Laser interference processing optimization method and system for improving micro-nano structure performance

Abstract

The invention provides a laser interference processing optimization method and system for improving the performance of a micro-nano structure, and relates to the technical field of laser micro-nano machining.The method comprises the steps that firstly, micro-nano structure sample processing parameters are extracted, a sensitive tracking model is constructed to analyze parameter sensitivity, and a lifting-retaining parameter set is obtained; according to the simulation modeling, processing and lifting a dynamic model diagram, analyzing the period consistency of the dynamic model diagram and a reference design diagram through foreground and background maximum flow cutting, and determining a structure period; if the local photoetching model diagram is of a periodic structure, projecting the actual local photoetching model diagram to a local photoetching model diagram of one type in a sine manner, analyzing the curve slope, and optimizing a periodic interference angle and a light beam wavelength; and if the structure is a non-periodic structure, interpolating a gradient processing chromaticity layout for the second-class local photoetching model diagram according to a chromaticity tradeoff criterion, and analyzing and optimizing a dynamic phase in real time. According to the method, high-precision optimization of the processing parameters of the micro-nano structure with consistency in different periods is realized, and the overall performance and stability of the micro-nano structure are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of laser micro-nano processing technology, and in particular to a laser interference processing optimization method and system for improving the performance of micro-nano structures. Background Art

[0002] With the rapid development of microelectronics, photonics, and advanced manufacturing, micro- and nanostructures, due to their unique optical, electrical, and mechanical properties, are widely used in high-precision devices such as photonic crystals, surface-enhanced Raman scattering substrates, microfluidic chips, and functional surfaces. Laser interference processing (LIM) is a maskless, non-contact, highly efficient, and low-cost method for fabricating micro- and nanostructures. This technology uses the interference of multiple coherent laser beams to generate a periodic intensity distribution, which in turn forms a regular pattern on the surface of a photosensitive material, enabling large-scale, rapid processing.

[0003] While laser interferometry (LIM) technology offers numerous advantages in maximizing the performance of micro-nanostructures, existing LIM processes cannot infer the potential for performance improvement based on their processing parameters, making it difficult to further define the direction for LIM optimization. Furthermore, existing LIM optimization methods have limitations, making it difficult to make appropriate optimization decisions for both periodic and non-periodic micro-nanostructure patterns, resulting in significant deviations in micro-nanostructure processing.

[0004] Secondly, due to the influence of laser fluctuations, environmental disturbances, and platform stability, the interference pattern is prone to phase drift and uneven fringes during large-area processing, resulting in poor structural repeatability and uniformity. In addition, the traditional multi-beam interference method is relatively fixed in the real-time optimization of the dynamic interference phase during processing, resulting in uneven or offset intensity distribution of the interference fringes, making it difficult to achieve fine energy control, resulting in overexposure or underexposure in some areas, thus affecting the molding quality and performance of the micro-nanostructure. Summary of the Invention

[0005] To this end, an embodiment of the present invention provides a laser interference processing optimization method and system for improving the performance of micro-nano structures, which is used to solve the problems in the existing technology that the potential for improving the performance of micro-nano structures cannot be inferred, it is difficult to make optimization decisions for different periodic structures, and phase drift is prone to occur during processing, which affects the molding quality and performance of micro-nano structures.

[0006] To solve the above problems, an embodiment of the present invention provides a laser interference processing optimization method for improving the performance of micro-nano structures, comprising: The current structural processing parameters of the micro-nanostructure samples are extracted, and a sensitive tracking model is constructed based on the coupling probability distribution mechanism of how pattern morphology changes affect the overall performance of the micro-nanostructure. The current structural processing parameters are changed in different directions using the sensitive tracking model and sensitivity improvement analysis is performed to obtain a set of improved and retained processing parameters. Simulating and modeling a processing lift dynamic model diagram of the micro-nanostructure sample according to the lift-retain processing parameter set, and analyzing the cycle consistency of the processing lift dynamic model diagram with respect to the benchmark micro-nanostructure design diagram through foreground and background maximum flow cutting to obtain a structural cycle analysis result; If the structural period analysis result shows a periodic structure, the actual local lithography model diagram of the processed micro-nanostructure sample is sinusoidally projected onto a type of local lithography model diagram according to the uniform periodic structure pattern and the slope analysis of the curve is performed to optimize the periodic interference angle and beam wavelength of the laser interference processing; If the structural period analysis result shows a non-periodic structure, chromaticity interpolation is performed for the second type of local lithography model diagram in accordance with the chromaticity trade-off criterion of laser processing phase interference to obtain a gradient processing chromaticity layout, and the dynamic phase of the laser interference processing non-periodic micro-nanostructure is optimized in real time by analyzing the gradient processing chromaticity layout.

[0007] Preferably, the process of extracting the current structural processing parameters of the micro-nanostructure sample, constructing a sensitive tracking model based on the coupling probability distribution mechanism of how pattern morphology changes affect the overall performance of the micro-nanostructure, and using the sensitive tracking model to change the current structural processing parameters along different directions and perform sensitivity improvement analysis to obtain the improved-retained processing parameter set includes: Obtain the application scenarios of micro-nano structures and the design and processing requirements that meet the application scenarios, extract one or more micro-nano structure samples currently produced on the laser interference processing line, and extract several current structural processing parameters of each micro-nano structure sample through the processing log; Acquire the performance indicators of the micro-nanostructure according to the application scenario, preset the sensitivity index threshold of the performance indicator under the most desired structural performance state according to the design and processing requirements, and acquire the micro-nanostructure knowledge graph based on big data; The micro-nanostructure knowledge graph is used to identify multiple performance indicators and several current structural processing parameters, output the coupling probability distribution mechanism of the pattern morphology change of the micro-nanostructure and the overall performance of the micro-nanostructure, and construct a sensitive tracking model for the impact of micro-nanostructure changes on micro-nanostructure performance based on the coupling probability distribution mechanism; Based on the sensitive tracking model, a plurality of sensitive tracking starting points for each performance indicator are randomly constructed. Starting from each sensitive tracking starting point, the value of the current structural processing parameter is changed in only one direction in the sensitive tracking model to generate a sensitive tracking trajectory. The above parameter value changing steps are repeated until each current structural processing parameter is changed, thereby obtaining a plurality of sensitive tracking trajectories. Introducing a sensitive element effect method to calculate the absolute mean and standard deviation corresponding to the element effect of each current structural processing parameter on each of the sensitive tracking trajectories, and determining the suspected sensitivity index of the linear impact of each current structural processing parameter on the performance index based on the absolute mean and standard deviation; If the suspected sensitivity index is less than the sensitivity index threshold and the suspected sensitivity index is a negative value, the current structural processing parameter corresponding to the suspected sensitivity index is retained and marked as a retained processing parameter; If the suspicious sensitivity index is greater than the sensitivity index threshold and the suspicious sensitivity index is positive, the current structural processing parameters corresponding to the suspicious sensitivity index are improved based on the design processing requirements and marked as improved processing parameters to obtain an improved-retained processing parameter set.

[0008] Preferably, the process of simulating and modeling a processing improvement dynamic model diagram of the micro-nanostructure sample according to the improvement-retention processing parameter set, analyzing the periodic consistency of the processing improvement dynamic model diagram with respect to the benchmark micro-nanostructure design diagram through foreground and background maximum flow cutting, and obtaining a structural periodicity analysis result comprises: Performing dynamic simulation reconstruction modeling on the micro-nanostructure sample in 3D model simulation software according to the lifting-retention processing parameter set to obtain a processing lifting dynamic model diagram of the micro-nanostructure sample; Extracting a benchmark micro-nanostructure design drawing and a processing decision of a laser interference lithography device for the benchmark micro-nanostructure design drawing through design processing requirements, treating each graph parameter in the processing improvement dynamic model graph as a dynamic graph construction node, and adding lithography source points and lithography sink points for constructing and processing the benchmark micro-nanostructure design drawing based on the processing decision; Constructing terminal edges and adjacent edges, establishing a periodic graph model of the processing improvement dynamic model graph benchmarked against the benchmark micro-nanostructure design graph based on the dynamic graph construction node, the lithography source point, the lithography sink point, the terminal edges, and the adjacent edges, solving the periodic graph model by maximum flow to obtain a foreground periodic set segment and a background periodic set segment; Counting the similarity sequences of the foreground periodic set segments and the background periodic set segments to obtain a periodic consistency index of a processing improvement dynamic model diagram benchmarked against a reference micro-nanostructure design diagram; If the periodic consistency index is greater than the preset index threshold, the micro-nanostructure sample of the processing improvement dynamic model diagram is calibrated as a periodic structure; if the periodic consistency index is less than the preset index threshold, the micro-nanostructure sample of the processing improvement dynamic model diagram is calibrated as a non-periodic structure.

[0009] Preferably, the process of constructing terminal edges and adjacent edges, establishing the processing improvement dynamic model graph based on the dynamic graph construction node, the lithography source point, the lithography sink point, the terminal edge, and the adjacent edge, and benchmarking the periodic graph model of the benchmark micro-nanostructure design graph processing, solving the periodic graph model by maximum flow, and obtaining the foreground periodic set segment and the background periodic set segment includes: Calculating the structural difference of the dynamic graph nodes between the processing improvement dynamic model graph and the benchmark micro-nanostructure design graph to obtain a graph dislocation function, connecting each dynamic graph node to a lithography source point and a lithography sink point to form N terminal edges, and calculating the allocation cost based on the graph dislocation function to obtain a terminal weight of each terminal edge; Connecting edges between adjacent dynamic graph nodes to form M adjacent edges, simultaneously calculating the cost of not being split between adjacent dynamic graph nodes based on the graph dislocation function, obtaining an adjacent weight of each adjacent edge, and combining the dynamic graph nodes, the lithography source point, the lithography sink point, the terminal edge, and the adjacent edge to establish a periodic graph model of the processing improvement dynamic model graph benchmarked against the benchmark micro-nanostructure design graph processing; A maximum flow algorithm is introduced to calculate the maximum flow of each dynamic graph node from the lithography source point to the lithography sink point on the periodic graph model, and a period set of the periodic graph model belonging to the lithography source point is generated, which is defined as a foreground period set segment, and a period set of the periodic graph model belonging to the lithography sink point is generated, which is defined as a background period set segment.

[0010] Preferably, if the structural period analysis result shows a periodic structure, then the process of sinusoidally projecting the actual local lithography model diagram of the processed micro-nanostructure sample onto a type of local lithography model diagram according to a uniform periodic structure pattern and performing curve slope analysis to optimize the periodic interference angle and beam wavelength of laser interference processing includes: If the structural period analysis result shows that the micro-nanostructure sample processed after the performance improvement is a periodic structure, a uniform periodic structure pattern of the micro-nanostructure sample is extracted through the processing improvement dynamic model diagram, and a sub-model diagram of the uniform periodic structure pattern is stripped out, which is defined as a type of local lithography model diagram; Retrieving, by means of a processing log, an actual local lithography model image processed by a laser interference lithography device within a preset time period for the micro-nanostructure sample following the uniform periodic structure pattern, and synchronously obtaining an actual interference angle value of the actual local lithography model generated by the laser interference lithography device during processing; Constructing a registration domain, performing model registration on the actual local lithography model image and the type of local lithography model image in the registration domain, obtaining a non-overlapping angle of a non-overlapping model image region between the actual local lithography model image and the type of local lithography model image, and constraining a periodic angle sampling range according to the non-overlapping angle; Dividing the periodic angle sampling range into a number of equally divided subsidiary sampling angles based on the actual interference angle value, rotating the model coordinate system of the actual local lithography model image along a direction perpendicular to the projection line corresponding to each subsidiary sampling angle so that the projection line is perpendicular to the rotated x-axis, and continuously accumulating and summing the graph parameter integrals of the graph nodes during the rotation process to form a one-dimensional periodic interference projection function; Obtaining a one-dimensional periodic interference projection function corresponding to the integral of each subsidiary sampling angle, storing the one-dimensional periodic interference projection function corresponding to the integral of each subsidiary sampling angle in a sinusoidal array unit mode, generating a two-dimensional periodic interference projection function, and drawing a necessary interference sinusoidal projection curve for transforming an actual local lithography model image into a type of local lithography model image based on the two-dimensional periodic interference projection function; The actual interference sinusoidal projection curve processed by the laser interference lithography equipment under the actual interference angle value is obtained, the peak slope deviation between the actual interference sinusoidal projection curve and the necessary interference sinusoidal projection curve is calculated, and the periodic interference angle and beam wavelength of the laser interference processed micro-nano structure are optimized according to the peak slope deviation.

[0011] Preferably, if the structural period analysis result shows a non-periodic structure, performing chromaticity interpolation for the second type of local lithography model diagram in accordance with the chromaticity trade-off criterion of laser processing phase interference to obtain a gradient processing chromaticity layout, and optimizing the dynamic phase of the laser interference processing non-periodic micro-nanostructure in real time by analyzing the gradient processing chromaticity layout includes: If the structural period analysis result shows that the micro-nanostructure sample processed after the performance improvement is a non-periodic structure, a sub-model diagram of a non-periodic processing of the micro-nanostructure sample is extracted through the processing improvement dynamic model diagram, which is defined as a second-type local lithography model diagram; Obtaining a preset processing strategy of the laser interference lithography equipment for the second type of local lithography model diagram, marking a predetermined processing surface of the laser interference lithography equipment on the second type of local lithography model diagram using the preset processing strategy, and determining a processing end vertex and a simulated normal vector of each processing end vertex based on all adjacent predetermined processing surfaces; Acquiring a chromaticity trade-off criterion for phase interference during laser interference lithography processing using 3D model simulation software through a big data network, calculating the grayscale of each processing termination vertex based on the simulated normal vector and simulated position information when the simulated normal vector is displayed and output in the 3D model simulation software, obtaining multiple pattern grayscale values, and constructing different phase interference-chromaticity interpolation fragments according to the chromaticity trade-off criterion; A bilinear interpolation algorithm is introduced to bilinearly interpolate different phase interference-chromaticity interpolation fragments from each processing end vertex to all adjacent predetermined processing surfaces based on the grayscale value of the pattern structure in the bilinear interpolation algorithm to generate a gradient processing chromaticity layout of the second type of local lithography model graph; Dividing the gradient processing chromaticity layout into a plurality of sub-layout blocks based on the preset processing strategy, obtaining the gradient chromaticity value presented by each sub-layout block and presetting a periodic chromaticity lookup table, and if the periodic chromaticity lookup table cannot find the gradient chromaticity value, then calibrating the sub-layout block corresponding to the gradient chromaticity value as a gradient layout block; The actual phase interference value when the laser interference lithography equipment processes the gradient plate block and the ideal phase interference value of the gradient plate block weighed accordingly according to the chromaticity trade-off criterion are obtained, the phase interference deviation value between the actual phase interference value and the ideal phase interference value is calculated, and the dynamic phase of the non-periodic uniform micro-nanostructure processed by laser interference is optimized in real time according to the phase interference deviation value.

[0012] An embodiment of the present invention further provides a laser interferometry processing optimization system for improving the performance of micro-nanostructures. The system is used to implement the above-mentioned laser interferometry processing optimization method for improving the performance of micro-nanostructures, and specifically includes: A parameter sensitivity analysis module is used to extract the current structural processing parameters of micro-nanostructure samples, construct a sensitive tracking model based on the coupling probability distribution mechanism of how pattern morphology changes affect the overall performance of the micro-nanostructure, and use the sensitive tracking model to change the current structural processing parameters in different directions and perform sensitivity improvement analysis to obtain a set of improved and retained processing parameters; a structural cycle analysis module for simulating and modeling a processing lift dynamic model diagram of the micro-nanostructure sample according to the lift-retain processing parameter set, and analyzing the cycle consistency of the processing lift dynamic model diagram with respect to the benchmark micro-nanostructure design diagram by foreground and background maximum flow cutting to obtain a structural cycle analysis result; a periodic structure optimization module configured to, if the structural period analysis result indicates a periodic structure, sinusoidally project the actual local lithography model diagram of the processed micro-nanostructure sample onto a class of local lithography model diagrams according to a uniform periodic structure pattern and perform curve slope analysis to optimize the periodic interference angle and beam wavelength of the laser interference processing; The non-periodic structure optimization module is used to perform chromaticity interpolation for the second type of local lithography model diagram in accordance with the chromaticity trade-off criterion of laser processing phase interference if the structural period analysis result shows a non-periodic structure, obtain a gradient processing chromaticity layout, and optimize the dynamic phase of the laser interference processing non-periodic micro-nano structure in real time by analyzing the gradient processing chromaticity layout.

[0013] An embodiment of the present invention also provides an electronic device, which includes a processor, a memory and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the above-mentioned laser interference processing optimization method for improving the performance of micro-nano structures.

[0014] An embodiment of the present invention further provides a computer storage medium storing a computer software product, wherein the computer software product includes a number of instructions for enabling a computer device to execute the above-mentioned laser interference processing optimization method for improving the performance of micro-nano structures.

[0015] It can be seen from the above technical solutions that the present invention has the following beneficial effects: The present invention provides a laser interference processing optimization method and system for improving the performance of micro-nanostructures. The method analyzes the sensitivity of processing parameters through a sensitive tracking model, determines the periodic properties of the structure in combination with maximum flow cutting, optimizes the interference angle and wavelength for periodic structures, and optimizes the dynamic phase of non-periodic structures through chromatic interpolation. This achieves high-precision optimization of processing parameters for micro-nanostructures with different periodic consistency, effectively improving the overall performance and stability of micro-nanostructures, and solving problems in the existing technology such as the difficulty in inferring performance improvement space, the single optimization strategy for periodic structures, and phase drift. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the implementation cases of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for use in the embodiments. By referring to the drawings, the features and advantages of the present invention will be more clearly understood. The drawings are for illustration only and should not be construed as limiting the present invention in any way. Those skilled in the art can derive other drawings based on these drawings without inventive effort. Among them: Figure 1 A flow chart of a laser interference processing optimization method for improving the performance of micro-nano structures provided by the present invention; Figure 2 A flow chart of a set of processing parameters to be enhanced and retained for the present invention; Figure 3 A flow chart showing the results of structural period analysis obtained by the present invention; Figure 4A flow chart of obtaining a foreground periodic set segment and a background periodic set segment according to the present invention; Figure 5 A flow chart for periodic structure optimization of the present invention; Figure 6 A flowchart for optimizing the non-periodic structure of the present invention; Figure 7 A block diagram of a laser interference processing optimization system for improving the performance of micro-nano structures provided by the present invention. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Example 1:

[0019] In order to solve the problems that the existing technology cannot infer the potential space for improving the performance of micro-nano structures, it is difficult to make optimization decisions for different periodic structures, and phase drift is prone to occur during processing, which affects the quality and performance of micro-nano structure molding. Figure 1 As shown, the present invention proposes a laser interference processing optimization method for improving the performance of micro-nano structures, comprising: S1: Extract the current structural processing parameters of the micro-nanostructure samples, build a sensitive tracking model based on the coupling probability distribution mechanism of how pattern morphology changes affect the overall performance of the micro-nanostructure, use the sensitive tracking model to change the current structural processing parameters along different directions and perform sensitivity improvement analysis to obtain the improved-retained processing parameter set; S2: Based on the lift-and-hold processing parameter set, a dynamic model diagram of the processing lift of the micro-nanostructure sample is simulated and modeled. The cycle consistency of the dynamic model diagram of the processing lift is compared with the benchmark micro-nanostructure design diagram through the maximum flow cutting of the foreground and background to obtain the structural cycle analysis results; S3: If the structural period analysis results indicate a periodic structure, the actual local lithography model of the processed micro-nanostructure sample is sinusoidally projected onto a type of local lithography model according to a uniform periodic structure pattern and a curve slope analysis is performed to optimize the periodic interference angle and beam wavelength of the laser interference processing; S4: If the structural period analysis results show a non-periodic structure, chromaticity interpolation is performed for the second-type local lithography model image according to the chromaticity trade-off criterion of laser processing phase interference to obtain a gradient processing chromaticity layout. The dynamic phase of the laser interference processing non-periodic micro-nanostructure is optimized in real time by analyzing the gradient processing chromaticity layout.

[0020] From the above technical solution, it can be seen that the present invention proposes a laser interference processing optimization method for improving the performance of micro-nano structures. By extracting the current structural processing parameters of micro-nano structure samples, constructing a sensitive tracking model and analyzing the parameter sensitivity, it can accurately lock the parameters that have a significant effect on the performance improvement of micro-nano structures, eliminate invalid parameters, and provide a key parameter set for subsequent processing optimization, effectively avoiding blind optimization and improving optimization efficiency. Based on the simulation modeling and processing improvement dynamic model diagram of the improvement-retention processing parameter set, the periodic consistency of the foreground and background maximum flow cutting analysis with the reference design diagram can be used to accurately judge the periodic properties of the micro-nano structure, provide a basis for the selection of subsequent targeted optimization strategies, and ensure the correctness of the optimization direction. For periodic structures, the actual local lithography model diagram is sinusoidally projected onto a type of local lithography model diagram and the slope of the curve is analyzed. The periodic interference angle and beam wavelength can be accurately optimized, so that the laser interference processing is more in line with the design requirements of the periodic structure, and the periodic accuracy and performance stability of the structure are improved. For non-periodic structures, chromaticity interpolation is performed according to the chromaticity trade-off principle to obtain a gradient processing chromaticity layout. By analyzing this layout and optimizing the dynamic phase in real time, this method can effectively solve problems such as phase drift during the processing of non-periodic structures, ensure precise energy regulation during the processing, and improve the molding quality and performance of non-periodic micro-nanostructures. This method optimizes processing parameters by constructing a sensitive tracking model, combines maximum flow cutting analysis to determine the structural period, and uses sinusoidal projection curve slope analysis and chromaticity interpolation dynamic phase optimization for periodic and non-periodic structures, respectively, to improve the performance and stability of micro-nanostructures.

[0021] Furthermore, in step S1, Figure 2 As shown, the specific steps include: S11: Obtain application scenarios of micro-nano structures and design and processing requirements that meet the application scenarios, extract one or more micro-nano structure samples currently produced on the laser interference processing line, and extract several current structural processing parameters of each micro-nano structure sample through the processing log; S12: Obtain performance indicators of micro-nano structures based on application scenarios, preset sensitivity index thresholds for performance indicators under the most desired structural performance state based on design and processing requirements, and obtain a knowledge graph of micro-nano structures based on big data; S13: Using the knowledge graph of micro-nanostructures to identify multiple performance indicators and several current structural processing parameters, output the coupling probability distribution mechanism of the pattern morphology changes of the micro-nanostructures and the overall performance of the micro-nanostructures, and construct a sensitive tracking model based on the coupling probability distribution mechanism to understand the impact of micro-nanostructure changes on micro-nanostructure performance; S14: Based on the sensitive tracking model, a plurality of sensitive tracking starting points for each performance indicator are randomly constructed. Starting from each sensitive tracking starting point, the value of the current structural processing parameter is changed in only one direction in the sensitive tracking model. At this time, a sensitive tracking trajectory is generated. The above parameter value changing steps are repeated until each current structural processing parameter is changed, thereby obtaining a plurality of sensitive tracking trajectories. S15: Introduce the sensitive element effect method to calculate the absolute mean and standard deviation corresponding to the element effect of each current structural processing parameter on each sensitive tracking trajectory, and determine the suspected sensitivity index of each current structural processing parameter's linear influence on the performance index based on the absolute mean and standard deviation; S16: If the suspected sensitivity index is less than the sensitivity index threshold and the suspected sensitivity index is a negative value, retain the current structural processing parameters corresponding to the suspected sensitivity index and mark them as retained processing parameters; S17: If the suspicious sensitivity index is greater than the sensitivity index threshold and the suspicious sensitivity index is positive, the current structural processing parameters corresponding to the suspicious sensitivity index are improved based on the design processing requirements and marked as improved processing parameters to obtain an improved-retained processing parameter set.

[0022] It should be noted that some laser interferometer lithography equipment may exhibit errors such as improper control during the processing of micro-nanostructures. These errors can cause deviations in the processing parameters of micro-nanostructures, resulting in performance that falls short of expectations. Therefore, to ensure that the performance of micro-nanostructures meets product requirements, it is necessary to explore potential improvements and optimize the processing parameters of the laser interferometer lithography equipment accordingly. However, existing methods for inferring potential improvements in micro-nanostructure performance lack a certain degree of precision, which can easily lead to negative optimization of the laser interferometer lithography equipment, resulting in instabilities such as cracks or collapse in the micro-nanostructures, significantly reducing their performance.

[0023] To address this issue, this method constructs a sensitive tracking model by leveraging the probability distribution mechanism of the coupling between the pattern morphology changes of micro-nanostructures and their overall performance. This model is used to explore and track the sensitivity of the current structural processing parameters of the micro-nanostructure sample to global improvements. This effectively identifies which parameters of the laser interference lithography equipment used to process the micro-nanostructure sample can improve its overall performance, and which parameters have no significant effect on overall performance improvement. This allows the precise identification of potential performance enhancement points, scales, and layouts for micro-nanostructures, ensuring that the overall performance of the micro-nanostructure meets the design and processing requirements. This provides a prerequisite benchmark and target for subsequent laser interference lithography equipment processing optimization, and provides a highly reliable optimization basis for laser interference processing.

[0024] It should be noted that today's structural processing parameters include the current size, area, volume, density and coverage of micro-nanostructures. The performance indicators of micro-nanostructures include surface roughness, hardness, elastic modulus, toughness, reflectivity, refractive index, absorptivity and conductivity. Since the performance improvement of micro-nanostructures is essentially determined by the change in the structural layout of the global pattern, for example, increasing the width of a certain part of the micro-nanostructure can improve its overall wind resistance, that is, there is a known echo coupling mechanism between the pattern change and the performance of the micro-nanostructure. Therefore, this method constructs a sensitive tracking model through the above-mentioned coupling probability distribution mechanism. The model can accurately track whether the changes in the micro-nanostructure meet the performance requirements specified by the design and processing requirements, thereby realizing the precise exploration of the improvement space of the processing parameters of each unit structure.

[0025] Specifically, starting from each sensitive tracking starting point in the sensitivity tracking model, the current structural processing parameter value is changed in a single direction, and the value of each structural processing variable is changed one by one to construct an exploration trajectory in the improvement space, that is, the change path of the input structural processing variable. This single variable change tracking method can isolate the local effect of each structural processing parameter variable, making it easier to estimate its direct impact on the model output. Each trajectory is equivalent to a screening experiment for performance-improving variables.

[0026] The sensitive element effect method calculates the absolute mean and standard deviation of the element effect of each current structural processing parameter on each sensitive tracking trajectory, thereby determining the suspected sensitivity index of the parameter's linear impact on the performance index. If the suspected sensitivity index is greater than the sensitivity index threshold and is positive, it means that changing the parameter can significantly improve the global performance of the micro-nanostructure sample. If the suspected sensitivity index is less than the sensitivity index threshold and is negative, it indicates that changing the parameter is unlikely to significantly improve the global performance of the micro-nanostructure sample, and therefore no improvement is necessary.

[0027] Further, in step S2, as Figure 3 As shown, the specific steps include: S21: reconstructing and modeling the micro-nanostructure sample by dynamic simulation in 3D model simulation software according to the lifting-retention processing parameter set to obtain a processing lifting dynamic model diagram of the micro-nanostructure sample; S22: extracting a benchmark micro-nanostructure design drawing and a processing decision of a laser interference lithography device for the benchmark micro-nanostructure design drawing based on the design processing requirements, treating each graph parameter in the processing improvement dynamic model graph as a dynamic graph construction node, and adding lithography source points and lithography sink points to construct the processing benchmark micro-nanostructure design drawing based on the processing decision; S23: Construct terminal edges and adjacent edges, establish a processing improvement dynamic model graph based on the dynamic graph construction nodes, lithography source points, lithography sink points, terminal edges and adjacent edges, benchmark the processing of the period graph model of the benchmark micro-nanostructure design graph, solve the period graph model through maximum flow, and obtain the foreground period set segment and the background period set segment; S24: Counting the similarity sequences of the foreground period set segments and the background period set segments to obtain a period consistency index of the processing improvement dynamic model diagram benchmarked against the benchmark micro-nanostructure design diagram; S25: If the periodic consistency index is greater than the preset index threshold, the micro-nano structure sample of the processing improvement dynamic model diagram is calibrated as a periodic structure; if the periodic consistency index is less than the preset index threshold, the micro-nano structure sample of the processing improvement dynamic model diagram is calibrated as a non-periodic structure.

[0028] It's important to note that micro-nanostructure designs are diverse, and different structural layouts can produce different performance effects. For example, based on the microstructures of biological epidermis, such as shark skin, lotus leaves, and moth eyes, it's possible to create multifunctional structures with features like drag reduction, anti-icing, and anti-fouling. However, the pattern layouts of some micro-nanostructures exhibit periodic consistency, while the pattern layouts of others can change drastically and lack periodic consistency.

[0029] However, existing methods for optimizing laser interferometer lithography equipment have limitations, making it difficult to develop optimized solutions tailored to the periodic characteristics of different micro-nanostructures. This results in micro-nanostructures with varying periodicities sharing the same optimization strategy during processing, which can easily lead to serious errors in controlling the interference angle, wavelength, and phase during laser interferometer processing. This can cause artifacts such as unclear edges and drift in the processed micro-nanostructures. Therefore, clarifying the periodic consistency of micro-nanostructures after performance improvement is crucial for optimizing laser interferometer processing.

[0030] To this end, this method uses the improvement-retention processing parameters to simulate and model the dynamic processing model after maximizing the performance of the micro-nanostructure, and obtains a processing improvement dynamic model diagram. Then, the optimal segmentation is found with reference to the baseline micro-nanostructure design diagram, and the regional connectivity of the processing improvement dynamic model diagram is constructed, that is, the processing improvement dynamic model diagram is benchmarked against the periodic diagram model of the baseline micro-nanostructure design diagram. According to the deviation feedback from the foreground period to the background period contained in the periodic diagram model, the periodic consistency of the processing pattern required for the micro-nanostructure sample after performance improvement can be revealed, thereby providing an accurate and reliable periodic analysis basis for the laser interference processing of micro-nanostructures. Through this method, the processing accuracy of laser interference lithography equipment for micro-nanostructures with different periodic patterns can be effectively improved, and the adaptability and flexibility of laser interference processing optimization can be significantly improved.

[0031] It should be noted that 3D model simulation software includes CAD, SolidWorks, and PROE. Since the machining improvement dynamic model diagram is generated through machining simulation reconstruction in 3D model simulation software, it is primarily constructed from local graphic parameters of different dynamic resolutions. Therefore, the dynamic graph construction node can depict the specific dynamic position of each graphic parameter in the machining improvement dynamic model diagram, making the subsequent capture of the coherence of the micro-nanostructure pattern more stable and accurate.

[0032] The lithography source node represents the periodic foreground, and all dynamic graph nodes associated with the regionally connected foreground category are connected to it via an edge. The lithography sink node represents the periodic background, and all dynamic graph nodes associated with the regionally connected background category are connected to it via an edge. These two nodes do not correspond to dynamic graph parameters in the model graph, but rather serve as additional logical endpoints introduced to facilitate regional coherent segmentation of micro-nanostructure patterns. This serves to clearly define the coherent attribution of each dynamic graph in the processing improvement dynamic model graph.

[0033] Because the benchmark micro-nanostructure design drawing has defined whether the micro-nanostructure pattern is periodically coherent, by combining the dynamic graph nodes, lithography source points, lithography sink points, terminal edges and adjacent edges, a processing improvement dynamic model diagram can be established to correspond to the periodic graph model of the benchmark micro-nanostructure design drawing.

[0034] If the periodic consistency index is greater than the preset index threshold, it indicates that the processing pattern required by the micro-nanostructure sample in the processing improvement dynamic model diagram is periodically coherent and has high periodic consistency. Therefore, the micro-nanostructure sample in the processing improvement dynamic model diagram is calibrated as a periodic structure. Conversely, it indicates that the processing pattern required by the micro-nanostructure sample may not extend according to the original pattern trajectory at a certain position or node, forming a non-periodically consistent pattern layout that may change suddenly at any time. Therefore, the micro-nanostructure sample in the processing improvement dynamic model diagram is calibrated as a non-periodic structure.

[0035] Further, in step S23, as Figure 4 As shown, the specific steps include: S231: Calculating the structural differences of the dynamic graph nodes between the processing improvement dynamic model graph and the benchmark micro-nanostructure design graph to obtain a graph dislocation function, connecting each dynamic graph node to a lithography source point and a lithography sink point to form N terminal edges, and calculating the allocation cost based on the graph dislocation function to obtain a terminal weight of each terminal edge; S232: Connecting edges between adjacent dynamic graph nodes to form M adjacent edges, and simultaneously calculating the cost of not being split between adjacent dynamic graph nodes based on the graph dislocation function to obtain the adjacent weight of each adjacent edge. Combining the dynamic graph nodes, lithography source points, lithography sink points, terminal edges, and adjacent edges, a processing improvement dynamic model graph is established to be benchmarked against a periodic graph model of the benchmark micro-nanostructure design graph processing. S233: Introduce the maximum flow algorithm to calculate the maximum flow of each dynamic graph node from the lithography source point to the lithography sink point on the periodic graph model, generate the period set of the periodic graph model belonging to the lithography source point, defined as the foreground period set segment, and generate the period set of the periodic graph model belonging to the lithography sink point, defined as the background period set segment.

[0036] It should be noted that regarding the construction details of the periodic graph model and the connectivity and attribution segmentation of foreground and background periods, the steps of this method are as follows: First, the graph misalignment of the processing-enhanced dynamic model graph relative to the baseline micro-nanostructure design graph is calculated. Because the baseline micro-nanostructure design graph serves as a coherent reference and has defined periodic coherence of the micro-nanostructure pattern, the graph misalignment reflects the dynamic coherence vector of the processing-enhanced dynamic model graph when the baseline micro-nanostructure design graph is used as a detailed reference. This reflects the trend of the coherence segmentation cost between each dynamic graph node.

[0037] Next, N terminal edges are formed from each dynamic graph node to the photolithography source and sink. These edges form a "cost model" for each dynamic graph node belonging to the periodic foreground or background, defining the connectivity properties of each dynamic graph node. The terminal weight of each terminal edge, calculated based on the graph misalignment function, represents the specific cost of assigning that dynamic graph node to the periodic foreground or background.

[0038] Next, adjacent graph nodes are connected in the same way to form M adjacency edges. Adjacency edges serve as clues to whether dynamic graph nodes belong to the same connected region, indicating the smoothness of connectivity between adjacent dynamic graph nodes. The adjacency weight of each adjacency edge, calculated based on the graph misalignment function, represents the specific cost of not separating adjacent dynamic graph nodes.

[0039] In summary, the control of terminal edges, terminal weights, adjacent edges, and adjacent weights determines the regional coherence segmentation behavior of the machining-enhanced dynamic model graph relative to the benchmark micro-nanostructure design graph. High-quality edge and edge weight settings lead to more accurate and natural regional connectivity segmentation results, significantly improving the accuracy of the micro-nanostructure periodic consistency calculation for the machining-enhanced dynamic model graph. Furthermore, the smoothing term of the adjacent weights helps maintain the coherence of the model graph and avoid the influence of narration noise during the simulated machining process.

[0040] It should be noted that the foreground represents the connectivity and emphasis of the graph, while the background represents the connectivity and de-emphasis of the graph. A periodic graph model, constructed by combining dynamic graph nodes, photolithography sources and sinks, terminal edges, and adjacent edges, depicts the foreground enhancement and background weakening of each dynamic graph node in the processing improvement dynamic model. Together, the foreground and background form a visually contrasting pattern, reflecting the regional periodic coherence of the processing improvement dynamic model graph relative to the benchmark micro-nanostructure design graph.

[0041] The maximum flow is calculated using the maximum flow algorithm. If the terminal edge weight of a dynamic graph node to the lithography source is greater than the adjacent edge weight to the lithography sink, it indicates that the dynamic graph node is more likely to belong to the foreground, and thus to the period set of the lithography source, forming a foreground period set segment. Conversely, it indicates that the dynamic graph node is more likely to belong to the background, and to the period set of the lithography sink, forming a background period set segment.

[0042] Further, in step S3, as Figure 5 As shown, the specific steps include: S31: If the structural period analysis result shows that the micro-nanostructure sample processed after the performance improvement is a periodic structure, a uniform periodic structure pattern of the micro-nanostructure sample is extracted through the processing improvement dynamic model map, and a sub-model map of the uniform periodic structure pattern is stripped out, which is defined as a type of local lithography model map; S32: Retrieving, through the processing log, an actual local lithography model image processed by the laser interference lithography equipment within a preset time period for the micro-nanostructure sample following the uniform periodic structure pattern, and simultaneously obtaining an actual interference angle value of the actual local lithography model generated by the laser interference lithography equipment; S33: constructing a registration domain, performing model registration on the actual local lithography model image and a type of local lithography model image in the registration domain, obtaining a non-overlapping angle of a non-overlapping model image region between the actual local lithography model image and the type of local lithography model image, and constraining a periodic angle sampling range according to the non-overlapping angle; S34: dividing the periodic angle sampling range into a number of equally divided subsidiary sampling angles based on the actual interference angle value, rotating the model coordinate system of the actual local lithography model image along a direction perpendicular to the projection line corresponding to each subsidiary sampling angle, so that the projection line is perpendicular to the rotated x-axis, and continuously accumulating and summing the graph parameter integrals of the pattern nodes during the rotation process to form a one-dimensional periodic interference projection function; S35: Obtaining a one-dimensional periodic interference projection function corresponding to the integral of each subsidiary sampling angle, storing the one-dimensional periodic interference projection function corresponding to the integral of each subsidiary sampling angle in a sinusoidal array unit pattern, generating a two-dimensional periodic interference projection function, and drawing a necessary interference sinusoidal projection curve for transforming the actual local lithography model image into a type of local lithography model image based on the two-dimensional periodic interference projection function; S36: Obtain the actual interference sinusoidal projection curve processed by the laser interference lithography equipment under the actual interference angle value condition, calculate the peak slope deviation between the actual interference sinusoidal projection curve and the necessary interference sinusoidal projection curve, and optimize the periodic interference angle and beam wavelength of the laser interference processed micro-nano structure according to the peak slope deviation.

[0043] It should be noted that the interference angle determines the periodicity of micro-nanostructure pattern processing, and precise control allows for flexible adjustment of the structural period. Therefore, for periodically consistent micro-nanostructure processing, the accuracy of the interference angle and laser beam are crucial. However, existing methods for optimizing the interference angle in laser interference processing have low precision and are prone to calibration errors, making it difficult to ensure that the laser beam processes micro-nanostructures with periodic consistency. They may also cause the beam wavelength to be too large or too small, resulting in problems such as uneven interference spot and fringe distortion.

[0044] This method obtains a uniform periodic structural pattern of a micro-nanostructure sample determined to be a periodic structure, as well as a local lithography model of the uniform periodic structural pattern. This represents the target periodic micro-nanostructure sample after performance improvement. The uniform periodic structural pattern is a structural pattern template that appears as a periodic pattern in the processing improvement dynamic model. Simultaneously, a local lithography model of the micro-nanostructure sample is obtained using a laser interferometer lithography apparatus to process the micro-nanostructure sample using the uniform periodic structural pattern. This represents actual data for the periodic micro-nanostructure sample after performance improvement.

[0045] This method aims to optimize the actual interference angle of the actual local lithography model image towards the interference angle required by a type of local lithography model image. In addition, there is a structural pattern gap between the actual local lithography model image and the type of local lithography model image due to performance improvement. To this end, this method first uses the registration field to obtain the non-overlapping model image area between the actual local lithography model image and the type of local lithography model image. This area is the structural pattern gap between the two, and based on the non-overlapping angle of the non-overlapping model image area, a periodic angle sampling range is constrained. This periodic angle sampling range can provide precise direction and angle guidance for the sinusoidal projection of the actual local lithography model image toward the type of local lithography model image, making the inverse transformation of the micro-nano structure from the actual local lithography model image to the type of local lithography model image more accurate.

[0046] It should be noted that the periodic angle sampling range is then divided into several equally divided subsidiary sampling angles to achieve more comprehensive and detailed projection information collection. Subsequently, the model coordinate system of the actual local lithography model image is rotated along the direction perpendicular to the projection line corresponding to each subsidiary sampling angle. The purpose is to rotate the actual local lithography model image according to the drift angle of the structural pattern difference, so that the actual local lithography model image is aligned with its own coordinate axis in the projection direction toward a type of local lithography model image. At this time, the cumulative intensity of the simulated projection line on the actual local lithography model image during the rotation process will obtain perspective image information at multiple different angles. This information records the sinusoidal profile of the structural pattern in one dimension.

[0047] However, the periodic processing variations of micro-nanostructures exhibit at least a two-dimensional pattern. Therefore, a sinusoidal array unit is further employed to store the corresponding integrals of the one-dimensional periodic interference projection function for each attached sampling angle. This function, formed by combining the one-dimensional sinusoidal profiles, represents a two-dimensional periodic interference projection function. This function represents the actual interference angle of the actual local lithography pattern, optimized to the necessary interference sinusoidal projection curve for the desired interference angle of a particular local lithography pattern. Ultimately, based on the peak slope deviation between the actual interference sinusoidal projection curve processed under the actual interference angle value and the necessary interference sinusoidal projection curve, the periodic interference angle and beam wavelength of laser interference processed micro-nanostructures can be precisely optimized.

[0048] Through this method, the function on the actual model diagram can be projected along the straight lines of different angles of the target model diagram, and the interference angle required by a type of local lithography model diagram can be used as the target. The directional sinusoidal projection calculation and optimization of the actual model diagram can be performed, thereby improving the accuracy of laser interference processing optimization of periodic micro-nanostructures and ensuring the performance improvement quality of micro-nanostructures.

[0049] Further, in step S4, as Figure 6 As shown, the specific steps include: S41: If the structural period analysis result shows that the micro-nanostructure sample processed after the performance improvement is a non-periodic structure, a sub-model graph of a non-periodic processing of the micro-nanostructure sample is extracted through the processing improvement dynamic model graph, which is defined as a second-type local lithography model graph; S42: Obtaining a preset processing strategy of the laser interference lithography device for the second type of local lithography model diagram, marking a predetermined processing surface of the laser interference lithography device on the second type of local lithography model diagram using the preset processing strategy, and determining a processing end vertex and a simulated normal vector of each processing end vertex based on all adjacent predetermined processing surfaces; S43: Using a big data network to obtain a chromaticity trade-off criterion for phase interference in a laser interference lithography process simulated by a 3D model simulation software. Based on the simulated normal vector and the simulated position information when the simulated normal vector is displayed and output in the 3D model simulation software, the grayscale of each processing termination vertex is calculated to obtain multiple pattern grayscale values. Different phase interference-chromaticity interpolation fragments are constructed according to the chromaticity trade-off criterion. S44: Introducing a bilinear interpolation algorithm, based on the grayscale value of the pattern, bilinearly interpolating different phase interference-chromaticity interpolation fragments from each processing end vertex to all adjacent predetermined processing surfaces in the bilinear interpolation algorithm, to generate a gradient processing chromaticity layout of the second type of local lithography model graph; S45: Dividing the gradient processing chromaticity map into a plurality of sub-map blocks based on a preset processing strategy, obtaining the gradient chromaticity value presented by each sub-map block and presetting a periodic chromaticity lookup table; if the periodic chromaticity lookup table cannot find the gradient chromaticity value, then calibrating the sub-map block corresponding to the gradient chromaticity value as the gradient map block; S46: Obtain the actual phase interference value when the laser interference lithography equipment processes the gradient plate block and the ideal phase interference value of the gradient plate block that is weighed accordingly according to the chromaticity trade-off criterion, calculate the phase interference deviation value between the actual phase interference value and the ideal phase interference value, and optimize the dynamic phase of the non-periodic uniform micro-nanostructure processed by laser interference in real time according to the phase interference deviation value.

[0050] It should be noted that when the structural period analysis results show that the micro-nanostructure sample processed after performance improvement is a non-periodic structure, it indicates that the structural pattern required for processing the sample may have irregular changes at any time, and may have specific offsets and regional jumps at certain positions or nodes, resulting in a gradual change in the structural pattern. The key to ensuring the performance improvement of the micro-nanostructure in the gradual change of the structural pattern lies in the pattern clarity, which depends on the accuracy of the real-time phase of the laser interference processing micro-nanostructure. If the phase is inaccurate, the interference pattern will be blurred, discontinuous, and uneven when processing the non-periodic gradual structural pattern.

[0051] To this end, the present method first obtains a type II local lithography model diagram for non-periodic processing of a micro-nanostructure sample as the target non-periodic micro-nanostructure sample after performance improvement. Then, the established processing surfaces of the laser interference lithography equipment for the type II local lithography model diagram are obtained. These established processing surfaces define the gradient trend of the structural pattern. Based on all adjacent established processing surfaces, the strategy switching turning point when the laser interference lithography equipment processes the type II local lithography model diagram, that is, the processing termination vertex, can be determined, and the simulated normal vector of each processing termination vertex can be obtained. The simulated normal vector represents the surface orientation of the processing termination vertex, and is an important clue for judging the gradient angle. It can ensure that the color rendering of the non-periodic gradient pattern on the subsequent type II local lithography model diagram is smoother and more realistic, and is especially suitable for non-periodic curved surfaces or smooth structures of micro-nanostructure samples.

[0052] It should be noted that although some 3D model simulation software has the function of tracking phase interference during the laser interference lithography simulation process, it does not have a visualization effect, which makes it impossible for the operator to clearly identify the starting gradient area of ​​the structural pattern on the second-type local lithography model diagram, making it difficult to accurately optimize the phase output of the laser interference in real time. To this end, this method uses big data to obtain the chromaticity trade-off criterion of the phase interference in the laser interference lithography process simulated by the 3D model simulation software, so that the 3D model simulation software can follow this criterion, perform chromaticity trade-off and assign corresponding values ​​for the phase interference parameters tracked during the simulation of the second-type local lithography model diagram, accurately display the non-periodic gradient of the structural pattern in the form of different chromatic definitions, generate a gradient processing chromaticity map of the second-type local lithography model diagram, and achieve a visualization effect of non-periodic gradient.

[0053] The simulated position information includes the simulated photolithography position, coordinate position, and viewpoint position of the final processing vertex. The grayscale value of the pattern is calculated based on the simulated normal vector and the simulated position information displayed when the simulated normal vector is output in the 3D model simulation software. This allows for the planning of a chromaticity interpolation comparison basis for each final processing vertex. This allows for smoother and more accurate chromaticity rendering when different phase interference-chromaticity interpolation fragments are bilinearly interpolated to all adjacent predetermined processing surfaces, following the chromaticity trade-off principle, significantly improving the reliability of the gradient processing chromaticity layout.

[0054] If a gradient chromaticity value cannot be found in the periodic chromaticity lookup table, it indicates that the structural pattern of the sub-plate has begun to deviate from the periodic chromaticity range, indicating that the structural pattern has begun to undergo a gradient phenomenon. In this case, the sub-plate block corresponding to the gradient chromaticity value is calibrated as a gradient plate block. This method can be used to perform reasonable chromaticity rendering for simulated non-periodic micro-nanostructure samples, visualize the gradient effect of the structural pattern of non-periodic micro-nanostructures, improve the starting and ending traceability accuracy of non-periodic gradient phenomena, ensure more sensitive and accurate real-time optimization of the dynamic phase during laser interferometry processing, and significantly improve the clarity and uniformity of laser processing of non-periodic consistent micro-nanostructure patterns.

[0055] Example 2:

[0056] like Figure 7 As shown, the present invention provides a laser interference processing optimization system for improving the performance of micro-nano structures. The system is used to implement the laser interference processing optimization method for improving the performance of micro-nano structures of the first embodiment, specifically comprising: The parameter sensitivity analysis module 100 is used to extract the current structural processing parameters of the micro-nanostructure sample, construct a sensitive tracking model based on the coupling probability distribution mechanism of how pattern morphology changes affect the overall performance of the micro-nanostructure, and use the sensitive tracking model to change the current structural processing parameters along different directions and perform sensitivity improvement analysis to obtain an improved-retained processing parameter set; The structural cycle analysis module 200 is used to simulate and model a processing lift dynamic model diagram of a micro-nanostructure sample based on the lift-and-retain processing parameter set, and analyze the cycle consistency of the processing lift dynamic model diagram with the reference micro-nanostructure design diagram through foreground and background maximum flow cutting to obtain a structural cycle analysis result; The periodic structure optimization module 300 is configured to, if the structural period analysis result indicates a periodic structure, perform sinusoidal projection of the actual local lithography model of the processed micro-nanostructure sample onto a class of local lithography model images according to a uniform periodic structure pattern and perform curve slope analysis to optimize the periodic interference angle and beam wavelength of the laser interference processing. The non-periodic structure optimization module 400 is used to perform chromaticity interpolation for the second type of local lithography model diagram in accordance with the chromaticity trade-off criterion of laser processing phase interference if the structural period analysis result shows a non-periodic structure, obtain a gradient processing chromaticity layout, and optimize the dynamic phase of the laser interference processing non-periodic micro-nano structure in real time by analyzing the gradient processing chromaticity layout.

[0057] A laser interference processing optimization system for improving the performance of micro-nano structures in this embodiment is used to implement the aforementioned laser interference processing optimization method for improving the performance of micro-nano structures. Therefore, the specific implementation methods of the laser interference processing optimization system for improving the performance of micro-nano structures can be seen in the embodiment part of the laser interference processing optimization method for improving the performance of micro-nano structures in the previous text. For example, the parameter sensitivity analysis module 100, the structural period analysis module 200, the periodic structure optimization module 300, and the non-periodic structure optimization module 400 are respectively used to implement steps S1, S2, S3, and S4 in the aforementioned laser interference processing optimization method for improving the performance of micro-nano structures. Therefore, its specific implementation methods can refer to the descriptions of the corresponding embodiments of each part. In order to avoid redundancy, they will not be repeated here.

[0058] Example 3:

[0059] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the above-mentioned laser interference processing optimization method for improving the performance of micro-nano structures.

[0060] Example 4:

[0061] An embodiment of the present invention provides a computer storage medium storing a computer software product. The computer software product includes several instructions for enabling a computer device to execute the above-mentioned laser interference processing optimization method for improving the performance of micro-nano structures.

[0062] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A laser interference processing optimization method for improving the performance of micro-nano structures, characterized in that: include: The current structural processing parameters of the micro-nanostructure samples are extracted, and a sensitive tracking model is constructed based on the coupling probability distribution mechanism of how pattern morphology changes affect the overall performance of the micro-nanostructure. The current structural processing parameters are changed in different directions using the sensitive tracking model and sensitivity improvement analysis is performed to obtain a set of improved and retained processing parameters. Simulating and modeling a processing lift dynamic model diagram of the micro-nanostructure sample according to the lift-retain processing parameter set, and analyzing the cycle consistency of the processing lift dynamic model diagram with respect to the benchmark micro-nanostructure design diagram through foreground and background maximum flow cutting to obtain a structural cycle analysis result; If the structural period analysis result shows a periodic structure, the actual local lithography model diagram of the processed micro-nanostructure sample is sinusoidally projected onto a type of local lithography model diagram according to the uniform periodic structure pattern and the slope analysis of the curve is performed to optimize the periodic interference angle and beam wavelength of the laser interference processing; If the structural period analysis result shows a non-periodic structure, chromaticity interpolation is performed for the second type of local lithography model diagram in accordance with the chromaticity trade-off criterion of laser processing phase interference to obtain a gradient processing chromaticity layout, and the dynamic phase of the laser interference processing non-periodic micro-nanostructure is optimized in real time by analyzing the gradient processing chromaticity layout.

2. The laser interference processing optimization method for improving micro-nanostructure performance according to claim 1, characterized in that: The process of extracting the current structural processing parameters of the micro-nanostructure sample, constructing a sensitive tracking model based on the coupling probability distribution mechanism of how pattern morphology changes affect the overall performance of the micro-nanostructure, and using the sensitive tracking model to change the current structural processing parameters along different directions and perform sensitivity improvement analysis to obtain an improved-retained processing parameter set includes: Obtain the application scenarios of micro-nano structures and the design and processing requirements that meet the application scenarios, extract one or more micro-nano structure samples currently produced on the laser interference processing line, and extract several current structural processing parameters of each micro-nano structure sample through the processing log; Acquire the performance indicators of the micro-nanostructure according to the application scenario, preset the sensitivity index threshold of the performance indicator under the most desired structural performance state according to the design and processing requirements, and acquire the micro-nanostructure knowledge graph based on big data; The micro-nanostructure knowledge graph is used to identify multiple performance indicators and several current structural processing parameters, output the coupling probability distribution mechanism of the pattern morphology change of the micro-nanostructure and the overall performance of the micro-nanostructure, and construct a sensitive tracking model for the impact of micro-nanostructure changes on micro-nanostructure performance based on the coupling probability distribution mechanism; Based on the sensitive tracking model, a plurality of sensitive tracking starting points for each performance indicator are randomly constructed. Starting from each sensitive tracking starting point, the value of the current structural processing parameter is changed in only one direction in the sensitive tracking model to generate a sensitive tracking trajectory. The above parameter value changing steps are repeated until each current structural processing parameter is changed, thereby obtaining a plurality of sensitive tracking trajectories. Introducing a sensitive element effect method to calculate the absolute mean and standard deviation corresponding to the element effect of each current structural processing parameter on each of the sensitive tracking trajectories, and determining the suspected sensitivity index of the linear impact of each current structural processing parameter on the performance index based on the absolute mean and standard deviation; If the suspected sensitivity index is less than the sensitivity index threshold and the suspected sensitivity index is a negative value, the current structural processing parameter corresponding to the suspected sensitivity index is retained and marked as a retained processing parameter; If the suspicious sensitivity index is greater than the sensitivity index threshold and the suspicious sensitivity index is positive, the current structural processing parameters corresponding to the suspicious sensitivity index are improved based on the design processing requirements and marked as improved processing parameters to obtain an improved-retained processing parameter set.

3. The laser interference processing optimization method for improving micro-nanostructure performance according to claim 1, characterized in that: The process of simulating and modeling a processing improvement dynamic model diagram of the micro-nanostructure sample according to the improvement-preservation processing parameter set, analyzing the periodic consistency of the processing improvement dynamic model diagram with respect to the benchmark micro-nanostructure design diagram through foreground and background maximum flow cutting, and obtaining a structural periodicity analysis result includes: Performing dynamic simulation reconstruction modeling on the micro-nanostructure sample in 3D model simulation software according to the lifting-retention processing parameter set to obtain a processing lifting dynamic model diagram of the micro-nanostructure sample; Extracting a benchmark micro-nanostructure design drawing and a processing decision of a laser interference lithography device for the benchmark micro-nanostructure design drawing through design processing requirements, treating each graph parameter in the processing improvement dynamic model graph as a dynamic graph construction node, and adding lithography source points and lithography sink points for constructing and processing the benchmark micro-nanostructure design drawing based on the processing decision; Constructing terminal edges and adjacent edges, establishing a periodic graph model of the processing improvement dynamic model graph benchmarked against the benchmark micro-nanostructure design graph based on the dynamic graph construction node, the lithography source point, the lithography sink point, the terminal edges, and the adjacent edges, solving the periodic graph model by maximum flow to obtain a foreground periodic set segment and a background periodic set segment; Counting the similarity sequences of the foreground periodic set segments and the background periodic set segments to obtain a periodic consistency index of a processing improvement dynamic model diagram benchmarked against a reference micro-nanostructure design diagram; If the periodic consistency index is greater than the preset index threshold, the micro-nanostructure sample of the processing improvement dynamic model diagram is calibrated as a periodic structure; if the periodic consistency index is less than the preset index threshold, the micro-nanostructure sample of the processing improvement dynamic model diagram is calibrated as a non-periodic structure.

4. The laser interference processing optimization method for improving micro-nanostructure performance according to claim 3, characterized in that: The process of constructing terminal edges and adjacent edges, establishing the processing improvement dynamic model graph based on the dynamic graph construction node, the lithography source point, the lithography sink point, the terminal edges and the adjacent edges, and benchmarking the periodic graph model of the benchmark micro-nanostructure design graph processing, solving the periodic graph model by maximum flow, and obtaining the foreground periodic set segment and the background periodic set segment includes: Calculating the structural difference of the dynamic graph nodes between the processing improvement dynamic model graph and the benchmark micro-nanostructure design graph to obtain a graph dislocation function, connecting each dynamic graph node to a lithography source point and a lithography sink point to form N terminal edges, and calculating the allocation cost based on the graph dislocation function to obtain a terminal weight of each terminal edge; Connecting edges between adjacent dynamic graph nodes to form M adjacent edges, simultaneously calculating the cost of not being split between adjacent dynamic graph nodes based on the graph dislocation function, obtaining an adjacent weight of each adjacent edge, and combining the dynamic graph nodes, the lithography source point, the lithography sink point, the terminal edge, and the adjacent edge to establish a periodic graph model of the processing improvement dynamic model graph benchmarked against the benchmark micro-nanostructure design graph processing; A maximum flow algorithm is introduced to calculate the maximum flow of each dynamic graph node from the lithography source point to the lithography sink point on the periodic graph model, and a period set of the periodic graph model belonging to the lithography source point is generated, which is defined as a foreground period set segment, and a period set of the periodic graph model belonging to the lithography sink point is generated, which is defined as a background period set segment.

5. The laser interference processing optimization method for improving micro-nanostructure performance according to claim 1, characterized in that: If the structural period analysis result shows a periodic structure, then the process of sinusoidally projecting the actual local lithography model diagram of the processed micro-nanostructure sample onto a type of local lithography model diagram according to a uniform periodic structure pattern and performing curve slope analysis to optimize the periodic interference angle and beam wavelength of the laser interference processing includes: If the structural period analysis result shows that the micro-nanostructure sample processed after the performance improvement is a periodic structure, a uniform periodic structure pattern of the micro-nanostructure sample is extracted through the processing improvement dynamic model diagram, and a sub-model diagram of the uniform periodic structure pattern is stripped out, which is defined as a type of local lithography model diagram; Retrieving, by means of a processing log, an actual local lithography model image processed by a laser interference lithography device within a preset time period for the micro-nanostructure sample following the uniform periodic structure pattern, and synchronously obtaining an actual interference angle value of the actual local lithography model generated by the laser interference lithography device during processing; Constructing a registration domain, performing model registration on the actual local lithography model image and the type of local lithography model image in the registration domain, obtaining a non-overlapping angle of a non-overlapping model image region between the actual local lithography model image and the type of local lithography model image, and constraining a periodic angle sampling range according to the non-overlapping angle; Dividing the periodic angle sampling range into a number of equally divided subsidiary sampling angles based on the actual interference angle value, rotating the model coordinate system of the actual local lithography model image along a direction perpendicular to the projection line corresponding to each subsidiary sampling angle so that the projection line is perpendicular to the rotated x-axis, and continuously accumulating and summing the graph parameter integrals of the graph nodes during the rotation process to form a one-dimensional periodic interference projection function; Obtaining a one-dimensional periodic interference projection function corresponding to the integral of each subsidiary sampling angle, storing the one-dimensional periodic interference projection function corresponding to the integral of each subsidiary sampling angle in a sinusoidal array unit mode, generating a two-dimensional periodic interference projection function, and drawing a necessary interference sinusoidal projection curve for transforming an actual local lithography model image into a type of local lithography model image based on the two-dimensional periodic interference projection function; The actual interference sinusoidal projection curve processed by the laser interference lithography equipment under the actual interference angle value is obtained, the peak slope deviation between the actual interference sinusoidal projection curve and the necessary interference sinusoidal projection curve is calculated, and the periodic interference angle and beam wavelength of the laser interference processed micro-nano structure are optimized according to the peak slope deviation.

6. The laser interference processing optimization method for improving micro-nanostructure performance according to claim 1, characterized in that: If the structural period analysis result shows a non-periodic structure, chromaticity interpolation is performed for the second type of local lithography model diagram according to the chromaticity trade-off criterion of laser processing phase interference to obtain a gradient processing chromaticity layout, and the dynamic phase of the laser interference processing non-periodic micro-nanostructure is optimized in real time by analyzing the gradient processing chromaticity layout. The process includes: If the structural period analysis result shows that the micro-nanostructure sample processed after the performance improvement is a non-periodic structure, a sub-model diagram of a non-periodic processing of the micro-nanostructure sample is extracted through the processing improvement dynamic model diagram, which is defined as a second-type local lithography model diagram; Obtaining a preset processing strategy of the laser interference lithography equipment for the second type of local lithography model diagram, marking a predetermined processing surface of the laser interference lithography equipment on the second type of local lithography model diagram using the preset processing strategy, and determining a processing end vertex and a simulated normal vector of each processing end vertex based on all adjacent predetermined processing surfaces; Acquiring a chromaticity trade-off criterion for phase interference during laser interference lithography processing using 3D model simulation software through a big data network, calculating the grayscale of each processing termination vertex based on the simulated normal vector and simulated position information when the simulated normal vector is displayed and output in the 3D model simulation software, obtaining multiple pattern grayscale values, and constructing different phase interference-chromaticity interpolation fragments according to the chromaticity trade-off criterion; A bilinear interpolation algorithm is introduced to bilinearly interpolate different phase interference-chromaticity interpolation fragments from each processing end vertex to all adjacent predetermined processing surfaces based on the grayscale value of the pattern structure in the bilinear interpolation algorithm to generate a gradient processing chromaticity layout of the second type of local lithography model graph; Dividing the gradient processing chromaticity layout into a plurality of sub-layout blocks based on the preset processing strategy, obtaining the gradient chromaticity value presented by each sub-layout block and presetting a periodic chromaticity lookup table, and if the periodic chromaticity lookup table cannot find the gradient chromaticity value, then calibrating the sub-layout block corresponding to the gradient chromaticity value as a gradient layout block; The actual phase interference value when the laser interference lithography equipment processes the gradient plate block and the ideal phase interference value of the gradient plate block weighed accordingly according to the chromaticity trade-off criterion are obtained, the phase interference deviation value between the actual phase interference value and the ideal phase interference value is calculated, and the dynamic phase of the non-periodic uniform micro-nanostructure processed by laser interference is optimized in real time according to the phase interference deviation value.

7. A laser interference processing optimization system for improving the performance of micro-nano structures, characterized in that: The system is used to implement the laser interference processing optimization method for improving the performance of micro-nano structures as described in any one of claims 1 to 6, specifically comprising: A parameter sensitivity analysis module is used to extract the current structural processing parameters of micro-nanostructure samples, construct a sensitive tracking model based on the coupling probability distribution mechanism of how pattern morphology changes affect the overall performance of the micro-nanostructure, and use the sensitive tracking model to change the current structural processing parameters in different directions and perform sensitivity improvement analysis to obtain a set of improved and retained processing parameters; a structural cycle analysis module for simulating and modeling a processing lift dynamic model diagram of the micro-nanostructure sample according to the lift-retain processing parameter set, and analyzing the cycle consistency of the processing lift dynamic model diagram with respect to the benchmark micro-nanostructure design diagram by foreground and background maximum flow cutting to obtain a structural cycle analysis result; a periodic structure optimization module configured to, if the structural period analysis result indicates a periodic structure, sinusoidally project the actual local lithography model diagram of the processed micro-nanostructure sample onto a class of local lithography model diagrams according to a uniform periodic structure pattern and perform curve slope analysis to optimize the periodic interference angle and beam wavelength of the laser interference processing; The non-periodic structure optimization module is used to perform chromaticity interpolation for the second type of local lithography model diagram in accordance with the chromaticity trade-off criterion of laser processing phase interference if the structural period analysis result shows a non-periodic structure, obtain a gradient processing chromaticity layout, and optimize the dynamic phase of the laser interference processing non-periodic micro-nano structure in real time by analyzing the gradient processing chromaticity layout.

8. An electronic device, characterized in that: The electronic device includes a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the laser interference processing optimization method for improving the performance of micro-nano structures as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that The computer storage medium stores a computer software product, which includes several instructions for enabling a computer device to execute the laser interference processing optimization method for improving the performance of micro-nano structures as described in any one of claims 1 to 6.

Images