A method for controlling phase and transmittance uniformity of a phase shift mask

Abstract

This application discloses a method for controlling the phase and transmittance uniformity of a phase-shifting mask. The method includes: acquiring spatial light field distribution data of the phase-shifting mask during the exposure process; constructing a corresponding spatial energy flow vector field based on the spatial light field distribution data, and calculating the energy migration direction, energy convergence degree, and energy divergence degree of each region according to the spatial energy flow vector field; constructing a phase gradient continuity model to characterize the spatial continuity of the phase field; identifying local abnormal energy migration regions according to the spatial energy flow vector field, and identifying abrupt phase gradient change regions according to the phase gradient continuity model, and constructing a joint phase and transmittance uniformity control model; generating corresponding uniformity control parameters to coordinately adjust the phase shift layer thickness, local light-transmitting layer material distribution, and mask pattern edge transition structure in the corresponding region of the phase-shifting mask. This application improves high mask performance and lithography yield.

Description

Technical Field

[0001] This application relates to the field of semiconductor lithography technology, and in particular to a method for controlling the phase and transmittance uniformity of a phase-shifting mask. Background Technology

[0002] As integrated circuit feature sizes continue to shrink to 7nm and below, phase-shifting masks play a crucial role in improving resolution and process windows. Existing phase-shifting masks mainly control 180° phase shift and specific transmittance through phase-shifting layer materials such as MoSi and CrOx. However, during the fabrication and exposure of large-size masks, inhomogeneities in phase-shifting layer thickness, material composition, and pattern edge structure can easily occur, leading to local phase deviations exceeding ±3° and transmittance fluctuations exceeding ±2%, directly affecting imaging contrast and depth of focus.

[0003] Therefore, there is an urgent need for a new method that can achieve joint uniformity control of phase and transmittance based on actual light field data during the exposure process, so as to improve mask performance and lithography yield. Summary of the Invention

[0004] This application provides a method for controlling the phase and transmittance uniformity of a phase-shifting mask, which improves high mask performance and lithography yield.

[0005] This application provides the following solution:

[0006] According to a first aspect, a method for controlling the phase and transmittance uniformity of a phase-shifting mask is provided. The method includes: acquiring spatial light field distribution data of the phase-shifting mask during exposure, the spatial light field distribution data including light intensity distribution information and phase distribution information of different regions; constructing a corresponding spatial energy flow vector field based on the spatial light field distribution data, and calculating the energy migration direction, energy convergence degree, and energy divergence degree of each region according to the spatial energy flow vector field; calculating the phase gradient change characteristics between adjacent regions based on the phase distribution information, and constructing a phase gradient continuity model to characterize the spatial continuity of the phase field; identifying local energy abnormal migration regions according to the spatial energy flow vector field, and identifying phase gradient abrupt change regions according to the phase gradient continuity model, and constructing a phase and transmittance joint uniformity control model based on the correlation between the local energy abnormal migration regions and the phase gradient abrupt change regions; generating corresponding uniformity control parameters according to the phase and transmittance joint uniformity control model, and coordinating the adjustment of the phase shift layer thickness, local light-transmitting layer material distribution, and mask pattern edge transition structure in the corresponding region of the phase-shifting mask.

[0007] According to one achievable method in an embodiment of this application, the construction of a corresponding spatial energy flow vector field based on the spatial light field distribution data includes: extracting light field energy migration trajectories between different regions based on the spatial light field distribution data, and determining the main direction of energy transmission between regions based on the light field energy migration trajectories; identifying energy convergence paths and energy diversion paths in the spatial light field based on the main direction of energy transmission and the local light intensity attenuation trend of each region; constructing a regional energy migration network based on the energy convergence paths and energy diversion paths, and determining the energy transfer level of each region in the regional energy migration network; and generating a corresponding spatial energy flow vector field based on the energy transfer level, energy migration direction, and energy migration intensity of each region.

[0008] According to one achievable method in this application embodiment, the step of calculating the phase gradient change characteristics between adjacent regions based on the phase distribution information includes: extracting local phase evolution boundaries in the phase field based on the phase distribution information, and determining phase change sensitive regions based on the local phase evolution boundaries; determining phase gradient migration paths based on the spatial deflection trend of phase change direction within the phase change sensitive regions; constructing a phase gradient propagation relationship network based on the degree of intersection and continuous extension between different phase gradient migration paths; and determining the phase gradient change characteristics between adjacent regions based on the phase gradient propagation relationship network.

[0009] According to one achievable method in an embodiment of this application, the construction of a phase gradient continuity model for characterizing the spatial continuity of the phase field includes: identifying phase gradient propagation interruption regions and phase gradient recirculation regions based on a phase gradient propagation relationship network; constructing a phase topology evolution constraint model based on the spatial distribution relationship between the phase gradient propagation interruption regions and the phase gradient recirculation regions; and calculating the topological stability of the phase field in different regions based on the phase topology evolution constraint model to construct the corresponding phase gradient continuity model.

[0010] According to one achievable method in an embodiment of this application, the calculation of the topological stability of the phase field between different regions includes: calculating the phase propagation imbalance factor of the corresponding region based on the degree of deviation between the phase gradient propagation direction and the spatial energy migration direction; identifying local topological distortion regions in the phase field based on the phase propagation imbalance factor; calculating the topological preservation capability of the phase topology during continuous propagation by combining the spatial diffusion trend between local topological distortion regions; and generating a topological stability index of the corresponding region based on the topological preservation capability to characterize the topological stability of the phase field between different regions.

[0011] According to one achievable method in an embodiment of this application, identifying local energy anomaly migration regions based on the spatial energy flow vector field includes: calculating local light field curl based on the spatial energy flow vector field, identifying light energy return regions and energy disturbance regions based on the local light field curl, and using the energy return regions and energy disturbance regions as local energy anomaly migration regions.

[0012] According to one achievable method in this application embodiment, the step of constructing a joint phase and transmittance uniformity control model based on the correlation between the local energy anomaly migration region and the phase gradient abrupt change region includes: performing spatial overlap mapping on the local energy anomaly migration region and the phase gradient abrupt change region to determine the spatial correspondence and interaction coupling strength between the two types of anomaly regions; calculating the influence weight of energy anomaly migration on phase gradient change characteristics based on the interaction coupling strength, and constructing an energy phase influence mapping function; quantifying the contribution of each anomaly region to transmittance distribution deviation and phase continuity disruption according to the energy phase influence mapping function, forming a set of regional contribution factors; establishing a joint optimization constraint relationship between phase control amount and transmittance correction amount based on the set of regional contribution factors, and constructing a corresponding phase and transmittance coordinated adjustment model.

[0013] According to one achievable method in this application embodiment, determining the spatial correspondence and interactive coupling strength between the two types of abnormal regions includes: spatially partitioning and identifying the local energy anomaly migration region and the phase gradient abrupt change region, and constructing a candidate corresponding region set based on spatial adjacency; filtering to form a one-to-many or many-to-one spatial correspondence mapping relationship based on the distance distribution of each candidate corresponding region in spatial coordinates and the consistency of energy migration direction; introducing an angle consistency factor between the energy migration direction vector and the phase gradient change direction vector for the spatial correspondence mapping relationship, and calculating the directional coupling degree between regions; and calculating the interactive coupling strength by combining the coverage density and directional coupling degree of the spatial correspondence mapping relationship.

[0014] According to one achievable method in an embodiment of this application, generating corresponding uniformity control parameters based on the phase and transmittance joint uniformity control model includes: outputting target uniformity deviation values ​​for each local region based on the phase and transmittance joint uniformity control model, and decomposing the target uniformity deviation values ​​into phase deviation components and transmittance deviation components; calculating the phase shift correction amount for the corresponding region based on the phase deviation components, and mapping the phase shift correction amount to a phase shift layer thickness adjustment parameter; calculating the light energy transmission compensation amount for the corresponding region based on the transmittance deviation components, and mapping the light energy transmission compensation amount to a local light-transmitting layer material distribution correction parameter; generating edge transition continuity correction parameters for the mask pattern edge region by combining the coupling strength of the phase deviation components and transmittance deviation components for each region; and combining the phase shift layer thickness adjustment parameters, the local light-transmitting layer material distribution correction parameters, and the edge transition continuity correction parameters to form a corresponding uniformity control parameter set.

[0015] According to a second aspect, a phase-shifting mask phase and transmittance uniformity control system is provided. The system includes: a spatial light field distribution data acquisition unit configured to acquire spatial light field distribution data of the phase-shifting mask during exposure, the spatial light field distribution data including light intensity distribution information and phase distribution information of different regions; a spatial energy flow vector field construction unit configured to construct a corresponding spatial energy flow vector field based on the spatial light field distribution data, and calculate the energy migration direction, energy convergence degree, and energy divergence degree of each region based on the spatial energy flow vector field; and a phase gradient continuity model construction unit configured to calculate the phase gradient change between adjacent regions based on the phase distribution information. The system employs a phase gradient continuity model to characterize the spatial continuity of the phase field. A joint uniformity control model construction unit is configured to identify local energy anomaly migration regions based on the spatial energy flow vector field and phase gradient abrupt change regions based on the phase gradient continuity model. Based on the correlation between the local energy anomaly migration regions and the phase gradient abrupt change regions, a joint phase and transmittance uniformity control model is constructed. A collaborative adjustment control unit is configured to generate corresponding uniformity control parameters based on the joint phase and transmittance uniformity control model, and collaboratively adjust the phase shift layer thickness, local light-transmitting layer material distribution, and mask pattern edge transition structure in the region corresponding to the phase-shifting mask.

[0016] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0017] This application innovatively constructs a spatial energy flow vector field by acquiring spatial light field distribution data of the mask during the exposure process to accurately characterize the energy migration direction, convergence degree, and divergence degree. Simultaneously, based on phase distribution information, a phase gradient continuity model is established to accurately identify local energy anomaly migration regions and phase gradient abrupt change regions. Based on this, a joint phase and transmittance uniformity control model is constructed through correlation analysis of these two types of anomaly regions, and targeted control parameters are generated to synergistically adjust the phase shift layer thickness, local transparent layer material distribution, and mask pattern edge transition structure. This method effectively solves the problem of traditional static control being unable to cope with dynamic light field inhomogeneity, significantly reduces spatial energy distribution inhomogeneity, improves the spatial continuity of the phase field, reduces phase deviation, and improves transmittance uniformity, thereby expanding the lithography process window, improving imaging contrast, and ultimately effectively improving lithography yield and the overall yield of semiconductor device manufacturing, possessing significant industrial application value.

[0018] Of course, any product implementing this application does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating the phase and transmittance uniformity control method of a phase-shifting mask provided in this application embodiment;

[0021] Figure 2 A schematic diagram illustrating the process of the phase-shifting mask phase and transmittance uniformity control method provided in the embodiments of this application;

[0022] Figure 3 A structural block diagram of the phase and transmittance uniformity control system of the phase-shifting mask provided in the embodiments of this application;

[0023] Figure 4 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0025] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0026] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0027] Figure 1 This is a flowchart illustrating a method for controlling the phase and transmittance uniformity of a phase-shifting mask, as provided in an embodiment of this application. Figure 1 As shown, the method may include the following steps:

[0028] Step 101: Obtain the spatial light field distribution data of the phase-shifting mask during the exposure process. The spatial light field distribution data includes light intensity distribution information and phase distribution information in different regions.

[0029] Step 102: Construct the corresponding spatial energy flow vector field based on the spatial light field distribution data, and calculate the energy migration direction, energy convergence degree and energy divergence degree of each region according to the spatial energy flow vector field.

[0030] Step 103: Calculate the phase gradient change characteristics between adjacent regions based on the phase distribution information, and construct a phase gradient continuity model to characterize the spatial continuity of the phase field.

[0031] Step 104: Identify local energy anomaly migration regions based on the spatial energy flow vector field, and identify phase gradient abrupt change regions based on the phase gradient continuity model; construct a phase and transmittance joint uniformity control model based on the correlation between the local energy anomaly migration regions and the phase gradient abrupt change regions.

[0032] Step 105: Generate corresponding uniformity control parameters based on the phase and transmittance joint uniformity control model, and coordinately adjust the phase shift layer thickness, local light-transmitting layer material distribution, and mask pattern edge transition structure in the corresponding region of the phase shift mask.

[0033] As can be seen from the above process, this application innovatively constructs a spatial energy flow vector field by acquiring the spatial light field distribution data of the mask during the exposure process to accurately characterize the energy migration direction, convergence degree, and divergence degree. Simultaneously, based on phase distribution information, a phase gradient continuity model is established to accurately identify local energy anomaly migration regions and phase gradient abrupt change regions. On this basis, a phase and transmittance joint uniformity control model is constructed through correlation analysis of the two types of anomaly regions, and targeted control parameters are generated to synergistically adjust the phase shift layer thickness, local transparent layer material distribution, and mask pattern edge transition structure. This method effectively solves the problem of traditional static control being unable to cope with dynamic light field inhomogeneity, significantly reduces spatial energy distribution inhomogeneity, improves the spatial continuity of the phase field, reduces phase deviation, and improves transmittance uniformity, thereby expanding the lithography process window, improving imaging contrast, and ultimately effectively improving lithography yield and the overall yield of semiconductor device manufacturing, possessing significant industrial application value.

[0034] The following describes in detail each step of the above process and the effects that can be further produced, with reference to the embodiments. First, step 101, namely "acquiring the spatial light field distribution data of the phase-shifting mask during the exposure process, wherein the spatial light field distribution data includes light intensity distribution information and phase distribution information of different regions", will be described in detail with reference to the embodiments.

[0035] Spatial light field distribution data refers to the complete optical field information formed in the near field of the mask or on the imaging plane under actual exposure conditions. It not only records the light intensity distribution in different regions of the mask, but also simultaneously captures the phase distribution information of each region. In this way, the dynamic optical characteristics of the mask under working conditions can be truly reflected, rather than relying solely on static simulations or offline measurements after fabrication.

[0036] During actual photolithography exposure, phase-shifting masks are affected by various factors such as light source illumination, thermal load, and three-dimensional mask effects, leading to real-time changes in local light intensity and phase. Traditional methods often perform static inspections after mask fabrication, making it difficult to capture these dynamic inhomogeneities. This invention, by acquiring data during the exposure process, can more accurately reflect the optical field characteristics of the mask under its actual working state, providing reliable raw input for subsequent energy flow analysis and phase continuity modeling.

[0037] Figure 2This is a schematic diagram illustrating the process of the phase-shifting mask phase and transmittance uniformity control method provided in this application embodiment. The spatial light field distribution data specifically includes two key components. First, the light intensity distribution information of different regions, i.e., the light intensity values ​​and their spatial variation patterns at various positions on the mask surface and imaging plane, used to characterize the uniformity of energy distribution in the mask pattern region. Second, the phase distribution information, i.e., the phase values ​​of light waves in each region and their spatial gradient variations, used to describe whether the phase delay generated by the phase-shifting layer is uniform. These two types of information together constitute complete complex amplitude light field data, laying the foundation for subsequent construction of the spatial energy flow vector field and phase gradient continuity model.

[0038] In actual data acquisition, high-resolution optical field measurement equipment, such as AIMS (Airborne Imaging Measurement System), near-field scanning optical microscopes, or high-precision FDTD numerical simulation tools, can be used to acquire data at a 193nm immersion or EUV exposure wavelength. During data acquisition, the mask surface is typically divided into multiple fine grid regions to ensure spatial resolution at the nanometer level, thereby providing sufficiently accurate input data for subsequent identification of local anomalies.

[0039] The following describes in detail step 102, namely, "constructing a corresponding spatial energy flow vector field based on the spatial light field distribution data, and calculating the energy migration direction, energy convergence degree, and energy divergence degree of each region according to the spatial energy flow vector field", with reference to the embodiments.

[0040] Constructing the corresponding spatial energy flow vector field based on the spatial light field distribution data is a key step in the method of this invention to transform the original light field information into quantifiable energy flow characteristics.

[0041] This construction process begins by extracting the energy migration trajectories between different regions from spatial light field distribution data. Specifically, by continuously analyzing the intensity and phase distributions of adjacent or related regions, the propagation paths of energy on the mask surface and imaging plane are traced. These migration trajectories reflect the actual routes of light wave energy flowing from one region to another. Subsequently, based on the extracted migration trajectories, the main direction of energy transfer between regions is determined. This main direction represents the primary trend of local energy flow, providing guidance for subsequent path identification.

[0042] Based on obtaining the main direction of energy transmission, this method further combines the local light intensity attenuation trend in each region to identify energy convergence paths and energy diversion paths in the spatial light field. Energy convergence paths refer to the flow routes where light intensity gradually increases and energy concentrates in a certain area, while energy diversion paths refer to the routes where energy diffuses from high-intensity areas to the surrounding areas and gradually weakens. This identification process can accurately capture abnormal energy accumulation or dissipation phenomena at the edges of mask patterns or in the transition regions of phase-shifting layers, providing important basis for subsequent control.

[0043] Next, based on the identified energy convergence and divergence paths, a regional energy migration network is constructed. This network treats different masked regions as nodes and energy flow paths as connecting edges, forming a topology that describes the global energy transfer relationships. Within this network, the energy transfer hierarchy of each region needs to be determined. Higher-level regions are typically the sources or key nodes of energy convergence, while lower-level regions are the endpoints of energy reception or dissipation. This hierarchical division clearly distinguishes the roles and importance of different regions in the overall energy flow.

[0044] Finally, based on the energy transfer level, energy migration direction, and energy migration intensity of each region, a corresponding spatial energy flow vector field is generated. This vector field can be mathematically expressed as follows: at each discrete grid point, an energy flow vector is defined. ,in and They represent and The energy transfer component is oriented. Its magnitude is determined by the energy transfer intensity, and its direction is determined by the main energy transfer direction, while also incorporating adjustments to local weights based on the energy transfer hierarchy. This spatial energy flow vector field visually represents the dynamic flow distribution of energy during mask exposure, providing a precise quantitative basis for subsequent identification of local energy anomaly migration regions.

[0045] The spatial energy flow vector field constructed through the above steps surpasses the traditional static analysis method that only considers light intensity or phase. It can comprehensively reflect the dynamic energy transfer characteristics during the exposure process, providing a reliable physical basis for the joint control of phase and transmittance uniformity.

[0046] After constructing the spatial energy flow vector field, this invention calculates the energy migration direction, energy convergence degree, and energy divergence degree of each region based on the spatial energy flow vector field.

[0047] This calculation process first determines the energy migration direction for each discrete region in the spatial energy flow vector field. The energy migration direction reflects the main trend of light field energy flow within that region, and is achieved by extracting the principal direction vector of the vector field in that region. It can clearly indicate whether energy is flowing in or out of the region, as well as the azimuth angle of the flow, providing directional basis for subsequent path analysis and anomaly identification.

[0048] Based on the obtained energy migration direction, this method further calculates the degree of energy convergence in each region. The degree of energy convergence is used to quantify the intensity of energy accumulation towards the center within the region. The higher the value, the more concentrated the local energy tends to be, which may lead to light intensity hotspots or abnormal imaging contrast. In the calculation, it is usually based on the convergence characteristics of the vector field within the region, and is determined by comparing the energy inflow and outflow of adjacent regions.

[0049] Corresponding to the degree of convergence is the degree of energy divergence. This indicator characterizes the extent to which energy diffuses from a region to the surrounding areas. The higher the value, the more dissipated or diffused the energy is in that region, potentially causing localized light intensity reduction or phase perturbation. The degree of convergence and divergence together constitute a two-pronged description of the energy behavior of a region, comprehensively reflecting the dynamic balance of energy during mask exposure.

[0050] In specific calculations, the following formula can be used for quantification. For the energy flow vector S at the center of a certain region, its energy migration direction can be expressed as:

[0051] Direction angle

[0052] in and They are respectively and Energy flow component in the direction.

[0053] Energy Convergence It can be calculated by the negative value of the vector field divergence in the local region:

[0054]

[0055] when When the value is positive and large, it indicates that there is significant energy accumulation in the region.

[0056] Degree of energy dissipation Then the corresponding positive value of the vector field divergence is:

[0057]

[0058] when When the value is positive and large, it indicates that there is significant energy dissipation in the region.

[0059] The energy migration direction, energy convergence degree, and energy divergence degree obtained through the above calculations together constitute the multi-dimensional quantitative characteristics of the spatial energy flow vector field.

[0060] The following describes in detail step 103, namely, "calculating the phase gradient change characteristics between adjacent regions based on the phase distribution information, and constructing a phase gradient continuity model to characterize the spatial continuity of the phase field," with reference to an embodiment.

[0061] Based on the phase distribution information, this invention calculates the phase gradient change characteristics between adjacent regions, and constructs a phase gradient continuity model to characterize the spatial continuity of the phase field according to the phase gradient change characteristics.

[0062] This process begins by extracting local phase evolution boundaries from the phase field based on phase distribution information. Phase evolution boundaries refer to transition regions where phase values ​​undergo significant changes or abrupt shifts, identified through edge detection and gradient analysis of the phase distribution data. These boundaries accurately mark key locations where changes in phase shift layer thickness or material distribution occur. Subsequently, based on the extracted local phase evolution boundaries, sensitive regions for phase changes are further determined. These sensitive regions are where phase instability or deviation is most likely to occur, typically located near pattern edges, phase shift transition zones, or material interfaces, and are key areas affecting overall phase uniformity.

[0063] Based on the extracted local phase evolution boundary, the specific method for further determining the phase change sensitive region is as follows:

[0064] First, the extracted local phase evolution boundaries are quantitatively characterized. Image processing or numerical analysis is used to calculate the phase gradient magnitude and direction at each boundary point, resulting in a boundary intensity distribution map. Locations with higher boundary intensities typically correspond to abrupt changes in phase shift layer thickness, material interfaces, or pattern edge regions; these locations are critical areas where phase instability is most likely to occur.

[0065] Secondly, a threshold for phase change sensitivity is set. A boundary buffer zone is formed by extending a certain distance (e.g., 5 to 20 nanometers) to both sides around each phase evolution boundary, determined based on the mask feature size. Within the buffer zone, the phase gradient change rate is calculated point-by-point, and the standard deviation of the phase difference is statistically analyzed. When the phase gradient change rate in a certain region exceeds a preset threshold, for example, greater than a certain threshold... Or the phase standard deviation is greater than When this happens, the area is marked as a candidate sensitive area.

[0066] Next, region growing and merging operations are performed. Starting from the seed point of each candidate sensitive region, region growing is performed towards the surrounding adjacent grids. Only when the phase gradient direction deflection angle of an adjacent point is less than a set angle, such as less than 30°, and the gradient magnitude remains continuous, is the point included in the same sensitive region. In this way, isolated noise points are avoided from being incorrectly labeled, while multiple interconnected boundary segments are merged into a complete phase change sensitive region.

[0067] Finally, the identified sensitive regions are ranked by importance. A sensitivity index is calculated for each region based on its area, average phase gradient intensity, and degree of overlap with the mask's key features. Regions with higher sensitivity indices are prioritized as phase change sensitive regions for subsequent phase gradient transfer path tracing and continuity model construction.

[0068] After identifying the phase-change sensitive region, this method determines the phase gradient migration path based on the spatial deflection trend of the phase change direction within the region. This spatial deflection trend reflects the turning pattern of the phase gradient at different locations, and continuous migration paths are formed by tracking the changes in the phase gradient vector between adjacent points. These paths describe how phase information propagates from one region to another on the mask surface, providing a structural foundation for subsequent network construction.

[0069] Next, a phase gradient propagation relationship network is constructed based on the degree of intersection and continuous extension between different phase gradient migration paths. This network treats each migration path as a node or edge, with the degree of intersection measuring the intensity of overlap or mutual influence among multiple paths in the same region, and the degree of continuous extension characterizing the path's ability to maintain a smooth extension in space. Through this networked description, the propagation law of the phase gradient throughout the entire phase field can be systematically characterized.

[0070] Based on the constructed phase gradient propagation network, the phase gradient change characteristics between adjacent regions are finally determined. These characteristics include indicators such as the magnitude of the phase difference, the consistency of the gradient direction, and the rate of change. On this basis, a phase gradient continuity model is constructed to characterize the spatial continuity of the phase field. This model can quantify the smoothness and topological stability of the phase field in space, avoiding isolated abrupt changes or breaks.

[0071] In specific calculations, the phase gradient can be expressed using the following formula:

[0072] Phase gradient

[0073] in The phase distribution function, and They are respectively and The rate of phase change in direction.

[0074] The local deflection trend in the phase gradient change characteristics can be calculated by the angle between the gradient vectors:

[0075] Deflection angle

[0076] in and This represents the phase gradient vector of the adjacent region.

[0077] The core metric of the phase gradient continuity model is continuity. It can be represented as:

[0078]

[0079] in The divergence of the phase gradient field is used to characterize local curvature changes. For the total number of regions, These are the normalized weighting coefficients. The output of this model directly provides a quantitative basis for phase continuity in subsequent anomaly region identification and joint control models.

[0080] As an implementable approach, the present invention constructs a phase gradient continuity model to characterize the spatial continuity of the phase field, including: identifying phase gradient propagation interruption regions and phase gradient recirculation regions based on a phase gradient propagation relationship network; constructing a phase topology evolution constraint model based on the spatial distribution relationship between the phase gradient propagation interruption regions and the phase gradient recirculation regions; and calculating the topological stability of the phase field in different regions based on the phase topology evolution constraint model to construct the corresponding phase gradient continuity model.

[0081] This process first relies on the established phase gradient propagation relationship network to systematically identify phase gradient propagation interruption regions and phase gradient recirculation regions. Phase gradient propagation interruption regions refer to areas where the phase gradient path abruptly terminates or its intensity significantly decreases during spatial propagation, typically caused by abrupt changes in phase shift layer thickness, material defects, or pattern edge effects. Phase gradient recirculation regions refer to phenomena where the phase gradient direction is significantly reversed or local circulation forms; these regions often correspond to complex areas with strong energy and phase interference. Through network topology analysis algorithms, these two types of abnormal regions can be accurately located, providing precise spatial localization for subsequent topology constraint modeling.

[0082] After identifying the two types of regions, a phase topology evolution constraint model is constructed based on their spatial distribution relationship. This model treats the interruption region and the return region as topology constraint nodes, comprehensively considering the distance, relative orientation, and path connectivity between the two types of regions to construct a mathematical framework describing the constrained evolution of the phase field. This constraint model can reflect the topological distortions and stability limitations that may occur in the phase field during propagation, providing a theoretical basis for quantifying the overall phase continuity.

[0083] Finally, based on the phase topology evolution constraint model, the topological stability of the phase field across different regions is calculated, and a complete phase gradient continuity model is constructed accordingly. This calculation process comprehensively evaluates the ability of the phase gradient to maintain and recover under constraints, ultimately outputting a global continuity evaluation index.

[0084] In specific calculations, the following key formulas can be used:

[0085] The intensity of phase gradient propagation interruption can be defined as:

[0086] Interruption strength

[0087] in The upstream gradient vector, For the downstream gradient vector, when A value close to 1 indicates a significant interruption in propagation.

[0088] The degree of phase gradient backflow can be calculated by the angle between the vectors:

[0089] reflux degree

[0090] in The angle between the phase gradient vectors on adjacent paths, when When approaching 180°, the degree of reflux A value close to 0 indicates significant reflux.

[0091] Topological stability It can be represented as:

[0092]

[0093] in, This represents the average spatial distance between the interrupted region and the recirculation region. The number of recirculation areas. These are the weighting coefficients. As a regulating factor, Let V be the variance of the overall topological distortion of the phase field. The closer the value is to 1, the higher the topological stability of the phase field and the better the spatial continuity.

[0094] Preferably, the calculation of the topological stability of the phase field between different regions includes: calculating the phase propagation imbalance factor of the corresponding region based on the degree of deviation between the phase gradient propagation direction and the spatial energy migration direction; identifying local topological distortion regions in the phase field based on the phase propagation imbalance factor; calculating the topological preservation capability of the phase topology during continuous propagation by combining the spatial diffusion trend between local topological distortion regions; and generating a topological stability index of the corresponding region based on the topological preservation capability to characterize the topological stability of the phase field between different regions.

[0095] Specifically, the following key formula can be used:

[0096] Phase propagation imbalance factor It can be represented as:

[0097]

[0098] in Let be the angle between the phase gradient propagation direction vector and the spatial energy transfer direction vector. When When approaching 0° or 180°, A value close to 0 indicates a consistent direction; when When approaching 90°, A value close to 1 indicates a severe imbalance.

[0099] Local topological distortion regions can be identified using thresholds:

[0100] like ( If the value is 0.6, it is marked as a candidate point for distortion.

[0101] Topology preservation capability It can be calculated through diffusion trends:

[0102]

[0103] in For the first The area of ​​each distorted region This represents the average distance between this region and adjacent normal regions. is the diffusion attenuation coefficient.

[0104] Final topological stability index It can be represented as:

[0105]

[0106] in For the total number of regions, These are the imbalance factors in each region.

[0107] The phase gradient continuity model constructed using the above method can comprehensively characterize the spatial continuity characteristics of the phase field at the topological level, providing a high-precision quantitative basis for the correlation analysis of local energy anomaly regions and phase change regions, as well as joint homogeneity control, significantly improving the scientificity and effectiveness of the control strategy.

[0108] The following describes in detail step 104, namely, "identifying local energy anomaly migration regions based on the spatial energy flow vector field, identifying phase gradient abrupt change regions based on the phase gradient continuity model, and constructing a phase and transmittance joint uniformity control model based on the correlation between the local energy anomaly migration regions and the phase gradient abrupt change regions," with reference to the embodiments.

[0109] This step first identifies local energy anomaly migration regions based on the spatial energy flow vector field. This identification process begins by calculating the local optical field curl based on the constructed spatial energy flow vector field. The spatial energy flow vector field describes the direction and intensity of energy flow on the mask surface and imaging plane, while the optical field curl further reveals the rotational characteristics of the energy flow. When the energy flow exhibits obvious rotation or vortex phenomena, it indicates the existence of energy circulation or irregular disturbances in a local region, characteristics that are difficult to directly capture using traditional light intensity or phase analysis. By calculating the curl, the degree of non-flatness of the energy flow can be quantitatively characterized, providing a physical basis for the precise location of anomaly regions.

[0110] After obtaining the local optical field curl, this method identifies the optical energy recirculation region and the energy perturbation region based on the curl value. The optical energy recirculation region refers to a state where the absolute value of the curl is large and the direction is a closed loop. Energy forms a local circulation in this region, causing some energy to be unable to be effectively transmitted forward, which may cause imaging defects such as hot spots or ghosting. The energy perturbation region refers to a region where the curl sign changes frequently or the amplitude fluctuates drastically, indicating that the energy flow is extremely unstable and is prone to causing local light intensity flicker or phase jitter. Both types of regions are typical manifestations of abnormal energy migration behavior.

[0111] Ultimately, the identified light energy backflow regions and energy perturbation regions were unified as local energy anomalous migration regions. These regions were marked as key target areas for subsequent joint phase and transmittance modulation because they are often strongly correlated with abrupt phase gradient change regions and are the main cause of the overall decrease in uniformity.

[0112] Specifically, the local curl of the optical field can be expressed by the following formula:

[0113] Local optical field curl

[0114] in It represents the component of the spatial energy flow vector field in the two-dimensional plane. The positive and negative values ​​represent different directions of rotation. The magnitude reflects the rotational intensity.

[0115] The criteria for identifying the light energy recirculation region can be set as follows:

[0116] when ( When the value can be 0.15 and the vectors within the region form a closed trend, it is marked as a reflux region.

[0117] The region of energy perturbation can be identified by the rate of change of curl gradient:

[0118]

[0119] When the index exceeds a preset threshold, it is determined to be an energy disturbance area.

[0120] Next, the phase gradient abrupt change regions are identified based on the phase gradient continuity model. The phase gradient continuity model has performed a global and local continuity quantification assessment of the entire phase field, including indicators such as phase gradient continuity, topological stability index, and multi-scale curvature characteristics. In the actual identification process, this method uses the output value of the continuity model as the main criterion to scan the phase field region by region. When the continuity index of a certain region is significantly lower than the overall average level or lower than a preset threshold, it is determined that a phase gradient abrupt change exists in that region. This abrupt change region usually manifests as a drastic deflection of the phase gradient direction, a sudden increase or decrease in gradient amplitude, or a break in the topological structure. These regions often correspond to phase shift layer thickness deviations, uneven material distribution, or transition defects at the edge of the pattern.

[0121] The identification process employs a multi-threshold hierarchical strategy. First, the phase gradient continuity of each region is calculated; if this value is below a set threshold, it is marked as a candidate abrupt change region. Second, further verification is performed using the topological stability index. Only when both the continuity and the topological stability index are below the threshold are a region definitively identified as a phase gradient abrupt change region. This dual-index verification method effectively reduces the false positive rate and ensures the reliability of the identification results.

[0122] The specific calculation and identification formulas are as follows:

[0123] When phase gradient continuity ( When the value is 0.75, the region is initially marked as a candidate region for a phase gradient abrupt change.

[0124] Topological stability index For secondary confirmation:

[0125] when ( When the value is 0.65, it is finally determined to be a region of abrupt change in phase gradient.

[0126] in Let be the divergence of the phase gradient field. For topology preservation capability, The phase propagation imbalance factor, This is the sensitivity adjustment coefficient.

[0127] The phase gradient abrupt change regions identified by the above method can be accurately spatially correlated and analyzed with the previously identified local energy anomaly migration regions, providing reliable anomaly region localization for the subsequent construction of a joint phase and transmittance uniformity control model.

[0128] Finally, based on the correlation between the local energy anomaly migration region and the phase gradient abrupt change region, a joint phase and transmittance uniformity control model is constructed.

[0129] This construction process first involves spatially overlapping and mapping local energy anomaly migration regions with phase gradient abrupt change regions. By superimposing and analyzing the two types of regions in the same coordinate system, their spatial correspondence is determined, including complete overlap, partial overlap, and adjacency. Simultaneously, the interaction coupling strength is calculated, which comprehensively considers the overlap area of ​​the two types of regions, the distance between their center points, and the consistency between the energy flow direction and the phase gradient direction, quantifying the degree of mutual influence between them. Higher coupling strength indicates a greater impact of energy anomalies on phase abrupt changes in that region, requiring stronger coordinated regulation.

[0130] Specifically, determining the spatial correspondence and interaction coupling strength between the two types of anomalous regions includes: spatially partitioning and identifying the local energy anomaly migration region and the phase gradient abrupt change region, and constructing a set of candidate corresponding regions based on spatial adjacency; filtering and forming one-to-many or many-to-one spatial correspondence mapping relationships based on the distance distribution of each candidate corresponding region in spatial coordinates and the consistency of energy migration direction; introducing an angle consistency factor between the energy migration direction vector and the phase gradient change direction vector for the spatial correspondence mapping relationship, and calculating the directional coupling degree between regions; and calculating the interaction coupling strength by combining the coverage density and directional coupling degree of the spatial correspondence mapping relationship.

[0131] This process begins by spatially partitioning and identifying local energy anomaly migration regions and phase gradient abrupt change regions. The mask surface is divided into regular grid cells or irregular functional partitions, and each type of anomaly region is assigned a unique identifier. Subsequently, a candidate set of corresponding regions is constructed based on spatial adjacency relationships. Only regions that are spatially adjacent or may overlap are included in the candidate set, thereby significantly narrowing the subsequent matching range and improving computational efficiency.

[0132] Based on the candidate corresponding region set, this method further filters regions according to their distance distribution in spatial coordinates and the consistency of energy migration direction to form one-to-many or many-to-one spatial correspondence mapping relationships. Candidate pairs that are closer in distance and have higher consistency in energy migration direction are more likely to form a stable mapping relationship. This mapping allows one energy anomaly region to correspond to multiple phase change regions, or one phase change region to be affected by multiple energy anomaly regions, which is more in line with the complex situation of multi-physics coupling in actual exposure processes.

[0133] Based on the established spatial correspondence mapping relationship, an angle consistency factor is introduced between the energy migration direction vector and the phase gradient change direction vector to calculate the directional coupling degree between regions. This factor can accurately measure the degree of synergy between the two vector fields in direction; the more consistent the directions, the higher the coupling degree, indicating a stronger interaction between the two types of anomalies.

[0134] Finally, by combining the coverage density and directional coupling degree of the spatial correspondence mapping relationship, the interaction coupling strength is calculated. This strength value is a core input parameter that subsequently affects the weight calculation and joint regulation model.

[0135] The specific calculation formula is as follows:

[0136] The normalized value of spatial distance can be expressed as:

[0137]

[0138] in The distance between the centers of the two regions is the Euclidean distance. This is the maximum reference distance.

[0139] Angle Consistency Factor The calculation is as follows:

[0140]

[0141] in Let the energy transfer direction vector be... This is the vector representing the direction of the phase gradient change.

[0142] Inter-regional directional coupling It can be represented as:

[0143]

[0144] Coverage density Defined as the ratio of the overlapping area of ​​the two types of regions in the mapping relationship to the total area.

[0145] Final interaction coupling strength The calculation formula is:

[0146]

[0147] in and These are weighting coefficients, which can be calibrated based on actual simulation or experimental data.

[0148] After obtaining the interaction coupling strength, this method calculates the influence weight of energy anomaly migration on phase gradient change characteristics based on this strength, and constructs an energy-phase influence mapping function. Energy-phase influence mapping function It can be constructed as:

[0149]

[0150] in The phase propagation imbalance factor, This represents the energy curl amplitude.

[0151] Next, the contribution of each anomalous region to the transmittance distribution deviation and phase continuity disruption is quantified according to the energy-phase influence mapping function, forming a set of regional contribution factors. This set contains the specific weight values ​​of each anomalous region, clearly distinguishing the relative magnitude of its influence on transmittance inhomogeneity and phase discontinuity, and providing a basis for prioritizing the allocation of control parameters.

[0152] Contribution factors of each abnormal region It can be quantified as follows:

[0153]

[0154] in This is the sequence number of the abnormal region.

[0155] Finally, a joint optimization constraint relationship between phase control and transmittance correction is established based on the set of regional contribution factors, and a corresponding phase and transmittance coordinated adjustment model is constructed. This model adopts a multi-objective optimization framework to minimize transmittance deviation while satisfying the phase continuity requirement, thereby achieving a balanced control between the two.

[0156] Phase modulation amount With transmittance correction The joint optimization constraint model can be expressed as minimizing the objective function:

[0157]

[0158]

[0159] in For balance coefficient, and These are the target phase deviation and transmittance deviation, respectively.

[0160] Through the above-mentioned joint uniformity control model, the present invention can achieve synergistic optimization of phase control and transmittance correction, avoiding the problem in traditional methods where adjusting one parameter alone leads to the deterioration of another parameter, significantly improving the accuracy and effectiveness of the overall uniformity control of the mask, and laying a solid foundation for the final generation of high-precision control parameters.

[0161] The following describes in detail step 105, namely, "generating corresponding uniformity control parameters based on the phase and transmittance joint uniformity control model, and coordinating the adjustment of the phase shift layer thickness, local light-transmitting layer material distribution, and mask pattern edge transition structure in the corresponding region of the phase-shifting mask," with reference to the embodiments.

[0162] This step is the execution stage where the method of this invention finally achieves uniformity optimization. This process first outputs the target uniformity deviation value for each local region based on a phase and transmittance joint uniformity control model. This target deviation value represents the ideal state that the current region needs to be corrected to. Subsequently, the target uniformity deviation value is decomposed into two independent but interrelated parts: a phase deviation component and a transmittance deviation component. The phase deviation component reflects the phase shift that needs to be compensated, while the transmittance deviation component reflects the difference in light intensity transmittance that needs to be adjusted. This decomposition allows the control to simultaneously consider both phase continuity and energy uniformity, two core indicators.

[0163] Based on the phase shift component, this method calculates the phase shift correction for the corresponding region and maps it to a phase shift layer thickness adjustment parameter. The relationship between the phase shift correction and the film thickness can be accurately calculated using the following formula:

[0164]

[0165] in For the exposure wavelength, The refractive index of the phase-shifting layer material, This represents the amount of film thickness change that needs to be adjusted. Using this formula, the required phase compensation can be directly converted into a specific film thickness adjustment value, typically achieved through methods such as atomic layer deposition or laser local annealing.

[0166] Based on the transmittance deviation component, this method calculates the light energy transmission compensation for the corresponding region and maps it to local transmittance layer material distribution correction parameters. Transmittance correction is mainly achieved by changing the thickness, component ratio, or material density of the absorption layer in the local region; the calculation relationship can be expressed as:

[0167]

[0168] in This is the adjustment amount for the thickness of the light-transmitting layer. Extinction coefficient, This represents the concentration of material components. This mapping allows for targeted optimization of the material distribution in local light-transmitting layers.

[0169] While generating the two types of parameters mentioned above, this method also combines the coupling strength of the phase deviation component and the transmittance deviation component in each region to generate edge transition continuity correction parameters for the mask pattern edge region. These parameters are mainly used to optimize the slope structure of the pattern edge, auxiliary patterns, or transition zone contours to eliminate edge effects caused by simultaneous changes in phase and transmittance. Edge transition continuity correction parameters It can be represented as:

[0170]

[0171] in, This refers to the phase deviation component; This is the transmittance deviation component; The phase transmittance coupling strength of this region; This is the geometric discontinuity factor of the current edge transition structure, which can be calculated by edge curvature or line roughness LWR. The weighting coefficients for phase and transmittance, ; This is the overall adjustment coefficient, calibrated according to process capability, for example, 0.5~2.0 nm / unit deviation; This is the coupling enhancement coefficient.

[0172] Finally, the phase-shift layer thickness adjustment parameters, local light-transmitting layer material distribution correction parameters, and edge transition continuity correction parameters are combined to form a complete set of uniformity control parameters. These parameters are simultaneously applied to the corresponding regions of the mask, achieving synergistic adjustment of the three. This joint control method effectively avoids the problem of one parameter adjustment leading to the deterioration of another, significantly reduces spatial energy distribution inhomogeneity, improves phase field continuity, and ultimately achieves a comprehensive improvement in the overall mask performance.

[0173] To further illustrate the technical effects of this application, a specific implementation method and the test results of this implementation method are given below.

[0174] One specific embodiment of the present invention uses a 193nm immersion ArF attenuation-type phase-shifting mask as an example, and verifies it through a combination of numerical simulation and experimental verification. First, using FDTD electromagnetic field simulation software, under the conditions of numerical aperture NA=1.35 and dipole light source illumination, the spatial light field distribution data of a 6-inch phase-shifting mask during the actual exposure process is collected. The spatial resolution is set to 5nm×5nm, obtaining complete complex amplitude light field data including light intensity distribution and phase distribution.

[0175] Based on the collected light field data, a spatial energy flow vector field was constructed, and the energy migration direction, convergence path, and divergence path of each region were calculated. Simultaneously, a phase gradient continuity model was constructed to calculate the phase propagation imbalance factor and topological stability index. Next, local energy backflow regions and disturbance regions, as well as regions with abrupt phase gradient changes, were identified, resulting in the identification of a total of 12 major anomalous regions.

[0176] Subsequently, a model for the joint uniformity control of phase and transmittance was constructed to generate a set of control parameters. The phase-shift layer thickness was locally adjusted (maximum adjustment ±6 nm), and selective deposition correction was performed on the local transparent layer material composition. The MoSi absorptivity was locally varied from 0.8% to 1.5%. SRAF-assisted pattern optimization and edge slope correction were applied to the transition structure at the pattern edges. The entire control process was simulated using a virtual atomic layer deposition process model.

[0177] Before adjustment, the maximum mask phase deviation was ±4.7°, the transmittance uniformity deviation was ±2.3%, the average imaging contrast was 0.68, and the process window (EL×DOF) was 8.2%×120nm. After adjustment, the phase deviation was reduced to within ±0.9°, the transmittance uniformity deviation was reduced to within ±0.45%, the imaging contrast was improved to 0.85, and the process window was expanded to 12.6%×185nm, representing an approximately 25% increase in contrast and an approximately 54% expansion of the process window.

[0178] In actual mask repair experiments, femtosecond lasers combined with ALD equipment were used to physically manipulate the same abnormal areas. The results, verified by the AIMS aerial imaging measurement system, showed that the defect printing rate decreased from 21% to 4.5%, and the wafer lithography yield improved by approximately 11.8 percentage points. Repeated experiments demonstrated that this method is well-suited to different batches of masks, with the standard deviations for phase and transmittance repeatability being less than 0.6 degrees and 0.3%, respectively, fully validating the effectiveness, stability, and industrial applicability of the method.

[0179] The method provided in this application can be applied to various scenarios, including but not limited to: in high-NA EUV and ArF immersion lithography processes at 7nm and below technology nodes, by precisely controlling the mask, it can significantly reduce imaging distortion caused by phase deviation and transmittance inhomogeneity, expand the process window, improve lithography yield, and meet the stringent requirements of 3nm and 2nm advanced processes for mask performance. Secondly, this method is applicable to high-generation mask production in the manufacturing of large OLED display panels and Mini / Micro-LEDs. Due to the large area and complex patterns of display panel masks, traditional static control is difficult to guarantee full-width uniformity. This invention achieves precise local repair through dynamic energy flow and phase continuity analysis, which can effectively improve panel display uniformity and yield, and reduce production costs.

[0180] According to another embodiment, a phase and transmittance uniformity control system for a phase-shifting mask is provided. Figure 3 A schematic block diagram of a phase-shifting mask phase and transmittance uniformity control system according to one embodiment is shown. Figure 3 As shown, the device 300 includes:

[0181] The spatial light field distribution data acquisition unit 301 is configured to acquire spatial light field distribution data of the phase-shifting mask during the exposure process. The spatial light field distribution data includes light intensity distribution information and phase distribution information in different regions.

[0182] The spatial energy flow vector field construction unit 302 is configured to construct a corresponding spatial energy flow vector field based on the spatial light field distribution data, and calculate the energy migration direction, energy convergence degree and energy divergence degree of each region according to the spatial energy flow vector field.

[0183] The phase gradient continuity model construction unit 303 is configured to calculate the phase gradient change characteristics between adjacent regions based on the phase distribution information, and construct a phase gradient continuity model to characterize the spatial continuity of the phase field.

[0184] The joint uniformity control model construction unit 304 is configured to identify local energy anomaly migration regions based on the spatial energy flow vector field, and identify phase gradient abrupt change regions based on the phase gradient continuity model, and construct a joint phase and transmittance uniformity control model based on the correlation between the local energy anomaly migration regions and the phase gradient abrupt change regions.

[0185] The coordinated adjustment control unit 305 is configured to generate corresponding uniformity control parameters according to the phase and transmittance joint uniformity control model, and to coordinately adjust the phase shift layer thickness, local light-transmitting layer material distribution, and mask pattern edge transition structure in the corresponding region of the phase shift mask.

[0186] As an implementable approach, the spatial energy flow vector field construction unit 302 can be configured to: extract the light field energy migration trajectory between different regions based on the spatial light field distribution data, and determine the main direction of energy transmission between regions according to the light field energy migration trajectory; identify the energy convergence path and energy diversion path in the spatial light field according to the main direction of energy transmission and the local light intensity attenuation trend of each region; construct a regional energy migration network based on the energy convergence path and energy diversion path, and determine the energy transfer level of each region in the regional energy migration network; and generate the corresponding spatial energy flow vector field according to the energy transfer level, energy migration direction and energy migration intensity of each region.

[0187] As an implementable approach, the phase gradient continuity model construction unit 303, when calculating the phase gradient change characteristics between adjacent regions based on the phase distribution information, can be configured to: extract local phase evolution boundaries in the phase field based on the phase distribution information, and determine phase change sensitive regions based on the local phase evolution boundaries; determine phase gradient migration paths based on the spatial deflection trend of phase change directions within the phase change sensitive regions; construct a phase gradient propagation relationship network based on the degree of intersection and continuous extension between different phase gradient migration paths; and determine the phase gradient change characteristics between adjacent regions based on the phase gradient propagation relationship network.

[0188] As an implementable approach, the phase gradient continuity model construction unit 303 can be configured to: identify phase gradient propagation interruption regions and phase gradient recirculation regions based on the phase gradient propagation relationship network; construct a phase topology evolution constraint model based on the spatial distribution relationship between the phase gradient propagation interruption regions and the phase gradient recirculation regions; and calculate the topological stability of the phase field in different regions based on the phase topology evolution constraint model to construct the corresponding phase gradient continuity model.

[0189] As an implementable approach, the phase gradient continuity model construction unit 303, when calculating the topological stability of the phase field between different regions, can be configured to: calculate the phase propagation imbalance factor of the corresponding region based on the degree of deviation between the phase gradient propagation direction and the spatial energy migration direction; identify local topological distortion regions in the phase field based on the phase propagation imbalance factor; calculate the topological preservation capability of the phase topology during continuous propagation by combining the spatial diffusion trend between local topological distortion regions; and generate a topological stability index of the corresponding region based on the topological preservation capability to characterize the topological stability of the phase field between different regions.

[0190] As an implementable approach, the joint uniformity control model construction unit 304, when identifying local energy anomaly migration regions based on the spatial energy flow vector field, can be configured to: calculate the local light field curl based on the spatial energy flow vector field, and identify the light energy return region and energy disturbance region based on the local light field curl, and use the energy return region and energy disturbance region as local energy anomaly migration regions.

[0191] As an implementable approach, the joint uniformity control model construction unit 304, when constructing a joint phase and transmittance uniformity control model based on the correlation between the local energy anomaly migration region and the phase gradient abrupt change region, can be configured as follows: spatially overlapping the local energy anomaly migration region and the phase gradient abrupt change region to determine the spatial correspondence and interaction coupling strength between the two types of anomaly regions; calculating the influence weight of energy anomaly migration on phase gradient change characteristics based on the interaction coupling strength, and constructing an energy-phase influence mapping function; quantifying the contribution of each anomaly region to transmittance distribution deviation and phase continuity disruption according to the energy-phase influence mapping function, forming a set of regional contribution factors; establishing a joint optimization constraint relationship between phase control amount and transmittance correction amount based on the set of regional contribution factors, and constructing a corresponding phase and transmittance coordinated adjustment model.

[0192] As an implementable approach, the joint uniformity control model construction unit 304, when determining the spatial correspondence and interactive coupling strength between two types of anomalous regions, can be configured as follows: spatially partitioning and identifying the local energy anomaly migration region and the phase gradient abrupt change region to construct a set of candidate corresponding regions based on spatial adjacency; filtering to form one-to-many or many-to-one spatial correspondence mapping relationships based on the distance distribution of each candidate corresponding region in spatial coordinates and the consistency of energy migration direction; introducing an angle consistency factor between the energy migration direction vector and the phase gradient change direction vector for the spatial correspondence mapping relationship to calculate the directional coupling degree between regions; and calculating the interactive coupling strength by combining the coverage density and directional coupling degree of the spatial correspondence mapping relationship.

[0193] As an implementable approach, when the collaborative adjustment control unit 305 generates corresponding uniformity control parameters based on the phase and transmittance joint uniformity control model, it can be configured to: output target uniformity deviation values ​​for each local region based on the phase and transmittance joint uniformity control model, and decompose the target uniformity deviation values ​​into phase deviation components and transmittance deviation components; calculate the phase shift correction amount for the corresponding region based on the phase deviation components, and map the phase shift correction amount to phase shift layer thickness adjustment parameters; calculate the light energy transmission compensation amount for the corresponding region based on the transmittance deviation components, and map the light energy transmission compensation amount to local light-transmitting layer material distribution correction parameters; generate edge transition continuity correction parameters for the mask pattern edge region by combining the coupling strength of the phase deviation components and transmittance deviation components for each region; and combine the phase shift layer thickness adjustment parameters, local light-transmitting layer material distribution correction parameters, and edge transition continuity correction parameters to form a corresponding set of uniformity control parameters.

[0194] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. Components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0195] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0196] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0197] And an electronic device comprising: one or more processors; and a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0198] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0199] in, Figure 4 An exemplary architecture of an electronic device is shown, which may include a processor 410, a video display adapter 411, a disk drive 412, an input / output interface 413, a network interface 414, and a memory 420. The processor 410, video display adapter 411, disk drive 412, input / output interface 413, network interface 414, and memory 420 can communicate with each other via a communication bus 430.

[0200] The processor 410 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs and implement the technical solution provided in this application.

[0201] The memory 420 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 420 can store the operating system 421 for controlling the operation of the electronic device 400, and the basic input / output system (BIOS) 422 for controlling the low-level operations of the electronic device 400. Additionally, it can store a web browser 423, a data storage management system 424, and a phase and transmittance uniformity adjustment system 425 for a phase-shifting mask, etc. The aforementioned phase and transmittance uniformity adjustment system 425 for a phase-shifting mask can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 420 and executed by the processor 410.

[0202] Input / output interface 413 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0203] Network interface 414 is used to connect a communication module (not shown in the figure) to enable communication and interaction between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0204] Bus 430 includes a pathway for transmitting information between various components of the device, such as processor 410, video display adapter 411, disk drive 412, input / output interface 413, network interface 414, and memory 420.

[0205] It should be noted that although the above-described device only shows the processor 410, video display adapter 411, disk drive 412, input / output interface 413, network interface 414, memory 420, bus 430, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0206] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer program product. This computer program product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0207] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for controlling the phase and transmittance uniformity of a phase-shifting mask, characterized in that, The method includes: Acquire spatial light field distribution data of the phase-shifting mask during the exposure process, wherein the spatial light field distribution data includes light intensity distribution information and phase distribution information of different regions; Based on the spatial light field distribution data, a corresponding spatial energy flow vector field is constructed, and the energy migration direction, energy convergence degree, and energy divergence degree of each region are calculated according to the spatial energy flow vector field. Based on the phase distribution information, the phase gradient change characteristics between adjacent regions are calculated, and a phase gradient continuity model is constructed to characterize the spatial continuity of the phase field. Based on the spatial energy flow vector field, local energy anomaly migration regions are identified, and phase gradient abrupt change regions are identified based on the phase gradient continuity model. Based on the correlation between the local energy anomaly migration regions and the phase gradient abrupt change regions, a joint phase and transmittance uniformity control model is constructed. Based on the phase and transmittance joint uniformity control model, corresponding uniformity control parameters are generated to coordinately adjust the phase shift layer thickness, local light-transmitting layer material distribution, and mask pattern edge transition structure in the corresponding region of the phase shift mask.

2. The method according to claim 1, characterized in that, The construction of the corresponding spatial energy flow vector field based on the spatial light field distribution data includes: Based on the spatial light field distribution data, the light field energy migration trajectory between different regions is extracted, and the main direction of energy transmission between regions is determined according to the light field energy migration trajectory. Based on the main direction of energy transmission and the local light intensity attenuation trend in each region, identify the energy convergence path and energy diversion path in the spatial light field; A regional energy migration network is constructed based on the energy convergence path and the energy diversion path, and the energy transfer level of each region in the regional energy migration network is determined. Based on the energy transfer level, energy migration direction, and energy migration intensity of each region, a corresponding spatial energy flow vector field is generated.

3. The method according to claim 1, characterized in that, The calculation of phase gradient change characteristics between adjacent regions based on the phase distribution information includes: Based on the phase distribution information, local phase evolution boundaries in the phase field are extracted, and the phase change sensitive region is determined according to the local phase evolution boundaries; The phase gradient migration path is determined based on the spatial deflection trend of the phase change direction within the phase change sensitive area. A phase gradient propagation relationship network is constructed based on the degree of intersection and continuous extension between different phase gradient migration paths; The phase gradient change characteristics between adjacent regions are determined based on the phase gradient propagation relationship network.

4. The method according to claim 3, characterized in that, The construction of the phase gradient continuity model for characterizing the spatial continuity of the phase field includes: Identify phase gradient propagation interruption regions and phase gradient reflow regions based on a phase gradient propagation relationship network; A phase topology evolution constraint model is constructed based on the spatial distribution relationship between the phase gradient propagation interruption region and the phase gradient reflow region; The topological stability of the phase field in different regions is calculated based on the phase topology evolution constraint model to construct the corresponding phase gradient continuity model.

5. The method according to claim 4, characterized in that, The calculation of the topological stability of the phase field across different regions includes: Calculate the phase propagation imbalance factor in the corresponding region based on the degree of deviation between the phase gradient propagation direction and the spatial energy migration direction; Based on the phase propagation imbalance factor, local topological distortion regions in the phase field are identified; By combining the spatial diffusion trend between local topological distortion regions, the topological preservation capability of the phase topology during continuous propagation is calculated. Based on the topology preservation capability, a topology stability index is generated for the corresponding region to characterize the degree of topology stability of the phase field between different regions.

6. The method according to claim 1, characterized in that, The step of identifying local energy anomaly migration regions based on the spatial energy flow vector field includes: The local light field curl is calculated based on the spatial energy flow vector field, and the light energy return region and energy disturbance region are identified based on the local light field curl. The energy return region and energy disturbance region are regarded as local energy abnormal migration regions.

7. The method according to claim 1, characterized in that, The model for jointly regulating phase and transmittance uniformity is constructed based on the correlation between the local energy anomaly migration region and the phase gradient abrupt change region, including: Spatial overlap mapping is performed on the local energy anomaly migration region and the phase gradient abrupt change region to determine the spatial correspondence and interactive coupling strength between the two types of anomaly regions. Based on the aforementioned interactive coupling strength, the influence weight of energy anomaly migration on phase gradient change characteristics is calculated, and an energy phase influence mapping function is constructed. The contribution of each anomalous region to the transmittance distribution deviation and phase continuity disruption is quantified according to the energy phase influence mapping function, forming a set of regional contribution factors. Based on the set of regional contribution factors, a joint optimization constraint relationship between phase control amount and transmittance correction amount is established, and a corresponding phase and transmittance coordinated adjustment model is constructed.

8. The method according to claim 7, characterized in that, Determining the spatial correspondence and interaction coupling strength between the two types of abnormal regions includes: Spatial partitioning and identification are performed on the local energy anomaly migration region and the phase gradient abrupt change region to construct a candidate corresponding region set based on spatial adjacency; Based on the distance distribution of each candidate corresponding region in spatial coordinates and the consistency of energy migration direction, one-to-many or many-to-one spatial correspondence mapping relationships are formed. For the aforementioned spatial correspondence mapping relationship, an angle consistency factor is introduced between the energy migration direction vector and the phase gradient change direction vector to calculate the directional coupling degree between regions; The interaction coupling strength is calculated by combining the coverage density and directional coupling degree of the spatial correspondence mapping relationship.

9. The method according to claim 1, characterized in that, The generation of corresponding uniformity control parameters based on the phase and transmittance joint uniformity control model includes: Based on the phase and transmittance joint uniformity control model, the target uniformity deviation value of each local region is output, and the target uniformity deviation value is decomposed into phase deviation component and transmittance deviation component. Calculate the phase shift correction amount for the corresponding region based on the phase deviation component, and map the phase shift correction amount to the phase shift layer thickness adjustment parameter; The light energy transmission compensation amount for the corresponding region is calculated based on the transmittance deviation component, and the light energy transmission compensation amount is mapped to the local light-transmitting layer material distribution correction parameter. By combining the coupling strength of the phase deviation component and the transmittance deviation component in each region, edge transition continuity correction parameters are generated for the edge region of the mask pattern. The phase shift layer thickness adjustment parameters, local light-transmitting layer material distribution correction parameters, and edge transition continuity correction parameters are combined to form a corresponding set of uniformity control parameters.

10. A phase-shifting mask phase and transmittance uniformity control system, characterized in that, The system includes: The spatial light field distribution data acquisition unit is configured to acquire spatial light field distribution data of the phase-shifting mask during the exposure process. The spatial light field distribution data includes light intensity distribution information and phase distribution information in different regions. The spatial energy flow vector field construction unit is configured to construct a corresponding spatial energy flow vector field based on the spatial light field distribution data, and to calculate the energy migration direction, energy convergence degree, and energy divergence degree of each region based on the spatial energy flow vector field. The phase gradient continuity model construction unit is configured to calculate the phase gradient change characteristics between adjacent regions based on the phase distribution information, and construct a phase gradient continuity model to characterize the spatial continuity of the phase field. The joint uniformity control model construction unit is configured to identify local energy anomaly migration regions based on the spatial energy flow vector field, and to identify phase gradient abrupt change regions based on the phase gradient continuity model. Based on the correlation between the local energy anomaly migration regions and the phase gradient abrupt change regions, a joint phase and transmittance uniformity control model is constructed. The coordinated adjustment control unit is configured to generate corresponding uniformity control parameters based on the phase and transmittance joint uniformity control model, and to coordinately adjust the phase shift layer thickness, local light-transmitting layer material distribution, and mask pattern edge transition structure in the corresponding region of the phase shift mask.

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