A near-field optical microscope optical path focusing positioning method

Abstract

The application belongs to the technical field of optical focusing, and specifically discloses a near-field optical microscope light path focusing positioning method, which comprises the following steps: determining a search space of light path focusing coordinates and a step length used for searching based on a laser wavelength and off-axis parabolic mirror data; determining an initial solution in the search space of the light path focusing coordinates, and using the solution searched in the search space to represent the light path focusing coordinates; performing iterative solving through a simulated annealing algorithm based on the initial solution, an initial temperature and a cooling rate, searching for a candidate solution in the search space, obtaining a new solution between a current solution and a new solution according to the step length in the iterative solving process, and screening the candidate solution from a new solution list; and determining an optimal solution in the searched candidate solution. Through reasonable search space determination, global optimization of the simulated annealing algorithm and a judgment standard of the maximum light intensity, the optimal optical focusing point can be efficiently and stably obtained when facing the multi-peak light intensity distribution caused by mirror diffraction.

Description

Technical Field

[0001] This application belongs to the field of optical focusing technology, and more specifically, relates to a method for focusing and positioning the optical path of a near-field optical microscope. Background Technology

[0002] In automated optical focusing design, spatial ergonomics and greedy algorithms are common focusing optimization approaches for focusing the optical path of scattering scanning near-field optical microscopes (s-SNOM). These algorithms can effectively solve optical focusing problems where the light intensity follows a Gaussian distribution. However, in the subwavelength domain where diffraction is significant, traditional algorithms struggle to quickly obtain the optimal optical focus point when faced with multi-peak light intensity distributions caused by specular diffraction. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, this application aims to solve the problem that traditional algorithms have difficulty quickly obtaining the optimal optical focal point when faced with multi-peak light intensity distribution caused by mirror diffraction.

[0004] To achieve the above objectives, in a first aspect, this application provides a near-field optical microscope optical path focusing and positioning method, the method comprising:

[0005] Based on the laser wavelength and off-axis parabolic mirror data, the search space for the optical path focusing coordinates and the step size used in the search are determined.

[0006] An initial solution is determined in the search space of the optical path focusing coordinates, and the solution found in the search space is used to characterize the optical path focusing coordinates.

[0007] Based on the initial solution, initial temperature, and cooling rate, the simulated annealing algorithm is used for iterative solution search. Candidate solutions are searched in the search space (the searched candidate solutions are added to the candidate solution list). During the iterative solution process, a new solution is obtained by stepping between the coordinates of the current solution and the coordinates of a randomly generated new solution and the new solution is added to the new solution list. Candidate solutions are then selected from the new solution list according to the Metropolis criterion.

[0008] Among the searched alternative solutions (list of alternative solutions), the one with the largest light intensity value is determined as the optimal solution.

[0009] Understandably, the search space and step size for the optical path focusing coordinates are first determined based on the laser wavelength and off-axis parabolic mirror data. This narrows the search range, avoids unnecessary calculations, and improves search efficiency. Furthermore, determining the initial solution provides a good starting point for subsequent iterative solutions.

[0010] Secondly, simulated annealing is an effective global optimization algorithm. It searches for the optimal solution step by step in the search space by simulating the physical annealing process. During the iterative solution process, the algorithm obtains a new solution by stepping between the coordinates of the current solution and the coordinates of a randomly generated new solution and adds the new solution to the list. This mechanism allows the algorithm to escape local optima to a certain extent and avoid getting trapped in local extrema, thereby increasing the probability of obtaining the global optimal solution.

[0011] Regarding the search capabilities of the simulated annealing algorithm: it can effectively escape local optima and reach the global optimum. This algorithm introduces probabilistic factors, meaning that even when encountering a local optimum, there is still a certain probability of continuing to explore other possible solutions. Its discrete randomness in generating new solutions is suitable for step-controlled optical displacement stages. Greedy algorithms: they are often prone to getting trapped in local optima, especially when multiple equal shortest paths exist, making it difficult to guarantee finding the global optimum, and they have poor noise resistance. Genetic algorithms and particle swarm optimization algorithms: although they also have the ability to escape local optima, compared to simulated annealing, these algorithms mainly solve continuous problems and are difficult to apply to the control of optical displacement stages.

[0012] Regarding the adaptability of the simulated annealing algorithm: Through flexible cooling progress and temperature scheduling, the search range and accuracy can be adaptively adjusted, showing good adaptability to off-axis parabolic mirrors with different laser wavelengths and parameters. Genetic algorithms and particle swarm optimization algorithms: These can adapt to different problem scales, but require higher computational resources, and may require frequent parameter adjustments in practical applications.

[0013] Regarding the solution quality of the simulated annealing algorithm: It can provide high-quality solutions in a short time, especially under complex multi-constraint conditions. In measuring the second-order electrical signal of a lock-in amplifier: When measuring near-field signals, the first-order electrical signal often comes from a high proportion of ambient light, causing near-field signal overload and making it difficult to measure; while higher-order electrical signals (third and fourth order) have low energy and poor signal-to-noise ratio before optical focusing is achieved. Therefore, using the second-order electrical signal as the solution object ensures the quality of the final optical focusing. Genetic algorithms and particle swarm optimization algorithms: They can find high-quality solutions, but their computational cost is high and their execution efficiency is relatively low, making them unsuitable for the purpose of fast optical focusing.

[0014] Regarding the robustness of simulated annealing: Through random perturbation and annealing mechanisms, it can effectively resist environmental noise and dynamic changes, enhancing its robustness. Genetic algorithms and particle swarm optimization algorithms: While possessing some robustness against perturbation, their stability and robustness are inferior to simulated annealing when facing complex noise.

[0015] Regarding the flexibility of the simulated annealing algorithm: it can flexibly address different problem requirements by adjusting parameters such as temperature function and cooling rate. Due to its simple principle and concise code, the simulated annealing algorithm is well-suited for secondary development to solve specific problems in near-field optical measurements. Genetic algorithms and particle swarm optimization algorithms: although flexible and configurable, parameter selection has a significant impact on the results, requiring extensive experience to set them appropriately.

[0016] The aforementioned advantages in search capability, adaptability, solution quality, perturbation resistance, and flexibility enable the simulated annealing algorithm to solve complex optical focusing problems more efficiently and stably in practical applications.

[0017] Finally, among the searched alternative solutions, the one with the largest light intensity value is determined as the optimal solution. Due to the multi-peak light intensity distribution caused by specular diffraction, directly searching for the maximum light intensity value may lead to misjudgment. However, through global search and stepwise optimization using the simulated annealing algorithm, this method can stably find the true optical focal point, i.e., the point with the largest light intensity value, in the multi-peak light intensity distribution.

[0018] Therefore, the near-field optical microscope optical path focusing and positioning method provided in this application, through reasonable search space determination, global optimization of simulated annealing algorithm and judgment criteria of maximum light intensity, can efficiently and stably obtain the optimal optical focus point when facing the multi-peak light intensity distribution caused by specular diffraction.

[0019] In one possible implementation, the above iterative solution includes:

[0020] A new solution is randomly generated in the neighborhood of the current solution. The current solution is used as the initial solution in the first iteration, and the temperature is used as the initial temperature in the first iteration.

[0021] After clearing the list of new solutions, obtain a new solution by stepping between the coordinates of the current solution and the coordinates of the new solution, and add the new solution to the list of new solutions;

[0022] Based on the current solution and the list of new solutions, according to the Metropolis criterion, determine whether to add the new solution in the list of new solutions to the list of candidate solutions; if a new solution is added to the list of candidate solutions in this iteration, then find the solution with the largest light intensity value from the solutions added to the list of candidate solutions in this iteration to update the current solution;

[0023] Determine whether the temperature of the next iteration has reached the termination temperature. If so, stop the iteration; otherwise, proceed to the next iteration.

[0024] In one possible implementation, the process of obtaining a new solution by stepping between the coordinates of the current solution and the coordinates of a randomly generated new solution, and then adding the new solution to the new solution list, includes:

[0025] At the coordinates of the current solution and the coordinates of the randomly generated new solution Define a path consisting of a polyline between coordinates A and B, with the path configured to pass through the inflection points sequentially. and inflection point ;

[0026] Along the path, the displacement stage is moved step by step, and the coordinates and corresponding light intensity values ​​are recorded. The recorded coordinates are used as new solutions and added to the new solution list.

[0027] It should be noted that the logic of the simulated annealing algorithm in the existing technology is as follows: Based on the current temperature and the current solution, a new solution coordinate range is randomly generated. After performing a Metropolis judgment on the light intensity value corresponding to the new solution coordinates, if the solution is determined to be retained, its coordinates and light intensity value are stored in the candidate solution list. When the simulated annealing reaches the termination temperature, the candidate solution coordinate with the largest corresponding light intensity value in the candidate solution list is selected as the optimal solution.

[0028] The improved simulated annealing algorithm provided in this application works as follows: Based on the current temperature and the current solution, a new set of coordinates within a certain range is randomly generated. A broken line is designed from the current coordinates to the (randomly generated) new coordinates. The light intensity is measured at all coordinates along the path to obtain a new set of solutions. After performing Metropolis judgment on each of these new solutions, the solutions that are judged to be true are retained, and their coordinates and light intensity values ​​are stored in the candidate solution list. When the simulated annealing reaches the termination temperature, the candidate solution with the largest corresponding light intensity value in the candidate solution list is selected as the optimal solution.

[0029] Therefore, during the iterative solution process, the algorithm obtains a new solution by stepping between the coordinates of the current solution and the coordinates of the randomly generated new solution and adds the new solution to the list. This mechanism enables the algorithm to escape local optima to a certain extent and avoid getting trapped in local extrema, thereby increasing the possibility of obtaining the global optimal solution.

[0030] In one possible implementation, the determination of whether to add a new solution from the new solution list to the alternative solution list, based on the Metropolis criterion, is achieved using the following formula:

[0031] ;

[0032] in, This represents the probability of accepting the new solution. This represents the light intensity corresponding to the current solution. This represents the light intensity corresponding to the new solution, where the new solution is any item in the list of new solutions. If so, the new solution will be accepted and added to the list of alternative solutions. ,and If the random number is greater than 0 and between 1, the new solution will be accepted and added to the list of alternative solutions.

[0033] In one possible implementation, the search space described above is determined through the following steps:

[0034] The center of the search space is determined based on the theoretical optical focus of the off-axis parabolic mirror.

[0035] The search space is determined based on the center of the search space and the preset search space size.

[0036] In one possible implementation, the above step size is determined through the following steps:

[0037] The step size is measured in units of length on the same order of magnitude as the laser wavelength.

[0038] In one possible implementation, determining the initial solution in the search space of the optical path focusing coordinates includes:

[0039] The initial focal plane is determined based on the focal length parameters of the off-axis parabolic mirror;

[0040] At the initial focal plane, perform coordinate traversal to obtain the light intensity value of the traversed coordinates; among the traversed coordinates, determine the one with the largest light intensity value as the initial solution.

[0041] In one possible implementation, the initial temperature is determined by the following formula:

[0042] ;

[0043] in, Indicates the initial temperature. This represents the maximum light intensity found during the coordinate traversal of the initial focal plane. This represents the minimum light intensity found during the coordinate traversal of the initial focal plane. It is a constant whose value is in the range of 0 to 1.

[0044] Secondly, this application provides a near-field optical microscope optical path focusing and positioning device, comprising:

[0045] The search configuration module is used to determine the search space for the optical path focusing coordinates and the step size used in the search based on the laser wavelength and off-axis parabolic mirror data.

[0046] The initial solution determination module is used to determine the initial solution in the search space of the optical path focusing coordinates. The solution found in the search space is used to characterize the optical path focusing coordinates.

[0047] The iteration module is used to iteratively solve the problem based on the initial solution, initial temperature, and cooling rate using the simulated annealing algorithm. It searches for candidate solutions in the search space. During the iterative solution process, it obtains a new solution by stepping between the coordinates of the current solution and the coordinates of a randomly generated new solution and adds the new solution to the new solution list. Then, it selects candidate solutions from the new solution list according to the Metropolis criterion.

[0048] The optimal solution determination module is used to determine the solution with the largest light intensity value from the searched alternative solutions as the optimal solution.

[0049] Thirdly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0050] It is understood that the beneficial effects of the second and third aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0051] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art:

[0052] The near-field optical microscope optical path focusing and positioning method provided in this application, through reasonable search space determination, global optimization of simulated annealing algorithm and judgment criteria of maximum light intensity, can efficiently and stably obtain the optimal optical focus point when facing multi-peak light intensity distribution caused by specular diffraction. Attached Figure Description

[0053] Figure 1 This is a schematic flowchart of the near-field optical microscope optical path focusing and positioning method provided in the embodiments of this application;

[0054] Figure 2 This is a schematic diagram of light intensity distribution provided in an embodiment of this application;

[0055] Figure 3 This is a schematic diagram of the polyline path search method provided in the embodiments of this application;

[0056] Figure 4 This is a schematic diagram of the structure of the near-field optical microscope optical path focusing and positioning device provided in the embodiments of this application;

[0057] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0060] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0061] The embodiments of this application are described below with reference to the accompanying drawings.

[0062] Figure 1 This is a schematic flowchart of the near-field optical microscope optical path focusing and positioning method provided in the embodiments of this application, as shown below. Figure 1 As shown, the method includes steps S1 to S7. Figure 2 This is a schematic diagram of light intensity distribution provided in an embodiment of this application. The method provided in this application can achieve light intensity distribution in the following ways: Figure 2 Under the light intensity distribution shown, the optimal optical focal point can be obtained efficiently and stably.

[0063] Step S1: Connect and read the lock-in amplifier data.

[0064] Specifically, the demodulation signal of the lock-in amplifier is read via a network cable or USB data cable.

[0065] A lock-in amplifier (LPA) is used in optical detection to extract weak optical signals from a strong noise background, significantly improving the signal-to-noise ratio (SNR). Its core principle is to use phase-sensitive detection technology to amplify only the input signal components that are in phase and frequency with the reference signal, suppressing irrelevant noise.

[0066] In this system, the input signal of the lock-in amplifier is a near-field optical signal modulated by a high-frequency vibrating needle tip, which is then converted into an electrical signal by a photodetector.

[0067] The output signal of the lock-in amplifier is: after filtering, it presents a low-frequency signal or a DC state (frequency around 0.1-10Hz).

[0068] Step S2: Connect and read the displacement stage data.

[0069] Specifically, it communicates with and controls the displacement stage via a network cable or USB data cable.

[0070] The displacement stage is used to control the spatial coordinates of the off-axis parabolic mirror in this system, and optical focusing is achieved by changing its spatial coordinates.

[0071] Step S3, construct the search space: calculate the size of the space to be searched and the step size based on the laser wavelength and off-axis parabolic mirror data.

[0072] Using visible-wavelength laser light for assistance, the optical path is first roughly focused near the tip-sample area (the space between the tip and the sample). Based on the theoretical optical focus of the off-axis parabolic mirror, the center of the search space is determined. Based on the search space center and a preset search space size (e.g., a square region with a side length of a preset number of steps), the search space is then defined; for example, the search space size is set to 50 steps. 50 steps The step size is 50 steps. The step size is determined by the order of magnitude of the laser wavelength used in the instrument. For example, if the laser wavelength is on the order of 10... When the step length is 0.01mm, the step size is 0.01mm.

[0073] Step S4, determine the initial solution: scan the initial focal plane in the search space (the initial focal plane is determined by the focal length parameter of the off-axis parabolic mirror) to obtain the light intensity value of the traversed coordinates; among the traversed coordinates, determine the one with the largest light intensity value as the initial solution to quickly determine the magnitude of the initial light intensity.

[0074] Definition of light intensity: In order to better observe the quality of near-field optical signals, the second-order electrical signal demodulated by the lock-in amplifier is used as the reference for the light intensity value at the tip-sample point.

[0075] Step S5, set initial parameters: set the initial temperature and cooling rate of the simulated annealing algorithm.

[0076] Take the maximum and minimum light intensity values ​​from the preliminary scan data (obtained through step S4 above), and perform the following calculation to obtain the initial temperature. .

[0077] ;

[0078] In the formula, This represents the minimum light intensity value in the preliminary scan data above. This represents the maximum light intensity value in the preliminary scan data above, where p is a constant within the range of (0,1), and here it is set to 0.1. The cooling rate is set to 0.9.

[0079] Step S6, Iterative Loop: Perform the iterative process of the simulated annealing algorithm. S6 includes the following steps S6-1 to S6-6.

[0080] Step S6-1, Generate a new solution: Based on the current solution, use the following polyline path search method to generate a new coordinate system, take all discrete points along the path as the new solution, and read the light intensity values ​​of these points.

[0081] Polyline path search method: Randomly generate a coordinate B within the defined search space. , , ), based on the coordinates A of the current solution ( , , ) and the new solution coordinates B( , , Connect A (a path consisting of broken lines) to form a path. , , Departing from M, passing through M ( , , ), N( , , ), B( , , The path contains One point, This represents the step size. The light intensity values ​​corresponding to these points are recorded and added to the new solution list (the new solution list is cleared before each iteration before performing the polyline path search). The coordinates mentioned above are in a three-dimensional Cartesian coordinate system, and each solution includes one coordinate value.

[0082] Figure 3 This is a schematic diagram of the polyline path search method provided in the embodiments of this application, such as... Figure 3 As shown, This represents the new solution obtained from a polyline path search. This represents the acceptance probability of each new solution (obtained through the Metropolis criterion). 010011010111 indicates whether the obtained new solution is accepted as a candidate solution, with 0 indicating acceptance as a candidate solution and 1 indicating non-acceptance as a candidate solution.

[0083] Step S6-2, Calculate the function difference: Calculate the difference in light intensity between the new solution and the current solution. .

[0084] Step S6-3, according to the Metropolis criterion, determines whether to add the new solution in the new solution list to the alternative solution list by the following formula.

[0085] ;

[0086] This represents the probability of accepting the new solution. This represents the light intensity corresponding to the current solution. This represents the light intensity corresponding to the new solution, where the new solution is any item in the list of new solutions. If so, the new solution will be accepted and added to the list of alternative solutions. ,and If the random number is greater than 0 and between 1, the new solution will be accepted and added to the list of alternative solutions.

[0087] Step S6-4, Update the current solution: If a new solution is added to the candidate solution list in this iteration, find the solution with the largest light intensity value from the solutions added to the candidate solution list in this iteration, and update the current solution.

[0088] Step S6-5, Cooling: Reduce the temperature according to the set cooling rate. , For the cooling rate, The temperature for this iteration. This is the temperature for the next iteration.

[0089] Step S6-6: Determine the termination condition: If the current temperature has reached the termination temperature, stop the iteration; otherwise, continue the iteration.

[0090] Use the selected termination temperature Determine whether the annealing process is complete. The algorithm ends.

[0091] Step S7, Output the optimal solution: Move the displacement stage to the coordinates of the optimal solution (the solution with the largest light intensity value is taken as the optimal solution), and fix it.

[0092] The focusing and positioning device for the near-field optical microscope provided in this application is described below. The focusing and positioning device for the near-field optical microscope described below can be referred to in correspondence with the focusing and positioning method for the near-field optical microscope described above.

[0093] Figure 4 This is a schematic diagram of the structure of the near-field optical microscope optical path focusing and positioning device provided in the embodiments of this application, as shown below. Figure 4 As shown, the device includes: a search configuration module 10, an initial solution determination module 20, an iteration module 30, and an optimal solution determination module 40. Wherein:

[0094] The search configuration module 10 is used to determine the search space for the optical path focusing coordinates and the step size used in the search based on the laser wavelength and off-axis parabolic mirror data.

[0095] The initial solution determination module 20 is used to determine the initial solution in the search space of the optical path focusing coordinates. The solution searched in the search space is used to characterize the optical path focusing coordinates.

[0096] The iteration module 30 is used to perform iterative solutions based on the initial solution, initial temperature and cooling rate using the simulated annealing algorithm. It searches for candidate solutions in the search space. During the iterative solution process, it obtains new solutions by stepping between the coordinates of the current solution and the coordinates of the randomly generated new solutions and adds the new solutions to the new solution list. It also selects candidate solutions from the new solution list according to the Metropolis criterion.

[0097] The optimal solution determination module 40 is used to determine the solution with the largest light intensity value from the searched alternative solutions as the optimal solution.

[0098] It is understood that the detailed functional implementation of each of the above units / modules can be found in the description in the aforementioned method embodiments, and will not be repeated here.

[0099] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0100] Based on the methods in the above embodiments, this application provides an electronic device. Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application, such as... Figure 5 As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute the methods in the above embodiments.

[0101] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0102] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0103] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0104] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0105] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0106] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0107] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0108] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for focusing and positioning the optical path of a near-field optical microscope, characterized in that, include: Based on the laser wavelength and off-axis parabolic mirror data, the search space for the optical path focusing coordinates and the step size used in the search are determined. An initial solution is determined in the search space of the optical path focusing coordinates, and the solution found in the search space is used to characterize the optical path focusing coordinates. Based on the initial solution, initial temperature, and cooling rate, the simulated annealing algorithm is used for iterative solution. Candidate solutions are searched in the search space. During the iterative solution process, a new solution is obtained by stepping between the coordinates of the current solution and the coordinates of a randomly generated new solution and adding the new solution to the list of new solutions. Candidate solutions are then selected from the list of new solutions according to the Metropolis criterion. Among the searched alternative solutions, the one with the largest light intensity value is determined as the optimal solution; The iterative solution includes: A new solution is randomly generated in the neighborhood of the current solution. The current solution is used as the initial solution in the first iteration, and the temperature is used as the initial temperature in the first iteration. After clearing the list of new solutions, obtain a new solution by stepping between the coordinates of the current solution and the coordinates of the randomly generated new solution, and add the new solution to the list of new solutions; Based on the current solution and the list of new solutions, according to the Metropolis criterion, determine whether to add the new solution in the list of new solutions to the list of candidate solutions; if a new solution is added to the list of candidate solutions in this iteration, then find the solution with the largest light intensity value from the solutions added to the list of candidate solutions in this iteration to update the current solution; The temperature for the next iteration is determined based on the cooling rate and the temperature of the current iteration. Determine whether the temperature of the next iteration has reached the termination temperature. If so, stop the iteration; otherwise, proceed to the next iteration. The step of obtaining a new solution by stepping between the coordinates of the current solution and the coordinates of a randomly generated new solution, and adding the new solution to the new solution list, includes: At the coordinates of the current solution and the coordinates of the randomly generated new solution Define a path consisting of a polyline between coordinates A and B, with the path configured to pass through the inflection points sequentially. and inflection point ; Along the path, the displacement stage is moved step by step and the coordinates and corresponding light intensity values ​​are recorded. The recorded coordinates are used as new solutions and added to the new solution list. The determination of whether to add a new solution from the new solution list to the candidate solution list, according to the Metropolis criterion, is achieved by the following formula: ； in, This represents the probability of accepting the new solution. This represents the light intensity corresponding to the current solution. This represents the light intensity corresponding to the new solution, where the new solution is any item in the list of new solutions. If so, the new solution will be accepted and added to the list of alternative solutions. ,and If the random number is greater than 0 and between 1, the new solution will be accepted and added to the list of alternative solutions.

2. The near-field optical microscope optical path focusing and positioning method according to claim 1, characterized in that, The search space is determined through the following steps: The center of the search space is determined based on the theoretical optical focus of the off-axis parabolic mirror. The search space is determined based on the center of the search space and the preset search space size.

3. The near-field optical microscope optical path focusing and positioning method according to claim 1, characterized in that, The step size is determined through the following steps: The step size is measured in units of length on the same order of magnitude as the laser wavelength.

4. The near-field optical microscope optical path focusing and positioning method according to claim 1, characterized in that, Determining the initial solution in the search space of the optical path focusing coordinates includes: The initial focal plane is determined based on the focal length parameters of the off-axis parabolic mirror; At the initial focal plane, perform coordinate traversal to obtain the light intensity value of the traversed coordinates; among the traversed coordinates, determine the one with the largest light intensity value as the initial solution.

5. The near-field optical microscope optical path focusing and positioning method according to claim 4, characterized in that, The initial temperature is determined by the following formula: ； in, Indicates the initial temperature. This represents the maximum light intensity found during the coordinate traversal of the initial focal plane. This represents the minimum light intensity found during the coordinate traversal of the initial focal plane. It is a constant whose value is in the range of 0 to 1.

6. A focusing and positioning device for a near-field optical microscope, characterized in that, The method applied to the near-field optical microscope optical path focusing and positioning method as described in any one of claims 1-5 includes: The search configuration module is used to determine the search space for the optical path focusing coordinates and the step size used in the search based on the laser wavelength and off-axis parabolic mirror data. The initial solution determination module is used to determine the initial solution in the search space of the optical path focusing coordinates. The solution found in the search space is used to characterize the optical path focusing coordinates. The iteration module is used to iteratively solve the problem based on the initial solution, initial temperature, and cooling rate using the simulated annealing algorithm. It searches for candidate solutions in the search space. During the iterative solution process, it obtains a new solution by stepping between the coordinates of the current solution and the coordinates of a randomly generated new solution and adds the new solution to the new solution list. Then, it selects candidate solutions from the new solution list according to the Metropolis criterion. The optimal solution determination module is used to determine the solution with the largest light intensity value from the searched alternative solutions as the optimal solution.

7. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1-5.

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