Optical proximity correction method

Abstract

The invention discloses an optical proximity correction method, and the method comprises the steps: obtaining an optimal segmentation range of a target graph according to exposure parameters, the target graph comprises a first edge and a second edge which are adjacent, and the first edge and the second edge form a corner of the target graph; performing simulated optical proximity correction according to the optimal segmentation range to obtain an optimal segmentation parameter combination; simulation optical proximity correction is conducted according to the optimal segmentation parameters, optimal strip-shaped auxiliary graph parameters are obtained, and strip-shaped auxiliary graphs are auxiliary graphs inserted to the peripheral sides of the corners; and generating a mask pattern according to the optimal segmentation parameter and the optimal strip-shaped auxiliary pattern parameter, wherein the mask pattern comprises a corrected target pattern and an inserted strip-shaped auxiliary pattern. According to the method, the strip-shaped auxiliary graph is inserted into the peripheral side of the corner of the target graph, so that the effect of solving the corner arcing problem is improved.

Description

Technical Field

[0001] The present application relates to the technical field of semiconductor devices and integrated circuits, and in particular to an optical proximity correction method. Background Art

[0002] With the development of the semiconductor integrated circuit manufacturing industry, device design dimensions are increasingly approaching the limits of photolithography imaging systems. The diffraction effect of light is becoming increasingly pronounced, leading to optical image degradation of the designed pattern. The actual pattern formed by photolithography is distorted relative to the pattern on the mask. This phenomenon is known as the optical proximity effect (OPE). To correct for this effect, optical proximity correction (OPC) has been proposed. This approach establishes an OPC model based on the consideration of offsetting the optical proximity effect. The mask pattern is then designed based on this OPC model, resulting in a pattern after photolithography that approximates the target pattern.

[0003] For high-energy ion implantation layers, thicker photoresist is required to prevent ion penetration into areas where ion implantation is not required, which could lead to device failure. However, excessively thick photoresist will inevitably cause pattern distortion. Due to the excessive thickness of the photoresist, the photoresist at the corners of the pattern cannot absorb enough light, resulting in corner rounding. The resulting pattern is severely distorted and the area is reduced, which in turn affects device performance. At the same time, due to the thick photoresist, it is difficult to establish an accurate OPC model. In view of this, adding block auxiliary patterns (serif) can be solved. However, due to the limitations of mask design rules (MRC) and process windows, the block auxiliary patterns cannot be infinitely increased, resulting in poor optimization effect on the corner rounding problem. Summary of the Invention

[0004] The present application provides an optical proximity correction method, which can solve the problem in the related art that inserting a block auxiliary model to solve the corner arc problem of the graphics in the photolithography process is poorly effective.

[0005] Obtaining an optimal segmentation range of a target pattern according to the exposure parameters, the target pattern comprising a first side and a second side adjacent to each other, the first side and the second side constituting a corner of the target pattern, the optimal segmentation range being a range of length values ​​of each segment when performing segmentation correction on the first side and the second side;

[0006] Performing simulated optical proximity correction according to the optimal segmentation range to obtain an optimal segmentation parameter combination, wherein the optimal segmentation parameter combination is a segmentation parameter combination with a minimum root mean square error, and the segmentation parameter combination includes the length of each segment and the number of segments;

[0007] Performing simulated optical proximity correction based on the optimal segmentation parameter combination to obtain optimal strip auxiliary pattern parameters, wherein the strip auxiliary pattern is an auxiliary pattern inserted around the corner, and the optimal strip auxiliary pattern parameters are strip auxiliary pattern parameters with the smallest root mean square error, and the strip auxiliary pattern parameters include a distance between the strip auxiliary pattern and the corner, a distance between the strip auxiliary patterns, and a width of the strip auxiliary pattern;

[0008] A mask pattern is generated according to the optimal segmentation parameters and the optimal strip-shaped auxiliary pattern parameters, wherein the mask pattern includes the corrected target pattern and the inserted strip-shaped auxiliary pattern.

[0009] In some embodiments, the exposure parameters include the numerical aperture of the exposure machine, the exposure wavelength, and light source parameters.

[0010] In some embodiments, obtaining an optimal segmentation range of the target graphic according to the exposure parameters includes:

[0011] Calculating the Nyquist value according to the numerical aperture, exposure wavelength, and light source parameters;

[0012] The optimal segmentation range is calculated based on the Nyquist value.

[0013] In some embodiments, performing simulated optical proximity correction according to the optimal segmentation range to obtain an optimal segmentation parameter combination includes:

[0014] Sampling each segment according to the optimal segment range to obtain possible values ​​of the length of each segment and possible values ​​of the number of segments;

[0015] The possible values ​​of the length of each segment and the possible values ​​of the number of segments form a parameter matrix;

[0016] Performing a simulated optical proximity correction on the parameter matrix to obtain a root mean square error of each parameter combination, wherein the parameter combination includes possible values ​​of the length of the segment and the corresponding possible values ​​of the number of segments;

[0017] The parameter combination with the smallest root mean square error is determined as the optimal segmentation parameter combination.

[0018] In some embodiments, sampling each segment according to the optimal segment range to obtain possible values ​​of the length of each segment and possible values ​​of the number of segments includes:

[0019] Within the optimal segmentation range, sampling is performed at intervals of a predetermined length to obtain possible values ​​of the length of each segment and possible values ​​of the number of segments.

[0020] In some embodiments, performing simulated optical proximity correction according to the optimal segmentation parameter combination to obtain optimal strip auxiliary graphic parameters includes:

[0021] Based on the optimal segmentation parameters, sampling the strip auxiliary graphic parameters to obtain possible values ​​of the strip auxiliary graphic parameters;

[0022] The possible values ​​of the optimal segmentation parameters and the strip auxiliary graphic parameters form a parameter matrix;

[0023] Performing a simulated optical proximity correction on the parameter matrix to obtain a root mean square error of each parameter combination, the parameter combination including possible values ​​of the optimal segmentation parameters and the strip auxiliary pattern parameters;

[0024] The possible values ​​of the strip auxiliary graphic parameters corresponding to the N groups of parameter combinations with the smallest root mean square errors are determined as the optimal strip auxiliary graphic parameters, where N is a natural number, N≥2.

[0025] The technical solution of this application has at least the following advantages:

[0026] The optimal segmentation range of the target pattern to be corrected is obtained through exposure parameters. The optimal parameter combination for optical proximity correction of the target pattern is determined based on the optimal segmentation range. The optimal parameters for inserting strip auxiliary patterns are determined based on the optimal parameter combination, and then the mask pattern is obtained. Subsequently, process verification can be carried out based on the mask pattern to obtain the optimal pattern solution, thereby improving the solution to the corner arc problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] 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.

[0028] Figure 1 is a flow chart of an optical proximity correction method provided by an exemplary embodiment of the present application;

[0029] Figure 2 This is a schematic diagram of an exemplary embodiment of the present application after a strip-shaped auxiliary pattern is inserted around the corner of a target pattern;

[0030] Figure 3 is a flow chart of an optical proximity correction method provided by an exemplary embodiment of the present application;

[0031] Figure 4 is a flow chart of an optical proximity correction method provided by an exemplary embodiment of the present application;

[0032] Figure 5 is a flowchart of an optical proximity correction method provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] refer to Figure 1 , which shows a flow chart of an optical proximity correction method provided by an exemplary embodiment of the present application, such as Figure 1 As shown, the method includes:

[0038] Step 101 : obtaining an optimal segmentation range of a target graphic according to exposure parameters, wherein the target graphic includes a first side and a second side adjacent to each other, and the first side and the second side constitute a corner of the target graphic.

[0039] The target pattern is a pattern requiring optical proximity correction and has a corner, which can be 90° or other angles. Prior to step 101, a pattern requiring optical proximity correction can be identified on the layout as a target pattern. For example, a pattern with a high degree of repetition per unit area (a threshold for the number of repetitions per unit area can be set; if the threshold is exceeded, the pattern is considered to have a high degree of repetition per unit area) can be identified as the target pattern.

[0040] At the same time, before step 101, a relatively simple OPC model must be constructed using exposure parameters and film layer information (including photoresist thickness and / or substrate reflectivity). The input parameters of this OPC model may include pattern information (including at least one of the size of the target pattern, the distance between target patterns, the coordinates of the inflection point, the length of the first side, and the length of the second side), a segment range (including the length range of each segment and at least one of the length and number of each segment), and stripe auxiliary pattern parameters (including at least one of the distance between the stripe auxiliary pattern and the corner, the distance between the stripe auxiliary patterns, and the width of the stripe auxiliary pattern). The output parameters may include the root mean square error (RMS) of each parameter combination (i.e., the RMS of the deviation between the simulated pattern edge and the target design position) and / or the mask pattern (a pattern after optical proximity correction, which may be a Post-OPC GDS file). Constructing a simple OPC model can implement optical proximity correction for thicker photolithography processes (generally, photoresists with a thickness greater than 500 nanometers (nm) are defined as thicker photoresists).

[0041] The exposure parameters are the parameters that the exposure machine that exposes the target pattern contributes to the optical proximity correction, and the required exposure parameters can be selected based on the established OPC model. For example, the exposure parameters may include the numerical aperture NA of the exposure machine, the exposure wavelength λ, and the light source parameter σ. The wavelength is used to characterize the wavelength of the light source used by the exposure machine, the numerical aperture is used to characterize the focusing ability of the lens of the exposure machine (which determines the resolution and depth of field), and the coherence coefficient is used to characterize the coherence parameter of the light source (which determines the interference characteristics of the light field). It should be noted that the exposure parameters required for the OPC model in the embodiment of the present application are only an example, and the exposure parameters input into the OPC model can be set by yourself according to the actual application scenario.

[0042] In optical proximity correction, the continuous edges of the target image need to be segmented, and each segment needs to be fine-tuned and corrected. Before performing segment correction, the optimal segment range needs to be determined. For example, Figure 2As shown, the first side L1 and the second side L2 of the target figure 200 are adjacent, and the first side and the second side constitute the corner of the target figure. In step 101, the first side and the second side need to be segmented and corrected. The optimal segmentation range is the range of length values ​​of each segment when the first side and the second side of the target figure are segmented and corrected.

[0043] For example, the segments of the first side L1 and the second side L2 may include a first corner segment SLC1, a second corner segment SLC2, a first subsegment SLRN, a second subsegment SLR2, and a third subsegment SLR3. The first corner segment SLC1 is a segment connected to the corner vertex on the first side, the second corner segment SLC2 is a segment connected to the corner vertex on the second side, the first subsegment SLRN is a segment adjacent to the first corner segment SLC1 on the first side, the second subsegment SLR2 is a segment adjacent to the first subsegment SLRN on the first side, and the third subsegment SLR3 is a segment adjacent to the second corner segment SLC2 on the second side.

[0044] Step 102 : Perform simulated optical proximity correction according to the optimal segmentation range to obtain an optimal segmentation parameter combination.

[0045] The optimal segmentation parameter combination is the one with the smallest root mean square error (RMS). The segmentation parameter combination includes the length and number of segments. The OPC model is used to simulate optical proximity correction for each segmentation parameter combination within the optimal segmentation range, obtaining the root mean square (RMS) error of each segmentation parameter combination. The segmentation parameter combination with the smallest RMS error is then determined as the optimal segmentation parameter combination.

[0046] Step 103 : Performing simulated optical proximity correction according to the optimal segmentation parameters to obtain optimal strip auxiliary pattern parameters. The strip auxiliary pattern is an auxiliary pattern inserted around the corner.

[0047] The strip auxiliary graphic is a graphic whose length is greater than its width. For example, it can be a rectangle, an ellipse, a rhombus, or a rounded rectangle. When the strip auxiliary graphic is not a rectangle but other strip graphics, it can be approximated as a rectangle, and the length of the approximated rectangle is used as the length of the strip auxiliary graphic, and the width of the approximated rectangle is used as the width of the strip auxiliary graphic. For example, if the strip auxiliary graphic is an ellipse, then twice the major axis of the ellipse is the length of the strip auxiliary graphic, and twice the minor axis of the ellipse is the width of the strip auxiliary graphic. Figure 2 As shown, the stripe-shaped auxiliary pattern 300 is inserted at the corner periphery side, and the central area (area a) generally does not have the stripe-shaped auxiliary pattern 300 inserted.

[0048] Optionally, the OPC model can be called to simulate optical proximity correction on the possible values ​​of the strip auxiliary graphics under the optimal segmentation parameters to obtain the root mean square error of each group of possible values, and the N groups of possible values ​​with the smallest root mean square error are determined as the optimal strip auxiliary graphics parameters, where N is a natural number, N≥2.

[0049] Step 104 : generating a mask pattern according to the optimal segmentation parameters and the optimal strip-shaped auxiliary pattern parameters. The mask pattern includes the corrected target pattern and the inserted strip-shaped auxiliary pattern.

[0050] The optimal segmentation parameter combination and the optimal strip auxiliary pattern parameters have been obtained through the above steps. The OPC model can be called to perform optical proximity correction based on the optimal segmentation parameter combination and the optimal strip auxiliary pattern parameters (the target pattern can be split into segments based on the optimal segmentation parameter combination to perform optical proximity correction to obtain a corrected target pattern, and the strip auxiliary pattern can be inserted based on the optimal strip auxiliary pattern parameters), and a mask pattern is generated, which includes the corrected target pattern and the inserted strip auxiliary pattern.

[0051] Optionally, the optimal strip auxiliary graphic parameters in step 103 include N groups of data, so the mask graphics obtained in step 104 include N groups of graphics. The N groups of graphics can be verified by the mask template design rules, and M (M is a natural number, M≤N) groups of graphics that meet the mask template design rules in the N groups of graphics are determined as candidate graphics. The M groups of candidate graphics are verified by the photolithography process, and finally the optimal layout graphics are obtained.

[0052] In summary, in the embodiments of the present application, the optimal segmentation range of the target pattern to be corrected is obtained through exposure parameters, the optimal parameter combination for optical proximity correction of the target pattern is determined based on the optimal segmentation range, and the optimal parameters for inserting the strip auxiliary pattern are determined based on the optimal parameter combination, thereby obtaining a mask pattern. Subsequently, process verification can be performed based on the mask pattern to obtain the optimal pattern solution, thereby improving the solution to the corner arc problem.

[0053] refer to Figure 3 , which actually shows a flow chart of an optical proximity correction method provided by an exemplary embodiment of the present application, the method is Figure 1 In an optional implementation of step 101 in the embodiment, the method includes:

[0054] Step 1011 , calculating the Nyquist value based on the numerical aperture, exposure wavelength, and light source parameters.

[0055] Exemplarily, step 1011 includes but is not limited to: calculating the Nyquist value according to the numerical aperture, exposure wavelength, and light source parameters using the following equation:

[0056]

[0057] Where Nyquist is the Nyquist value, NA is the numerical aperture, λ is the exposure wavelength, σ is the light source parameter, and A is a constant. The mapping relationship f between the Nyquist value and the numerical aperture, exposure wavelength, and light source parameters, as well as the constant A, can be set or calculated based on the actual application scenario.

[0058] Step 1012: Calculate and obtain the optimal segmentation range based on the Nyquist value.

[0059] For example, the optimal segmentation range can be calculated based on the Nyquist value using the following formula:

[0060] 1.5Nyquist≤P-SLC≤3Nyquist

[0061] 1.5Nyquist≤P-SLR≤3Nyquist

[0062] Where P-SLC is the length of each segment in the first side L1, and P-SLR is the length of each segment in the second side L2. It should be noted that the constants 1.5 and 3 in the above formula can be set according to actual application. Therefore, the constant 1.5 can be replaced by the constant C1, and the constant 3 can be replaced by the constant C2, as long as C1 < C2.

[0063] refer to Figure 4 , which shows a flowchart of an optical proximity correction method provided by an exemplary embodiment of the present application, the method is Figure 1 In an optional implementation of step 102 in the embodiment, the method includes:

[0064] Step 1021: Sample each segment according to the optimal segment range to obtain possible values ​​of the length of each segment and possible values ​​of the number of segments.

[0065] Exemplarily, step 1021 includes but is not limited to: within the optimal segmentation range, sampling is performed at intervals of a predetermined length (for example, the predetermined length may be 10 nanometers) to obtain possible values ​​of the length of each segment and possible values ​​of the number of segments.

[0066] For example, based on the optimal segmentation range, each segment can be sampled at intervals of a predetermined length using the following formula to obtain possible values ​​for the length of each segment and the number of segments:

[0067] L1=2·P-SLC1+2·P-SLRN+n1·P-SLR2

[0068] L2=2·P-SLC2+n2·P-SLR3

[0069] Among them, L1 is the length of the first side, L2 is the length of the second side, P-SLC1 is a possible value of the first corner segment SLC1, P-SLRN is a possible value of the first sub-segment SLRN, P-SLR2 is a possible value of the second sub-segment SLR2, P-SLC2 is a possible value of the second corner segment SLC2, P-SLR3 is a possible value of the third sub-segment SLR3, n1 is a possible value of the number of segments of the first side, and n2 is a possible value of the number of segments of the second side.

[0070] Step 1022: Construct a parameter matrix from the possible values ​​of the length of each segment and the possible values ​​of the number of segments.

[0071] For example, as described above, the parameter matrix formed according to the possible values ​​of the length of each segment and the possible values ​​of the number of segments can be:

[0072] P-SLC1=[50nm, 60nm, 70nm,...]

[0073] P-SLC2=[40nm, 50nm, 60nm,...]

[0074] P-SLRN=[30nm, 40nm, 50nm,…]

[0075] n1,n2∈[the range of integers allowed by Nyquist]

[0076] Step 1023 , simulate optical proximity correction is performed on the parameter matrix to obtain the root mean square error of each parameter combination, where the parameter combination includes possible values ​​of the length of the segment and the possible values ​​of the corresponding number of segments.

[0077] For example, the OPC model can be called to simulate the optical proximity correction of the parameter matrix to obtain the root mean square error of each parameter combination, where each parameter combination includes the possible values ​​of the length of the segment and the possible values ​​of the corresponding number of segments. The formula for calculating the root mean square error is:

[0078]

[0079] Among them, RMS is the root mean square error, EPE i is the deviation value of the i-th position (the deviation value between the simulated graphic edge and the target design position), W i is the weight of the deviation value at the i-th position, and n is the number of positions selected for the root mean square error (n and i are natural numbers, i ≥ 1, n ≥ i). It should be noted that the average root mean square error in the embodiment of the present application can be the deviation value at the corner only to simplify the calculation.

[0080] Step 1024: Determine the parameter combination with the smallest root mean square error as the optimal segmentation parameter combination.

[0081] refer to Figure 5 , which shows a flowchart of an optical proximity correction method provided by an exemplary embodiment of the present application, the method is Figure 1 In an optional implementation of step 103 in the embodiment, the method includes:

[0082] Step 1031 : Based on the optimal segmentation parameters, the strip auxiliary graphic parameters are sampled to obtain possible values ​​of the strip auxiliary graphic parameters.

[0083] Before executing step 1031, it may be determined whether a strip-shaped auxiliary graphic can be inserted. For example, it may be determined whether the following formula is satisfied:

[0084] Minimum spacing>w1+2·space1

[0085] Among them, the minimum spacing is the minimum spacing of the area where the target pattern is located, w1 is the width of the strip auxiliary pattern, and space1 is the distance between the strip auxiliary pattern and the corner.

[0086] like Figure 2 As shown, the strip auxiliary pattern parameters include the distance space1 between the strip auxiliary pattern 300 and the corner, the distance space2 between the strip auxiliary patterns 300, and the width w1 of the strip auxiliary pattern. For example, the area where the strip auxiliary pattern can be inserted can be determined based on the optimal segmentation parameters and the pattern information, and then the strip auxiliary pattern parameters can be sampled to obtain possible values ​​of the strip auxiliary pattern parameters.

[0087] Step 1032: Construct a parameter matrix from possible values ​​of the optimal segmentation parameter strip auxiliary graphic parameters.

[0088] The method of constructing the parameter matrix can be referred to Figure 4 Step 1022 in the embodiment is not described in detail here.

[0089] Step 1033 , simulate optical proximity correction on the parameter matrix to obtain the root mean square error of each parameter combination, where the parameter combination includes possible values ​​of the optimal segmentation parameters and strip auxiliary pattern parameters.

[0090] Exemplarily, the OPC model may be called to simulate optical proximity correction on the parameter matrix to obtain the root mean square error of each parameter combination, where each parameter combination includes possible values ​​of the optimal segmentation parameters and strip auxiliary pattern parameters.

[0091] Step 1034 : Determine the possible values ​​of the strip auxiliary graphic parameters corresponding to the N parameter combinations with the smallest root mean square errors as the optimal strip auxiliary graphic parameters.

[0092] As mentioned above, I will not elaborate on it here.

[0093] 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. An optical proximity correction method, characterized in that: include: Obtaining an optimal segmentation range of a target pattern according to the exposure parameters, the target pattern comprising a first side and a second side adjacent to each other, the first side and the second side constituting a corner of the target pattern, the optimal segmentation range being a range of length values ​​of each segment when performing segmentation correction on the first side and the second side; Performing simulated optical proximity correction according to the optimal segmentation range to obtain an optimal segmentation parameter combination, wherein the optimal segmentation parameter combination is a segmentation parameter combination with a minimum root mean square error, and the segmentation parameter combination includes the length of each segment and the number of segments; Performing simulated optical proximity correction based on the optimal segmentation parameter combination to obtain optimal strip auxiliary pattern parameters, wherein the strip auxiliary pattern is an auxiliary pattern inserted around the corner, and the optimal strip auxiliary pattern parameters are strip auxiliary pattern parameters with the smallest root mean square error, and the strip auxiliary pattern parameters include a distance between the strip auxiliary pattern and the corner, a distance between the strip auxiliary patterns, and a width of the strip auxiliary pattern; A mask pattern is generated according to the optimal segmentation parameters and the optimal strip-shaped auxiliary pattern parameters, wherein the mask pattern includes the corrected target pattern and the inserted strip-shaped auxiliary pattern.

2. The method according to claim 1, characterized in that The exposure parameters include the numerical aperture of the exposure machine, the exposure wavelength, and the light source parameters.

3. The method according to claim 2, characterized in that The step of obtaining an optimal segmentation range of the target graphic according to the exposure parameters includes: Calculating the Nyquist value according to the numerical aperture, exposure wavelength, and light source parameters; The optimal segmentation range is calculated based on the Nyquist value.

4. The method according to claim 1, wherein The performing of simulated optical proximity correction according to the optimal segmentation range to obtain an optimal segmentation parameter combination includes: Sampling each segment according to the optimal segment range to obtain possible values ​​of the length of each segment and possible values ​​of the number of segments; The possible values ​​of the length of each segment and the possible values ​​of the number of segments form a parameter matrix; Performing a simulated optical proximity correction on the parameter matrix to obtain a root mean square error of each parameter combination, wherein the parameter combination includes possible values ​​of the length of the segment and the corresponding possible values ​​of the number of segments; The parameter combination with the smallest root mean square error is determined as the optimal segmentation parameter combination.

5. The method according to claim 4, characterized in that The step of sampling each segment according to the optimal segment range to obtain possible values ​​of the length of each segment and possible values ​​of the number of segments includes: Within the optimal segmentation range, sampling is performed at intervals of a predetermined length to obtain possible values ​​of the length of each segment and possible values ​​of the number of segments.

6. The method according to claim 1, characterized in that The step of performing simulated optical proximity correction according to the optimal segmentation parameter combination to obtain optimal strip auxiliary graphic parameters includes: Based on the optimal segmentation parameters, sampling the strip auxiliary graphic parameters to obtain possible values ​​of the strip auxiliary graphic parameters; The possible values ​​of the optimal segmentation parameters and the strip auxiliary graphic parameters form a parameter matrix; Performing a simulated optical proximity correction on the parameter matrix to obtain a root mean square error of each parameter combination, the parameter combination including possible values ​​of the optimal segmentation parameters and the strip auxiliary pattern parameters; The possible values ​​of the strip auxiliary graphic parameters corresponding to the N groups of parameter combinations with the smallest root mean square errors are determined as the optimal strip auxiliary graphic parameters, where N is a natural number, N≥2.

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