A method and system for three-dimensional modeling and optimization of a VCSEL chip structure

Abstract

The application relates to the technical field of VCSEL chip three-dimensional modeling and optical simulation numerical optimization, and discloses a VCSEL chip structure three-dimensional modeling optimization method and system. In the VCSEL chip three-dimensional modeling and optical simulation numerical optimization scene, the method introduces a cyclic iteration process of dynamic monitoring, intelligent evaluation, defect positioning, adaptive optimization and calculation recovery, effectively solves the problems that in the prior art, due to the complex structure, simulation calculation is unstable and the result is inaccurate because the grid quality is poor.

Description

Technical Field

[0001] This invention relates to the technical field of VCSEL chip 3D modeling and optical simulation numerical optimization, specifically to a VCSEL chip structure 3D modeling optimization method and system. Background Technology

[0002] In the field of precision manufacturing of optical chips, especially for devices like vertical-cavity surface-emitting lasers (VCSELs) that require extremely high structural precision, 3D modeling and simulation play a crucial role. However, existing modeling tools often struggle to accurately reproduce the complex 3D heterojunction structure and photonic crystal distribution characteristics of VCSEL devices, leading to discrepancies between simulated optical performance results and actual devices. This severely impacts the efficiency and accuracy of new product development.

[0003] To overcome the limitations of existing VCSEL arrays in terms of overall output power and heat dissipation, the design team began exploring a novel structural design. This new design introduces a non-uniformly distributed photonic crystal structure, aiming to more precisely control light extraction efficiency and optimize heat dissipation paths. This new structure, which combines large-scale regularity with small-scale fine structures and complex geometries, presents a significant challenge to traditional modeling methods. Summary of the Invention

[0004] This invention provides a method and system for optimizing 3D modeling of VCSEL chip structures, aiming to solve the problems in existing 3D modeling and optical simulation of VCSEL chips, such as difficulty in accurately reproducing complex structures, deviation between simulation results and actual devices, poor mesh quality leading to unstable calculations and inaccurate results.

[0005] The technical solution of this application is as follows: In a first aspect, this application discloses a three-dimensional modeling and optimization method for VCSEL chip structures, applied to the scenario of three-dimensional modeling and optical simulation numerical optimization of VCSEL chips. The method includes: Based on the completed three-dimensional structural mesh model of the VCSEL chip, numerical simulation calculations of the chip's optical performance were initiated. During the execution of numerical simulation calculations, the overall running status of the simulation calculations is continuously monitored; Based on the real-time monitored operating status data, the stability of the current numerical simulation calculation is quantitatively evaluated. If it is determined that there is an instability problem in the calculation process, the current numerical simulation calculation is interrupted and the complete calculation status data of the current simulation iteration is stored. Retrieve the stored simulation calculation status data, locate the problem area in the 3D structural mesh model that causes calculation anomalies, and perform quantitative analysis on the quality of mesh cells in the problem area to determine the corresponding mesh defect type; Based on the identified mesh problem areas and the matched mesh defect types, the corresponding adaptive mesh optimization strategy is invoked to complete the optimization of the mesh structure. After the mesh optimization process is completed, the optimized 3D structural mesh model is loaded, and the numerical simulation calculation is resumed from the pre-stored state breakpoint. The simulation monitoring, defect location, mesh optimization, and calculation recovery process is executed iteratively until the numerical simulation results meet the preset stable convergence conditions.

[0006] This technical solution enables dynamic monitoring, problem identification, adaptive optimization, and computational recovery of the 3D modeling and optical simulation process of VCSEL chips. It effectively solves the problems of computational instability and inaccurate results caused by mesh quality issues in complex structure simulation, and significantly improves simulation efficiency and accuracy.

[0007] Furthermore, the operational status data includes the simulation iteration convergence progress, the preset computational stability quantification index, and the real-time fluctuation range of the core optical simulation parameters.

[0008] This technical solution can provide multi-dimensional and detailed operational status data, making the assessment of the stability of numerical simulation calculations more comprehensive and accurate, thereby enabling the timely detection and handling of potential instability issues.

[0009] In some preferred embodiments, the problem areas include: the edge regions of non-uniformly distributed photonic crystal holes inside the VCSEL chip, the regions inside the holes, and the transition regions at the interface between the photonic crystal structure and the heterojunction.

[0010] This technical solution can clearly identify the key areas in the VCSEL chip structure that are prone to computational anomalies, making problem localization more accurate and providing clear guidance for subsequent mesh optimization.

[0011] As a technological improvement, adaptive mesh optimization strategies include any one or more combinations of the following: For the key areas of the microstructure of the VCSEL chip, local mesh subdivision and densification processing is performed according to the preset fine mesh generation rules; For substandard mesh cells, the cell topology and connectivity are adaptively adjusted. Based on the chip light field distribution prediction results, the grid density of the corresponding region is dynamically adjusted. Build a mesh buffer transition zone in adjacent regions where the mesh size changes abruptly to achieve a smooth connection between meshes of different sizes.

[0012] This technical solution provides diverse and flexible mesh optimization methods, allowing for the adoption of the most suitable optimization strategies for different types of mesh defects and regional characteristics, thereby more effectively improving mesh quality and ensuring the accuracy and stability of simulation calculations.

[0013] To improve the solution, the problem areas in the 3D structured mesh model that cause computational anomalies were located, including: Detect local optical field anomaly data during the simulation process, and extract the corresponding local simulation sub-region from the original VCSEL chip geometric model with the optical field anomaly location as the center; The material property parameters of the local simulation sub-region are reset to the standard ideal design values, and electromagnetic field simulation calculations are performed to obtain the standard local light field approximate distribution results of the local simulation sub-region. By comparing and analyzing the standard local light field approximation distribution results with the actual abnormal light field characteristics obtained from simulation, the light field anomalies can be distinguished as originating from local segregation problems of material properties or geometric mesh defects.

[0014] This technical solution enables precise differentiation of the root cause of computational anomalies by comparing and analyzing actual abnormal light fields with ideal light fields. It determines whether the anomaly is due to material property issues or geometric mesh defects, providing a basis for decision-making in subsequent targeted optimization.

[0015] Based on the above, this application further proposes that if the optical field anomaly is determined to be caused by local segregation of material properties, the method further includes a local mesh adaptive reconstruction step, which includes: Based on the characteristics of local physical field distortion caused by local segregation of materials, a virtual physical boundary adapted to the actual field distribution is constructed. Perform local mesh topology reconstruction on the surrounding area of ​​the virtual physical boundary to generate a completely new mesh vertex and mesh edge structure; A boundary layer mesh structure with an adaptive field distribution is generated in the regions on both sides of the virtual physical boundary, so that the mesh cell shape conforms to the distribution characteristics of the virtual physical interface.

[0016] This technical solution addresses the problem of localized material segregation by constructing virtual physical boundaries and reconstructing local meshes. This allows the mesh structure to better adapt to the actual physical field distribution, thereby more accurately capturing the impact of material segregation on optical performance.

[0017] Furthermore, a boundary layer mesh structure with an adapted field distribution is generated in the regions on both sides of the virtual physical boundary, including: Collect local geometric feature information of the virtual physical boundary, as well as local physical field gradient distribution information caused by material segregation; Detect the spatial deviation angle between the direction of the physical field gradient extension and the local normal direction of the virtual physical boundary; If a deviation angle is detected, the generation and extension direction of the boundary layer mesh is adaptively adjusted to ensure that the mesh generation direction is consistent with the physical field gradient direction. Based on the anisotropic distribution characteristics of material segregation, the size and shape of the boundary layer mesh cells are adjusted differentially. Mesh refinement is performed in the direction of the physical field gradient, and mesh sparseness is performed in the direction perpendicular to the physical field gradient.

[0018] This technical solution enables the adaptive adjustment of the generation direction, size, and shape of the boundary layer mesh based on the gradient direction of the physical field and the anisotropic characteristics of material segregation. This allows the mesh to more accurately capture the subtle changes in the physical field in the boundary region, further improving simulation accuracy.

[0019] As a further improvement, local mesh topology reconstruction is performed, including: The outline of the virtual physical boundary is smoothed and optimized to eliminate sharp protrusions and irregular shapes in the boundary. Based on the smoothed and optimized virtual physical boundary features, the corresponding mesh generation control parameters are matched and adjusted. A transition region is constructed at the junction of the reconstructed mesh region and the surrounding original mesh region. By adjusting the mesh size and cell connection method within the transition region, a smooth transition between the old and new meshes is achieved.

[0020] This technical solution can effectively avoid sharp boundaries and abrupt changes in mesh size that may occur during mesh reconstruction by smoothly optimizing virtual physical boundaries and constructing transition regions, thus ensuring a smooth connection between the old and new meshes and maintaining overall mesh quality and computational stability.

[0021] To enhance functionality and achieve a smooth transition between the old and new meshes, the following measures are included: Obtain the element parameter information of the reconstructed mesh region and the surrounding original mesh region, and identify the differences between the two regions in terms of mesh element size, shape, and topology. Based on the differences, determine the grid size gradient and cell morphology evolution rules in the transition region; Based on the size gradient and morphological evolution rules, a continuous gradient mesh is generated in the transition region to achieve a smooth transition between the reconstructed region and the original mesh. Based on geometric distance and topological adjacency, a mesh adaptive connection algorithm is constructed to automatically establish stable connection relationships between reconstructed mesh cells and surrounding original mesh cells.

[0022] This technical solution enables a seamless and high-quality smooth transition between the reconstructed region and the original mesh by finely identifying the differences between the old and new meshes, determining the gradient rules, and constructing an adaptive connection algorithm, effectively avoiding simulation errors caused by mesh discontinuity.

[0023] Secondly, this application also discloses a VCSEL chip structure 3D modeling and optimization system for performing a VCSEL chip structure 3D modeling and optimization method. The system includes: The computation startup module is used to initiate numerical simulation calculations of the optical performance of a VCSEL chip based on a three-dimensional structural mesh model. The status monitoring module is used to continuously monitor the real-time running status data of the numerical simulation calculation during the process. The stability assessment module is used to quantitatively assess the stability of the current numerical simulation calculation based on real-time monitored running status data. If it is determined that there is an instability problem in the calculation process, the current numerical simulation calculation will be interrupted and the complete calculation status data of the current simulation iteration will be stored. The problem localization module is used to retrieve stored simulation calculation status data, locate the problem area in the 3D structure mesh model that causes calculation anomalies, and perform quantitative analysis on the quality of mesh cells in the problem area to determine the corresponding mesh defect type. The optimization execution module is used to call the corresponding adaptive mesh optimization strategy based on the identified mesh problem areas and the matched mesh defect types to complete the optimization processing of the mesh structure. The computation recovery module is used to load the optimized 3D structural mesh model after the mesh optimization process is completed, recover from the pre-stored state breakpoints, and continue to execute numerical simulation calculations. The loop control module is used to coordinate and control the various functional modules to perform the optimization process iteratively until the numerical simulation calculation reaches a stable convergence state.

[0024] This technical solution provides a complete 3D modeling and optimization system for VCSEL chip structures. Through modular design and collaborative work, it achieves automated and intelligent optimization of the simulation process for complex VCSEL chip structures, significantly improving simulation efficiency and the reliability of results. Beneficial effects

[0025] The VCSEL chip structure 3D modeling optimization method and system disclosed in this application introduces a cyclic iterative process of dynamic monitoring, intelligent evaluation, defect location, adaptive optimization and computational recovery in the VCSEL chip 3D modeling and optical simulation numerical optimization scenario. This effectively solves the problems of unstable simulation calculation and inaccurate results caused by the difficulty in accurately reproducing complex structures and poor mesh quality in the prior art.

[0026] Specifically, this method can monitor the simulation calculation status in real time. Once an instability is detected, it can promptly interrupt the calculation and save the state, preventing the continuation of invalid calculations. Subsequently, by retrieving the stored state data, it accurately locates the problem areas in the 3D structural mesh model that cause calculation anomalies and quantitatively analyzes the types of mesh defects. This overcomes the limitation of traditional methods in accurately identifying defects in complex microstructure meshes. Based on the identified defect types, the system can invoke corresponding adaptive mesh optimization strategies to refine the mesh structure. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a three-dimensional modeling and optimization method for VCSEL chip structure provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a VCSEL chip structure three-dimensional modeling and optimization system provided in an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Reference Figure 1 , Figure 1 This is a flowchart illustrating a method for optimizing 3D modeling of a VCSEL chip structure according to an embodiment of the present invention. Applied to scenarios involving 3D modeling and optical simulation numerical optimization of VCSEL chips, the method includes: S11. Based on the completed three-dimensional structural mesh model of the VCSEL chip, start the numerical simulation calculation of the chip's optical performance. S12, during the execution of the numerical simulation calculation, the overall running status of the simulation calculation is continuously monitored; S13. Based on the real-time monitored operating status data, quantitatively evaluate the stability of the current numerical simulation calculation. If it is determined that there is an instability problem in the calculation process, interrupt the current numerical simulation calculation and store the complete calculation status data of the current simulation iteration. S14, retrieve the stored simulation calculation status data, locate the problem area in the three-dimensional structural mesh model that causes the calculation anomaly, and perform quantitative analysis on the quality of the mesh cells in the problem area to determine the corresponding mesh defect type; S15, based on the identified mesh problem areas and the matched mesh defect types, invoke the corresponding adaptive mesh optimization strategy to complete the optimization processing of the mesh structure; S16. After the mesh optimization process is completed, load the optimized three-dimensional structural mesh model, restore from the pre-stored state breakpoint and continue to execute the numerical simulation calculation. S17 iteratively executes the simulation monitoring, defect location, mesh optimization, and calculation recovery process until the numerical simulation calculation results meet the preset stable convergence conditions.

[0030] This application introduces an adaptive mesh optimization strategy to achieve high-precision modeling and stable simulation of complex VCSEL chip structures. It effectively solves the problems of poor mesh quality and inaccurate simulation results when traditional methods are used to process complex structures, and significantly improves the R&D efficiency and performance optimization capabilities of VCSEL chips.

[0031] To better understand the proposed 3D modeling and optimization method for VCSEL chip structures, it is necessary to explain some key terms involved. A VCSEL chip, or Vertical-Cavity Surface-Emitting Laser chip, is an important type of semiconductor laser characterized by laser emission perpendicular to the chip surface, and is widely used in optical communication, 3D sensing, and other fields. The 3D structure mesh model is a mesh representation formed by discretizing the geometric structure of the VCSEL chip before numerical simulation, and it forms the basis of the simulation calculation. Numerical simulation calculation refers to the process of simulating and predicting the optical performance of the VCSEL chip using a computer, typically involving solving electromagnetic field equations. Simulation iteration convergence progress refers to the degree to which the calculation results gradually approach a stable solution during the numerical simulation calculation. A preset computational stability quantification index is a standard used to measure whether the simulation calculation process is stable; for example, it can be set as the fluctuation range of the computational residual. The real-time fluctuation amplitude of the core optical simulation parameters refers to the change amplitude of key optical parameters (such as light field intensity, mode gain, etc.) with time or iteration steps during the simulation process. Mesh cell quality is an important indicator of mesh quality, typically including cell shape, size, aspect ratio, etc. Mesh defect type refers to the specific problems existing in the mesh elements, such as distortion, negative volume, excessively large or small size, etc. Adaptive mesh optimization strategy is a method that automatically adjusts the mesh structure based on the type of mesh defect and the characteristics of the problem region. A state breakpoint refers to the saved current calculation state when the simulation calculation is interrupted, so that the calculation can be resumed from that point later. Preset stability convergence conditions refer to the criteria for ending the simulation calculation, such as the accuracy requirements of the calculation results or the limit on the number of iterations.

[0032] The core of the VCSEL chip structure 3D modeling optimization method proposed in this application lies in ensuring the stability and accuracy of VCSEL chip optical performance numerical simulation calculations through an iterative optimization process.

[0033] First, based on the constructed 3D structural mesh model of the VCSEL chip, numerical simulation calculations of the chip's optical performance are initiated. In practice, the 3D structural mesh model of the VCSEL chip can be constructed in various ways. For example, CAD software can be used for geometric modeling, and then mesh generation tools (such as finite element mesh generators) can be used to discretize the geometric model, generating tetrahedral, hexahedral, or hybrid mesh elements. Another approach is to utilize image processing technology to extract geometric features from the scanning electron microscope (SEM) image of the VCSEL chip and automatically generate a mesh model based on these features. When initiating numerical simulation calculations, different simulation software and algorithms can be selected according to the specific application scenario of the VCSEL chip and the required simulation accuracy. For example, the finite-difference time-domain (FDTD) method, the finite element method (FEM), or rigorous coupled-wave analysis (RCWA) can be used.

[0034] Secondly, during the execution of numerical simulation calculations, the overall operational status of the simulation is continuously monitored. Operational status data is crucial for evaluating simulation stability. For example, the iterative convergence progress within the simulation software can be obtained in real time through a programming interface, observing whether it is steadily declining or exhibiting oscillations. Simultaneously, some quantitative indicators of computational stability can be preset, such as monitoring computational residuals and energy conservation errors, and setting their thresholds. Furthermore, the fluctuation amplitude of core optical simulation parameters can be monitored in real time, such as the light field intensity, mode gain, and loss parameters within the VCSEL chip, observing their changing trends during the iteration process. This data can be recorded in log files or displayed in real time through a visualization interface, allowing engineers to understand the simulation progress promptly.

[0035] Secondly, based on real-time monitored operational status data, the stability of the current numerical simulation calculation is quantitatively evaluated. If instability is detected, the current numerical simulation is interrupted, and the complete calculation status data of the current simulation iteration is stored. Quantitative evaluation can be achieved in several ways. For example, a threshold can be set; if the iteration convergence progress fails to reach a preset rate of decline in multiple consecutive iterations, or if reverse fluctuations occur, it is considered unstable. Alternatively, instability can also be determined when preset computational stability quantification indicators (such as residuals) exceed a set threshold, or when the real-time fluctuation amplitude of core optical simulation parameters exceeds the normal range. Once instability is determined, the system will immediately interrupt the current numerical simulation to avoid invalid computation and resource waste. Simultaneously, for subsequent recovery and optimization, the system will store the complete calculation status data of the current simulation iteration, including the current mesh model, solver status, calculated intermediate results, boundary conditions, and all other necessary information.

[0036] Next, the stored simulation calculation state data is retrieved to locate the problem region in the 3D structural mesh model that is causing the calculation anomalies. The quality of the mesh elements in the problem region is then quantitatively analyzed to determine the corresponding mesh defect type. Locating the problem region is a crucial step in resolving simulation instability. For example, by analyzing the simulation results before the interruption, the specific spatial locations of phenomena such as abnormal light field distribution and energy non-conservation can be identified. These anomaly locations often correspond to areas with poor mesh quality. After locating the problem region, the quality of the mesh elements in that region needs to be quantitatively analyzed. This can be achieved by calculating the geometric parameters of the mesh elements, such as the aspect ratio, distortion, and Jacobian determinant value. Based on these parameters, the corresponding mesh defect type can be determined, such as whether there are overstretched elements, negative volume elements, sharp corner elements, or insufficient or excessive mesh density.

[0037] Then, based on the identified mesh problem areas and matching mesh defect types, the corresponding adaptive mesh optimization strategy is invoked to optimize the mesh structure. The adaptive mesh optimization strategy is selected according to different mesh defect types and the characteristics of the problem area. For example, if the problem area contains overstretched mesh elements, a local mesh reconstruction strategy can be used to regenerate the mesh in that area, making its element shapes more regular. If the mesh density is insufficient, a local mesh refinement strategy can be used to increase the number of mesh elements in the problem area, improving simulation accuracy. If abrupt changes in mesh size lead to poor connectivity, a mesh buffer transition zone can be constructed to achieve smooth connection between meshes of different sizes. These strategies can be used individually or in combination to achieve the best optimization results.

[0038] After mesh optimization is complete, the optimized 3D structural mesh model is loaded, and the numerical simulation calculation resumes from the pre-stored state breakpoint. Once mesh optimization is complete, the system loads the optimized mesh model into the simulation environment. Because complete computational state data has been stored previously, the simulation calculation can resume from the interrupted state breakpoint without starting from scratch. This significantly saves computation time and improves optimization efficiency. After resuming computation, the simulation process continues and re-enters the loop of monitoring the running status.

[0039] Finally, the simulation monitoring, defect location, mesh optimization, and computational recovery process is iteratively executed until the numerical simulation results meet the preset stability convergence conditions. This iterative process is the core of this application. Through continuous monitoring, evaluation, optimization, and recovery, the system can gradually eliminate mesh defects, improve simulation stability, and ultimately ensure that the numerical simulation results meet the preset stability convergence conditions. These preset stability convergence conditions may include the computational residual reaching a certain minimum, the core optical parameters changing less than a certain threshold in multiple consecutive iterations, or the error between the simulation results and experimental data being within an acceptable range.

[0040] The VCSEL chip structure 3D modeling optimization method proposed in this application effectively solves the problems of poor mesh quality, unstable simulation results and inaccuracy in traditional VCSEL chip 3D modeling and optical simulation by introducing a set of iterative adaptive mesh optimization mechanism.

[0041] Specifically, in the above-mentioned three-dimensional modeling and optimization method for VCSEL chip structure, the problem area includes the edge region of non-uniformly distributed photonic crystal holes inside the VCSEL chip, the internal region of the holes, and the boundary transition region between the photonic crystal structure and the heterojunction interface.

[0042] The edge regions of the non-uniformly distributed photonic crystal holes within the VCSEL chip refer to the boundaries between the photonic crystal holes and the surrounding material. Due to the complexity of their geometry and abrupt changes in material properties, these regions are prone to mesh quality issues and computational instability in numerical simulations. The internal regions of the holes refer to the internal space of the photonic crystal holes, and the details of their internal structure significantly affect optical performance. Improper meshing can also affect simulation accuracy and stability. Furthermore, the transition regions between the photonic crystal structure and the heterojunction interface are the connecting parts between different material layers and structural layers in the VCSEL chip. The optical field distribution at these interfaces is complex, requiring extremely high mesh quality, and are common areas that cause computational anomalies.

[0043] This application's solution explicitly identifies specific regions within the VCSEL chip prone to computational anomalies, enabling more targeted localization of problem areas causing such anomalies in the 3D mesh model. By pre-identifying these high-risk regions, such as the edges and interiors of non-uniformly distributed photonic crystal holes within the VCSEL chip, and the transitional regions between the photonic crystal structure and the heterojunction interface, the simulation system can be guided to perform more detailed monitoring and analysis in these critical areas, thereby improving the efficiency and accuracy of problem localization. This clear region definition facilitates subsequent mesh cell quality quantification analysis and mesh defect type determination, providing precise input for invoking adaptive mesh optimization strategies.

[0044] In some of the embodiments described above in this application, an adaptive mesh optimization strategy is proposed to optimize the mesh structure based on the identified mesh problem regions and the matched mesh defect types. However, in the complex 3D structure modeling and optical simulation scenarios of VCSEL chips, the types of mesh defects are diverse and their distribution is complex. A single or general mesh optimization strategy may be difficult to solve all problems efficiently and thoroughly, which may lead to poor optimization results, or even fail to effectively eliminate simulation instability factors in some cases, thereby affecting the convergence speed and accuracy of the simulation calculation.

[0045] In response, this application further proposes that the aforementioned adaptive mesh optimization strategy includes any one or more of the following combinations: For the key microstructure regions of the VCSEL chip, local mesh subdivision and densification processing is performed according to preset fine mesh generation rules; For substandard mesh cells, the cell topology and connectivity are adaptively adjusted. Based on the chip light field distribution prediction results, the grid density of the corresponding region is dynamically adjusted. Build a mesh buffer transition zone in adjacent regions where the mesh size changes abruptly to achieve a smooth connection between meshes of different sizes.

[0046] Specifically, "performing local mesh refinement and densification processing on key microstructure regions of the VCSEL chip according to preset fine mesh generation rules" refers to targeting microstructure regions in the VCSEL chip that significantly affect optical performance, such as resonant cavities, photonic crystal hole edges, and heterojunction interfaces. These regions experience drastic changes in the optical field, requiring higher mesh resolution to accurately capture physical phenomena. By using preset fine mesh generation rules, such as those based on geometric curvature, material interfaces, or expected field gradient distribution, the mesh in these key regions is locally refined and densified to improve simulation accuracy.

[0047] The phrase "adaptively adjust the topology and connectivity of substandard mesh cells" can be understood as follows: when quality issues are detected in mesh cells, such as excessive aspect ratio, distortion, or excessively small or large volume, these problems may lead to numerical calculation divergence or decreased accuracy. This strategy automatically identifies and adjusts the topology of these substandard mesh cells through algorithms. For example, it converts tetrahedral cells to hexahedral cells or improves the cell shape by reconnecting adjacent vertices, thereby improving the overall mesh quality and ensuring the stability and accuracy of the calculation.

[0048] In practical applications, "dynamically adjusting the mesh density of corresponding regions based on the chip's optical field distribution prediction results" specifically involves using the predicted optical field distribution information inside the chip obtained from preliminary simulations or theoretical analysis to make targeted adjustments to the mesh. In regions where the optical field intensity changes drastically or has a large gradient, such as optical waveguides or areas with concentrated resonant modes, the mesh density is increased to more accurately capture the details of the optical field; while in regions where the optical field is relatively flat or has a low intensity, the mesh density can be appropriately reduced to save computational resources. The aim is to optimize computational efficiency while ensuring simulation accuracy.

[0049] Furthermore, "constructing a mesh buffer transition zone in adjacent regions with abrupt changes in mesh size to achieve a smooth connection between meshes of different sizes" refers to the fact that direct abrupt changes in size can lead to numerical errors or instability between regions with significant differences in mesh density, such as when transitioning from a coarse mesh region to a fine mesh region. By introducing one or more intermediate mesh layers to form a gradual transition region, the mesh size and shape can smoothly transition from one region to another, avoiding a sharp decline in mesh quality and thus ensuring the stability and accuracy of numerical calculations.

[0050] The proposed solution provides a variety of targeted adaptive mesh optimization strategies, enabling the selection of the most suitable optimization method based on the specific mesh defect types and problem areas identified in the 3D structure mesh model of the VCSEL chip. For example, for critical microstructure regions, local mesh refinement ensures accurate capture of complex geometric and physical phenomena; for substandard mesh cells, adaptive adjustment of their topology and connectivity directly improves the mesh's geometric quality and eliminates potential sources of computational instability; dynamically adjusting mesh density based on chip light field distribution prediction results allows computational resources to be concentrated in critical regions while maintaining overall computational efficiency; and constructing buffer transition zones in regions of abrupt mesh size changes effectively avoids numerical errors and divergences caused by mesh discontinuities, thereby comprehensively improving mesh quality and adaptability and providing a more stable and accurate foundation for subsequent numerical simulation calculations.

[0051] In the above-mentioned VCSEL chip structure 3D modeling optimization method, the problem region in the 3D structure mesh model that causes calculation anomalies includes: Detect local optical field anomaly data during the simulation process, and extract the corresponding local simulation sub-region from the original VCSEL chip geometric model with the optical field anomaly location as the center; The material property parameters of the local simulation sub-region are reset to the standard ideal design values, and electromagnetic field simulation calculations are performed to obtain the standard local light field approximate distribution results of the local simulation sub-region. The standard local light field approximation distribution results are compared and analyzed with the actual abnormal light field characteristics obtained by simulation to distinguish whether the light field anomaly originates from local segregation problems of material properties or geometric mesh defects.

[0052] Specifically, during the numerical simulation process, the simulation data must first be monitored in real time to identify any local optical field anomalies. Local optical field anomalies can be understood as phenomena such as drastic fluctuations, spikes, discontinuities, or abnormal energy dissipation in the optical field distribution at specific locations within the simulation area, contrary to expectations. Once such anomalies are detected, a local simulation sub-region containing the anomaly is precisely extracted from the complete geometric model of the original VCSEL chip, centered on the location of the optical field anomaly. This aims to narrow the scope of analysis, focus on the specific location of the problem, and improve diagnostic efficiency.

[0053] Furthermore, to accurately determine the root cause of the anomaly, the material property parameters of the local simulation sub-region are reset to standard ideal design values. This means that within this sub-region, all material parameters (such as refractive index, absorption coefficient, etc.) are set to the defect-free ideal values ​​expected during the VCSEL chip design. Based on this, an independent electromagnetic field simulation calculation is performed on this local simulation sub-region to obtain the standard local optical field approximation distribution result of the local simulation sub-region under ideal material conditions. This result represents the optical field distribution state that the region should have in the absence of material defects.

[0054] Subsequently, the approximate distribution of the standard local light field obtained through the above steps is compared and analyzed with the actual local light field characteristics, including anomalies, obtained during the complete chip simulation. This comparison allows for precise differentiation between whether the light field anomaly stems from local segregation issues in material properties (i.e., deviations between actual material parameters and design values) and geometric mesh defects (such as poor mesh quality or mesh distortion). For example, if the actual anomalous light field differs significantly from the standard ideal light field in its distribution pattern, but its overall trend remains similar to the ideal situation, it may indicate material segregation; if the actual anomalous light field exhibits violent and irregular fluctuations, completely inconsistent with the ideal light field, it is more likely to indicate geometric mesh defects.

[0055] This application's solution, by introducing the concept of local simulation sub-regions and combining them with comparative simulations under ideal material conditions, effectively decomposes complex global simulation anomalies into locally controllable diagnostic tasks. It is precisely because of the independent simulation of local regions under ideal conditions and the precise comparison of the results with actual anomalous light fields that the system can conduct in-depth analysis and accurate judgment of the root causes of computational anomalies from both physical and geometric dimensions. This method avoids blindly guessing or attempting repairs directly in complex global models, thereby improving the accuracy and efficiency of problem localization.

[0056] Through the above technical solution, this application can achieve precise localization and cause differentiation of problem areas causing computational anomalies in the 3D structural mesh model of VCSEL chips. Compared with simply identifying problem areas, this solution further provides the ability to determine whether the anomaly originates from local material property segregation or geometric mesh defects. This precise diagnostic capability allows subsequent optimization processing to be more targeted. For example, for material segregation problems, material parameters or processes can be adjusted, while for geometric mesh defects, corresponding mesh optimization strategies can be invoked, thereby significantly improving the efficiency and effectiveness of the 3D modeling optimization method for VCSEL chips and avoiding unnecessary repetitive calculations and trial-and-error processes.

[0057] In some of the embodiments described above in this application, by comparing the standard local light field approximation distribution results with the actual abnormal light field characteristics obtained from simulation, it is possible to distinguish whether the light field anomaly originates from a local segregation problem in material properties or a geometric mesh defect problem. However, when the light field anomaly is determined to be caused by local segregation in material properties, traditional mesh optimization strategies may not be able to fully capture the local physical field distortion characteristics caused by material segregation, thereby affecting the simulation accuracy and convergence.

[0058] In response, this application further proposes a local mesh adaptive reconstruction step to more accurately handle computational instability caused by local segregation of material properties.

[0059] If the optical field anomaly is determined to be caused by local segregation of material properties, the method further includes a local mesh adaptive reconstruction step, which includes: Based on the characteristics of local physical field distortion caused by local segregation of materials, a virtual physical boundary adapted to the actual field distribution is constructed. Perform local mesh topology reconstruction on the surrounding area of ​​the virtual physical boundary to generate a completely new mesh vertex and mesh edge structure; A boundary layer mesh structure with an adaptive field distribution is generated in the regions on both sides of the virtual physical boundary, so that the mesh cell shape conforms to the distribution characteristics of the virtual physical interface.

[0060] Specifically, when the aforementioned VCSEL chip structure 3D modeling optimization method identifies during simulation calculations that the optical field anomaly is caused by local segregation of material properties within the VCSEL chip, traditional geometric mesh optimization may not be able to effectively resolve the resulting physical field distortion. Therefore, this application proposes a local mesh adaptive reconstruction strategy. "Constructing virtual physical boundaries adapted to the actual field distribution" refers to abstracting one or more virtual, non-geometric boundaries in the simulation model based on the non-uniform distribution and distortion characteristics of physical fields such as electromagnetic fields, temperature fields, or stress fields caused by local material segregation. These virtual physical boundaries aim to accurately depict the gradient changes and discontinuities of the actual physical field distribution, providing a precise reference for subsequent mesh reconstruction.

[0061] Furthermore, "performing local mesh topology reconstruction in the surrounding area of ​​the virtual physical boundary to generate entirely new mesh vertex and edge structures" refers to re-dividing and connecting existing mesh cells near the identified virtual physical boundary. This includes deleting existing mesh cells and generating new mesh vertices and edges based on the morphology and field distribution characteristics of the virtual physical boundary, thereby constructing a mesh topology structure that better matches the actual physical field distribution. This aims to eliminate the mesh-physical field mismatch problem caused by material segregation and improve the mesh quality in local areas.

[0062] Furthermore, "generating boundary layer mesh structures adapted to the field distribution in the regions on both sides of the virtual physical boundary, so that the mesh cell shape conforms to the distribution characteristics of the virtual physical interface" refers to generating boundary layer meshes with specific orientation and size distribution on both the inner and outer sides of the virtual physical boundary. The cells of these boundary layer meshes are typically designed to be refined along the physical field gradient direction, and their shape (e.g., aspect ratio) is consistent with the local normal direction or field gradient direction of the virtual physical interface. The purpose is to accurately capture drastic changes in the physical field near the boundary, such as reflection, refraction, or absorption of light at material interfaces, thereby significantly improving the accuracy of simulation results.

[0063] This application's approach, when identifying optical field anomalies caused by localized material segregation, no longer relies solely on general mesh optimization strategies. Instead, it constructs virtual physical boundaries to characterize the actual physical field distortion features. This introduction of virtual physical boundaries allows subsequent local mesh topology reconstruction and boundary layer mesh generation to directly adapt to the complex physical field distribution induced by material segregation, rather than merely focusing on geometric structures. By generating entirely new mesh vertex and edge structures and ensuring that the mesh cell shapes conform to the distribution characteristics of the virtual physical interface, this approach can more precisely capture drastic changes in the physical field in local regions, effectively avoiding simulation instability or non-convergence issues caused by mismatch between the mesh and the actual physical field distribution.

[0064] This application further proposes a boundary layer mesh structure that generates an adaptive field distribution in the regions on both sides of the virtual physical boundary, the structure comprising: Collect local geometric feature information of the virtual physical boundary, as well as local physical field gradient distribution information caused by material segregation; Detect the spatial deviation angle between the direction of the physical field gradient extension and the local normal direction of the virtual physical boundary; If a deviation angle is detected, the generation and extension direction of the boundary layer mesh is adaptively adjusted to ensure that the mesh generation direction is consistent with the physical field gradient direction. Based on the anisotropic distribution characteristics of material segregation, the size and shape of the boundary layer mesh cells are adjusted differentially. Mesh refinement is performed in the direction of the physical field gradient, and mesh sparseness is performed in the direction perpendicular to the physical field gradient.

[0065] Specifically, acquiring local geometric feature information of the virtual physical boundary refers to obtaining geometric attribute data such as curvature, normal direction, and connection relationship between adjacent elements of the virtual physical boundary in local regions. Simultaneously, the local physical field gradient distribution information caused by material segregation can be understood as analyzing the numerical results of local physical fields (such as optical and electromagnetic fields) in the simulation calculations to extract their rate of change and direction in space, thus characterizing the degree and trend of the influence of material segregation on the physical field distribution. The purpose is to provide accurate input data for subsequent mesh generation direction and size adjustment.

[0066] Specifically, detecting the spatial deviation angle between the physical field gradient extension direction and the local normal direction of the virtual physical boundary refers to calculating the angle between the physical field gradient vector and the local normal vector of the virtual physical boundary. If this angle is greater than a preset threshold, a deviation angle is determined to exist. Its purpose is to identify situations where the physical field distribution and the geometric boundary are not perfectly aligned, providing a basis for adaptively adjusting the mesh generation direction.

[0067] In practical applications, if a deviation angle is detected, the generation and extension direction of the boundary layer mesh is adaptively adjusted to align with the direction of the physical field gradient. This means that when generating the boundary layer mesh, it is no longer simply extended along the normal direction of the geometric boundary, but adjusted according to the direction of the actual physical field gradient. For example, through interpolation or projection algorithms, the principal axis direction of the mesh cells is aligned with the direction of the most drastic change in the physical field. The purpose is to enable the mesh cells to more effectively capture rapid changes in the physical field and improve simulation accuracy.

[0068] Furthermore, based on the anisotropic distribution characteristics of material segregation, the size and shape of the boundary layer mesh cells are adjusted differentially. Mesh refinement is performed along the physical field gradient direction, while mesh sparserization is performed along the direction perpendicular to the physical field gradient. Specifically, the anisotropic distribution characteristic refers to the varying degrees of influence of material segregation on the physical field in different spatial directions. Mesh refinement along the physical field gradient direction can, for example, reduce the mesh cell length in that direction to more precisely resolve drastic changes in the physical field; while mesh sparserization along the direction perpendicular to the physical field gradient can, for example, appropriately increase the mesh cell length in that direction to reduce the number of mesh cells while maintaining accuracy, thereby improving computational efficiency. The aim is to optimize mesh resource allocation and improve computational efficiency while ensuring simulation accuracy.

[0069] This application's solution first collects local geometric feature information of the virtual physical boundary and local physical field gradient distribution information caused by material segregation, obtaining comprehensive data on boundary morphology and physical field variation trends. Through this technical solution, this application achieves higher-precision matching between the boundary layer mesh and the actual physical field distribution. Specifically, by aligning the mesh generation direction with the physical field gradient direction, the mesh's ability to capture drastic changes in the physical field is significantly improved, avoiding simulation errors caused by mesh-field mismatch. Simultaneously, based on the anisotropic distribution characteristics of material segregation, the size and shape of the mesh cells are differentially adjusted, with density increased in key physical field gradient directions and sparser in secondary directions. This not only ensures the accuracy of simulation results but also optimizes the allocation of mesh resources, effectively reducing unnecessary mesh numbers and thus improving computational efficiency. This refined and adaptive boundary layer mesh generation method significantly enhances the ability of VCSEL chips to handle local material segregation problems in 3D modeling, thereby strengthening the stability and convergence of numerical simulation calculations and providing more reliable data support for VCSEL chip performance optimization.

[0070] In some embodiments described above in this application, if the optical field anomaly is determined to be caused by local segregation of material properties, a local mesh topology reconstruction is performed on the surrounding region of the virtual physical boundary to generate a completely new mesh vertex and edge structure. However, in practical applications, simply performing local mesh topology reconstruction may result in poor mesh quality and new mesh defects due to irregular initial shape of the virtual physical boundary or improper connection between the reconstructed region and the surrounding original mesh region, thereby affecting the stability and accuracy of subsequent numerical simulation calculations.

[0071] In this regard, this application further proposes that the steps for performing the aforementioned local mesh topology reconstruction include: The contour of the virtual physical boundary is smoothed and optimized to eliminate sharp protrusions and irregular shapes in the boundary. Based on the smoothed and optimized virtual physical boundary features, the corresponding mesh generation control parameters are matched and adjusted. A transition region is constructed at the junction of the reconstructed mesh region and the surrounding original mesh region. By adjusting the mesh size and cell connection method within the transition region, a smooth transition between the old and new meshes is achieved.

[0072] Specifically, smoothing and optimizing the contours of virtual physical boundaries involves applying geometric smoothing algorithms, such as Laplacian smoothing, Gaussian smoothing, or curvature-based smoothing methods, to correct the geometric contours of the virtual physical boundaries. This eliminates potential sharp protrusions, depressions, or irregular jagged shapes. The goal is to ensure that the boundary geometry of the reconstructed region is more regular and continuous, laying the foundation for the generation of high-quality meshes.

[0073] Specifically, based on the smoothed and optimized virtual physical boundary characteristics, the corresponding mesh generation control parameters are matched and adjusted. This can be understood as the system dynamically adjusting the parameters used to generate the mesh, such as the maximum / minimum size of the mesh cells, the mesh growth rate, and the number and thickness of the boundary layer mesh, according to the new boundary geometry characteristics, such as curvature distribution and local dimensions, after the boundary smoothing process is completed. The purpose is to ensure that the generated mesh can better fit the optimized boundary and avoid mesh distortion or quality degradation caused by parameter mismatch.

[0074] In practical applications, constructing a transition region at the junction of the reconstructed mesh region and the surrounding original mesh region refers to setting up a buffer region with a specific thickness between the reconstructed region and the unreconstructed original mesh region after local mesh topology reconstruction to avoid abrupt changes in mesh size or topology. By adjusting the size and connection method of the mesh cells within this transition region, such as using gradient meshing techniques or multi-scale mesh connection algorithms, a smooth transition between the old and new meshes is achieved. The purpose is to ensure the continuity and consistency of the entire mesh model and prevent numerical simulation instability or inaccurate results caused by abrupt mesh changes.

[0075] The proposed solution eliminates geometric irregularities that could lead to mesh quality problems by smoothing and optimizing the contours of the virtual physical boundary, thus enabling subsequent mesh generation to be based on a more ideal geometric foundation.

[0076] This application further proposes specific methods for achieving a smooth transition between the old and new meshes, including: Obtain the cell parameter information of the reconstructed mesh region and the surrounding original mesh region, and identify the differences between the two regions in terms of mesh cell size, shape, and topology. Based on the aforementioned differences, determine the grid size gradient and cell morphology evolution rules for the transition region; Based on the size gradient and morphological evolution rules, a continuous gradient mesh is generated in the transition region to achieve a smooth transition between the reconstructed region and the original mesh. Based on geometric distance and topological adjacency, a mesh adaptive connection algorithm is constructed to automatically establish stable connection relationships between reconstructed mesh cells and surrounding original mesh cells.

[0077] Specifically, after performing mesh reconstruction, it is first necessary to obtain detailed element parameter information of the newly generated reconstructed mesh region and its adjacent original mesh regions. This parameter information may include morphological indices such as the average size of the mesh elements, aspect ratio, skewness, and Jacobian determinant value, as well as the topological connectivity of the elements. By comparing and analyzing these parameters, specific differences in mesh characteristics between the two regions can be identified, such as abrupt changes in size, significant changes in element shape, or incompatibility in connectivity methods.

[0078] Based on the identified differences, it is necessary to further determine the mesh size gradient and element shape evolution rules in the transition region. The size gradient refers to the rate and direction of the gradual change in mesh element size from the reconstructed region to the original mesh region, ensuring that the size change is smooth and controllable. The element shape evolution rules define how the shape of the mesh elements (e.g., from quadrilaterals to triangles, or from regular to irregular shapes) gradually evolves in the transition region to adapt to the mesh characteristics of the two ends. These rules can be determined based on preset mesh quality standards and geometric topological constraints.

[0079] In practical applications, a continuous gradient mesh is generated between the reconstructed region and the original mesh region based on a defined size gradient and morphological evolution rules. This means that mesh cells within the transition region will smoothly transition from the characteristics of one region to the characteristics of the other region according to a preset gradient and rules, avoiding sudden changes in mesh size or shape. For example, a multi-layer mesh generation technique can be used, gradually adjusting mesh parameters in each layer to achieve a smooth overall transition.

[0080] Furthermore, to ensure stable connections between reconstructed mesh cells and surrounding original mesh cells, an adaptive mesh connectivity algorithm based on geometric distance and topological adjacency is required. This algorithm can automatically detect and establish connections between different mesh cells, such as by sharing nodes, edges, or faces to ensure mesh continuity. Geometric distance is used to determine the proximity of adjacent cells, while topological adjacency is used to identify the actual connection method between cells. This adaptive connectivity algorithm effectively avoids mesh breaks or overlaps, ensuring the integrity and consistency of the entire mesh model.

[0081] This application's solution systematically analyzes the differences in mesh characteristics between the reconstructed region and the original mesh region, and accordingly formulates refined mesh size gradients and element morphology evolution rules, thereby generating a continuously gradient transition mesh between the two. This gradient mesh effectively avoids abrupt changes in mesh size and morphology, fundamentally eliminating mesh quality issues that could lead to instability in numerical simulations. Simultaneously, by constructing a mesh adaptive connection algorithm based on geometric distance and topological adjacency, a seamless connection between the reconstructed mesh and the original mesh is ensured, avoiding computational errors or non-convergence issues caused by improper connections. It is precisely because of this refined transition region mesh generation and intelligent connection mechanism that the entire VCSEL chip's three-dimensional structural mesh model maintains high quality and stability even after local reconstruction, thus guaranteeing the accuracy and reliability of subsequent optical simulation calculations.

[0082] The above technical solutions significantly improve the quality and stability of the entire VCSEL chip's 3D structure mesh model after local mesh reconstruction. Specifically, by accurately identifying the differences between the old and new meshes and formulating gradient rules, drastic changes in mesh size and shape can be effectively avoided, thereby reducing mesh distortion and the generation of low-quality cells. Furthermore, the adaptive connectivity algorithm ensures topological continuity between the reconstructed region and the original mesh region, eliminating potential mesh breakage or overlap issues. These improvements work together to make the numerical simulation process more stable, converge faster, and produce higher accuracy and reliability in the final simulation results, effectively solving the problems of simulation instability or inaccuracy that may occur after mesh reconstruction in traditional methods.

[0083] In some preferred embodiments, it is assumed that a region of the VCSEL chip requires mesh reconstruction due to localized material segregation. After smoothing the virtual physical boundary and reconstructing the local mesh topology, the new reconstructed mesh region may use finer mesh cells, while its surrounding area consists of the relatively larger original mesh. To achieve a smooth transition between the two, the system first acquires the parameter information of the mesh cells in the reconstructed region (e.g., regular tetrahedrons with a side length of 0.1 micrometers) and the mesh cells in the original mesh region (e.g., irregular tetrahedrons with a side length of 0.5 micrometers), identifying significant differences in size and shape.

[0084] Next, based on these differences, the system determines a grid size gradient from 0.1 micrometers to 0.5 micrometers and sets rules for the evolution of cell morphology from regular tetrahedrons to irregular tetrahedrons. For example, it can be defined that in the transition region, the average side length of the grid cells increases by 0.05 micrometers at regular intervals, while allowing the skewness of the cells to gradually increase.

[0085] Subsequently, the system will generate a continuous transition mesh zone between the reconstructed region and the original mesh region based on these gradual gradients and evolution rules. The mesh cells in this transition zone will gradually and smoothly transition from the fine, regular mesh near the reconstructed region to the coarse, irregular mesh near the original mesh region, ensuring that there are no sudden changes in size or shape.

[0086] Finally, to ensure the integrity of the entire mesh model, the system runs a mesh adaptive connectivity algorithm. This algorithm detects the geometric distance and topological adjacency between newly generated mesh cells and surrounding original mesh cells within the transition region. For example, if a face of a transition mesh cell is geometrically very close to a face of an original mesh cell and their normal directions are aligned, the algorithm will automatically establish a shared face connection between them, or insert new nodes and edges as necessary to ensure topological continuity. This automatically establishes stable connections between all new and old mesh cells, avoiding the complexity and potential errors of manual adjustments.

[0087] refer to Figure 2 , Figure 2 This is a schematic diagram of a VCSEL chip structure 3D modeling and optimization system provided in an embodiment of the present invention, used to execute the above-described VCSEL chip structure 3D modeling and optimization method. The system includes: The computation startup module is used to initiate numerical simulation calculations of the optical performance of a VCSEL chip based on a three-dimensional structural mesh model. The status monitoring module is used to continuously monitor the real-time running status data of the numerical simulation calculation during the process. The stability assessment module is used to quantitatively assess the stability of the current numerical simulation calculation based on real-time monitored running status data. If it is determined that there is an instability problem in the calculation process, the current numerical simulation calculation will be interrupted and the complete calculation status data of the current simulation iteration will be stored. The problem location module is used to retrieve the stored simulation calculation status data, locate the problem area in the three-dimensional structure mesh model that causes the calculation anomaly, and perform quantitative analysis on the quality of the mesh cells in the problem area to determine the corresponding mesh defect type. The optimization execution module is used to call the corresponding adaptive mesh optimization strategy based on the identified mesh problem areas and the matched mesh defect types to complete the optimization processing of the mesh structure. The computation recovery module is used to load the optimized 3D structural mesh model after the mesh optimization process is completed, recover from the pre-stored state breakpoints, and continue to execute numerical simulation calculations. The loop control module is used to coordinate and control the various functional modules to perform the optimization process iteratively until the numerical simulation calculation reaches a stable convergence state.

[0088] This system functionalizes and modularizes each step in the 3D modeling and optimization method for VCSEL chip structures, forming an automated and intelligent optimization closed loop. The computation initiation module starts the simulation, while the state monitoring and stability assessment modules monitor the simulation process in real time, ensuring timely detection and handling of instability issues. The problem localization and optimization execution modules specifically address mesh defects, and the computation recovery module ensures that the optimized simulation can efficiently recover from interruptions. Finally, the loop control module coordinates the entire process, enabling high-precision modeling and stable, accurate numerical simulation of the complex structure of VCSEL chips. This effectively overcomes the problems of poor mesh quality, unstable simulation results, and inaccuracy inherent in traditional modeling tools when dealing with complex VCSEL structures, significantly improving the R&D efficiency and performance optimization capabilities of VCSEL chips.

[0089] The core of the VCSEL chip structure 3D modeling and optimization system proposed in this application lies in the automation and intelligence of VCSEL chip 3D modeling and optical simulation numerical optimization through the collaborative work of various functional modules.

[0090] Specifically, the computation startup module is used to initiate numerical simulation calculations of the VCSEL chip's optical performance based on its 3D structural mesh model. This module can be implemented as a software interface that receives user commands or pre-defined scheduled tasks, calls the underlying simulation engine, loads the VCSEL chip's 3D structural mesh model and related physical parameters, boundary conditions, etc., and initializes the simulation calculation process. For example, this module could be a command-line tool that receives the model file path and simulation parameters as input and then starts a background simulation process; alternatively, it could be a button in a graphical user interface that triggers the simulation startup logic when clicked by the user.

[0091] The status monitoring module continuously monitors the real-time running status data of the numerical simulation calculation. This module can be designed as an independent process or thread, interacting with the simulation engine to obtain information such as the simulation iteration convergence progress, computational stability quantification indicators, and real-time fluctuations of core optical simulation parameters. This data can be transmitted via shared memory, message queues, or network communication. For example, the status monitoring module can periodically query the simulation engine's application programming interface to obtain the current iteration step number, residual values, etc., and store them in a database or memory for subsequent analysis.

[0092] The stability assessment module quantifies the stability of the current numerical simulation calculation based on real-time monitored operational status data. If instability is detected, the current numerical simulation is interrupted, and the complete calculation status data of the current simulation iteration is stored. This module can include a series of preset assessment algorithms and thresholds, such as analyzing the slope of the convergence curve, fluctuation amplitude, or abnormal jumps in specific parameters to determine stability. When instability is detected, the module sends an interrupt command to the simulation engine and triggers a data storage mechanism to save key data such as the current mesh model, solver state, and calculated intermediate results to persistent storage for later recovery.

[0093] The problem localization module retrieves stored simulation calculation status data to locate the problem region in the 3D structural mesh model that causes computational anomalies. It then performs quantitative analysis of the mesh cell quality within the problem region to determine the corresponding mesh defect type. This module can integrate data analysis tools to post-process the simulation results before the interruption; for example, it can initially determine the problem area by visualizing abnormal light field distributions or energy non-conservation regions. Subsequently, the module can invoke mesh quality analysis algorithms to calculate the geometric indices of the mesh cells within the problem region and classify them into specific mesh defect types according to preset rules.

[0094] The optimization execution module, based on the identified mesh problem areas and matched mesh defect types, invokes corresponding adaptive mesh optimization strategies to optimize the mesh structure. This module can maintain a strategy library containing various mesh optimization algorithms, such as local mesh refinement, element topology adjustment, dynamic mesh density adjustment, or construction of mesh buffer transition zones. Based on the defect type and region information provided by the problem location module, the optimization execution module intelligently selects or combines the most suitable optimization strategy and applies it to the 3D structural mesh model to generate the optimized mesh.

[0095] The computation recovery module is used to load the optimized 3D structural mesh model after mesh optimization, recover from the pre-stored state breakpoint, and continue the numerical simulation calculation. This module manages the loading and recovery process of the simulation state. After mesh optimization is complete, it reads the previously saved simulation state data from the storage medium and reloads it into the simulation engine. Simultaneously, it replaces the old mesh model with the optimized one, ensuring that the simulation can seamlessly continue from the breakpoint without starting from scratch.

[0096] The loop control module is used to coordinate and control the various functional modules to iteratively execute the optimization process until the numerical simulation calculation reaches a stable convergence state. This module is the coordinator and scheduler of the entire system. It is responsible for managing the entire lifecycle of the optimization process, including starting the calculation, triggering monitoring, evaluating stability, interrupting the calculation, locating the problem, executing optimization, resuming the calculation, and determining whether to terminate the loop based on preset convergence conditions. For example, the loop control module can be a state machine that determines which functional module to activate next based on the current simulation state and evaluation results, thereby achieving automated and closed-loop management of the entire optimization process.

[0097] In traditional VCSEL chip 3D modeling and optical simulation workflows, engineers often need to manually intervene when numerical simulations become unstable or inaccurate, spending a significant amount of time troubleshooting, mesh correction, and resimulating. This manual intervention is inefficient and makes it difficult to guarantee the accuracy and consistency of corrections, especially when dealing with complex VCSEL structures, where problem localization and mesh optimization are extremely challenging.

[0098] The VCSEL chip structure 3D modeling and optimization system proposed in this application significantly improves the efficiency and reliability of VCSEL chip 3D modeling and optical simulation through its modular design and automated iterative mechanism. This system can monitor the simulation status in real time, automatically identify and locate mesh defects, and intelligently call appropriate optimization strategies for correction, ultimately achieving stable convergence of the simulation calculation. Compared to traditional manual intervention and discretization toolchains, this system provides an integrated and intelligent solution, greatly reducing manual operation, shortening the R&D cycle, and improving the accuracy and reliability of simulation results, providing strong technical support for the innovative design and performance optimization of VCSEL chips.

[0099] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for three-dimensional modeling and optimization of a VCSEL chip structure, characterized in that, The method, applied to numerical optimization scenarios for 3D modeling and optical simulation of VCSEL chips, includes: Based on the completed three-dimensional structural mesh model of the VCSEL chip, numerical simulation calculations of the chip's optical performance were initiated. During the execution of the numerical simulation calculation, the overall running status of the simulation calculation is continuously monitored; Based on the real-time monitored operating status data, the stability of the current numerical simulation calculation is quantitatively evaluated. If it is determined that there is an instability problem in the calculation process, the current numerical simulation calculation is interrupted and the complete calculation status data of the current simulation iteration is stored. Retrieve the stored simulation calculation status data, locate the problem area in the three-dimensional structural mesh model that causes the calculation anomaly, and perform quantitative analysis on the mesh cell quality of the problem area to determine the corresponding mesh defect type; Based on the identified mesh problem areas and the matched mesh defect types, the corresponding adaptive mesh optimization strategy is invoked to complete the optimization of the mesh structure. After the mesh optimization process is completed, the optimized 3D structural mesh model is loaded, and the numerical simulation calculation is resumed from the pre-stored state breakpoint. The simulation monitoring, defect location, mesh optimization, and calculation recovery process is executed iteratively until the numerical simulation results meet the preset stable convergence conditions.

2. The method of claim 1, wherein, The operational status data includes the simulation iteration convergence progress, preset computational stability quantification indicators, and real-time fluctuation amplitude of core optical simulation parameters.

3. The method of claim 1, wherein, The problem areas include: the edge region of the non-uniformly distributed photonic crystal holes inside the VCSEL chip, the region inside the holes, and the transition region at the interface between the photonic crystal structure and the heterojunction.

4. The method of claim 1, wherein, The adaptive mesh optimization strategy includes any one or more combinations of the following: For the key microstructure regions of the VCSEL chip, local mesh subdivision and densification processing is performed according to preset fine mesh generation rules; For substandard mesh cells, the cell topology and connectivity are adaptively adjusted. Based on the chip light field distribution prediction results, the grid density of the corresponding region is dynamically adjusted. Build a mesh buffer transition zone in adjacent regions where the mesh size changes abruptly to achieve a smooth connection between meshes of different sizes.

5. The VCSEL chip structure three-dimensional modeling and optimization method according to claim 1, characterized in that, The problem regions in the localized 3D structural mesh model that cause computational anomalies include: Detect local optical field anomaly data during the simulation process, and extract the corresponding local simulation sub-region from the original VCSEL chip geometric model with the optical field anomaly location as the center; The material property parameters of the local simulation sub-region are reset to the standard ideal design values, and electromagnetic field simulation calculations are performed to obtain the standard local light field approximate distribution results of the local simulation sub-region. The standard local light field approximation distribution results are compared and analyzed with the actual abnormal light field characteristics obtained by simulation to distinguish whether the light field anomaly originates from local segregation problems of material properties or geometric mesh defects.

6. The VCSEL chip structure three-dimensional modeling and optimization method according to claim 5, characterized in that, If the optical field anomaly is determined to be caused by local segregation of material properties, the method further includes a local mesh adaptive reconstruction step, which includes: Based on the characteristics of local physical field distortion caused by local segregation of materials, a virtual physical boundary adapted to the actual field distribution is constructed. Perform local mesh topology reconstruction on the surrounding area of ​​the virtual physical boundary to generate a completely new mesh vertex and mesh edge structure; A boundary layer mesh structure with an adaptive field distribution is generated in the regions on both sides of the virtual physical boundary, so that the mesh cell shape conforms to the distribution characteristics of the virtual physical interface.

7. The VCSEL chip structure three-dimensional modeling and optimization method according to claim 6, characterized in that, The generation of a boundary layer mesh structure with an adapted field distribution in the regions on both sides of the virtual physical boundary includes: Collect local geometric feature information of the virtual physical boundary, as well as local physical field gradient distribution information caused by material segregation; Detect the spatial deviation angle between the direction of the physical field gradient extension and the local normal direction of the virtual physical boundary; If a deviation angle is detected, the generation and extension direction of the boundary layer mesh is adaptively adjusted to ensure that the mesh generation direction is consistent with the physical field gradient direction. Based on the anisotropic distribution characteristics of material segregation, the size and shape of the boundary layer mesh cells are adjusted differentially. Mesh refinement is performed in the direction of the physical field gradient, and mesh sparseness is performed in the direction perpendicular to the physical field gradient.

8. The VCSEL chip structure three-dimensional modeling and optimization method according to claim 6, characterized in that, The process of performing local mesh topology reconstruction includes: The contour of the virtual physical boundary is smoothed and optimized to eliminate sharp protrusions and irregular shapes in the boundary. Based on the smoothed and optimized virtual physical boundary features, the corresponding mesh generation control parameters are matched and adjusted. A transition region is constructed at the junction of the reconstructed mesh region and the surrounding original mesh region. By adjusting the mesh size and cell connection method within the transition region, a smooth transition between the old and new meshes is achieved.

9. The VCSEL chip structure three-dimensional modeling and optimization method according to claim 8, characterized in that, The smooth transition between the old and new meshes includes: Obtain the cell parameter information of the reconstructed mesh region and the surrounding original mesh region, and identify the differences between the two regions in terms of mesh cell size, shape, and topology. Based on the aforementioned differences, determine the mesh size gradient and cell morphology evolution rules of the transition region; Based on the grid size gradient and the unit shape evolution rules, a continuous gradient grid is generated in the transition area to achieve a smooth transition between the reconstructed area and the original grid. Based on geometric distance and topological adjacency, a mesh adaptive connection algorithm is constructed to automatically establish a stable connection between the reconstructed region and the original mesh.

10. A three-dimensional modeling and optimization system for VCSEL chip structure, characterized in that, The system is used to perform the VCSEL chip structure three-dimensional modeling and optimization method according to any one of claims 1-9, the system comprising: The computation startup module is used to initiate numerical simulation calculations of the optical performance of a VCSEL chip based on a three-dimensional structural mesh model. The status monitoring module is used to continuously monitor the real-time running status data of the numerical simulation calculation during the process. The stability assessment module is used to quantitatively assess the stability of the current numerical simulation calculation based on the real-time monitored running status data. If it is determined that there is an instability problem in the calculation process, the current numerical simulation calculation will be interrupted and the complete calculation status data of the current simulation iteration will be stored. The problem localization module is used to retrieve the stored simulation calculation status data, locate the problem area in the three-dimensional structural mesh model that causes the calculation anomaly, and perform quantitative analysis on the quality of the mesh cells in the problem area to determine the corresponding mesh defect type. The optimization execution module is used to call the corresponding adaptive mesh optimization strategy based on the identified mesh problem areas and the matched mesh defect types to complete the optimization processing of the mesh structure. The computation recovery module is used to load the optimized 3D structural mesh model after the mesh optimization process is completed, recover from the pre-stored state breakpoints, and continue to execute numerical simulation calculations. The loop control module is used to coordinate and control the various functional modules to perform the optimization process iteratively until the numerical simulation calculation reaches a stable convergence state.

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