced by similar amounts of such physical effects. When evaluated against each other with respect to matching, similar amounts of physical effects from the two images will be offset.

[0075] Alternatively, another sequence of frames 810 may be acquired in a slightly different manner. The sequence of frames 810 includes a first sequence 812 acquired under optimal focus conditions and a second sequence 814 acquired under out-of-focus conditions. The first sequence 812 and the second sequence 814 are acquired alternately but in an ABBA manner. For example, the tool is first configured with optimal focus, and a focused frame 816 is acquired. Then, the tool is configured with an out-of-focus condition defined by the x-coordinate of the tool settings 604, and an out-of-focus frame 818 is acquired. Then, as shown in sequence 800, there is no return to configuring the tool with optimal focus. Instead, the tool continues the configuration of the out-of-focus condition, and a third frame 820 is captured. Then, the tool is configured back to optimal focus and a fourth frame is captured, and so on, until the entire sequence 810 is acquired. Compared to sequence 800, sequence 810 assumes that the variation of the carbonization effect between the two sequences 812 and 814 is further eliminated. Therefore, when the two combined images are evaluated against each other with respect to the degree of match, it is less likely that they will be affected by this effect.

[0076] Although the above example illustrates alternating between optimal and non-optimal conditions for one selected tool parameter, this approach may be applicable to multiple tool parameters. For example, where the selected parameters include focus and NA, such as shown in 602, a first frame sequence may be acquired under optimal conditions for the two parameters, and a second frame sequence may be acquired under non-optimal conditions for the two parameters, as defined by each given tool setting in 602 (e.g., tool setting 604, where the values ​​of the two parameters are defined by specific x and y coordinates).

[0077] It should be noted that although for illustrative purposes, Figure 6 602 in exemplifies a combination between two tool parameters, but in some cases, the selected tool parameter set may include more than two parameters. In this case, the tool parameter space of 602 may be represented in a multi-dimensional parameter space, where each specific tool setting is characterized by the values ​​of multiple tool parameters. For example, in the case where the tool parameter set includes three parameters focus, NA and astigmatism, 602 may be represented as a three-dimensional (3D) parameter space, where the x-axis, y-axis and z-axis represent one of the three parameters respectively. Each point in the 3D parameter space is characterized / defined by three parameter values ​​for three axes.

[0078] Once the dataset is acquired, multiple image sets (e.g. Figure 1The method of claim 1 , wherein the metrology algorithm is optimized ( 210 ) by a recipe optimizer 106 in the process to satisfy at least one metrology metric, the at least one metrology metric comprising tool matching. As described above, a metrology algorithm may be considered as part of a metrology recipe that may be used by a metrology tool to perform metrology operations on a sample. A metrology algorithm is typically associated with a specific metrology application. A metrology application refers to what a customer / user is typically interested in measuring on a sample. As a non-limiting example, a metrology application may be one of the following: critical dimension (CD) metrology, overlay (OVL), measurement-based inspection (MBI), and critical dimension uniformity (CDU), among others.

[0079] Since the acquired data set represents the expected tool variation over time in a single tool or between different tools, the data set can be used to evaluate metrological metrics of matching and optimize metrological algorithms at least with respect to matching. Matching represents the measurement variation between different tools (or the measurement variation of one tool over time), so matching is also called tool matching or tool-to-tool matching. Matching is related to the repeatability of measurement data from different images of the same given feature acquired by different tools. In some embodiments, at least one metrological metric may further include one or more additional metrics, such as accuracy, correlation, and sensitivity, as will be described below.

[0080] Various optimization methods can be used to tune the algorithm parameters. Figure 3 A generalized flow chart of an exemplary process for optimizing a metrology algorithm according to certain embodiments of the presently disclosed subject matter is depicted.

[0081] As described above, the metrology algorithm typically includes a large set of algorithm parameters that characterize the metrology algorithm. In some embodiments, the metrology algorithm can be optimized with respect to an algorithm parameter set selected from a plurality of algorithm parameter groups. As an example, the algorithm parameter set can be selected based on known knowledge. For example, the selected algorithm parameter set can include common parameters such as smoothing, derivatives (e.g., the magnitude and type of the derivatives), etc., and / or many other customized algorithm parameters related to a specific algorithm. Smoothing typically represents a low-pass filter to be applied to the image. The derivative (e.g., its magnitude and type) represents a high-pass filter to be applied to the image.

[0082] The value of each algorithm parameter from the selected set may be varied (302) a plurality of times, thereby generating a plurality of algorithm settings corresponding to a plurality of combinations of varying values ​​of the algorithm parameter set. As an example, the value of a given parameter may be varied within a predefined range according to an interval.

[0083] For each given algorithm setting in a plurality of algorithm settings, a plurality of measurement data sets corresponding to the plurality of image sets may be obtained (304) using a metrology algorithm configured with the given algorithm setting. The plurality of measurement data sets may be evaluated with respect to at least one metrology metric. Once the plurality of algorithm settings have been traversed through and the corresponding measurement data sets obtained thereby evaluated, the metrology algorithm may be optimized (306) based on the evaluation.

[0084] Figure 4 A generalized flow chart of an exemplary process for evaluating measurement data with respect to at least one metrology metric, in accordance with certain embodiments of the presently disclosed subject matter, is depicted.

[0085] The measurement data may be evaluated using an objective function (also referred to as a loss function) for at least one metrology metric. The objective function used herein may refer to a separate objective function for evaluating each separate measurement data set for a particular tool setup, and may refer to an overall objective function for evaluating the overall performance of multiple measurement data sets.

[0086] Specifically, for each given algorithm setting, a separate value of a separate objective function may be calculated (402) based on each measurement data set (e.g., obtained from a corresponding image set of the corresponding tool setting). An overall value of an overall objective function may be calculated based on multiple measurement data sets (e.g., corresponding to multiple image sets of multiple tool settings). The algorithm setting having the best overall value may be selected (404) from the multiple algorithm settings. In the event that the best overall value satisfies a specification of at least one metrological metric (e.g., a goodness of fit specification), the individual values ​​of the multiple measurement data sets obtained under the selected algorithm setting may be verified (406) with respect to the specification to determine whether further evaluation is required.

[0087] Figure 5 A generalized flow diagram of an exemplary authentication process as described in block 406 is shown in accordance with some embodiments of the presently disclosed subject matter.

[0088] In response to the individual values ​​of each measurement data set satisfying the specification, optionally, one or more tool settings (of the plurality of tool settings) that result in the one or more measurement data sets having relatively poor performance with respect to the specification may be identified (502). One or more new tool settings having a finer resolution may be selected (504) from a range of tool settings characterized by the one or more tool settings, and one or more new image sets may be acquired under the one or more new tool settings. Similarly, as described above with respect to block 304, one or more measurement data sets corresponding to the one or more new image sets may be obtained (506) using a metrology algorithm configured with the selected algorithm settings, and the one or more new measurement data sets may be evaluated with respect to at least one metrology metric.

[0089] As an example, if all of the new measurement data sets also meet the specification of at least one metrology metric, the selected algorithm settings may be used to optimize the metrology algorithm. In the event that at least some of the new measurement data sets do not meet the specification of at least one metrology metric (or in the event that at least some of the plurality of measurement data sets described above with respect to 406 do not meet the specification), one or more new algorithm parameters may be selected from the plurality of algorithm parameters and added to the set of algorithm parameters (e.g., in addition to previously existing algorithm parameters in the set, or in place of some of them). In the event that the specification is still not met after adjusting the new algorithm parameters, steps may be taken to consider tightening the tolerance for tool parameter changes.

[0090] continue Figure 6 , 606 depicts the algorithm parameter space characterized by two selected tool parameters: smoothing and derivative shown in the x-direction and y-direction, respectively. The values ​​of these two parameters are varied multiple times, respectively, resulting in multiple combinations of the changed values ​​of these two parameters represented by the points in 606. Each point represents a specific algorithm setting configured by the corresponding smoothing value and derivative value.

[0091] As mentioned above, in reference Figure 2 After the data is collected, a data set including a plurality of image sets corresponding to the plurality of tool settings in 602 is obtained, and the data set can be used to optimize a metrology algorithm.

[0092] Specifically, for each algorithm setting specified in 606, a metrology algorithm may be configured with the specific algorithm setting, and a plurality of measurement data sets corresponding to the plurality of image sets may be obtained using the metrology algorithm so configured. As described above, the plurality of measurement data sets may be evaluated with respect to at least one metrology metric using individual objective functions and an overall objective function.

[0093] Take goodness of fit as an example of at least one metrological metric. Assume that algorithm setting 608 has the best overall loss (i.e., the overall value of the overall objective function) from multiple algorithm settings when evaluated. The overall value also meets the goodness of fit specification. In this case, in addition to the overall loss representing the average amount of tool variation, the individual losses of each of the multiple measurement data sets obtained under algorithm setting 608 need to be verified to ensure that the individual measurements produced by all tool variations meet the specification.

[0094] Assume that after verification, it is identified that the tool settings within the range 610, while still meeting the specification, have poor matching performance with respect to the remaining tool settings in 602. In this case, one or more new tool settings with finer resolution can be optionally selected from the range 610. As shown, the range 610 has been finely sampled, and five new tool settings 612 that are close to the original tool settings are selected within the range (indicated by larger dots compared to the original tool settings). Five new image sets are acquired at the five new tool settings 612.

[0095] Therefore, five new measurement data sets corresponding to the five new image sets are obtained using the metrology algorithm configured with the algorithm settings 608. The five new measurement data sets are then evaluated for matching. In the event that the five new measurement data sets also meet the matching specification, the metrology algorithm may be optimized using the selected algorithm settings 608 that provide the best overall loss, while their individual losses meet the matching specification even with finer sampling.

[0096] In the event that at least some of the five new measurement data sets do not meet the goodness of fit specification, or even at an earlier stage, in the event that at least some of the individual losses in the plurality of measurement data sets do not meet the specification, it indicates that the algorithm parameter model with the two selected parameters is not complex enough to be adjusted to meet the specification. In this case, one or more new algorithm parameters may be selected from the plurality of algorithm parameters characterizing the algorithm. For example, an outlier removal strength parameter may be selected and added to the algorithm parameter set (e.g., in addition to smoothing and derivative magnitude). In this case, the algorithm parameter space 606 becomes a three-dimensional parameter space, and measurement data may be acquired and evaluated under the new algorithm settings.

[0097] In the event that the fit specification is still not met after adding / replacing one or more new algorithm parameters and optimizing the algorithm, this indicates that the metrology algorithm is simply not able to meet the fit specification under this range of tool variations. In this case, the tolerances for tool parameter variations should be tightened or limited. For example, the tool engineer may be updated so that the values ​​of the focus and NA parameters should be limited to a limited range (e.g., avoiding ranges in the tool parameter space that may result in an out-of-specification condition) to ensure that the specification is met. This may be accomplished, for example, through more frequent maintenance and calibration, etc. On the other hand, if the algorithm performs well under a particular tool variation, the specification range may be increased accordingly, indicating that less calibration will be required on that particular variation, resulting in shorter maintenance time.

[0098] As mentioned above, the match represents the measurement variation between different tools (or the measurement variation of a tool over time), so the match is also called tool match or tool-to-tool match. The match is related to the repeatability of the measurement data from different images of the same given feature acquired by different tools. To evaluate the match, during the data collection phase (see above for reference), Figure 2 During the process, a data set representing inter-tool variations is collected, such as multiple image sets as described above.

[0099] The loss function of the criterion for evaluating the degree of match may be configured, for example, by calculating the variation between the measurement data of different images acquired under different tool variations based on the degree of match measure.

[0100] As an example, in Figure 8 In the data collection scheme shown in , where two images are collected for each site representing the tool deviation between two tools, two subsets A and B are obtained, each subset containing a corresponding image of the two images for each site. The individual objective functions can be configured to represent a difference metric for evaluating the difference between corresponding measurements from the two subsets. In some cases, the difference metric can be based on distance. Examples of such difference metrics may include, for example, Lasso, Ridge regression, Lp norm (p can be an integer), etc. The overall objective function can be configured to represent a difference metric for evaluating the difference between multiple measurements corresponding to multiple tool settings.

[0101] In some embodiments, in addition to the degree of match, the at least one metrology metric may further include one or more additional metrics, such as accuracy, correlation, and sensitivity, as will be described below.

[0102] Specifically, precision refers to the closeness of consistency between independent measurements (by the same metrology tool) on the same feature of the same area / site of the sample. For example, high precision means that independent measurements of the same feature are repeatable (i.e., the measurements have small deviations from each other, and the measurement distributions are relatively close). In some embodiments, precision can be regarded as measuring repeatability. In some other embodiments, precision may include two components: repeatability and reproducibility. Repeatability refers to the measurement of the distribution of measurement results, in which continuous measurements are repeated on the same site of the sample without any operator intervention. The cause of the variation in repeated measurements can be mainly due to the statistical properties of the tool signal (e.g., SEM signal), and the interpretation of the new signal set by the measurement algorithm included in the recipe. Reproducibility refers to another measure of the distribution of measurement results, in which measurements are obtained from different sites of the same sample at different times. Other sources of variation between independent measurements are considered: wafer alignment, SEM autofocus, pattern recognition, tool stability, etc.

[0103] To evaluate the performance of the algorithm in terms of accuracy, an accuracy dataset may be collected by acquiring images of a given feature on the sample (e.g., a structural feature from a given site on the sample, or the same type of feature from different regions / sites on the sample) through a metrology tool. The objective function may be configured to represent the deviation between the measurement data of different images acquired for a given feature.

[0104] As an example, an objective function configured to evaluate accuracy may be illustrated as follows:

[0105] L 精确度 = difference measure (y1,y2,...y n )

[0106] y1 to y n Represents the measurements of a particular run of n passes scanning a given one or more sites. Similarly, as described above, the difference metric can be, for example, the deviation in the distance of the measurements of each pass to the average measurement of multiple passes or any Lp norm (p can be any integer), etc.

[0107] Correlation refers to the relationship between the measurement data obtained from the image and the corresponding reference true measurement data associated therewith. The objective function of the criterion for evaluating the correlation may be configured to represent the difference between the measurement data and the corresponding reference true measurement data.

[0108] In order to evaluate the correlation, it is necessary to provide benchmark real measurement data. As an example, the objective function configured to evaluate the correlation can be illustrated as follows:

[0109] L 相关性 = difference measure (y 真实 ,y 预测 )

[0110] y 预测 represents the measurement obtained from the image. 真实 represents the reference true measurement data provided, for example, by a reference metrology algorithm or by a customer. The objective function represents a difference metric for the difference between the evaluation measurement and the corresponding reference true measurement.

[0111] Sensitivity refers to how sensitive a measurement is to changes in the size of a feature of a sample. As an example, if a feature of a sample (e.g., the width of a structural element) changes from 10 nm to 10.1 nm, a high sensitivity indicates that the corresponding measurement should be sensitive to changes of this size, and the measurement results should reflect this change. Sensitivity can be evaluated in cases where a dataset includes images with varying sizes of feature structures (such as synthetic training images specifically simulated with varying sizes of certain structural elements) and their corresponding ground truth data.

[0112] The objective function of the criterion for evaluating sensitivity may be configured, for example, by estimating a linear regression function between a plurality of measurement data and associated reference true data. As an example, the linear regression may be estimated as, for example, measurement data = gain * reference true + offset.

[0113] In some cases, a metrology algorithm may be optimized to meet a specific metrology metric using an objective function for a specific metric. In some other cases, a metrology algorithm may be optimized to meet multiple metrology metrics, such as matching, accuracy, sensitivity, and correlation. In this case, the overall objective / loss function may include various components of a specific objective function configured for a specific metric, where corresponding weights may be applied for the specific objective function. For example, the overall objective function may be expressed as follows:

[0114] L 总 =a1L 精确度 +a2L 匹配度 +a3L 相关性 +a4L 灵敏度

[0115] Once optimized using an overall objective function configured with the above components, the metrology algorithm can be used at runtime to process input images from any tool and provide runtime measurements with optimized tool-to-tool deviations while meeting correlation, sensitivity, and accuracy requirements.

[0116] In some cases, one or more additional objective / loss functions may be added to the total loss function, optionally in addition to or in lieu of the above exemplary components, and the present disclosure is not limited to a particular representation and / or number of components included in the total cost function.

[0117] In some embodiments, criteria for one or more metrology metrics may be predetermined according to customer specifications and / or based on previous inspection experience.

[0118] It should be noted that the metrology algorithm used in this article may refer to any type of algorithm that can be used to perform metrology operations for a metrology application, such as a conventional metrology algorithm and / or an algorithm based on machine learning (ML). In the case where the metrology algorithm is based on ML and includes an ML model, the optimization of the algorithm can be considered as part of the training process of the ML model. The ML model can be optimized until the loss function (i.e., the objective function) satisfies a predefined criterion. In some cases, the loss function refers to an overall loss function, which may include one or more loss functions specifically configured to evaluate one or more metrology metrics including a degree of match.

[0119] As an example, weights and / or thresholds of the ML model may be initially selected prior to training, and may be further iteratively adjusted or modified during training to achieve an optimal set of weights and / or thresholds in the trained ML model. After each iteration, the difference between the actual output produced by the ML model and the target output associated with the corresponding training data may be determined according to a particular loss function. Such a difference may be referred to as an error value. Training may be determined to be complete when one or more loss functions representing one or more error values ​​are less than corresponding predetermined values, or when a limited change in performance between iterations is achieved (which may be predetermined).

[0120] It should be noted that the examples shown in the present disclosure (e.g., exemplary metrology applications, exemplary metrology metrics, optimization processes, and image acquisition processes, etc.) are described for exemplary purposes and should not be considered to limit the present disclosure in any way. Other appropriate examples / implementations may be used in addition to or in lieu of the above.

[0121] One of the advantages of certain embodiments of the subject matter of the present disclosure as described herein is that an automatic metrology recipe setup and optimization process is provided, which generates a metrology recipe / algorithm that provides measurement data that meets the specifications of one or more metrology metrics, such as accuracy, matching, sensitivity and / or correlation, and the trade-offs therebetween, thereby providing optimal algorithm performance. This optimization is fully automatic and does not rely on user experience and proficiency.

[0122] Another advantage of certain embodiments of the subject matter of the present disclosure as described herein is a data generation process that is capable of acquiring a data set from a single tool that represents expected tool changes between different tools or on a single tool over time, which avoids the necessity of taking an entire fleet of tools offline, thereby significantly improving system throughput.

[0123] It is to be understood that the disclosure is not limited in its application to the details set forth in the description contained herein or illustrated in the drawings.

[0124] In this detailed description, many specific details are set forth in order to fully understand the present disclosure. However, it will be appreciated by those skilled in the art that the subject matter of the present disclosure may be practiced without these specific details. In other cases, well-known methods, procedures, components and circuits are not described in detail to avoid obscuring the subject matter of the present disclosure.

[0125] Unless otherwise specifically stated, as is apparent from this discussion, it should be understood that throughout the specification, discussions utilizing terms such as "obtain," "inspect," "change," "configure," "acquire," "optimize," "use," "select," "evaluate," "compute," "validate," "satisfy," "tighten," "identify," "combine," etc. refer to one or more actions and / or one or more processes of a computer that manipulates data and / or transforms data into other data, the data being represented as physical quantities, such as electronic quantities, and / or the data representing physical objects. The term "computer" should be broadly interpreted to encompass any type of hardware-based electronic device having data processing capabilities, including, as non-limiting examples, the inspection systems, metrology systems, and corresponding portions thereof disclosed in the present application.

[0126] As used herein, the terms "non-transitory memory" and "non-transitory storage medium" should be broadly interpreted to cover any volatile or non-volatile computer memory suitable for the subject matter of the present disclosure. The terms should be considered to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store one or more instruction sets. The terms should also be understood to include any medium capable of storing or encoding an instruction set executed by a computer and causing the computer to perform any one or more of the methods of the present disclosure. Thus, the terms should be considered to include, but are not limited to, read-only memory ("ROM"), random access memory ("RAM"), magnetic disk storage media, optical storage media, flash memory devices, and the like.

[0127] The term "sample" used in this specification should be broadly interpreted to cover any type of physical object or substrate used to manufacture semiconductor integrated circuits, magnetic heads, flat panel displays, and other semiconductor products, including wafers, masks, graticules, and other structures, and combinations and / or parts thereof. The sample is also referred to herein as a semiconductor sample, and the sample can be produced by manufacturing equipment that performs the corresponding manufacturing process.

[0128] The term "inspection" used in this specification should be broadly interpreted to cover any type of operation involving defect detection, defect review and / or various types of defect classification, segmentation, and / or metrology operations during and / or after the sample manufacturing process. Inspection is performed by using a non-destructive inspection tool during or after the manufacture of the sample to be inspected. As a non-limiting example, the inspection process may include runtime scanning (in a single scan or multiple scans), imaging, sampling, detection, review, measurement, classification and / or other operations performed on the sample or part thereof using the same or different inspection tools. Similarly, the inspection may be performed before the sample to be inspected is manufactured, and the inspection may include, for example, generating one or more inspection recipes and / or other setup operations. Note that unless otherwise specifically stated, the term "inspection" or its derivatives used in this specification are not limited to the resolution or size of the inspection area. As non-limiting examples, various non-destructive inspection tools include scanning electron microscopes (SEMs), atomic force microscopes (AFMs), optical inspection tools, etc.

[0129] The term "metrology operation" as used in this specification should be broadly interpreted to cover any metrology operation procedure for extracting metrology information related to one or more structural elements on a semiconductor sample. In some embodiments, the metrology operation may include measurement operations, such as critical dimension (CD) measurements performed on certain structural elements on the sample, including but not limited to the following: dimensions (e.g., line width, line spacing, contact diameter, element size, edge roughness, grayscale statistics, etc.); element shape; distance within or between elements; related angles; overlap information associated with elements corresponding to different design levels, etc. For example, by using image processing techniques to analyze measurement results such as measured images. It should be noted that unless otherwise specifically stated, the term "metrology" or its derivatives used in this specification are not limited to measurement techniques, measurement resolution, or the size of the inspection area.

[0130] The term "defect" as used in this specification should be interpreted broadly to cover any type of abnormal or undesirable feature / functionality formed on a sample. In some cases, a defect may be a defect of interest (DOI), which is a real defect that has some impact on the functionality of the manufactured device, and the customer is therefore interested in the defect to be detected. For example, any "inhibited" defect that may cause yield loss may be denoted as a DOI. In some other cases, a defect may be a negligible impairment (also known as a "false alarm" defect) because it has no impact on the functionality of the finished device and does not affect yield.

[0131] The term "design data" as used in this specification should be interpreted broadly to encompass any data indicative of a hierarchical physical design (layout) of a sample. The design data may be provided by a respective designer, and / or may be derived from a physical design (e.g., by complex simulations, simple geometric and Boolean operations, etc.). The design data may be provided in different formats, such as, as non-limiting examples, the GDSII format, the OASIS format, etc. The design data may be presented in a vector format, a grayscale intensity image format, or other formats.

[0132] The terms "one or more images" or "image data" as used in this specification should be broadly interpreted to cover any original image / frame of a sample captured by an inspection tool during the manufacturing process, derivatives of the captured images / frames obtained by various pre-processing stages, and / or computer-generated synthetic images (in some cases based on design data). Depending on the specific manner of scanning (e.g., one-dimensional scanning such as line scanning, two-dimensional scanning in x and y directions, or point scanning at specific points, etc.), the image data can be represented in different formats, for example, as a grayscale distribution, a two-dimensional image, or discrete pixels, etc. It should be noted that in some cases, in addition to the image (e.g., captured image, processed image, etc.), the image data mentioned in this document may also include digital data associated with the image (e.g., metadata, manual attributes, etc.). It should also be noted that an image or image data may include data related to a processing step / layer of interest or multiple processing steps / layers of a sample.

[0133] It should be understood that, unless otherwise specifically stated, certain features of the subject matter of the present disclosure described in the context of separate embodiments may also be provided in combination in a single embodiment. Conversely, various features of the subject matter of the present disclosure described in the context of a single embodiment may also be provided individually or in any suitable subcombination. In this detailed description, many specific details are set forth in order to fully understand the methods and apparatus.

[0134] It should also be understood that the system according to the present disclosure can be implemented at least in part on a suitably programmed computer. Likewise, the present disclosure contemplates a computer program readable by a computer for performing the method of the present disclosure. The present disclosure further contemplates a non-transitory computer readable memory for performing the method of the present disclosure, the non-transitory computer readable memory tangibly embodying a program of instructions executable by a computer.

[0135] The present disclosure is capable of other embodiments and can be practiced and executed in various ways. Therefore, it should be understood that the words and terms used herein are for descriptive purposes and should not be considered restrictive. Therefore, those skilled in the art will understand that the concepts upon which the present disclosure is based can be easily used as the basis for designing other structures, methods and systems for achieving the several purposes of the subject matter of the present disclosure.

[0136] Those skilled in the art will readily appreciate that various modifications and changes may be made to the embodiments of the present disclosure described above without departing from the scope of the present invention as defined by the appended claims.

Claims

1. A computerized metrology system, the system comprising an inspection tool operably connected to a processing circuit system, wherein: The processing circuit system is configured to: obtaining a tool parameter set selected from a plurality of tool parameters characterizing the inspection tool, varying a value of each tool parameter from the set a plurality of times to generate a plurality of tool settings corresponding to a plurality of combinations of varied values ​​of the tool parameter set, and configuring the inspection tool with each given tool setting from the plurality of tool settings; and The inspection tool is configured to: Responsive to configuring with each given tool setup, acquiring a set of images of at least one site on the capture sample with the given tool setup, thereby obtaining a plurality of image sets corresponding to the plurality of tool setups and representing expected tool variation over time within a single tool or between different tools; Wherein the processing circuitry is further configured to optimize a metrology algorithm using the plurality of image sets to satisfy at least one metrology metric, the at least one metrology metric comprising tool matching.

2. The computerized metrology system of claim 1, wherein the metrology algorithm is optimized with respect to a set of algorithm parameters selected from a plurality of algorithm parameters characterizing the metrology algorithm.

3. The computerized metrology system of claim 2, wherein the processing circuitry is further configured to optimize the metrology algorithm by: varying the value of each algorithm parameter from the set a plurality of times, thereby generating a plurality of algorithm settings corresponding to a plurality of combinations of varied values ​​of the set of algorithm parameters; for each given algorithm setting, obtaining a plurality of measurement data sets corresponding to the plurality of image sets using the metrology algorithm configured with the given algorithm setting, and evaluating the plurality of measurement data sets with respect to the at least one metrology metric; as well as The metrology algorithm is optimized based on the evaluation.

4. The computerized metrology system of claim 3, wherein said evaluating comprises: For each given algorithm setting, calculating a separate value of a separate objective function based on each measurement data set, and calculating an overall value of the overall objective function based on the plurality of measurement data sets; selecting, from the plurality of algorithm settings, an algorithm setting having a best overall value that satisfies a specification of the at least one metrology metric; as well as The specification with respect to the at least one metrological metric verifies the individual values ​​of each of the plurality of measurement data sets obtained under the selected algorithm settings to determine whether further evaluation is required.

5. The computerized metrology system of claim 4, wherein said verifying comprises: responsive to the individual values ​​for each measurement data set satisfying the specification, identifying one or more tool settings that resulted in one or more measurement data sets having relatively poor performance with respect to the specification; selecting one or more new tool settings having a finer resolution from the range of tool settings characterized by the one or more tool settings, and acquiring one or more new sets of images at the one or more new tool settings; as well as One or more measurement data sets corresponding to the one or more new image sets are obtained using the metrology algorithm configured with the selected algorithm settings, and the one or more new measurement data sets are evaluated with respect to the at least one metrology metric.

6. The computerized metrology system of claim 4, wherein said verifying comprises: In response to at least some of the plurality of measurement data sets having individual values ​​that do not meet the specification, one or more new algorithm parameters are selected to be added to the algorithm parameter set, or tolerances for tool parameter variations are tightened.

7. The computerized metrology system of claim 1 , wherein the processing circuitry is configured to acquire the set of images for each given tool setting by: For a given site among the at least one site, alternately acquiring a first frame sequence and a second frame sequence, wherein the first frame sequence is acquired with a tool setting having an optimal condition of the algorithm parameter set, and the second frame sequence is acquired with the given tool setting; and The first sequence of frames is combined into a first image and the second sequence of frames is combined into a second image, respectively, wherein the image set includes the first image and the second image of the given location.

8. The computerized metrology system of claim 1, wherein the at least one metrology metric further comprises one or more of precision, correlation, and sensitivity.

9. The computerized metrology system of claim 1, wherein the set of tool parameters includes focus, astigmatism, and numerical aperture.

10. The computerized metrology system of claim 2, wherein the set of algorithm parameters includes smoothing and derivatives.

11. A computerized metrology method, the method comprising: obtaining a set of tool parameters selected from a plurality of tool parameters characterizing an inspection tool; varying the value of each tool parameter from the set a plurality of times, thereby generating a plurality of tool settings corresponding to a plurality of combinations of the varied values ​​of the set of tool parameters; configuring the inspection tool with each given tool setting of the plurality of tool settings; as well as In response to receiving a plurality of image sets from the inspection tool corresponding to the plurality of tool settings and representing expected tool variations over time in a single tool or between different tools, a metrology algorithm is optimized using the plurality of image sets to satisfy at least one metrology metric, the at least one metrology metric comprising tool matching.

12. The computerized metrology method of claim 11, wherein the metrology algorithm is optimized with respect to a set of algorithm parameters selected from a plurality of algorithm parameters characterizing the metrology algorithm.

13. The computerized metrology method of claim 12, wherein said optimizing comprises: varying the value of each algorithm parameter from the set a plurality of times, thereby generating a plurality of algorithm settings corresponding to a plurality of combinations of varied values ​​of the set of algorithm parameters; for each given algorithm setting, obtaining a plurality of measurement data sets corresponding to the plurality of image sets using the metrology algorithm configured with the given algorithm setting, and evaluating the plurality of measurement data sets with respect to the at least one metrology metric; as well as The metrology algorithm is optimized based on the evaluation.

14. The computerized metrology method of claim 13, wherein said evaluating comprises: For each given algorithm setting, calculating a separate value of a separate objective function based on each measurement data set, and calculating an overall value of the overall objective function based on the plurality of measurement data sets; selecting, from the plurality of algorithm settings, an algorithm setting having a best overall value that satisfies a specification of the at least one metrology metric; as well as The specification with respect to the at least one metrological metric verifies the individual values ​​of each of the plurality of measurement data sets obtained under the selected algorithm settings to determine whether further evaluation is required.

15. The computerized metrology method of claim 14, wherein said verifying comprises: responsive to the individual values ​​for each measurement data set satisfying the specification, identifying one or more tool settings that resulted in one or more measurement data sets having relatively poor performance with respect to the specification; selecting one or more new tool settings having a finer resolution from the range of tool settings characterized by the one or more tool settings, and acquiring one or more new sets of images at the one or more new tool settings; as well as One or more measurement data sets corresponding to the one or more new image sets are obtained using the metrology algorithm configured with the selected algorithm settings, and the one or more new measurement data sets are evaluated with respect to the at least one metrology metric.

16. The computerized metrology method of claim 14, wherein said verifying comprises: In response to at least some of the plurality of measurement data sets having individual values ​​that do not meet the specification, one or more new algorithm parameters are selected to be added to the algorithm parameter set, or tolerances for tool parameter variations are tightened.

17. The computerized metrology method of claim 11, wherein the processing circuitry is configured to acquire the set of images for each given tool setting by: For a given site among the at least one site, alternately acquiring a first frame sequence and a second frame sequence, wherein the first frame sequence is acquired with a tool setting having an optimal condition of the algorithm parameter set, and the second frame sequence is acquired with the given tool setting; and The first sequence of frames is combined into a first image and the second sequence of frames is combined into a second image, respectively, wherein the image set includes the first image and the second image of the given location.

18. The computerized metrology method of claim 11, wherein the at least one metrology metric further comprises one or more of precision, correlation, and sensitivity.

19. The computerized metrology method of claim 11, wherein the set of tool parameters includes focus, astigmatism, and numerical aperture.

20. A non-transitory computer-readable storage medium tangibly embodying a program of instructions that, when executed by a computer, cause the computer to perform a metrology method comprising: obtaining a set of tool parameters selected from a plurality of tool parameters characterizing an inspection tool; varying the value of each tool parameter from the set a plurality of times, thereby generating a plurality of tool settings corresponding to a plurality of combinations of the varied values ​​of the set of tool parameters; configuring the inspection tool with each given tool setting of the plurality of tool settings; as well as In response to receiving a plurality of image sets from the inspection tool corresponding to the plurality of tool settings and representing expected tool variations over time in a single tool or between different tools, a metrology algorithm is optimized using the plurality of image sets to satisfy at least one metrology metric, the at least one metrology metric comprising tool matching.

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