Inversion photoetching method and system based on adaptive threshold regularization

Abstract

The invention discloses an inversion photoetching method and system based on adaptive threshold regularization, and relates to the technical field of inversion photoetching and optical proximity correction in computational lithography, and the method comprises the following steps: obtaining an initial mask image; performing optimization processing on the initial mask image by adopting a CTM algorithm to obtain a gray value mask containing SRAFs; performing region division to obtain a gray value mask after region division; performing dynamic threshold adjustment and optimization on each region in the gray value mask after region division by adopting a self-adaptive threshold regularization algorithm to obtain a binary mask containing SRAFs (Static Random Access Frameworks); the dynamic threshold adjustment optimization is to adjust the threshold corresponding to each region in the gray value mask after region division in a manner of adaptively adjusting a parameter a, and determine the optimal threshold of each region; the parameter a is a parameter in a regularization function in an adaptive threshold regularization algorithm. According to the invention, the binary mask can be obtained, the industrial manufacturing requirement is met, and the mask imaging precision is improved.

Description

Technical Field

[0001] The present application relates to the technical field of inversion lithography and optical proximity correction in computational lithography, and in particular to an inversion lithography method and system based on adaptive threshold regularization. Background Art

[0002] With the rapid development of semiconductor manufacturing technology, the design of integrated circuits has become increasingly complex, and the amount of mask layout data has surged to the billion level. In this context, the role of mask binarization is particularly significant. It ensures that the pattern can be transferred from the mask to the silicon wafer with extremely high accuracy during the lithography process. This step is crucial for accurately replicating the microscopic features in integrated circuits. In addition, the use of electron beam lithography machines to manufacture masks determines that optical proximity correction requires the output of a binary mask to prepare mask data. Therefore, mask binarization is not only the key to improving production efficiency, ensuring product quality, and ensuring manufacturability, but also an important driving force for technological innovation in the semiconductor industry.

[0003] The process of mask binarization involves converting the grayscale value mask into a binary form containing only two states: fully transparent and fully opaque. This conversion is crucial to meeting the standards in industrial production. However, the current traditional mask binarization technology relies on a single threshold method, which results in low precision in mask manufacturing and inaccurate mask imaging. Summary of the invention

[0004] The purpose of the present application is to provide an inversion lithography method and system based on adaptive threshold regularization, which can improve the accuracy of mask imaging.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In a first aspect, the present application provides an inversion lithography method based on adaptive threshold regularization, and the inversion lithography method based on adaptive threshold regularization includes the following steps.

[0007] An initial mask image is obtained; the initial mask image is a mask image obtained after initializing a target mask, and the target mask is a mask to be processed.

[0008] The initial mask image is optimized by using a CTM algorithm to obtain a grayscale value mask containing SRAFs.

[0009] According to the grayscale value of each area in the grayscale value mask containing SRAFs, the grayscale value mask containing SRAFs is divided into regions to obtain a grayscale value mask after region division.

[0010] An adaptive threshold regularization algorithm is used to dynamically adjust and optimize the threshold values ​​of each region in the gray value mask after the region division to obtain a binary mask containing SRAFs; the dynamic threshold adjustment optimization refers to adjusting the threshold values ​​corresponding to each region in the gray value mask after the region division by adaptively adjusting parameter a, thereby determining the optimal threshold value for each region; the parameter a refers to a parameter in the regularization function in the adaptive threshold regularization algorithm.

[0011] In a second aspect, the present application provides an inversion lithography system based on adaptive threshold regularization, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the inversion lithography method based on adaptive threshold regularization described in the first aspect.

[0012] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0013] The present application provides an inversion lithography method and system based on adaptive threshold regularization, which optimizes the initial mask image through the CTM algorithm to obtain a gray value mask containing SRAFs. This optimization process based on the CTM algorithm can preliminarily guarantee the accuracy of mask imaging. By dividing the gray value mask containing SRAFs into regions, it is convenient to adjust the threshold of each region in the gray value mask containing SRAFs. By introducing the parameter a in the regularization function, the threshold corresponding to each region in the gray value mask after the region division is adjusted by adaptively adjusting the parameter a, so as to determine the optimal threshold of each region, and finally obtain a more accurate and reliable binary mask containing SRAFs. This method can improve the details of each region, thereby reflecting the characteristics of the overall mask and improving the accuracy of mask imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A schematic flow chart of an inversion lithography method based on adaptive threshold regularization provided in one embodiment of the present application.

[0016] Figure 2 A schematic diagram of an inversion lithography method based on adaptive threshold regularization provided in one embodiment of the present application.

[0017] Figure 3 This is a graph showing how the regularization function value of the adjustment parameter a varies with the mask value in the range of 0 to 0.5 according to an embodiment of the present application.

[0018] Figure 4 A graph showing how the regularization function value of the adjustment parameter a varies with the mask value in the range of 0.5 to 1 according to an embodiment of the present application.

[0019] Figure 5 This is a loss comparison curve diagram of the imaging process of the adaptive threshold method, CTM algorithm and single threshold method provided in one embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] like Figure 1 As shown, this embodiment proposes an inversion lithography method based on adaptive threshold regularization. The inversion lithography method based on adaptive threshold regularization specifically includes the following steps.

[0022] Step S1, obtaining an initial mask image. The initial mask image is a mask image obtained after initializing a target mask, and the target mask is a mask to be processed.

[0023] Step S2: using a CTM (Continuous Transmission Mask) algorithm to optimize the initial mask image and obtain a grayscale value mask containing SRAFs (Sub-Resolution Assist Features).

[0024] Step S3: According to the distribution of the grayscale values ​​of each region in the grayscale value mask containing SRAFs, the grayscale value mask containing SRAFs is divided into regions to obtain a grayscale value mask after region division.

[0025] In this embodiment, the gray value mask after area division includes a main feature area and SRAFs areas of different orders. Among them, the main feature area refers to the main graphic area of ​​the gray value mask obtained by the CTM algorithm. Based on the main graphic area, the main graphic area diffuses outward, and according to the distance and grayscale distribution, different grayscale areas are obtained in sequence, which are called SRAFs areas of different orders. The main graphic area is a graphic similar to the target mask part, and then according to the distance and grayscale value, the main graphic area is expanded outward in sequence to form a first-order SRAFs area, a second-order SRAFs area, and a third-order SRAFs area.

[0026] Step S4, using an adaptive threshold regularization algorithm, dynamically adjust and optimize the thresholds of each region in the gray value mask after region division to obtain a binary mask containing SRAFs. The dynamic threshold adjustment optimization refers to adjusting the thresholds corresponding to each region in the gray value mask after region division by adaptively adjusting parameter a, thereby determining the optimal threshold for each region. Parameter a refers to a parameter in the regularization function in the adaptive threshold regularization algorithm.

[0027] In this embodiment, step S1 acquires an initial mask image, which specifically includes the following steps.

[0028] Step S11, reading the pattern of the target mask.

[0029] Step S12: Perform initialization processing according to the graphics of the target mask to obtain an initial mask image.

[0030] In this embodiment, step S2 uses the CTM algorithm to optimize the initial mask image to obtain a gray value mask containing SRAFs, which specifically includes the following steps.

[0031] The CTM algorithm is used to optimize the pixels of the initial mask image, and the pixels of 0 and 1 are optimized to pixels between 0 and 1, so as to obtain a gray value mask containing SRAFs. Among them, the pixels of the initial mask image are 0 and 1, and the pixels of the gray value mask containing SRAFs are between 0 and 1.

[0032] In this embodiment, step S4 adopts an adaptive threshold regularization algorithm to perform dynamic threshold adjustment and optimization on each region in the gray value mask after region division to obtain a binary mask containing SRAFs, which specifically includes the following steps.

[0033] Step S41, using an adaptive threshold regularization algorithm, adding the regularization function of the adaptive threshold regularization algorithm to the objective function of the CTM algorithm in the form of a regularization term, the objective function including the loss function and the regularization term. Taking the minimization of the objective function as the optimization goal, the gray value mask after the region division is optimized, and before the optimization starts, an initial parameter a is set for each region in the gray value mask after the region division.

[0034] Step S42: Adopting the gradient descent method, by adaptively adjusting the initial parameter a corresponding to each region, the threshold corresponding to each region is iteratively optimized and adjusted, so that the optimal threshold corresponding to each region can be determined, and a binary mask containing SRAFs is obtained.

[0035] In this embodiment, the regularization function is added to the objective function in the form of a regularization term. Therefore, the regularization function is actually a regularization term. In the regularization function, the following relationship exists between the regularization function value and the mask value of the regularization function.

[0036] (1) When the mask value is 0 and 1, the regularization function value is the smallest and both are 0.

[0037] (2) When the mask value is any value between 0 and 1, the regularization function value has only one controllable maximum value. By adjusting the size of parameter a, the maximum value of the regularization function value can be adjusted so that the regularization function reaches the maximum value under different mask values.

[0038] The principle of the inversion lithography method based on adaptive threshold regularization in this embodiment is as follows.

[0039] Based on the graphics of the target mask, the CTM algorithm is first used to optimize the initial mask image. In this process, the pattern error is set as the objective function to ensure the high accuracy of imaging. After completing this step, the gray value mask containing SRAFs can be obtained; secondly, according to the distribution of the gray value of the gray value mask containing SRAFs, the gray value mask containing SRAFs is dynamically divided into the main feature area and SRAFs areas of different orders, laying the foundation for subsequent fine optimization; then the regularization function is used, which is integrated into the objective function. This is the key to achieve adaptive threshold adjustment and ensure that each area can achieve the optimal binarization effect; finally, the main feature area and SRAFs areas of different orders are jointly optimized. In this process, the optimal threshold of each area can be adaptively adjusted, and the global optimal solution is gradually approached through iterative optimization, and finally the accurate binarization of the mask image is achieved, which can significantly improve the accuracy of mask imaging and effectively solve the problem of low accuracy of traditional methods.

[0040] This embodiment introduces a regularization term that can adaptively adjust the threshold, which not only optimizes the binarization problem in mask design and manufacturing, but also significantly improves the accuracy of mask imaging. It dynamically adjusts the threshold to adapt to the diversity and subtle differences of lithography masks, thereby overcoming the limitations of traditional mask binarization technology in processing complex patterns.

[0041] In order to make the technical solution of this embodiment clearer, the specific implementation steps of the technical solution of this embodiment are described in detail below in the form of examples.

[0042] Step 101: read the pattern of the target mask and initialize to obtain an initial mask image.

[0043] Step 102, using the CTM algorithm to optimize the initial mask image. In order to ensure the accuracy of mask imaging, edge placement error (EPE) and pattern error (PE) are used as indicators to evaluate the accuracy of mask imaging. In this embodiment, the pattern error is defined as the loss function in the optimization process. In practical applications, the edge placement error or other indicators can also be defined as the loss function. This is used as the objective function of the subsequent optimization steps. The objective function is designed to minimize the error of mask imaging and ensure the high fidelity of the mask image. The formula is as follows.

[0044] in, To calculate the loss, and Respectively represent the number of rows and columns of the target graph, and are the row and column indices respectively, represents the target graphic, Represents the wafer image, represents the initial mask image, Represents the imaging of the mask after each optimization.

[0045] In this embodiment, after the optimization is completed, the CTM algorithm will generate a pixel map with intensity values ​​between 0 and 1, that is, a grayscale value mask containing SRAFs.

[0046] Step 103: A grayscale value mask including SRAFs can be obtained through step 102. According to the grayscale value distribution, the grayscale value mask including SRAFs is dynamically divided into a main feature region and SRAFs regions of different orders.

[0047] Step 104: The main feature area of ​​the gray value mask and the SRAFs areas of different orders can be obtained through step 103. In order to dynamically adjust the optimal threshold that meets each area, this embodiment designs a regularization function, which reaches the minimum value when the mask value is 0 and 1, both of which are 0, and there is only one controllable maximum point in the interval of the mask value from 0 to 1. This embodiment defines this parameter as a. By adjusting the parameter a, this maximum point can be flexibly adjusted, so the threshold can be adaptively adjusted using this regularization function. Figure 3 The graph of the regularization function value of the adjustment parameter a as the mask value changes from 0 to 0.5. Figure 4 It is a graph showing the change of the regularization function value of the adjustment parameter a with the mask value in the range of 0.5 to 1, where: Figure 3 and Figure 4 The six curves in the figure represent the specific changes of the regularization function by adjusting the value of parameter a. Figure 3 and Figure 4 It can be seen that the regularization function reaches its minimum value when the mask value is 0 and 1, both of which are 0, and there is only one controllable maximum point in the interval of mask value from 0 to 1, and this maximum point can be adjusted by adjusting the parameter a.

[0048] Step 105, adding the regularization function in step 104 as a regularization term to the objective function, which is essentially a cost function, with minimizing the cost function as the goal. At the same time, in step 105, it is necessary to select a suitable weight for the regularization term so that it can ensure the accuracy of mask imaging and the obtained mask is in a binary form.

[0049] Step 106: Jointly optimize the main feature region and SRAFs regions of different orders in step 103, assign an initial parameter a to each region, and adaptively adjust the parameter a corresponding to each region through the gradient descent method, and then determine whether the cost function is less than the set value. If so, stop adjusting and optimizing, and obtain the optimal threshold that meets each region. Otherwise, repeatedly iteratively adjust the parameter a until the cost function is less than the set value, indicating that all regions have their own optimal thresholds, thereby obtaining a binary mask containing SRAFs.

[0050] Step 107: Verify the effectiveness of the technical solution of this embodiment by comparing it with the traditional method. The method of this embodiment is compared with the mask optimized by only using the CTM algorithm and the single threshold algorithm in the optimization process. Figure 5The loss comparison curves of the imaging process of the adaptive threshold regularization method, CTM algorithm and single threshold algorithm of this embodiment show that although the loss of the mask after the CTM algorithm optimization is small, the final loss is 66.2696, but the optimized mask is a gray value mask, not a binary mask, which does not meet the actual production and manufacturing requirements. Although the single threshold method completes the binarization of the mask, its loss is too large, and the final loss is 130.3158, which cannot guarantee the accuracy of mask manufacturing, and the accuracy of mask imaging is low. However, by adopting the method of this embodiment, the mask finally obtained not only meets the binary mask required for production and manufacturing, but also takes into account the accuracy of mask imaging.

[0051] This embodiment can adaptively adjust the threshold of each region to achieve accurate matching of the optimal threshold of each region. Compared with the traditional inversion lithography method, this embodiment not only optimizes the binarization effect of the local region, but also helps to discover the global optimal solution, thereby significantly improving the accuracy of mask imaging. This adaptive threshold adjustment mechanism provides strong support for the accurate binarization of the mask and meets the strict requirements of high-end semiconductor manufacturing. In addition, by dividing the grayscale value mask containing SRAFs into multiple regions and applying an adaptive threshold regularization adjustment strategy to each region, not only the flexibility of the design is ensured, but also the process of finding the global optimal solution is effectively promoted. Therefore, the accuracy of mask binarization can be significantly improved, the accuracy of mask imaging can be guaranteed, and a more accurate and efficient mask production technology is provided for the semiconductor manufacturing field.

[0052] This embodiment adopts an adaptive threshold regularization adjustment strategy to ensure the simulation degree and manufacturability of the mask during the mask optimization process. First, the CTM algorithm is used to optimize the initial mask image to generate a gray value mask containing SRAFs; then the generated gray value mask containing SRAFs is dynamically divided into main feature areas and SRAFs areas of different orders according to the distribution of its gray value; then, the gray value mask after regional division is used as input to optimize it; at the same time, the adaptive threshold regularization adjustment strategy is integrated into the mask optimization process. During the optimization process, the main feature areas and SRAFs areas of different orders of the gray value mask after regional division are dynamically adjusted. The optimization result selects the most appropriate threshold for each area, and ensures the accuracy of mask imaging, and finally obtains a binary mask containing SRAFs. Among them, this embodiment designs a regularization function specifically for optimizing the mask binarization process. The regularization function reaches its minimum value when the mask value is 0 and 1, both of which are 0, and there is only one controllable maximum point in the interval of mask value from 0 to 1. By adjusting the parameter a, this maximum point can be flexibly changed. The regularization function can achieve precise control of the threshold. This design enables different areas of the mask image to automatically adapt to the optimal threshold, effectively ensuring the manufacturability of the binary mask and the accurate presentation of SRAFs.

[0053] In an exemplary embodiment, an inversion lithography system based on adaptive threshold regularization is also provided, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned inversion lithography method based on adaptive threshold regularization.

[0054] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0055] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An inversion lithography method based on adaptive threshold regularization, characterized in that: The inversion lithography method based on adaptive threshold regularization includes: Acquire an initial mask image; the initial mask image is a mask image obtained after initializing a target mask, and the target mask is a mask to be processed; The initial mask image is optimized by using a CTM algorithm to obtain a gray value mask including SRAFs; According to the grayscale values ​​of each region in the grayscale value mask containing the SRAFs, the grayscale value mask containing the SRAFs is divided into regions to obtain a grayscale value mask after the region division; An adaptive threshold regularization algorithm is used to dynamically adjust and optimize the threshold values ​​of each region in the gray value mask after the region division to obtain a binary mask containing SRAFs; the dynamic threshold adjustment optimization refers to adjusting the threshold values ​​corresponding to each region in the gray value mask after the region division by adaptively adjusting parameter a, thereby determining the optimal threshold value for each region; the parameter a refers to a parameter in the regularization function in the adaptive threshold regularization algorithm.

2. The inversion lithography method based on adaptive threshold regularization according to claim 1, characterized in that: Obtain an initial mask image, including: Read the pattern of the target mask; An initialization process is performed according to the pattern of the target mask to obtain an initial mask image.

3. The inversion lithography method based on adaptive threshold regularization according to claim 1, characterized in that: The initial mask image is optimized using the CTM algorithm to obtain a gray value mask containing SRAFs, specifically including: The pixels of the initial mask image are optimized by using a CTM algorithm, and the pixels of 0 and 1 are optimized to pixels between 0 and 1, thereby obtaining the gray value mask containing SRAFs; the pixels of the initial mask image are 0 and 1, and the pixels of the gray value mask containing SRAFs are between 0 and 1.

4. The inversion lithography method based on adaptive threshold regularization according to claim 1, characterized in that: The loss function of the CTM algorithm is the pattern error.

5. The inversion lithography method based on adaptive threshold regularization according to claim 4, characterized in that: The expression of the loss function is: ； in, To calculate the loss, and Respectively represent the number of rows and columns of the target graph, and are the row and column indices respectively, represents the target graphic, Represents the wafer image, represents the initial mask image, Represents the imaging of the mask after each optimization.

6. The inversion lithography method based on adaptive threshold regularization according to claim 1, characterized in that: Adopting an adaptive threshold regularization algorithm, dynamically adjusting and optimizing the threshold values ​​of each region in the gray value mask after the region division, and obtaining a binary mask containing SRAFs, specifically including: Adopting the adaptive threshold regularization algorithm, adding the regularization function of the adaptive threshold regularization algorithm to the objective function of the CTM algorithm in the form of a regularization term, optimizing the gray value mask after the region division with minimization of the objective function as the optimization goal, and setting an initial parameter a for each region in the gray value mask after the region division during the optimization process; The gradient descent method is used to adaptively adjust the initial parameter a corresponding to each region to determine the optimal threshold corresponding to each region, and obtain a binary mask containing SRAFs.

7. An inversion lithography system based on adaptive threshold regularization, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the inversion lithography method based on adaptive threshold regularization according to any one of claims 1 to 6.

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