Method for establishing opc model, electronic device, storage medium and program product

Abstract

The present disclosure provides a method, an electronic device, a storage medium and a program product for establishing an OPC model. The method comprises: determining a plurality of parameter items related to the OPC model, wherein each parameter item comprises a corresponding parameter entry set; for each parameter item, determining a weight coefficient set associated with the corresponding parameter entry set of the parameter item, wherein each weight coefficient in the weight coefficient set is within a corresponding boundary range; and constructing the OPC model based on a plurality of weight coefficient sets respectively associated with the plurality of parameter items.

Description

Technical Field

[0001] This disclosure relates primarily to integrated circuits, and more specifically to methods for establishing optical proximity correction (OPC) models, electronic devices, computer-readable storage media, and computer program products. Background Technology

[0002] Photolithography is a crucial process in integrated circuit manufacturing. It utilizes photochemical reactions and chemical and physical etching methods to transfer patterns pre-prepared on a mask onto a substrate. The photolithography process can be described using optical and chemical models and mathematical formulas. When light shines on the mask, it diffracts; this diffraction is collected by a projection lens and converges onto the photoresist surface—this imaging process is optical. The image projected onto the photoresist excites a photochemical reaction, and after baking, the photoresist becomes locally soluble in the developer—this is a chemical process.

[0003] Typically, the pattern on a mask is projected onto photoresist using an exposure system. Due to imperfections in the optical system and diffraction effects, the pattern on the photoresist and the pattern on the mask are not perfectly identical. Optical proximity correction (OPC) uses computational methods to correct the pattern on the mask, ensuring that the pattern projected onto the photoresist conforms as closely as possible to the design requirements. Summary of the Invention

[0004] According to an example embodiment of this disclosure, a scheme for establishing an optical proximity correction (OPC) model is provided.

[0005] In a first aspect of this disclosure, a method for constructing an OPC model is provided. The method includes: determining a plurality of parameter terms associated with the OPC model, wherein each parameter term includes a corresponding set of parameter entries; for each parameter term, determining a set of weight coefficients associated with the corresponding set of parameter entries for that parameter term, wherein each weight coefficient in the set of weight coefficients is within a corresponding boundary range; and constructing the OPC model based on the plurality of weight coefficient sets associated with the plurality of parameter terms respectively.

[0006] In a second aspect of this disclosure, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to perform the method according to the first aspect of this disclosure.

[0007] In a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to a first aspect of this disclosure.

[0008] In a fourth aspect of this disclosure, a computer program product is provided, which includes program code that, when executed by a processor, implements the method according to a first aspect of this disclosure.

[0009] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0011] Figure 1 A schematic diagram of an example architecture in which embodiments of the present disclosure can be implemented is shown;

[0012] Figure 2 A flowchart of a method for establishing an OPC model according to an embodiment of the present disclosure is shown;

[0013] Figure 3 A flowchart of a method for determining a set of weight coefficients associated with a corresponding set of parameter entries for each parameter item, according to an embodiment of the present disclosure, is shown.

[0014] Figure 4 A flowchart of a method for determining the association effect associated with a parameter item according to an embodiment of the present disclosure is shown;

[0015] Figure 5 A schematic diagram illustrating the implementation process for determining an initial set of coefficients according to an embodiment of the present disclosure is shown;

[0016] Figure 6 A flowchart of a method for determining a set of weight coefficients associated with each of a plurality of parameter items, according to embodiments of the present disclosure, is shown; and

[0017] Figure 7 A block diagram of an electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0018] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0019] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] As briefly mentioned earlier, optical proximity correction (OPC) uses computational methods to correct the pattern on a mask so that the pattern projected onto the photoresist conforms as closely as possible to the design requirements. Model-based optical proximity correction (hereinafter referred to as "OPC model") has been widely used. Establishing an OPC model is a crucial step in the optical proximity correction process. When establishing an OPC model, fitting calculations are performed to minimize the difference between the simulation results and the actual measurement results; for example, minimizing the difference between the pattern simulated by the OPC model and the photoresist pattern.

[0021] In the process of modeling the OPC model, algorithms are typically used to simulate a series of optical and photoresistive effect parameters in the lithography machine. The optimal solution is obtained by optimizing the corresponding algorithm parameters, thereby establishing the OPC model. The algorithm parameters can include a series of influence factors or weight coefficients for each of the optical and photoresistive effect parameters.

[0022] One related technique is to use a genetic algorithm for OPC modeling. In this genetic algorithm, a specified number of model parameters are generated based on an initial OPC seed model and algorithm parameters (e.g., maximum, minimum, step size, etc.). The optimal solution is then obtained by calculating the generated model parameters, thereby establishing an OPC model with improved accuracy. However, in practical applications, genetic algorithms tend to result in slow iterative convergence of the OPC model, and the optimal solution is strongly correlated with the generated algorithm parameters. Therefore, the ability of genetic algorithms to find the optimal solution is limited.

[0023] Another related technique employs a linear regression algorithm based on the Least Absolute Value Shrinkage and Selection Operator (LASSO) to build an OPC model. However, this method may suffer from overfitting, meaning that the influence factors calculated using this method may be too large or too small. Influence factors that are too large or too small cannot be used to build an accurate OPC model.

[0024] To at least address the aforementioned problems, embodiments of this disclosure provide a method for establishing an OPC model. The method includes: determining multiple parameter items associated with the OPC model, wherein each parameter item includes a corresponding set of parameter entries; for each parameter item, determining a set of weight coefficients associated with the corresponding set of parameter entries, wherein each weight coefficient in the set of weight coefficients is within a corresponding boundary range; and constructing the OPC model based on the multiple sets of weight coefficients associated with the multiple parameter items respectively.

[0025] The method for building an OPC model disclosed herein is based on controlling the weight coefficients to model the OPC model. By controlling the weight coefficients for each parameter during the OPC modeling process, overfitting can be prevented during the modeling and solving process. Furthermore, the obtained weight coefficients can be flexibly set within the range required by the user, based on the actual needs of the lithography process, thereby further improving the accuracy and flexibility of the OPC model.

[0026] Figure 1 Example environments 100 in which various embodiments of this disclosure can be implemented are shown. For example... Figure 1 As shown, electronic device 110 can receive a first mask pattern 102 and a target pattern 104. The target pattern 104 is a complete or partial wafer pattern desired to be obtained on a silicon wafer. The first mask pattern 102 can be a mask pattern of a complete circuit layout or a portion thereof.

[0027] A first mask pattern 102 is associated with a target pattern 104. The first mask pattern 102 may be a mask pattern determined based on the target pattern 104. In some embodiments, the first mask pattern 102 may be the same as the target pattern 104. In some embodiments, the first mask pattern 102 may be a modified target pattern 104.

[0028] In some embodiments, an OPC model 112 is deployed in the electronic device 110. The OPC model 112 can use computational methods to modify a mask pattern (e.g., a first mask pattern 102) and obtain a modified mask pattern (e.g., a second mask pattern 106), so that the pattern projected onto the photoresist conforms as closely as possible to the design requirements, for example, it is closer to the target pattern 104.

[0029] In some embodiments, the OPC model 112 in the electronic device 110 can determine the pattern edges in the target pattern 104 based on the received target pattern 104, and can obtain the pattern edges of the simulated pattern of the first mask pattern 102 after photolithography through simulation operations. The OPC model 112 in the electronic device 110 can compare the pattern edges in the target pattern 104 with the pattern edges of the simulated pattern to determine the edge placement error. The OPC model 112 corrects the first mask pattern 102 by minimizing or making the edge placement error an acceptable value, and outputs a second mask pattern 106. Accordingly, the second mask pattern 106 is a corrected version of the first mask pattern 102 obtained based on the OPC model 112. Compared with the wafer pattern obtained based on the first mask pattern 102, the wafer pattern obtained based on the second mask pattern 106 is closer to the target pattern 104.

[0030] In the process of establishing an OPC model, an important indicator for evaluating the model's fit is the linewidth (critical dimension; CD, also known as the "key dimension"). For example, the measured linewidth of the lithographic pattern obtained after the test pattern is exposed on photoresist can be measured. The fit of the OPC model is evaluated by comparing this measured linewidth with the simulated linewidth in the simulated pattern obtained by simulating the test pattern using the OPC model. The smaller the difference between the measured and simulated linewidths, the better the fit of the OPC model, and correspondingly, the higher the accuracy of the model. In addition, one or more other indicators can be used to evaluate the accuracy of the OPC model, and this disclosure does not limit this method.

[0031] In some embodiments, electronic device 110 may determine multiple parameter items associated with an OPC model, wherein each parameter item includes a corresponding set of parameter entries. For each parameter item, electronic device 110 may determine a set of weight coefficients associated with the corresponding set of parameter entries for that parameter item, wherein each weight coefficient in the set of weight coefficients is within a corresponding boundary range. Electronic device 110 may construct the OPC model based on the multiple sets of weight coefficients associated with the multiple parameter items respectively.

[0032] Electronic device 110 can be any device with computing capabilities. As a non-limiting example, electronic device 110 can be any type of fixed electronic device, mobile electronic device, or portable electronic device, including but not limited to desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, multimedia computers, mobile phones, smart home devices, wearable electronic devices, etc. In some embodiments, all or some components of electronic device 110 can be distributed in the cloud. This disclosure does not limit the specific type of electronic device 110.

[0033] Figure 2 A flowchart of a method 200 for establishing an OPC model according to some embodiments of the present disclosure is shown. For ease of discussion, it will be combined with... Figure 1 Let's describe method 200. Figure 2 Method 200 can be found in Figure 1 The steps are executed at electronic device 110 and any suitable electronic device. Furthermore, the numbers in the flowchart do not indicate the order in which these steps are executed; some or all of these steps may be executed in parallel, or their execution order may be interchanged, and this disclosure does not limit this.

[0034] In block 202, electronic device 110 can determine multiple parameter items associated with the OPC model. In some embodiments, each parameter item includes a corresponding set of parameter entries.

[0035] In some embodiments, the plurality of parameter items include one or more of the following: parameter items for optical effects, or parameter items for photoresistive effects.

[0036] Parameters related to optical effects may include, but are not limited to, one or more of the following: light source distribution, illumination intensity, light source depth of focus, and lens aperture value. Each parameter may also include a corresponding set of parameter entries, which may include one or more parameter entries. For example, the set of parameter entries corresponding to the parameter item light source distribution may include one or more of the following parameter entries: light source shape, partial coherence factor, polarization direction, degree of polarization, angle of incidence, light intensity uniformity, symmetry, phase distribution, and imaging-related derivative parameters. The set of parameter entries corresponding to the parameter item illumination intensity may include one or more of the following parameter entries: exposure dose, scanning / exposure speed, dose uniformity, etc. The set of parameter entries corresponding to the parameter item light source depth of focus may include one or more of the following parameter entries: mask pattern and periodicity, projection lens numerical aperture, aberrations, etc. The set of parameter entries corresponding to the parameter item lens aperture value may include one or more of the following parameter entries: numerical aperture, aperture stop, maximum angle of incidence, process window, etc. It is understood that the above is only illustrative and for explanatory purposes. This disclosure does not limit the parameters for optical effects or the parameters included in each parameter.

[0037] In some embodiments, the parameters for photoresistivity may include, but are not limited to, one or more of the following: parameters simulating acid-base diffusion effects and parameters simulating shrinkage after drying. Each parameter may also include a corresponding set of parameter entries, which may include one or more parameter entries. For example, the set of parameter entries corresponding to the parameter for simulating acid-base diffusion effects may include one or more of the following parameter entries: exposure parameters, diffusion parameters, reaction parameters, thermal process parameters, etc. The set of parameter entries corresponding to the parameter for simulating shrinkage after drying may include one or more of the following parameter entries: material property parameters, process parameters, shrinkage and stress parameters, etc. It is understood that the above is merely illustrative and for purposes of explanation. This disclosure does not limit the parameters for optical effects or the parameters included in each parameter.

[0038] In the following text, for ease of description, each parameter term is defined as P, and each parameter term includes corresponding parameter entries x. Accordingly, for a parameter term Pi, it can be represented as Pi = {x} i1 ,x i2 ,…,x ik}, where i is the number of parameter terms used in the OPC model, and i is a positive integer greater than or equal to 1, and k is the number of parameter entries in parameter term Pi. It is understood that different parameter terms may have the same or different number of parameter entries, and this disclosure does not impose any limitation on this. Taking the parameter term light source distribution as an example, it can be represented as the parameter term light source distribution P1={ x 11 ,x 12 , x 13 ,x 14 , x 15 ,x 16 , x 17 ,x 18 , x 19}, where x 11 Used to characterize the shape of the light source, x 12 Used to characterize the partial coherence factor, x 13 Used to characterize polarization direction, x 14 Used to characterize the degree of polarization, x 15 Used to characterize the angle of incidence, x 16 Used to characterize the uniformity of light intensity, x 17 Used to characterize symmetry, x 18 Used to characterize the phase distribution, and x 19 These are used to characterize imaging-related derived parameters. Other parameters can be described and understood in a similar manner to the examples above, and will not be elaborated upon here for the sake of brevity.

[0039] In some embodiments, each parameter associated with the OPC model has a corresponding effect or influence on the lithography process. For example, parameter P1 produces effect E1, parameter P2 produces effect E2, and so on. Accordingly, the total effect of all parameter terms in the OPC model on the lithography process can be referred to as the total effect E. all Therefore, E all =E1+E2+…+Ei (where i is the number of parameter terms used in the OPC model).

[0040] In block 204, for each parameter item, the electronic device 110 can determine a set of weight coefficients associated with the corresponding set of parameter entries for that parameter item. In some embodiments, each weight coefficient in the set of weight coefficients is within a corresponding boundary range.

[0041] In some embodiments, each parameter entry in the parameter terms has a corresponding influence factor or weight coefficient, used to characterize the degree of influence of the corresponding parameter entry on the photolithography process. The weight coefficients corresponding to each parameter entry constitute a weight coefficient group associated with the parameter entry set of that parameter term. The weight coefficients in the weight coefficient group, together with the corresponding parameter entries, generate the influence effect of that parameter entry on the photolithography process. The following will be illustrated using an OPC model with three parameter terms (P1, P2, and P3), each parameter term having two parameter entries (i.e., the number of parameter entries in the parameter entry set included in each parameter term is 2).

[0042] For parameter P1, which includes two parameter entries, parameter P1 can be represented as: P1 = {x} 11 ,x 12 Similarly, parameter P2 and parameter P3 can be represented as: P2 = { x 21 ,x 22}, and P3={ x 31 ,x 32}

[0043] In some embodiments, each parameter entry in each parameter item has a corresponding weighting coefficient. For example, parameter entry x in parameter item P1 11 With weighting coefficient β 11 Parameter entry x in parameter item P1 12 With weighting coefficient β 12 Weighting coefficient β 11 and β 12 The weighted coefficient group associated with parameter term P1; the parameter entries x in parameter term P2 21 With weighting coefficient β 21 Parameter entry x in parameter item P2 22 With weighting coefficient β22 Weighting coefficient β 21 and β 22 The weight coefficient group associated with parameter term P2 is formed; and the parameter entries x in parameter term P3 are... 31 With weighting coefficient β 31 Parameter entry x in parameter item P3 32 With weighting coefficient β 32 Weighting coefficient β 31 and β 32 The weighted coefficient group is associated with parameter P3.

[0044] It is understood that the above examples are merely illustrative and for purposes of explanation. The number of parameter items in an OPC model can be arbitrary, and the number of parameter items in the corresponding parameter item set for each parameter item can also be arbitrary; this disclosure does not impose any limitations on this.

[0045] In some embodiments, weighting coefficients and corresponding parameter entries together generate the effect of the parameter entry on the photolithography process. Electronic device 110 can determine the effect of a parameter entry based on multiple parameter entries and their corresponding weighting coefficients. Electronic device 110 can sum the effects of multiple parameter entries to determine the total effect of multiple parameter entries related to the OPC model on the photolithography process.

[0046] For example, the total effect can be determined based on the following formula (1):

[0047] E all = x 11 β 11 + x 12 β 12+ x 21 β 21+ x 22 β 22+ x 31 β 31+ x 32 β 32 (1)

[0048] In some embodiments, each weight coefficient in the weight coefficient group lies within a corresponding boundary range. In other words, the weight coefficients associated with each parameter entry lie within a corresponding boundary range, which can be in numerical form and can be represented as [a, b], where a and b can be any values ​​greater than 0, less than 0, or equal to 0, and a can be less than b. The weight coefficients can lie within the boundary range and can include boundary values.

[0049] In some embodiments, the boundary range corresponding to each weighting coefficient can be a default setting. In some embodiments, the boundary range corresponding to each weighting coefficient can be preset. The boundary range can be associated with processes, optical effects or photoresist effects, patterns on layers, etc. For example, the user can specify the boundary range based on parameter entry x. j Various factors, such as the manufacturing process, optical or photoresist effects, and patterns, are used to set the value of parameter x. j The corresponding boundary ranges for the weight coefficients. This disclosure does not limit the values ​​of the corresponding boundary ranges for each weight coefficient. The specific implementation process for determining the weight coefficients will be described in detail below with reference to the accompanying drawings.

[0050] In some embodiments, electronic device 110 iteratively executes the steps in block 204. Electronic device 110 may determine the weight coefficient group for each parameter item in a predefined order. This predefined order may be determined according to a standard, and is not limited thereto in this disclosure.

[0051] In box 206, electronic device 110 can construct an OPC model based on multiple weight coefficient groups that are associated with multiple parameter items respectively.

[0052] In some embodiments, the electronic device 110 constructs an OPC model based on the weight coefficient set for each parameter item determined in step 204. The electronic device 110 may construct the OPC model based on various known or future-developed technologies, and this disclosure does not limit this.

[0053] Advantageously, the method for building an OPC model proposed herein is based on controlling the weight coefficients to model the OPC model. By controlling the weight coefficients for each parameter during the OPC modeling process, overfitting can be prevented during the modeling and solving process. Furthermore, the solved weight coefficients can be flexibly set within the range required by the user, based on the actual needs of the lithography process, thereby further improving the accuracy and flexibility of the OPC model.

[0054] Figure 3 A flowchart illustrating a method 300 for determining a set of weight coefficients associated with a corresponding set of parameter entries for each parameter item, according to an embodiment of this disclosure, is shown. (This will be combined with...) Figure 1 Let's describe method 300. Figure 3 Method 300 can be found in Figure 1The steps are executed at electronic device 110 and any suitable electronic device. Furthermore, the numbers in the flowchart do not indicate the order in which these steps are executed; some or all of these steps may be executed in parallel, or their execution order may be interchanged, and this disclosure does not limit this. Figure 3 The method described above can be applied to every parameter item; in other words, it can be applied when determining the set of weight coefficients associated with the corresponding set of parameter entries for each parameter item. Figure 3 The method in the middle.

[0055] At box 302, electronic device 110 can determine an initial set of coefficients for the set of corresponding parameter entries for the parameter item based on the association effect associated with the parameter item and based on the minimum angle regression algorithm.

[0056] In some embodiments, the association effect associated with a parameter item can be the residual effect in the total effect after removing the effects of one or more parameter items whose weight coefficients have been determined. Let's continue with the example of the three parameter items P1, P2, and P3 above. Assuming that the corresponding weight coefficients for parameter item P1 have been determined, i.e., the effect E1 produced by parameter item P1 has been determined, then for parameter item P2, its association effect is the residual effect after removing the effect E1 produced by P1 from the total effect. Similarly, assuming that the corresponding weight coefficients for parameter items P1 and P2 have been determined, i.e., the effect E1 produced by parameter item P1 and the effect E2 produced by parameter item P2 have been determined, then for parameter item P3, its association effect is the residual effect after removing the effects E1 produced by P1 and the effect E2 produced by P2 from the total effect.

[0057] The following will combine Figure 4 This describes the process of determining the associated effects of parameter terms. Figure 4 A flowchart is shown for a method 400 for determining the association effect associated with a parameter item according to an embodiment of the present disclosure.

[0058] At block 402, electronic device 110 can determine one or more determined parameter items from a plurality of parameter items. In some embodiments, one or more weight coefficient groups associated with each of the one or more determined parameter items have been determined. In other words, a determined parameter item can refer to a parameter item whose associated weight coefficient group has been determined. For example, among parameter items P1, P2, and P3, if the weight coefficient associated with parameter item P1 has been determined, then parameter item P1 can be a determined parameter item.

[0059] At box 404, electronic device 110 can determine the deterministic effect associated with one or more determined parameter items based on the weight coefficients in one or more determined weight coefficient groups. As previously mentioned, the weight coefficients are used to characterize the influence or effect of the corresponding parameter item on the photolithography process. Electronic device 110 can multiply the determined weight coefficients with the corresponding parameter items to determine the influence of the parameter item on the photolithography process. Accordingly, for a parameter item, the influence or effect of the parameter item on the photolithography process, i.e., the deterministic effect, can be determined by calculating the product of all parameter items in the parameter item with the corresponding weight coefficients. Since the weight coefficient associated with parameter item P1 has been determined, electronic device 110 can determine the deterministic effect of parameter item P1 using formula (2).

[0060] E1=x 11 β 11 + x 12 β 12 (2)

[0061] At box 406, electronic device 110 can remove the deterministic effect determined at box 404 from the total effect to obtain the associated effect related to the parameter item of the weight coefficient to be determined.

[0062] For example, let's continue with the three parameters P1, P2, and P3 from the previous text. Assume that the corresponding weighting coefficients for parameter P1 have been determined, and the definite effect E1 has been identified—that is, the effect E1 produced by parameter P1 can be determined. Then, for parameter P2, whose weighting coefficients are yet to be determined, its association effect is the residual effect after removing the effect E1 produced by P1 from the total effect. In other words, the association effect of parameter P2 is (E... all -E1).

[0063] Return now Figure 3 After determining the association effect associated with the parameter item, the electronic device 110 can determine the initial coefficient set for the corresponding set of parameter entries for that parameter item at block 302 based on the minimum angular regression algorithm. The following will combine... Figure 5 Describe the process by which electronic device 110 determines the initial coefficient set based on the minimum angle regression algorithm. Figure 5 A schematic diagram illustrating the implementation process for determining an initial set of coefficients according to an embodiment of the present disclosure is shown.

[0064] Figure 5 The following example illustrates this: For parameter P1, parameter P1 includes two parameter entries x. 11 and x 12 And the association effect associated with parameter P1 is E, such as Figure 5 As shown.

[0065] In some embodiments, the electronic device 110 can determine the parameter item among multiple parameter items that has the highest correlation (e.g., distance) with the association effect E. The electronic device 110 can sort the multiple parameter items according to the correlation between each parameter item and the association effect E, thereby determining a predetermined order. In some embodiments, the electronic device 110 can determine the correlation between a parameter item and the association effect based on the angle between the parameter item and the association effect. The smaller the angle, the greater the correlation. Figure 5 For example, in two parameter entries, parameter entry x 11 and x 12 Among them, parameter entry x 11 The angle between the associated effect E and the parameter entry x is smaller than that between the two. 12 The angle between the associated effect E and the parameter x is such that the electronic device 110 can determine the parameter entry x. 11 The correlation with the correlation effect E is stronger. Therefore, the electronic device 110 can also determine the two parameter entries x. 11 and x 12 The predetermined order is x 11 x 12 .

[0066] In some embodiments, during the first calculation phase, for parameter entry x 11 Perform calculations. Electronic device 110 can transmit parameter entry x... 11 Along the parameter entry x 11 The direction is shifted according to a predetermined step size, and after each shift, the residual Ex between the shifted parameter entry and the associated effect is calculated. This is in response to the current parameter entry x. 11 The degree of association with the associated effect E is determined by the next parameter item (parameter item x in this example) in a predetermined order. 12 The correlation degree is consistent with the correlation effect E, that is, the residual lies on the angle bisector of the two parameter items (e.g., Figure 5 (As indicated by the dashed line in the diagram), the next parameter entry can then be added to the calculation process, i.e., the second calculation stage. Electronic device 110 can determine the coefficient C1 determined in the first calculation stage as the coefficient for parameter entry x in that first calculation stage. 11 A definite coefficient.

[0067] After adding the next parameter entry in the manner described above, the electronic device 110 can continue to add parameter entry x in the second calculation stage in the same manner as described above. 11 and x 12 along Figure 5 The dashed line in the diagram is shifted, and the coefficients C2 and C3 in this stage are determined.

[0068] The electronic device 110 can continue to the next calculation stage in the manner described above, and in each calculation stage, parameter entries are added to the calculation in a predetermined order. The electronic device 110 can repeat the above steps until the calculated residual with respect to the associated effect is sufficiently small, or until all parameter entries for that parameter item have been calculated.

[0069] After determining the coefficients obtained in each calculation stage, the electronic device 110 can determine the corresponding initial coefficients for each parameter item. In some embodiments, the electronic device 110 can use the coefficients determined for each parameter item in the last calculation stage as the initial coefficients determined for that parameter item in the minimum angle regression algorithm, where the initial coefficients represent the initial weight coefficients of that parameter item. Accordingly, the electronic device 110 can obtain a set of initial coefficients for that parameter item, wherein the initial coefficients in the set are arranged in a predetermined order (i.e., a predetermined order determined according to the degree of association between the parameter item and the associated effect).

[0070] Understandable, Figure 5 The implementation process described is merely illustrative; the electronic device 110 can be configured for any number of parameter entries according to... Figure 5 The implementation process calculates the initial coefficients for each parameter entry.

[0071] In some embodiments, the electronic device 110 may determine the weight coefficient of a parameter entry based on the corresponding boundary range of each parameter entry and the initial coefficient of each parameter entry. In some embodiments, for parameter item Pi={x i1 ,x i2 ,…,x ik}, in accordance with Figures 3-5 After the implementation process described herein has determined the corresponding initial coefficients for each parameter entry in the parameter item, the electronic device 110 can determine the weight coefficients of each parameter entry.

[0072] In some embodiments, the electronic device 110 may sequentially obtain the initial coefficients to be updated from the initial coefficient set based on a predetermined order. In some embodiments, the predetermined order is determined based on the degree of association between the parameter entries in the corresponding parameter entry set of the parameter items and the associated effect. In other words, after the predetermined order for the parameter entries is determined, the order of the initial coefficients associated with them is also determined accordingly.

[0073] For each acquired initial coefficient, the electronic device 110 can compare the value of the initial coefficient with the boundary range of the parameter entry corresponding to the initial coefficient. In response to the initial coefficient value exceeding the corresponding boundary range, the electronic device 110 can update the initial coefficient value to a first boundary value within the corresponding boundary range. In some embodiments, the electronic device 110 can update the initial coefficient value to a boundary value within the corresponding boundary range that is closer to the initial coefficient value. For example, if the corresponding boundary range includes a first boundary value and a second boundary value, in response to the difference between the first boundary value and the initial coefficient being less than the difference between the second boundary value and the initial coefficient, the electronic device 110 can update the initial coefficient value to the first boundary value within the corresponding boundary range. In some embodiments, in response to the initial coefficient value being within the corresponding boundary range, the electronic device 110 can maintain the initial coefficient value.

[0074] The electronic device 110 can sequentially obtain the initial coefficients to be updated from the initial coefficient group according to a predetermined order, and update the value of each initial coefficient to be updated in the manner described above, thereby determining the corresponding weight coefficient for each parameter entry in the set of corresponding parameter entries for that parameter item.

[0075] By modeling the OPC model based on the control of weight coefficients, overfitting problems can be prevented during the modeling and solving process. Furthermore, the weight coefficients obtained from the solution can be flexibly set within the range required by the user according to the actual needs of the lithography process, thereby further improving the accuracy and flexibility of the OPC model.

[0076] In some embodiments, after determining the weighting coefficients for a parameter item, the electronic device 110 can determine the deterministic effect of that parameter item based on the weighting coefficients and each parameter entry, and can also remove the deterministic effect of that parameter item from the association effect to obtain an updated association effect, and continue according to... Figures 3-5 The method involves determining the corresponding weight coefficient for each of the remaining parameter items. In some embodiments, the electronic device 110 may sort multiple parameter items related to the OPC model according to a pre-set standard, and sequentially determine the set of weight coefficients associated with each parameter item in that order. It is understood that the association effect (the effect produced by removing the parameter item with determined weight coefficients from the total effect) is different for different parameter items.

[0077] Figure 6 A flowchart illustrating a method 600 for determining a group of weight coefficients associated with each of a plurality of parameter items, according to an embodiment of the present disclosure. (This will be combined with...) Figure 1 To describe method 600. Figure 6 Method 600 can be found in Figure 1 The steps are executed at electronic device 110 and any suitable electronic device. Furthermore, the numbers in the flowchart do not indicate the order in which these steps are executed; some or all of these steps may be executed in parallel, or their execution order may be interchanged, and this disclosure does not limit this.

[0078] In box 602, electronic device 110 can determine, for the current parameter item, an initial set of coefficients associated with the set of corresponding parameter entries for that parameter item based on a minimum angle regression algorithm. Electronic device 110 can then proceed as follows: Figure 5 The exemplary process described determines the initial set of coefficients for this parameter item, which will not be elaborated here for the sake of simplicity.

[0079] At block 604, after determining the initial coefficient set of the corresponding parameter entries for the current parameter item, electronic device 110 can obtain the initial coefficient to be updated from the initial coefficient set for the current parameter item. At block 606, electronic device 110 compares the value of the initial coefficient with the corresponding boundary range to determine whether the value of the initial coefficient is within the corresponding boundary range. In response to the value of the initial coefficient being within the corresponding boundary range, electronic device 110 maintains the value of the initial coefficient as the weight coefficient corresponding to the initial coefficient at block 612. In response to the value of the initial coefficient exceeding the corresponding boundary range, electronic device 110 updates the value of the initial coefficient to a first boundary value within the corresponding boundary range at block 608. In some embodiments, electronic device 110 may update the value of the initial coefficient to a boundary value within the corresponding boundary range that is closer to the value of the initial coefficient.

[0080] At block 610, electronic device 110 determines whether all initial coefficients in the current initial coefficient group have been updated. In response to the fact that all initial coefficients in the current initial coefficient group have not yet been updated, at block 618, electronic device 110 retrieves the next initial coefficient to be updated from the current initial coefficient group in a predetermined order. In some embodiments, the predetermined order may be determined based on the correlation between the parameter entries in the corresponding parameter entry set of the parameter item and the associated effect, which can be understood with reference to the preceding description; for the sake of brevity, it will not be repeated here.

[0081] Electronic device 110 iteratively executes the operations at boxes 604, 606, 608 (or 612; depending on the judgment result at box 606), 610, and 618 until all initial coefficients in the initial coefficient group have been updated.

[0082] In response to the completion of updating all initial coefficients in the current initial coefficient set, electronic device 110 proceeds to box 619 and determines whether the determination of weight coefficients for all parameter items is complete. If complete, electronic device 110 proceeds to the operation in box 620, and the method flow ends. If not yet complete, electronic device 110, at box 614, removes the determined effect of the current parameter item from the current association effect to obtain the updated association effect. At box 616, electronic device 110 executes the step of obtaining the next parameter item and proceeds to box 602 to iteratively specify the step of obtaining the initial coefficient set for the next parameter item. Electronic device 110 can follow... Figure 6 The steps are executed sequentially until the weight coefficient group for each parameter item is obtained.

[0083] You can refer to the relevant process mentioned above to understand this. Figure 6 For the sake of brevity, the various steps involved will not be described in detail here.

[0084] Table 1 shows a comparison of the weighting coefficients obtained using the method according to embodiments of the present disclosure and the weighting coefficients obtained using related technologies under the same conditions.

[0085] Table 1

[0086] As can be seen from the data in Table 1, under the same conditions, the method according to the embodiments of this disclosure can prevent overfitting problems in the modeling and solving process, and can flexibly set the weight coefficients obtained by solving to a range that meets the user's needs according to the actual requirements of the lithography process, thereby further improving the accuracy and flexibility of the OPC model.

[0087] Figure 7 A schematic block diagram of an example device 700 that can be used to implement embodiments of the present disclosure is shown. Device 700 can be used to implement... Figure 1 The electronic device 110. As shown, the device 700 includes a processing unit 701, such as a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) 702 or loaded from storage unit 708 into random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of the device 700. The processing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0088] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0089] Processing unit 701 executes the various methods and processes described above, such as methods 200, 300, 400, and 600. For example, in some embodiments, methods 200, 300, 400, and 600 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by processing unit 701, one or more steps of methods 200, 300, 400, and 600 described above may be performed. Alternatively, in other embodiments, processing unit 701 may be configured to execute methods 200, 300, 400, and 600 by any other suitable means (e.g., by means of firmware).

[0090] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0091] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0093] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0094] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for establishing an optical proximity effect corrected OPC model, comprising: Identify multiple parameter items associated with the OPC model, where each parameter item includes a set of corresponding parameter entries; For each parameter item, a set of weighted coefficients is determined that is associated with the set of corresponding parameter entries for the parameter item, wherein each weighted coefficient in the set of weighted coefficients is within a corresponding boundary range. This includes: determining an initial set of coefficients for the set of corresponding parameter entries for the parameter item based on the association effect associated with the parameter item and based on a minimum angle regression algorithm, wherein the association effect represents the residual effect after removing the effect of one or more parameter items for which the weighted coefficient set has been determined from the total effect generated by the plurality of parameter items; and updating the value of the initial coefficient based on the corresponding boundary range of the initial coefficient for each initial coefficient in the initial set of coefficients to obtain the set of weighted coefficients associated with the set of corresponding parameter entries for the parameter item. as well as The OPC model is constructed based on multiple weight coefficient groups associated with the multiple parameter items respectively.

2. The method according to claim 1, wherein the plurality of parameter items includes one or more of the following: a plurality of parameter items for optical effects, or a plurality of parameter items for photoresistive effects.

3. The method of claim 2, wherein the plurality of parameters for the optical effect include two or more of the following: light source distribution, light intensity, light source depth of focus, and lens aperture value.

4. The method of claim 2, wherein the plurality of parameters for the photoresist effect include two or more of the following: simulated acid-base diffusion effect parameters and simulated shrinkage effect parameters after drying.

5. The method of claim 1, wherein one or more initial coefficients in the initial coefficient set are respectively associated with one or more parameter entries in the corresponding parameter entry set of the parameter item.

6. The method according to claim 5, further comprising: One or more determined parameter items are determined from among the plurality of parameter items, wherein one or more weight coefficient groups associated with each of the one or more determined parameter items have been determined; Based on the weight coefficients in the determined weight coefficient groups, a deterministic effect associated with one or more determined parameter items is determined, wherein the deterministic effect represents the influence or effect of the parameter item on the photolithography process; as well as The deterministic effect is removed from the total effect produced by the plurality of parameter terms to obtain the associated effect related to the parameter terms.

7. The method of claim 5, wherein updating the value of the initial coefficient based on the corresponding boundary range of the initial coefficient comprises: In response to the value of the initial coefficient exceeding the corresponding boundary range, the value of the initial coefficient is updated to a first boundary value within the corresponding boundary range; or In response to the value of the initial coefficient being within the corresponding boundary range, the value of the initial coefficient is maintained.

8. The method of claim 7, wherein the corresponding boundary range includes the first boundary value and the second boundary value, and wherein the difference between the first boundary value and the initial coefficient is less than the difference between the second boundary value and the initial coefficient.

9. The method of claim 7, wherein updating the value of the initial coefficient based on the corresponding boundary range of the initial coefficient further comprises: The initial coefficients to be updated are obtained sequentially from the initial coefficient set according to a predetermined order. The predetermined order is determined based on the degree of association between the parameter entries in the corresponding parameter entry set of the parameter item and the associated effect.

10. An electronic device, the device comprising: One or more processors; as well as A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to perform the method as described in any one of claims 1-9.

11. A computer-readable storage medium having a computer program stored thereon, the computer program implementing the method as described in any one of claims 1-9 when executed by a processor.

12. A computer program product comprising program code that, when executed by a processor, implements the method as described in any one of claims 1-9.

Images