Photomask data processing method, device, equipment, medium and program product

Abstract

This application relates to a method, apparatus, device, medium, and program product for photomask data processing. The method includes: acquiring current photomask design data; processing the current photomask design data to obtain pattern density information and pattern size information for each target region; generating a heat map characterizing the defect degree of each target region; acquiring target regions with defect degrees greater than or equal to a defect threshold; determining defect classification for the target regions based on the pattern density information and pattern size information; when the defect classification includes pattern density classification, acquiring the target area of ​​the target region; when the target area is greater than an area threshold, identifying functional regions of the target region and extracting key signal paths within the functional regions; determining splitting boundaries based on the functional regions and key signal paths; and adjusting the current photomask design data based on the splitting boundaries. This method allows for pre-optimization of the photomask design data.

Description

Technical Field

[0001] This application relates to the field of semiconductor technology, and in particular to a photomask data processing method, apparatus, device, medium, and program product. Background Technology

[0002] The loading effect in chemical mechanical polishing (CMP) refers to the uneven polishing rate caused by different pattern densities during the polishing process.

[0003] In traditional IC design and manufacturing technology, when placing different patterns on the TQV (Test Quality Verification) photomask, SRAM is usually grouped together in a certain area. Currently, the common practice is to use experience to rationally place different patterns during photomask design in order to reduce the loading effect in chemical mechanical polishing (CMP).

[0004] However, as the manufacturing process continues to iterate, the loading effect in CMP becomes more and more obvious, resulting in the manufactured photomasks not meeting the requirements. Summary of the Invention

[0005] Therefore, it is necessary to provide a photomask data processing method, apparatus, device, medium, and program product that can optimize photomask design data in advance to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a photomask data processing method, the method comprising:

[0007] Obtain the current photomask design data;

[0008] The current photomask design data is processed to obtain pattern density information and pattern size information for each target region;

[0009] Based on the pattern density information and pattern size information of each target region, a heat map is generated to characterize the degree of defect in each target region.

[0010] The target region with a defect severity greater than or equal to the defect threshold is obtained, and the defect classification of the target region is determined based on the pattern density information and pattern size information of the target region.

[0011] Adjusting the current photomask design data based on the defect classification includes: when the defect classification includes pattern density classification, obtaining the target area of ​​the target region where the defect degree is greater than or equal to a defect threshold; when the target area is greater than the area threshold, performing functional region identification on the target region where the target area is greater than the area threshold, and extracting key signal paths within the identified functional regions; determining a split boundary based on the functional regions and the key signal paths; and adjusting the current photomask design data based on the split boundary.

[0012] In one optional embodiment, adjusting the current photomask design data based on the split boundary includes: determining an optional range of the channel width of the filled virtual pattern based on the split boundary; determining the channel width of the filled virtual pattern based on the optional range of the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, the pattern density of each split region after splitting, and the pattern density of the target region; determining the pattern type of the filled virtual pattern based on the ratio of the area of ​​the N-type active region to the area of ​​the P-type active region in the target region, wherein the pattern type includes an N-type virtual pattern or a P-type virtual pattern; and filling the target region with a virtual pattern based on the split boundary, the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, and the pattern type of the filled virtual pattern.

[0013] In one optional embodiment, processing the current photomask design data to obtain pattern density information and pattern size information for each target region includes: extracting features from the current photomask design data using a pre-trained feature extraction network of the photomask data processing model to obtain a multi-scale feature map; predicting the multi-scale feature map using a density prediction network of the photomask data processing model to obtain pattern density information for each target region; and predicting the multi-scale feature map using a size prediction network of the photomask data processing model to obtain pattern size information for each target region.

[0014] The step of using the size prediction network of the photomask data processing model to predict the multi-scale feature map and obtain pattern size information for each target region includes:

[0015] The size prediction network of the photomask data processing model is used to split each of the multi-scale feature maps to obtain several pixels.

[0016] The multi-scale feature maps of adjacent layers are aligned by aligning the pixels in the multi-scale feature maps of adjacent layers.

[0017] Based on the aligned multi-scale feature maps, predictions are made to obtain the pixel-level mask of each target region, the position of the pattern size, and the size statistical histogram corresponding to each size of the pattern.

[0018] In one optional embodiment, generating a heat map characterizing the degree of defect in each of the target regions based on the pattern density information and pattern size information of each target region includes:

[0019] Based on the pattern density information of each target region, a corresponding first color is assigned to different target regions, and the target region with a higher pattern density is assigned a darker first color.

[0020] Based on the pattern size information of each target region, a corresponding second color is assigned to different target regions. The target region with a larger pattern size is assigned a darker second color. The first color and the second color are different.

[0021] Based on the target regions after assigning the first color and the second color, a heat map characterizing the degree of defect in each target region is generated.

[0022] In one optional embodiment, adjusting the current photomask design data based on the defect classification further includes:

[0023] When the target area is less than or equal to the area threshold, the pattern density of the macro region corresponding to the target area is calculated. If the pattern density of the target area is less than or equal to the pattern density of the macro region, a first virtual pattern is filled in the target area. If the pattern density of the target area is greater than the pattern density of the macro region, a second virtual pattern is filled in the target area. The pattern density of the first virtual pattern is greater than the pattern density of the second virtual pattern.

[0024] When the defect classification includes pattern size classification, patterns with a pattern size greater than a size threshold are placed in the target area where the pattern density is greater than a density threshold, and virtual pattern filling is performed in the adjacent area of ​​the target area.

[0025] After adjusting the current photomask design data based on the defect classification, the process includes:

[0026] The wafer electrical testing is performed based on the adjusted current photomask design data, including: mapping the pattern density information and pattern size information of each target region to uniform electrical parameters; connecting the unsplit target regions and the regions obtained by splitting the target regions in parallel; and performing wafer electrical testing based on the parallel unsplit target regions, the split target regions, and the electrical parameters.

[0027] In one optional embodiment, the adjustment of the current photomask design data based on the defect classification includes:

[0028] Based on the adjusted target area, new current photomask design data is obtained, and the process of processing the current photomask design data to obtain pattern density information and pattern size information of each target area continues until the defect degree of each target area in the heat map is less than the defect threshold.

[0029] Output the adjusted current photomask design data.

[0030] Secondly, this application also provides a photomask data processing apparatus, the apparatus comprising:

[0031] The data acquisition module is used to acquire the current photomask design data;

[0032] The processing module is used to process the current photomask design data to obtain pattern density information and pattern size information for each target area;

[0033] A heat map generation module is used to generate a heat map characterizing the degree of defects in each of the target regions based on the pattern density information and pattern size information of each target region.

[0034] The defect classification determination module is used to acquire the target area where the defect degree is greater than or equal to the defect threshold, and determine the defect classification of the target area based on the pattern density information and pattern size information of the target area;

[0035] The adjustment module is used to adjust the current photomask design data based on the defect classification, including: when the defect classification includes pattern density classification, obtaining the target area of ​​the target region where the defect degree is greater than or equal to a defect threshold; when the target area is greater than the area threshold, performing functional region identification on the target region where the target area is greater than the area threshold, and extracting key signal paths within the identified functional regions; determining a split boundary based on the functional regions and the key signal paths; and adjusting the current photomask design data based on the split boundary.

[0036] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the above embodiments.

[0037] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0038] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0039] The aforementioned photomask data processing method, apparatus, equipment, medium, and program product acquire current photomask design data; process the current photomask design data to obtain pattern density information and pattern size information for each target region; generate a heat map characterizing the defect degree of each target region based on the pattern density information and pattern size information of each target region; acquire the target regions whose defect degree is greater than or equal to a defect threshold, and determine the defect classification of the target regions based on the pattern density information and pattern size information of the target regions; adjust the current photomask design data based on the defect classification, including: when the defect classification includes a pattern density classification, obtaining... The target area of ​​the target region where the defect severity is greater than or equal to the defect threshold is selected. When the target area is greater than the area threshold, functional region identification is performed on the target region where the target area is greater than the area threshold, and key signal paths within the identified functional regions are extracted. Based on the functional regions and the key signal paths, a split boundary is determined. Based on the split boundary, the current photomask design data is adjusted. This pre-adjustment of the photomask design data reduces risk. A heat map is generated based on pattern density information and pattern size information. Based on the heat map, high-risk locations and corresponding defect classifications are determined, optimizing the loading effect in CMP and reducing overall risk. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating a photomask data processing method in one embodiment;

[0042] Figure 2 This is a schematic diagram of the structure of a photomask data processing model in one embodiment;

[0043] Figure 3This is a comparison image of aligned and unaligned feature maps in one embodiment;

[0044] Figure 4 This is a flowchart of the heatmap generation steps in one embodiment;

[0045] Figure 5 This is a schematic diagram of a superimposed heatmap in one embodiment;

[0046] Figure 6 This is a flowchart of the current photomask design data adjustment steps in one embodiment;

[0047] Figure 7 This is a structural block diagram of a photomask data processing device in one embodiment;

[0048] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0050] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0051] In one embodiment, such as Figure 1 As shown, a photomask data processing method is provided. This embodiment illustrates the method applied to a server, but it is understood that the method can also be applied to a terminal, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0052] S102: Obtain the current photomask design data.

[0053] The current photomask design data is based on the photolithography layout and GDS files (such as GDSII files). The photolithography layout refers to the physical layout drawn during the chip design process, which defines the geometry of the semiconductor device and determines the pattern of each layer on the chip (such as transistors, metal interconnects, and contact holes). The GDS file is a binary file format commonly used in the semiconductor industry to store and transmit the chip's photomask layout data, containing geometric patterns such as polygons, paths, and text.

[0054] During the research and development phase, the photomask can be a single pixel size, that is, the size of the photomask is 1*1 pixels. The photomask will be divided into dozens of areas for placing different patterns. Different areas correspond to different GDSII files. All GDSII files are combined and mapped onto the photomask to obtain the current photomask design data.

[0055] In some optional embodiments of this application, the target layer in the photolithography pattern and the file data corresponding to the target layer in the GDS file are obtained, and the file data is mapped to the photolithography pattern based on the pattern position to obtain the current photomask design data.

[0056] S104: Process the current photomask design data to obtain pattern density information and pattern size information for each target area.

[0057] The target area is a fixed area region. A single photomask pattern can be divided into multiple fixed area regions. By processing the current photomask design data, the pattern density information and pattern size information of each target area can be obtained.

[0058] The pattern density information includes the pattern density of each target region, and may also include the average, maximum, and minimum values ​​of the pattern density.

[0059] The pattern size information includes the position information of each pattern, pixel-level segmentation mask, and size statistics histogram. The position information of the pattern can include the bounding box coordinates of the pattern. The pixel-level segmentation mask means that each feature map is aligned at the pixel level. The size statistics histogram is the proportion of each pattern size in a target area, that is, the ratio of the number of patterns of this size to the number of patterns of all sizes.

[0060] In some optional embodiments, the current photomask design data is preprocessed before being processed, wherein the preprocessing includes at least one of nonlocal mean denoising, adaptive histogram equalization to enhance contrast, and data normalization to improve the accuracy of the output.

[0061] S106: Based on the pattern density and pattern size information of each target region, generate a heat map to characterize the degree of defects in each target region.

[0062] The heat map is used to characterize the degree of defects in each target area. The degree of defects refers to the level of danger. For example, if the patterns (such as metal lines, contact holes, transistors, etc.) in a certain area of ​​the chip are very densely distributed, the polishing pressure will be high, which may lead to over-polishing or dielectric layer erosion. If the pattern size in a certain area of ​​the chip is small (such as line width, spacing, etc.), for example, close to the resolution limit of the lithography machine, it will cause the pattern to be blurred or broken, resulting in the superposition of lithography errors. If the image size is large, it will lead to a significant etching micro-load effect.

[0063] Therefore, based on the pattern density and pattern size information of each target region, a heat map is generated to characterize the degree of defect in each target region. For example, different levels of heat map display methods are assigned to different pattern densities, and different levels of heat map display methods are also assigned to different pattern sizes. Finally, the two display methods are superimposed to obtain the heat map. The different levels of heat map display methods are used to represent different degrees of defect.

[0064] S108: Obtain the target area where the defect degree is greater than or equal to the defect threshold, and determine the defect classification of the target area based on the pattern density information and pattern size information of the target area.

[0065] The defect threshold can be customized, and the target area with a defect degree greater than or equal to the defect threshold can be marked, for example, by selecting it by boxing or by making special annotations at the corresponding target area.

[0066] The defect classification of the target area includes pattern density information and pattern size information. For example, high risk may be due to pattern density, pattern size, or a combination of both.

[0067] S110: Adjust the current photomask design data based on defect classification.

[0068] If the heatmap contains the aforementioned special markings, indicating a target area with a defect level greater than or equal to the defect threshold, then the current photomask design data needs to be adjusted to reduce the risk.

[0069] Wherein, when the defect classification includes pattern density classification, the target area of ​​the target region where the defect degree is greater than or equal to the defect threshold is obtained. When the target area is greater than the area threshold, functional region identification is performed on the target region where the target area is greater than the area threshold, and key signal paths within the identified functional regions are extracted. A split boundary is determined based on the functional regions and the key signal paths. The current photomask design data is adjusted based on the split boundary. Specifically, adjusting the current photomask design data based on the split boundary may include: determining the channel width of the filled virtual pattern based on the split boundary. The optional range of the channel width of the filled virtual pattern is determined based on the optional range of the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, the pattern density of each split region after splitting, and the pattern density of the target region. The pattern type of the filled virtual pattern is determined based on the ratio of the area of ​​the N-type active region to the area of ​​the P-type active region in the target region. The pattern type includes an N-type virtual pattern or a P-type virtual pattern. Virtual pattern filling is performed in the adjacent area of ​​the target region based on the splitting boundary, the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, and the pattern type of the filled virtual pattern.

[0070] Specifically, if the target area is larger than the area threshold, meaning the target region is a macroscopic area, then it's necessary to determine the splitting boundary—that is, where to split the target region. Determining the splitting boundary requires considering the integrity of the functional areas and the location of critical signal paths. Specifically, the hierarchical structure of the layout can be analyzed to identify functional areas. For example, large empty spaces typically appear between analog modules, memory arrays, power management areas, or large logic functional blocks; splitting should prioritize the boundaries of these predefined functional modules. For instance, consider a chip containing a CPU core and a large SRAM, with a large empty space between them. The first choice for splitting is to divide along the boundary line between the CPU and SRAM. Furthermore, for highly symmetrical circuits (such as differential pairs, current mirrors) and arrays of precisely matched devices (such as capacitor arrays in an ADC), they must be preserved as a whole, and splitting from the middle is prohibited. Splitting can only be done on the periphery of these arrays. Therefore, it's necessary to first identify the functional areas; the specific identification method can be achieved through a neural network model, which is not specifically limited here. Secondly, the extraction of critical signal paths can be achieved by using corresponding EDA tools to analyze the critical signal paths within the target area. These critical signal paths may include high fan-out clock lines, high-speed data buses, and critical control signals. Decoupling must avoid these critical signal paths or reserve complete channels for them to prevent excessively long windings or additional parasitic RC from being introduced due to decoupling, which could lead to timing violations or deterioration of signal integrity.

[0071] After determining the splitting boundaries, it is necessary to determine the specific appearance of the filling virtual pattern. Specifically, the appearance of the filling virtual pattern in this application is defined by splitting principles, which include at least: density control principles and pattern type principles. The density control principles determine the channel width and pattern density of the filling virtual pattern.

[0072] Specifically, the splitting is not a physical cutting on the map, but rather the introduction of a buffer zone of a certain width composed of a virtual pattern. This channel physically divides the original large plots into two or more independent blocks. The channel width is a key adjustable parameter (e.g., from 2μm to 10μm) used to control the magnitude of density variation.

[0073] Instead of arbitrarily filling the cutting channels with virtual patterns, intelligent filling is performed based on the density of the original target area and surrounding areas. The goal is to ensure that the weighted average of the density of each segmented region and the density of the cutting channel itself does not differ from the global density of the target area by more than a density threshold, such as 5%.

[0074] Specifically, iterative calculations can be performed using the density analysis engine of an EDA tool. If the density drops too much after splitting, a high-density virtual pattern is filled into the channel or the channel width is reduced; if the change is small, a medium-density pattern is filled to smooth the transition.

[0075] The pattern type principle stems from the fact that in advanced processes, PMOS typically uses embedded silicon-germanium source / drain to introduce compressive stress and improve hole mobility. This process step is highly sensitive to the total area (open area) of the PMOS active region. If there are too many P-type regions in the dummy pattern, it can deceive the process equipment, causing it to react in areas where silicon-germanium epitaxy should not be performed, leading to stress runaway and performance fluctuations.

[0076] Therefore, before splitting, the ratio of N-type active area to P-type active area within the target region is precisely calculated. In areas requiring virtual patterns (such as cutting channels or new blocks), filling is not random; instead, N-type or P-type virtual patterns are selectively inserted based on the current N / P ratio of the area. If the P-type ratio in the area is close to the upper limit, only N-type virtual patterns (usually NMOS virtual patterns connected to the VSS) are allowed to be inserted; conversely, the opposite applies. After splitting, the N / P ratio of each new block is calculated separately. If the PMOS ratio of a block exceeds the limit, N-type virtual patterns need to be deliberately added to its cutting channels or internal blank areas to bring the overall ratio back within the allowable range.

[0077] When the target area exceeds a certain area threshold, it is necessary to adjust the floorplan of the entire photomask. This involves dividing the target area and filling it with a virtual pattern to prevent excessively large plots from creating a macroscopic loading effect. For example, in the photomask floorplan, there might be a plot with an area of ​​64mm². 2 Once detected, the plot needs to be split without affecting the original function. The principles for splitting the plot are: 1. Ensure the integrity of the plot's function; 2. The pattern density of the entire plot, whether macroscopic or microscopic, cannot change by more than 5%; 3. The uniformity of the N / P MOS ratio, especially the aperture ratio of PMOS. The change in the N / P MOS ratio before and after splitting cannot exceed 3%. This is mainly because virtual patterns also have N / P MOS differences. Inserting too many PMOS will increase the aperture ratio of PMOS and affect subsequent processes.

[0078] In some optional embodiments, after adjusting the current photomask design data based on defect classification, the process includes: obtaining new current photomask design data based on the adjusted target areas, and continuing to process the current photomask design data to obtain pattern density information and pattern size information for each target area until the defect degree of each target area in the heat map is less than the defect threshold; and outputting the adjusted current photomask design data.

[0079] Specifically, in order to obtain compliant photomask design data, this application, after obtaining new current photomask design data based on the adjusted target area, processes the new current photomask design data again to obtain pattern density and pattern size information for each target area. This process continues until the defect degree of each target area in the heat map is less than the defect threshold, i.e., the risk level meets the requirements, at which point the adjusted current photomask design data is output. If there are still target areas in the heat map with a defect degree greater than or equal to the defect threshold, adjustments can be made again. This reduces the risk level during photomask design, thereby ensuring the accuracy of photomask fabrication.

[0080] The above-described photomask data processing method involves: acquiring current photomask design data; processing the current photomask design data to obtain pattern density information and pattern size information for each target region; generating a heatmap characterizing the defect severity of each target region based on the pattern density information and pattern size information; acquiring target regions with defect severity greater than or equal to a defect threshold, and determining the defect classification of the target regions based on the pattern density information and pattern size information; and adjusting the current photomask design data based on the defect classification, including: when the defect classification includes pattern density classification, acquiring the target area of ​​the target regions with defect severity greater than or equal to the defect threshold; when the target area is greater than the area threshold, identifying functional regions of the target regions with target areas greater than the area threshold, extracting key signal paths within the identified functional regions, determining splitting boundaries based on the functional regions and the key signal paths, and adjusting the current photomask design data based on the splitting boundaries. This pre-adjustment of the photomask design data reduces risk, and a heatmap is generated based on the pattern density information and pattern size information. This heatmap is used to determine high-risk locations and corresponding defect classifications, optimizing the loading effect in CMP and reducing overall risk.

[0081] In some optional embodiments, the current photomask design data is processed to obtain pattern density information and pattern size information for each target region, including: extracting features from the current photomask design data using a pre-trained feature extraction network of the photomask data processing model to obtain multi-scale feature maps; predicting the multi-scale feature maps using a density prediction network of the photomask data processing model to obtain pattern density information for each target region; and predicting the multi-scale feature maps using a size prediction network of the photomask data processing model to obtain pattern size information for each target region.

[0082] Specifically, in combination Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a photomask data processing model in one embodiment. In this embodiment, the photomask data processing model includes an input layer, a feature extraction network, a density prediction network, and a size prediction network.

[0083] The input layer is used to receive the current photomask design data.

[0084] The feature extraction network is used to extract multi-scale feature maps. This network includes convolutional layers, each comprising four convolutional blocks. Each block includes a 2D convolutional layer (Conv2D), a batch normalization (BN) layer, a rectified linear unit (ReLU), and a max-pooling layer. The 2D convolutional layer extracts local spatial features (such as edges and textures) from the image or feature map. The batch normalization layer normalizes each batch of data (mean = 0, variance = 1), accelerating training and mitigating gradient vanishing / exploding. The rectified linear unit introduces non-linearity to enhance the model's expressive power. The max-pooling layer reduces dimensionality (decreasing computation) while preserving salient features and enhancing translation invariance.

[0085] Optionally, the feature extraction network may also include an attention mechanism to enhance the response in key regions. The feature extraction network may also include air conditioning spatial pyramid pooling to capture multi-scale features.

[0086] This density prediction network can employ a U-Net-based encoding / decoding architecture and progressively recover spatial resolution through transposed convolutions, ultimately outputting pattern density information. This pattern density information includes an image at the same resolution as the input, as well as the pattern density of each target region within the image. In other embodiments, the pattern density information may also include global density statistics, such as average density, maximum density, and minimum density.

[0087] Optionally, the density prediction network is specifically used to generate a smooth density map based on the local pixel distribution to avoid discrete noise. Here, a local pixel refers to the number of pixels in this layer of feature map within a target region. A layer of feature map can be divided into multiple target regions, and a smooth density map can be generated based on the pixel distribution within the target region.

[0088] This size prediction branch can include an object detection framework based on R-CNN, which includes a feature pyramid network to process multi-scale patterns and generate candidate boxes based on the target region, and then determine the position of the pattern, pixel-level segmentation mask, and size statistical histogram based on the candidate boxes.

[0089] In the above embodiments, pattern density and pattern size are detected through a model to measure risk.

[0090] In one optional embodiment, the size prediction network of the photomask data processing model is used to predict the multi-scale feature maps to obtain the pattern size information of each target region. This includes: splitting each multi-scale feature map into several pixels using the size prediction network of the photomask data processing model; aligning the pixels in the multi-scale feature maps of adjacent layers to align the multi-scale feature maps of adjacent layers; and predicting based on the aligned multi-scale feature maps to obtain the pixel-level mask of each target region, the position of the pattern size, and the size statistical histogram corresponding to the pattern of each size.

[0091] Each layer of GDSII has a corresponding feature pattern, i.e., a pixel-level mask, which is used to align the feature maps of the preceding and following layers.

[0092] Specifically, each multi-scale feature map is split into several pixels; by aligning the pixels in the multi-scale feature maps of adjacent layers, the multi-scale feature maps of adjacent layers are aligned. For example, the feature pattern is split into countless pixels of fixed size, and the error is reduced by aligning pixels with pixels. Specifically, multiple pixels in the large-scale feature map are aligned to a single pixel in the small-scale feature map. After alignment, the outline of the pattern is measured.

[0093] Combination Figure 3 As shown, Figure 3 This is a comparison image of aligned and misaligned feature maps in one embodiment. The image on the left is a schematic diagram of aligned feature maps. After feature alignment, patterns in different environments can be accurately measured. The image on the right is a schematic diagram of misaligned feature maps. When feature maps are misaligned, the layer is not affected, but it will affect the measurement of patterns in different environments, leading to measurement errors. As a result, the final position information of the pattern may be incorrect, such as incorrect bounding box coordinates.

[0094] The above embodiments involve feature map alignment, which ensures the accuracy of pattern measurement under different environments, thereby ensuring the accuracy of the acquired pattern size information.

[0095] In some optional embodiments, a heat map characterizing the degree of defect in each target region is generated based on the pattern density information and pattern size information of each target region. This includes: assigning a corresponding first color to different target regions based on the pattern density information of each target region, with the first color being darker for target regions with higher pattern density; assigning a corresponding second color to different target regions based on the pattern size information of each target region, with the second color being darker for target regions with larger pattern size, and the first color and the second color being different; and generating a heat map characterizing the degree of defect in each target region based on the target regions after assigning the first color and the second color.

[0096] The pattern density and pattern size are both within a target area. Pattern density is macroscopic, while size is microscopic. Based on pattern density and pattern size, the corresponding defect level can be determined. For example, a density > 60 or a size > 100um is high risk. If the density is 30 and the size is 200um in a certain target area, a heat map similar to a temperature distribution will be generated, with low temperature around the area and high temperature inside the area.

[0097] The system pre-defines the correspondence between each pattern density range and the first color, as well as the correspondence between each pattern size and the second color. The target area with a higher pattern density is assigned a darker first color, and the target area with a larger pattern size is assigned a darker second color. Therefore, only the pattern density range needs to be defined.

[0098] Optionally, the pattern density information may include the maximum and minimum values ​​of the pattern density, and the pattern size information may also include the maximum and minimum values ​​of the pattern size. The maximum range can be determined based on the maximum and minimum values, and then the pattern density range corresponding to each color can be determined based on the number of the first color and the number of the second color, as well as the corresponding maximum and minimum values.

[0099] For example, based on the maximum and minimum values ​​of pattern density in the pattern density information, the maximum range of pattern density can be determined. Then, the number of first colors is obtained. Based on the maximum range of pattern density and the number of first colors, the pattern density range corresponding to each first color is determined. Finally, the higher the density within the pattern density range, the darker the corresponding first color. Similar processing is performed on the pattern size information, and finally, the two are superimposed to obtain the final heatmap.

[0100] Combination Figure 4 As shown, Figure 4 This is a flowchart of the heatmap generation steps in one embodiment. In this embodiment, the target area with high pattern density is assigned a darker first color, and the target area with large pattern size is assigned a darker second color. Finally, the two are superimposed to obtain the final heatmap. For example... Figure 5 , Figure 5 This is a schematic diagram of a superimposed heatmap in one embodiment.

[0101] In the above embodiments, the pattern density information and pattern size information are processed separately to obtain a heat map, which can characterize the degree of defects in the target area.

[0102] In some optional embodiments, adjusting the current photomask design data based on defect classification includes: calculating the pattern density of the macroscopic region corresponding to the target area when the target area is less than or equal to an area threshold; filling the target area with a first virtual pattern when the pattern density of the target area is less than or equal to the pattern density of the macroscopic region; filling the target area with a second virtual pattern when the pattern density of the target area is greater than the pattern density of the macroscopic region, wherein the pattern density of the first virtual pattern is greater than the pattern density of the second virtual pattern; when the defect classification includes pattern size classification, placing patterns with a pattern size greater than a size threshold into the target area with a pattern density greater than a density threshold, and filling the target area with virtual patterns in the adjacent area; after adjusting the current photomask design data based on the defect classification, the process includes: performing wafer electrical testing based on the adjusted current photomask design data, including: mapping the pattern density information and pattern size information of each target area to uniform electrical parameters; connecting the unsplit target areas and the areas obtained by splitting the target areas in parallel; and performing wafer electrical testing based on the parallel unsplit target areas, the split target areas, and the electrical parameters.

[0103] In this embodiment, high-risk and low-risk areas can be distinguished based on process characteristics and data received from the actual process. For example, in the actual etching process, different pattern densities have different etching rates. This results in different depths of etched trenches in high-density areas compared to low-density areas. Based on this characteristic, the etching depth of different pattern density areas is collected to determine at which pattern density the etching depth is considered dangerous. Similar processing can be applied to pattern size, which will not be elaborated here.

[0104] Combination Figure 6 , Figure 6 This is a flowchart of the current photomask design data adjustment steps in one embodiment. In this embodiment, if the defect classification includes pattern density classification, for example, if the pattern density in the target area is too low, and the area of ​​the target area with a defect severity greater than or equal to the defect threshold is less than or equal to the area threshold, then virtual pattern filling is performed in the target area. This allows for micro-adjustment of the target area to eliminate local non-uniformity. If the area of ​​the target area with a defect severity greater than or equal to the defect threshold is greater than the area threshold, then the target area is split and virtual pattern filling is performed. This involves splitting the area and inserting a virtual pattern, which is equivalent to macro-adjustment of the target area to eliminate global non-uniformity as described above.

[0105] In this case, if the area of ​​the target region is less than or equal to the area threshold, such as a very small area, for example, a 60um*60um area, the calculated pattern density is only 10. However, on a macroscopic scale, for example, within an area of ​​1mm*1mm, the pattern density is 60. On a microscopic scale, this can be improved by filling only this specific low-density area with a virtual pattern. For the virtual pattern, the filling virtual pattern can be dynamically adjusted based on the calculated data. When the pattern density in the microscopic area is less than the pattern density in the macroscopic area, a high-density virtual pattern, i.e., the first virtual pattern, can be used. When the pattern density in the microscopic area is greater than the pattern density in the macroscopic area, a low-density virtual pattern can be used. The purpose of both methods is to balance the pattern density of the entire photomask, both macroscopically and microscopically.

[0106] If the defect classification includes pattern size classification and is caused by the pattern size being too large, the pattern position can be changed. For example, a pattern with a pattern size larger than the size threshold can be placed in a target area with a pattern density greater than the density threshold, and virtual pattern filling can be performed in the adjacent area of ​​the target area to increase the density of the micro-area.

[0107] In this process, the pattern density meets requirements at the microscopic level. At the macroscopic level, large, low-density areas are broken down into smaller areas, which are then rearranged to create areas with uniform pattern density. Finally, WAT (Wafer Electrical Atmosphere Testing) is performed for homogenization. Generally, whether it's a large macroscopic area or a small microscopic area, the electrical properties are essentially the same. Regardless of the processing method, numerous logic circuits are needed to connect the various regions in parallel to prevent functional loss. Ultimately, because the entire process, both macroscopically and microscopically, tends towards uniformity, this uniform pattern density significantly improves micro-loading for CMP (Chemical Mechanical Polishing), greatly enhancing the uniformity of Vt within the wafer.

[0108] In the above embodiments, different adjustments are made for different defect classifications to correct the current photomask design data and lay the foundation for the accuracy of subsequent photomask fabrication.

[0109] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0110] Based on the same inventive concept, this application also provides a photomask data processing apparatus for implementing the photomask data processing method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more of the photomask data processing apparatus embodiments provided below can be found in the limitations of the photomask data processing method described above, and will not be repeated here.

[0111] In one exemplary embodiment, such as Figure 7 As shown, a photomask data processing device is provided, including: a data acquisition module 701, a processing module 702, a heat map generation module 703, a defect classification determination module 704, and an adjustment module 705, wherein:

[0112] Data acquisition module 701 is used to acquire the current photomask design data;

[0113] Processing module 702 is used to process the current photomask design data to obtain pattern density information and pattern size information for each target area;

[0114] The heat map generation module 703 is used to generate a heat map that characterizes the degree of defects in each target area based on the pattern density information and pattern size information of each target area.

[0115] The defect classification determination module 704 is used to obtain target areas where the defect degree is greater than or equal to the defect threshold, and to determine the defect classification of the target area based on the pattern density information and pattern size information of the target area.

[0116] The adjustment module 705 is used to adjust the current photomask design data based on defect classification, including: when the defect classification includes pattern density classification, obtaining the target area of ​​the target region where the defect degree is greater than or equal to a defect threshold; when the target area is greater than the area threshold, performing functional region identification on the target region where the target area is greater than the area threshold, and extracting key signal paths within the identified functional regions; determining a split boundary based on the functional regions and the key signal paths; and adjusting the current photomask design data based on the split boundary.

[0117] In one optional embodiment, the adjustment module 705 is specifically used to determine an optional range of the channel width of the filled virtual pattern based on the split boundary, and to determine the channel width of the filled virtual pattern based on the optional range of the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, the pattern density of each split region after splitting, and the pattern density of the target region. Based on the ratio of the area of ​​the N-type active region to the area of ​​the P-type active region in the target region, the pattern type of the filled virtual pattern is determined, the pattern type including the N-type virtual pattern or the P-type virtual pattern. Based on the split boundary, the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, and the pattern type of the filled virtual pattern, virtual pattern filling is performed in the adjacent area of ​​the target region.

[0118] In one optional embodiment, the processing module 702 is further configured to extract features from the current photomask design data to obtain a multi-scale feature map by using a feature extraction network of the photomask data processing model that has been pre-trained; to predict the multi-scale feature map by using a density prediction network of the photomask data processing model to obtain pattern density information of each target region; and to predict the multi-scale feature map by using a size prediction network of the photomask data processing model to obtain pattern size information of each target region.

[0119] The aforementioned processing module 702 is further configured to split each multi-scale feature map into several pixels through the size prediction network of the photomask data processing model; to align the pixels in the multi-scale feature maps of adjacent layers; and to make predictions based on the aligned multi-scale feature maps to obtain the pixel-level mask of each target region, the position of the pattern size, and the size statistical histogram corresponding to the pattern of each size.

[0120] In one optional embodiment, the heatmap generation module 703 is further configured to assign a corresponding first color to different target regions based on the pattern density information of each target region, wherein the target region with a higher pattern density is assigned a darker first color; assign a corresponding second color to different target regions based on the pattern size information of each target region, wherein the target region with a larger pattern size is assigned a darker second color, and the first color and the second color are different; and generate a heatmap characterizing the degree of defect in each target region based on the target regions after assigning the first color and the second color.

[0121] In one optional embodiment, the adjustment module 705 is further configured to: calculate the pattern density of the macroscopic region corresponding to the target area when the target area is less than or equal to an area threshold; fill the target area with a first virtual pattern when the pattern density of the target area is less than or equal to the pattern density of the macroscopic region; fill the target area with a second virtual pattern when the pattern density of the target area is greater than the pattern density of the macroscopic region, wherein the pattern density of the first virtual pattern is greater than the pattern density of the second virtual pattern; when the defect classification includes pattern size classification, place patterns with a pattern size greater than a size threshold into the target area with a pattern density greater than a density threshold, and fill the adjacent area of ​​the target area with virtual patterns; perform wafer electrical testing based on the adjusted current photomask design data, including: mapping the pattern density information and pattern size information of each target area to uniform electrical parameters; connecting the unsplit target areas and the areas obtained by splitting the target areas in parallel; and performing wafer electrical testing based on the parallel unsplit target areas, the split target areas, and the electrical parameters.

[0122] In one optional embodiment, the above apparatus further includes: a loop module, configured to obtain new current photomask design data based on the adjusted target area, and continue to execute the steps of processing the current photomask design data to obtain pattern density information and pattern size information of each target area until the defect degree of each target area in the heat map is less than the defect threshold; and output the adjusted current photomask design data.

[0123] Each module in the aforementioned photomask data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0124] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores current photomask design data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a photomask data processing method.

[0125] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0126] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0127] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0128] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0129] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, pattern processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0130] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0131] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for processing photomask data, characterized in that, The method includes: Obtain the current photomask design data; The current photomask design data is processed to obtain pattern density information and pattern size information for each target region; Based on the pattern density information and pattern size information of each target region, a heat map is generated to characterize the degree of defect in each target region, wherein the degree of defect refers to the degree of danger. The target region with a defect severity greater than or equal to the defect threshold is obtained, and the defect classification of the target region is determined based on the pattern density information and pattern size information of the target region. Adjusting the current photomask design data based on the defect classification includes: when the defect classification includes pattern density classification, obtaining the target area of ​​the target region where the defect degree is greater than or equal to the defect threshold; when the target area is greater than the area threshold, performing functional region identification on the target region where the target area is greater than the area threshold, and extracting key signal paths within the identified functional regions; determining the splitting boundary based on the functional regions and the key signal paths; and adjusting the current photomask design data based on the splitting boundary. The adjustment of the current photomask design data based on the split boundary includes: The channel width of the filled virtual pattern is determined based on the split boundary, and the channel width of the filled virtual pattern is determined based on the selectable range of the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, the pattern density of each split region after splitting, and the pattern density of the target region. Based on the ratio of the area of ​​the N-type active region to the area of ​​the P-type active region within the target area, the pattern type of the virtual pattern to be filled is determined, and the pattern type includes an N-type virtual pattern or a P-type virtual pattern. Virtual pattern filling is performed in the vicinity of the target area based on the split boundary, the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, and the pattern type of the filled virtual pattern.

2. The method according to claim 1, characterized in that, The process of processing the current photomask design data to obtain pattern density information and pattern size information for each target region includes: By pre-training a feature extraction network for a photomask data processing model, feature extraction is performed on the current photomask design data to obtain a multi-scale feature map. The density prediction network of the photomask data processing model is used to predict the multi-scale feature map to obtain the pattern density information of each target region. The size prediction network of the photomask data processing model is used to predict the multi-scale feature map to obtain the pattern size information of each target region. The step of using the size prediction network of the photomask data processing model to predict the multi-scale feature map and obtain pattern size information for each target region includes: The size prediction network of the photomask data processing model is used to split each of the multi-scale feature maps to obtain several pixels. The multi-scale feature maps of adjacent layers are aligned by aligning the pixels in the multi-scale feature maps of adjacent layers. Based on the aligned multi-scale feature maps, predictions are made to obtain the pixel-level mask of each target region, the position of the pattern size, and the size statistical histogram corresponding to each size of the pattern.

3. The method according to claim 1 or 2, characterized in that, The step of generating a heat map characterizing the defect degree of each target region based on the pattern density and pattern size information of each target region includes: Based on the pattern density information of each target region, a corresponding first color is assigned to different target regions, and the target region with a higher pattern density is assigned a darker first color. Based on the pattern size information of each target region, a corresponding second color is assigned to different target regions. The target region with a larger pattern size is assigned a darker second color. The first color and the second color are different. Based on the target regions after assigning the first color and the second color, a heat map characterizing the degree of defect in each target region is generated.

4. The method according to claim 1 or 2, characterized in that, The adjustment of the current photomask design data based on the defect classification also includes: When the target area is less than or equal to the area threshold, the pattern density of the macro region corresponding to the target area is calculated. If the pattern density of the target area is less than or equal to the pattern density of the macro region, a first virtual pattern is filled in the target area. If the pattern density of the target area is greater than the pattern density of the macro region, a second virtual pattern is filled in the target area. The pattern density of the first virtual pattern is greater than the pattern density of the second virtual pattern. When the defect classification includes pattern size classification, patterns with a pattern size greater than a size threshold are placed in the target area where the pattern density is greater than a density threshold, and virtual pattern filling is performed in the adjacent area of ​​the target area. After adjusting the current photomask design data based on the defect classification, the process includes: The wafer electrical testing is performed based on the adjusted current photomask design data, including: mapping the pattern density information and pattern size information of each target region to uniform electrical parameters; connecting the unsplit target regions and the regions obtained by splitting the target regions in parallel; and performing wafer electrical testing based on the parallel unsplit target regions, the split target regions, and the electrical parameters.

5. The method according to claim 1 or 2, characterized in that, After adjusting the current photomask design data based on the defect classification, the process includes: Based on the adjusted target area, new current photomask design data is obtained, and the process of processing the current photomask design data to obtain pattern density information and pattern size information of each target area continues until the defect degree of each target area in the heat map is less than the defect threshold. Output the adjusted current photomask design data.

6. A photomask data processing device, characterized in that, The device includes: The data acquisition module is used to acquire the current photomask design data; The processing module is used to process the current photomask design data to obtain pattern density information and pattern size information for each target area; A heat map generation module is used to generate a heat map characterizing the degree of defects in each of the target regions based on the pattern density information and pattern size information of each target region, wherein the degree of defects refers to the degree of danger. The defect classification determination module is used to acquire the target area where the defect degree is greater than or equal to the defect threshold, and determine the defect classification of the target area based on the pattern density information and pattern size information of the target area; An adjustment module is used to adjust the current photomask design data based on the defect classification, including: when the defect classification includes pattern density classification, obtaining the target area of ​​the target region where the defect degree is greater than or equal to a defect threshold; when the target area is greater than the area threshold, performing functional region identification on the target region where the target area is greater than the area threshold, and extracting key signal paths within the identified functional regions; determining a split boundary based on the functional regions and the key signal paths; and adjusting the current photomask design data based on the split boundary. The adjustment of the current photomask design data based on the split boundary includes: determining the selectable range of the channel width of the filled virtual pattern based on the split boundary, and determining the channel width of the filled virtual pattern based on the selectable range of the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, the pattern density of each split region after splitting, and the pattern density of the target region. Based on the ratio of the area of ​​the N-type active region to the area of ​​the P-type active region within the target area, the pattern type of the virtual pattern to be filled is determined, and the pattern type includes an N-type virtual pattern or a P-type virtual pattern. Virtual pattern filling is performed in the vicinity of the target area based on the split boundary, the channel width of the filled virtual pattern, the pattern density of the filled virtual pattern, and the pattern type of the filled virtual pattern.

7. The apparatus according to claim 6, characterized in that, The processing module is also used for: By pre-training a feature extraction network for a photomask data processing model, feature extraction is performed on the current photomask design data to obtain a multi-scale feature map. The density prediction network of the photomask data processing model is used to predict the multi-scale feature map to obtain the pattern density information of each target region. The size prediction network of the photomask data processing model is used to predict the multi-scale feature map to obtain the pattern size information of each target region. The step of using the size prediction network of the photomask data processing model to predict the multi-scale feature map and obtain pattern size information for each target region includes: The size prediction network of the photomask data processing model is used to split each of the multi-scale feature maps to obtain several pixels. The multi-scale feature maps of adjacent layers are aligned by aligning the pixels in the multi-scale feature maps of adjacent layers. Based on the aligned multi-scale feature maps, predictions are made to obtain the pixel-level mask of each target region, the position of the pattern size, and the size statistical histogram corresponding to each size of the pattern.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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