Post-processing method and device for simulation results of multiple process steps of glass substrate based on APDL

Abstract

This invention discloses a post-processing method and apparatus for multi-process simulation results of glass substrates based on APDL, relating to the field of electronic packaging simulation technology. Its main purpose is to solve the problem of low efficiency in existing simulation data post-processing. The method mainly includes: acquiring the simulation results of the glass substrate to be processed; constructing a parameterized command set for cloud map rendering applicable to all process steps and a process step-activation unit height mapping model; calling the parameterized command set for cloud map rendering to generate an effective simulation cloud map for each process step based on the glass substrate simulation results, obtaining a sequence of cloud maps for all process steps indexed by the process step; and extracting multi-physical quantity data for each process step from the glass substrate simulation results based on the process step-activation unit height mapping model, and storing the multi-physical quantity data in a structured data file indexed by the process step number. This method is mainly used for post-processing of multi-process simulation results of glass substrates.

Description

Technical Field

[0001] This invention relates to the field of electronic packaging simulation technology, and in particular to a post-processing method and apparatus for simulation results of multiple process steps on glass substrates based on APDL. Background Technology

[0002] Post-processing of multi-process simulation results for glass substrates refers to the visualization rendering and data extraction of the massive simulation output data generated during multiple consecutive process steps, such as deposition, etching, bonding, and cooling, to output full-process results that support interactive analysis. Post-processing of warp simulation results for glass substrates is a core aspect of simulation analysis. The visualization of contour maps and the accurate extraction of key data such as displacement and stress directly determine the scientific validity of the process optimization conclusions.

[0003] Currently, in the multi-step simulation post-processing of existing glass substrates, manual configuration is largely relied upon. From cloud map generation to data extraction, manual intervention to adjust parameters is indispensable, which consumes a lot of processing time. At the same time, the extracted data contains a lot of interference data, and the data is fragmented and difficult to trace, resulting in low overall efficiency of post-processing and poor support for the analysis of subsequent simulation data. Summary of the Invention

[0004] In view of this, the present invention provides a post-processing method and apparatus for simulation results of multiple process steps of glass substrate based on APDL, the main purpose of which is to solve the problem of low post-processing efficiency of existing simulation data.

[0005] According to one aspect of the present invention, a post-processing method for simulation results of multiple process steps of glass substrates based on APDL is provided, comprising: Obtain the simulation solution results of the glass substrate to be processed, and construct a set of parameterized command for cloud map rendering and a process step-activation cell height mapping model applicable to all process steps; The cloud map rendering parameterization command set is invoked, and based on the simulation solution results of the glass substrate, an effective simulation cloud map for each process step in the entire process step is generated, resulting in a sequence of cloud maps for the entire process step indexed by the process step. The effective simulation cloud map is rendered with the effective simulation region of the corresponding process step as the spatial constraint. Based on the process step-activation unit height mapping model, the multi-physical quantity data of each process step is extracted from the simulation solution of the glass substrate, and the multi-physical quantity data is stored in a structured data file with the process step number as the index. The sequence of cloud maps for the entire process step is associated with the multi-physical quantity data in the structured data file based on the process step number.

[0006] According to another aspect of the present invention, a post-processing apparatus for simulation results of multiple process steps of glass substrates based on APDL is provided, comprising: The module is used to obtain the simulation solution results of the glass substrate to be processed, and to build a set of parameterized command for cloud map rendering and a process step-activation cell height mapping model applicable to all process steps; The cloud map generation module is used to call the cloud map rendering parameterized command set, generate an effective simulation cloud map for each process step in the whole process step according to the simulation solution of the glass substrate, and obtain a whole process step cloud map sequence indexed by the process step. The effective simulation cloud map is rendered with the effective simulation area of ​​the corresponding process step as the spatial constraint. The data extraction module is used to extract multi-physical quantity data of each process step from the simulation solution of the glass substrate according to the process step-activation unit height mapping model, and store the multi-physical quantity data into a structured data file with the process step number as the index. The sequence of cloud maps for the entire process step is associated with the multi-physical quantity data in the structured data file based on the process step number.

[0007] According to another aspect of the present invention, a storage medium is provided, wherein at least one executable instruction is stored therein, the executable instruction causing a processor to perform an operation corresponding to the post-processing method for the simulation results of multi-process steps of glass substrate based on APDL described above.

[0008] According to another aspect of the present invention, a terminal is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the post-processing method of the simulation results of the multi-process step of glass substrate based on APDL.

[0009] By employing the above-described technical solutions, the technical solutions provided by the embodiments of the present invention have at least the following advantages: This invention provides a post-processing method and apparatus for multi-process step simulation results of glass substrates based on APDL. In this embodiment, the simulation results of the glass substrate to be processed are obtained, and a set of parameterized command for cloud map rendering and a process step-activation unit height mapping model applicable to all process steps are constructed. The parameterized command set is invoked, and based on the glass substrate simulation results, an effective simulation cloud map for each process step is generated, resulting in a sequence of process step cloud maps indexed by the process step. The effective simulation cloud map is rendered with the effective simulation region of the corresponding process step as a spatial constraint. Based on the process step-activation unit height mapping model, multi-physical quantity data for each process step is extracted from the glass substrate simulation results, and the multi-physical quantity data is stored in a structured data file using the process step number as an index. This significantly reduces the reliance on manual processing in the post-processing process while ensuring accuracy, thereby greatly improving the efficiency of glass substrate simulation result post-processing.

[0010] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, it can be implemented according to the contents of the specification. Furthermore, in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0011] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 The flowchart of a post-processing method for simulation results of multiple process steps of glass substrate based on APDL provided by an embodiment of the present invention is shown. Figure 2 A flowchart of a method for generating a full process step cloud map sequence provided by an embodiment of the present invention is shown; Figure 3 This image shows a simulated cloud map rendering effect of glass substrate warpage provided by an embodiment of the present invention; Figure 4 This invention provides a block diagram of a post-processing device for simulating multi-process steps of a glass substrate based on APDL, according to an embodiment of the present invention. Figure 5 A schematic diagram of the structure of a terminal provided in an embodiment of the present invention is shown. Detailed Implementation

[0012] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0013] To address the problem of low post-processing efficiency in existing simulation data, this invention provides a post-processing method for multi-process simulation results of glass substrates based on APDL, such as... Figure 1 As shown, the method includes: 101. Obtain the simulation solution results of the glass substrate to be processed, and construct a set of parameterized command for cloud map rendering and a process step-activation cell height mapping model applicable to all process steps.

[0014] The simulation results of the glass substrate to be processed are the simulation data of the glass substrate that needs to be post-processed. This data can come from the original result file output by the preceding finite element simulation solver after performing numerical calculations on the multi-process manufacturing process of the glass substrate. The contour rendering parametric command set includes APDL parametric commands that automatically configure basic parameters and rendering rules such as viewpoint, background, and magnification of the contour. APDL stands for ANSYS Parametric Design Language. It is a scripting programming language for ANSYS simulation software that allows users to control and automate the simulation analysis process through programming structures such as parameters, variables, loops, and conditional statements. By pre-building the contour rendering parametric command set, unified automatic rendering of contours for all process steps can be achieved.

[0015] In this embodiment of the invention, a reusable and standardized post-processing system is established to avoid repetitive configuration for each subsequent process step. The parameterized command set for cloud map rendering is equivalent to a unified visualization specification, ensuring that all cloud maps are consistent in terms of background, viewpoint, and color mapping. The process step-activation unit height mapping model records the thickness range of the effective simulation area under each process step, solving the problem of unclear data extraction boundaries caused by changes in the activation unit height of different process steps. For example, in the 28 process steps of a glass substrate, step 1 activates only the glass layer, steps 2 to 27 gradually activate the ABF and Cu layers, and step 28 activates the green oil layer. This mapping model can automatically record the activation height corresponding to each step, laying the foundation for accurate extraction in the future. Through the above pre-configuration, the post-processing process is transformed from requiring manual intervention to adjust parameters at each step to automatic reuse across all process steps after a single construction, thereby improving the consistency and efficiency of subsequent cloud map review and multi-physical quantity data extraction processes.

[0016] 102. Call the cloud map rendering parameterization command set, and generate an effective simulation cloud map for each process step in the entire process step according to the simulation solution of the glass substrate, so as to obtain a sequence of cloud maps for the entire process step indexed by the process step.

[0017] In this invention, the effective simulation cloud map is rendered using the effective simulation area of ​​the corresponding process step as spatial constraints. To avoid interference from invalid information in observation and judgment, all process steps are traversed, and the effective simulation area of ​​each process step is automatically selected for cloud map rendering. Simultaneously, the cloud map file name includes the physical quantity type and process step number, forming a clear index structure. For example, the warp displacement cloud map of step 5 is saved as warp-5.jpg, and the stress cloud map of step 5 is saved as stress-5.jpg. By automatically executing the parameter settings of the cloud map generation process based on the established cloud map rendering specifications, a complete sequence of process step cloud maps with consistent perspective, unified color scale, and containing only the effective area can be obtained without manual intervention to adjust the viewpoint, background, and magnification of each cloud map individually, supporting intuitive comparative analysis across process steps.

[0018] 103. Based on the process step-activation unit height mapping model, extract the multi-physical quantity data of each process step from the simulation solution of the glass substrate, and store the multi-physical quantity data in a structured data file using the process step number as an index.

[0019] In this embodiment of the invention, based on the activation unit height corresponding to each process step in the process step-activation unit height mapping model, the data extraction spatial range for that process step is automatically determined. Within this range, the maximum stress value of the glass layer and the maximum and minimum warpage displacement values ​​of the overall model are extracted simultaneously, while recording the location coordinates of these extreme values ​​and the corresponding activation unit heights. After extraction, all data is written line by line into a structured text file according to the process step sequence number, forming a clear data table. For example, the 5th line of the data file corresponds to the 5th process step, containing information such as the maximum stress value, maximum and minimum warpage value, and corresponding node coordinates for that step. Since each cloud map in the full process step cloud map sequence is associated with the corresponding process step sequence number, and the multi-physical quantity data also corresponds to the process step sequence number, the full process step cloud map sequence and the multi-physical quantity data in the structured data file are associated based on the process step sequence number. By associating based on the process step sequence number, scattered and unrelated original simulation values ​​can be transformed into traceable and searchable structured data assets.

[0020] In one embodiment of the present invention, for further illustration and limitation, such as Figure 2 As shown, the process of generating the full process step cloud map sequence includes: 1021. Traverse the parameterized commands in the cloud map rendering parameterized command set to generate a cloud map basic template.

[0021] 1022. By iterating through all process steps using a loop command, a valid simulation cloud diagram is generated for each process step by calling the cloud diagram basic template.

[0022] 1023. Using the process step as an index, construct a sequence of valid simulation cloud maps for all process steps into a full process step cloud map sequence.

[0023] The effective simulation cloud maps include warpage displacement cloud maps and stress cloud maps. In this embodiment of the invention, each parameterized command in the pre-built cloud map rendering parameterized command set is traversed. For example, commands such as configuring a white background, fixing the 2D main view perspective, setting the deformation magnification factor, setting the number of contour lines to 9, enabling mesh display, and shifting the color bar to the left are executed sequentially to generate a reusable cloud map base template. This template defines unified rendering rules for all cloud maps. Furthermore, through APDL... The DO loop command iterates through all process steps, for example, from step 1 to step 28. For each process step, it first loads the simulation results for that step, then calls the generated contour map template, and generates warpage displacement contour maps and stress contour maps based on the loaded simulation results. These are then automatically saved as JPEG files according to the combination rules of physical quantity type and process step number. For example, they are saved as "Warpage-5.jpg" and "Stress-5.jpg". Finally, all valid simulation contour maps generated from all process steps are sorted and indexed according to the process step number from smallest to largest, constructing a complete sequence of contour maps for all process steps. In subsequent manual analysis, the warpage or stress contour maps of any process step can be quickly retrieved and compared using the process step number. The command iterates through the parameterized commands in the contour map rendering parameterization command set to generate a contour map template, including the definition of basic parameters for contour map generation and the configuration of standardized rendering rules. The definition of basic parameters for contour map generation includes defining the contour map saving format, defining the contour map magnification factor, deformation magnification factor, and ensemble element filtering rules. The cloud map standardized rendering rule configuration includes background and redundant information hiding, invalid display turning off, unified view configuration, fine display settings, color bar optimization, etc.

[0024] In one embodiment of the present invention, for further explanation and limitation, the effective simulation cloud map generation process of any process step includes: Load the simulation results data of the process step; Filter the cells that represent the active state from the simulation results data, and take the area covered by the cells as the effective simulation area; The cloud map base template is invoked, and the simulation solution data is rendered using the effective simulation area as the spatial clipping boundary to generate the effective simulation cloud map of the process step.

[0025] In this embodiment of the invention, to ensure that the cloud map of each process step only reflects the unit region actually participating in the physical process, the effective simulation region is filtered based on the unit activation state under the current process step, so that the cloud map is rendered only for the effective simulation region. Specifically, firstly, the simulation solution data of the current process step is loaded using the SET command, and the displacement field, stress field, and other information of this step are read into memory. Subsequently, the units with the state marked as LIVE are filtered from the loaded results using the ESEL,S,LIVE command, i.e., the effective units that are active in the current process step. The spatial range covered by these units is determined as the effective simulation region, while inactive EKILL units are excluded from subsequent rendering. Finally, a pre-built cloud map base template is called, which has fixed rendering rules such as background color, viewing direction, deformation magnification factor, and number of contour lines. During the rendering process, the filtered effective simulation region is used as the spatial clipping boundary, and only the nodes and units within the region are rendered, while units outside the region are not displayed or are displayed with a uniform background color, thereby generating the effective simulation cloud map of the current process step. For example, when only the glass layer is activated in the first process step, the contour map only shows the warping deformation and stress distribution of the glass layer; after some ABF layers are activated in the second process step, the contour map automatically expands to show the newly added activated areas, while the layers that have not yet been activated are not displayed, ensuring that the content of the contour map is accurately matched with the actual physical state of the process step.

[0026] In a specific application example, the automated generation of contour maps can be achieved through the execution of the following parameterized commands. Define the contour map magnification factor (PictureMAG) and deformation magnification factor (DeformMAG=20) to visually magnify microscale warping deformation to a identifiable level, adapting to the visualization requirements of microscale warping deformation of glass substrates. Configure a white background using the / RGB and / PLOPTS commands, and disable the display of time, date, and software identifiers to maintain visual consistency in the contour maps. Configure the contour map output format to JPEG using the / SHOW command; configure the naming format of the contour map files using the naming rules command, the naming format including the physical quantity type and process step number. Configure a fixed viewing angle as a two-dimensional main view using the / DIST, / ANG, and / VIEW commands, and fix the viewing distance and rotation angle to ensure consistent viewing angles across different time steps. Configure the number of contour lines to a preset value (preferably 9) using the / CONT command, and automatically match the range of physical quantity values; enable grid display using the / GLINE command. The color bar is configured to be fixed on the left side of the view using the / UDOC command to avoid overlapping with the model area and ensure the integrity of the cloud map information. The above describes the generation process of the basic cloud map template. The automated generation process of cloud maps for all process steps includes: 1) Entering the simulation post-processing module using the / POST1 command, disabling the graphics enhancement mode, and enabling the dual-display function of the structural contours before and after deformation for easy comparison of warping deformation; 2) Filtering the active units of each process step using the ESEL,S,LIVE command, rendering cloud maps only on the effective simulation area, and filtering out invalid displays of non-active units; 3) Through The DO loop command iterates through all process steps (TimesY=1 to 30), automatically calling the PLNSOL command to generate warpage displacement contour maps and first principal stress contour maps of the glass layer for each time step; 4) During the loop, the / DSCALE command automatically loads the preset deformation amplification factor to achieve clear visualization of micro-scale warpage deformation, and the / SHOW command automatically saves the contour maps and names them according to the rules; 5) After the loop ends, the / REPLOT command completes the redrawing and saving of the contour maps, realizing the automated batch generation of contour maps for all process steps. The final generated glass substrate warpage simulation contour map rendering effect is as follows: Figure 3 As shown.

[0027] In one embodiment of the present invention, for further explanation and limitation, the process of extracting multi-physical quantity data for any of the aforementioned process steps includes: Load the simulation solution data corresponding to the process step from the simulation solution results of the glass substrate; From the process step-activation unit height mapping model, the target activation unit height coordinates matching the process step are identified, and the target activation unit height coordinates are used as spatial constraints to determine the data extraction spatial range. Traverse the data extraction space range and collect the warping displacement data of each unit node in the direction perpendicular to the glass substrate, and extract the warping displacement extreme value and the node coordinates of the unit node corresponding to the warping displacement extreme value as warping displacement data. Among the unit nodes that satisfy the data extraction spatial range, glass layer unit nodes belonging to the glass layer unit set of the glass substrate are selected, the stress data of the glass layer unit nodes are traversed, and the maximum first principal stress and the node coordinates of the unit node corresponding to the maximum first principal stress are extracted as glass layer stress data.

[0028] In this embodiment of the invention, to achieve automated and spatially constrained extraction of multi-physical quantity data at any process step, the data extraction range for each process step is dynamically determined through the mapping relationship between the process step and the height of the activation unit. Within this range, the warpage displacement and glass layer stress are extracted synchronously. Specifically, firstly, the simulation solution data of the current process step is loaded from the glass substrate simulation solution results, and the displacement field, stress field, and other information of this step are read into memory. Subsequently, from the pre-constructed process step-activation unit height mapping model, which includes the mapping relationship between different process steps and their corresponding activation unit heights, the corresponding target activation unit height coordinates are identified according to the current process step number. These height coordinates reflect the thickness direction range of the glass substrate that has been activated in the current process step. Using these height coordinates as a spatial threshold, only node data whose thickness direction coordinates do not exceed this threshold are extracted, thus forming the data extraction spatial range. Based on this, the warping displacement data of all element nodes within this spatial range are traversed, the maximum and minimum warping displacement values ​​are extracted, and the coordinates of the nodes corresponding to these two extreme values ​​are recorded as warping displacement data. Simultaneously, within this spatial range, glass layer element nodes belonging to the glass layer element set are further selected, and the first principal stress data of these nodes are traversed, the maximum value is extracted, and the coordinates of the node corresponding to this maximum value are recorded as glass layer stress data. Through the above process, the data extraction range is automatically adapted to each process step. Furthermore, warping displacement and glass layer stress are extracted synchronously and associated according to each process step, allowing engineers to directly compare the extreme values ​​and their locations under the same process step, thus providing a data foundation for analyzing the coupling relationship between stress and deformation.

[0029] In one embodiment of the present invention, for further explanation and limitation, the construction process of the process step-activation unit height mapping model includes: Obtain the thickness parameters of each functional layer of the glass substrate and the thickness parameters of the composite stack; For the glass layer process step, the thickness direction coordinate of the top of the glass core board is used as the height of the glass layer activation unit, and a mapping relationship is established between the height of the glass layer activation unit and the glass layer process step. For multiple circuit process steps, the number of ABF layers or Cu layers is matched according to parity for each circuit process step. For each process step, the multi-layer accumulation height is calculated based on the ABF layer sequence or Cu layer sequence corresponding to the circuit process step, as well as the thickness parameters of each functional layer and the thickness parameters of the composite stack. The multi-layer accumulation height is then used as the layer activation unit height of the circuit process step to establish a mapping relationship. For the SM green oil process step, the thickness direction coordinates of the top of the ABF layer are used as the activation unit height to establish a mapping relationship with the SM green oil process step; For the final process step, the total thickness of the glass substrate is used as the height of the activation unit to establish a mapping relationship with the final process step.

[0030] In this embodiment of the invention, the functional layers include a glass layer, an ABF layer, a Cu layer, and an SM green oil layer. The thickness parameters of each functional layer and the composite stack of the glass substrate are predefined thickness parameters, including the thickness of the glass layer, the ABF layer, the Cu layer, the SM green oil layer, and the CU+ABF composite stack. The model is constructed mainly based on the activation sequence and thickness accumulation law of each functional layer during the multi-process manufacturing of the glass substrate, automatically calculating and assigning the corresponding activation unit height coordinates for each process step. Specifically, for the glass layer process step, the thickness direction coordinate of the top of the glass core board is used as the activation unit height, and a mapping relationship between this height and the glass layer process step is established. For multiple circuit process steps, the number of ABF layers or Cu layers is automatically matched according to the parity of the process step number. For each circuit process step, based on the ABF layer sequence or Cu layer sequence corresponding to that step, combined with the predefined functional layer thickness parameters, the multi-layer cumulative height is calculated by accumulation, and this cumulative height is used as the activation unit height of that circuit process step to establish a mapping relationship. For example, in step 2, an ABF layer is activated, and the accumulated height is the top of the glass layer plus the thickness of one ABF layer. In step 3, another Cu layer is added on top of this, and so on, gradually increasing the height. For the SM green oil process step, the thickness direction coordinates of the top of the ABF layer are used as the mapping relationship for the activation unit height. For the last process step, the total thickness of the glass substrate is used as the mapping relationship for the activation unit height. Through the mapping model constructed in this step, each process step obtains a unique corresponding activation unit height coordinate, providing an accurate and process step-adaptive threshold basis for automatically determining the spatial range during subsequent multi-physical quantity data extraction, and completely eliminating the calculation deviation caused by manually matching process steps and height coordinates.

[0031] In a specific application example, the process step-activation cell height mapping model is constructed through the following steps: 1) Based on APDL parameterization commands, the thicknesses of each functional layer (H_SM, H_ABF, H_Cu, H_glass) and the stack thickness (H_OneCUABF) of the glass substrate are predefined, providing a basis for height coordinate calculation; 2) Through The IF conditional statement establishes a mapping rule between each time step and the activation cell type and layer: Time step 1: Activate only the glass layer, with the height coordinate being the top coordinate of the glass core (Y_TOPglass); Time steps 2-27: Automatically match the number of ABF / Cu activation layers according to parity, and calculate the total height Hhigh in the corresponding thickness direction = Y_TOPglass + abf_count × H_ABF + cu_count × H_Cu; Time step 28: Activate the SM green oil layer, with the height coordinates being the top coordinates of the top layer ABF (Y_TOPABF); Time step 29: All cells activated, height coordinate is the total thickness of the substrate (H_total). 3) Through The DIM command defines a height array (Hhigh_Array) to automatically store and retrieve the height coordinates at each time step.

[0032] In one embodiment of the present invention, for further explanation and limitation, the multi-physical quantity data is stored in a structured data file using the process step number as an index, including: The warp displacement data is structured and written into a warp displacement data file according to a preset warp displacement field format. The glass layer stress data is structured and written into a glass layer stress data file according to a preset glass stress field format.

[0033] In this embodiment of the invention, to achieve structured storage and path association of multi-physical quantity data, the warp displacement data and glass layer stress data extracted from each process step are written into corresponding structured data files according to predefined field formats. The multi-physical quantity data includes warp displacement data and glass layer stress data, and the structured data files are in txt format, including warp displacement data files and glass layer stress data files. The preset warp displacement field format includes process step number, extreme displacement node number, active unit height, coordinates of the extreme displacement in different directions, and displacement amount. For example, "process step number, maximum displacement node number, maximum displacement X coordinate, maximum displacement Y coordinate, maximum Y-direction displacement, minimum displacement node number, minimum displacement X coordinate, minimum displacement Y coordinate, minimum Y-direction displacement, active unit height". The preset glass stress field format includes process step number, maximum stress node number, coordinates of the maximum stress in different directions, maximum first principal stress of the glass layer, and active unit height. For example, "process step number, maximum stress node number, maximum stress X coordinate, maximum stress Y coordinate, maximum first principal stress of the glass layer, active unit height". The above data can be obtained through APDL. The VWRITE command writes data in a structured manner according to the above field format, using comma delimiters to ensure that the data can be directly imported into software such as Excel and Origin for subsequent analysis.

[0034] Specifically, examples of the warp displacement data file format are shown in Table 1 below; examples of the glass stress data file format are shown in Table 2 below.

[0035] Table 1:

[0036] Table 2:

[0037] In addition, the warp displacement data file and the glass layer stress data file are stored in the same directory as the effective simulation contour map sequence, so that the effective simulation contour map sequence is associated with the structured data file path.

[0038] In one embodiment of the present invention, for further explanation and limitation, before storing the multi-physical quantity data into a structured data file, the method further includes: The stress threshold of the glass core plate of the glass substrate is retrieved, and the stress threshold of the glass core plate is used as the stress data verification benchmark and compared with the maximum first stress of each process step. An anomaly label is generated for the process step where the maximum first stress is greater than the stress threshold of the glass core plate, so as to mark the anomaly in the glass layer stress data file. The warping displacement data is filtered according to the upper and lower threshold values ​​of the displacement amount, so that the filtered valid warping displacement data is written into the warping displacement data file.

[0039] To ensure the physical rationality and engineering usability of the data entering the structured data file, this embodiment of the invention introduces a rationality verification mechanism before data storage. This mechanism automatically filters and marks anomalies in the extracted multi-physical quantity data using preset material and displacement thresholds. Specifically, based on APDL parameterization commands, a stress threshold for the glass core is defined. For example, when the glass core is BF33, the stress threshold is 2σ_allow, and this stress threshold is used as the benchmark for stress data verification. During the extraction of glass layer stress data, the maximum extracted first principal stress is compared with the allowable stress threshold using the IF condition judgment command. If the maximum stress of the glass layer in a certain process step is greater than 2σ_allow, the VWRITE command marks that process step as an anomaly in the data file, such as marking it as "stress exceeds limit," thus achieving automatic identification of abnormal data. For warpage displacement data, upper and lower limit thresholds for displacement are set to filter extreme abnormal values ​​caused by simulation calculation errors, ensuring data validity.

[0040] All APDL commands involved in the above embodiments are encapsulated into executable scripts and connected with glass substrate simulation preprocessing and solution scripts. Only a few parameters such as cloud map magnification and data saving path need to be modified to achieve one-click operation of simulation postprocessing and complete the automated generation of cloud maps and data extraction of the entire process.

[0041] This invention provides a post-processing method for multi-process simulation results of glass substrates based on APDL. In this embodiment, the simulation results of the glass substrate to be processed are obtained, and a set of parameterized command for cloud map rendering and a process step-activation unit height mapping model applicable to all process steps are constructed. The parameterized command set is invoked, and based on the glass substrate simulation results, an effective simulation cloud map for each process step is generated, resulting in a sequence of process step cloud maps indexed by the process step. The effective simulation cloud map is rendered with the effective simulation region of the corresponding process step as spatial constraint. Based on the process step-activation unit height mapping model, multi-physical quantity data for each process step is extracted from the glass substrate simulation results, and the multi-physical quantity data is stored in a structured data file using the process step number as an index. This significantly reduces the reliance on manual processing in the post-processing process while ensuring accuracy, thereby greatly improving the efficiency of glass substrate simulation result post-processing.

[0042] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this embodiment of the invention provides a post-processing device for simulation results of multiple process steps of glass substrates based on APDL, such as... Figure 4 As shown, the device includes: Module 31 is used to obtain the simulation solution results of the glass substrate to be processed, and to build a set of parameterized command for cloud map rendering and a process step-activation cell height mapping model applicable to all process steps; The cloud map generation module 32 is used to call the cloud map rendering parameterized command set, generate an effective simulation cloud map for each process step in the whole process step according to the simulation solution of the glass substrate, and obtain a whole process step cloud map sequence indexed by the process step, wherein the effective simulation cloud map is rendered with the effective simulation area of ​​the corresponding process step as the spatial constraint. Data extraction module 33 is used to extract multi-physical quantity data of each process step from the simulation solution of the glass substrate according to the process step-activation unit height mapping model, and store the multi-physical quantity data into a structured data file with the process step number as the index. The sequence of cloud maps for the entire process step is associated with the multi-physical quantity data in the structured data file based on the process step number.

[0043] Furthermore, the cloud map generation module 32 includes: The first generation unit is used to traverse the parameter commands in the cloud map rendering parameterization command set and generate a cloud map basic template. The second generation unit is used to traverse all process steps through a loop command, and for each process step, call the cloud map basic template to generate a valid simulation cloud map. The building unit is used to construct a sequence of full-process-step cloud maps by indexing the effective simulation cloud maps of all process steps.

[0044] Furthermore, in specific application scenarios, the second generation unit is specifically used to load the simulation solution data of the process step; Filter the cells that represent the active state from the simulation results data, and take the area covered by the cells as the effective simulation area; The simulation result data is rendered by calling the cloud map base template and using the effective simulation area as the spatial clipping boundary to generate the effective simulation cloud map of the process step. The effective simulation cloud map includes a warpage displacement cloud map and a stress cloud map.

[0045] Furthermore, the data extraction module 33 includes: The loading unit is used to load the simulation solution data corresponding to the process step from the simulation solution results of the glass substrate. The determining unit is used to identify the target activation unit height coordinates matching the process step from the process step-activation unit height mapping model, and to determine the data extraction spatial range using the target activation unit height coordinates as spatial constraints. The process step-activation unit height mapping model includes the mapping relationship between different process steps and their corresponding activation unit heights. The first extraction unit is used to traverse the warping displacement data of each unit node in the direction perpendicular to the glass substrate within the data extraction space, and extract the warping displacement extreme value and the node coordinates of the unit node corresponding to the warping displacement extreme value as warping displacement data. The second extraction unit is used to filter out glass layer unit nodes belonging to the glass layer unit set of the glass substrate from the unit nodes that meet the data extraction spatial range, traverse the stress data of the glass layer unit nodes, and extract the maximum first principal stress and the node coordinates of the unit node corresponding to the maximum first principal stress as glass layer stress data.

[0046] Furthermore, the construction module 31 includes: The acquisition unit is used to acquire the thickness parameters of each functional layer of the glass substrate and the thickness parameters of the composite stack, wherein the functional layers include a glass layer, an ABF layer, a Cu layer and an SM green oil layer; For the glass layer process step, the thickness direction coordinate of the top of the glass core board is used as the height of the glass layer activation unit, and a mapping relationship is established between the height of the glass layer activation unit and the glass layer process step. For multiple circuit process steps, the number of ABF layers or Cu layers is matched according to parity for each circuit process step. For each process step, the multi-layer accumulation height is calculated based on the ABF layer sequence or Cu layer sequence corresponding to the circuit process step, as well as the thickness parameters of each functional layer and the thickness parameters of the composite stack. The multi-layer accumulation height is then used as the layer activation unit height of the circuit process step to establish a mapping relationship. For the SM green oil process step, the thickness direction coordinates of the top of the ABF layer are used as the activation unit height to establish a mapping relationship with the SM green oil process step; For the final process step, the total thickness of the glass substrate is used as the height of the activation unit to establish a mapping relationship with the final process step.

[0047] Furthermore, the multi-physical quantity data includes warpage displacement data and glass layer stress data, and the structured data file includes a warpage displacement data file and a glass layer stress data file; the data extraction module 33 further includes: The first writing unit is used to write the warp displacement data into a warp displacement data file in a structured manner according to a preset warp displacement field format, wherein the preset warp displacement field format includes process step number, extreme displacement node number, activation unit height, coordinates and displacement amount in different directions of the extreme displacement. The second writing unit is used to write the glass layer stress data into a glass layer stress data file in a structured manner according to a preset glass stress field format. The preset glass stress field format includes the process step number, the maximum stress node number, the coordinates of the maximum stress in different directions, the maximum first principal stress of the glass layer, and the height of the activation unit. The warpage displacement data file and the glass layer stress data file are stored in the same directory as the effective simulation cloud map sequence, so that the effective simulation cloud map sequence is associated with the structured data file path.

[0048] Furthermore, the data extraction module 33 also includes: The verification unit is used to retrieve the glass core plate stress threshold of the glass substrate, and compare the glass core plate stress threshold as the stress data verification benchmark with the maximum first stress of each process step, and generate an anomaly label for the process step where the maximum first stress is greater than the glass core plate stress threshold, so as to mark the anomaly in the glass layer stress data file. The filtering unit is used to filter the warp displacement data according to the upper and lower threshold values ​​of the displacement amount, so as to write the filtered valid warp displacement data into the warp displacement data file.

[0049] This invention provides a post-processing device for multi-process simulation results of glass substrates based on APDL. In this embodiment, the simulation results of the glass substrate to be processed are obtained, and a set of parameterized command sets for cloud map rendering and a process step-activation unit height mapping model applicable to all process steps are constructed. The parameterized command set for cloud map rendering is invoked, and based on the glass substrate simulation results, an effective simulation cloud map for each process step is generated, resulting in a sequence of process step cloud maps indexed by the process step. The effective simulation cloud map is rendered with the effective simulation region of the corresponding process step as a spatial constraint. Based on the process step-activation unit height mapping model, multi-physical quantity data for each process step is extracted from the glass substrate simulation results, and the multi-physical quantity data is stored in a structured data file using the process step number as an index. This significantly reduces the reliance on manual processing in the post-processing process while ensuring accuracy, thereby greatly improving the efficiency of post-processing of glass substrate simulation results.

[0050] According to one embodiment of the present invention, a storage medium is provided, the storage medium storing at least one executable instruction, which can execute the post-processing method for simulation results of multi-process steps of glass substrate based on APDL in any of the above method embodiments.

[0051] Figure 5 The diagram shows a structural schematic of a terminal according to an embodiment of the present invention. The specific implementation of the present invention is not limited to the specific implementation of the terminal.

[0052] like Figure 5 As shown, the terminal may include: a processor 402, a communication interface 404, a memory 406, and a communication bus 408.

[0053] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0054] Communication interface 404 is used for network communication with other devices such as clients or other servers.

[0055] The processor 402 is used to execute program 410, specifically the relevant steps in the above embodiment of the post-processing method for simulation results of multi-process steps of glass substrate based on APDL.

[0056] Specifically, program 410 may include program code that includes computer operation instructions.

[0057] Processor 402 may be a central processing unit (CPU), a specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The terminal may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0058] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0059] Specifically, program 410 can be used to cause processor 402 to perform the following operations: Obtain the simulation solution results of the glass substrate to be processed, and construct a set of parameterized command for cloud map rendering and a process step-activation cell height mapping model applicable to all process steps; The cloud map rendering parameterization command set is invoked, and based on the simulation solution results of the glass substrate, an effective simulation cloud map for each process step in the entire process step is generated, resulting in a sequence of cloud maps for the entire process step indexed by the process step. The effective simulation cloud map is rendered with the effective simulation region of the corresponding process step as the spatial constraint. Based on the process step-activation unit height mapping model, the multi-physical quantity data of each process step is extracted from the simulation solution of the glass substrate, and the multi-physical quantity data is stored in a structured data file with the process step number as the index. The sequence of cloud maps for the entire process step is associated with the multi-physical quantity data in the structured data file based on the process step number.

[0060] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A post-processing method for simulation results of multiple process steps of glass substrates based on APDL, characterized in that, include: Obtain the simulation solution results of the glass substrate to be processed, and construct a set of parameterized command for cloud map rendering and a process step-activation cell height mapping model applicable to all process steps; The cloud map rendering parameterization command set is invoked, and based on the simulation solution results of the glass substrate, an effective simulation cloud map for each process step in the entire process step is generated, resulting in a sequence of cloud maps for the entire process step indexed by the process step. The effective simulation cloud map is rendered with the effective simulation region of the corresponding process step as the spatial constraint. Based on the process step-activation unit height mapping model, the multi-physical quantity data of each process step is extracted from the simulation solution of the glass substrate, and the multi-physical quantity data is stored in a structured data file with the process step number as the index. The sequence of cloud maps for the entire process step is associated with the multi-physical quantity data in the structured data file based on the process step number.

2. The method according to claim 1, characterized in that, The process of generating the full process step cloud map sequence includes: Iterate through the parameterized commands in the cloud map rendering parameterized command set to generate a basic cloud map template; By iterating through all process steps using a loop command, and for each process step, the cloud map base template is called to generate an effective simulation cloud map; Using process steps as an index, the effective simulation cloud maps of all process steps are constructed into a sequence of full process step cloud maps.

3. The method according to claim 2, characterized in that, The effective simulation contour plot generation process for any process step includes: Load the simulation results data of the process step; Filter the cells that represent the active state from the simulation results data, and take the area covered by the cells as the effective simulation area; The simulation result data is rendered by calling the cloud map base template and using the effective simulation area as the spatial clipping boundary to generate the effective simulation cloud map of the process step. The effective simulation cloud map includes a warpage displacement cloud map and a stress cloud map.

4. The method according to claim 1, characterized in that, The process step-activation unit height mapping model includes the mapping relationship between different process steps and their corresponding activation unit heights; The multi-physical quantity data includes warpage displacement data and glass layer stress data; The process of extracting multi-physical quantity data for any of the aforementioned process steps includes: Load the simulation solution data corresponding to the process step from the simulation solution results of the glass substrate; From the process step-activation unit height mapping model, the target activation unit height coordinates matching the process step are identified, and the data extraction spatial range is determined by using the target activation unit height coordinates as spatial constraints. The process step-activation unit height mapping model includes the mapping relationship between different process steps and their corresponding activation unit heights. Traverse the data extraction space range and collect the warping displacement data of each unit node in the direction perpendicular to the glass substrate, and extract the warping displacement extreme value and the node coordinates of the unit node corresponding to the warping displacement extreme value as warping displacement data. Among the unit nodes that satisfy the data extraction spatial range, glass layer unit nodes belonging to the glass layer unit set of the glass substrate are selected, the stress data of the glass layer unit nodes are traversed, and the maximum first principal stress and the node coordinates of the unit node corresponding to the maximum first principal stress are extracted as glass layer stress data.

5. The method according to claim 4, characterized in that, The construction process of the process step-activation unit height mapping model includes: The thickness parameters of each functional layer of the glass substrate and the thickness parameters of the composite stack are obtained, wherein the functional layers include a glass layer, an ABF layer, a Cu layer and an SM green oil layer; For the glass layer process step, the thickness direction coordinate of the top of the glass core board is used as the height of the glass layer activation unit, and a mapping relationship is established between the height of the glass layer activation unit and the glass layer process step. For multiple circuit process steps, the number of ABF layers or Cu layers is matched according to parity for each circuit process step. For each process step, the multi-layer accumulation height is calculated based on the ABF layer sequence or Cu layer sequence corresponding to the circuit process step, as well as the thickness parameters of each functional layer and the thickness parameters of the composite stack. The multi-layer accumulation height is then used as the layer activation unit height of the circuit process step to establish a mapping relationship. For the SM green oil process step, the thickness direction coordinates of the top of the ABF layer are used as the activation unit height to establish a mapping relationship with the SM green oil process step; For the final process step, the total thickness of the glass substrate is used as the height of the activation unit to establish a mapping relationship with the final process step.

6. The method according to claim 4, characterized in that, The multi-physical quantity data includes warpage displacement data and glass layer stress data, and the structured data file includes warpage displacement data file and glass layer stress data file; Using the process step number as an index, the multi-physical quantity data is stored in a structured data file, including: According to the preset warp displacement field format, the warp displacement data is structured and written into the warp displacement data file. The preset warp displacement field format includes process step number, extreme displacement node number, active unit height, coordinates and displacement amount in different directions of the extreme displacement. According to the preset glass stress field format, the glass layer stress data is structured and written into the glass layer stress data file. The preset glass stress field format includes process step number, maximum stress node number, coordinates of maximum stress in different directions, maximum first principal stress of glass layer, and activation unit height. The warping displacement data file and the glass layer stress data file are stored in the same directory as the effective simulation cloud map sequence, so that the effective simulation cloud map sequence is associated with the structured data file path.

7. The method according to claim 6, characterized in that, Before storing the multi-physical quantity data into a structured data file, the method further includes: The stress threshold of the glass core plate of the glass substrate is retrieved, and the stress threshold of the glass core plate is used as the stress data verification benchmark and compared with the maximum first stress of each process step. An anomaly label is generated for the process step where the maximum first stress is greater than the stress threshold of the glass core plate, so as to mark the anomaly in the glass layer stress data file. The warping displacement data is filtered according to the upper and lower threshold values ​​of the displacement amount, so that the filtered valid warping displacement data is written into the warping displacement data file.

8. A post-processing device for simulation results of multiple process steps of glass substrates based on APDL, characterized in that, include: The module is used to obtain the simulation solution results of the glass substrate to be processed, and to build a set of parameterized command for cloud map rendering and a process step-activation cell height mapping model applicable to all process steps; The cloud map generation module is used to call the cloud map rendering parameterized command set, generate an effective simulation cloud map for each process step in the whole process step according to the simulation solution of the glass substrate, and obtain a whole process step cloud map sequence indexed by the process step. The effective simulation cloud map is rendered with the effective simulation area of ​​the corresponding process step as the spatial constraint. The data extraction module is used to extract multi-physical quantity data of each process step from the simulation solution of the glass substrate according to the process step-activation unit height mapping model, and store the multi-physical quantity data into a structured data file with the process step number as the index. The sequence of cloud maps for the entire process step is associated with the multi-physical quantity data in the structured data file based on the process step number.

9. A storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the post-processing method of the simulation results of multi-process steps of glass substrate based on APDL as described in any one of claims 1-7.

10. A terminal, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the post-processing method of the simulation results of multi-process steps of glass substrate based on APDL as described in any one of claims 1-7.

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