Method of establishing an optical proximity correction model and method of optical proximity correction
By dividing pattern groups according to the proximity coefficient between patterns and establishing a matching optical proximity correction model, the problem of large errors in the patterning process of semiconductor products is solved, achieving higher pattern formation accuracy and lower defect rate.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-09-03
- Publication Date
- 2026-03-24
AI Technical Summary
In the existing technology, the use of a single OPC model during the patterning process of semiconductor products leads to large patterning errors, especially resulting in high defect rates in specific areas.
Pattern groups are divided according to the proximity coefficient between patterns, and a matching optical proximity correction model is established for each pattern group. Different OPC models are created using simulation software or test mask software.
It improves the matching degree of the optical proximity correction model, reduces the error and defect rate during pattern modeling, and improves the accuracy of pattern formation.
Smart Images
Figure CN115755528B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of semiconductor technology, and specifically to a method for establishing an optical proximity correction model and an optical proximity correction method. Background Technology
[0002] In the semiconductor manufacturing industry, there are many limitations to realizing semiconductor product designs during the patterning stage. When there is insufficient resolution in aspects such as masks, photoresists, and process conditions, the designed pattern may not be formed (realized). As semiconductor density increases, the size of patterns formed by photolithography becomes smaller and smaller. However, the actual pattern formed often deviates from the original design for various reasons, resulting in many defects.
[0003] To overcome this weakness, OPC (Optical Proximity Correction) is performed. OPC aims to accurately shape the desired pattern according to its design. However, in a single layer, all patterns within a semiconductor chip are modeled using the same OPC model. This can easily lead to inaccuracies in the OPC model used in specific areas, which is a significant drawback. Figure 1 and Figure 2 As shown, on the designed chip, the three different pattern groups, Group A, Group B, and Group C, all use the same OPC model, Model A. This can easily lead to inconsistencies between the OPC model and the pattern group, resulting in significant errors and high defect rates in pattern modeling. Figure 2 As shown, the vertical axis represents the difference between the ideal pattern and the actual pattern. When Model A is used as the standard model, and Group A, Group B and Group C are all modeled using Model A, the pattern modeling of Group B and Group C shows a large error. Summary of the Invention
[0004] The purpose of this disclosure is to provide a method for establishing an optical proximity correction model and an optical proximity correction method. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general description, nor is it intended to identify key / important components or to describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.
[0005] According to a first aspect of the embodiments of this disclosure, a method for establishing an optical proximity correction model is provided, comprising:
[0006] For each designed pattern, the pattern groups are divided according to the closeness coefficient between the patterns;
[0007] Establish a matching optical proximity correction model for each of the divided pattern groups;
[0008] The proximity coefficient includes at least one parameter among pattern shape similarity / similarity, pattern density, distance between the pattern and its surrounding patterns, and pattern function similarity / similarity.
[0009] According to a second aspect of the present disclosure, a method for optical proximity correction is provided, comprising:
[0010] The steps of the method for establishing an optical proximity correction model in the first aspect mentioned above;
[0011] Optical proximity correction is performed on each of the pattern groups using an optical proximity correction model that matches each of the pattern groups.
[0012] According to a third aspect of the present disclosure, an apparatus for establishing an optical proximity correction model is provided, comprising:
[0013] The partitioning module is used to divide the designed patterns into pattern groups based on the closeness coefficient between the patterns.
[0014] The modeling module is used to create a matching optical proximity correction model for each of the divided pattern groups.
[0015] The proximity coefficient includes at least one parameter among pattern shape similarity / similarity, pattern density, distance between the pattern and its surrounding patterns, and pattern function similarity / similarity.
[0016] According to a fourth aspect of the present disclosure, an apparatus for optical proximity correction is provided, comprising:
[0017] The apparatus for establishing the optical proximity correction model mentioned in the third aspect above;
[0018] The correction module is used to perform optical proximity correction on each of the pattern groups using an optical proximity correction model that matches each of the pattern groups.
[0019] According to a fifth aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a first program and / or a second program stored in the memory and executable on the processor;
[0020] The processor executes the first program to implement the method for establishing an optical proximity correction model as described in the first aspect.
[0021] And / or,
[0022] The processor executes the second program to implement the optical proximity correction method described in the second aspect.
[0023] According to a sixth aspect of the present disclosure, a computer-readable storage medium is provided having a first program and / or a second program stored thereon.
[0024] The first program is executed by the processor to implement the method for establishing an optical proximity correction model as described in the first aspect.
[0025] And / or,
[0026] The second program is executed by the processor to implement the optical proximity correction method described in the second aspect.
[0027] One aspect of the technical solution provided by the embodiments of this disclosure may include the following beneficial effects:
[0028] The method for establishing an optical proximity correction model provided in this disclosure divides the pattern groups according to the proximity coefficient between the patterns for each designed pattern, and establishes a matching optical proximity correction model for each divided pattern group. This greatly improves the matching degree of the optical proximity correction model corresponding to each pattern group, thereby significantly reducing the error and defect rate generated during pattern modeling when using each optical proximity correction model to model the matching pattern group.
[0029] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description, or some features and advantages may be inferred from the description or determined without question, or may be learned by practicing embodiments of this disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description
[0030] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0031] Figure 1 A schematic diagram of three pattern groups composed of the designed patterns is shown;
[0032] Figure 2 This demonstrates a method using existing technology for... Figure 1 A bar chart showing the error between the ideal and actual patterns after pattern modeling of the three pattern groups;
[0033] Figure 3 A flowchart illustrating a method for establishing an optical proximity correction model according to an embodiment of this disclosure is shown.
[0034] Figure 4 It shows Figure 3 A flowchart of an embodiment of dividing pattern groups according to the proximity coefficient between each pattern is shown in the example.
[0035] Figure 5 It shows Figure 3 A flowchart of another embodiment of dividing pattern groups according to the proximity coefficient between each pattern in the illustrated embodiment;
[0036] Figure 6 It shows Figure 3 A flowchart of another embodiment of dividing pattern groups according to the proximity coefficient between each pattern in the illustrated embodiment;
[0037] Figure 7 It shows Figure 3 A flowchart of another embodiment of dividing pattern groups according to the proximity coefficient between each pattern in the illustrated embodiment;
[0038] Figure 8 It shows Figure 3 A schematic diagram of an embodiment of dividing pattern groups according to the proximity coefficient between each pattern is shown in the example.
[0039] Figure 9 It shows Figure 3 A schematic diagram of another embodiment of dividing pattern groups according to the proximity coefficient between each pattern in the illustrated embodiment;
[0040] Figure 10 A flowchart of an optical proximity correction method according to another embodiment of this disclosure is shown;
[0041] Figure 11 It shows the use of Figure 10 The optical proximity correction method of the illustrated embodiment is for Figure 1 The error histogram between the ideal pattern and the actual pattern after pattern modeling of the three pattern groups. Detailed Implementation
[0042] Embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.
[0043] The accompanying drawings illustrate various structural schematics according to embodiments of the present disclosure. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.
[0044] In the context of this disclosure, when a layer / element is referred to as being "above" another layer / element, the layer / element may be directly above the other layer / element, or there may be an intermediate layer / element between them. Additionally, if a layer / element is "above" another layer / element in one orientation, then when the orientation is reversed, the layer / element may be "below" the other layer / element.
[0045] like Figure 3 As shown, one embodiment of this disclosure provides a method for establishing an optical proximity correction model, including:
[0046] S10. For each designed pattern, divide the patterns into groups based on the proximity coefficient between them. The proximity coefficient includes at least one parameter among pattern shape similarity / similarity, pattern density, distance between the pattern and its surrounding patterns, and pattern function similarity / similarity.
[0047] When grouping patterns, at least one of four parameters can be used: similarity or difference in pattern shape, pattern density, distance between a pattern and its surrounding patterns, and similarity or difference in pattern function. For example, isolated patterns can be grouped based on two parameters: pattern shape and pattern function. One or two of these parameters can be considered when grouping patterns.
[0048] For example, the parameter value for pattern shape similarity / difference can be set to 1 when two patterns have the same shape, and 0 when two patterns have different shapes.
[0049] For example, the parameter value for pattern function similarity / difference can be set to 1 when two patterns have the same function, and 0 when two patterns have different functions.
[0050] like Figure 4 As shown, in some embodiments, when the proximity coefficient includes only one parameter, pattern density, the pattern groups are divided according to the proximity coefficient between each pattern, including:
[0051] S101. Select an initial pattern from the designed patterns and use this initial pattern as the initial pattern combination.
[0052] S102. Select a candidate pattern from the patterns surrounding the pattern combination and add it to the pattern combination; the candidate pattern is a pattern whose distance from the center point of the pattern combination is less than a preset threshold.
[0053] S103. Calculate the pattern density of the pattern combination after adding the candidate patterns.
[0054] S104. If the calculated pattern density is less than or equal to the preset density threshold, then the candidate pattern is determined to remain in the pattern combination; otherwise, the candidate pattern is excluded from the pattern combination.
[0055] S105. Turn to select an alternative pattern from the patterns surrounding the pattern combination and add it to the pattern combination until there are no alternative patterns in the patterns surrounding the pattern combination. The resulting pattern combination is a pattern group.
[0056] Repeat steps S101-S105 until the pattern groups for all patterns are completed.
[0057] like Figure 5 As shown, the closeness coefficient includes only one parameter: pattern density. Based on the pattern density between each pattern, all patterns are divided into three pattern groups: Group A, Group B, and Group C according to steps S101-S105.
[0058] In some implementations, when the proximity coefficient only includes the same parameter of pattern shape difference, the pattern group is divided according to the proximity coefficient between each pattern, including: dividing patterns with the same pattern shape into the same pattern group.
[0059] In some implementations, when the proximity coefficient only includes the same parameter for pattern function difference, the pattern group is divided according to the proximity coefficient between each pattern, including: dividing patterns with the same pattern function into the same pattern group.
[0060] like Figure 6 As shown, in some embodiments, when the proximity coefficient includes only one parameter—the distance between a pattern and its surrounding patterns—the pattern groups are divided based on the proximity coefficients between the patterns, including:
[0061] S10-1. Select a reference pattern from the pre-designed patterns and calculate the distance between the reference pattern and the patterns around it.
[0062] S10-2. Patterns whose distance from the reference pattern is less than or equal to a preset distance threshold are grouped together with the reference pattern into the same pattern group.
[0063] Repeat steps S10-1 to S10-2 until all patterns have been divided.
[0064] like Figure 7 As shown, in some embodiments, when the proximity coefficient includes only one parameter—the distance between a pattern and its surrounding patterns—the method of grouping patterns based on the proximity coefficient between each pattern further includes:
[0065] S10-3. After all patterns have been divided, if there is more than one isolated pattern group, the more than one isolated pattern group shall be divided according to the differences in pattern shape and / or pattern function, and multiple isolated pattern groups with the same pattern shape and / or the same pattern function shall be divided into the same pattern group; wherein, an isolated pattern group is a pattern group containing only one pattern.
[0066] For example, for all designed patterns, pattern groups can be formed based on the parameter of the distance between each pattern and its surrounding patterns. Specifically, a reference pattern is selected, the distance between the reference pattern and its surrounding patterns is calculated, and patterns whose distance from the reference pattern is less than or equal to a preset distance threshold are grouped into the same pattern group as the reference pattern. The reference pattern can be arbitrarily selected.
[0067] like Figure 8 As shown, based on the distance between each pattern and its surrounding patterns, the patterns are divided into five groups: Group A, Group B, Group C, Group D, and Group E. Group A, Group B, and Group C each contain more than one pattern: Group A has four patterns, Group B has six patterns, and Group C has four patterns. OPC models Model A, Model B, and Model C are created for Group A, Group B, and Group C, respectively. Group D and Group E are both isolated pattern groups. Since the patterns in Group D and Group E are identical, they are grouped into the same pattern group, and the same OPC model, Model D, is created for this same pattern group. Model D applies to both Group D and Group E.
[0068] In some implementations, the proximity coefficient includes four parameters: similarity or difference in pattern shape, pattern density, distance between the pattern and its surrounding patterns, and similarity or difference in pattern function. Pattern groups are divided based on the proximity coefficient between each pattern, including:
[0069] If all designed patterns have a pattern density that reaches a preset density threshold, a distance between each pattern and its surrounding patterns that reaches a preset distance threshold, and all patterns have the same function, then all patterns are divided into different pattern groups according to the similarities and differences in their patterns.
[0070] like Figure 9As shown, all patterns are divided into three pattern groups: Group A, Group B, and Group C, according to their shapes.
[0071] In some implementations, all designed patterns can be grouped according to the pattern shape as a parameter. Specifically, patterns with the same shape are grouped into the same pattern group.
[0072] In some implementations, all designed patterns can be grouped according to a single parameter: pattern function. Specifically, patterns with the same function are grouped into the same pattern group.
[0073] S20. Establish a matching OPC model for each of the divided pattern groups.
[0074] The OPC models corresponding to each pattern group can be created using tools such as simulation software or test mask software. Different pattern groups use their own corresponding OPC models. The same pattern group corresponds to the same OPC model. For example... Figure 9 As shown, all patterns are divided into three pattern groups, Group A, Group B and Group C, according to their shapes. OPC models Model A, Model B and Model C are established for pattern groups Group A, Group B and Group C respectively.
[0075] Using the proximity coefficient as a benchmark for pattern grouping can improve the matching accuracy of the OPC model. The proximity coefficient can be set according to actual needs.
[0076] The method for establishing an optical proximity correction model provided in this disclosure divides the pattern groups according to the proximity coefficient between the patterns for each designed pattern, and establishes a matching optical proximity correction model for each divided pattern group. This greatly improves the matching degree of the optical proximity correction model corresponding to each pattern group, thereby significantly reducing the error and defect rate generated during pattern modeling when using each optical proximity correction model to model the matching pattern group.
[0077] Another embodiment of this application provides a method for optical proximity correction, including:
[0078] The steps of the method for establishing an optical proximity correction model in any of the above embodiments;
[0079] Optical proximity correction was performed on each pattern group using an OPC model that matched each pattern group.
[0080] For example, such as Figure 10 As shown, an optical proximity correction method in some embodiments of this example includes:
[0081] S10. For each designed pattern, divide the patterns into groups based on the proximity coefficient between them. The proximity coefficient includes at least one parameter among pattern shape similarity / similarity, pattern density, distance between the pattern and its surrounding patterns, and pattern function similarity / similarity.
[0082] S20. Establish a matching OPC model for each of the divided pattern groups.
[0083] The OPC models corresponding to each pattern group can be created using tools such as simulation software or test mask software. Different pattern groups use their own corresponding OPC models.
[0084] S30. Optical proximity correction is performed on each pattern group using an OPC model that matches each pattern group.
[0085] The use of multiple OPC models can improve the accuracy of optical proximity correction and reduce the dispersion between patterns.
[0086] An optical proximity correction model is used to simulate the pattern of a matching pattern group to obtain a detection pattern. The sub-patterns in the detection pattern correspond one-to-one with the micro-patterns in the pattern. The sub-patterns in the detection pattern are detected one by one. When the feature size corresponding to the sub-pattern in the detection pattern is 0, the micro-pattern on the pattern group corresponding to the sub-pattern is marked.
[0087] Two or more OPC models can be used within a single chip. There is no limit to the type or number of OPC models used.
[0088] For example, after setting up a pattern group according to the above four standards, the result can be predicted using the corresponding OPC model. The prediction result can be confirmed by CD (Critical Dimension) or Profile. The CD (Critical Dimension) or Profile of each point within the pattern group can be statistically analyzed.
[0089] To create patterns according to the design, OPC is required when manufacturing the mask. When using OPC, different OPC models are used for each pattern within the chip, depending on its shape, density, distance from surrounding patterns, and function. This embodiment sets up groups according to patterns. Reducing OPC targeting scatter can improve accuracy.
[0090] like Figure 11 The image shows the optical proximity correction method using an embodiment of this application. Figure 1The error between the ideal pattern and the actual pattern obtained after optical proximity correction for all the patterns shown is... Figure 2 In contrast, after using Model B and Model C to model the patterns for Group B and Group C respectively, Figure 11 The error shown is significantly smaller than Figure 2 The error shown.
[0091] The optical proximity correction method provided in this disclosure divides the designed patterns into pattern groups based on the proximity coefficient between the patterns, and establishes a matching optical proximity correction model for each pattern group. This greatly improves the matching degree of the optical proximity correction model corresponding to each pattern group. By using each optical proximity correction model to model the matching pattern groups, the error and defect rate generated during pattern modeling are greatly reduced.
[0092] Another embodiment of this application provides an apparatus for establishing an optical proximity correction model, comprising:
[0093] The partitioning module is used to divide the designed patterns into pattern groups based on the closeness coefficient between the patterns.
[0094] The modeling module is used to create a matching optical proximity correction model for each of the divided pattern groups.
[0095] The proximity coefficient includes at least one parameter among pattern shape similarity / similarity, pattern density, distance between the pattern and its surrounding patterns, and pattern function similarity / similarity.
[0096] Another embodiment of this application provides an optical proximity correction apparatus, comprising:
[0097] Apparatus for establishing an optical proximity correction model according to any of the above embodiments;
[0098] The correction module is used to perform optical proximity correction on each of the pattern groups using an optical proximity correction model that matches each of the pattern groups.
[0099] Another embodiment of this application provides an electronic device, including a memory, a processor, and a first program and / or a second program stored in the memory and executable on the processor;
[0100] The processor executes the first program to implement the method for establishing an optical proximity correction model according to any of the above embodiments.
[0101] And / or,
[0102] The processor executes the second program to implement the optical proximity correction method of any of the above embodiments.
[0103] Another embodiment of this application provides a computer-readable storage medium storing a first program and / or a second program thereon, the first program being executed by a processor to implement the method for establishing an optical proximity correction model according to any of the above embodiments.
[0104] And / or,
[0105] The second program is executed by the processor to implement the optical proximity correction method of any of the above embodiments.
[0106] The above description does not provide detailed explanations of the technical aspects of each layer's patterning, etching, etc. However, those skilled in the art should understand that various technical means can be used to form layers and regions of the desired shape. Furthermore, to form the same structure, those skilled in the art can also design methods that are not entirely identical to those described above. Additionally, although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination.
[0107] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.
Claims
1. A method for establishing an optical proximity correction model, characterized in that, include: For each designed pattern, the pattern groups are divided according to the closeness coefficient between the patterns; Establish a matching optical proximity correction model for each of the divided pattern groups; The proximity coefficient includes at least one parameter among pattern density and the distance between the pattern and its surrounding patterns; The process of dividing pattern groups based on the proximity coefficient between the patterns includes: Select an initial pattern from the designed patterns, and use the initial pattern as the pattern combination; Select a candidate pattern from the patterns surrounding the pattern combination and add it to the pattern combination; the candidate pattern is a pattern whose distance from the center point of the pattern combination is less than a preset threshold. Calculate the pattern density of the pattern combination after adding the candidate patterns; If the calculated pattern density is less than or equal to a preset density threshold, then the candidate pattern is determined to remain in the pattern combination; otherwise, the candidate pattern is excluded from the pattern combination. Turn to select an alternative pattern from the patterns surrounding the pattern combination and add it to the pattern combination until there are no alternative patterns left in the patterns surrounding the pattern combination. The resulting pattern combination is then taken as a pattern group.
2. The method according to claim 1, characterized in that, The process of dividing pattern groups based on the proximity coefficient between the patterns includes: If all designed patterns have a pattern density that reaches a preset density threshold, a distance between each pattern and its surrounding patterns that reaches a preset distance threshold, and all patterns have the same function, then all patterns will be divided into different pattern groups according to the similarities and differences in their patterns.
3. A method for optical proximity correction, characterized in that, include: The steps of the method for establishing an optical proximity correction model according to any one of claims 1-2; Optical proximity correction is performed on each of the pattern groups using an optical proximity correction model that matches each of the pattern groups.
4. An apparatus for establishing an optical proximity correction model, characterized in that, include: The partitioning module is used to divide the designed patterns into pattern groups based on the closeness coefficient between the patterns. The modeling module is used to create a matching optical proximity correction model for each of the divided pattern groups. The proximity coefficient includes at least one parameter among pattern density and the distance between the pattern and its surrounding patterns; The partitioning module is further specifically used for: Select an initial pattern from the designed patterns, and use the initial pattern as the pattern combination; Select a candidate pattern from the patterns surrounding the pattern combination and add it to the pattern combination; the candidate pattern is a pattern whose distance from the center point of the pattern combination is less than a preset threshold. Calculate the pattern density of the pattern combination after adding the candidate patterns; If the calculated pattern density is less than or equal to a preset density threshold, then the candidate pattern is determined to remain in the pattern combination; otherwise, the candidate pattern is excluded from the pattern combination. Turn to select an alternative pattern from the patterns surrounding the pattern combination and add it to the pattern combination until there are no alternative patterns left in the patterns surrounding the pattern combination. The resulting pattern combination is then taken as a pattern group.
5. An optical proximity correction device, characterized in that, include: The optical proximity correction model established by the device of claim 4; The correction module is used to perform optical proximity correction on each of the pattern groups using an optical proximity correction model that matches each of the pattern groups.
6. An electronic device, characterized in that, Includes a memory, a processor, and a first program and / or a second program stored in the memory and executable on the processor; The processor executes the first program to implement the method as described in any one of claims 1-2. And / or, The processor executes the second program to implement the method as described in claim 3.
7. A computer-readable storage medium having a first program and / or a second program stored thereon, characterized in that, The first program is executed by the processor to implement the method as described in any one of claims 1-2. And / or, The second program is executed by the processor to implement the method as described in claim 3.
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