OPC modeling measurement graphic data selection method

Abstract

The present application relates to the field of semiconductor integrated circuit manufacturing technology, and specifically to a method for selecting measurement pattern data for OPC modeling. The method comprises: providing a mask data file and an OPC initial model corresponding to a specific layer of a semiconductor device; selecting a mask pattern whose actual feature size falls within a first feature size range from the mask data file as a test pattern, and all test patterns form a test pattern set; the first feature size includes the minimum design feature size of the specific semiconductor device layer; optimizing the OPC initial model using the test pattern set to generate a first OPC model; simulating each test pattern in the test pattern set based on the first OPC model to determine a valid test pattern set in the test pattern set; deleting mask patterns in the mask data file whose actual feature size is smaller than the actual feature size of the minimum valid test pattern, so that the remaining mask patterns serve as measurement pattern data for OPC modeling.

Description

Technical Field

[0001] The present application relates to the field of semiconductor integrated circuit manufacturing technology, and in particular to an OPC (Optical Proximity Correction) modeling and measurement graphic data selection method. Background Art

[0002] As chip size continues to shrink, graphics continue to shrink, and the rules for designing exposure graphics for each layer of the chip become increasingly complex, the light source used is also developing in a complex direction toward coordinated optimization of the light source and mask. As a result, many small-sized layout graphics are increasingly subject to deformation of the projected graphics due to diffraction and interference of the exposure light during the exposure process. This phenomenon is known as the optical proximity effect.

[0003] To correct for the optical proximity effect-induced projected pattern distortion, OPC technology has been introduced in the semiconductor integrated circuit manufacturing industry. The core of this technology is the creation of an OPC model capable of compensating for projected pattern distortion. Based on this OPC model, an OPC pattern is inserted onto the original reticle to create a photolithography mask pattern that corrects for the optical proximity effect. While optical proximity effect still occurs during exposure using a reticle with this pattern, the OPC model accounts for this effect when designing the reticle pattern. As a result, the projected pattern after exposure is closer to the desired target pattern.

[0004] In the OPC modeling process, the related technology needs to measure the projection patterns of almost all test patterns on the mask to obtain measurement data, and optimize and adjust the OPC model based on the measurement data until the final optimized and adjusted OPC model can make the projection pattern closer to the target pattern to a greater extent.

[0005] However, the projection patterns measured by related technologies include many invalid patterns whose feature sizes are smaller than the feature sizes of the smallest valid patterns. If these measurement data are used to optimize and adjust the OPC model, it will have an adverse effect on the accuracy of the model, and more time will be needed to clear the measurement data of these invalid patterns in the later stage. Summary of the Invention

[0006] The present application provides a method for selecting measurement graphic data for OPC modeling, which can solve the problem in the related art that the accuracy of the OPC model established based on the measurement graphic data is adversely affected by the inclusion of invalid graphics in the measurement graphic data.

[0007] In order to solve the technical problems described in the background technology, the present application provides an OPC modeling measurement graphic data selection method, which includes the following steps performed in sequence:

[0008] Providing a mask data file and an OPC initial model corresponding to a specific layer of a semiconductor device; the OPC initial model is pre-established based on optical system parameters corresponding to the specific layer of the semiconductor device;

[0009] Selecting, from the mask data file, mask patterns whose actual feature sizes fall within a first feature size range as test patterns, and all test patterns form a test pattern set; the first feature size includes a minimum design feature size of the semiconductor specific device layer;

[0010] Optimizing the OPC initial model using the test pattern set to generate a first OPC model;

[0011] Simulating each of the test patterns in the test pattern set based on the first OPC model to determine a valid test pattern set in the test pattern set;

[0012] The mask patterns whose actual feature size is smaller than the actual feature size of the minimum effective test pattern are deleted from the mask data file, so that the remaining mask patterns are used as measurement pattern data for OPC modeling.

[0013] Optionally, the first characteristic size range is continuous;

[0014] The minimum design feature size of the semiconductor specific device layer is located in the middle area of ​​the first feature size range.

[0015] Optionally, the first characteristic size range includes an upper limit value and a lower limit value;

[0016] The upper limit of the first characteristic size range is in the range of 1.2 to 1.5 times the minimum design characteristic size;

[0017] The lower limit of the first feature size range is 0.5 to 0.8 times the minimum design feature size.

[0018] Optionally, the step of simulating each of the test patterns in the test pattern set based on the first OPC model to determine a valid test pattern set in the test pattern set includes:

[0019] Simulating each of the test patterns in the test pattern set based on the first OPC model to predict a predicted feature size corresponding to each of the test patterns;

[0020] determining whether the predicted characteristic size of each of the test patterns is valid;

[0021] The test pattern whose predicted characteristic size is valid is a valid test pattern, and the test pattern whose predicted characteristic size is invalid is an invalid test pattern.

[0022] Optionally, the step of simulating each of the test patterns in the test pattern set based on the first OPC model to predict a predicted feature size corresponding to each of the test patterns includes:

[0023] determining a central area of ​​each of the test patterns in the test pattern set;

[0024] Based on the first OPC model, a central area of ​​each of the test patterns is simulated to predict a predicted feature size corresponding to each of the test patterns.

[0025] Optionally, the step of determining whether the predicted characteristic size of each test pattern is valid includes:

[0026] Setting a minimum target feature size corresponding to the test pattern;

[0027] Based on the size relationship between the predicted feature size of each test pattern and the minimum target feature size, it is determined whether the predicted feature size of each test pattern is valid.

[0028] Optionally, the step of determining whether the predicted feature size of each test pattern is valid based on the size relationship between the predicted feature size of each test pattern and the minimum target feature size includes:

[0029] When the predicted feature size of the test pattern is smaller than the minimum target feature size, it is determined that the predicted feature size is invalid and the test pattern is an invalid test pattern.

[0030] Optionally, the step of determining whether the predicted feature size of each test pattern is valid based on the size relationship between the predicted feature size of each test pattern and the minimum target feature size includes:

[0031] When the predicted feature size of the test pattern is greater than or equal to the minimum target feature size, it is determined that the predicted feature size is valid and the test pattern is a valid test pattern.

[0032] Optionally, the minimum target characteristic size ranges from 70% to 98% of the minimum design characteristic size.

[0033] Optionally, the step of deleting the mask patterns in the mask data file whose actual feature size is smaller than the actual feature size of the minimum valid test pattern so that the remaining mask patterns serve as measurement pattern data for OPC modeling includes:

[0034] Determine the minimum actual feature size of the test pattern set in which the predicted feature size is valid as the boundary effective feature size of the mask data file;

[0035] The mask patterns whose actual feature size is smaller than the boundary effective feature size are deleted from the mask data file, so that the remaining mask patterns are used as measurement pattern data for OPC modeling.

[0036] The technical solution of this application has at least the following advantages: It can remove invalid pattern data from the mask data file and select only valid pattern data as measurement pattern data for OPC modeling, which can save time in establishing the OPC model and improve the accuracy of the established OPC model. By screening small-scale test patterns to determine whether the test patterns are valid, data sorting efficiency can be improved, and the measurement pattern data ultimately obtained is valid, avoiding the subsequent waste of time in screening the validity of the measurement pattern data. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 A flow chart of a method for selecting OPC modeling and measurement graphic data provided by an embodiment of the present application is shown;

[0039] Figure 1a A schematic diagram of the structure of a mask data file provided in an embodiment of the present application is shown;

[0040] Figure 1b Shown from Figure 1 The mask data file 100 shown is a schematic diagram of selecting a test pattern set N;

[0041] Figure 2 A schematic diagram showing a first characteristic size range T provided by an embodiment is shown;

[0042] Figure 3 A schematic diagram of a process for generating a first OPC model provided by an embodiment is shown;

[0043] Figure 4 The embodiment is shown based on Figure 3 The first OPC model shown is a schematic diagram of simulating a test pattern set N to determine a valid test pattern set N*;

[0044] Figure 5An embodiment is shown in Figure 4 Schematic diagram of determining a minimum valid test pattern Nmin based on determining a valid test pattern set N*, and selecting measurement pattern data M* based on the minimum valid test pattern Nmin. DETAILED DESCRIPTION

[0045] The following is a clear and complete description of the technical solutions in this application in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0046] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0047] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to internal connections between two components; they can refer to wireless connections or wired connections. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0048] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0049] Figure 1 The flow chart of the method for selecting OPC modeling measurement graphic data provided by an embodiment of the present application is shown. Figure 1 As can be seen from the figure, the OPC modeling measurement graphic data selection method includes the following steps performed in sequence:

[0050] Step S1: providing a mask data file and an OPC initial model corresponding to a specific layer of a semiconductor device.

[0051] The OPC initial model is pre-established based on optical system parameters corresponding to a specific layer of the semiconductor device.

[0052] Reference Figure 1a , which shows a schematic diagram of the structure of the mask data file provided by the embodiment of the present application, from Figure 1a It can be seen that the mask data file 100 corresponding to a specific layer of the semiconductor device includes a mask pattern set M consisting of several mask patterns M1, M2...Mx. The mask data file 100 is used to form a target pattern on the specific layer of the semiconductor device, and each mask pattern has a corresponding actual feature size.

[0053] Step S2: selecting mask patterns with actual feature sizes within a first feature size range from the mask data file as test patterns, and all test patterns form a test pattern set.

[0054] The first feature size includes a minimum designed feature size of a specific layer of the semiconductor device.

[0055] It should be explained that each specific layer of the semiconductor device has a corresponding minimum design feature size that complies with the design rules. Step S2 involves selecting, from the mask pattern set in the mask data file, a mask pattern that meets a specific condition, namely, an actual feature size within a first feature size range, as the test pattern. All test patterns constitute the test pattern set. Mask patterns with actual feature sizes within the first feature size range are considered small-size mask patterns.

[0056] Reference Figure 1b , which shows that from Figure 1 The mask data file 100 shown is a schematic diagram of selecting a test pattern set N. Figure 1b It can be seen that the test pattern set N is a subset of the mask pattern set M, and the actual feature size CD of each test pattern N1, N2...Ny in the test pattern set N is R It belongs to the first characteristic size range T. The test patterns N1, N2...Ny of the selected test pattern set N are all small-sized patterns.

[0057] In order to make the step S2 of this embodiment select a small-sized mask pattern from the mask data file 100 as the test pattern N1, N2...Ny, the actual feature size CD of the test pattern N1, N2...Ny is R Belongs to the first feature size range T. The first feature size range T needs to include the minimum design feature size CD of the semiconductor specific device layer. min , so that the first feature size range T is the minimum design feature size CD min The selected area nearby.

[0058] Reference Figure 2 , which shows a schematic diagram of the first characteristic size range T provided by the embodiment, from Figure 2 It can be seen that the first characteristic size range T is continuous, and the first characteristic size range T includes the upper limit value T max and the lower limit T minx , the minimum design feature size CD of a specific semiconductor device layer min , located in the middle area Tc of the first characteristic size range T. For example, the upper limit of the first characteristic size range is 1.2 to 1.5 times the minimum design characteristic size CD min The lower limit of the first characteristic size range is 0.5 to 0.8 times the minimum design characteristic size CD min .

[0059] Step S3: Optimizing the OPC initial model using the test pattern set to generate a first OPC model.

[0060] The number of test patterns selected in step S2 is relatively small compared to the total number of mask patterns in the reticle data file. Therefore, using a test pattern set containing a smaller number of patterns, the time required to optimize the initial OPC model and generate the first OPC model is shortened. This first OPC model is used to perform optical proximity correction on small-sized mask patterns.

[0061] Reference Figure 3 , which shows a schematic flow chart of generating a first OPC model provided by an embodiment.

[0062] Step S4: Based on the first OPC model, simulate each of the test patterns in the test pattern set to determine a valid test pattern set in the test pattern set.

[0063] Reference Figure 4 , which shows an embodiment based on Figure 3 The first OPC model is a schematic diagram of simulating a test pattern set N to determine a valid test pattern set N*. The valid test pattern set N* is a subset of the test pattern set N and includes a plurality of valid test patterns Np...Nq.

[0064] The step of simulating the test pattern set based on the first OPC model to determine the valid test pattern set includes the following steps S41 to S42 performed in sequence:

[0065] Step S41: Based on the first OPC model, simulate each of the test patterns in the test pattern set to predict and obtain a predicted feature size corresponding to each of the test patterns.

[0066] Due to the characteristics of the optical system itself, some small-sized mask patterns with smaller actual feature sizes will be significantly smaller than the minimum design feature size of the design rules after exposure. Therefore, they cannot be used as OPC modeling measurement graphic data and are invalid graphics.

[0067] In step S41 of this embodiment, the first OPC model obtained in step S3 is used to simulate each test pattern in the test pattern set, paving the way for subsequent determination of invalid patterns in the test pattern set.

[0068] Optionally, the central area of ​​each test pattern in the test pattern set may be determined first; then, based on the first OPC model, the central area of ​​each test pattern may be simulated to predict the characteristic size corresponding to each test pattern, so as to implement step S41.

[0069] Step S42: determining whether the predicted characteristic size of each test pattern is valid; a test pattern with a valid predicted characteristic size is a valid test pattern; a test pattern with an invalid predicted characteristic size is an invalid test pattern.

[0070] The step of determining whether the predicted characteristic size of each test pattern is valid may include the following steps S421 to S422 performed in sequence:

[0071] Step S421: setting a minimum target feature size corresponding to the test pattern; the value range of the minimum target feature size is 70% to 98% of the minimum design feature size.

[0072] Step S422: determining whether the predicted feature size of each test pattern is valid based on the size relationship between the predicted feature size of each test pattern and the minimum target feature size.

[0073] When the predicted feature size of the test pattern is smaller than the minimum target feature size, it is determined that the predicted feature size is invalid and the test pattern is an invalid test pattern.

[0074] When the predicted feature size of the test pattern is greater than or equal to the minimum target feature size, it is determined that the predicted feature size is valid and the test pattern is a valid test pattern.

[0075] Step S5: Deleting the mask patterns in the mask data file whose actual feature size is smaller than the actual feature size of the minimum valid test pattern, so that the remaining mask patterns are used as measurement pattern data for OPC modeling.

[0076] Step S51: determining the minimum actual feature size of the test pattern set for which the predicted feature size is valid, and using it as the boundary effective feature size of the mask data file.

[0077] Step S52: Deleting the mask patterns in the mask data file whose actual feature size is smaller than the boundary effective feature size, so that the remaining mask patterns are used as measurement pattern data for OPC modeling.

[0078] Reference Figure 5 , which shows an embodiment in Figure 4 Schematic diagram of determining a minimum valid test pattern Nmin based on determining a valid test pattern set N*, and selecting measurement pattern data M* based on the minimum valid test pattern Nmin.

[0079] As can be seen from the above, this application can remove invalid pattern data from the mask data file and only select valid pattern data as the measurement pattern data for OPC modeling, which can save time in establishing the OPC model and improve the accuracy of the established OPC model. By screening small-scale test patterns to determine whether the test patterns are valid, data sorting efficiency can be improved, and the measurement pattern data finally obtained are all valid pattern data, avoiding the subsequent waste of time in screening the validity of the measurement pattern data.

[0080] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of this application.

Claims

1. A method for selecting OPC modeling measurement graphic data, characterized in that: The OPC modeling measurement graphic data selection method includes the following steps performed in sequence: Providing a mask data file and an OPC initial model corresponding to a specific layer of a semiconductor device; the OPC initial model is pre-established based on optical system parameters corresponding to the specific layer of the semiconductor device; Selecting, from the mask data file, mask patterns whose actual feature sizes fall within a first feature size range as test patterns, and all test patterns form a test pattern set; the first feature size includes a minimum design feature size of a specific layer of the semiconductor device; Optimizing the OPC initial model using the test pattern set to generate a first OPC model; Simulating each of the test patterns in the test pattern set based on the first OPC model to determine a valid test pattern set in the test pattern set; The mask patterns whose actual feature size is smaller than the actual feature size of the minimum effective test pattern are deleted from the mask data file, so that the remaining mask patterns are used as measurement pattern data for OPC modeling.

2. The OPC modeling measurement graphic data selection method according to claim 1, wherein: The first characteristic size range is continuous; The minimum design feature size of the specific layer of the semiconductor device is located in the middle area of ​​the first feature size range.

3. The OPC modeling measurement graphic data selection method according to claim 2, wherein: The first characteristic size range includes an upper limit value and a lower limit value; The upper limit of the first characteristic size range is in the range of 1.2 to 1.5 times the minimum design characteristic size; The lower limit of the first feature size range is 0.5 to 0.8 times the minimum design feature size.

4. The OPC modeling measurement graphic data selection method according to claim 1, wherein: The step of simulating each of the test patterns in the test pattern set based on the first OPC model to determine a valid test pattern set in the test pattern set includes: Simulating each of the test patterns in the test pattern set based on the first OPC model to predict a predicted feature size corresponding to each of the test patterns; determining whether the predicted characteristic size of each of the test patterns is valid; The test pattern whose predicted characteristic size is valid is a valid test pattern, and the test pattern whose predicted characteristic size is invalid is an invalid test pattern.

5. The OPC modeling measurement graphic data selection method according to claim 4, wherein: The step of simulating each of the test patterns in the test pattern set based on the first OPC model to predict a predicted feature size corresponding to each of the test patterns includes: determining a central area of ​​each of the test patterns in the test pattern set; Based on the first OPC model, a central area of ​​each of the test patterns is simulated to predict a predicted feature size corresponding to each of the test patterns.

6. The OPC modeling measurement graphic data selection method according to claim 4, wherein: The step of determining whether the predicted characteristic size of each of the test patterns is valid comprises: Setting a minimum target feature size corresponding to the test pattern; Based on the size relationship between the predicted feature size of each test pattern and the minimum target feature size, it is determined whether the predicted feature size of each test pattern is valid.

7. The OPC modeling measurement graphic data selection method according to claim 6, wherein: The step of determining whether the predicted feature size of each test pattern is valid based on the size relationship between the predicted feature size of each test pattern and the minimum target feature size includes: When the predicted feature size of the test pattern is smaller than the minimum target feature size, it is determined that the predicted feature size is invalid and the test pattern is an invalid test pattern.

8. The OPC modeling measurement graphic data selection method according to claim 6, wherein: The minimum target characteristic size has a value range of 70% to 98% of the minimum design characteristic size.

9. The OPC modeling measurement graphic data selection method according to claim 6, wherein: The step of determining whether the predicted feature size of each test pattern is valid based on the size relationship between the predicted feature size of each test pattern and the minimum target feature size includes: When the predicted feature size of the test pattern is greater than or equal to the minimum target feature size, it is determined that the predicted feature size is valid and the test pattern is a valid test pattern.

10. The OPC modeling measurement graphic data selection method according to claim 4, wherein: The step of deleting the mask patterns in the mask data file whose actual feature size is smaller than the actual feature size of the minimum valid test pattern so that the remaining mask patterns serve as measurement pattern data for OPC modeling includes: Determine the minimum actual feature size of the test pattern set in which the predicted feature size is valid as the boundary effective feature size of the mask data file; The mask patterns whose actual feature size is smaller than the boundary effective feature size are deleted from the mask data file, so that the remaining mask patterns are used as measurement pattern data for OPC modeling.

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