A method, apparatus, and medium for optimizing a vertical superlattice grating coupler

Abstract

The application relates to a method, device and medium for optimizing a vertical super-structure grating coupler, the method comprising: initializing a dielectric constant distribution in a two-dimensional design area and stretching the dielectric constant distribution into an etching layer height; based on the initialized dielectric constant distribution, performing continuous topology optimization; in the continuous topology optimization process, performing smoothing processing on the dielectric constant distribution according to a preset filtering radius; when a change of a target function is in a predefined threshold range, introducing random disturbance; converting the dielectric constant after the continuous topology optimization into two discrete values; designing a geometric structure of the vertical super-structure grating coupler according to the discrete dielectric constant distribution obtained through optimization; and performing performance simulation on the designed vertical super-structure grating coupler to evaluate coupling efficiency and bandwidth parameters. The application provides an excellent input / output interface solution for realizing a large-scale super-compact super-structure photonic integrated circuit.

Description

Technical Field

[0001] The present invention relates to the field of metaphotonic devices and vertical grating couplers, and in particular to a method, device and medium for optimizing vertical metagrating couplers. Background Art

[0002] As an important optical input and output interface, grating couplers have become widely used in photonic devices such as lidar (LiDAR), optical communications, and sensors due to their flexible layout, simple optical path design, high integration, and broadband characteristics. Vertical grating couplers, in particular, reduce the stringent requirements for light source placement, making them easier to form large-scale, ultra-compact photonic integrated circuits, thereby enabling ultra-miniaturized systems. This characteristic of vertical grating couplers is particularly important for the large-scale transmit and receive antenna arrays used in photonic integrated lidar. However, the low coupling efficiency of conventional vertical grating couplers has been a difficult problem to overcome. This is primarily due to the short coupling distance between the light source and the grating portion after vertical incidence, making it very easy for the incident light to leak out from the bottom of the grating. In particular, when the grating structure is designed with weak coupling, only a small amount of light is coupled into the grating structure and then routed to the output waveguide, resulting in very low coupling efficiency. Furthermore, to achieve low optical transmission loss, single-mode transmission of light in the waveguide is required. The waveguide width of silicon optical routing in photonic integrated circuits is typically 0.5μm. Therefore, it is generally necessary to connect a tapered spot converter (except for the focusing grating coupler) between the vertical grating coupler and the optical routing waveguide. Only when the tapered spot converter is long enough can the two achieve low-loss adiabatic conversion. For example, the most commonly used 10μm grating coupler needs to pass through a tapered spot converter with a length of several hundred μm to be efficiently converted into a 0.5μm wide single-mode waveguide. This undoubtedly becomes a huge obstacle to the high integration of grating couplers. Since the curvature of the focusing grating coupler is difficult to maintain stable during processing, it has a great impact on its performance, and therefore is relatively limited in practical applications.

[0003] In recent years, the concept of super-photonic and the application of inverse design method make the design of photonic devices have more design freedom, and various super-compact and high-performance super-photonic devices emerge. Among them, the super-photonic grating coupler breaks the traditional periodic structure and realizes the complex spatial structure within the sub-wavelength precision, making it possible to have a super-compact and high-performance grating coupler. The commonly used inverse design methods include DBS, genetic algorithm, shape topology optimization, deep learning and adjoint method. Among them, the DBS algorithm is the simplest and can find the global optimal solution, but the calculation is time-consuming and the optimization efficiency is very low. The shape topology optimization only optimizes the structure edge, and the design freedom is limited. Deep learning needs a lot of data collection work before optimization, and the optimization effect is general. The adjoint method is a high-efficiency optimization algorithm. For single-port input and single-port output devices, only one forward simulation and one adjoint simulation are needed in each iteration to obtain the global optimization structure. Therefore, in recent years, many scholars have applied it to the optimization research of super-photonic vertical grating coupler. But as we all know, this method is sensitive to the initial structure and easy to converge to local optimum, which has certain limitations in device optimization. SUMMARY

[0004] The application provides a method, device and medium for optimizing a vertical super-photonic grating coupler, which aims to solve the problems of low coupling efficiency and large area of traditional vertical grating coupler, and improve the integration and performance of photonic integrated circuit.

[0005] To achieve the above-mentioned purpose, the first aspect of the application provides a method for optimizing a vertical super-photonic grating coupler, comprising the following steps:

[0006] Initialize the dielectric constant distribution in the two-dimensional design area, make the dielectric constant between silicon and silicon dioxide or between silicon and air in a random manner, and stretch it to the etching layer height;

[0007] Based on the initialized dielectric constant distribution, continuous topology optimization is carried out;

[0008] During the continuous topology optimization, the dielectric constant distribution is smoothed according to the preset filtering radius;

[0009] When the change of the objective function is within the predefined threshold range, random disturbance is introduced to adjust the dielectric constant distribution in the design area;

[0010] The dielectric constant after continuous topology optimization is converted into two discrete values, and the conversion includes limiting the dielectric constant distribution to two discrete values of silicon and silicon dioxide or silicon and air by applying a binary algorithm;

[0011] According to the discrete dielectric constant distribution obtained by optimization, the geometric structure of the vertical super-photonic grating coupler is designed.

[0012] The designed vertical superlattice grating coupler is simulated to evaluate its coupling efficiency and bandwidth parameters.

[0013] Further, the continuous topology optimization comprises:

[0014] performing a forward simulation to calculate electromagnetic field distribution within the design region;

[0015] based on the forward simulation results, establishing an objective function to evaluate mode overlap integral between the output waveguide fundamental mode and the incident Gaussian light source;

[0016] performing an adjoint simulation to determine sensitivity of the objective function with respect to permittivity distribution within the design region;

[0017] using the sensitivity information, adjusting the permittivity distribution within the design region to optimize the objective function;

[0018] repeating the above steps until the objective function converges or reaches a predetermined number of iterations.

[0019] Further, after the forward simulation and the adjoint simulation are completed, a high-frequency noise portion in the design region is smoothed according to a preset filtering radius, and a smoothing method comprises:

[0020] According to the filtering radius, the permittivity value of the high-frequency noise portion is modified so that the permittivity within the filtering radius is the same value;

[0021] After filtering is completed, the permittivity distribution in the design region is updated, and subsequent steps are performed.

[0022] Further, when the change in the objective function is within a predefined threshold range, a method for adjusting the permittivity distribution within the design region by introducing random perturbations comprises:

[0023] calculating a figure of merit change between successive iterations;

[0024] comparing the figure of merit change with the predefined threshold range;

[0025] when the figure of merit change is within the threshold range, randomly perturbing the permittivity distribution within the design region based on a random perturbation method;

[0026] adjusting the permittivity distribution within the design region according to the random perturbation;

[0027] After introducing the random perturbation, the forward simulation and the adjoint simulation are continued.

[0028] Further, the random perturbation comprises:

[0029] Randomly select several dielectric constant cells;

[0030] Randomly increase or decrease the dielectric constant value of the selected cell, and limit the dielectric constant value after random increase or decrease to the dielectric constant range between silicon and silicon dioxide or between silicon and air;

[0031] Repeat the above steps multiple times to achieve random perturbation of the overall dielectric constant distribution;

[0032] After the random perturbation is completed, the dielectric constant distribution within the design region is updated for use in subsequent optimization steps.

[0033] Furthermore, the method of converting the dielectric constant after continuous topology optimization into two discrete values ​​includes:

[0034] setting two target discrete values ​​of dielectric constant, corresponding to the dielectric constants of silicon and silicon dioxide or silicon and air, respectively;

[0035] For each dielectric constant cell in the optimization area, assign its dielectric constant to one of the target discrete values ​​based on how close its current value is to the two target discrete values;

[0036] A binarization algorithm is applied to convert the continuous dielectric constant distribution within the design area into a distribution containing only the two target discrete values ​​mentioned above.

[0037] Furthermore, based on the optimized discrete dielectric constant distribution, a method for designing the geometric structure of the vertical metagrating coupler includes:

[0038] Extracting the optimized dielectric constant distribution pattern;

[0039] Based on the extracted dielectric constant distribution pattern, the geometric structure of the vertical metagrating coupler is defined.

[0040] Furthermore, the criterion for convergence of the objective function is:

[0041] When the objective function value is equal to the preset threshold, the objective function is considered to have converged, where the threshold is 1, indicating that the optimization process is stopped when the objective function value is equal to this value;

[0042] When the rate of change of the objective function is within a predetermined threshold range, the objective function is considered to have converged, where the predetermined threshold is [0,10 -8 ], indicating that when the rate of change of the objective function is within this interval, the optimization process is stopped.

[0043] Furthermore, in order to prevent abnormal situations such as infinite execution of optimization, the number of optimization iterations is limited. When the number of optimization iterations reaches the preset value n, the optimization is forced to stop regardless of whether it converges or not. n is set to 500, which means that the optimization process is stopped after 500 generations of optimization iterations.

[0044] To achieve the above object, the first aspect of the present application provides an electronic device comprising a processor and a memory, wherein the processor is configured to implement the steps of the method for optimizing a vertical metasurface grating coupler when executing a computer program stored in the memory.

[0045] To achieve the above object, the first aspect of the present application provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is configured to implement the steps of the method for optimizing a vertical metasurface grating coupler when executed by a processor.

[0046] Advantages of the present application:

[0047] Compared with the prior art, the present application provides a method, device and medium for optimizing a vertical metasurface grating coupler, which effectively alleviates the problem of local optimal convergence in the traditional concomitant optimization method by introducing a "random disturbance" strategy and a "continuous topology optimization" method. During the continuous topology optimization process, when the change of the objective function is within a preset threshold range, the introduction of random disturbance can dynamically adjust the dielectric constant distribution in the design area, avoiding premature convergence to a local optimal solution. Compared with traditional optimization techniques such as the concomitant method and the DBS algorithm, this method can more flexibly and efficiently find a global optimal solution. In addition, the present application adjusts the geometric shape of the grating through smoothing processing, and converts the optimized dielectric constant distribution into two discrete values using a binary algorithm, so that the design result has higher processing feasibility and practical application value, solving the contradiction between high integration and processability in traditional methods. Finally, the optimized grating coupler has higher coupling efficiency and bandwidth performance, meeting the demand of modern photonic integrated circuits for ultra-compact and efficient input-output interfaces. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows.

[0049] Figure 1 is a flowchart of a method for optimizing a vertical metasurface grating coupler disclosed by the embodiments of the present application.

[0050] Figure 2 is a design flowchart of a VMGC disclosed by the embodiments of the present application.

[0051] Figure 3 is an optimized structure diagram of a small-size single-layer VMGC disclosed by the embodiments of the present application.

[0052] Figure 4 is an optimized structure diagram using a Lumopt tool.

[0053] Figure 5 This is a schematic diagram of a single-polarization VMGC structure disclosed in an embodiment of the present invention.

[0054] Figure 6 This is a performance diagram of a single-polarization VMGC device disclosed in an embodiment of the present invention.

[0055] Figure 7 This is a schematic diagram of the polarization beam splitting VMGC structure disclosed in an embodiment of the present invention.

[0056] Figure 8 This is a performance diagram of a polarization beam splitting VMGC device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0058] According to an embodiment of the present invention, it should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the following method, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0059] like Figure 1 、 Figure 2 As shown, the present invention provides a method for optimizing a vertical metagrating coupler, comprising the following steps:

[0060] Step S100 , initializing the dielectric constant distribution within the two-dimensional design area, making the dielectric constant randomly distributed between silicon and silicon dioxide or between silicon and air, and stretching it to the height of the etching layer;

[0061] The design region refers to the optimized area of ​​a grating coupler, the specific spatial range within a photonic device where performance optimization is desired. This region is two-dimensional and represents a cross-section of the entire grating structure, encompassing the distribution of different materials, the optical path, and the photon coupling region. The size and shape of the design region determine the functionality and performance of the grating coupler. By initializing, optimizing, and adjusting the dielectric constant within this design region, efficient light coupling and minimized transmission losses can be achieved.

[0062] Initializing the dielectric constant distribution within the design area means assigning an initial dielectric constant value to each pixel unit in the design area at the beginning of the optimization process. The dielectric constant is the response characteristic of a material in an electric field, which directly affects the propagation characteristics of light. At this stage, the value of the dielectric constant is randomly distributed, usually between silicon (Si) and silicon dioxide (SiO2), or between silicon and air. This randomly distributed dielectric constant provides a starting point for the subsequent optimization process. The optimization algorithm will adjust the dielectric constant of each pixel unit on this basis to optimize the light coupling efficiency and other performance indicators. Through this initialization method, the design area can provide more design freedom for subsequent topology optimization, thereby helping to achieve a more efficient vertical grating coupler structure.

[0063] Step S200: performing continuous topology optimization based on the initialized dielectric constant distribution;

[0064] Continuous topology optimization allows the material or dielectric constant in the design area to change continuously in space. Specifically, the continuous topology optimization process includes multiple steps: the first is forward simulation, which calculates the electromagnetic field distribution in the design area by simulating the incidence and propagation of the light source, and calculates the objective function value based on this to evaluate the optical coupling effect of the current design. Then, based on the adjoint simulation, the adjoint electric field of the design area is obtained, and the sensitivity of the objective function relative to the dielectric constant distribution in the design area is calculated, and the optimal gradient descent direction (i.e., the negative gradient direction) is determined, thereby obtaining information on how to adjust the dielectric constant distribution in the design area. This adjustment information will drive the optimization process, gradually adjusting the distribution of the dielectric constant to improve the coupling efficiency and bring the design closer to the final goal.

[0065] In short, continuous topology optimization adjusts the distribution of materials in the design area through repeated iterations, and uses objective functions and sensitivity analysis to adjust the dielectric constant distribution in the design area to achieve the best optical coupling effect.

[0066] Step S300: During the continuous topology optimization process, the dielectric constant distribution is smoothed according to a preset filter radius;

[0067] It can be understood that after the forward simulation and the accompanying simulation are completed, the dielectric constant distribution is smoothed according to the preset filter radius to filter out the sharp areas; the purpose of this stage is to avoid discontinuous mutations or high-frequency noise (such as checkerboard effects) in the structure, thereby ensuring that the optimized structure is physically feasible.

[0068] Step S400: When the change of the objective function is within a predefined threshold range, random perturbations are introduced to adjust the dielectric constant distribution within the design area;

[0069] During the optimization process, the objective function may slow down, indicating that the design has reached a relatively stable state. However, this does not necessarily mean that the global optimal solution has been reached. Introducing random perturbations can further advance the optimization process and adjust the dielectric constant distribution within the design area, thereby breaking through the limitations of the local optimum and finding a more ideal solution.

[0070] Specifically, the optimization process first calculates the rate of change of the objective function (also known as the figure of merit, FOM) between successive iterations. This rate of change reflects the progress of the design optimization. If the rate of change falls within a preset threshold, it indicates that the current optimization process has stalled. To break this stagnation, the system decides whether to introduce random perturbations based on the trend of the objective function. The magnitude and direction of the random perturbations are determined based on the change in the results of the previous iteration.

[0071] When the objective function changes slightly, it means that the optimization direction tends to be stable and may stagnate near a local optimal solution. To avoid this situation, the dielectric constant of each pixel unit in the design area will be randomly adjusted slightly. These minor adjustments are usually made by randomly changing the value of the dielectric constant to ensure that they remain within the physically feasible range, that is, the dielectric constant remains between silicon (Si) and air (or silicon dioxide). Such perturbations can break the optimization bottleneck, allowing the optimization algorithm to continue to move forward, thereby further improving the design performance on a global scale.

[0072] Furthermore, the introduction of random perturbations does not imply arbitrary optimization, but rather is precisely controlled and calculated. The amplitude and timing of the perturbations are precisely set to ensure they effectively advance the optimization process without causing uncontrolled changes in the design. For example, the amplitude of the random perturbations can be set to 1 percent of the dielectric constant, a level that ensures the perturbations are effective without disrupting the optimized structure.

[0073] Preferably, the random perturbation process is divided into the following steps:

[0074] During the optimization process, the design area is divided into many small cells. Each cell has a dielectric constant value that represents the optical properties of the material in that area. To introduce perturbations, it is first necessary to randomly select some cells whose dielectric constants will be adjusted.

[0075] Once the cells that need to be adjusted are selected, their dielectric constant values ​​are randomly increased or decreased slightly. The magnitude of the increase or decrease is random, with the aim of breaking the limitations of the existing design and allowing the optimization process to explore a wider design space. The increased or decreased dielectric constant values ​​will be limited according to a preset range to ensure that the adjusted values ​​still meet physical requirements. Specifically, these adjusted dielectric constant values ​​must be between silicon (Si) and silicon dioxide (SiO2), or between silicon (Si) and air.

[0076] To ensure the optimization process fully explores the design space, perturbations are repeated multiple times rather than just a single one. Each perturbation is performed in a different cell, adjusting the dielectric constant multiple times, resulting in a more comprehensive and uniform perturbation across the design area. This repeated perturbation allows the design to further transcend local optimal solutions and identify potentially more optimal solutions.

[0077] Once the perturbation is complete, the dielectric constant distribution within the design region will change, meaning the distribution obtained during the original optimization process has been adjusted to a new state. This new dielectric constant distribution will be used as input for subsequent optimization processes, such as forward simulation and adjoint simulation, to further optimize the optocoupler design.

[0078] Step S500: converting the dielectric constant after continuous topology optimization into two discrete values, wherein the conversion includes applying a binarization algorithm to limit the dielectric constant distribution to two discrete values ​​of silicon and silicon dioxide or silicon and air;

[0079] In continuous topology optimization, the dielectric constant within the design region is a continuous value, representing the transition region between different materials. However, in actual manufacturing, photonic devices usually only use a few different materials (such as silicon, silicon dioxide, air, etc.), and the dielectric constants of these materials are discrete. Therefore, the purpose of binarization is to convert these continuous dielectric constant values ​​into discrete material states, such as a binary distribution between silicon and air or silicon and silicon dioxide.

[0080] Before binarization, two target discrete values ​​need to be assigned to the dielectric constant. These two discrete values ​​correspond to the dielectric constants of silicon and silicon dioxide (or silicon and air), respectively. By setting these two target values, each pixel unit in the design area will be forced to be classified as one of the materials. Specifically, the dielectric constant value will be compared. If it is close to the dielectric constant of silicon (such as a higher dielectric constant), the pixel unit is assigned to silicon; if it is close to the dielectric constant of air or silicon dioxide (such as a lower dielectric constant), the pixel unit is assigned to air or silicon dioxide.

[0081] During the binarization process, a simple threshold is set to determine whether the dielectric constant of each pixel is close to silicon or air / silicon dioxide. For example, if the dielectric constant of a pixel is greater than or equal to the average of the dielectric constants of silicon and air, the dielectric constant of that pixel is set to the value of silicon. Otherwise, it is set to the value of air or silicon dioxide. This processing method ensures that the dielectric constant within the design area ultimately has only two discrete values, which meets the requirements of actual materials.

[0082] After binarization, the dielectric constant of each pixel within the design area is assigned a specific value, transforming the entire design area into a discrete distribution composed of two materials (such as silicon and air / silicon dioxide). This binary dielectric constant distribution directly corresponds to the actual grating structure, making its physical properties more easily compatible with the manufacturing process.

[0083] Step S600: designing the geometric structure of the vertical metagrating coupler according to the discrete dielectric constant distribution obtained by optimization;

[0084] In the aforementioned optimization step, a discrete dielectric constant distribution consisting of two materials (such as silicon and air or silicon dioxide) has been obtained. These distributions are obtained after optimization and represent the dielectric constant value of each pixel unit. In step S600, it is first necessary to extract the design pattern from this discrete dielectric constant distribution. These patterns include information such as the specific shape of the grating, its optical properties and its distribution in space, forming the basic geometric structure of the grating coupler.

[0085] Based on the extracted dielectric constant distribution pattern, the entire grating coupler geometry is then defined. This geometry encompasses not only the basic shape of the grating (such as the width, spacing, and thickness of the grating stripes), but also the design of the upper and lower layers of the grating, the angle of the light source incident, and the layout of the waveguide. This process maps the optimized theoretical design to the actual physical structure, ensuring that the design achieves optical coupling while meeting manufacturing requirements.

[0086] Step S700: Perform performance simulation on the designed vertical metagrating coupler to evaluate its coupling efficiency and bandwidth parameters.

[0087] Performance simulations are performed using electromagnetic simulation tools that simulate the physical phenomena of light propagation, reflection, and refraction in grating couplers. These simulations allow evaluation of parameters such as coupling efficiency, wavelength dependence, and bandwidth. Specific operating conditions, such as the wavelength, polarization state, and angle of incidence of the incident light source, are input during the simulations to comprehensively evaluate the design's performance under various operating conditions.

[0088] During the simulation, the coupling efficiency can be obtained by calculating the mode overlap integral between the output waveguide port and the incident light source. The higher the coupling efficiency, the more incident light can be effectively transferred into the output waveguide.

[0089] Through simulation, the performance of the grating coupler at different wavelengths can be obtained, and then the bandwidth can be evaluated. The size of the bandwidth determines the range of signal frequencies that the grating coupler can support. The larger the bandwidth, the better the performance of the coupler at multiple wavelengths.

[0090] In addition to coupling efficiency and bandwidth, performance simulation can also evaluate other important performances of the grating coupler, such as polarization-dependent loss, optical transmission loss, beam quality, etc. For example, in multimode waveguide or polarization beam splitting applications, the grating coupler needs to ensure low polarization-dependent loss and efficiently transmit optical signals. If these performance indicators do not meet the requirements, further optimization of the design, improvement of material selection or adjustment of the geometry may be needed.

[0091] Once the performance simulation is complete, the design team needs to compare the simulation results with the initial design goals. If the performance indicators such as coupling efficiency, bandwidth, etc. meet the predetermined requirements, the design is successful and can enter the manufacturing stage. If some indicators do not meet the target, it may be necessary to return to the design stage for further adjustment and optimization.

[0092] In the method of the embodiment, it can be understood that the whole includes three optimization processes of initialization, continuous topology optimization, and binarization.

[0093] The initialization process includes the initialization setting of the device structure size, optimization parameters and optimization objective function (FOM). First, based on the physical basis, the basic structure of the vertical meta-grating coupler (VMGC) based on the 220nm SOI platform is constructed, including a 2μm thick SiO2 buried oxygen layer, a 220nm thick partially etched Si lower grating, a fully etched Si upper grating, a SiO2 or air isolation layer between the upper and lower gratings, a 0.5μm wide 220nm thick Si block as the output waveguide of the grating coupler, and a Gaussian light source located directly above the VMGC. The optimization parameters include the dielectric constant / effective refractive index distribution of the 220nm Si layer optimization region, the partial etching thickness of the lower grating Si layer, the full etching thickness of the upper grating Si layer, the minimum pixel unit size and the Gaussian light source mode field radius. The optimization region is set as the two-dimensional cross section of the grating. The optimization objective function is determined according to the device function, for example, the FOM of the single-polarization VMGC is set as the mode overlap integral between the TE0 fundamental mode of the output waveguide and the incident Gaussian light source.

[0094] The continuous topology optimization process includes four steps: one forward simulation, one accompanying simulation, filtering and random perturbation evaluation and implementation. The forward simulation is to obtain an output beam at the output port when the incident Gaussian light source is placed directly above the grating coupler. The accompanying simulation is to receive a coupled beam directly above the grating coupler when the TE0 waveguide light source is placed at the output waveguide port. By comparing the electric field distribution of the optimized area in the two simulations, the sensitivity information of each pixel unit in the optimized area can be obtained, thereby obtaining the dielectric constant distribution in the gradient descent direction. The filtering step is to smooth the high-frequency noise (such as spikes, checkerboards) in the design area according to the preset filtering radius to prevent the occurrence of non-physical tiny structures, control the minimum feature size, avoid local oscillations in the gradient, and improve the stability of the optimization. The random perturbation evaluation and implementation step is based on the rate of change ΔFOM of the previous FOM values. j Make a judgment, if ΔFOM j If the value is within the pre-set threshold range (θ, β), a random perturbation Δeps is applied to the entire optimization region. i , that is, the dielectric constant eps of each pixel unit i+1 =eps i +Δeps i When the number of optimization iterations is equal to the preset value n or the FOM approaches the set expected value α or the rate of change of the FOM value ΔFOM j When the expected setting range [0,γ] is met, the continuous optimization process ends.

[0095] The binarization process is to binarize the values ​​between the dielectric constants of Si and air in the optimized area after the optimization process is completed. This method adopts the mean binarization method, that is, the dielectric constant eps of each pixel unit is i ≥(eps Si +eps air )*0.5, let eps i =eps Si , otherwise, let eps i =eps air , where the subscript i represents the i-th pixel unit. So the dielectric constant is eps air The pixel unit is air, and the dielectric constant is eps Si The pixel unit is silicon, and an optimized grating structure can be obtained.

[0096] Furthermore, based on the existing Si processing technology and test conditions, the partial etching thickness of the lower grating Si layer is set to 110nm, the full etching thickness of the upper grating Si layer is set to 110nm, the minimum pixel unit size is set to 40nm, and the isolation layer thickness is 0, to ensure the processability and testability of the device.

[0097] The initial dielectric constant distribution of the grating optimization area adopts a random value between the dielectric constants of air and Si, that is, eps i ∈[1,12.0873].

[0098] The optimization objective function FOM of the single polarization VMGC is shown in formula (1), and the set FOM expected value α=1, that is, the coupling efficiency is 100%. The set ΔFOM j Expected value γ = 1 × 10 -8 Therefore, when FOM=α or 0≤ΔFOM j When ≤γ, the continuous optimization process ends. Where j represents the jth iteration:

[0099]

[0100] Among them, FOM represents the optimization objective function, λ(i) is the wavelength, E λ(i) is the electric field intensity related to wavelength, is the conjugate complex number of the electric field intensity, and dS is the integration of small area elements in the design area.

[0101] In the random disturbance evaluation and implementation phase, the evaluation threshold range (θ, β) and the random disturbance Δeps i It is based on the optimization results and multiple tests. β is the ΔFOM obtained from previous optimizations. j When β is too large, the introduction of random perturbations too early will interfere with the normal progress of the optimization process; when β is too small, the introduction of random perturbations too late may result in the optimization ending with a local optimal solution. i The value will also affect the optimization process, Δeps i When the value range is too large, the optimized structure will fail; Δeps i If the value is too small, the perturbation effect cannot be achieved, and the effect is similar to no perturbation. In this method, one percent of the initial dielectric constant distribution is used as the global random perturbation.

[0102] Secondly, the optimization efficiency and effectiveness of this embodiment are significantly improved. Table 1 compares the optimization efficiency and effectiveness of the design method of the present invention with Lumerical's Lumopt, a similar reverse design tool using the adjoint method, using a small-scale single-layer VMGC of 3.6μm*3.6μm as an example. As can be seen from this comparison, the optimization efficiency of the present invention is approximately three times that of Lumopt, significantly improving both device performance and processability.

[0103] Table 1 Comparison of optimization efficiency and effect between the method of the present invention and the lumopt tool

[0104] Method of the present invention Lumopt Optimize structure See Figure 3 See Figure 4 Number of iterations 189 488 Time-consuming 15.75 hours 46.6 hours Coupling efficiency 53.32％ 48.18％ 3dB bandwidth 71nm 35nm

[0105] This embodiment adopts the above design method to design a single polarization VMGC with the structure as follows: Figure 5 The single-polarization VMGC can couple a Gaussian beam in free space into a 0.5 μm wide single-mode waveguide with a coupling efficiency of over 95% and a 3 dB bandwidth of 92 nm. The device performance curve is shown in Figure 6 shown.

[0106] This embodiment adopts the above design method and also designs a polarization beam splitting VMGC, the structure of which is as follows: Figure 7 The single-polarization VMGC can couple Gaussian beams with different polarization angles in free space into two 0.5μm wide single-mode waveguides, respectively. The total coupling efficiency is maintained at around 80%, and the polarization-dependent loss is less than 0.16dB. The device performance curve is shown in Figure 8 shown.

[0107] The present invention provides an ultra-compact, high-efficiency vertical metagrating coupler and design method for use in metaphotonic integrated circuits. This design effectively addresses the issues of low coupling efficiency and large footprint of vertical grating couplers, achieving high optimization efficiency and excellent results. This invention provides an excellent input and output interface solution for the implementation of large-scale, ultra-compact metaphotonic integrated circuits, and is expected to have broad application prospects in a wide range of fields, including photonic integrated lidar, optical communications, and optical imaging.

[0108] According to another aspect of this embodiment, an electronic device is provided, including a processor and a memory, wherein the processor is configured to implement the steps of the method when executing a computer program stored in the memory.

[0109] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] In the several embodiments provided in this embodiment, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, and can be electrical or other forms.

[0111] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0112] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0113] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for optimizing a vertical metagrating coupler, characterized in that: The steps include: Initializing the dielectric constant distribution within the two-dimensional design area so that the dielectric constant is randomly distributed between silicon and silicon dioxide or between silicon and air, and stretching it to the height of the etched layer; performing continuous topology optimization based on the initialized dielectric constant distribution; During the continuous topology optimization process, the dielectric constant distribution is smoothed according to the preset filter radius; When the change of the objective function is within a predefined threshold range, random perturbations are introduced to adjust the dielectric constant distribution within the design area; Converting the dielectric constant after continuous topology optimization into two discrete values, wherein the conversion includes applying a binarization algorithm to limit the dielectric constant distribution to two discrete values ​​of silicon and silicon dioxide or silicon and air; Based on the optimized discrete dielectric constant distribution, the geometric structure of the vertical metagrating coupler is designed; The performance of the designed vertical metagrating coupler was simulated to evaluate its coupling efficiency and bandwidth parameters.

2. The method for optimizing a vertical metagrating coupler according to claim 1, wherein: The continuous topology optimization includes: Perform forward simulations to calculate the electromagnetic field distribution within the design area; Based on the forward simulation results, an objective function is established to evaluate the mode overlap integral between the output waveguide fundamental mode and the incident Gaussian light source. Perform adjoint simulations to determine the sensitivity of the objective function with respect to the dielectric constant distribution within the design region; Using the sensitivity, adjusting the dielectric constant distribution within the design area to optimize the objective function; The steps of forward simulation, establishing the objective function, performing the accompanying simulation, and adjusting the dielectric constant distribution in the design region are repeated until the objective function converges or a predetermined number of iterations is reached.

3. The method for optimizing a vertical metagrating coupler according to claim 2, wherein: After the forward simulation and the accompanying simulation are completed, the high-frequency noise in the design area is smoothed according to the preset filter radius. The smoothing method includes: According to the filter radius, the dielectric constant value of the high-frequency noise part is modified so that the dielectric constant within the filter radius is the same value; After filtering is complete, update the dielectric constant distribution in the design area and proceed to the next steps.

4. The method for optimizing a vertical metagrating coupler according to claim 2, wherein: When the change in the objective function is within a predefined threshold range, the method of introducing random perturbations to adjust the dielectric constant distribution within the design area includes: Calculate the change in quality factor between consecutive iterations; comparing the quality factor change to a predefined threshold range; When the quality factor change is within the threshold range, randomly perturbing the dielectric constant distribution within the design area based on a random perturbation method; adjusting the dielectric constant distribution within the design area according to the random perturbation; After introducing the random perturbations, the forward and adjoint simulations are continued.

5. The method for optimizing a vertical metagrating coupler according to claim 4, wherein: The random disturbance includes: Randomly select several dielectric constant cells; Randomly increase or decrease the dielectric constant value of the selected cell, and limit the dielectric constant value after random increase or decrease to the dielectric constant range between silicon and silicon dioxide or between silicon and air; Repeating the random selection and random increase and decrease steps multiple times to achieve random perturbation of the overall dielectric constant distribution; After the random perturbation is completed, the dielectric constant distribution within the design region is updated for use in subsequent optimization steps.

6. The method for optimizing a vertical metagrating coupler according to claim 1, wherein: Methods for converting the dielectric constant after continuous topology optimization into two discrete values ​​include: setting two target discrete values ​​of dielectric constant, corresponding to the dielectric constants of silicon and silicon dioxide or silicon and air, respectively; For each dielectric constant cell in the optimization area, assign its dielectric constant to one of the target discrete values ​​based on how close its current value is to the two target discrete values; A binarization algorithm is applied to convert the continuous dielectric constant distribution within the design area into a distribution containing only the two target discrete values ​​mentioned above.

7. The method for optimizing a vertical metagrating coupler according to claim 1, wherein: Based on the optimized discrete dielectric constant distribution, the method for designing the geometric structure of the vertical metagrating coupler includes: Extracting the optimized dielectric constant distribution pattern; Based on the extracted dielectric constant distribution pattern, the geometric structure of the vertical metagrating coupler is defined.

8. The method for optimizing a vertical metagrating coupler according to claim 2, wherein: The criterion for convergence of the objective function is: When the objective function value is equal to the preset threshold, the objective function is considered to have converged, where the threshold is 1, indicating that the optimization process is stopped when the objective function value is equal to this value; When the rate of change of the objective function is within a predetermined threshold range, the objective function is considered to have converged, wherein the predetermined threshold range is [0, 10 -8 ], indicating that when the rate of change of the objective function is within the predetermined threshold range, the optimization process is stopped; The conditions for forcing optimization to stop are: When the number of optimization iterations reaches the preset value n, the optimization is forced to stop regardless of whether it converges or not.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor is configured to implement the steps of the method for optimizing a vertical metagrating coupler as claimed in any one of claims 1 to 8 when executing a computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing a vertical metagrating coupler according to any one of claims 1 to 8 are executed.

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