Prediction of the electrical properties of semiconductor samples

Abstract

Measurement data D providing information on multiple structural parameters of a semiconductor sample metrology obtaining a model providing information of a relationship between at least some of the structural parameters and one or more electrical properties of the sample; and metrology and generating a recipe for a test tool, wherein the recipe enables a ratio between a first acquisition rate of data providing information of the one or more given structural parameters and a second acquisition rate of data providing information of other structural parameters of the plurality of structural parameters to satisfy the criterion.

Description

[Technical Field]

[0001] The subject matter of this disclosure generally relates to the field of sample testing, and more specifically to the automation of sample testing. [Background technology]

[0002] The current demands for high density and high performance associated with the ultra-large-scale integration of manufactured devices necessitate submicron feature areas, increased transistor and circuit speeds, and improved reliability. Such demands require the formation of device feature areas with high precision and uniformity, which in turn necessitates careful monitoring of the manufacturing process, including automated testing of the device while it is still in the form of a semiconductor wafer.

[0003] Testing processes are used at various steps during semiconductor manufacturing to measure the dimensions of a sample and / or to detect and classify defects in the sample (e.g., automated defect classification (ADC), automated defect review (ADR), etc.). [Overview of the project]

[0004] In accordance with a particular aspect of the subject matter of this disclosure, a system including a processor and memory circuit (PMC) is provided, wherein the PMC provides measurement data D that provides information on multiple structural parameters of a semiconductor sample. metrology Obtaining the model and D metrologyThe method involves using to determine one or more given structural parameters among a plurality of structural parameters that influence at least one given electrical property of a sample according to an influence criterion, and generating a policy for a test tool, the policy being configured to generate such that the ratio between a first acquisition rate of data providing information on one or more given structural parameters and a second acquisition rate of data providing information on other structural parameters among the plurality of structural parameters satisfies the criterion.

[0005] According to some embodiments, the policy enables the test tool to acquire a data amount that provides information on each of one or more given structural parameters, which is greater than the data amount that provides information on each of the other structural parameters among a plurality of structural parameters.

[0006] According to some embodiments, the structural parameter includes at least one of the following: a parameter that provides information on the dimensions of one or more structural features of a sample; a parameter that provides information on the shape of one or more structural features of a sample; a parameter that provides information on the location of one or more structural features of a sample; and a parameter that provides information on the material of one or more structural features of a sample.

[0007] According to some embodiments, the influence criterion is such that one or more given structural parameters have a greater influence on a given electrical characteristic than other structural parameters among a plurality of structural parameters.

[0008] According to some embodiments, the system is configured to acquire electrical measurements that provide information on a given electrical property of a sample, and to use at least the electrical measurements and data that provide information on one or more given structural parameters to determine whether one or more given structural parameters affect a given electrical property according to an influence criterion.

[0009] According to some embodiments, the system is configured to send commands to a test tool to perform a test on the sample using a policy, to obtain data providing information on one or more given structural parameters, and to use the data and model to predict the given electrical properties of the sample.

[0010] According to some embodiments, the system is configured to send commands to a test tool to perform testing of a sample using a policy, to acquire data providing information on one or more given structural parameters, and to use the data and model to refine the lithography process of the sample.

[0011] According to some embodiments, the system sends commands to a test tool to perform testing on a new sample using a policy, and provides measurement data D that provides information on multiple structural parameters of the sample. metrology / new To obtain the model and D metrology / new The method involves using a test tool to determine, for at least one given electrical property of a sample, one or more second given structural parameters among a plurality of structural parameters that influence the given electrical property according to an influence criterion, and updating the policy for testing a new sample, wherein the policy is configured to update such that the ratio between a first acquisition rate of data providing information on one or more second given structural parameters and a second acquisition rate of data providing information on other structural parameters among the plurality of structural parameters satisfies the criterion.

[0012] According to some embodiments, the system is configured to send commands to a test tool to perform testing of a new sample according to an updated policy, to acquire data providing information on one or more second given structural parameters of the new sample, and to use the data and model to predict the given electrical properties of the new sample.

[0013] According to some embodiments, the system sends a command to a test tool to perform a test on a new sample using a strategy, and metrology data D that provides information on a plurality of structural parameters of the sample metrology / new is acquired, and using the model and D metrology / new for at least one given electrical characteristic of the sample, one or more second given structural parameters among the plurality of structural parameters that affect the given electrical characteristic are determined according to an influence criterion, and the strategy is updated for testing the new sample by the test tool, wherein the strategy is configured to perform updating that enables a ratio between a first acquisition rate of data providing information on the one or more second given structural parameters and a second acquisition rate of data providing information on other structural parameters among the plurality of structural parameters to satisfy a criterion.

[0014] According to some embodiments, the system is configured to send a command to the test tool to perform a test on a new sample according to the updated strategy, acquire data providing information of the one or more second given structural parameters of the new sample, and predict a given electrical characteristic of the new sample using the data and the model.

[0015] According to some embodiments, the system is configured to send a command to the test tool to perform a test on a new sample according to the updated strategy, acquire data providing information of the one or more second given structural parameters, and rework the lithography process for the new sample using the data and the model.

[0016] According to some embodiments, a sample includes a first feature formed in a first layer by a first lithography process, and a second feature formed in a second layer embedded under the first layer and formed by a second lithography process before the first lithography process, and the system is configured to direct electrons of a primary electron beam to interact with the first feature, direct electrons of the primary electron beam to interact with the second feature, generate a detection signal in response to electrons scattered or reflected from at least one of the first feature and the second feature, and control a test tool to determine at least one spatial relationship between the first feature and the second feature using at least the detection signal.

[0017] According to some embodiments, a ratio between a first acquisition rate of data providing information of one or more given structural parameters and a second acquisition rate of data providing information of other structural parameters depends on data providing information of variations of the one or more given structural parameters across a plurality of samples.

[0018] In accordance with certain aspects of the presently disclosed subject matter, by a processor and memory circuitry (PMC), metrology data D providing information of a plurality of structural parameters of a semiconductor sample metrology is acquired, a model providing information of a relationship between at least part of the structural parameters and one or more electrical characteristics of the sample is acquired, and using the model and D metrology for at least one given electrical characteristic of the sample, one or more given structural parameters among the plurality of structural parameters that affect the given electrical characteristic are determined according to an influence criterion, and a strategy for a test tool is generated, wherein the strategy enables a ratio between a first acquisition rate of data providing information of the one or more given structural parameters and a second acquisition rate of data providing information of other structural parameters among the plurality of structural parameters to satisfy a criterion.

[0019] According to some embodiments, the strategy enables the test tool to acquire a larger amount of data providing information on each of one or more given structural parameters than the amount of data providing information on each of the other structural parameters among the plurality of structural parameters.

[0020] According to some embodiments, the structural parameters include at least one of a parameter providing information on a dimension of one or more structural features of the sample, a parameter providing information on a shape of one or more structural features of the sample, a parameter providing information on a location of one or more structural features of the sample, and a parameter providing information on a material of one or more structural features of the sample.

[0021] According to some embodiments, the influence criterion is such that the one or more given structural parameters affect a given electrical property more than other structural parameters among the plurality of structural parameters.

[0022] According to some embodiments, the method includes sending a command to a test tool to perform a test on the sample using the strategy, acquiring data providing information on one or more given structural parameters, and predicting a given electrical property of the sample using the data and a model.

[0023] According to some embodiments, the method includes sending a command to a test tool to perform a test on the sample using the strategy, acquiring data providing information on one or more given structural parameters, and adjusting a lithography process for the sample using the data and a model.

[0024] According to some embodiments, the method includes sending a command to a test tool to perform a test on a new sample using the strategy, and metrology data D providing information on a plurality of structural parameters of the sample metrology / new acquiring, and using the model and D metrology / newThe method involves using to determine one or more second given structural parameters among a plurality of structural parameters that influence at least one given electrical property of a sample according to an influence criterion, and updating the policy for testing a new sample with the test tool, the policy being updated such that the ratio between a first acquisition rate of data providing information on one or more second given structural parameters and a second acquisition rate of data providing information on other structural parameters among the plurality of structural parameters satisfies the criterion.

[0025] In accordance with other specific aspects of the subject matter of this disclosure, a non-temporary computer-readable medium is provided that, when executed by a processor, contains instructions causing the processor to perform the operations described above.

[0026] Among the advantages of certain embodiments of the subject matter of this disclosure is the ability to understand the correlation between measurement data and the electrical properties of a sample. According to some embodiments, the proposed solution is dynamic and provides continuous learning. According to some embodiments, the proposed solution provides smart and centralized acquisition of measurement data of a sample related to predicting the electrical properties of the sample. According to some embodiments, the proposed solution is effective even when the sample production process is complex and involves a large number of different parameters. According to some embodiments, the proposed solution makes it possible to predict the electrical properties of a sample even in the early stages of the production line. According to some embodiments, the proposed solution makes it possible to predict the electrical properties using on-device measurements of the sample. According to some embodiments, the proposed solution makes it possible to rework the lithography process of the sample. According to some embodiments, the proposed solution improves the yield of the production line.

[0027] To understand this disclosure and to see how it can actually be put into practice, embodiments will now be described, with reference to the accompanying drawings, merely as non-limiting examples. [Brief explanation of the drawing]

[0028] [Figure 1] This is a generalized block diagram of a test system according to a particular embodiment of the subject matter of this disclosure. [Figure 2] For example, this is a generalized flowchart of a method for testing samples that can be performed during the training phase. [Figure 2A] This figure shows a method for determining the relationship between one or more structural parameters of a sample and one or more electrical properties of the sample. [Figure 3] For example, this is a generalized flowchart of a method for testing a sample that can be performed during the runtime phase. [Figure 3A] This figure shows a method for determining the relationship between one or more structural parameters of a sample and one or more electrical properties of the sample. [Figure 3B] This is a generalized flowchart of a method for predicting one or more electrical properties of a sample. [Figure 4] This is a generalized flowchart of a method for obtaining data that provides information on one or more structural parameters of multiple layers of a sample. [Modes for carrying out the invention]

[0029] In the following detailed description, numerous specific details are included to provide a complete understanding of the Disclosure. However, it will be understood by those skilled in the art that the subject matter of the Disclosure can be practiced without these specific details. In other cases, well-known methods, procedures, components, and circuits are not described in detail so as not to obscure the subject matter of the Disclosure.

[0030] Unless otherwise specified, as will be apparent from the following discussion, discussions throughout this specification using terms such as “acquire,” “use,” “send,” “determine,” “generate,” “update,” and “enable” refer to computer operations and / or processes that manipulate and / or convert data into other data, and such data is represented as physical quantities such as electron quantities, and / or such data represents physical objects. The term “computer” should be interpreted broadly to include any kind of hardware-based electronic device having data processing capabilities, including, as an example not limited to, System 103 and each of its parts disclosed in this application.

[0031] As used herein, the terms “non-temporary memory” and “non-temporary storage medium” should be interpreted broadly to encompass any volatile or non-volatile computer memory suitable for the subject matter of this disclosure.

[0032] As used herein, the term “sample” should be interpreted broadly to include any type of wafer, mask, and other structure, combination thereof, and / or parts thereof, used to produce semiconductor integrated circuits, magnetic heads, flat panel displays, and other semiconductor manufacturing products.

[0033] As used herein, the term “testing” should be interpreted broadly to encompass any kind of measurement-related operation, as well as operations related to the detection and / or classification of defects in a sample during production. Testing is performed by using non-destructive testing tools during or after production of the sample to be tested. As an unspecified example, the testing process may include runtime scanning (single or multiple scans), sampling, review, measurement, classification, and / or other operations performed in relation to the sample or a portion thereof using the same or different inspection tools. Similarly, testing may be performed before production of the sample to be tested and may include, for example, the generation of a test policy and / or other setup operations. Unless otherwise specified, it should be noted that the term “testing” or its derivatives as used herein is not limited in terms of the resolution or size of the inspection area.

[0034] As used herein, the term “defect” should be interpreted broadly to include any kind of abnormal or undesirable feature formed on or within a sample.

[0035] As used herein, the term “design data” should be interpreted broadly to encompass any data that demonstrates the hierarchical physical design (layout) of a sample. Design data may be provided by the respective designer and / or derived from the physical design (e.g., by complex simulations, simple geometric and Boolean operations, etc.). Design data may be provided in various formats, such as GDSII format, OASIS format, etc., as non-limiting examples. Design data may be presented in vector format, grayscale intensity image format, or otherwise.

[0036] Unless otherwise specified, it should be acknowledged that certain features of the subject matter of this 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 this disclosure described in the context of a single embodiment may also be provided separately or in any suitable subcombination. Numerous specific details are described in the following detailed description to provide a complete understanding of the methods and apparatus.

[0037] With this in mind, we will now focus on Figure 1, which shows a functional block diagram of a test system according to a particular embodiment of the subject matter of this disclosure. The test system 100 shown in Figure 1 can be used to test a sample (e.g., a wafer and / or a portion thereof) as part of a sample manufacturing process. The illustrated test system 100 includes a computer-based system 103 that can automatically determine measurement-related information (and optionally defect-related information) using images acquired during sample manufacturing. The system 103 can be operably connected to one or more low-resolution test tools 101 and / or one or more high-resolution test tools 102 and / or other test tools. The test tools are configured to capture images and / or review captured images and / or enable or perform measurements related to captured images. The system 103 can further be operably connected to a CAD server 110 and a data repository 109.

[0038] System 103 includes a processor and memory circuit (PMC) 104 operably connected to a hardware-based input interface 105 and a hardware-based output interface 106. The PMC 104 is configured to perform all the processing necessary to operate System 103 (see methods described in Figures 2, 2A, 3, 3A, and 3B, which can be performed at least partially by System 103), and includes a processor (not shown separately) and memory (not shown separately). The processor of the PMC 104 can be configured to execute several functional modules according to computer-readable instructions implemented in non-temporary computer-readable memory contained within the PMC. Such functional modules are referred to below as contained within the PMC. The functional modules contained within the PMC 104 include a deep neural network (DNN) 112. The DNN 112 is configured to enable data processing using machine learning algorithms to output application-relevant data based on images of a sample.

[0039] As an unrestricted example, the layers of DNN112 can be organized according to a convolutional neural network (CNN) architecture, a recurrent neural network architecture, a recursive neural network architecture, a generative adversarial network (GAN) architecture, or other. Optionally, at least some of the layers may be organized into multiple DNN subnetworks. Each layer of a DNN can contain a number of fundamental computational elements (CEs), commonly referred to in the art as dimensions, neurons, or nodes.

[0040] Typically, the computational elements of a given layer can be connected to the CEs of preceding and / or succeeding layers. Each connection between the CEs of the preceding and succeeding layers is associated with a weighting value. A given CE can receive inputs from the CE of the previous layer through each connection, and each given connection is associated with a weighting value that can be applied to the input of the given connection. The weighting value can determine the relative strength of the connections, and therefore the relative influence of each input on the output of the given CE. A given CE can be configured to compute an activation value (e.g., a weighted sum of inputs) and further derive an output by applying an activation function to the computed activation. The activation function can be, for example, an identity function, a deterministic function (e.g., linear, sigmoid, threshold, etc.), a probability function, or other appropriate function. The output from a given CE can be sent to the CE of the succeeding layer through each connection. Similarly, as described above, each connection at the output of a CE can be associated with a weighting value that can be applied to the output of the CE before it is received as an input to the CE of the succeeding layer. In addition to weighted values, there may be thresholds (including limiting functions) associated with connections and CEs.

[0041] The weights and / or thresholds for DNN112 can be initially selected before training and can be further adjusted or modified through iterations during training to achieve an optimal set of weights and / or thresholds in the trained DNN. After each iteration, the difference (also called the loss function) between the actual output produced by DNN112 and the target output associated with each training set of data can be determined. This difference is sometimes called the error value. Training can be considered complete when the cost function or loss function indicating the error value falls below a predetermined value, or when a limited change in performance between iterations is achieved. Optionally, at least some (if any) of the DNN subnetworks may be trained separately before training the entire DNN.

[0042] System 103 is configured to receive input data via input interface 105. Input data may include data produced by the test tool (and / or its derivatives and / or associated metadata), and / or data produced and / or stored in one or more data repositories 109, and / or CAD server 110, and / or other associated data repositories. It should be noted that input data may include images (e.g., captured images, images derived from captured images, simulated images, composite images, etc.) and associated numerical data (e.g., metadata, handcrafted attributes, etc.). It should be further noted that image data may include data related to the layer in question, and / or data related to one or more other layers of the sample.

[0043] System 103 is further configured to process at least a portion of the received input data and send the results (or a portion thereof) via the output interface 106 to a storage system 107, a test tool, a computer-based graphical user interface (GUI) 108 for rendering the results, and / or an external system (e.g., a FAB yield management system (YMS)). The GUI 108 can be further configured to allow user-specified input related to the operating system 103.

[0044] As an example without limitation, the sample may be tested with one or more low-resolution testing machines 101 (e.g., optical inspection systems, low-resolution SEMs, etc.). The resulting data providing information on low-resolution images of the sample (hereinafter referred to as low-resolution image data 121) can be sent to system 103 directly or via one or more intermediate systems. Alternatively or additionally, the sample may be tested with high-resolution machines 102 (e.g., scanning electron microscopes (SEMs) and / or atomic force microscopes (AFMs)). The resulting data providing information on high-resolution images of the sample (hereinafter referred to as high-resolution image data 122) can be sent to system 103 directly or via one or more intermediate systems.

[0045] It should be noted that image data can be received and processed along with its associated metadata (e.g., pixel size, text description of defect type, parameters of the image capture process, etc.).

[0046] Once the system has processed the input data (e.g., low-resolution image data and / or high-resolution image data, optionally along with other data such as design data, synthesis data, etc.), the system 103 can send the results (e.g., instruction-related data 123 and / or 124) to one of the test tools, store the results (e.g., defect attributes, defect classification, etc.) in the storage system 107, render the results via the GUI 108, and / or send them to an external system (e.g., to YMS).

[0047] Those skilled in the art will readily recognize that the teachings of the subject matter of this disclosure are not constrained to the system shown in Figure 1, and that equivalent and / or modified functions may be linked or separated in other ways and implemented in appropriate combinations of software, firmware and / or hardware.

[0048] It should be noted that the test system shown in Figure 1 can be implemented in a distributed computing environment, and the functional modules shown in Figure 1 can be distributed across several local and / or remote devices and linked by a communication network. In other embodiments, it should be further noted that at least one of the test tool 102, system 107, and / or GUI 108 may be outside the test system 100 and may operate by communicating data with system 103 via input interface 105 and output interface 106. System 103 may be implemented as a standalone computer used with the test tool. Alternatively, each function of the system may be integrated, at least partially, into one or more test tools.

[0049] Next, let's look at Figure 2, which illustrates the training phase that can be performed before the runtime phase.

[0050] The method shown in Figure 2 provides measurement data D, which contains information on multiple structural parameters of the semiconductor sample. metrology This includes obtaining (operation 200). If the sample contains multiple layers, data providing information on structural parameters can be obtained for all or at least some of the layers.

[0051] Structural parameters may include at least one of the following: parameters that provide information about the dimensions or shape of structural features of a sample, and parameters that provide information about the location of structural features of a sample. For example, D metrology This provides information on structural parameters, including, for example, critical dimension (CD), critical dimension uniformity (CDU), overlay, edge placement error, 3D profile, shape of structural features, and process variation error.

[0052] D metrologyAt least a portion of this can be acquired / determined by test tools such as optical and / or electron beam test tools. In some embodiments, the test tool may be a high-resolution test tool (see, for example, reference numeral 102, corresponding to a scanning electron microscope (SEM)). If necessary, the system 103 further processes the data provided by the test tool (e.g., images, and / or image metadata) to obtain data D metrology It is possible to make a decision.

[0053] According to some embodiments, D metrology This provides information on structural parameters, including data that provides information about the material of the sample (e.g., the material used to produce the structural features of the sample). This may include, for example, the type of material, stoichiometric variations, atomic defects, interfaces, and material morphology. These parameters can be measured using an electronic test tool, for example, one that includes a material sensitivity detector. When the electronic test tool illuminates the sample with an electron beam, the interaction between the electrons and the sample provides unique photons, which can be sensed by the detector. The reflected photons provide information about the material properties of the sample (e.g., X-ray photons provide information about the material properties).

[0054] The method in Figure 2 involves obtaining a model (operation 210) that provides information about the electrical properties of the sample. In particular, the model can provide information about the relationship between one or more structural parameters of the sample (as described above) (e.g., material, dimensions / location / 3D profile of structural features, etc.) and one or more electrical properties of the sample. Examples of electrical properties include transient, pulsed, and DC properties (e.g., voltage, current, capacitance, conductance, etc.), carrier transport, charge trapping and emission, etc.

[0055] According to some embodiments, the model can be generated using Ginestra® Simulation Software, which is described, for example, at https: / / www.appliedmaterials.com / products / applied-mdlx-ginestra-simulation-software (the content and documentation cited in this link are incorporated herein by reference). This is not limited to other electrical models, which may be used (for example, electrical models may be constructed using correlation techniques, etc.).

[0056] This method involves the model and D metrology The process further includes using (operation 220) to determine one or more given structural parameters among a plurality of structural parameters that affect a given electrical property of a sample according to an influence criterion for at least one given electrical property of the sample. In some embodiments, variations in one or more given structural parameters with respect to design data affect a given electrical property according to an influence criterion.

[0057] In particular, the influence criterion can be defined as the fact that one or more given structural parameters (and / or their variation relative to the design data) have a greater impact on the given electrical properties of a sample than the variation (and / or their variation relative to the design data) of other structural parameters among a group of structural parameters.

[0058] In other words, according to some embodiments, a variation in a given electrical property (e.g., with respect to the expected or designed electrical property) correlates best with a variation in one or more given structural parameters than with a variation in other structural parameters. The behavior of one or more given structural parameters is identified as being strongly linked to the behavior of a given electrical property. Therefore, one or more given structural parameters are more relevant than other structural parameters for analyzing the behavior of a given electrical property of a sample.

[0059] In some embodiments, the influence criterion can be defined as the level of correlation between one or more given structural parameters and a given electrical characteristic exceeding a predefined threshold.

[0060] While a given electrical characteristic was referred to above, this also applies to multiple different given electrical characteristics. For each given electrical characteristic, one or more given sets of structural parameters can be identified as influencing the given electrical characteristic according to influence criteria (which may differ for each given electrical characteristic).

[0061] Figure 2A shows possible operations that can be performed in operation 220. The method in Figure 2A provides information on multiple structural parameters of the sample, data D metrology or this may include injecting the variation in the design data into an electrical model (obtained in operation 210) (operation 2201). The electrical model is D metrology Estimate one or more electrical characteristics related to (operation 2202).

[0062] The method in Figure 2A performs the analysis to provide data D that gives information on one or more estimated electrical properties (or their variation from the design electrical properties) and multiple structural parameters of the sample. metrology This may further include determining the relationship between (or its variation to the design data) (operation 2203).

[0063] Operation 2203 may include performing a multivariate analysis. The multivariate analysis takes the following as input: - Data D provides information on multiple structural parameters of the sample. metrology , and / or its variation to the design data, - One or more estimated electrical properties of the sample (as estimated by the model), and / or their variation from the design electrical properties (it is desirable to understand the correlation of these one or more electrical properties with the structural parameters of the sample). Receive.

[0064] According to some embodiments, the multivariate analysis may further receive actual electrical measurements of one or more electrical properties of a sample as input.

[0065] According to some embodiments, multivariate analysis is D metrology This involves performing various correlation analyses (e.g., linear correlation analysis) between the electrical properties (or their variations) of the sample and the electrical properties (or their variations). In some embodiments, the correlation analysis is performed independently between each of one or more electrical properties and each of one or more structural parameters.

[0066] According to some embodiments, multivariate analysis includes using a machine learning network, such as a deep neural network (e.g., DNN112), to determine the relationship between one or more electrical properties of a sample and one or more structural parameters of the sample. The machine learning network takes as input: - Data D provides information on multiple structural parameters of the sample. metrology , and / or its variation to the design data, - One or more estimated electrical properties of the sample (as estimated by the model), and / or their variation from the design electrical properties (it is desirable to understand the correlation of these one or more electrical properties with the structural parameters of the sample). The sample is received. In some embodiments, actual electrical measurements of the electrical properties of the sample are provided.

[0067] In some embodiments, training of the machine learning network is supervised. In particular, a physical model may be provided to the machine learning network. The physical model provides instructions, for example, regarding physical correlations (e.g., between the electrical properties and structural parameters of a sample), which helps the machine learning network better predict the relationship between the electrical properties and structural features of a sample. For example, the physical model shows that capacitance is a function of the area of ​​the sample. The physical model corrects the output of the deep neural network, for example, if the deep neural network performs overfitting when performing multivariate analysis.

[0068] The method in Figure 2 further includes generating a policy (test policy) for the test tool (operation 230).

[0069] Assume that in operation 220, it is determined that one or more given structural parameters influence a given electrical property of a sample according to an influence criterion. In particular, this can correspond to one or more given structural parameters having a greater influence on this given electrical property than other structural parameters.

[0070] The strategy can be designed to focus data acquisition by the test tool on one or more given structural parameters, or data that provides information about them.

[0071] In particular, the policy enables a criterion to be met by the ratio between a first acquisition rate of data providing information on one or more given structural parameters and a second acquisition rate of data providing information on other structural parameters among a plurality of structural parameters. This criterion can be defined, for example, as the first acquisition rate being higher than the second acquisition rate. The acquisition rates, in particular, provide information on the number of samples acquired and / or the number of samples acquired per unit time.

[0072] In some embodiments, the first acquisition rate can be made greater than the second acquisition rate by a predefined value that can be selected by the operator, for example.

[0073] In some embodiments, N structural parameters P1~P N Assume that the following is analyzed. Assume that a given structural parameter (which affects a given electrical characteristic according to an influence criterion) corresponds to parameter P2. In some embodiments, the policy is such that the acquisition rate of data providing information about P2 is greater than the acquisition rate of data providing information about P1, greater than the acquisition rate of data providing information about P3, ..., and P N This allows for a data acquisition rate greater than the acquisition rate of data providing information for one structural parameter. This example is not limited. In other words, this corresponds to a data acquisition rate greater than the data acquisition rate of data providing information for each of the other structural parameters.

[0074] According to some embodiments, the ratio satisfies the criterion when the number of samples (data volume) obtained for one or more given structural parameters is greater than the number of samples (data volume) obtained for the data providing information for each of the other structural parameters. In some embodiments, this corresponds to the data volume providing information for each of the given structural parameters being greater than the data volume providing information for each of the other structural parameters.

[0075] In some embodiments, operation 230 can take into account variations in one or more given structural parameters across multiple samples. Variations in one or more given structural parameters can be measured, for example, using a fitted statistical index and compared to a threshold. For example, the standard deviation of a given structural parameter relative to other samples can be determined. However, this is not limiting. If a given structural parameter is identified as stable across multiple samples, it is determined that the given structural parameter influences one or more given electrical properties according to an influence criterion, but the acquisition rate of data providing information about this given structural parameter does not need to be higher than the acquisition rates of other structural parameters (since its value can be predicted from other samples). Conversely, if a given structural parameter is identified as influencing a given electrical property and varying between samples, its acquisition rate can be higher than, or set to be greater than, the acquisition rates of each of the other structural parameters.

[0076] According to some embodiments, measurement data D metrology It is assumed that (obtained in operation 200) was obtained by a previous policy that allowed for the acquisition of data providing information on one or more given structural parameters at a previous first acquisition rate, and data providing information on other structural parameters among multiple structural parameters at a previous second acquisition rate. The criterion is that the policy generated in operation 230 is - To acquire data providing information on one or more given structural parameters at a new first acquisition rate that is greater than the previous first acquisition rate. - To acquire data that provides information on other structural parameters at a new second acquisition rate that is lower than the previous second acquisition rate. It can be defined as enabling at least one of the following.

[0077] According to some embodiments, the policy allows the test tool to dedicate more time to acquiring data that provides information about one or more given structural parameters compared to data that provides information about other parameters among multiple structural parameters.

[0078] Given multiple given structural parameters, the first acquisition rate is not necessarily the same for each of the given structural parameters. Similarly, the second acquisition rate is not necessarily the same for each of the other structural parameters.

[0079] Since data acquisition using test tools is practically limited (e.g., due to time and / or cost constraints), data acquisition focuses on data that affects the electrical properties (of interest) of the sample. Thus, smart sampling is achieved.

[0080] As described in reference to operation 2202, the sample model is data D metrology It is used to predict one or more electrical properties of a sample (or their variation from expected properties) based on (or their variation from the design data).

[0081] According to some embodiments, the method shown in Figure 2 may include comparing predictions of one or more electrical properties of a sample with actual measurements of these electrical properties (operation 240). Depending on the level of agreement between them, it may be possible to determine how valid the relationship (determined in operation 2203) and / or model (obtained in operation 210) are. According to some embodiments, if the level of agreement does not meet the criteria, the method may be repeated (e.g., with the same sample and / or with a different sample).

[0082] As described above, the method shown in Figure 2, which can be performed during the training phase, can be repeated for multiple samples.

[0083] Next, let's look at Figure 3.

[0084] The method shown in Figure 3 can be performed, for example, during the runtime phase. In some embodiments, it can be performed during real-time testing of the sample.

[0085] This method provides measurement data D that provides information on multiple structural parameters of the semiconductor sample. metrology This includes obtaining (operation 300). Data D metrology An example of this is already provided by referring to operation 200.

[0086] D metrology At least a portion of these can be obtained by test tools such as optical test and / or electron beam test tools. In some embodiments, the test tool may be a high-resolution test tool (e.g., SEM). In some embodiments, D metrology At least a portion of these can be obtained using material-sensitive detectors (e.g., photon detectors), as described above.

[0087] In some embodiments, at least one first sample tested during runtime and at least one second sample used during the training phase (see Figure 2) are similar according to a similarity criterion. The similarity criterion can be defined, for example, as the first sample (or at least a first region thereof) and the second sample (or at least a second region thereof) containing the same physical components or containing similar zones of the semiconductor sample (e.g., similar dies, cells, etc.).

[0088] The acquisition can be performed by the test tool according to the policy generated during the training phase (see Figure 2). In particular, commands can be sent to the test tool to perform the test according to the policy. As a result, measurement data D metrology / new D is acquired. metrology / newThis primarily includes data that provides information on one or more given structural parameters (data providing information on other structural parameters can also be acquired, but as mentioned above, the acquisition / sampling rate of data providing information on one or more given structural parameters can be selected to be greater than that of data providing information on other structural parameters). The strategy allows data acquisition by the test tool to be focused on structural parameters related to at least one given electrical property (or multiple given electrical properties to be analyzed) that should be analyzed.

[0089] This method uses a model (which is acquired in operation 210 during the training phase) and D metrology / new The process further includes using (operation 320) to determine one or more given structural parameters among a plurality of structural parameters that influence at least one given electrical property of a sample according to an influence criterion. The influence criterion can be the same as the influence criterion defined in operation 220 and is therefore not described again.

[0090] The operation 320 that can be performed during the runtime test of the sample is the same as the operation 220 that is performed during the training phase.

[0091] Although operation 220 has already been performed during the training phase, performing operation 320 (during the runtime phase) can offer several advantages. In fact, structural parameters that affect a given electrical property can change progressively over time (even for the same sample). For example, during the training phase, given structural parameters P1~P3 (parameters P1~P N Assume that (among the parameters) is determined to be the structural parameter most relevant to a given electrical characteristic. During runtime, it may be discovered that parameters P1, P2, and P4 are now the parameters most relevant to the given electrical characteristic. This example is not limiting.

[0092] According to some embodiments, operation 320 may include the operation shown in Figure 3A.

[0093] The method in Figure 3A provides data D, which provides information on multiple structural parameters of the sample. metrology / new or this may include injecting the variation in the design data into an electrical model (the electrical model is obtained in operation 210) (operation 3201). The electrical model is D metrology / new Estimate one or more electrical properties of the sample related to (e.g., including the given electrical properties described above) (operation 3202).

[0094] The method in Figure 3A performs the analysis to obtain one or more estimated electrical properties (or their variation from the design electrical properties) and data D that provides information on multiple structural parameters of the sample. metrology / new Alternatively, this may further include determining the relationship between the design data and its variations (operation 3203). Operation 3203 may include performing a multivariate analysis. Operation 3203 is similar to operation 2203 and will not be described again.

[0095] The method in Figure 3 further includes updating the policy (operation 330). As described above, it may be identified in operation 320 that structural parameters (different from those identified during the training phase) affect a given electrical characteristic. Therefore, the policy is updated to take the results of this analysis into account.

[0096] Assume that in operation 320, it is identified that a second given structural parameter influences a given electrical characteristic according to an influence criterion. The policy can be updated so that the ratio between a first acquisition rate of data providing information on one or more second given structural parameters and a second acquisition rate of data providing information on other structural parameters among the multiple structural parameters satisfies the criterion. In particular, the first acquisition rate can be higher than the second acquisition rate. In some embodiments, the first acquisition rate of data providing information on each of the one or more second given structural parameters is greater than the second acquisition rate of data providing information on each of the other structural parameters among the multiple structural parameters.

[0097] According to some embodiments, the ratio satisfies the criterion when the number of samples (data volume) obtained for one or more second given structural parameters is greater than the number of samples (data volume) obtained for data providing information on other structural parameters. In some embodiments, the data volume providing information on each of the one or more second structural parameters is greater than the data volume providing information on each of the other structural parameters.

[0098] According to some embodiments, measurement data D metrology / new It is assumed that (obtained in operation 300) was obtained using a policy that allows obtaining data providing information on one or more second given structural parameters using a previous first acquisition rate, and obtaining data providing information on other structural parameters among multiple structural parameters using a previous second acquisition rate. The criterion is that the policy generated in operation 330 is - To acquire data that provides information on one or more given second structural parameters using a new first acquisition rate that is higher than the previous first acquisition rate, - To acquire data that provides information on other structural parameters using a new second acquisition rate that is lower than the previous second acquisition rate. It can be defined as enabling at least one of the following.

[0099] According to some embodiments, the policy allows the test tool to spend more time acquiring data that provides information about one or more second given structural parameters compared to data that provides information about other parameters among multiple structural parameters.

[0100] According to some embodiments, operation 330 can take into account variations in one or more second given structural parameters across multiple samples. Variations in one or more second given structural parameters can be measured, for example, using a fitted statistical index and compared to, for example, a threshold. For example, the standard deviation of the second given structural parameter relative to other samples can be determined. However, this is not limiting. If a second given structural parameter is identified as stable across multiple samples, it is determined that the second given structural parameter influences one or more given electrical properties according to an influence criterion, but the acquisition rate of data providing information about this second given structural parameter does not need to be higher than the acquisition rates of the other structural parameters (since its value can be predicted from the other samples). Conversely, if a second given structural parameter is identified as influencing a given electrical property and varying between samples, its acquisition rate can be higher than, or set to be greater than, the acquisition rates of each of the other structural parameters.

[0101] As shown in Figure 3 (see reference numeral 340), according to some embodiments, the method of Figure 3 can be repeated iteratively. In particular, after the policy has been updated (operation 330), it is possible to return to operation 300, where data acquisition is performed by the test tool based on the policy.

[0102] In some embodiments, operations 320 and 330 may be repeated to further improve the policy.

[0103] According to some embodiments, this method is used in accordance with the latest update of the policy, data D metrology / new After obtaining the data, the operation may include predicting at least one given electrical property of the sample (operation 350).

[0104] This strategy allows data acquisition to affect the given electrical characteristics of data D. metrology / new It became possible to selectively concentrate on it. Therefore, operation 350 can include operations such as those shown in Figure 3B.

[0105] The method in Figure 3B provides data D, which contains information on one or more structural parameters of the sample. metrology / new This may include, or injecting the variation in the design data into an electrical model (operation 3501).

[0106] The electrical model is D metrology / new Estimate at least one given electrical characteristic related to (operation 3502). Thus, a prediction of a given electrical characteristic is obtained as required. This prediction can be performed for various layers of the sample at several stages of the sample production process. This prediction can even be performed in the early stages of the sample production process without the need to perform actual electrical tests on the sample.

[0107] As shown in Figure 3, according to some embodiments, this method can correct the production process (operation 360) using a prediction of at least one given electrical characteristic. In fact, if at least one given electrical characteristic is expected to deviate from the design value, this method may include correcting one or more given structural parameters of the sample that affect the given electrical characteristic. According to some embodiments, this may include reworking the lithography process of the sample. In particular, this may include reworking the resist (also called photoresist) during the lithography stage. This is a cost-effective way to trigger process correction since the sample (wafer) is not discarded.

[0108] Next, let's look at Figure 4.

[0109] As described with reference to operations 200 and 300, the acquisition of data providing information about the sample is performed by the test tool.

[0110] According to some embodiments, data acquisition may include the following operations in accordance with the method described in U.S. Patent No. 9,046,475 (which is incorporated herein by reference in its entirety).

[0111] Assume the sample contains at least two layers. The first layer contains a first three-dimensional feature produced using a first lithography process. The second three-dimensional feature is formed in a second layer embedded beneath the first layer and formed by the second lithography process prior to the first lithography process.

[0112] This method includes performing a multi-step imaging sequence (using an electron beam test tool such as a SEM) to generate images of at least portions of first and second feature regions. This may include inducing electrons in the primary electron beam to interact with the first feature region during the first imaging step (e.g., so that the electrons in the primary electron beam have an incident energy of at least 2000 electron volts) (operation 400). This method may further include inducing electrons in the primary electron beam to interact with the second feature region during the second imaging step (e.g., so that the electrons in the primary electron beam have an incident energy of at least 5000 electron volts) (operation 410). This method may further include generating a detection signal in response to electrons scattered or reflected from at least one of the first and second feature regions (operation 420). This method may further include determining at least one spatial relationship between the first and second feature regions based on the detection signal (operation 430). This may include, for example, determining an overlay between the two feature regions. In some embodiments, spatial relationships can be determined using information representing a first feature region. This information can be generated by inducing electrons in a primary electron beam to interact with the first region without substantially interacting with the second region. This information may include, for example, the width of the first feature region. However, this is not limiting.

[0113] In other words, this acquisition method provides data D that offers information on multiple structural parameters across multiple layers of the sample. metrology This makes it possible to obtain the data.

[0114] This acquisition method can be used in particular in operation 360. In fact, this acquisition method allows for the acquisition of structural parameters across multiple lithographic layers of a sample, and can therefore be used to rework one or more of the lithographic layers (which are typically produced by depositing a resistive material onto a substrate / layer, exposing the resistive material by a photolithography process, and developing the exposed resistive material to create a pattern that defines several areas to be etched later). Since the photoresist is placed on top of numerous layers deposited during the lithography stage (e.g., hard mask, bottom anti-reflective coating, which are later removed), the underlayer alignment of the various layers is facilitated by using this acquisition method. As a result, it facilitates the rework process of lithographic layers.

[0115] It should be understood that the present invention is not limited in its application to the details described herein or shown in the drawings.

[0116] It will also be understood that the system according to the present invention can be implemented, at least in part, on a properly programmed computer. Similarly, the present invention envisions a computer program readable by a computer for performing the method of the present invention. The present invention further envisions a non-temporary computer-readable memory that tangibly embodies a program of computer-executable instructions for performing the method of the present invention.

[0117] Other embodiments of the present invention are possible and can be practiced and implemented in various ways. Therefore, it should be understood that the terminology and language used herein are for illustrative purposes only and should not be considered limiting. Those skilled in the art will therefore recognize that the concepts upon which this disclosure is based can be readily utilized as a basis for designing other structures, methods, and systems to accomplish some of the objectives of the subject matter of this disclosure.

[0118] Those skilled in the art will readily recognize that various modifications and changes can be applied to the embodiments of the invention described above without departing from the scope of the invention as defined by the appended claims.

Claims

1. A system including a processor and memory circuit (PMC), wherein the PMC is - Measurement data D that provides information on multiple structural parameters of the semiconductor sample metrology To acquire, - To obtain a model that provides information on the relationship between at least some of the structural parameters and one or more electrical properties of the semiconductor sample, - The aforementioned model and the aforementioned D metrology Using this, determine one or more given structural parameters among the plurality of structural parameters that affect at least one given electrical property of the sample according to the influence criteria, - To generate a policy for a test tool, the policy enabling the test tool to obtain a data amount that provides information on each of one or more given structural parameters that is greater than the data amount that provides information on each of the other structural parameters among the plurality of structural parameters. A system configured to perform the following actions.

2. The aforementioned structural parameters are, A parameter that provides information on the dimensions of one or more structural feature parts of the aforementioned sample. A parameter that provides information on the shape of one or more structural feature parts of the sample, A parameter that provides information on the location of one or more structural feature parts of the sample, and A parameter that provides material information for one or more structural feature parts of the aforementioned sample. The system according to claim 1, comprising at least one of the following.

3. The system according to claim 1, wherein the influence criterion is such that one or more given structural parameters have a greater influence on the given electrical characteristics than other structural parameters among the plurality of structural parameters.

4. Electrical measurement values ​​are obtained that provide information on the given electrical characteristics of the sample. At a minimum, the electrical measurement values ​​and data providing information on one or more given structural parameters are used to determine whether one or more given structural parameters affect the given electrical characteristics according to the influence criteria. The system according to claim 1, configured as follows.

5. To perform the test on the sample using the aforementioned method, a command is sent to the test tool: Obtain data that provides information on one or more given structural parameters, Using the aforementioned data and model, predict the given electrical characteristics of the sample. The system according to claim 1, configured as follows.

6. To perform the test on the sample using the aforementioned method, a command is sent to the test tool: Obtain data that provides information on one or more given structural parameters, The lithography process for the sample is modified using the aforementioned data and model. The system according to claim 1, configured as follows.

7. Sending a command to the test tool to perform testing of a new sample using the aforementioned method, Measurement data D provides information on multiple structural parameters of the aforementioned sample. metrology / new To acquire, The aforementioned model and the D metrology / new Using this, determine one or more second given structural parameters among the plurality of structural parameters that affect at least one given electrical property of the sample according to the influence criteria, This involves updating the strategy for testing the new sample using the aforementioned test tool. The aforementioned measures enable the ratio between a first acquisition rate of data providing information on one or more second given structural parameters and a second acquisition rate of data providing information on other structural parameters among the plurality of structural parameters to satisfy the criteria. The system according to claim 1, configured to perform the following:

8. A command is sent to the test tool to perform the test of the new sample in accordance with the updated policy: Data is obtained that provides information on one or more second given structural parameters of the new sample. Using the aforementioned data and model, predict the given electrical properties of the new sample. The system according to claim 7, configured as described above.

9. A command is sent to the test tool to perform the test of the new sample in accordance with the updated policy: Data providing information on one or more second given structural parameters is obtained, The lithography process for the new sample is modified using the aforementioned data and model. The system according to claim 7, configured as described above.

10. The sample includes a first feature formed in a first layer by a first lithography process, and a second feature formed in a second layer embedded beneath the first layer and formed by a second lithography process prior to the first lithography process, and the system is The electrons of the primary electron beam are guided to interact with the first feature part, The electrons of the primary electron beam are guided to interact with the second characteristic part, A detection signal is generated in response to electrons scattered or reflected from at least one of the first feature portion and the second feature portion. At least one spatial relationship between the first feature portion and the second feature portion is determined using at least the detection signal. The system according to claim 1, configured to control a test tool in such a way.

11. The system according to claim 1, wherein the ratio between a first acquisition rate of data providing information on one or more given structural parameters and a second acquisition rate of data providing information on other structural parameters depends on data providing information on variations of one or more given structural parameters across a plurality of samples.

12. Processor and memory circuitry (PMC) - Measurement data D that provides information on multiple structural parameters of the semiconductor sample metrology To acquire, - To obtain a model that provides information on the relationship between at least some of the structural parameters and one or more electrical properties of the sample, - The aforementioned model and the aforementioned D metrology Using this, determine one or more given structural parameters among the plurality of structural parameters that affect at least one given electrical property of the sample according to the influence criteria, - To generate a policy for a test tool, the policy enabling the test tool to obtain a data amount that provides information on each of one or more given structural parameters that is greater than the data amount that provides information on each of the other structural parameters among the plurality of structural parameters. A method that includes this.

13. The aforementioned structural parameters are, A parameter that provides information on the dimensions of one or more structural feature parts of the aforementioned sample. A parameter that provides information on the shape of one or more structural feature parts of the sample, A parameter that provides information on the location of one or more structural feature parts of the sample, and A parameter that provides material information for one or more structural feature parts of the aforementioned sample. The method according to claim 12, comprising at least one of the following.

14. The method according to claim 12, wherein the influence criterion is such that one or more given structural parameters have a greater influence on the given electrical characteristics than other structural parameters among the plurality of structural parameters.

15. Sending a command to the test tool to perform the test of the sample using the aforementioned method, To obtain data that provides information on one or more given structural parameters, Using the aforementioned data and the aforementioned model, predict the given electrical characteristics of the sample. The method according to claim 12, including the method described in claim 12.

16. Sending a command to the test tool to perform the test of the sample using the aforementioned method, To obtain data that provides information on one or more given structural parameters, Modify the lithography process of the sample using the aforementioned data and model. The method according to claim 12, including the method described in claim 12.

17. Sending a command to the test tool to perform testing of a new sample using the aforementioned method, Measurement data D provides information on multiple structural parameters of the aforementioned sample. metrology / new To acquire, The aforementioned model and the D metrology / new Using this, determine one or more second given structural parameters among the plurality of structural parameters that affect at least one given electrical property of the sample according to the influence criteria, This involves updating the strategy for testing the new sample using the aforementioned test tool. The aforementioned measures enable the ratio between a first acquisition rate of data providing information on one or more second given structural parameters and a second acquisition rate of data providing information on other structural parameters among the plurality of structural parameters to satisfy the criteria. The method according to claim 12, including the method described in claim 12.

18. A non-temporary computer-readable medium containing instructions, wherein when the instructions are executed by a processor and memory circuit (PMC), the PMC receives the instructions. - Measurement data D that provides information on a plurality of structural parameters of a semiconductor sample metrology is acquired - To obtain a model that provides information on the relationship between at least some of the structural parameters and one or more electrical properties of the sample, - The aforementioned model and the aforementioned D metrology Using this, determine one or more given structural parameters among the plurality of structural parameters that affect at least one given electrical property of the sample according to the influence criteria, - To generate a policy for a test tool, the policy enabling the test tool to obtain a data amount that provides information on each of one or more given structural parameters that is greater than the data amount that provides information on each of the other structural parameters among the plurality of structural parameters. A non-temporary computer-readable medium that enables the execution of actions including [specific actions].

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