Optimization processing method and device of spectrograph and spectrograph

Abstract

The invention provides an optimization processing method and device of a spectrograph and the spectrograph, and relates to the field of optical technology and the like. The method comprises the following steps: determining an optimization object, wherein the optimization object comprises a first metasurface grating in the spectrometer; determining an optimization mode matched with the optimization object according to an optimization demand corresponding to the optimization object; and performing optimization processing on the structure of the optimization object according to the determined optimization mode to obtain a target optimization structure. By applying the scheme of the invention, the performance of the metasurface grating and the like can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of optical technology, and in particular to a method and device for optimizing a spectrometer and a spectrometer. Background Art

[0002] A spectroscope is a scientific instrument that decomposes complex light into spectral lines. It can be used to measure, for example, light reflected from an object's surface. Traditional spectrometers are typically large, which limits their application scenarios. Summary of the Invention

[0003] The present disclosure provides a method and device for optimizing a spectrometer, and a spectrometer.

[0004] A method for optimizing a spectrometer, comprising:

[0005] Determining an optimization object, the optimization object comprising: a first metasurface grating in the spectrometer;

[0006] Determining an optimization method that matches the optimization object according to the optimization requirements corresponding to the optimization object;

[0007] The structure of the optimization object is optimized according to the optimization method to obtain a target optimized structure.

[0008] A spectrometer optimization processing device includes: a first determination module, a second determination module and a structure optimization module;

[0009] The first determining module is used to determine an optimization object, and the optimization object includes: a first metasurface grating in the spectrometer;

[0010] The second determining module is configured to determine an optimization method that matches the optimization object according to the optimization requirements corresponding to the optimization object;

[0011] The structure optimization module is used to optimize the structure of the optimization object according to the optimization method to obtain a target optimized structure.

[0012] A spectrometer comprising:

[0013] A first metasurface grating, wherein the structure of the first metasurface grating conforms to a target optimized structure, wherein the target optimized structure is obtained by optimizing the structure of the first metasurface grating according to a matching optimization method, and wherein the matching optimization method is determined based on the optimization requirements corresponding to the first metasurface grating.

[0014] An electronic device, comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method as described above.

[0018] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method as described above.

[0019] A computer program product comprises a computer program / instruction, which implements the above method when executed by a processor.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0022] Figure 1 This is a flow chart of an embodiment of the optimization processing method of the spectrometer disclosed in the present invention;

[0023] Figure 2 is a schematic diagram of the rule structure described in the present disclosure;

[0024] Figure 3 is a schematic diagram of the free-form structure described in the present disclosure;

[0025] Figure 4 Schematic diagram of the structure of the spectrometer disclosed in the present invention;

[0026] Figure 5 A schematic diagram of a spectrometer according to the present disclosure in which the second metasurface grating and the focusing lens are combined into one metasurface device;

[0027] Figure 6 This is a schematic diagram of the relationship between phase and radius described in the present disclosure;

[0028] Figure 7 A schematic diagram of the customized phase gradient 1 described in the present disclosure;

[0029] Figure 8 A schematic diagram of the customized phase gradient 2 described in the present disclosure;

[0030] Figure 9 A schematic diagram of the customized phase gradient 3 described in the present disclosure;

[0031] Figure 10 Schematic diagram of the metasurface design method disclosed in the present invention;

[0032] Figure 11 1 is a schematic diagram of the structure of an embodiment 1100 of the optimization processing device for a spectrometer disclosed herein;

[0033] Figure 12 A schematic block diagram of an electronic device 1200 that can be used to implement embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0034] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0035] Furthermore, it should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " as used herein generally indicates that the associated objects are in an "or" relationship.

[0036] Figure 1 Flowchart of an embodiment of the optimization processing method of the spectrometer disclosed in the present invention. Figure 1 As shown, the following specific implementation methods are included.

[0037] In step 101, an optimization object is determined, and the optimization object includes: a first metasurface grating (metastructure grating) in a spectrometer.

[0038] In step 102, an optimization method matching the optimization object is determined according to the optimization requirements corresponding to the optimization object.

[0039] In step 103, the structure of the optimization object is optimized according to the determined optimization method to obtain a target optimized structure.

[0040] It can be seen that the spectrometer in the scheme described in the present disclosure can adopt a metasurface grating. Metasurface refers to a device composed of an artificial two-dimensional structure arranged at a subwavelength, which can realize flexible and effective control of the polarization, amplitude, phase, propagation mode and other characteristics of electromagnetic waves. Metasurface has the properties of being ultra-light and ultra-thin. Accordingly, metasurface optical devices made based on metasurfaces have the advantages of excellent optical performance, small size, high integration, etc. compared with traditional optical devices. They are also compatible with traditional complementary metal oxide semiconductor (CMOS) processes, and can easily achieve large-scale mass production and processing, so that they can be applied to various application scenarios such as augmented reality wearable devices, virtual reality wearable devices, and mobile terminal lenses.

[0041] However, there are certain problems with metasurface gratings in practical applications. Therefore, the solution described in this disclosure optimizes its structure in a certain way so that a metasurface grating can be produced based on the obtained target optimized structure, thereby improving the performance of the metasurface grating.

[0042] Accordingly, the first metasurface grating in the spectrometer can be determined as the optimization object, and according to the optimization requirements corresponding to the optimization object, an optimization method matching the optimization object can be determined, and then the structure of the optimization object can be optimized according to the determined optimization method to obtain the target optimized structure.

[0043] Optimization requirements refer to problems that need to be overcome. For example, the bandwidth of a metasurface grating is usually not wide enough (tens of nanometers), and the efficiency of different polarizations varies greatly. For example, the efficiency of x-polarization incidence is 90%, and the efficiency of y-polarization incidence is 30%. For this optimization requirement, the optimization method that matches the optimization object can be determined as the first optimization method. For another example, the efficiency of a metasurface grating will be lower when the period is small and the diffraction angle is large. For this optimization requirement, the optimization method that matches the optimization object can be determined as the second optimization method.

[0044] The specific implementations of the first optimization method and the second optimization method are described below.

[0045] 1) The first optimization method

[0046] In some embodiments of the present disclosure, in response to determining that the optimization method is the first optimization method, prior condition information can be first obtained, and then the target optimization structure can be determined based on the prior condition information and each candidate structure. The target optimization structure includes: a candidate structure selected from each candidate structure and geometric parameter information of the selected candidate structure.

[0047] The specific candidate structures can be determined according to actual needs. Each candidate structure is usually a regular structure, for example, Figure 2 is a schematic diagram of the regular structure described in the present disclosure, such as Figure 2 As shown, the eight rule structures (two in each row) can be determined as candidate structures. Alternatively, the rule structures can be screened based on prior knowledge to remove some rule structures that may have poor effects, and the remaining rule structures can be determined as candidate structures.

[0048] In some embodiments of the present disclosure, the prior condition information may include: period and incident angle. Accordingly, the optimal geometric parameter information corresponding to each candidate structure can be determined separately based on the satisfaction of the prior condition information, and the evaluation results of the optimal geometric parameter information corresponding to each candidate structure can be obtained separately, and then the candidate structure with the best evaluation result and the optimal geometric parameter information corresponding to the candidate structure with the best evaluation result can be determined as the target optimization structure.

[0049] Specifically, in some embodiments of the present disclosure, a particle swarm optimization (PSO) algorithm may be used to determine the optimal geometric parameter information corresponding to each candidate structure.

[0050] There is no limitation on how to determine the prior condition information. For example, the prior condition information can be determined by combining optical design and simulation (ZEMAX) software and grating equations.

[0051] The grating equation can be shown as follows:

[0052]

[0053] n0 is the refractive index of the medium in the incident direction, k0 is the incident wave vector, θ0 is the incident angle in the x direction, m is the diffraction order, which can be 0, +1, +2, -1, -2, etc., P0 is the period in the x direction, n m is the refractive index of the medium in the outgoing direction, θ m is the diffraction angle.

[0054] For example, the period in the prior condition information can be 882nm, and the incident angle can be 60 degrees, which can be determined using ZEMAX software and verified using the grating equation. For example, after calculating the diffraction angle based on the period and incident angle, the obtained diffraction angle needs to be consistent with the corresponding result in ZEMAX.

[0055] Afterwards, the particle swarm optimization algorithm can be used to determine the optimal geometric parameter information corresponding to each candidate structure.

[0056] The particle swarm optimization algorithm is a type of swarm intelligence algorithm. It is designed by simulating the hunting behavior of bird flocks. Assuming that there is only one food source in the area (the optimal solution in the optimization problem), the task of the bird flock is to find this food source. During the entire search process, the birds will transmit their respective information to each other to let other birds know their location. Through such collaboration, they can determine whether the solution they have found is the optimal solution, and at the same time, they will also transmit the information of the optimal solution to the entire bird flock. Eventually, the entire bird flock can gather around the food source, which is usually said to have found the optimal solution and the problem has converged.

[0057] To implement the particle swarm optimization algorithm, you first need to define initialization parameters, such as the fitness function for the problem, set the swarm size (number of particles) and number of iterations, randomly initialize the position and velocity of each particle, and set the individual optimal position of each particle and the global optimal position of the entire swarm. Iterative optimization is then performed. For each particle, the new velocity is updated based on its current position and velocity, the new position is updated based on the new velocity, the fitness function value is calculated based on the new position, and the individual optimal position of the particle and the global optimal position of the entire swarm are then updated. The particle swarm gradually approaches the optimal solution through iterative updates. The convergence condition is usually set as reaching the maximum number of iterations (such as 100) or the fitness change is less than a threshold (such as 1×10-6). The solution corresponding to the global optimal position is then output as the optimal solution to the optimization problem.

[0058] Among them, when updating the velocity and position of particles, the following formula is usually used:

[0059] Speed ​​update:

[0060] v(t+1) = w * v(t) + c1 * r1 * (pbest - x(t)) + c2 * r2 * (gbest - x(t)); (2)

[0061] Location Updates:

[0062] x(t+1) = x(t) + v(t+1); (3)

[0063] v(t) represents the velocity of the particle at time t, x(t) represents the position of the particle at time t, w represents the inertia weight, and the inertia weight can be dynamically adjusted (such as linearly decaying from 0.9 to 0.4) to balance the global exploration and local fine search capabilities. c1 and c2 represent learning factors, which are usually set to 2. r1 and r2 represent random numbers between 0 and 1, which are used to introduce randomness. pbest represents the individual best position of the particle, and gbest represents the global best position of the entire group.

[0064] In the scheme described in this disclosure, the geometric parameter information of the candidate structure can be mapped as particles in a multidimensional parameter space. Each particle represents a set of possible parameter combinations, with its position corresponding to the parameter value and its velocity determining the direction of parameter adjustment. Furthermore, the fitness function can be directly linked to the efficiency of the grating, for example, using the transmittance or reflectance of a target diffraction order (e.g., -1) as a core metric. The fitness function can also incorporate additional constraints, such as bandwidth, sidelobe suppression, and polarization sensitivity, to achieve multi-objective optimization.

[0065] Assuming that there are 5 candidate structures, the corresponding optimal geometric parameter information can be obtained for each candidate structure. Figure 2 Taking the first structure in the first row shown in the figure as an example, the optimal geometric parameters may include the optimal radius and optimal thickness of the cylinder, and then the evaluation results of the optimal geometric parameter information corresponding to each candidate structure can be obtained respectively. For example, the evaluation results may refer to the diffraction effect, etc., and then the candidate structure with the best evaluation result and the optimal geometric parameter information corresponding to the candidate structure can be determined as the target optimization structure.

[0066] Through the above processing, a metasurface grating with high efficiency, wide bandwidth and small polarization difference can be obtained.

[0067] Assume that the candidate structure in the target optimization structure is Figure 2 For the second structure in the third row shown in , its geometric parameter information may include: thickness of the structure (Hsi) = 0.93655um, refractive index (nsi) = 3.38@1573nm, radius (R) = 0.2um, 0.0601574um, x-direction period (Px) = 0.882um, y-direction period (Py) = 0.48um. In addition, experiments show that the tolerance tolerance corresponding to the target optimized structure is high (thickness ±20nm, diameter ±20nm, and refractive index ±0.05 can all guarantee an efficiency of more than 80%), 1495-1675nm>80% efficiency, and there is no resonance, 1520-1620nm>90% efficiency, and the xy polarization efficiency difference is very small, 1520-1580nm difference is within 1%, 1580-1600nm difference is 1%, and 1600-1626nm difference is 2%, that is, the maximum polarization efficiency difference in the entire working wavelength is about 2%, and 1495-1675nm and 1520-1620nm represent wavelengths.

[0068] 2) Second optimization method

[0069] In some embodiments of the present disclosure, for the first metasurface grating, in response to determining that the optimization method is the second optimization method, a free-form structure can be determined by the adjoint method, and then the free-form structure can be determined as the target optimization structure. Figure 3 Schematic diagram of the free-form structure described in the present disclosure.

[0070] That is, if an extreme metagrating with a large diffraction angle is required, a free-form metagrating can be designed through inverse design.

[0071] In some embodiments of the present disclosure, an objective function and an initial structure may be determined first. The initial structure may include: a random dielectric distribution within a silicon layer. Then, a binary structure that meets the requirements may be determined through continuous iteration based on the objective function and the initial structure. The binary structure is a binary structure of silicon and air, and the binary structure may be determined as the desired target optimization structure.

[0072] Among them, the objective function can be defined as: taking the power efficiency of a specific diffraction order (such as the +1 order) as the optimization target (FoM), for example, the goal of a 75° deflector is to maximize the transmittance of the incident light in the +1 diffraction channel.

[0073] The initial structure is a random dielectric distribution within the silicon layer (between the dielectric constants of air and silicon). Forward simulation and adjoint simulation can then be performed in sequence. The forward simulation includes calculating the electromagnetic field distribution of the current structure at the target wavelength and evaluating the FoM based on the electromagnetic field distribution. The adjoint simulation includes calculating the dielectric constant of each position point in the current structure by backpropagating the gradient of the objective function and adjusting the sensitivity to FoM (using the principle of electromagnetic field reciprocity). Furthermore, parameter updates can be performed, i.e., adjusting the dielectric constant of each position point based on its corresponding sensitivity, thereby gradually approaching the silicon / air binary structure.

[0074] Through the above processing, multi-physics field collaborative optimization can be achieved, that is, during the optimization process, the algorithm can automatically control the structure to support multiple Bloch modes and coordinate their coupling relationships, and can maximize the objective function through mathematical optimization methods such as the Moving Asymptotes Method (MMA) while satisfying process constraints (such as minimum feature size). In addition, robust design can also be achieved, such as manufacturing error modeling, that is, introducing the simulation of structural expansion (over-etch) and corrosion (under-etch) in the iteration to generate a design that is insensitive to geometric errors, and achieving an efficiency-robustness balance, that is, sacrificing part of the theoretical efficiency in exchange for experimental stability to ensure the performance of the actual device.

[0075] Bloch modes can be divided into propagating modes (with an effective refractive index between air and silicon) and evanescent modes (rapid decay) based on their propagation characteristics. The Bloch modes can also be combined with the Fabry-Perot cavity model, providing a powerful theoretical framework for analyzing the diffraction characteristics of gratings. An incident plane wave simultaneously excites multiple Bloch modes, each of which is reflected multiple times in the cavity. Part of the energy is reflected back to the original medium by the cavity mirror. The modes exchange energy through structural perturbations. During each reflection, the mode energy is scattered to different diffraction orders according to the angle distribution. In addition, the output fields of each Bloch mode in the target diffraction channel must be phase-matched, and the total field intensity in the target diffraction channel can be maximized by optimizing the mode excitation intensity and scattering efficiency.

[0076] With the help of multiple Bloch modes, more optimization degrees of freedom can be provided, and the non-intuitive nanostructure makes the field distributions of each mode highly overlapping, promoting energy exchange between modes. The scattered fields of all modes in the target diffraction channel can be coherently superimposed through algorithm optimization, and the efficiency is significantly higher than the contribution of a single mode. In addition, the evanescent mode can provide an auxiliary role. That is, although the evanescent mode does not propagate directly, its field distribution affects the boundary conditions at the interface, indirectly regulating the coupling efficiency of the propagation mode.

[0077] In short, through the second optimization method, a metagrating with large diffraction angle and high efficiency can be obtained, that is, a free-form metasurface grating can be reverse-engineered to achieve high efficiency, etc.

[0078] Compared with the second optimization method, the first optimization method has a simpler structure and is relatively easy to process, while the second optimization method requires higher processing accuracy, that is, high-precision processing is required.

[0079] In some embodiments of the present disclosure, the spectrometer may further include: a second metasurface grating. Accordingly, in response to determining that the second metasurface grating is set separately from the focusing lens in the spectrometer, the second metasurface grating may also be determined as an optimization object.

[0080] Figure 4 Schematic diagram of the structure of the spectrometer disclosed in this disclosure. Figure 4As shown, the system may include a light source, a collimation system, a first metasurface grating, a prism, a second metasurface grating, a focusing lens, and a CMOS chip. The light source can emit light in a specific wavelength band for sample illumination or direct analysis. The collimation system can convert the divergent light emitted by the light source into parallel light, allowing the light beam to enter the grating at a predetermined angle. The first metasurface grating can disperse the incident light to different angles according to wavelength. The combination of the prism and the grating can achieve secondary dispersion and chromatic aberration correction. The second metasurface grating can redirect the dispersed light and correct the distortion of the first metasurface grating through metasurface phase adjustment. The focusing lens can focus light of different wavelengths to different positions on the CMOS chip, forming spatially separated spectral lines. The CMOS chip can record the intensity of each wavelength of light through a pixel array and output digital spectral data. The second metasurface grating can also be identified as an optimization target and optimized using the same optimization method as the first metasurface grating.

[0081] In practical applications, the focusing lens can use a metasurface or a spherical mirror for focusing, or the second metasurface grating and the focusing lens can be combined into one metasurface device.

[0082] Figure 5 Schematic diagram of a spectrometer after the second metasurface grating and focusing lens are combined into one metasurface device as described in the present disclosure. Figure 5 As shown, tilt focusing can be achieved by superimposing a second metasurface grating and a focusing lens.

[0083] In some embodiments of the present disclosure, the metasurface design method of the metasurface device can also be determined, and full-field simulation processing can be performed based on the metasurface design method.

[0084] The traditional metasurface design method is to first scan the unit structure, that is, by adjusting the parameters of a single unit structure (such as size and shape), calculating its phase response at a specific angle of incidence, and establishing a phase-parameter mapping relationship. Then, based on the phase-parameter mapping relationship, each unit structure is arranged into a metasurface array according to the phase requirements. Then, the entire metasurface array can be simulated and verified for performance. Among them, the metasurface is composed of many tiny units, each of which can be called a unit structure. The shape of the unit structure can be various shapes such as square pillars, cylinders, and elliptical pillars.

[0085] However, when the incident light enters the substrate at an angle greater than 45°, according to Snell's law, the angle converted to air may exceed 90°, resulting in total reflection. At this time, the light field transmission efficiency of the unit structure approaches 0, and the traditional method cannot obtain an effective phase-parameter mapping relationship, which will cause the subsequent full-field simulation results to deviate seriously from the design goals.

[0086] In some embodiments of the present disclosure, the overall phase of the metasurface device can be obtained, and a custom phase gradient generated based on prior experience can be obtained, and then the metasurface design method can be determined by combining the overall phase and the custom phase gradient.

[0087] In addition, in some embodiments of the present disclosure, the blazed grating phase corresponding to the second metasurface grating and the cylindrical oblique focusing mirror phase corresponding to the focusing lens can be obtained respectively, and the sum of the blazed grating phase and the cylindrical oblique focusing mirror phase can be determined as the overall phase.

[0088] That is:

[0089] φall=φ1+φ2; (4)

[0090] Among them, φ1 represents the phase of the blazed grating, φ2 represents the phase of the cylindrical oblique focusing mirror, and φall represents the overall phase, that is, the phase of the entire metasurface.

[0091] φ1=-(2*pi / wavelength)*x_location*sin(theta_in); (5)

[0092] Where wavelength represents the incident wavelength, x_location represents the horizontal coordinate of the location point, and theta_in represents the incident angle, such as 45°.

[0093] φ2=-(2*pi / wavelength)*(sqrt((x_location-xf)^2+zf^2)–f); (6)

[0094] Wherein, f represents the focus of oblique focusing, xf represents the distance from the position point to the focus along the x direction, xf = f*sin(theta_out), zf represents the distance from the position point to the focus along the z direction, zf = f*cos(theta_out), theta_out represents the exit angle, and theta_out can be calculated using the grating equation.

[0095] In addition, φ2 can also be replaced by the ZEMAX binary surface phase (the binary surface phase includes the hyperbolic phase).

[0096] The binary face phase can be expressed as:

[0097]

[0098] Among them, the first term in (7) represents the even-order aspheric surface base, c represents the curvature, k represents the quadratic surface constant, ρ represents the radial coordinate, and the value range is usually 0≤ρ≤ρnorm, ρnorm represents the normalized radius (Norm Radius), which is usually set to the semi-aperture of the optical surface, the second term in (7) represents the high-order term of the even-order aspheric surface, Ai represents the set coefficient, n represents the set order, and the third term in (7) represents the diffraction phase modulation term, Φ(ρ) represents the phase function, λ0 represents the designed central wavelength, and n mat Represents the refractive index of the material.

[0099] In addition, according to process requirements, the size of the unit structure is usually between 80nm and (period - 80)nm, and the interval between unit structures also needs to be between 80nm and (period - 80)nm. The phase of the unit structure is usually between 0 and 2π. Then, taking the period = 600nm as an example, the size range of the unit structure that meets the 2π phase is between 80nm and 520nm. According to prior experience, no matter how the incident angle, period, etc. change, a unified custom phase gradient can be determined.

[0100] Taking the shape of the unit structure as a cylinder as an example, based on multiple sets of data, Figure 6 This is a schematic diagram of the relationship between phase and radius summarized in this disclosure. Figure 7 is a schematic diagram of the custom phase gradient 1 described in the present disclosure, Figure 8 Schematic diagram of the custom phase gradient 2 described in the present disclosure, Figure 9 This is a schematic diagram of the custom phase gradient 3 described in the present disclosure. Figure 6 After the relationship between phase and radius is shown, a custom phase gradient can be determined based on the relationship, such as Figures 7 to 9 As shown in FIG, there are three possible customized phase gradients, any of which can be used in practical applications.

[0101] In this way, after obtaining the overall phase and customized phase gradient of the metasurface device respectively, the required metasurface design method can be determined by combining the two.

[0102] For example, for each position point in the overall phase (the value is also between 0 and 2π), the corresponding radius can be determined by querying the custom phase gradient, and the unit structure closest to the determined radius can be arranged at the position point. In practical applications, 16 or 32 different unit structures are usually set for selection, and the radius of different unit structures can be different. In the same way, the arrangement of the unit structures of all position points can be completed, thereby obtaining the desired metasurface design method, that is, the desired metasurface array, such as Figure 10 As shown, Figure 10This is a schematic diagram of the metasurface design method disclosed in the present invention. Furthermore, full-field simulation verification of performance, etc. can be performed based on the metasurface array.

[0103] In addition, in traditional metasurface design methods, it is assumed that there is no electromagnetic coupling between the unit structures, and the design is performed only by scanning a single unit structure. However, when the incidence angle is large, the near-field coupling and periodic effects between the unit structures are significant, and independent design will lead to full-field simulation mismatch, etc. The solution described in the present disclosure avoids the above problems as much as possible by customizing the phase gradient and directly performing global optimization on the complete metasurface.

[0104] In addition, when performing full-field simulation, the setting of substrate thickness will significantly affect the simulation results. Substrate thickness refers to the thickness of the base material supporting the metasurface structure.

[0105] In some embodiments of the present disclosure, when performing full-field simulation processing, in response to determining that accurate calculation results with a predetermined accuracy need to be obtained, calculations can be performed based on a substrate thickness greater than or equal to 5um; otherwise, calculations can be performed based on a substrate thickness of 1 to 2um.

[0106] The thickness of the substrate in actual applications is usually in the hundreds of microns, while simulations can usually only simulate the micron level, so a relatively accurate method is needed to calculate the accurate efficiency, etc. Therefore, by simulating the optimal solution after adding anti-reflection through infinitely thick substrates (infinitely thick substrates, theoretically optimal efficiency results), as well as the efficiency under the condition of finitely thick substrates (1-10um), we determine how to design the closest to the optimal solution. Experiments show that when the substrate thickness is 1, 2, and 4um, the efficiency difference between the two methods is large, while when the substrate thickness is 3, 5, 6, 7, 8, 9, and 10um, the efficiency difference is small. Therefore, when performing full-field simulation processing, if the accuracy of the calculation results is not high, the calculation can be performed according to a substrate thickness of 1 to 2um to save calculation time. Conversely, the calculation can be performed according to a substrate thickness greater than or equal to 5um to improve the accuracy of the calculation results.

[0107] The above is an introduction to the method embodiment. The following is a further explanation of the solution disclosed in the present disclosure through an apparatus embodiment.

[0108] Figure 11 FIG. 1 is a schematic diagram of the structure of an embodiment 1100 of the optimization processing device for the spectrometer disclosed in the present invention. Figure 11 As shown, it includes: a first determination module 1101 , a second determination module 1102 and a structure optimization module 1103 .

[0109] The first determining module 1101 is used to determine an optimization object, where the optimization object includes: a first metasurface grating in a spectrometer.

[0110] The second determining module 1102 is configured to determine an optimization method that matches the optimization object according to the optimization requirements corresponding to the optimization object.

[0111] The structure optimization module 1103 is used to optimize the structure of the optimization object according to the determined optimization method to obtain a target optimized structure.

[0112] In some embodiments of the present disclosure, the optimization method may include: a first optimization method. In response to determining that the optimization method is the first optimization method, the structural optimization module 1103 may obtain prior condition information, and may determine the target optimization structure based on the prior condition information and each candidate structure. The target optimization structure includes: a candidate structure selected from each candidate structure and geometric parameter information of the selected candidate structure.

[0113] In some embodiments of the present disclosure, the prior condition information may include: period and incident angle. The structural optimization module 1103 can determine the optimal geometric parameter information corresponding to each candidate structure based on the satisfaction of the prior condition information, and can obtain the evaluation results of the optimal geometric parameter information corresponding to each candidate structure. Thereafter, the candidate structure with the best evaluation result and the optimal geometric parameter information corresponding to the candidate structure with the best evaluation result can be determined as the target optimization structure.

[0114] In some embodiments of the present disclosure, the structure optimization module 1103 may use a particle swarm optimization algorithm to determine the optimal geometric parameter information corresponding to each candidate structure.

[0115] In addition, in some embodiments of the present disclosure, the optimization method may include: a second optimization method, and the structural optimization module 1103, in response to determining that the optimization method is the second optimization method, can determine a free-form structure through the adjoint method, and can determine the free-form structure as the target optimization structure.

[0116] In some embodiments of the present disclosure, the structural optimization module 1103 can determine the objective function and the initial structure, and the initial structure may include: random dielectric distribution within the silicon layer, and can determine a binary structure that meets the requirements through continuous iteration based on the objective function and the initial structure, and the binary structure is a binary structure of silicon and air, and then the binary structure can be determined as the target optimization structure.

[0117] In some embodiments of the present disclosure, the spectrometer may further include: a second metasurface grating. In response to determining that the second metasurface grating is set separately from the focusing lens in the spectrometer, the first determination module 1101 may also determine the second metasurface grating as an optimization object.

[0118] In addition, in some embodiments of the present disclosure, the structure optimization module 1103 can determine the metasurface design method of the metasurface device in response to determining that the second metasurface grating and the focusing lens are combined into a metasurface device, and can perform full-field simulation processing based on the metasurface design method.

[0119] In some embodiments of the present disclosure, the structure optimization module 1103 can obtain the overall phase of the metasurface device and can obtain a custom phase gradient generated based on prior experience, and then can determine the metasurface design method by combining the overall phase and the custom phase gradient.

[0120] In some embodiments of the present disclosure, the structure optimization module 1103 can respectively obtain the blazed grating phase corresponding to the second metasurface grating and the cylindrical oblique focusing mirror phase corresponding to the focusing lens, and can determine the sum of the blazed grating phase and the cylindrical oblique focusing mirror phase as the overall phase.

[0121] In addition, in some embodiments of the present disclosure, when the structure optimization module 1103 performs full-field simulation processing, in response to determining that an accurate calculation result with a predetermined accuracy needs to be obtained, the calculation can be performed based on a substrate thickness greater than or equal to 5um; otherwise, the calculation can be performed based on a substrate thickness of 1 to 2um.

[0122] The solution disclosed in the present invention also discloses a spectrometer, which includes: a first metasurface grating, the structure of the first metasurface grating conforms to the target optimization structure, the target optimization structure is obtained by optimizing the structure of the first metasurface grating according to a matching optimization method, and the matching optimization method is determined according to the optimization requirements corresponding to the first metasurface grating.

[0123] In addition, the target optimization structure may include: a candidate structure selected from the candidate structures and geometric parameter information of the selected candidate structure, or a free-form structure.

[0124] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0125] Figure 12 A schematic block diagram of an electronic device 1200 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0126] like Figure 12 As shown, the electronic device 1200 includes a computing unit 1201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1202 or a computer program loaded from a storage unit 1208 into a random access memory (RAM) 1203. Various programs and data required for the operation of the electronic device 1200 can also be stored in the RAM 1203. The computing unit 1201, the ROM 1202, and the RAM 1203 are connected to each other via a bus 1204. An input / output (I / O) interface 1205 is also connected to the bus 1204.

[0127] Multiple components in the electronic device 1200 are connected to the I / O interface 1205, including an input unit 1206, such as a keyboard, a mouse, etc.; an output unit 1207, such as various types of displays, speakers, etc.; a storage unit 1208, such as a magnetic disk, an optical disk, etc.; and a communication unit 1209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1209 allows the electronic device 1200 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0128] The computing unit 1201 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSP), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1201 performs the various methods and processes described above, such as the methods described in the present disclosure. For example, in some embodiments, the methods described in the present disclosure can be implemented as a computer software program that is tangibly included in a machine-readable medium, such as a storage unit 1208. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1200 via the ROM 1202 and / or the communication unit 1209. When the computer program is loaded into the RAM 1203 and executed by the computing unit 1201, one or more steps of the methods described in the present disclosure can be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured to execute the method described in the present disclosure in any other appropriate manner (for example, by means of firmware).

[0129] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), system on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0130] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0131] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0132] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0133] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with embodiments of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0134] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0135] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0136] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for optimizing a spectrometer, characterized in that: include: Determining an optimization object, the optimization object comprising: a first metasurface grating in the spectrometer; Determining an optimization method that matches the optimization object according to the optimization requirements corresponding to the optimization object; The structure of the optimization object is optimized according to the optimization method to obtain a target optimized structure.

2. The method according to claim 1, characterized in that The optimization methods include: a first optimization method; Optimizing the structure of the optimization object according to the optimization method to obtain the target optimized structure includes: In response to determining that the optimization method is the first optimization method, obtaining prior condition information; The target optimized structure is determined according to the prior condition information and each candidate structure. The target optimized structure includes: a candidate structure selected from each candidate structure and geometric parameter information of the selected candidate structure.

3. The method according to claim 2, characterized in that The prior condition information includes: period and incident angle; Determining the target optimized structure based on the prior condition information and each candidate structure includes: On the premise of satisfying the a priori condition information, respectively determining the optimal geometric parameter information corresponding to each candidate structure; Obtain evaluation results of the optimal geometric parameter information corresponding to each candidate structure; The candidate structure with the best evaluation result and the optimal geometric parameter information corresponding to the candidate structure with the best evaluation result are determined as the target optimized structure.

4. The method according to claim 3, characterized in that The determining of the optimal geometric parameter information corresponding to each candidate structure includes: The particle swarm optimization algorithm is used to determine the optimal geometric parameter information corresponding to each candidate structure.

5. The method according to claim 1, wherein The optimization method includes: a second optimization method; Optimizing the structure of the optimization object according to the optimization method to obtain the target optimized structure includes: In response to determining that the optimization method is the second optimization method, a free-form structure is determined by an adjoint method, and the free-form structure is determined as the target optimization structure.

6. The method according to claim 5, characterized in that Determining the free-form structure by the adjoint method and determining the free-form structure as the target optimized structure includes: determining a target function and an initial structure, the initial structure comprising: a random dielectric distribution within a silicon layer; According to the objective function and the initial structure, a binary structure that meets the requirements is determined through continuous iteration, where the binary structure is a binary structure of silicon and air, and the binary structure is determined as the target optimized structure.

7. The method according to claim 1, characterized in that The spectrometer further includes: a second metasurface grating; The method further includes: in response to determining that the second metasurface grating is disposed separately from the focusing lens in the spectrometer, also determining the second metasurface grating as the optimization object.

8. The method according to claim 7, characterized in that In response to determining that the second metasurface grating and the focusing lens are combined into a metasurface device, a metasurface design method of the metasurface device is determined, and a full-field simulation process is performed according to the metasurface design method.

9. The method according to claim 8, characterized in that The method of determining the metasurface design of the metasurface device includes: Obtaining the overall phase of the metasurface device; Get custom phase gradients generated based on a priori experience; The metasurface design is determined by combining the overall phase and the custom phase gradient.

10. The method according to claim 9, characterized in that Acquiring the overall phase of the metasurface device includes: respectively acquiring a blazed grating phase corresponding to the second metasurface grating and a cylindrical oblique focusing lens phase corresponding to the focusing lens; The sum of the blazed grating phase and the cylindrical oblique focusing mirror phase is determined as the overall phase.

11. The method according to claim 8, characterized in that The full-field simulation process includes: When performing the full-field simulation process, in response to determining that an accurate calculation result with a predetermined accuracy needs to be obtained, the calculation is performed based on a substrate thickness greater than or equal to 5 μm; otherwise, the calculation is performed based on a substrate thickness of 1 to 2 μm.

12. A spectrometer optimization processing device, characterized in that: include: a first determination module, a second determination module, and a structure optimization module; The first determining module is used to determine an optimization object, and the optimization object includes: a first metasurface grating in the spectrometer; The second determining module is configured to determine an optimization method that matches the optimization object according to the optimization requirements corresponding to the optimization object; The structure optimization module is used to optimize the structure of the optimization object according to the optimization method to obtain a target optimized structure.

13. A spectrometer, characterized in that: include: A first metasurface grating, wherein the structure of the first metasurface grating conforms to a target optimized structure, wherein the target optimized structure is obtained by optimizing the structure of the first metasurface grating according to a matching optimization method, and wherein the matching optimization method is determined based on the optimization requirements corresponding to the first metasurface grating.

14. The spectrometer according to claim 13, characterized in that The target optimized structure includes: a candidate structure selected from the candidate structures and geometric parameter information of the selected candidate structure, or a free-form structure.

15. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 11.

16. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 11.

17. A computer program product, characterized in that The method comprises a computer program / instruction, which implements the method according to any one of claims 1 to 11 when executed by a processor.

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