Methods, apparatuses, and media for layout processing

Abstract

According to embodiments of this disclosure, a method, apparatus, and medium for pattern processing are provided. In the method, parameter information related to a lithography machine and a wafer in a lithography process is acquired. The lithography machine is used to form a wafer pattern on the wafer based on a target pattern. Based on the parameter information, a target state representation corresponding to the target pattern is determined from a plurality of candidate state representations indicating the electron beam scattering state. Based on the target state representation, a correction result for the target pattern is determined. In this manner, the state representation of the electron beam scattering state corresponding to the target pattern can be determined based on the parameter information of the lithography machine used and the parameter information of the wafer used for lithography, thereby simulating the lithography process more accurately. This ensures that the state representation used for lithography process simulation can more accurately describe the scattering state of the electron beam used by the lithography machine, improving the reliability of the simulation process and thus more accurately determining the correction result for the target pattern.

Description

Technical Field

[0001] The embodiments of this disclosure primarily relate to the field of integrated circuits, and more specifically, to methods, apparatus, and media for layout processing. Background Technology

[0002] With the rapid development of integrated circuit manufacturing technology, the process nodes of traditional integrated circuits are gradually decreasing, and the size of integrated circuit devices is constantly shrinking. Wafer manufacturing processes involve precisely transferring the patterns of different layers onto a wafer at a certain scale using photolithography. Direct-write electron beam lithography (EBL) is a high-precision nanofabrication technology that uses a focused electron beam to directly write patterns onto a substrate coated with electron resist, without the need for a photomask. However, current direct-write electron beam lithography technology has some problems that affect wafer quality. Summary of the Invention

[0003] In a first aspect of this disclosure, a method for layout processing is provided. In this method, parameter information relating to a lithography apparatus and a wafer in a lithography process is acquired, the lithography apparatus being used to form a wafer pattern on the wafer based on a target layout; based on the parameter information, a target state representation corresponding to the target layout is determined from a plurality of candidate state representations indicating electron beam scattering states; and based on the target state representation, a correction result for the target layout is determined.

[0004] In a second aspect of this disclosure, a method for determining the electron beam scattering state is provided. In this method, based on the corresponding energy distributions of multiple sample electron beams on a sample wafer, multinomial sample reference energy deposition data corresponding to each of the multiple sample electron beams are determined, wherein the multiple sample electron beams have different cross-sections; based on the cross-sectional characteristics of each sample electron beam, multinomial sample simulated energy deposition data corresponding to each of the multiple sample electron beams are obtained; and based on the multinomial sample reference energy deposition data and the multinomial sample simulated energy deposition data, multiple candidate sample state representations are determined, wherein the multiple candidate sample state representations indicate the scattering state of the multiple sample electron beams.

[0005] In a third aspect of this disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled to the processor. The memory has instructions stored therein, which, when executed by the processor, cause the electronic device to perform a method for layout processing according to a first aspect of this disclosure and / or a method for determining the electron beam scattering state according to a second aspect.

[0006] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium. When executed by a processor, the computer program implements the method for layout processing according to the first aspect of this disclosure and / or the method for determining the electron beam scattering state according to the second aspect.

[0007] As will be understood from the following description, according to some embodiments of this disclosure, parameter information related to the lithography equipment and wafer in the lithography process is obtained. The lithography equipment is used to form a wafer pattern on the wafer based on a target layout. Based on the parameter information, a target state representation corresponding to the target layout is determined from multiple candidate state representations indicating the electron beam scattering state. Based on the target state representation, a correction result for the target layout is determined. In this way, the state representation of the electron beam scattering state corresponding to the target layout can be determined based on the parameter information of the lithography equipment used and the parameter information of the wafer used for lithography, thereby simulating the lithography process more accurately. Thus, on the one hand, it ensures that the state representation used for lithography process simulation can more accurately describe the scattering state of the electron beam used by the lithography equipment, improving the accuracy and reliability of the simulation process. On the other hand, when changing the lithography equipment or the electron beam type of the lithography equipment, the point spread function used to correct the target layout can be quickly and accurately determined, thereby determining the correction result for the target layout more accurately.

[0008] It should be understood that the content described in this summary section 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

[0009] 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: Figure 1 A schematic diagram of an example environment in which the various embodiments of this disclosure can be implemented is shown; Figure 2 A flowchart illustrating an example process for layout processing according to some embodiments of the present disclosure is shown; Figure 3 A flowchart is shown as an example process for determining candidate state representations according to some embodiments of the present disclosure; Figure 4 A schematic diagram of energy deposition data according to some embodiments of the present disclosure is shown; Figure 5 A flowchart illustrating an example process for determining the electron beam scattering state according to some embodiments of the present disclosure is shown; and Figure 6 A block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented is shown. Detailed Implementation

[0010] 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.

[0011] 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.

[0012] Figure 1 A schematic diagram of an example environment 100 in which the various embodiments of the present disclosure can be implemented is shown. The example environment 100 may generally include an electronic device 110.

[0013] Electronic device 110 acquires a target layout 120 (also called a "design layout") as input. The target layout 120 includes one or more patterns to be processed, such as target pattern 122. It is expected that a pattern corresponding to target pattern 122 can be obtained on the wafer after photolithography. To prevent distortion of the image formed on the wafer due to light diffraction, the placement of each pattern in the target layout 120 needs to be determined through simulation signals. It should be understood that... Figure 1 The layouts, masks, and shapes, sizes, and numbers of target graphics shown are merely exemplary and not limiting. The scope of this disclosure is not limited in this respect.

[0014] Electronic device 110 uses a point spread function, which represents the electron beam scattering state, to simulate the photolithography process for the target layout 120 (i.e., the design layout) to obtain the processed target layout 120. The processed target layout 120 includes a processed target pattern 122 (also referred to as the processed target pattern), which is a modified target pattern 122. Compared to the target pattern 122 in the target layout 120, the size and / or position of the processed target pattern 122 in the processed target layout 120 has changed. For example, in the processed target layout 120, the processed target pattern 122 is the output pattern after OPC, which can also be referred to as the "OPC-post pattern". The aforementioned changes in size and / or position of the processed target pattern 122 compared to the unprocessed target pattern 122 can be determined by electronic device 110. In other words, the displacement during the OPC process can be determined by electronic device 110.

[0015] In example environment 100, electronic device 110 can be any type of computing-capable device, including terminal devices or server devices. Terminal devices can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. Server devices can include, for example, computing systems / servers, such as mainframes, edge computing nodes, computing devices in cloud environments, and so on.

[0016] It should be understood that the structure and function of environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure. Figure 1 The target layout 120, the processed target layout 120, and the graphics therein shown are merely exemplary and are not intended to limit the scope of this disclosure. Exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings.

[0017] As mentioned earlier, in direct-write electron beam lithography, the lithography machine uses a high-energy electron beam to scan and expose a substrate coated with electron resist point by point or shape, according to a pre-designed pattern data file (i.e., the design layout). The exposed substrate is then immersed in a specific chemical developer to form the lithographic pattern. However, the scattering of high-energy electrons (e.g., forward scattering and backscattering) causes adjacent areas to also receive a certain amount of exposure, resulting in a difference between the lithographic pattern and the design pattern.

[0018] Currently, most methods utilize proximity effect correction algorithms to modify design patterns and compensate for neighboring regions. During the modification process, the point spread function, used to describe the electron beam scattering state, can be used to simulate the photolithography process and generate a simulated photolithography pattern. Based on the differences between the simulated pattern and the design pattern, the design pattern is then modified. Therefore, how to more accurately simulate the photolithography process to improve the accuracy of the simulation is a problem that urgently needs to be solved by those skilled in the art.

[0019] Some embodiments of this disclosure provide a scheme for layout processing. In this scheme, parameter information related to the lithography equipment and wafer in the lithography process is acquired. The lithography equipment is used to form a wafer pattern on the wafer based on a target layout. Based on the parameter information, a target state representation corresponding to the target layout is determined from a plurality of candidate state representations indicating the electron beam scattering state. Based on the target state representation, a correction result for the target layout is determined.

[0020] In this way, based on the parameter information of the lithography machine and the wafer used for lithography, the state representation of the electron beam scattering state corresponding to the target layout can be determined, thereby simulating the lithography process more accurately. This ensures, on the one hand, that the state representation of the simulated lithography process can more accurately describe the scattering state of the electron beam used by the lithography machine. On the other hand, when changing the lithography machine or the type of electron beam, the point spread function used to correct the target layout can be quickly and accurately determined, thus better determining the proximity effect correction result for the target layout.

[0021] The following description, with reference to the accompanying drawings, outlines exemplary embodiments of this disclosure.

[0022] Figure 2 A flowchart of an example process 200 for determining a candidate state according to some embodiments of the present disclosure is shown. In some embodiments, process 200 may be performed by, for example... Figure 1 The illustrated electronic device 110 (e.g., a layout processing device) performs the operation. It should be understood that process 200 may also include additional boxes not shown and / or some (or some) of the boxes shown may be omitted, and the scope of this disclosure is not limited in this respect.

[0023] In frame 210, electronic device 110 can acquire parameter information related to the lithography equipment and wafer in the lithography process. The lithography equipment is used to form a wafer pattern on the wafer based on a target layout. The target layout is the design layout used for the lithography process.

[0024] In some embodiments, for a specific lithography task, the electronic device 110 may first determine the lithography machine used to perform the task and the wafer used for lithography to obtain parameter information related to the lithography machine and parameter information related to the wafer. In some embodiments, the parameter information related to the lithography machine may include the cross-sectional type of the electron beam related to the lithography machine. In some embodiments, the parameter information related to the lithography machine may include electron beam attribute information (e.g., the diameter of the electron beam used by the lithography machine, the cross-section of the electron beam, the electron beam step size or scanning mode, etc.), scanning positioning and overlay information (e.g., primary / secondary deflector resolution, laser interferometer positioning, real-time height measurement, etc.), and exposure dose and write speed information (e.g., exposure dose and write speed, etc.). In some embodiments, the parameter information related to the wafer may include wafer information, such as geometric and surface information (e.g., wafer diameter / thickness, global flatness or surface roughness, etc.), material and optical / charge property information (e.g., conductive layer, reflectivity / stack or coefficient of thermal expansion, etc.), and photoresist information, etc.

[0025] In block 220, electronic device 110 can determine the target state representation corresponding to the target layout from a plurality of candidate state representations indicating the electron beam scattering state based on parameter information.

[0026] In some embodiments, the state representation of the electron beam (also referred to as the "point spread function") indicates the scattering state of the electron beam, which can be used to describe the energy distribution absorbed in the electron resist layer. The electronic device 110 can use the state representation of the electron beam to accurately predict the exposure pattern corresponding to the design layout.

[0027] In some embodiments, the electronic device 110 may pre-store multiple state representations (i.e., candidate state representations) corresponding to multiple different electron beams. For example, electron beam A has a rectangular cross-section with a dimension B, corresponding to a first state representation. Electron beam C has a circular cross-section with a dimension D, corresponding to a second state representation.

[0028] Figure 3 A flowchart of an example process 300 for determining candidate state representations according to some embodiments of the present disclosure is shown. Process 300 can be performed by, for example... Figure 1 The illustrated electronic device 110 (e.g., a layout processing device) performs this operation. Figure 3As shown in box 310, electronic device 110 simulates electron beams of different sizes and shapes. For example, electronic device 110 can determine multiple different electron beams by changing conditions such as the cross-sectional shape, size, and energy of the electron beam. Electronic device 110 can determine the initial state representation (i.e., the initial point spread function) of each electron beam through calculation or lookup. Initial state representations are mostly default functions, with low accuracy and reliability, and cannot accurately describe the electron beam scattering state.

[0029] Subsequently, for each of the multiple electron beams, the electronic device 110 can determine the energy distribution of the electron beam on the wafer using a simulation model based on the electron beam information of that electron beam, as one of the corresponding energy distributions. The electron beam information includes at least one of the following: the cross-sectional type of the electron beam (e.g., circular, rectangular, or triangular), the cross-sectional size of the electron beam, or the energy intensity of the electron beam.

[0030] For example, electronic device 110 can use experimental methods such as Monte Carlo simulation to simulate multiple electron beams of different shapes (including rectangles, triangles, circles, etc.) and sizes to obtain corresponding simulation results representing the actual energy distribution of each electron beam on the wafer. Based on the corresponding energy distribution of multiple electron beams on the wafer, electronic device 110 can determine multiple reference energy deposition data corresponding to each of the multiple electron beams.

[0031] In some embodiments, the reference energy deposition data can be an energy deposition map. In block 320, the electronic device 110 can calculate from the determined multiple reference energy deposition data to obtain energy deposition maps of electron beams with different cross-sectional sizes and shapes. For example, the electronic device 110 can process multiple simulation results separately, such as performing energy statistics on each level to obtain an energy deposition map corresponding to the simulation result of each electron beam.

[0032] For example, for a target electron beam among multiple electron beams, the electronic device 110 may first divide the wafer into multiple regions (e.g., divide the wafer into...). The electronic device 110 can then determine the energy distribution of a target electron beam in multiple regions of the wafer based on the energy distribution of a specific electron beam (e.g., the target electron beam) determined above on the wafer. Subsequently, based on the energy distributions of the multiple regions, reference energy deposition data (i.e., an energy deposition map) corresponding to the target electron beam is determined as one of multiple reference energy deposition data. In some embodiments, if the electron beam shape is circular, the electronic device 110 can perform radial normalization on the simulation results to ensure that the energy sums to 1, thereby obtaining the trend of energy variation along the radial direction of the cross-section, as one of multiple reference energy deposition data.

[0033] Subsequently, the electronic device 110 can simulate the energy distribution of the corresponding electron beams on the wafer using the initial state representation to obtain simulated energy deposition data corresponding to each of the multiple electron beams. In some embodiments, the electronic device 110 can simulate the energy distribution of the electron beams in different ways according to different cross-sectional characteristics of the target electron beams. For example, if the cross-section of an electron beam is circular, the energy distribution of the electron beam can be simulated using curve fitting. If the cross-section of an electron beam is triangular, the energy distribution of the electron beam can be simulated using two-dimensional fitting.

[0034] In some embodiments, for a target electron beam among multiple electron beams, the electronic device 110 can simulate the energy distribution of the target electron beam on the wafer based on the cross-sectional characteristics of the target electron beam and using an initial state representation indicating the electron beam scattering state. Thus, the electronic device 110 can acquire simulated energy deposition data corresponding to the target electron beam.

[0035] For example, in box 330, the electronic device 110 can determine whether the cross-section of the electron beam is circular. If the cross-section of the electron beam is circular, proceed to box 340. If the cross-section of the electron beam is not circular, proceed to box 350.

[0036] In box 340, electronic device 110 can simulate energy distribution using a curve fitting algorithm. Exemplarily, the cross-sectional features include the radius of the cross-section, and the reference energy deposition data includes the trend of energy variation along the radius of the cross-section. For a target electron beam among multiple electron beams, electronic device 110 can fit the reference energy deposition data based on the radius of the electron beam cross-section using an initial state representation (i.e., a point spread function) corresponding to the target electron beam. Exemplarily, the initial state representation can be a bigaussian function model. The bigaussian function model can be expressed as: (1) in The function values ​​are from the double Gaussian function model. These are the weighting coefficients. The diffusion scale parameter for the first Gaussian component is... This is the diffusion scale parameter for the second Gaussian component. The center of the Gaussian distribution, This is the normalization factor.

[0037] Electronic device 110 can use a double Gaussian function model to fit the trend of energy variation along the radial direction of the cross section in order to determine the simulated energy deposition data corresponding to the target electron beam.

[0038] Electronic device 110 can update the initial state representation based on the difference between simulated energy deposition data and reference energy deposition data corresponding to the target electron beam, thereby determining a candidate state representation corresponding to the target electron beam as one of a plurality of candidate state representations. For example, electronic device 110 can determine the difference between the energy change trend fitted using a double Gaussian function model and the reference energy deposition data by subtraction. Electronic device 110 updates the initial state representation based on the determined difference. This process is repeated, and after a specified number of iterations or if the difference between the simulated energy deposition data and the reference energy deposition data is less than a threshold, the updated initial state representation can be used as one of the candidate state representations.

[0039] In box 350, electronic device 110 can simulate energy distribution using a two-dimensional fitting algorithm. When the cross-section is rectangular or other non-circular, the cross-sectional features can include the dimensions of the electron beam's cross-section. Electronic device 110 can divide the energy distribution map formed by the electron beam on the wafer into a [division] based on the dimensions of the wafer surface. The energy distribution map includes the regions irradiated by the electron beam (i.e., regions with distributed energy) and the regions not irradiated by the electron beam (i.e., regions without distributed energy). Subsequently, the electronic device 110 can create a... The simulation matrix is ​​the same size as the energy distribution map. The electronic device 110 can initialize the simulation matrix based on the energy distribution map. For example, if a region in the simulation matrix corresponds to the target electron beam, that region is set to a first value. If a region does not correspond to the target electron beam, that region is set to a second value. For example, if the region corresponding to the first region in the simulation matrix has an energy distribution, the value of that region is set to 1. Subsequently, the electronic device 110 can set the values ​​of regions other than the first region to 0.

[0040] In some embodiments, due to the large scale of the matrix, there may be a situation where the first region in the simulation matrix differs from the energy distribution region (i.e., the cross-section of the electron beam) in the energy distribution map. For example, the edge of the energy distribution region differs from the edge of the first region. In this case, the electronic device 110 can correct the first region using an interpolation algorithm. Subsequently, the electronic device 110 can perform a convolution operation on the simulation matrix using the initial state representation (i.e., the initial point spread function) corresponding to the electron beam, and perform sum-normalization processing on the convolved simulation matrix and the reference energy deposition data (i.e., the energy distribution map) to determine the difference between the two. For example, the electronic device 110 can perform normalization on the simulated energy deposition data and the reference energy deposition data respectively to obtain a normalized simulation matrix and a normalized reference matrix. Subsequently, the electronic device 110 can determine feedback information for evaluating the simulated energy deposition data based on the difference between the normalized simulation matrix and the normalized reference matrix. The electronic device 110 can update the initial state representation based on the feedback information. Electronic device 110 can update the initial state representation based on the difference between simulated energy deposition data (i.e., simulation matrix) and reference energy deposition data to determine a candidate state distribution as one of a plurality of candidate state distributions.

[0041] Figure 4 A schematic diagram of energy deposition data 400 according to some embodiments of the present disclosure is shown. For example... Figure 4 As shown, the energy deposition data 400 includes simulated energy deposition data 410 and reference energy deposition data 420, and the initial state distribution can be a double Gaussian function model. The electronic device 110 can update the double Gaussian function model based on the difference between the simulated energy deposition data 410 and the reference energy deposition data 420. Then, proceed to box 360. In box 360, the electronic device 110 can acquire the fitted parameters. In some embodiments, the electronic device 110 can determine the parameters of the current double Gaussian function model as a candidate state distribution if the number of updates to the double Gaussian function model parameters exceeds a threshold or the difference between the simulated energy deposition data 410 and the reference energy deposition data 420 is less than a threshold.

[0042] In some embodiments, the electronic device 110 can determine the target state representation corresponding to the electron beam used by the lithography machine from multiple candidate state representations based on the cross-sectional characteristics of the target electron beam, the parameter information of the lithography machine, and the parameter information of the wafer.

[0043] Continue to refer to Figure 2In block 230, electronic device 110 can determine a correction result for a target layout based on a target state representation. The correction result for the target layout can indicate an energy compensation value for the target layout. In some embodiments, electronic device 110 can simulate a lithography process based on the determined target state representation to obtain a predicted lithography pattern. Subsequently, electronic device 110 can determine an energy compensation value for the target layout based on the difference between the obtained predicted lithography pattern and the target layout (i.e., the design layout).

[0044] In some embodiments, the electronic device 110 can divide the target layout into multiple regions. In this case, the correction result includes multiple energy compensation values ​​for the multiple regions. For each of the multiple regions, the electronic device 110 can determine the energy deposition data corresponding to that region based on the target state representation corresponding to the electron beam and the layout information corresponding to that region in the target layout. Subsequently, the electronic device 110 can determine the predicted energy distribution of each region on the wafer based on the energy deposition data determined for each of the multiple regions, and then determine the predicted lithography pattern formed based on the target layout. The electronic device 110 can determine the predicted lithography pattern formed based on the target layout based on the multiple energy deposition data determined for each of the multiple regions. The electronic device 110 can determine the correction result for the target layout based on the difference between the predicted lithography pattern and the target layout.

[0045] In summary, in the embodiments of this disclosure, the electronic device determines a target state representation that can indicate the scattering state of the electron beam from multiple candidate state representations as the point spread function in the lithography simulation process, based on the parameter information of the lithography machine (including at least the parameter information of the electron beam used by the lithography machine) and wafer information. In this way, on the one hand, it ensures that the state representation used for lithography process simulation can more accurately describe the scattering state of the electron beam used by the lithography machine, improving the accuracy and reliability of the simulation process. On the other hand, when changing the lithography machine or the type of electron beam used in the lithography machine, the point spread function used to correct the target layout can be quickly and accurately determined, thereby better determining the proximity effect correction result for the target layout. Furthermore, the accuracy of wafers produced using the lithography machine is improved.

[0046] In some embodiments of this disclosure, a scheme for determining the electron beam scattering state is provided. In this scheme, based on the corresponding energy distributions of multiple sample electron beams on a sample wafer, multinomial sample reference energy deposition data corresponding to each of the multiple sample electron beams are determined. The multiple sample electron beams have different cross-sections. Accordingly, based on the cross-sectional characteristics of each sample electron beam, multinomial sample simulated energy deposition data corresponding to each of the multiple sample electron beams are obtained. Further, based on the multinomial sample reference energy deposition data and the multinomial sample simulated energy deposition data, multiple candidate sample state representations are determined, indicating the scattering state of the multiple sample electron beams.

[0047] In this way, for different types of sample electron beams, the sample point spread function (i.e., candidate sample state representation) corresponding to each sample electron beam can be determined. This allows for rapid and accurate determination of the sample point spread function even when changing the lithography equipment or the type of sample electron beam. Therefore, based on the point spread function corresponding to different types of electron beams, the proximity effect correction results for the target layout can be better determined.

[0048] The following will be referenced Figure 5 This describes how to determine the candidate sample state representation that indicates the scattering state of the sample electron beam. Figure 5 A flowchart illustrating an example process 500 for determining the electron beam scattering state according to some embodiments of the present disclosure is shown. In some embodiments, process 500 may be performed by, for example... Figure 1 The illustrated electronic device 110 (e.g., a device for determining the electron beam scattering state) performs this action.

[0049] In block 510, electronic device 110 determines multiple sample reference energy deposition data corresponding to each of the multiple sample electron beams based on the corresponding energy distributions of the multiple sample electron beams on the sample wafer. The multiple sample electron beams have different cross-sections. In some examples, electronic device 110 can determine multiple different sample electron beams by changing conditions such as the cross-sectional shape, size, and energy of the sample electron beams.

[0050] For each of a plurality of sample electron beams, the electronic device 110 can determine the energy distribution of that sample electron beam on a sample wafer using a simulation model based on the sample electron beam information of that sample electron beam, as one of the corresponding energy distributions. The sample electron beam information may include the cross-sectional type of the electron beam, such as circular, rectangular, or triangular. The sample electron beam information may include the cross-sectional dimensions of the electron beam. Alternatively / additionally, the sample electron beam information may include the energy intensity of the electron beam.

[0051] In some embodiments, the sample reference energy deposition data can be a sample energy deposition map. The electronic device 110 can calculate from the determined multiple sample reference energy deposition data to obtain sample energy deposition maps of sample electron beams with different cross-sectional sizes and shapes. In some examples, for a target sample electron beam among multiple sample electron beams, the electronic device 110 can first divide the sample wafer into multiple sample regions (e.g., dividing the sample wafer into...). (The matrix). Further, the electronic device 110 can determine the energy distribution of the target sample electron beam in multiple sample regions of the sample wafer based on the energy distribution of a sample electron beam (e.g., the target electron beam) determined above on the sample wafer. Subsequently, the electronic device 110 determines the sample reference energy deposition data (i.e., the sample energy deposition map) corresponding to the target sample electron beam based on the energy distribution of the multiple regions, as one of the multiple sample reference energy deposition data.

[0052] In some embodiments, if the sample electron beam is circular, the electronic device 110 can perform radial normalization on the simulation results so that the energy satisfies the constraint that the sum is 1, so as to obtain the variation trend of the sample electron beam energy along the radial direction of the cross section, as one of the multiple sample reference energy deposition data.

[0053] The preceding text describes the determination of multi-sample reference energy deposition data corresponding to multiple sample electron beams. The following text will continue to refer to... Figure 5 This describes how to obtain multi-sample simulated energy deposition data corresponding to multiple sample electron beams.

[0054] Continuing with process 500, in block 520, the electronic device 110 acquires multivariate sample simulation energy deposition data corresponding to each of the multiple sample electron beams based on the cross-sectional characteristics of each sample electron beam. In some embodiments, the electronic device 110 can simulate the energy distribution of the sample electron beams in different ways according to different cross-sectional characteristics of the target sample electron beams. For example, if the cross-section of a sample electron beam is circular, the energy distribution of the sample electron beam can be simulated using curve fitting. If the cross-section of a sample electron beam is triangular, the energy distribution of the sample electron beam can be simulated using two-dimensional fitting.

[0055] In some embodiments, the electronic device 110 can simulate the energy distribution of the target sample electron beam on the sample wafer based on the cross-sectional characteristics of the target sample electron beam and using an initial sample state representation indicating the scattering state of the target sample electron beam. In this way, the electronic device 110 can acquire sample simulation energy deposition data corresponding to the target sample electron beam.

[0056] In some embodiments, if the electronic device 110 determines that the cross-section of the target sample electron beam is circular, it can use a curve fitting algorithm to simulate the energy distribution of the target sample electron beam. The cross-sectional characteristics of the target sample electron beam with a circular cross-section include the radius of the cross-section. Sample reference energy deposition data includes the trend of energy variation along the radial direction of the cross-section.

[0057] Specifically, the electronic device 110 can fit the sample reference energy deposition data based on the radius of the sample electron beam cross-section using an initial sample state representation (i.e., a sample point spread function) corresponding to the target sample electron beam. For example, the initial sample state representation can be a double Gaussian function model. That is, the electronic device 110 can use a double Gaussian function model to fit the energy variation trend along the radial direction of the cross-section to determine the simulated energy deposition data corresponding to the target sample electron beam.

[0058] In some embodiments, if the electronic device 110 determines that the cross-section of the target sample electron beam is non-circular (e.g., rectangular or any other suitable non-circular), it can use a two-dimensional fitting algorithm to simulate the energy distribution. The cross-sectional characteristics of the target sample electron beam with a non-circular cross-section may include the dimensions of the cross-section.

[0059] Specifically, the electronic device 110 determines a sample simulation matrix associated with the target sample electron beam based on the size of the target sample electron beam. A first sample region in the sample simulation matrix corresponding to the target sample electron beam is set as a first sample value. Correspondingly, the electronic device 110 can set non-first sample regions in the sample simulation matrix as second sample values. Further, the electronic device 110 can determine sample simulation energy deposition data by performing convolution on the sample simulation matrix and the initial sample state representation.

[0060] The preceding text describes the acquisition of multi-sample simulated energy deposition data corresponding to multiple sample electron beams. The following text will continue to refer to... Figure 5 This describes how to determine multiple candidate sample state representations. Continuing with process 500, in block 530, the electronic device 110 determines multiple candidate sample state representations based on multiple sample reference energy deposition data and multiple sample simulated energy deposition data. The multiple candidate sample state representations indicate the scattering state of multiple sample electron beams. In some embodiments, for a target sample electron beam among the multiple sample electron beams, the electronic device 110 can update the initial sample state representation of the target sample electron beam's scattering state based on the difference between the sample simulated energy deposition data corresponding to the target sample electron beam and its corresponding sample reference energy deposition data, thereby determining the candidate sample state representation corresponding to the target sample electron beam. In this way, the electronic device 110 can acquire the candidate sample state representations corresponding to each of the multiple sample electron beams.

[0061] In some embodiments, for a target sample electron beam with a non-circular cross-section, the electronic device 110 may determine the candidate sample state representation corresponding to the target sample electron beam in the following manner.

[0062] Electronic device 110 performs normalization on the sample simulated energy deposition data and the sample reference energy deposition data respectively to obtain a normalized sample simulation matrix and a normalized sample reference matrix. Accordingly, electronic device 110 determines sample feedback information for evaluating the sample simulated energy deposition data based on the difference between the normalized sample simulation matrix and the normalized sample reference matrix. Then, electronic device 110 updates the initial sample state representation based on the sample feedback information. As an example, electronic device 110 can update the initial sample state representation based on the difference between the sample simulated energy deposition data (i.e., the sample simulation matrix) and the sample reference energy deposition data (i.e., the sample reference matrix) to determine a candidate sample state distribution as one of multiple candidate sample state distributions.

[0063] In some embodiments, for a target sample electron beam with a circular cross-section, the electronic device 110 may determine the candidate sample state representation corresponding to the target sample electron beam in the following manner.

[0064] Electronic device 110 can determine the difference between the energy change trend fitted using a double Gaussian function model and the sample reference energy deposition data by subtraction. Based on the determined difference, electronic device 110 updates the initial state representation. This process is repeated, and after a specified number of iterations or when the difference between the sample simulated energy deposition data and the sample reference energy deposition data is less than a threshold, the updated initial sample state representation is used as one of the candidate sample state representations.

[0065] In this way, the sample point spread function corresponding to different types of sample electron beams can be determined. It should be understood that the specific process for determining the candidate sample state representation (i.e., the sample point spread function) can be found in the section above on the specific process of candidate state representation, and will not be repeated here.

[0066] In summary, by changing the lithography equipment or the type of sample electron beam used in the lithography equipment, the sample point spread function can be determined quickly and accurately. This sample point spread function can then be used to correct the target layout. Therefore, based on the sample point spread functions corresponding to different types of sample electron beams, the proximity effect correction results for the target layout can be better determined.

[0067] Figure 6 A block diagram is shown of an electronic device 600 in which one or more embodiments of the present disclosure may be implemented. The electronic device 600 may, for example, be used to implement... Figure 1 The electronic device 110 shown. It should be understood that, Figure 6 The electronic device 600 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein.

[0068] like Figure 6 As shown, electronic device 600 is in the form of a general-purpose electronic device. Components of electronic device 600 may include, but are not limited to, one or more processors 610 or processing units, memory 620, storage device 630, one or more communication units 640, one or more input devices 650, and one or more output devices 660. The processing unit may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 620. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 600.

[0069] Electronic device 600 typically includes multiple computer storage media. Such media can be any available media accessible to electronic device 600, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 620 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 630 can be a removable or non-removable medium and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data (e.g., training data for training) and can be accessed within electronic device 600.

[0070] Electronic device 600 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 6 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 620 may include computer program product 625 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0071] The communication unit 640 enables communication with other electronic devices via a communication medium. Additionally, the functionality of the components of the electronic device 600 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the electronic device 600 can operate in a networked environment using logical connections to one or more other servers, networked personal computers (PCs), or another network node.

[0072] Input device 650 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 660 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 600 can also communicate with one or more external devices (not shown) via communication unit 640 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 600, or with any device that enables electronic device 600 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interfaces (not shown).

[0073] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores one or more computer instructions, wherein one or more computer instructions are executed by a processor to implement the methods described above.

[0074] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0075] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0076] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0077] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0078] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive, nor is it limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the implementations disclosed herein.

Claims

1. A method for layout processing, comprising: Acquire parameter information related to the lithography machine and wafer in the lithography process, wherein the lithography machine is used to form a wafer pattern on the wafer based on the target layout; Based on the parameter information, a target state representation corresponding to the target layout is determined from multiple candidate state representations indicating the electron beam scattering state; as well as Based on the target state representation, a correction result is determined for the target layout.

2. The method of claim 1, wherein, The multiple candidate state representations are determined in the following manner: Based on the corresponding energy distribution of multiple electron beams on the wafer, multiple reference energy deposition data corresponding to the multiple electron beams are determined, and the multiple electron beams have different cross-sections; For the target electron beam among the plurality of electron beams, Based on the cross-sectional characteristics of the target electron beam, the energy distribution of the target electron beam on the wafer is simulated using an initial state representation indicating the scattering state of the electron beam, in order to obtain simulated energy deposition data corresponding to the target electron beam. as well as Based on the difference between the simulated energy deposition data and the reference energy deposition data corresponding to the target electron beam, the initial state representation is updated to determine a candidate state representation corresponding to the target electron beam as one of the plurality of candidate state representations.

3. The method of claim 2, wherein, The corresponding energy distributions of the plurality of electron beams are determined in the following manner: For each of the plurality of electron beams, based on the electron beam information of that electron beam, a simulation model is used to determine the energy distribution of that electron beam on the wafer, which is then considered as one of the corresponding energy distributions. The electron beam information includes at least one of the following: The cross-sectional type of the electron beam, The cross-sectional dimensions of the electron beam, or The energy intensity of the electron beam.

4. The method of claim 3, wherein, The multiple reference energy deposition data include multiple energy deposition maps, and Determining the multiple reference energy deposition data based on the corresponding energy distribution includes: For the target electron beam among the plurality of electron beams, Based on the energy distribution of the target electron beam on the wafer, determine the energy distribution of the target electron beam in multiple regions of the wafer. Based on the energy distribution of the multiple regions, reference energy deposition data corresponding to the target electron beam is determined as one of the multiple reference energy deposition data.

5. The method of claim 2, wherein, The target electron beam has a circular cross-section, the cross-sectional feature of which includes the radius of the cross-section, and the reference energy deposition data includes the trend of energy variation along the radius of the cross-section. The simulated energy distribution includes: Based on the radius, the energy distribution is simulated using a double Gaussian function model to determine the simulated energy deposition data corresponding to the target electron beam.

6. The method of claim 2, wherein, The target electron beam has a non-circular cross-section, and the cross-sectional features include the dimensions of the cross-section. The simulated energy distribution includes: Based on the size of the target electron beam, a simulation matrix associated with the target electron beam is determined, wherein a first region in the simulation matrix corresponding to the target electron beam is set to a first value, and non-first regions in the simulation matrix are set to a second value; and The simulated energy deposition data is determined by performing convolution on the simulation matrix and the initial state representation.

7. The method according to claim 6, characterized in that, Updating the initial state representation based on the difference between the simulated energy deposition data and the reference energy deposition data includes: By performing normalization on the simulated energy deposition data and the reference energy deposition data respectively, a normalized simulation matrix and a normalized reference matrix are obtained; Based on the difference between the normalized simulation matrix and the normalized reference matrix, feedback information for evaluating the simulated energy deposition data is determined; and Based on the feedback information, the initial state representation is updated.

8. The method according to claim 1, characterized in that, The parameter information includes wafer information related to the wafer and the cross-sectional type of the electron beam related to the lithography machine. Determining the target state representation includes: Based on the wafer information and the cross-section type, the target state representation that matches the wafer information and the cross-section information is determined from the plurality of candidate state representations.

9. The method according to claim 1, characterized in that, Determining the correction result based on the target state representation includes: For each of the multiple regions of the target layout, based on the target state representation and the layout information corresponding to that region in the target layout, energy deposition data corresponding to that region is determined; and Based on the energy deposition data determined for the multiple regions respectively, a correction result for the target layout is determined.

10. The method according to claim 9, characterized in that, The correction result includes multiple energy compensation values ​​corresponding to the multiple regions respectively, and The correction result is determined based on the energy deposition data, including: Based on the energy deposition data, a predicted lithography pattern is determined based on the target layout; and The correction result is determined based on the difference between the predicted lithographic pattern and the target layout.

11. A method for determining the state of electron beam scattering, comprising: Based on the corresponding energy distribution of multiple sample electron beams on the sample wafer, multiple sample reference energy deposition data corresponding to the multiple sample electron beams are determined, and the multiple sample electron beams have different cross sections. Based on the cross-sectional characteristics of each sample electron beam in the plurality of sample electron beams, multi-sample simulated energy deposition data corresponding to the plurality of sample electron beams are obtained respectively; as well as Based on the multi-sample reference energy deposition data and the multi-sample simulated energy deposition data, multiple candidate sample state representations are determined, and the multiple candidate sample state representations indicate the scattering state of the multiple sample electron beams.

12. The method of claim 11, wherein the plurality of sample electron beams includes a target sample electron beam, and the candidate sample state representation corresponding to the target sample electron beam is determined by: Based on the cross-sectional characteristics of the target sample electron beam, and using an initial sample state representation indicating the scattering state of the target sample electron beam, the energy distribution of the target sample electron beam on the sample wafer is simulated to obtain sample simulation energy deposition data corresponding to the target sample electron beam; and Based on the difference between the simulated energy deposition data of the sample and the sample reference energy deposition data corresponding to the electron beam of the target sample, the initial sample state representation is updated to determine the candidate sample state representation corresponding to the electron beam of the target sample, as one of the plurality of candidate sample state representations.

13. The method of claim 11, wherein the corresponding energy distribution of each of the plurality of sample electron beams on the sample wafer is determined by: Based on the sample electron beam information, the energy distribution of the sample electron beam on the sample wafer was determined using a simulation model, and The sample electron beam information includes at least one of the following: The cross-sectional type of the electron beam in this sample. The cross-sectional dimensions of the sample electron beam, or The energy intensity of the sample electron beam.

14. The method of claim 13, wherein the plurality of sample reference energy deposition data comprises a plurality of sample energy deposition maps, and The determination of the multiple sample reference energy deposition data based on the corresponding energy distribution includes: For the target sample electron beam among the multiple sample electron beams, Based on the energy distribution of the target sample electron beam on the sample wafer, determine the energy distribution of the target sample electron beam in multiple regions of the sample wafer. Based on the energy distribution of the multiple regions, the sample reference energy deposition data corresponding to the electron beam of the target sample is determined as one of the multiple sample reference energy deposition data.

15. The method of claim 12, wherein the cross-section of the target sample electron beam is circular, the cross-sectional feature includes the radius of the cross-section, and the sample reference energy deposition data includes the trend of energy variation along the radial direction of the cross-section on the cross-section. The simulated energy distribution includes: Based on the radius, the energy distribution is simulated using a double Gaussian function model to determine the sample simulated energy deposition data corresponding to the target sample electron beam.

16. The method of claim 12, wherein the cross-section of the target sample electron beam is non-circular, and the cross-sectional feature includes the dimensions of the cross-section, and The simulated energy distribution includes: Based on the size of the target sample electron beam, a sample simulation matrix associated with the target sample electron beam is determined. In the sample simulation matrix, the first sample region corresponding to the target sample electron beam is set as the first sample value, and the non-first sample region in the sample simulation matrix is ​​set as the second sample value. as well as The sample simulation energy deposition data is determined by performing convolution on the sample simulation matrix and the initial sample state representation.

17. The method of claim 16, wherein updating the initial sample state representation based on the difference between the simulated sample energy deposition data and the sample reference energy deposition data corresponding to the electron beam of the target sample comprises: By performing normalization on the sample simulation energy deposition data and the sample reference energy deposition data respectively, a normalized sample simulation matrix and a normalized sample reference matrix are obtained. Based on the difference between the normalized sample simulation matrix and the normalized sample reference matrix, sample feedback information for evaluating the sample simulation energy deposition data is determined; as well as The initial sample state representation is updated based on the sample feedback information.

18. An electronic device, comprising: processor; as well as A memory coupled to the processor, the memory having instructions stored therein, which, when executed by the processor, cause the device to perform the method as described in any one of claims 1-10, and / or the method as described in any one of claims 11-17.

19. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as claimed in any one of claims 1-10, and / or the method as claimed in any one of claims 11-17.

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