Joint optimization method and device based on overlay error, equipment and medium

Abstract

The embodiment of the invention discloses a joint optimization method and device based on overlay errors, equipment and a medium. The method comprises the following steps: acquiring sample measurement data, and determining to-be-adjusted parameters in an initial overlay error compensation model; grouping the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and respectively determining a candidate overlay error compensation model corresponding to the candidate model parameter group and a reference overlay error compensation model corresponding to the reference model parameter group; determining a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model; and grouping the sample measurement data to obtain a candidate measurement data set and a reference measurement data set, and determining a target sampling scheme according to the candidate measurement data set, the reference measurement data set and the target overlay error compensation model. The applicability of the model is improved, the accuracy and applicability of the sampling scheme are improved, and the production efficiency is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of semiconductor technology, and in particular to a joint optimization method, device, equipment and medium based on overlay error. Background Art

[0002] With the development of modern information technology such as artificial intelligence, the demand for chips is increasing, and the performance requirements are getting higher and higher. High-performance chips need to be realized through advanced manufacturing processes. At present, the industry has achieved the research and development and mass production of 3-2nm chips. In the research and development of advanced processes, the ability to control overlay errors is one of the key factors affecting mass production and product yield. Usually, the overlay error needs to be controlled within 1 / 3 of the process node. The requirements of advanced processes are higher, about 1 / 4 to 1 / 5, which poses a huge challenge to the control of overlay errors. Factors affecting overlay errors include the design, process, measurement and compensation correction methods of overlay marks. In order to obtain better overlay error compensation capabilities, wafer fabs gradually use high-order models to replace traditional linear models to improve model compensation accuracy. Since high-order models require more measurement data for characterization, wafer fabs will use wafer-by-wafer and exposure field-by-exposure methods to improve model accuracy, which reduces production efficiency; in addition, different sampling schemes during overlay error measurement will also have a certain impact on the results, so how to improve the accuracy and applicability of sampling schemes is crucial. Summary of the invention

[0003] The present invention provides a joint optimization method, device, equipment and medium based on overlay error to improve the applicability of an overlay error compensation model, further improve the accuracy and applicability of a sampling scheme determined based on the overlay error compensation model, and improve production efficiency.

[0004] According to one aspect of the present invention, a joint optimization method based on overlay error is provided, comprising:

[0005] Acquire sample measurement data of a sample wafer and determine parameters to be adjusted in a pre-built initial overlay error compensation model;

[0006] Grouping the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and determining, based on the initial overlay error compensation model, candidate overlay error compensation models corresponding to the candidate model parameter group and reference overlay error compensation models corresponding to the reference model parameter group;

[0007] Determining a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model;

[0008] The sample measurement data are grouped to obtain a candidate measurement data group and a reference measurement data group, and a target sampling scheme is determined according to the candidate measurement data group, the reference measurement data group and the target overlay error compensation model.

[0009] According to another aspect of the present invention, there is provided a joint optimization device based on overlay error, comprising:

[0010] A parameter acquisition module, used to acquire sample measurement data of a sample wafer and determine parameters to be adjusted in a pre-built initial overlay error compensation model;

[0011] a parameter grouping module, configured to group the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and determine, based on the initial overlay error compensation model, a candidate overlay error compensation model corresponding to the candidate model parameter group and a reference overlay error compensation model corresponding to the reference model parameter group;

[0012] A target model determination module, configured to determine a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model;

[0013] The target sampling scheme determination module is used to group the sample measurement data to obtain a candidate measurement data group and a reference measurement data group, and determine a target sampling scheme according to the candidate measurement data group, the reference measurement data group and the target overlay error compensation model.

[0014] According to another aspect of the present invention, there is provided an electronic device, comprising:

[0015] one or more processors;

[0016] A memory for storing one or more programs;

[0017] When one or more programs are executed by one or more processors, the one or more processors can execute any one of the joint optimization methods based on overlay errors provided by the embodiments of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a processor to implement any one of the joint optimization methods based on overlay errors provided in the embodiments of the present invention when executed.

[0019] An embodiment of the present invention provides a joint optimization scheme based on overlay error, which obtains sample measurement data of a sample wafer and determines the parameters to be adjusted in a pre-constructed initial overlay error compensation model; groups the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and based on the initial overlay error compensation model, respectively determines the candidate overlay error compensation model corresponding to the candidate model parameter group and the reference overlay error compensation model corresponding to the reference model parameter group; determines a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model; groups the sample measurement data to obtain a candidate measurement data group and a reference measurement data group, and determines a target sampling scheme according to the candidate measurement data group, the reference measurement data group and the target overlay error compensation model. The above scheme, by grouping the parameters to be adjusted and based on the reference overlay error compensation model, determines the target overlay error compensation model from the candidate overlay error compensation models corresponding to each candidate model parameter group, thereby realizing the determination of the target overlay error compensation model on the basis of ignoring irrelevant parameters in the model, reducing the number of measurement data used subsequently and improving the applicability of the target overlay error compensation model. At the same time, by grouping the sample measurement data and determining the target sampling scheme according to the target overlay error compensation model, the grouped candidate measurement data group and the reference measurement data group, the optimization of the sampling scheme based on the overlay error is realized, and the accuracy and applicability of the determined target sampling scheme are improved. In addition, the embodiment of the present invention realizes the joint optimization based on the overlay error compensation model and the overlay error sampling scheme, so that the determined target sampling scheme is more adapted to the actual production situation and the production efficiency is improved.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1A is a flow chart of a joint optimization method based on overlay error provided in Embodiment 1 of the present invention;

[0023] Figure 1B is a parameter residual relationship diagram provided by the first embodiment of the present invention;

[0024] Figure 1Cis a parameter relative residual relationship diagram provided in the first embodiment of the present invention;

[0025] Figure 2A is a flow chart of a joint optimization method based on overlay error provided in Embodiment 2 of the present invention;

[0026] Figure 2B This is a sampling relative correction relationship diagram provided by the second embodiment of the present invention;

[0027] Figure 2C This is a result comparison diagram provided by Embodiment 2 of the present invention;

[0028] Figure 2D This is a sampling scheme comparison diagram provided by Embodiment 2 of the present invention;

[0029] Figure 3 is a structural schematic diagram of a joint optimization device based on overlay error provided in Embodiment 3 of the present invention;

[0030] Figure 4 It is a structural schematic diagram of an electronic device for implementing a joint optimization method based on overlay error provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0032] Embodiment 1

[0033] Figure 1A A flow chart of a joint optimization method based on overlay error is provided in Example 1 of the present invention. This embodiment is applicable to the situation of determining the optimal compensation model and exposure field sampling scheme based on overlay error measurement data. The method can be executed by a joint optimization device based on overlay error, which can be implemented in software and / or hardware and can be configured in an electronic device that carries the joint optimization function based on overlay error.

[0034] See also Figure 1A The joint optimization method based on overlay error shown includes:

[0035] S110 , obtaining sample measurement data of a sample wafer, and determining parameters to be adjusted in a pre-built initial overlay error compensation model.

[0036] The sample wafer refers to a wafer used to provide overlay error measurement data. Exemplarily, the sample wafer may be a specially manufactured test wafer with densely distributed measurement marks.

[0037] Among them, the sample measurement data refers to the overlay error measurement data obtained based on the sample wafer. Exemplarily, the sample measurement data may include wafer parameters such as wafer size, exposure field size and exposure field position distribution. The embodiment of the present invention does not impose any limitation on the way of obtaining sample measurement data, which can be set by technicians based on experience. Exemplarily, it can be obtained by measurement with dedicated equipment in the wafer fab. It should be noted that the more sample measurement data there is, the higher the accuracy of the optimization result; if there is a batch of sample measurement data, the sample measurement data can be pre-processed, such as aggregation processing.

[0038] The initial overlay error compensation model refers to a pre-built model for compensating overlay error measurement data. Exemplarily, the initial overlay error compensation model can be a first-order model or a high-order model, which is not specifically limited in the embodiment of the present invention. The parameters to be adjusted refer to the parameters involved in model optimization in the initial overlay error compensation model. Exemplarily, all possible combinations of parameters to be adjusted in the initial overlay error compensation model can be determined by traversing or optimizing the algorithm loop.

[0039] Exemplarily, the expression of the third-order initial overlay error compensation model is:

[0040]

[0041] Among them, x and y represent the coordinates in the exposure field; X and Y represent the coordinates within the wafer range; OVL_X and OVL_Y represent the overlay error; k1, k2, k3, k4, k5, k6, k7, k8, k9, k 10 , k 11 , k 12 , k 13 , k 14 , k 15 , k 16 , k 17 , k 18 , k 19 , k 20 、Tx 10 、Tx 01 、Tx 20 、Tx 11 、Tx 02 、Tx 30 、Tx 21 、Tx 12 、Tx 03 、Ty 10 、Ty 01 、Ty 20 、Ty 11 、Ty 02 、Ty 30 、Ty21 、Ty 12 、Ty 03 It should be noted that the highest compensable parameter of the third-order initial overlay error compensation model is the third-order term.

[0042] It should be noted that there are 38 parameters to be adjusted in the above three-order initial overlay error compensation model. In actual situations, some of the parameters to be adjusted do not have corresponding compensation actuators and cannot be compensated, such as k9, k 15 , k 17 , k 18 and k 20 , ignoring these five parameters to be adjusted, there are 33 parameters to be adjusted. Generally, the more parameters, the more offset features can be represented, and the better the compensation effect. However, the more parameters, the more data required for model fitting. In addition, different production line equipment and products have different process characteristics. The correction values ​​of some parameters are very small, almost 0, and their contribution to the compensation effect is negligible. These parameters can be discarded in the optimization. Through the model with a small number of parameters, the compensation ability of the model with all 33 parameters selected can be obtained.

[0043] S120, grouping the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and determining, based on the initial overlay error compensation model, candidate overlay error compensation models corresponding to the candidate model parameter group and reference overlay error compensation models corresponding to the reference model parameter group.

[0044] Among them, the candidate model parameter group refers to a data group including some parameters to be adjusted. The reference model parameter group refers to a data group including all parameters to be adjusted. The candidate overlay error compensation model refers to a model determined based on the initial overlay error compensation model and the candidate model parameter group. The candidate overlay error compensation model only includes the parameters to be adjusted in the candidate model parameter group. It should be noted that one candidate model parameter group corresponds to one candidate overlay error compensation model. The reference overlay error compensation model refers to a model determined based on the initial overlay error compensation model and the parameter model parameter group. The parameters to be adjusted included in the reference data group are trained in the reference overlay error compensation model, that is, the reference overlay error compensation model includes all parameters to be adjusted.

[0045] The embodiment of the present invention does not impose any limitation on the grouping method, which can be set by a technician based on experience or needs. For example, the parameters to be adjusted can be grouped according to a preset grouping interval threshold. The embodiment of the present invention does not impose any limitation on the size of the preset grouping interval threshold, which can be set by a technician based on experience.

[0046] S130: Determine a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model.

[0047] The target overlay error compensation model refers to the most suitable model determined from the candidate overlay error compensation models. The target overlay error compensation model can be used to determine the target sampling scheme.

[0048] In an optional embodiment, a target overlay error compensation model is determined based on sample measurement data, candidate overlay error compensation models and a reference overlay error compensation model, including: inputting the sample measurement data into each candidate overlay error compensation model respectively to obtain a corresponding candidate residual value, and inputting the sample measurement data into the reference overlay error compensation model to obtain a reference residual value; and determining the target overlay error compensation model from each candidate overlay error compensation model based on the candidate residual value and the reference residual value.

[0049] Among them, the candidate residual value is determined based on the residual values ​​output by the candidate overlay error compensation model. Exemplarily, the candidate residual value can be determined from the residual values ​​output by the candidate overlay error compensation model based on a preset selection method. The embodiment of the present invention does not impose any limitation on the preset selection method, which can be set by the technician based on experience or needs. It should be noted that the preset selection methods corresponding to different candidate overlay error compensation models can be the same or different, and the embodiment of the present invention does not impose specific limitations on this. Exemplarily, the preset selection method can be maximum selection, mean selection, variance selection, or weighted superposition selection of multiple statistics.

[0050] The reference residual value is determined based on the residual values ​​output by the reference overlay error compensation model. Exemplarily, the reference residual value can be determined from the residual values ​​output by the reference overlay error compensation model based on a preset selection method.

[0051] It should be noted that the output result of the candidate overlay error compensation model includes a model quantity (which can be represented by the model) and a residual value (which cannot be represented by the model), and the embodiment of the present invention determines the candidate residual value by using a preset selection method and the residual values ​​output by the candidate overlay error compensation model. Correspondingly, the output result of the reference overlay error compensation model includes a model quantity and a residual value, and the embodiment of the present invention determines the reference residual value by using a preset selection method and the residual values ​​output by the reference overlay error compensation model.

[0052] Exemplarily, based on candidate residual values ​​and reference residual values, a target overlay error compensation model is determined from each candidate overlay error compensation model, including: a parameter residual relationship diagram can be generated through the candidate residual values ​​and the reference residual values, and a target model parameter group is determined from the candidate model parameter group according to the parameter residual relationship diagram; and the candidate overlay error compensation model corresponding to the target model parameter group is used as the target overlay error compensation model.

[0053] The parameter residual relationship diagram refers to the relationship diagram between the number of parameters to be adjusted in the candidate model parameter group with the minimum candidate residual value and the corresponding candidate residual value under any number of parameters. The target model parameter group refers to the most suitable candidate model parameter group.

[0054] For example, see Figure 1B As shown in the parameter residual relationship diagram. As the number of parameters to be adjusted in the candidate model parameter group increases, the corresponding candidate residual value gradually decreases. When the number of parameters to be adjusted in the candidate model parameter group increases to 10, the trend slows down. Based on this, the final number of parameter items and the final parameter item selection can be determined, that is, the target model parameter group is determined.

[0055] For example, if the reference model parameter group is used as a reference benchmark and the relative candidate residual value is used, reducing the parameter items one by one will lead to a gradual decrease in compensation accuracy. When the number is reduced to a certain number, the compensation capability deteriorates sharply. The final parameter items, i.e., the target model parameter group, can be determined based on the preset compensation accuracy threshold. Figure 1C As shown in the parameter relative residual relationship diagram, selecting 10 parameters to be adjusted can achieve an accuracy of more than 95% when all parameters to be adjusted are selected. The embodiment of the present invention does not impose any limitation on the size of the preset compensation accuracy threshold, which can be set by technicians based on experience or needs, or determined repeatedly through a large number of experiments. Among them, the relative candidate residual value can be determined based on the candidate residual value and the reference residual value. Exemplarily, the relative candidate residual value is determined based on the candidate residual value with the reference residual value as the reference. Among them, the reference residual value refers to the residual value of the reference model parameter group.

[0056] It can be understood that by introducing the candidate residual value and the reference residual value to determine the target overlay error compensation model, the accuracy of the determined target overlay error compensation model is improved.

[0057] S140, grouping the sample measurement data to obtain a candidate measurement data group and a reference measurement data group, and determining a target sampling scheme according to the candidate measurement data group, the reference measurement data group and a target overlay error compensation model.

[0058] Among them, the candidate measurement data group refers to a group including some sample measurement data. The reference measurement data group refers to a group including all sample measurement data. The target sampling scheme refers to the sampling strategy finally determined. Exemplarily, the target sampling scheme may include a target sampling number and a target sampling position. The target sampling number refers to the number of exposure fields on the sample wafer that participate in sampling. The target sampling position refers to the position of the exposure field on the sample wafer that participates in sampling.

[0059] An embodiment of the present invention provides a joint optimization scheme based on overlay error, which obtains sample measurement data of a sample wafer and determines the parameters to be adjusted in a pre-constructed initial overlay error compensation model; groups the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and based on the initial overlay error compensation model, respectively determines the candidate overlay error compensation model corresponding to the candidate model parameter group and the reference overlay error compensation model corresponding to the reference model parameter group; determines a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model; groups the sample measurement data to obtain a candidate measurement data group and a reference measurement data group, and determines a target sampling scheme according to the candidate measurement data group, the reference measurement data group and the target overlay error compensation model. The above scheme, by grouping the parameters to be adjusted and based on the reference overlay error compensation model, determines the target overlay error compensation model from the candidate overlay error compensation models corresponding to each candidate model parameter group, thereby realizing the determination of the target overlay error compensation model on the basis of ignoring irrelevant parameters in the model, reducing the number of measurement data used subsequently and improving the applicability of the target overlay error compensation model. At the same time, by grouping the sample measurement data and determining the target sampling scheme according to the target overlay error compensation model, the grouped candidate measurement data group and the reference measurement data group, the optimization of the sampling scheme based on the overlay error is realized, and the accuracy and applicability of the determined target sampling scheme are improved. In addition, the embodiment of the present invention realizes the joint optimization based on the overlay error compensation model and the overlay error sampling scheme, so that the determined target sampling scheme is more adapted to the actual production situation and the production efficiency is improved.

[0060] Embodiment 2

[0061] Figure 2AIt is a flow chart of a joint optimization method based on overlay error provided in the second embodiment of the present invention. Based on the above embodiments, this embodiment further refines the operation of "grouping the sample measurement data to obtain candidate measurement data groups and reference measurement data groups" into "determining the number of parameters of the target adjustment parameters in the target overlay error compensation model, and determining the number of samples of the sample measurement data; based on the number of parameters and the number of samples, determining the sample sampling number interval, and grouping the sample measurement data according to the sample sampling number interval to obtain a candidate measurement data group including some sample measurement data, and a reference measurement data group including all sample measurement data", so as to improve the grouping mechanism of the sample measurement data. It should be noted that for the parts not described in detail in the embodiments of the present invention, reference may be made to the descriptions of other embodiments.

[0062] See also Figure 2A The joint optimization method based on overlay error shown includes:

[0063] S210, obtaining sample measurement data of a sample wafer, and determining parameters to be adjusted in a pre-built initial overlay error compensation model.

[0064] S220, grouping the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and based on the initial overlay error compensation model, respectively determining a candidate overlay error compensation model corresponding to the candidate model parameter group and a reference overlay error compensation model corresponding to the reference model parameter group.

[0065] S230: Determine a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model.

[0066] S240: Determine the number of parameters of the target adjustment parameters in the target overlay error compensation model, and determine the number of samples of the sample measurement data.

[0067] Among them, the target adjustment parameter refers to the parameter to be adjusted in the target model parameter group corresponding to the target overlay error compensation model. The target adjustment parameter can be understood as the parameter to be adjusted in the target overlay error compensation model that participates in the model optimization. The number of parameters refers to the number of target adjustment parameters in the target overlay error compensation model. The number of samples refers to the number of sample measurement data.

[0068] S250. Determine a sample sampling quantity interval based on the number of parameters and the number of samples, and group the sample measurement data according to the sample sampling quantity interval to obtain a candidate measurement data group including some sample measurement data and a reference measurement data group including all sample measurement data.

[0069] The sample sampling number interval can be used to indicate grouping of sample measurement data. Exemplarily, the sample sampling number interval determines the minimum number of sample measurement data in the candidate measurement data group, and the sample sampling number interval can group the candidate measurement data group to determine the candidate measurement data group corresponding to each sample sampling number sub-interval.

[0070] It should be noted that the number of sample measurement data in each candidate measurement data group should be greater than or equal to the number of parameters of the target adjustment parameter to ensure that each target adjustment parameter has at least one sample measurement data to avoid overfitting.

[0071] In an optional embodiment, based on the number of parameters and the number of samples, a sample sampling number interval is determined, including: determining the quantitative distance between the number of parameters and the number of samples; dividing the quantitative distance according to a preset sample sampling interval to obtain sample sampling number sub-intervals; and generating a sample sampling number interval including sample sampling number sub-intervals.

[0072] The quantity distance refers to the difference between the number of parameters and the number of samples. The preset sample sampling interval refers to the size of the interval for dividing the quantity distance. The size of the preset sample sampling interval in the embodiment of the present invention is not limited in any way and can be set by technicians based on experience. The sample sampling quantity sub-interval refers to the quantity interval of the sample measurement data corresponding to each candidate measurement data group.

[0073] S260: Determine a target sampling scheme according to the candidate measurement data set, the reference measurement data set and the target overlay error compensation model.

[0074] In an optional embodiment, a target sampling scheme is determined based on a candidate measurement data group, a reference measurement data group and a target overlay error compensation model, including: inputting the candidate measurement data group into the target overlay error compensation model to obtain a candidate correction amount, and inputting the reference measurement data group into the target overlay error compensation model to obtain a reference correction amount; determining a target measurement data group from the candidate measurement data group based on the reference correction amount and the candidate correction amount, and determining a target sampling scheme based on the target measurement data group.

[0075] The candidate correction amount refers to the model amount output by the candidate overlay error compensation model. The reference correction amount refers to the model amount output by the reference overlay error compensation model. The target measurement data group refers to the group containing sample measurement data used to determine the target sampling scheme.

[0076] It can be understood that by introducing the candidate correction amount and the reference correction amount to determine the target measurement data set, the accuracy of the determined target measurement data set is improved.

[0077] In an optional embodiment, a target measurement data set is determined from a candidate measurement data set based on a reference correction and candidate corrections, including: for any candidate measurement data set, determining candidate correction differences between each candidate correction corresponding to the candidate measurement data set and the corresponding reference correction, and determining a target correction difference based on the candidate correction differences; and determining a target measurement data set from the candidate measurement data set based on the target correction differences corresponding to each candidate measurement data.

[0078] Among them, the candidate correction amount difference refers to the difference between the candidate correction amount and the reference correction amount. The target correction amount difference can be determined based on the candidate correction amount difference. Exemplarily, the target correction amount difference can be determined based on the candidate correction amount difference based on a preset selection method. The preset selection method can be maximum value selection, mean selection, variance selection, or weighted superposition selection of multiple statistics. It should be noted that one candidate measurement data group corresponds to one target correction amount difference, and the target correction amount difference is determined from multiple candidate correction amount differences corresponding to the candidate measurement data group.

[0079] It can be understood that by introducing candidate correction amount differences, determining target correction amount differences, and then determining the target measurement data set from the candidate measurement data sets based on the target correction amount differences, the accuracy of the determined target measurement data set is improved.

[0080] In an optional embodiment, a target measurement data group is determined from the candidate measurement data group based on the target correction value difference corresponding to each candidate measurement data, including: determining a graph measurement data group from each candidate measurement data group corresponding to a sample sampling number sub-interval based on each target correction value difference corresponding to a sample sampling number sub-interval; generating a sampling correction relationship graph based on the graph measurement data group corresponding to each sample sampling number sub-interval; and determining the target measurement data group from the graph measurement data group based on the sampling correction relationship graph.

[0081] The graph measurement data set refers to the candidate measurement data set with the minimum target correction difference under any same sample number in the sample sampling number subinterval. The sampling correction relationship graph refers to the relationship graph between the sample number of the sample measurement data in the graph measurement data set and the corresponding target correction difference.

[0082] It can be understood that by constructing the sampling correction relationship diagram, a visual display of the sampling effect of each measurement data group is achieved, thereby improving the accuracy and applicability of the determined target measurement data group.

[0083] For example, based on the target overlay error compensation model or reference overlay error compensation model determined by the first stage optimization, the model correction A (i.e., reference correction) is calculated using the read dense measurement data (i.e., sample measurement data); the number of samples is reduced one by one from the dense measurement data through traversal or optimization algorithms, and then the target overlay error compensation model is used to calculate the model correction B (i.e., candidate correction) after the number of samples is reduced; since the amount of data used for model parameter fitting is reduced, it is necessary to substitute the fitted parameters into the position of the dense measurement data and then calculate the model correction at the dense measurement point. The candidate correction difference is calculated based on the model correction A and the model correction B, and the result is saved. Here, the candidate correction difference is calculated by taking the difference between the model correction A and the model correction B, and then calculating the statistical value, which can be the maximum, mean, variance, or weighted superposition of multiple statistical values. When the number of samples of the fitting model is reduced one by one to the minimum amount of data required for the target overlay error compensation model, the loop ends.

[0084] Exemplarily, the target sampling scheme is determined by the target correction difference values ​​saved in all possible candidate sampling schemes saved in the iterative process. Specifically, a method similar to the model optimization process can be adopted to determine the relative target correction difference values ​​of each image measurement data set under a partial sampling number, taking the reference measurement data set of the full sampling number as a reference. Figure 2B As shown in the sampling relative correction relationship diagram, the more samples there are, the more accurate the model fitting result is. If the compensation capacity under dense sampling is 100%, the compensation accuracy will gradually decrease as the number of samples is reduced. The target sampling scheme can be determined by setting the lower limit of the compensation accuracy. When 10 exposure field data are selected for compensation, the dense sampling compensation accuracy can reach more than 87%. Among them, the relative target correction amount difference can be determined based on the target correction amount difference and the reference correction amount difference. Exemplarily, the reference correction amount difference is used as a reference, and the relative target correction amount difference is determined according to the target correction amount difference. Among them, the reference correction amount difference refers to the correction amount difference of the reference measurement data group. Exemplarily, the reference correction amount difference can be 0.

[0085] The embodiment of the present invention provides a sampling optimization scheme based on overlay error measurement data, which is refined into the operations of determining the number of parameters of the target adjustment parameters in the target overlay error compensation model and determining the number of samples of the sample measurement data by grouping the sample measurement data to obtain candidate measurement data groups and reference measurement data groups; determining the sample sampling number interval based on the number of parameters and the number of samples, and grouping the sample measurement data according to the sample sampling number interval to obtain a candidate measurement data group including some sample measurement data and a reference measurement data group including all sample measurement data, thereby improving the grouping mechanism of the sample measurement data. The above scheme, by introducing the number of parameters and the number of samples, determines the sample sampling number interval, and groups the sample measurement data according to the sample sampling number interval, realizes that each target adjustment parameter in the target overlay error compensation model has at least one sample measurement data, avoids overfitting, and improves the accuracy of the subsequent determination of the target sampling scheme based on the candidate measurement data group.

[0086] The technical solution provided by the embodiment of the present invention can be divided into two parts, namely, model optimization (determining the target overlay error compensation model) and sampling optimization (determining the target sampling scheme), which realizes the trade-off optimization between the overlay error compensation accuracy and the throughput rate. Specifically, the present invention combines the overlay error compensation model parameter configuration and the sampling optimization of the measurement data. On the premise of optimizing and determining the parameter configuration of the target overlay error compensation model, the measurement data sampling optimization is performed based on the optimization model, so that the best sampling scheme can be obtained while ensuring the best compensation accuracy.

[0087] For example, see Figure 2C The following is a comparison of the results obtained by using the method provided by the embodiment of the present invention. Figure 2C (a) is an ideal exposure field distribution diagram. Figure 2C (b) is the exposure field distribution after compensation using all parameters to be adjusted and all sample measurement data, Figure 2C (c) is the exposure field distribution after compensation using randomly selected model parameters and random sample measurement data, Figure 2C (d) shows the target adjustment parameters of the target overlay error compensation model after optimization by this method, and the exposure field distribution after compensation for a target measurement data set with the same number as the random sample measurement data.

[0088] For example, see Figure 2D The sampling diagram shown in Figure 1 is shown in Figure 2. Figure 2D (a) is the target sampling scheme obtained using randomly selected model parameters and random sample measurement data, Figure 2D(b) is a target sampling scheme obtained by using the target adjustment parameters of the target overlay error compensation model and the target measurement data set with the same number of random sample measurement data. It can be seen from the figure that the optimization method of the embodiment of the present invention can obtain a compensation effect equivalent to that of using all the parameters to be adjusted and all the sampling data. Among them, the black squares represent the selected sampling positions. The white squares represent the unselected sampling positions.

[0089] An embodiment of the present invention provides a joint optimization method based on overlay error, which combines overlay error model parameter configuration and sampling optimization, and performs sampling optimization on the basis of optimizing and determining a target overlay error compensation model, thereby improving the overall optimization performance.

[0090] Embodiment 3

[0091] Figure 3 This is a schematic diagram of the structure of a joint optimization device based on overlay error provided in the third embodiment of the present invention. This embodiment is applicable to the case of determining the best compensation model and exposure field sampling scheme based on overlay error measurement data. The method can be executed by a joint optimization device based on overlay error, which can be implemented in software and / or hardware, and can be configured in an electronic device that carries the joint optimization function based on overlay error.

[0092] like Figure 3 As shown, the device includes: a parameter acquisition module 310, a parameter grouping module 320, a target model determination module 330 and a target sampling scheme determination module 340. Among them,

[0093] The parameter acquisition module 310 is used to acquire sample measurement data of the sample wafer and determine the parameters to be adjusted in the pre-built initial overlay error compensation model;

[0094] A parameter grouping module 320 is used to group the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and determine, based on the initial overlay error compensation model, a candidate overlay error compensation model corresponding to the candidate model parameter group and a reference overlay error compensation model corresponding to the reference model parameter group;

[0095] A target model determination module 330, configured to determine a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model;

[0096] The target sampling scheme determination module 340 is used to group the sample measurement data to obtain a candidate measurement data group and a reference measurement data group, and determine a target sampling scheme according to the candidate measurement data group, the reference measurement data group and the target overlay error compensation model.

[0097] An embodiment of the present invention provides a joint optimization scheme based on overlay error, which obtains sample measurement data of a sample wafer and determines the parameters to be adjusted in a pre-constructed initial overlay error compensation model; groups the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and based on the initial overlay error compensation model, respectively determines the candidate overlay error compensation model corresponding to the candidate model parameter group and the reference overlay error compensation model corresponding to the reference model parameter group; determines a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model; groups the sample measurement data to obtain a candidate measurement data group and a reference measurement data group, and determines a target sampling scheme according to the candidate measurement data group, the reference measurement data group and the target overlay error compensation model. The above scheme, by grouping the parameters to be adjusted and based on the reference overlay error compensation model, determines the target overlay error compensation model from the candidate overlay error compensation models corresponding to each candidate model parameter group, thereby realizing the determination of the target overlay error compensation model on the basis of ignoring irrelevant parameters in the model, reducing the number of measurement data used subsequently and improving the applicability of the target overlay error compensation model. At the same time, by grouping the sample measurement data and determining the target sampling scheme according to the target overlay error compensation model, the grouped candidate measurement data group and the reference measurement data group, the optimization of the sampling scheme based on the overlay error is realized, and the accuracy and applicability of the determined target sampling scheme are improved. In addition, the embodiment of the present invention realizes the joint optimization based on the overlay error compensation model and the overlay error sampling scheme, so that the determined target sampling scheme is more adapted to the actual production situation and the production efficiency is improved.

[0098] Optionally, the target sampling scheme determination module 340 includes:

[0099] A quantity determination unit, used to determine the parameter quantity of the target adjustment parameter in the target overlay error compensation model, and determine the sample quantity of the sample measurement data;

[0100] The sample measurement data grouping unit is used to determine a sample sampling quantity interval based on the parameter quantity and the sample quantity, and group the sample measurement data according to the sample sampling quantity interval to obtain a candidate measurement data group including some sample measurement data and a reference measurement data group including all sample measurement data.

[0101] Optionally, the sample measurement data grouping unit is specifically used to:

[0102] determining a quantitative distance between the parameter quantity and the sample quantity;

[0103] Dividing the quantity distance according to a preset sample sampling interval to obtain sample sampling quantity sub-intervals;

[0104] A sample sampling number interval including the sample sampling number sub-interval is generated.

[0105] Optionally, the target sampling scheme determination module 340 includes:

[0106] A correction amount determination unit, configured to input the candidate measurement data set into the target overlay error compensation model to obtain a candidate correction amount, and input the reference measurement data set into the target overlay error compensation model to obtain a reference correction amount;

[0107] The target sampling scheme determining unit is used to determine a target measurement data set from the candidate measurement data set according to the reference correction amount and the candidate correction amount, and determine a target sampling scheme based on the target measurement data set.

[0108] Optionally, the target sampling scheme determination unit includes:

[0109] A correction value difference determination subunit is used to determine, for any candidate measurement data group, candidate correction value differences between each candidate correction value corresponding to the candidate measurement data group and the corresponding reference correction value, and determine a target correction value difference according to the candidate correction value differences;

[0110] The target measurement data set determination subunit is used to determine a target measurement data set from the candidate measurement data sets according to the target correction value difference corresponding to each of the candidate measurement data sets.

[0111] Optionally, the target measurement data group determination subunit is specifically used to:

[0112] Determine the image measurement data set from each of the candidate measurement data sets corresponding to the sample sampling number sub-intervals according to each of the target correction amount differences corresponding to the sample sampling number sub-intervals;

[0113] Generate a sampling correction relationship diagram according to the graph measurement data group corresponding to each of the sample sampling quantity sub-intervals;

[0114] According to the sampling correction relationship diagram, a target measurement data set is determined from the diagram measurement data set.

[0115] Optionally, the target model determination module 330 includes:

[0116] A residual value determination unit, configured to input the sample measurement data into each of the candidate overlay error compensation models to obtain a corresponding candidate residual value, and input the sample measurement data into the reference overlay error compensation model to obtain a reference residual value;

[0117] A target model determination unit is used to determine a target overlay error compensation model from each of the candidate overlay error compensation models based on the candidate residual value and the reference residual value.

[0118] The joint optimization device based on overlay error provided in the embodiment of the present invention can execute the joint optimization method based on overlay error provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing each joint optimization method based on overlay error.

[0119] In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of the sample measurement data and parameters to be adjusted are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0120] Embodiment 4

[0121] Figure 4 It is a structural schematic diagram of an electronic device for implementing a joint optimization method based on overlay error provided by Embodiment 4 of the present invention. Electronic device 410 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0122] like Figure 4 As shown, the electronic device 410 includes at least one processor 411, and a memory connected to the at least one processor 411 in communication, such as a read-only memory (ROM) 412, a random access memory (RAM) 413, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 411 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 412 or the computer program loaded from the storage unit 418 to the random access memory (RAM) 413. In the RAM 413, various programs and data required for the operation of the electronic device 410 can also be stored. The processor 411, the ROM 412, and the RAM 413 are connected to each other via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0123] Multiple components in the electronic device 410 are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, a mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, an optical disk, etc.; and a communication unit 419, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 419 allows the electronic device 410 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0124] The processor 411 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 411 performs the various methods and processes described above, such as a joint optimization method based on overlay error.

[0125] In some embodiments, the joint optimization method based on overlay error may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 410 via the ROM 412 and / or the communication unit 419. When the computer program is loaded into the RAM 413 and executed by the processor 411, one or more steps of the joint optimization method based on overlay error described above may be performed. Alternatively, in other embodiments, the processor 411 may be configured to perform the joint optimization method based on overlay error in any other appropriate manner (e.g., by means of firmware).

[0126] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0127] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0128] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0129] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0130] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0131] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0132] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0133] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A joint optimization method based on overlay error, characterized in that: include: Acquire sample measurement data of a sample wafer and determine parameters to be adjusted in a pre-built initial overlay error compensation model; Grouping the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and determining, based on the initial overlay error compensation model, candidate overlay error compensation models corresponding to the candidate model parameter group and reference overlay error compensation models corresponding to the reference model parameter group; Determining a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model; The sample measurement data are grouped to obtain a candidate measurement data group and a reference measurement data group, and a target sampling scheme is determined according to the candidate measurement data group, the reference measurement data group and the target overlay error compensation model.

2. The method according to claim 1, characterized in that The step of grouping the sample measurement data to obtain a candidate measurement data group and a reference measurement data group includes: Determining the number of parameters of the target adjustment parameters in the target overlay error compensation model, and determining the number of samples of the sample measurement data; Based on the number of parameters and the number of samples, a sample sampling number interval is determined, and the sample measurement data is grouped according to the sample sampling number interval to obtain a candidate measurement data group including some sample measurement data and a reference measurement data group including all sample measurement data.

3. The method according to claim 2, characterized in that The determining of the sample sampling quantity interval based on the parameter quantity and the sample quantity includes: determining a quantitative distance between the parameter quantity and the sample quantity; Dividing the quantity distance according to a preset sample sampling interval to obtain sample sampling quantity sub-intervals; A sample sampling number interval including the sample sampling number sub-interval is generated.

4. The method according to claim 1, characterized in that The determining a target sampling scheme according to the candidate measurement data set, the reference measurement data set and the target overlay error compensation model comprises: Inputting the candidate measurement data set into the target overlay error compensation model to obtain a candidate correction amount, and inputting the reference measurement data set into the target overlay error compensation model to obtain a reference correction amount; A target measurement data set is determined from the candidate measurement data set according to the reference correction amount and the candidate correction amount, and a target sampling scheme is determined based on the target measurement data set.

5. The method according to claim 4, characterized in that The step of determining a target measurement data set from the candidate measurement data set according to the reference correction amount and the candidate correction amount comprises: For any candidate measurement data set, determining candidate correction amount differences between each candidate correction amount corresponding to the candidate measurement data set and the corresponding reference correction amount, and determining a target correction amount difference according to the candidate correction amount differences; A target measurement data set is determined from the candidate measurement data sets according to the target correction value difference corresponding to each of the candidate measurement data sets.

6. The method according to claim 5, characterized in that The step of determining a target measurement data set from the candidate measurement data set according to the target correction value difference corresponding to each candidate measurement data set comprises: Determine the image measurement data set from each of the candidate measurement data sets corresponding to the sample sampling number sub-intervals according to each of the target correction amount differences corresponding to the sample sampling number sub-intervals; Generate a sampling correction relationship diagram according to the graph measurement data group corresponding to each of the sample sampling quantity sub-intervals; According to the sampling correction relationship diagram, a target measurement data set is determined from the diagram measurement data set.

7. The method according to claim 1, characterized in that The step of determining a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model comprises: Inputting the sample measurement data into each of the candidate overlay error compensation models to obtain a corresponding candidate residual value, and inputting the sample measurement data into the reference overlay error compensation model to obtain a reference residual value; Based on the candidate residual values ​​and the reference residual values, a target overlay error compensation model is determined from the candidate overlay error compensation models.

8. A joint optimization device based on overlay error, characterized in that: include: A parameter acquisition module, used to acquire sample measurement data of a sample wafer and determine parameters to be adjusted in a pre-built initial overlay error compensation model; a parameter grouping module, configured to group the parameters to be adjusted to obtain a candidate model parameter group and a reference model parameter group, and determine, based on the initial overlay error compensation model, a candidate overlay error compensation model corresponding to the candidate model parameter group and a reference overlay error compensation model corresponding to the reference model parameter group; A target model determination module, configured to determine a target overlay error compensation model according to the sample measurement data, the candidate overlay error compensation model and the reference overlay error compensation model; The target sampling scheme determination module is used to group the sample measurement data to obtain a candidate measurement data group and a reference measurement data group, and determine a target sampling scheme according to the candidate measurement data group, the reference measurement data group and the target overlay error compensation model.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a joint optimization method based on overlay error as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a joint optimization method based on overlay error as described in any one of claims 1 to 7 is implemented.

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