A method and device for predicting quartz glass through-hole etching based on digital twin

Abstract

The present invention discloses a quartz glass through-hole etching prediction method and device based on digital twin, the prediction method comprises: obtaining historical data and real-time data of the quartz glass through-hole etching process; obtaining virtual visualization attributes of the historical data and real-time data of the quartz glass through-hole etching process, and establishing a digital twin model according to the virtual visualization attributes; training according to the digital twin model to obtain a prediction model; simulating and predicting the through-hole etching results according to the prediction model, and iteratively optimizing the prediction model according to the real-time data. The present invention does not need to be optimized through repeated experiments, and can automatically optimize the current experimental parameters based on the results of the currently etched glass through-hole and the data mapped to the digital twin model, which not only saves manpower and financial resources, but also improves experimental efficiency; at the same time, it can monitor in real time and provide quality assurance to ensure the stability and reliability of the etching process.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twin simulation, and in particular to a quartz glass through-hole etching prediction method and device based on digital twin. Background Art

[0002] Compared with the traditional two-dimensional interconnection technology, three-dimensional (3D) interconnection technology has the advantages of lower transmission loss, higher integration density, lower power dissipation and the ability to achieve heterogeneous integration. In the field of three-dimensional interconnection, the traditional through-silicon via (TSV) interposer has various problems, such as high electrical loss and high processing cost. To solve these problems, glass interposers are considered to be a superior alternative to silicon interposers due to their excellent electrical performance and low cost.

[0003] The common processing method for glass through holes on glass adapters is to first modify the quartz glass with an ultrafast laser and then etch it with hydrofluoric acid. However, the etching process is too slow and the morphology of the through hole is difficult to control. Operators are usually required to observe the size of the through hole and record the experimental data to stop etching in time. Obviously, this traditional method is overly dependent on manual labor, and because laboratory chemical reactions are relatively dangerous, it is difficult to predict and monitor the experimental process. At the same time, the control of hydrofluoric acid concentration and the optimization of parameters such as temperature also require repeated experiments, resulting in huge losses of manpower and financial resources.

[0004] Currently, there is a lack of methods to create digital twins based on relevant data from the hydrofluoric acid etching process of quartz glass through holes, train an optimization model, and thus predict and optimize the processing results. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention constructs a physical model of the process of hydrofluoric acid etching a quartz glass through hole, a twin data model, and a connection and interaction relationship between the physical model and the twin data model. Through the combination of physical entity and virtual space, the problem in the prior art that it is impossible to effectively monitor, analyze and predict the experiment of the laser-modified glass through hole etching process is solved, and the efficiency of hydrofluoric acid etching of quartz glass is improved.

[0006] In order to achieve the above object, the present invention provides a quartz glass through-hole etching prediction method based on digital twin, comprising:

[0007] Obtain historical and real-time data of the quartz glass through-hole etching process;

[0008] Acquire virtual visualization attributes of historical data and real-time data of a quartz glass through-hole etching process, and establish a digital twin model according to the virtual visualization attributes;

[0009] Training the digital twin model to obtain a prediction model;

[0010] The through-hole etching result is simulated and predicted according to the prediction model, and the prediction model is iteratively optimized according to the real-time data.

[0011] Furthermore, the virtual visualization attributes of obtaining historical data and real-time data of the quartz glass through-hole etching process include:

[0012] Initializing the object attribute parameters according to the physical properties involved in the process of etching the quartz glass with the etching solution to obtain the initialized object attribute parameters;

[0013] Describing the virtual visualization attributes of the physical attributes according to the initialized object attribute parameters to obtain a physical entity model, and establishing a component library to manage the physical entity model and store the virtual visualization attributes;

[0014] The physical properties directly affect the etching rate and the geometric characteristics of the through hole; the object attribute parameters include the physical and chemical properties of the quartz glass and the etching solution.

[0015] Further, the physical entity model includes a micro-scale model and a macro-scale model;

[0016] The Lennard-Jones potential energy function is used in the micro-scale model to simplify the interaction between adjacent SiO2 in quartz glass into the interaction between two SiO2 molecules.

[0017] Furthermore, building a digital twin model includes:

[0018] Acquire twin data of a quartz glass through-hole etching process according to the component library;

[0019] Performing dynamic unified modeling based on the twin data to obtain a unified ontology model of the quartz glass through-hole etching process data;

[0020] The unified ontology model is subjected to data processing to obtain a digital twin model.

[0021] Furthermore, the prediction model obtained by training the digital twin model includes:

[0022] Preprocessing the real-time data to obtain preprocessed real-time data;

[0023] The digital twin model is trained based on the preprocessed real-time data to obtain a prediction model.

[0024] Further, the prediction model includes a first model obtained using an advanced machine learning algorithm and a second model obtained using a complex time series analysis method;

[0025] Wherein, the first model is used to predict the feasible probability and effect of the current etching reaction;

[0026] The second model is used to predict the time series data of the current etching task and monitor the probability of abnormalities occurring during the etching process.

[0027] Furthermore, the second model introduces the Damköhler number to analyze and predict the chemical reaction ratio and diffusion rate of the etching solution.

[0028] Furthermore, the above method also includes:

[0029] The etching results predicted by the prediction model are sent to the user and the etching process is optimized based on the monitored real-time data.

[0030] The present invention also provides a quartz glass through-hole etching prediction device based on digital twin, comprising:

[0031] A data acquisition module is used to acquire historical data and real-time data of the quartz glass through-hole etching process;

[0032] A model building module, used to obtain virtual visualization attributes of historical data and real-time data of the quartz glass through-hole etching process, and to build a digital twin model according to the virtual visualization attributes;

[0033] A model training module, used to train the digital twin model to obtain a prediction model;

[0034] The prediction optimization module is used to simulate and predict the through-hole etching result according to the prediction model, and iteratively optimize the prediction model according to the real-time data.

[0035] The present invention also provides a device, which includes a processor and a memory, wherein the memory stores instructions, and the instructions can be executed by the processor to perform the above method.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention bypasses the physical simulation modeling process of chemical reactions by creating and training a prediction model, thereby predicting and monitoring the results and processes of through-glass etching. The need for physical simulation modeling is eliminated, and the physical simulation modeling process of traditional chemical reactions is avoided. The digital twin model is based on actual data and can accurately predict and monitor the results and processes of through-glass etching. The application of this prediction model makes the etching process more efficient and convenient.

[0038] 2. The present invention does not require repeated experiments for optimization. It can automatically optimize the current experimental parameters based on the results of the currently etched glass through-holes and the data mapped to the digital twin model, which not only saves manpower and financial resources, but also improves experimental efficiency.

[0039] 3. Based on the data and prediction model mapped to the digital twin model, it can provide the monitoring results of the etching process to meet the quality indicators of the through-hole, including the size, shape, surface quality, etc. This real-time monitoring and quality assurance ensures the stability and reliability of the etching process.

[0040] 4. Based on the etching process monitoring results and through-hole indicators, the present invention can simulate the etching process and estimate the support and expansion conditions required for etching. This resource estimation helps to better plan resource allocation and improve resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A flowchart of a quartz glass through-hole etching prediction method based on digital twins of the present invention is shown;

[0043] Figure 2 A schematic diagram of the process of completing integrated management of data cleaning in the present invention is shown;

[0044] Figure 3 A framework diagram of a quartz glass through-hole etching prediction method based on digital twinning of the present invention is shown;

[0045] Figure 4 A schematic diagram of a quartz glass through-hole etching prediction device based on digital twin of the present invention is shown;

[0046] Figure 5 A schematic diagram of an electronic device of the present invention is shown;

[0047] Figure 6 A schematic diagram of a computer readable medium of the present invention is shown. DETAILED DESCRIPTION

[0048] It should be noted that digital twin technology is an advanced engineering method that combines physical models, real-time sensor data and historical data to build a highly accurate digital copy of the actual object. This digital model can continuously and dynamically reflect, analyze and predict the operating conditions of the actual object throughout its life cycle. This technology mainly consists of three key elements: physical space, virtual space and the close connection between the two. In digital twin technology, the physical space represents real entities, such as machines, equipment or buildings. The virtual space contains every detail and characteristic of the physical space and is a highly refined digital image. The connection between the two refers to the transmission of data and the feedback of information. Specifically, the data collected by the sensor in real time in the physical space is input into the virtual space, and the virtual space can also send information to the physical space to realize real-time monitoring and adjustment of the actual object.

[0049] At present, digital twin technology cannot be applied to the processing of through-holes in quartz glass modified by hydrofluoric acid etching ultrafast laser. The main reasons are:

[0050] 1. Dependence on manual operation and lack of data collection function: In the process of hydrofluoric acid etching of quartz glass through holes, many operations still rely on manual intervention. If the etching device does not have a data collection function, it cannot provide the real-time data required for the digital twin model. The core of digital twin technology is to collect, transmit and analyze data in real time. If this data cannot be automatically obtained, it is difficult to build an accurate model.

[0051] 2. Uncertainty and dynamic changes in chemical reactions: The chemical reaction rate between hydrofluoric acid and quartz glass is not fixed, but is affected by many factors, such as temperature, hydrofluoric acid concentration, etc. These parameters are constantly changing during the reaction, making it very difficult to establish a stable and reliable prediction model. Due to the dynamic nature of the reaction process, traditional static models cannot accurately capture the actual situation, so digital twin models cannot accurately predict this dynamic process.

[0052] 3. Multiple factors affecting the etching process and difficulty in modeling: The success of etching and the quality indicators of through-holes are usually not only determined by equipment and operation, but also affected by multiple factors, such as material properties, environmental conditions, etc. These factors are difficult to accurately simulate through physical simulation, and even using simulation software such as COMSOL, it is difficult to take into account all possible chemical changes. This complexity makes it impossible to accurately describe the etching process through traditional simulation and prediction methods, and also affects the application of digital twin technology.

[0053] Based on this, the present invention provides a quartz glass through-hole etching prediction method based on digital twin, so as to realize effective data monitoring, analysis and prediction of the laser-modified glass through-hole etching process, and obtain a glass through-hole that meets user needs.

[0054] The following will be combined with the specific embodiments of the present invention and the drawings of the specification to clearly and completely describe the technical solutions in 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] like Figure 1 , a quartz glass through-hole etching prediction method based on digital twin, wherein the etching solution is hydrofluoric acid, comprises the following steps,

[0057] S101. Acquire historical data and real-time data of a quartz glass through-hole etching process.

[0058] Specifically, it is necessary to set up detection tools including temperature sensors, hydrofluoric acid concentration sensors, etching rate sensors and surface morphology scanners in the etching equipment. Among them, the temperature sensor is used to monitor the temperature change during the etching process, the hydrofluoric acid concentration sensor is used to monitor the concentration of hydrofluoric acid in real time; the etching rate sensor is used to measure the etching rate; and the surface morphology scanner is used to obtain the morphology information of the through hole. These detection tools can not only collect real-time data, but also record historical data, including all parameter information in the previous etching process. These data include a variety of information such as hydrofluoric acid concentration, temperature, etching rate, through-hole morphology, etc., while the historical data contains the changing trend of the past etching process, and the real-time data represents the instantaneous state of the current etching process.

[0059] S102: Acquire virtual visualization attributes of historical data and real-time data of a quartz glass through-hole etching process, and establish a digital twin model according to the virtual visualization attributes.

[0060] Get virtual visualization of historical and real-time data of the quartz glass through-hole etching process, including:

[0061] S1021. Initialize object attribute parameters according to physical properties involved in a process in which an etching solution etches quartz glass, and obtain initialized object attribute parameters.

[0062] Among them, the physical properties directly affect the etching rate and the geometric characteristics of the through hole. For example, they include the initial temperature of the etching device, the initial concentration of hydrofluoric acid, the size of the through hole after laser modification, the roughness and verticality of the hole. The selection of the initial temperature and the concentration of hydrofluoric acid directly affects the etching rate and the quality of the through hole; the size, roughness and verticality of the through hole after laser modification determine the geometric characteristics and surface quality of the through hole; the initial values ​​of these physical properties can also be determined according to the through hole index and the specification requirements of the etched glass through hole. The object attribute parameters include the physical and chemical properties of quartz glass and etching solution, including the density, hardness, melting point of quartz glass, the material of the etching device, structural parameters, the diameter and depth of the through hole; the chemical composition, thermal conductivity coefficient, thermal expansion coefficient, etc. of quartz glass.

[0063] Physical entity models include micro-scale models and macro-scale models.

[0064] In the macroscale modeling of quartz glass through-hole etching, the overall etching process is considered to provide a more complete understanding of the behavior of quartz glass:

[0065] Overall Behavior Considerations: The macroscale model focuses on the overall physical and chemical behavior of the quartz glass via etching process. This includes changes in the quartz glass surface during etching, material removal, and the overall effects of via formation.

[0066] SiO2 particle covalent bond destruction: The model takes into account the covalent bond destruction between SiO2 particles during hydrofluoric acid etching in detail. This is because during the etching process, hydrofluoric acid will cause the covalent bonds of SiO2 particles to break, thus affecting the structure and stability of quartz glass.

[0067] Material removal effects: The macroscale model also takes into account the material removal effects caused by etching. This includes the effect of hydrofluoric acid on the quartz glass surface and the effect of the removed material on the overall via structure.

[0068] Simulation of complexity: Due to the complexity of the hydrofluoric acid etching process, the model achieves accurate simulation of the entire macro-scale etching process by considering factors such as covalent bond destruction and material removal.

[0069] By deeply analyzing various aspects of the etching process at a macroscopic scale, a more comprehensive and accurate method is provided for predicting quartz glass through-hole etching.

[0070] When modeling quartz glass at the microscale, the complexity of covalent bonds leads to high computational costs for accurate calculations. Therefore, a simplified interaction potential energy function is used, with the following steps:

[0071] Microscale modeling is used to describe the interaction forces between quartz glass atoms through physical equations. The atoms in quartz glass mainly interact with each other through covalent bonds. The covalent bond interaction forces are very complex, so it is necessary to use a simplified potential energy function. During the hydrofluoric acid etching process, the covalent bonds between SiO2 particles are destroyed, which further increases the complexity of the simulation.

[0072] Adopting Lennard-Jones (LJ) potential energy function: The Lennard-Jones (LJ) potential energy function is selected to construct the potential energy function of the interaction between SiO2 particles. The LJ potential energy function is a model commonly used to describe the interaction between molecules, which has a simple and effective form. The LJ potential energy function includes two parts, attraction and repulsion, which can well approximate the interaction between atoms.

[0073] SiO2 particles have complex interactions: SiO2 is a complex structure composed of oxygen atoms and silicon atoms, which includes multiple interactions such as oxygen-oxygen, oxygen-silicon, and silicon-silicon. This complexity leads to increased computational cost of particle simulation, and detailed modeling of these interactions is required. The atoms in quartz glass mainly interact through covalent bonds. The covalent bond interaction force is very complex, so it is necessary to use a simplified potential energy function. During the hydrofluoric acid etching process, the covalent bonds between SiO2 particles are broken, which further increases the complexity of the simulation.

[0074] Therefore, this embodiment simplifies the entire SiO2 into a single particle: In order to reduce the computational cost, the complex structure of SiO2 is simplified and the entire SiO2 is regarded as a single particle. The simplified interaction potential function between SiO2 particles only considers the interaction between this single particle and other particles, which greatly reduces the computational complexity.

[0075] The interaction potential between SiO2 particles, specifically the interaction potential between two SiO2 particles, is designated as LJ potential and can be expressed as:

[0076] U(r ij )=4ε[(σ / r ij ) 12 -(σ / r ij ) 6 ],

[0077] Where: r ij is the distance between two SiO2 particles; σ is the distance between two molecules when the potential energy (PE) is zero; ε is the absolute value of the minimum potential energy. In order to establish the SiO2 particle interaction potential function, the molecular mass m, σ and ε mentioned in the LJ potential need to be specified. These parameters can be determined based on some of the macroscopic properties of the material (density ρ = 2.2 g / cm 3, melting temperature Tm=1938K and elastic constant C11=76.6GPa).

[0078] There is an exact relationship between m and σ, namely m = ρσ 3 , we only need to determine σ and ε to determine the LJ potential between SiO2 particles.

[0079] Establish a molecular dynamics model: Use molecular dynamics methods to model and simulate the movement and interaction between SiO2 particles. By simulating the behavior of quartz glass by molecular dynamics, its structure and properties can be revealed at the microscopic level. By simulating the microscopic heating process of SiO2 particles and the process of applying strain to the lattice after relaxation, the microscopic parameters are linked to the melting temperature Tm and elastic constant C11 of quartz glass, and the values ​​of m, ε and σ parameters in the LJ potential function of SiO2 particles are determined, and the interaction potential between SiO2 particles can be determined.

[0080] By simulating the process of microscopic heating and relaxation of SiO2 particles and applying strain to the lattice, the parameters are associated with the macroscopic properties of quartz glass. In the simulation, the σ and ε parameters in the LJ potential function of SiO2 particles are adjusted, and the melting point and elastic constant of quartz glass are calculated for different parameters, so that the simulated macroscopic properties are close enough to the experimental results of quartz glass, so as to determine the parameter values ​​in the LJ potential function.

[0081] Through the above steps, a digital twin model that can effectively simulate the microscopic behavior of quartz glass was established, and appropriate simplification was adopted in the process. This method of simplifying the whole into a single particle greatly shortened the calculation time of a single simulation without affecting the accuracy of the simulation, so as to balance the accuracy and computational efficiency of the model.

[0082] S1022: obtaining a physical entity model by describing the virtual visualization attributes of the physical attributes according to the initialized object attribute parameters, and establishing a component library to manage the physical entity model and store the virtual visualization attributes.

[0083] A component library is established to manage these physical entity models and store the virtual visualization attributes. The physical entity model includes the structure of the etching device, the chemical reaction simulation involved in the through-hole process, the morphology of the through-hole after laser modification and other elements. The object attribute parameters just initialized are abstracted into virtual entities, and these entities are given specific visualization features; for example, quartz glass may be abstracted as a three-dimensional entity with a specific shape and size; the attributes of this virtual entity will be the basis for subsequent simulation and analysis, and the establishment of the component library ensures the unified management and utilization of these virtual entities, which is convenient for use in the construction of digital twin models.

[0084] Building a digital twin model includes:

[0085] S1023. Acquire twin data of the quartz glass through-hole etching process according to the component library.

[0086] S1024. Perform dynamic unified modeling based on the twin data to obtain a unified ontology model of the quartz glass through-hole etching process data.

[0087] Specifically, the dynamic unified modeling process is as follows: adopting the unified modeling technology of quartz glass through-hole processing process data based on ontology, defining the standard concept of processing process status data, using RDP formal system overview, XML formal syntax analysis and RDF formal semantic analysis, establishing the attribute association between data information, describing the constraint relationship of concept attributes, and establishing the unified ontology model of quartz glass through-hole processing process data including quartz glass material ontology, processing subsystem ontology and processing process ontology.

[0088] S1025. Perform data processing on the unified ontology model to obtain a digital twin model.

[0089] Specifically, after the unified ontology model is built, Figure 2 As shown, integrated management is completed by data cleaning through data filling, data denoising, and data de-redundancy, and the glass through-hole etching process data is integratedly managed to obtain a digital twin model.

[0090] Among them, data cleaning includes: first, processing all data locations that need to be interpolated, completing the first round of interpolation operations, and obtaining a new and more complete data set. Then, the interpolation process continues until the number of iterations reaches the preset end condition, which is usually set between 5 and 20 rounds; if the results of the current round of interpolation are no different from those of the previous round, the iteration will end in advance, and the final interpolation result will be output after the end. The processing data is denoised using a clustering algorithm based on K-means. At the same time, by using a file similarity comparison algorithm, those duplicate and redundant file data are removed to maintain data accuracy and consistency. Finally, the integrated managed processing data will be stored in a MySQL or Oracle database for further analysis and application. Through this step, a complete, accurate, and clear data set is obtained, which is the basis for establishing a predictive model. It not only provides detailed information about the real-time etching process, but also has been finely processed to provide reliable data support for model training and optimization. This fine data processing process is the key to ensuring the accuracy of the digital twin model and the basis for ensuring the accuracy of the experimental results.

[0091] S103: Train the digital twin model to obtain a prediction model.

[0092] The real-time collected data is prepared through appropriate preprocessing methods to ensure the quality and consistency of the data. Then, the preprocessed data is mapped to the digital twin model. After the data preprocessing is completed, the digital twin model is trained to obtain the prediction model. The data mapping and preprocessing process includes data normalization, denoising, and feature extraction preprocessing steps to ensure that the data input to the model is of high quality and interpretability.

[0093] The prediction model includes a first model obtained using an advanced machine learning algorithm and a second model obtained using a complex timing analysis method; wherein the first model is used to predict the feasible probability and effect of the current etching reaction, that is, to determine whether the reaction in the etching process is in line with expectations and whether it will cause unexpected situations; the second model is used to predict the time series data of the current etching task and monitor the probability of abnormalities occurring during the etching process, which helps to predict operational abnormalities during the etching process, such as problems such as uneven etching caused by a rapid increase in equipment temperature.

[0094] Specifically, the real-time data collected during the hydrofluoric acid etching process of quartz glass through-holes is mapped to the digital twin model to form a reactive data set. This data set includes various reactive parameters, such as etching rate, hydrofluoric acid concentration, and through-hole morphology. The data mapped to the digital twin model is converted into time series data during the etching process of quartz glass through-holes. This time series data covers the changes in operating parameters of various etching processes, such as etching time, laser power, and etching depth.

[0095] Specifically, a first model is created, which uses advanced machine learning algorithms, such as deep neural networks, to train based on reactive data mapped to the digital twin model. The model has the ability to learn and optimize, and can predict the feasible probability and effect of the current reaction. By monitoring the output of the first model, the state of the reaction process can be understood in real time, and the etching parameters can be adjusted in time to maintain the stability and consistency of the etching process.

[0096] Specifically, a second model is created that focuses on the prediction of time series data and the monitoring of abnormal probability. The second model uses complex time series analysis techniques, such as long short-term memory networks (LSTM), and is trained based on time series data mapped to the digital twin model. The model is able to predict the time series data of the current etching task and monitor the probability of abnormalities in real-time tasks. By monitoring the output of the second model, any abnormal situation can be quickly identified and responded to to avoid errors and waste of resources in the etching process.

[0097] After the first model and the second model are established, the data mapped to the specific concepts related to the hydrofluoric acid etching of quartz glass through holes, that is, the distribution of historical execution results, can be used to predict the execution results of the current etching task. By analyzing the historical data, the typical execution results of the etching task under similar conditions can be understood, and the possible results of the current task can be predicted using this information.

[0098] In some embodiments, the second model introduces the Damköhler number to analyze and predict the chemical reaction ratio and diffusion rate of the etching solution. The Damköhler number Da is expressed as:

[0099]

[0100] Where k is the kinetic constant; C is the concentration of hydrofluoric acid; L is the diffusion length; n is the reaction order; and D is the Knudsend diffusion coefficient. When Da<1 and L<500μm, the reaction belongs to a chemical mechanism; when Da>1 and L>500μm, the reaction belongs to a diffusion mechanism. When etching is controlled by diffusion, such as in the deep microchannels of glass through-holes, an increase in hydrofluoric acid pressure will lead to a decrease in the diffusion rate, thereby resulting in a decrease in the reaction ratio. The relationship between its diffusion coefficient and pressure is expressed as,

[0101]

[0102] Wherein, λ represents the mean free path of the gas under etching conditions; R is the gas constant; Na is the Avogadro number; T is the temperature; P is the pressure; and σ is the molecular diameter.

[0103]

[0104] Where: M is the molar mass (≈104 g / mol); r is the radius of the glass through hole.

[0105] The Damköller number is a key performance indicator that reflects the relationship between the chemical reaction ratio and diffusion rate of hydrofluoric acid etching quartz glass. Based on the value of the Damköller number, it can be determined whether the reaction belongs to a chemical mechanism or a diffusion mechanism.

[0106] In the second model, the chemical reaction and diffusion mechanism of hydrofluoric acid etching of quartz glass are simulated. The model simulates the influence of diffusion control and chemical reaction in the etching process, as well as the influence of these influences on the Damköller number. To simulate the optimization of the etching process: Use the second model to optimize the etching process, and monitor the changes of the Damköller number in real time by adjusting the input parameters. According to the changes in the Damköller number, adjust the diffusion mechanism and etching mechanism, aspect ratio, pressure, temperature and other physical parameters in the etching process.

[0107] In some embodiments, after training the first model and the second model, a verification step is performed to ensure the accuracy of the model output. Specifically, the output results of the first model and the second model are compared with the actual experimental execution results, and the model is run in shadow mode: that is, the model is run while the experiment is being conducted, but the output content of the model is not visible to the experimenter. In this way, it is evaluated whether the prediction of the model is consistent with the actual situation.

[0108] In some embodiments, during the verification process, it was found that there was a certain variance in the predictions of a few individual experiments, that is, the success rate of the model prediction was high, but the actual results were not as expected. However, in large-scale experiments, it was found that the prediction success rate of the model and the accuracy of various abnormal situations were very consistent with the actual execution results. This verification result strengthens the reliability and effectiveness of the ultrafast laser-assisted hydrofluoric acid etching method for processing quartz glass through holes developed based on digital twin technology.

[0109] Through the above steps, the first model and the second model were successfully established to accurately predict and monitor the quartz glass through-hole etching, which also completed Figure 3 The creation process of AI models in the paper. The application of these models makes the method more intelligent, efficient and reliable in the process of hydrofluoric acid etching of quartz glass through holes.

[0110] S104, simulating and predicting through-hole etching results according to the prediction model, and iteratively optimizing the prediction model according to the real-time data.

[0111] like Figure 3 As shown in the figure, while the hydrofluoric acid etching of quartz glass through-holes is in progress, the real-time collected data is mapped to the digital twin model through the acquisition layer, data layer and theoretical model, realizing real-time monitoring of the status of the hydrofluoric acid etching of quartz glass through-holes process. In the model layer, the AI ​​model is used to predict the ongoing etching process and evaluate the probability of various abnormal events.

[0112] In some embodiments, the result of the current hydrofluoric acid etching process of quartz glass and the parameters mapped to the experimental operation are determined, the parameters of the etching process are adjusted according to the preset reaction parameters and the process optimization algorithm, and the adjusted execution result of etching the quartz glass through hole is obtained;

[0113] In some implementations, determining the data mapped to the etching process by the current result, and adjusting and optimizing the etching-related parameters according to the analysis results of the current experimental data;

[0114] In some embodiments, etching experiments are performed using parameters obtained by applying digital twin optimization in an etching device, and the etching process is monitored in real time.

[0115] In some embodiments, when adjusting the parameters based on the preset reaction parameters and the process optimization algorithm, the actual results of the current hydrofluoric acid etching quartz glass process are first determined, and these results are mapped to the parameters of the experimental operation. According to the preset reaction parameters and the process optimization algorithm, the parameters of the etching process are adjusted. These parameters may include but are not limited to hydrofluoric acid concentration, temperature, etching rate, etc. By optimizing these parameters, the etching process can be made more efficient and stable, ensuring the quality and accuracy of the through hole.

[0116] In some embodiments, parameter adjustment and optimization based on data analysis results can also be more refined and optimized based on the data mapped to the etching process according to the current results. By analyzing the current data, it is possible to understand the specific changes and characteristics in the etching process. According to these analysis results, the parameters involved in etching can be adjusted and optimized. The fine-tuning of these parameters may include laser power, etching time, etching depth, etc. Through this refined adjustment, it is possible to better adapt to changes in the etching process and ensure the morphology and surface quality of the through hole.

[0117] In some embodiments, during the etching process, the causal relationship between the results of the current through-holes obtained by etching and the data mapped to the digital twin model will be analyzed. Through this analysis, the relationship between the etching parameters and the etching results can be understood, and the key factors affecting the etching quality can be found. Based on these causal relationships, the first adjustment can be made to optimize the etching parameters. This optimization process may include adjusting the etching parameters or optimizing the settings of the etching device. Such adjustments are based on in-depth analysis of experimental data and feedback from the digital twin model, ensuring the accuracy and stability of the experimental process.

[0118] In some embodiments, during the etching process, the results of multiple executions of changing one or more parameters under the same etching conditions are also compared. This comparative analysis can help determine the degree of influence of each parameter on the etching results. Based on these analyses, a second adjustment can be made to optimize the setting of the etching parameters. The adjustment at this stage is more refined, and it takes into account the interaction between different parameters and their combined impact on the etching results. Through this multiple iterative adjustment, the optimal combination of etching parameters can be found, making the etching process more accurate and efficient.

[0119] In some embodiments, Figure 3 As shown, through the data acquisition layer, the results of the current experimental process can be determined and mapped to the parameters of the experimental operation. According to the preset reaction parameters and process optimization algorithm, the parameters of the experimental process and / or experimental operation are adjusted to obtain the adjusted experimental process execution results, thereby achieving the purpose of optimizing the reaction process and reaction conditions.

[0120] In some embodiments, first, based on the training data, according to the predetermined optimization target (such as yield), a surrogate model is trained, which can represent a class of reactions. Then, by setting the acquisition function and maximizing the function, a series of parameter points are selected for experiments. Next, experiments are conducted based on these parameter points, and the experimental results are used to update the surrogate posterior model as a new surrogate model. These steps are repeated until the predetermined optimization target (such as yield) is no longer improved or the resources are exhausted. In this process, the parameter points obtained are the "optimal parameters" after optimization. Further, the experimental operation before adjustment and the experimental operation after adjustment are respectively performed multiple times, and the execution data are analyzed to verify the adjustment efficiency. For example: for an etching parameter, the etching scenario before optimization and the scenario after optimization are respectively executed multiple times, and the improvement efficiency is verified by executing data analysis.

[0121] The step S104 completes Figure 3 Optimization of the application layer.

[0122] S105 , the etching result predicted by the prediction model is sent to the user and the etching process is optimized according to the monitored real-time data.

[0123] The execution time required for the hydrofluoric acid etching of glass through-holes can also be predicted based on the data mapped to the digital twin model to provide support for the etching experiment: the time distribution of historical parameters in the execution of the etching process is collected, and the execution time required under the current etching parameters is predicted based on the time distribution. The monitoring results of the current etching process, the indicators of the current etching task, and the execution time of the current etching process are sent to the user. The prediction results of the abnormal probability of the etching process, the monitoring results of the data mapped to the digital twin model, the indicators and execution time of the etching task are provided to the user in the form of a report to provide support for decision-making. The execution effect before and after the etching process optimization is simulated and displayed to the user, showing the effect of the optimization of the reaction process and the operation process: for example, how much the efficiency can be improved after the operation of changing the hydrofluoric acid concentration; how much the efficiency can be improved if the initial temperature of the etching reaction is increased; receiving the optimization index input by the user, the optimization index may include: the depth-to-width ratio of the through-hole, the surface roughness of the through-hole, and the optimization method. Exemplarily, the optimization object is the hydrofluoric acid concentration control, and the optimization method is to automatically add hydrofluoric acid equipment of different concentrations. The execution effect before and after the optimization is determined based on the automatic realization of hydrofluoric acid concentration control. The etching experiment support conditions input by the user are received, and etching parameters matching the etching experiment support conditions are searched based on different business indicators and execution time.

[0124] The step S105 completes Figure 3 Visual interaction, virtual etching platform and etching experimental device at the middle decision-making layer.

[0125] Example 2

[0126] like Figure 4 As shown, a quartz glass through-hole etching prediction device based on digital twin includes:

[0127] A data acquisition module is used to acquire historical data and real-time data of the quartz glass through-hole etching process;

[0128] A model building module, used to obtain virtual visualization attributes of historical data and real-time data of the quartz glass through-hole etching process, and to build a digital twin model according to the virtual visualization attributes;

[0129] A model training module, used to train the digital twin model to obtain a prediction model;

[0130] A prediction optimization module, used to simulate and predict the through-hole etching result according to the prediction model, and iteratively optimize the prediction model according to the real-time data;

[0131] The user interaction module is used to send the etching results predicted by the prediction model to the user and optimize the etching process according to the monitored real-time data.

[0132] Example 3

[0133] like Figure 5 As shown, an electronic device is presented in the form of a general data processing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different electronic device components (including the storage unit 520 and the processing unit 510), a display unit 540, etc.

[0134] The storage unit 520 stores a computer-readable program, which may be a source program or a code of a read-only program. The program may be executed by the processing unit 510, so that the processing unit 510 performs the steps of various embodiments of the present invention. For example, the processing unit 510 may perform the following steps: Figure 1 Steps shown.

[0135] The storage unit 520 may include a random access storage unit (RAM) 521 and / or a cache storage unit 522, and may further include a read-only storage unit (ROM) 523; the storage unit 520 may also include a program of (at least one) program module 524, such program module 524 includes but is not limited to: operating the electronic device, one or more application programs, other program modules and program data, each of these examples or some combination may include the implementation of a network environment.

[0136] The electronic device 500 may also communicate with one or more external devices 400 (e.g., a keyboard, a display, a network device, a Bluetooth device, etc.) so that a user can interact with the electronic device 500 via these external devices 400, and / or the electronic device 500 can communicate with one or more other data processing devices (e.g., a router, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 550, and may also be performed through a network adapter 560 with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet). The network adapter 560 may communicate with other modules of the electronic device 500 via a bus 530.

[0137] It should be noted that although Figure 5 Not shown, other hardware and / or software modules may be used in the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID electronic devices, tape drives, and data backup storage electronic devices.

[0138] Example 4

[0139] like Figure 6 As shown, a computer readable medium, the computer program can be stored on one or more computer readable media. The computer readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, but is not limited to, an electronic device, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. The readable storage medium includes: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0140] When the computer program is executed by one or more data processing devices, the computer-readable medium is enabled to implement the above-mentioned method of the present invention.

[0141] Through the description of the above implementation modes, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation mode of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a data processing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the present invention.

[0142] Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Python, C++, and Python. ++ The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on the remote computing device or server.

[0143] The combined implementation of the above steps makes the etching experiment no longer a single trial and error process, but an iterative optimization process based on in-depth analysis and digital twin models. This method ensures the stability, efficiency and accuracy of the etching process, and provides reliable technical support for quartz glass through-hole processing.

[0144] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A quartz glass through-hole etching prediction method based on digital twin, characterized in that: include, Obtain historical and real-time data of the quartz glass through-hole etching process; Acquire virtual visualization attributes of historical data and real-time data of a quartz glass through-hole etching process, and establish a digital twin model according to the virtual visualization attributes; Training the digital twin model to obtain a prediction model; Simulating and predicting through-hole etching results according to the prediction model, and iteratively optimizing the prediction model according to the real-time data; The virtual visualization attributes of obtaining historical data and real-time data of the quartz glass through-hole etching process include: Initializing the object attribute parameters according to the physical properties involved in the process of etching the quartz glass with the etching solution to obtain the initialized object attribute parameters; Describing the virtual visualization attributes of the physical attributes according to the initialized object attribute parameters to obtain a physical entity model, and establishing a component library to manage the physical entity model and store the virtual visualization attributes; Wherein, the physical properties directly affect the etching rate and the geometric characteristics of the through hole; the object attribute parameters include the physical and chemical properties of the quartz glass and the etching solution; The physical entity model includes a micro-scale model and a macro-scale model; The Lennard-Jones potential energy function is used in the micro-scale model to simplify the interaction between adjacent SiO2 in quartz glass into the interaction between two SiO2 molecules.

2. The method for predicting quartz glass through-hole etching based on digital twinning according to claim 1, characterized in that: Building a digital twin model includes: Acquire twin data of a quartz glass through-hole etching process according to the component library; Performing dynamic unified modeling based on the twin data to obtain a unified ontology model of the quartz glass through-hole etching process data; The unified ontology model is subjected to data processing to obtain a digital twin model.

3. The method for predicting quartz glass through-hole etching based on digital twinning according to claim 1, characterized in that: The prediction model obtained by training the digital twin model includes: Preprocessing the real-time data to obtain preprocessed real-time data; The digital twin model is trained based on the preprocessed real-time data to obtain a prediction model.

4. The method for predicting quartz glass through-hole etching based on digital twinning according to claim 1, characterized in that: The prediction model includes a first model obtained using an advanced machine learning algorithm and a second model obtained using a complex time series analysis method; Wherein, the first model is used to predict the feasible probability and effect of the current etching reaction; The second model is used to predict the time series data of the current etching task and monitor the probability of abnormalities occurring during the etching process.

5. The method for predicting quartz glass through-hole etching based on digital twinning according to claim 4, characterized in that: The second model introduces the Damköhler number to analyze and predict the chemical reaction ratio and diffusion rate of the etching solution.

6. The method for predicting quartz glass through-hole etching based on digital twinning according to any one of claims 1 to 5, characterized in that: Also includes, The etching results predicted by the prediction model are sent to the user and the etching process is optimized based on the monitored real-time data.

7. A quartz glass through-hole etching prediction device based on digital twinning as claimed in claim 1, characterized in that: include, A data acquisition module is used to acquire historical data and real-time data of the quartz glass through-hole etching process; A model building module, used to obtain virtual visualization attributes of historical data and real-time data of the quartz glass through-hole etching process, and to build a digital twin model according to the virtual visualization attributes; A model training module, used to train the digital twin model to obtain a prediction model; The prediction optimization module is used to simulate and predict the through-hole etching result according to the prediction model, and iteratively optimize the prediction model according to the real-time data.

8. A device, characterized in that: The method comprises a processor and a memory, wherein the memory stores instructions, and the instructions can be executed by the processor to perform the method according to any one of claims 1 to 6.

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