Digital production supervision method and system

Abstract

The application provides a kind of digital production supervision method and system, belong to computer field, solve the problem of crack, residual metal, cleaning not thoroughly etc., often rely on the optical inspection or chemical analysis of quartz piece after cleaning, once find problem, have missed the best correction opportunity, lead to rework rate high, low efficiency problem.The above method includes: in the stage of cleaning quartz processing piece after hot processing, automatically match cleaning formula based on quartz processing piece information, the cleaning formula includes washing liquid ratio and cleaning time-temperature control curve;Real-time acquisition of cleaning liquid state change parameter in cleaning tank, the cleaning liquid state parameter includes conductivity and oxidation-reduction potential;The quality defect of the quartz processing piece is evaluated based on the cleaning liquid state change parameter.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a digital production monitoring method and system. Background Technology

[0002] In high-end manufacturing industries such as semiconductors, LEDs, and photovoltaics, quartz materials are widely used in the manufacture of key components such as reactor liners, process boats, and mask supports due to their excellent thermal stability, chemical inertness, and optical transparency. After undergoing multiple processes such as cold working, thermoforming, and welding, these quartz parts may have residual contaminants such as particles, metal ions, and organic matter on their surfaces. If these contaminants are not thoroughly removed, they will directly affect the stability and yield of the devices in high-cleanliness processes.

[0003] Currently, mainstream cleaning processes employ a multi-stage process involving ultrasonic cleaning, acid washing, and ultrapure water rinsing. However, for issues such as cracks, residual metal, and incomplete cleaning, the process often relies on optical inspection or chemical analysis of the cleaned quartz parts. By the time problems are discovered, the optimal time for correction has already passed, resulting in high rework rates and low efficiency. Summary of the Invention

[0004] This application provides a digital production monitoring method and system that can solve the current problems of cracks, residual metal, and incomplete cleaning, which often rely on optical inspection or chemical analysis of the cleaned quartz parts. Once a problem is discovered, the best time for correction has been missed, resulting in high rework rates and low efficiency.

[0005] The first aspect of this application provides a digital production supervision method, including:

[0006] During the cleaning stage of the quartz parts after heat treatment, a cleaning formula is automatically matched based on the information of the quartz parts. The cleaning formula includes the washing solution ratio and the cleaning time-temperature control curve.

[0007] Real-time acquisition of cleaning fluid state change parameters in the cleaning tank, including conductivity and redox potential;

[0008] The quality defects of the quartz machined parts are evaluated based on the state change parameters of the cleaning fluid.

[0009] Optionally, the assessment of quality defects in the quartz workpiece based on the state change parameters of the cleaning fluid includes:

[0010] The abnormal ion release of the quartz workpiece is evaluated based on the state change parameters of the cleaning fluid.

[0011] The quality defects of the quartz machined parts are assessed based on the abnormal ion release.

[0012] Optionally, it also includes:

[0013] The number and size distribution of particles stripped from the fluid per unit time are recorded in real time, serving as a dynamic response curve for the surface cleanliness of the device.

[0014] The assessment of quality defects in the quartz machined parts based on the state change parameters of the cleaning fluid includes:

[0015] The microparticle release mode was used to analyze whether the quartz part had microcracks caused by edge stress accumulation.

[0016] Optionally, it also includes:

[0017] In the rinsing heating zone, the heat distribution of the quartz workpiece during the heating process is captured by an infrared thermal imager.

[0018] Interference fringe patterns were collected in the rinsing heating zone by irradiation with polarized light.

[0019] The quality defects of the quartz machined part are assessed based on the heat distribution and interference fringe pattern during the heating process.

[0020] Optionally, the assessment of quality defects in the quartz workpiece based on the heat distribution and interference fringe pattern during the heating process includes:

[0021] Determining the heat retention area based on the heat distribution during the heating process of quartz parts;

[0022] Determining the fringe distortion region based on the interference fringe pattern;

[0023] The thermal retention area and stripe distortion area are detected using an image fusion algorithm, and a classifier is used to determine whether they are stress accumulation areas.

[0024] Optional, also includes:

[0025] During the drying stage after cleaning, the wake information of the drying airflow after passing through the quartz workpiece is obtained, and the drying airflow is a constant temperature and pressure airflow.

[0026] The wake flow field is determined based on the wake information in order to evaluate the quality defects of the quartz part according to the wake flow field. The quality defects include the presence of liquid film thickness difference curves and / or surface foreign matter adhesion defects in the quartz part.

[0027] Optionally, the parameters for the change in the state of the cleaning fluid also include pH and surface tension, and the method further includes:

[0028] Based on the real-time acquired parameters of the state change of the cleaning fluid in the cleaning tank, the current level of contamination of the cleaning fluid is assessed.

[0029] The cleaning intensity parameters are adjusted according to the current contamination level of the cleaning fluid. The cleaning intensity parameters include at least one of ultrasonic frequency, cleaning duration, or cleaning temperature.

[0030] A second aspect of this application provides a digital production monitoring system, comprising:

[0031] A matching unit is used to automatically match a cleaning formula based on the information of the quartz workpiece during the cleaning stage after heat treatment. The cleaning formula includes the washing solution ratio and the cleaning time-temperature control curve.

[0032] The acquisition unit is used to acquire the state change parameters of the cleaning fluid in the cleaning tank in real time, including conductivity and redox potential.

[0033] An evaluation unit is used to evaluate the quality defects of the quartz machined part based on the state change parameters of the cleaning fluid.

[0034] A third aspect of this application provides an electronic device, including a memory and a processor, wherein the processor is used to execute a computer program stored in the memory to implement the steps of the above-described digital production monitoring method.

[0035] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the digital production monitoring method described above.

[0036] In summary, the digital production monitoring method provided in this application automatically matches a cleaning formula based on the information of the quartz parts during the cleaning stage after heat treatment. The cleaning formula includes the washing solution ratio and a cleaning time-temperature control curve. Real-time acquisition of cleaning solution state change parameters in the cleaning tank, including conductivity and redox potential, is also performed. The quality defects of the quartz parts are assessed based on these cleaning solution state change parameters. This shifts the focus from post-processing detection to process judgment, significantly reducing rework costs associated with discovering problems only after cleaning. The cleaning intensity is intelligently matched to each batch of products based on its contamination difficulty, avoiding the waste of time and reagents by re-washing lightly contaminated products. Dynamic monitoring of contaminant leaching during the cleaning process allows for timely adjustment of cleaning parameters, improving the thorough removal rate of contaminants. The cleaning solution state change signal serves as an implicit quality indicator, enhancing sensitivity to latent defects such as microcracks and embedded impurities. This not only frees the quartz cleaning process from the traditional passive mode relying on end-of-line detection, achieving feedforward defect identification based on process characteristics, but also establishes a precise matching logic between products and the cleaning process, significantly reducing rework rates and redundant cleaning solution usage. It can improve the yield and stability of quartz components in high-cleanliness processes, while providing traceable and evolving digital monitoring capabilities for the production line, significantly enhancing the intelligence level and controllability of high-end manufacturing processes.

[0037] Correspondingly, the systems, electronic devices, and computer-readable storage media provided in the embodiments of the present invention also have the above-mentioned technical effects. Attached Figure Description

[0038] Figure 1 A flowchart illustrating a possible digital production monitoring method provided in this application embodiment;

[0039] Figure 2 A schematic structural block diagram of a possible digital production monitoring system provided for embodiments of this application;

[0040] Figure 3 A schematic diagram of the hardware structure of a possible digital production monitoring system provided in this application embodiment;

[0041] Figure 4 A schematic structural block diagram of a possible electronic device provided in an embodiment of this application;

[0042] Figure 5 This is a schematic structural block diagram of a possible computer-readable storage medium provided for embodiments of this application. Detailed Implementation

[0043] This application provides a digital production monitoring method and related equipment, which can solve the current problems of cracks, residual metal, and incomplete cleaning, which often rely on optical inspection or chemical analysis of the cleaned quartz parts. Once a problem is discovered, the best time for correction has been missed, resulting in high rework rates and low efficiency.

[0044] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0045] Please see Figure 1 The flowchart of a digital production supervision method provided in this application embodiment may specifically include:

[0046] S110-S130.

[0047] S110, during the cleaning stage of the hot-processed quartz parts, a cleaning formula is automatically matched based on the quartz parts information. The cleaning formula includes the washing solution ratio and the cleaning time-temperature control curve.

[0048] S120, real-time acquisition of cleaning fluid state change parameters in the cleaning tank, the cleaning fluid state parameters including conductivity and redox potential.

[0049] S130, assess the quality defects of the quartz workpiece based on the state change parameters of the cleaning fluid.

[0050] Understandably, after heat treatment (welding, annealing, etc.), different types of contaminants (particles, organic matter, metal ions) may remain on the surface of quartz parts. The cleaning process is essentially a synergistic process controlled by temperature, chemical reaction rate, interfacial diffusion rate, and cleaning solution composition. Different contaminants have specific cleaning windows, defined by the active state of the cleaning solution (e.g., pH, conductivity, ORP). During cleaning, continuously monitoring the conductivity and redox potential (ORP) of the cleaning solution can indirectly reflect the degree of cleaning reaction and the dissolution state of contaminants. For example, conductivity increases when metal ions are dissolved; ORP undergoes a transition when organic matter is oxidized and decomposed. These trends can be modeled and compared with an empirical database to predict the type and degree of contamination in quartz parts in advance. Based on the source process path of the quartz parts (e.g., heat treatment temperature, atmosphere, usage location, etc.), structural characteristics, and historical defect records, the system automatically calls up matching formulas from the cleaning database. These formulas include chemical cleaning solution concentration, cleaning sequence, cleaning time for each stage, and temperature profiles to achieve precise contamination removal. The above method addresses the issues of delayed detection of quality defects and uncontrollable process during the cleaning stage of quartz parts in the high-end manufacturing industry. By introducing dynamic parameter monitoring and formula matching mechanisms during the cleaning process, it enables feedforward judgment of quality defects and digital control of the production process, avoiding the problems of high rework rate and low efficiency caused by the traditional cleaning-then-inspection model.

[0051] For example, after the quartz parts have undergone heat treatment (such as welding, thermoforming, or annealing), the system automatically reads the complete process history information of that batch of quartz parts through the MES platform, including heat treatment temperature, time, and type of environmental atmosphere (e.g., , The system uses parameters such as cleaning agent type (e.g., argon, etc.), historical defect tags, and application location (e.g., mask frame, process boat, reactor substrate). Based on these parameters, the system automatically matches the most suitable cleaning formula by comparing it with a pre-set cleaning process database. The formula includes the type of cleaning agent (e.g., HF, argon, etc.). , The system incorporates various parameters such as concentration ratios, cleaning step sequence, cleaning time settings, and temperature-time curves to ensure that different types of contaminants can be effectively removed within the optimal window.

[0052] For example, during the cleaning operation, the system will acquire the state parameters of the cleaning solution in real time through online sensing modules deployed in the cleaning tank, mainly including conductivity and ORP (reflects oxidizing / reducing capacity of the solution). These parameters can be collected every 30 seconds to form a state curve for the entire cleaning process. Simultaneously, turbidimeters, ultraviolet absorption spectrometers, etc., can be connected as needed as auxiliary means to improve the sensitivity of monitoring organic contaminants and colloidal impurities. The real-time data during this process will be compared and analyzed with a standard state model constructed from historical cleaning data: if the conductivity shows an abnormal increase (such as a large amount of metal ions dissolving), the system can automatically infer potential welding overheating or surface coating residue problems; if the ORP remains below the set lower limit for a long period, it indicates that the oxidant activity is depleted, requiring an increase in oxidant concentration or an extension of this stage; if the curves for both parameters show overall sluggishness, it may suggest the presence of cracks or a dense contamination layer on the device surface hindering the cleaning reaction.

[0053] For example, based on the above real-time judgment, the system will automatically adjust the cleaning process, such as appropriately extending the pickling time, switching to a backup formula, inserting an intermediate rinsing stage, or issuing an alert for subsequent non-destructive testing (such as surface scattering intensity analysis or local XPS testing), to avoid device scrapping due to incomplete cleaning. Furthermore, after the cleaning task is completed, the system will perform correlation analysis between the cleaning fluid state curve and the final quartz part cleanliness test data (such as particle count, surface residual metal concentration analysis, and microcrack detection), and feed the processing results back to the cleaning formula database. Through machine learning algorithms, the system continuously optimizes the multi-dimensional mapping relationship from process path to cleaning response to detection label.

[0054] In summary, the digital production monitoring method provided in the embodiments of this application automatically matches a cleaning formula based on the information of the quartz parts during the cleaning stage after heat treatment. The cleaning formula includes the washing solution ratio and a cleaning time-temperature control curve. Real-time acquisition of cleaning solution state change parameters in the cleaning tank, including conductivity and redox potential, is also performed. The quality defects of the quartz parts are assessed based on these cleaning solution state change parameters. This transforms the process from post-inspection to in-process judgment, significantly reducing rework costs associated with discovering problems only after cleaning. The cleaning intensity is intelligently matched to each batch of products based on its contamination difficulty, avoiding the waste of time and reagents by re-washing lightly contaminated products. Dynamic monitoring of contaminant leaching during the cleaning process allows for timely adjustment of cleaning parameters, improving the thorough removal rate of contaminants. The cleaning solution state change signal serves as an implicit quality indicator, enhancing sensitivity to latent defects such as microcracks and embedded impurities. This not only frees the quartz cleaning process from the traditional passive mode relying on end-of-line detection, achieving feedforward defect identification based on process characteristics, but also constructs a precise matching logic between products and the cleaning process, significantly reducing rework rates and redundant cleaning solution usage. It can improve the yield and stability of quartz components in high-cleanliness processes, while providing traceable and evolving digital monitoring capabilities for the production line, significantly enhancing the intelligence level and controllability of high-end manufacturing processes.

[0055] In some examples, assessing the quality defects of the quartz workpiece based on the parameters of change in the cleaning fluid state includes:

[0056] The abnormal ion release of the quartz workpiece is evaluated based on the state change parameters of the cleaning fluid.

[0057] The quality defects of the quartz machined parts are assessed based on the abnormal ion release.

[0058] For example, during the cleaning process, the system continuously acquires parameters related to the state changes of the cleaning solution, especially conductivity and redox potential (ORP). Conductivity reflects the overall concentration of ions in the cleaning solution, particularly the dynamic changes in the dissolution of metal ions, sodium ions, aluminum ions, and iron ions from the quartz surface; while ORP reflects the concentration of oxidants (such as...) in the cleaning solution. , Changes in reactivity between ) and reducing pollutants (such as organic matter, surface carbon residue, etc.).

[0059] For example, the system compares the collected conductivity time series with the "standard release curve" in the database. The standard curve is a parameter trend recorded during the cleaning of a large number of normal quartz parts, and has obvious segmented characteristics: for example, the conductivity rises slowly in the initial stage, then rises sharply at a certain time point, indicating that surface ions begin to be released in large quantities, and then tends to stabilize. If the current quartz part exhibits the following abnormal ion release during the cleaning process, an early warning will be triggered. For example, an abnormal increase in conductivity in the early stage (e.g., exceeding the historical median value in the first 30 seconds) may indicate residual metal from welding spatter on the surface; a continuous slow and unstable increase in conductivity within a preset time period indicates uneven release of contaminants, possibly with impurities embedded in surface microcracks; and ORP remaining in the low range and fluctuating abnormally indicates that a large amount of oxidant has been consumed but contaminants have not been effectively removed, suggesting heavy organic impurities or abnormal surface tension. After determining the ion release status, the system further combines the ion release rate and peak value amplitude, using rule-based or trained models (such as cluster analysis, SVM classification, etc.) to classify the current device into four quality levels: no obvious defects, excessive surface contamination, potential microcracks, and requiring subsequent physical inspection. For example, rapid release of high concentrations of metal ions is often related to anomalies such as welding overheating or tool cross-contamination in the upstream process; while a slow and continuously increasing ion release rate may be related to microcracks caused by internal doping of the quartz, structural fatigue, or localized stress concentration. Finally, the system can issue instructions based on the ion release assessment results even before cleaning is complete, such as automatically extending the current cleaning cycle, changing the cleaning solution or adding oxidant, inserting optical surface scanning, or prompting for enhanced next inspection processes. This achieves forward-looking control of potential quality problems, avoiding irreversible defects or batch rework. This process can also synchronously update the assessment data to the MES system and process optimization model, forming a closed-loop learning mechanism for the cleaning process path. This method of inferring quality defects based on the state parameters of the cleaning fluid significantly improves the intelligence and predictive control capabilities of the cleaning process, and reduces hidden quality risks in the production of quartz parts.

[0060] In some examples, it also includes:

[0061] The number and size distribution of particles stripped from the fluid per unit time are recorded in real time, serving as a dynamic response curve for the surface cleanliness of the device.

[0062] The assessment of quality defects in the quartz machined parts based on the state change parameters of the cleaning fluid includes:

[0063] The microparticle release mode was used to analyze whether the quartz part had microcracks caused by edge stress accumulation.

[0064] For example, to further enhance the early identification capability of microscopic defects in quartz parts, the system also includes the following steps: real-time recording of the number and size distribution of particles detached from the cleaning fluid per unit time, and using this as a dynamic response curve of the surface cleanliness of the quartz device. Specifically, the system integrates a high-sensitivity particle counting module (such as an online particle monitoring device based on laser scattering or optical obscuration) at the cleaning tank outlet or circulation pipeline, recording the concentration and size distribution of detached particles in the fluid at a frequency of seconds or minutes (e.g., segmented statistics of 0.1–1 μm, 1–5 μm, and 5–10 μm and above), forming a complete particle release time series diagram of the cleaning process. Based on this particle release data, the system further analyzes its change patterns to infer possible microstructural anomalies in the quartz parts. One key discrimination mechanism is: using particle release patterns to analyze whether there are microcracks caused by edge stress accumulation in the quartz parts. This analysis is based on the following principles: Under normal surface release mode, if there are no obvious defects on the quartz surface, the particle release rate may be slightly higher in the early stage of cleaning (removing surface dust and processing residues), then rapidly decreases and tends to stabilize; the particle size is concentrated in the range of less than 1 μm, with a uniform distribution and no sudden increase. Under stress-induced microcrack release mode, if there is edge stress accumulation (such as internal stress not being fully released due to heat treatment or machining), during the cleaning process, under high temperature or chemical action, this area is very likely to generate microcracks that propagate, thereby releasing characteristic particles. This release mode is characterized by a sudden jump in particle concentration at a certain moment (such as after 20 minutes of pickling); an abnormally high proportion of medium-to-large particle size (such as 3–10 μm) in the particle size distribution; a high correlation between the duration of the particle release peak and changes in the state parameters of the cleaning solution (such as an increase in conductivity); and statistical reproducibility of similar curves across multiple different devices, constituting a defect label sample. The system utilizes the aforementioned characteristics and, through pre-trained machine learning models (such as pattern matching based on Dynamic Time Warping (DTW) or LSTM sequence analysis networks), determines in real time whether the current particle release curve conforms to the "stress-induced microcrack" characteristic model. If it does, the system determines that the device may have a microcrack potential. At this point, the system can automatically execute the following measures: prompt for additional high-magnification visual inspection or stress testing (such as polarized light interferometry), mark the batch of products as at risk, or insert a neutralization treatment and secondary rinsing stage in real time to prevent crack expansion. By jointly modeling particle release information with state parameters such as cleaning fluid conductivity and ORP, the system not only achieves a quantifiable judgment of the degree of contamination removal but also makes the invisible defect of crack stress release explicit as a fluid dynamic signal. This greatly improves the feedforward control capability of quartz component cleaning quality supervision and the accuracy of identifying hidden structural anomalies, further ensuring its stability and reliability in high-cleanliness processes.

[0065] In some examples, it also includes:

[0066] In the rinsing heating zone, the heat distribution of the quartz workpiece during the heating process is captured by an infrared thermal imager.

[0067] Interference fringe patterns were collected in the rinsing heating zone by irradiation with polarized light.

[0068] The quality defects of the quartz machined part are assessed based on the heat distribution and interference fringe pattern during the heating process.

[0069] For example, to further enhance the non-destructive assessment capability of the structural integrity and microscopic defects of quartz parts, the following monitoring steps are also included: capturing the thermal distribution spectrum of the quartz part during the heating process in the rinsing heating zone using an infrared thermal imager, and obtaining the interference fringe spectrum through polarized light illumination, and assessing the quality defects of the quartz part based on the combined results of both. This method achieves visualization and quantitative analysis of internal stress, cracks, and material inhomogeneity through dual-channel detection of thermal field response and optical interference.

[0070] For example, in the rinsing and heating phase of the cleaning process (usually a stage where high-purity water or neutralizing solution is used for rinsing after cleaning, while the temperature is gradually increased to 60–90°C), the system is equipped with an infrared thermal imaging device to capture the surface heat distribution of each quartz part. Because quartz has good infrared transmittance, its surface and shallow thermal response can be effectively observed within a reasonable wavelength range (e.g., 8–14 μm). The principle behind this step is that discontinuities in heat conduction and changes in the heat capacity of defect areas will lead to abnormal local temperature rises or hysteresis in thermal conduction. For example, if the quartz part has microcracks, inclusions, or stress concentrations, these areas will exhibit the following during the heating process: dark spots with significantly lower or delayed temperature rises on the thermal distribution map; asymmetrical thermal diffusion characteristics in local edge areas; and a temperature rise curve slope that is significantly different from the normal area. Simultaneously, in the same heating phase, a polarized light source is used to illuminate the quartz part at an oblique incidence, and an interference fringe pattern (such as equal-inclination interference, equal-thickness interference, etc.) is recorded by an interference image receiver placed on the back side. This method is based on the birefringence properties and stress-photoelasticity of quartz. When stress gradients or structural disturbances (such as edge bending or stress concentration at microcrack tips) exist, abnormal distortions (non-equidistant, offset), localized densification or disappearance, fringe skipping, or the formation of singularities in the interference fringes will occur. The system extracts stress distribution information from the interference fringe map using image processing algorithms (such as fringe enhancement, phase unwrapping, and spectral comparison) and performs spatial alignment and feature correlation with the infrared thermal distribution map. If both types of maps show abnormal signals in a local area (such as edge corners, welded areas, or openings), for example, hot spots combined with disordered fringe superposition, this area will be marked with high confidence as a suspected area with structural defects. Finally, the system integrates these results to form a defect assessment framework that combines the temperature response with acquired thermal images and optical interferograms, outputting a structural health status evaluation report for each quartz part, marking the coordinates and risk level of abnormal areas, and triggering subsequent high-precision verification steps (such as white light interferometer retesting or stress relief heat treatment recommendations). This integrated approach enables high-throughput, high-resolution defect localization and classification without physical contact or destructive operations, making it particularly suitable for identifying internal micro-stress or latent cracks that are difficult to detect using traditional conductivity / ORP monitoring. This technology significantly enhances the quartz cleaning process's detection capabilities in both cleanliness and structural integrity dimensions, and drives the entire cleaning, inspection, and process feedback chain towards data-driven, intelligent, closed-loop control.

[0071] It is important to emphasize that in conventional experimental or non-destructive testing systems, in order to induce defect responses or stimulate stress distribution, it is often necessary to set up a separate heating source, set a heating rate, and perform temperature control in a dedicated testing environment. This not only increases equipment costs and operational complexity but also leads to low testing efficiency and difficulty in integrating into mass production processes. In this solution, however, there is no need to add a new heating control module. Instead, it cleverly leverages the rinsing heating stage in the cleaning process to complete the thermal excitation and optical interference detection of the device, thus embedding stress defect identification. In the cleaning process of quartz devices, the rinsing stage is designed to use ultrapure water or neutralizing solution at 60–90°C for hot rinsing to reduce surface tension and promote the removal of particles and chemical residues. Quartz materials exhibit excellent thermal response characteristics in this temperature range, sufficient to induce heterogeneous thermal conduction behavior and stress photoelastic interference responses in localized stress zones or crack areas.

[0072] In some examples, the assessment of quality defects in the quartz workpiece based on the heat distribution and interference fringe patterns during the heating process includes:

[0073] Determining the heat retention area based on the heat distribution during the heating process of quartz parts;

[0074] Determining the fringe distortion region based on the interference fringe pattern;

[0075] The thermal retention area and stripe distortion area are detected using an image fusion algorithm, and a classifier is used to determine whether they are stress accumulation areas.

[0076] For example, during the rinsing and heating process, an infrared thermal imager continuously captures the dynamic changes in the surface temperature of the quartz workpiece and generates a time-series thermal distribution map. The system analyzes the thermal maps at different time points to extract the thermal diffusion velocity field and local temperature rise curves. If certain areas significantly lag behind in heating when other parts have approached stable temperatures, or if the local temperature rise slope is significantly lower than the surrounding area, these areas are marked as thermal stagnation zones. The root causes of thermal stagnation typically include: non-uniform microstructure within the quartz (such as inclusions and bubbles), local stress concentration affecting the heat conduction path, and surface cracks. These phenomena all lead to time delays and spatial distortions in thermal diffusion, becoming important thermal response characteristics for subsequent defect analysis.

[0077] Simultaneously, the interference fringe pattern of the quartz component during the heating process is acquired using a polarized light interferometry detection system. This pattern appears as a series of intensity fringes, the distribution of which directly reflects the stress field changes within the material. After image processing analysis of the interference pattern (such as edge detection, phase unwrapping, and fringe feature extraction), the system can identify the following fringe distortion characteristics: locally dense or sparse fringes; fringe distortion, breakage, or formation of closed loops; and fringe trajectories deviating from the theoretical stress distribution direction. These abnormal fringe areas are marked as fringe distortion regions, usually resulting from non-uniform stress or stress accumulation at the tips of microcracks within the material. Finally, the system spatially aligns (registers) the thermal distribution map and the interference fringe pattern, and uses image fusion algorithms (such as multi-scale feature fusion, edge-texture joint analysis, or the attention mechanism fusion module in a convolutional neural network) to construct a fused feature map. In this fused map, the system extracts spatially overlapping or highly similar regions from the two types of patterns, i.e., regions that are both thermally trapped and accompanied by interference fringe distortion, as suspected stress anomaly regions. The multidimensional features of these regions (such as region area, shape, thermal gradient change rate, and stripe offset amplitude) are input into a pre-trained classifier, which can employ Support Vector Machine (SVM), Random Forest (RF), or a lightweight neural network model to determine whether they are stress accumulation regions. The classifier's training samples are derived from labeled stress-defect and non-defect regions in historical cleaning batches, and the judgment accuracy is continuously optimized through supervised learning.

[0078] In some examples, it also includes:

[0079] During the drying stage after cleaning, the wake information of the drying airflow after passing through the quartz workpiece is obtained, and the drying airflow is a constant temperature and pressure airflow.

[0080] The wake flow field is determined based on the wake information in order to evaluate the quality defects of the quartz part according to the wake flow field. The quality defects include the presence of liquid film thickness difference curves and / or surface foreign matter adhesion defects in the quartz part.

[0081] For example, after cleaning and entering the drying stage, the system introduces a constant-temperature and constant-pressure airflow (such as temperature-controlled nitrogen, clean dry air, or high-purity argon) that flows stably over the surface of the quartz part from above or the side, forming a controllable laminar flow drying environment. During the evaporation of the water film on the quartz part surface, any abnormal surface morphology (such as uneven liquid film residue, microparticles, scratches, stress-induced warping, etc.) will disturb the local boundary layer structure, velocity distribution, and vortex formation of the airflow. These disturbances will manifest as pressure perturbations, velocity shear fluctuations, or abnormal temperature and humidity gradients in the wake field after the airflow passes through the quartz device. The system is equipped with a high-precision wake monitoring device at the tail of the quartz part, which can employ one or a combination of the following methods: a laser Doppler velocimeter to capture minute velocity changes in real time; a miniature hot-wire anemometer to measure the spatial distribution of temperature and velocity in the airflow; particle image velocimetry combined with labeled particles to visualize the wake structure; and an infrared thermal imager / gas refractometer to capture thermal disturbances or optical density distortion patterns in the airflow. The system acquires spatial velocity distribution maps and local turbulence structures of the wake using the aforementioned device, reconstructs the flow field, and can use CFD approximate solutions or neural field modeling algorithms to extract the following defect-related feature patterns:

[0082] Liquid film thickness difference curve identification: If the speed of the drying airflow suddenly slows down or micro vortices are generated above a certain area, it is often caused by a thicker water film or limited evaporation in that area. Based on this, the system extracts the airflow disturbance contour map and derives the liquid film thickness difference profile curve on the quartz surface to determine whether there is cleaning residue.

[0083] Foreign object attachment identification: If a continuous point-like vortex or flow field shear distortion zone is formed in the wake, it indicates that the airflow is obstructed at that location, which is very likely to correspond to particles, residues or scratches and depressions adhering to the surface. The system performs spatial registration on the disturbance center and outputs the coordinates of the surface defect.

[0084] For example, the system maps and aligns the abnormal wake region with the three-dimensional structural model of the quartz device, and outputs an analysis report that includes the liquid film thickness distribution curve, the judgment result of local attachments, and the wake disturbance index distribution map. It can also overlay the state of the front cleaning fluid and particle release data to collaboratively decide whether the device needs to undergo secondary rinsing, microwave heating drying compensation, or enter the quality inspection review path.

[0085] Understandably, retrieving the residual state of quartz device surfaces using wake field information not only avoids the additional contamination or resolution limitations caused by contact or imaging-based interference detection, but also captures drying anomaly signals that are difficult to detect with the naked eye in a highly sensitive manner. This is particularly suitable for detecting latent defects in quartz devices after cleaning, such as thick liquid film areas, uneven hydrophobicity distribution, and localized particle redeposition. Combined with multi-stage process monitoring methods, it significantly improves the intelligent closed-loop control level of the entire cleaning-drying-detection process, playing a crucial role in ensuring the high cleanliness and thermal stability requirements of semiconductor quartz devices.

[0086] In some examples, the parameters for the change in the state of the cleaning fluid also include pH and surface tension, and the method further includes:

[0087] Based on the real-time acquired parameters of the state change of the cleaning fluid in the cleaning tank, the current level of contamination of the cleaning fluid is assessed.

[0088] The cleaning intensity parameters are adjusted according to the current contamination level of the cleaning fluid. The cleaning intensity parameters include at least one of ultrasonic frequency, cleaning duration, or cleaning temperature.

[0089] For example, during the cleaning process, the system synchronously collects real-time state changes of the cleaning solution through a multi-sensor array, including but not limited to: pH, reflecting the neutralization degree and reactivity of acid / alkali components in the cleaning solution. If the pH gradually deviates from the initial value (e.g., a strong acid becomes neutral), it usually indicates that the active ingredients in the cleaning solution have been consumed by contaminants; surface tension, measured by online drop weight method or bubble pressure method. Higher surface tension indicates a reduced ability of the cleaning solution to remove contaminants, such as residual organic matter or surfactant decay; conductivity, measuring changes in ion concentration, used to identify metal ion release and particle dissolution; ORP, characterizing the remaining capacity of active substances in the redox system, reflecting the ability to remove organic pollutants. Based on the changing trends of the above parameters and comparison with historical samples, the system uses a multi-index fusion model, such as a weighted decision tree, fuzzy rule system, or neural network regression model, to determine the pollution level, classifying the current cleaning solution state into three levels: clean state, moderately polluted state, and severely polluted state, and associating it with preset process response logic.

[0090] For example, once the contamination level is determined, the system will automatically invoke the cleaning process control module to adaptively adjust the cleaning intensity parameters to compensate for the loss of cleaning efficiency caused by the decline in cleaning fluid performance. When the cleaning fluid is in a moderately contaminated state, the current cleaning step (such as pickling or rinsing) will be extended by 5–15% to ensure sufficient surface reaction time. If the system identifies a "severely contaminated" state accompanied by highly viscous contaminant residue (such as organic colloids), the system will increase the ultrasonic frequency or switch to a low-frequency, high-power mode to improve peeling efficiency. When the surface tension increases significantly, the cleaning fluid temperature can be increased (e.g., from 60°C to 75°C) to reduce liquid viscosity and enhance the decontamination diffusion capacity. This adjustment process is a real-time closed-loop control mechanism that can dynamically compensate before cleaning is completed, thereby preventing problems such as incomplete cleaning, residue, or secondary adsorption of particles due to the degradation of cleaning fluid performance.

[0091] Understandably, by introducing pH and surface tension—two key parameters characterizing the activity and interfacial behavior of the cleaning fluid—the dimensionality and accuracy of contamination monitoring are significantly improved. Simultaneously, by combining the linkage control of contamination level and cleaning intensity parameters, an adaptive cleaning scheduling mechanism based on cleaning fluid state perception is constructed. This mechanism is particularly crucial in the cleaning of quartz parts because contamination sources are complex, such as heat-treated oxide layers, residual flux, and particulate adsorption; a fixed cleaning scheme cannot cover all variables. This method effectively improves cleaning efficiency per unit time, reduces the probability of rework due to abnormalities, and ensures that the cleaning quality of quartz devices remains highly controllable, making it one of the core guarantees for achieving high-yield quartz part production. The wake velocity field can be reconstructed using a CFD approximation model or a neural flow field estimation model. Determine the standard deviation of flow velocity disturbance >Threshold, marking anomalies; can map wake disturbance types to liquid film thickness curves / particle attachment regions.

[0092] The above describes the digital production supervision method in the embodiments of this application. The following describes the digital production supervision system in the embodiments of this application.

[0093] Please see Figure 2 This application describes one embodiment of a digital production monitoring system, which may include:

[0094] The matching unit 201 is used to automatically match a cleaning formula based on the information of the quartz workpiece during the cleaning stage after heat treatment. The cleaning formula includes the washing solution ratio and the cleaning time-temperature control curve.

[0095] The acquisition unit 202 is used to acquire the state change parameters of the cleaning fluid in the cleaning tank in real time. The state parameters of the cleaning fluid include conductivity and redox potential.

[0096] Evaluation unit 203 is used to evaluate the quality defects of the quartz machined part based on the state change parameters of the cleaning fluid.

[0097] In summary, the digital production monitoring system provided in the above embodiments automatically matches a cleaning formula based on the information of the quartz parts during the cleaning stage after heat treatment. The cleaning formula includes the washing solution ratio and a cleaning time-temperature control curve. It also acquires real-time parameters of the cleaning solution's state changes in the cleaning tank, including conductivity and redox potential. Based on these parameters, it assesses the quality defects of the quartz parts. This shifts the focus from post-processing detection to process judgment, significantly reducing rework costs associated with discovering problems only after cleaning. The system intelligently matches the cleaning intensity to each batch of products based on their contamination difficulty, avoiding wasting time and reagents by re-washing lightly contaminated items. Dynamic monitoring of contaminant leaching during cleaning allows for timely adjustment of cleaning parameters, improving the complete removal rate of contaminants. The cleaning solution's state change signal serves as an implicit quality indicator, enhancing sensitivity to latent defects such as microcracks and embedded impurities. This not only frees the quartz cleaning process from the traditional passive mode relying on end-of-pipe detection, achieving feedforward defect identification based on process characteristics, but also establishes a precise matching logic between products and the cleaning process, significantly reducing rework rates and redundant cleaning solution usage. It can improve the yield and stability of quartz components in high-cleanliness processes, while providing traceable and evolving digital monitoring capabilities for the production line, significantly enhancing the intelligence level and controllability of high-end manufacturing processes.

[0098] above Figure 2 The digital production monitoring system in this application embodiment has been described from the perspective of modular functional entities. The following section describes the digital production monitoring system in this application embodiment in detail from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 One embodiment of the digital production monitoring system 300 in this application includes:

[0099] The system includes an input device 301, an output device 302, a processor 303, and a memory 304, wherein the number of processors 303 can be one or more. Figure 3 Taking a processor 303 as an example. In some embodiments of this application, the input device 301, output device 302, processor 303, and memory 304 can be connected via a bus or other means, wherein... Figure 3 Taking the example of a connection between China and Israel via a bus.

[0100] Specifically, by calling the operation instructions stored in memory 304, processor 303 executes the following steps:

[0101] During the cleaning stage of the quartz parts after heat treatment, a cleaning formula is automatically matched based on the information of the quartz parts. The cleaning formula includes the washing solution ratio and the cleaning time-temperature control curve.

[0102] Real-time acquisition of cleaning fluid state change parameters in the cleaning tank, including conductivity and redox potential;

[0103] The quality defects of the quartz machined parts are evaluated based on the state change parameters of the cleaning fluid.

[0104] By calling the operation instructions stored in memory 304, processor 303 is also used to execute... Figure 1 Any of the methods in the corresponding embodiments.

[0105] Please see Figure 4 , Figure 4 A schematic diagram illustrating an embodiment of the electronic device provided in this application.

[0106] like Figure 4 As shown, this application embodiment provides an electronic device, including a memory 410, a processor 420, and a computer program 411 stored in the memory 420 and executable on the processor 420. When the processor 420 executes the computer program 411, it performs the following steps:

[0107] During the cleaning stage of the quartz parts after heat treatment, a cleaning formula is automatically matched based on the information of the quartz parts. The cleaning formula includes the washing solution ratio and the cleaning time-temperature control curve.

[0108] Real-time acquisition of cleaning fluid state change parameters in the cleaning tank, including conductivity and redox potential;

[0109] The quality defects of the quartz machined parts are evaluated based on the state change parameters of the cleaning fluid.

[0110] In practical implementation, when the processor 420 executes the computer program 411, it can achieve... Figure 1 Any of the corresponding implementation methods in the embodiments.

[0111] Since the electronic device described in this embodiment is the device used to implement a digital production supervision system in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0112] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided in this application.

[0113] like Figure 5 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 511 is stored. When the computer program 511 is executed by a processor, it performs the following steps:

[0114] During the cleaning stage of the quartz parts after heat treatment, a cleaning formula is automatically matched based on the information of the quartz parts. The cleaning formula includes the washing solution ratio and the cleaning time-temperature control curve.

[0115] Real-time acquisition of cleaning fluid state change parameters in the cleaning tank, including conductivity and redox potential;

[0116] The quality defects of the quartz machined parts are evaluated based on the state change parameters of the cleaning fluid.

[0117] By calling the operation instructions stored in memory 304, processor 303 is also used to execute... Figure 1 Any of the methods in the corresponding embodiments.

[0118] This application provides a computer program product comprising one or more computer instructions. When these computer program instructions are loaded and executed on a computer, they generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0119] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0123] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A digital production supervision method, characterized in that, include: During the cleaning stage of the quartz parts after heat treatment, a cleaning formula is automatically matched based on the information of the quartz parts. The cleaning formula includes the washing solution ratio and the cleaning time-temperature control curve. Real-time acquisition of cleaning fluid state change parameters in the cleaning tank, including conductivity and redox potential; In the rinsing and heating zone, the heat distribution of the quartz workpiece during the heating process is captured by an infrared thermal imager, and interference fringe patterns are collected by polarized light irradiation in the rinsing and heating zone. Spatial alignment is performed between the thermal distribution and the interference fringe pattern. An image fusion algorithm is used to detect overlapping or highly similar regions between the thermal retention region and the fringe distortion region. The multidimensional features of the overlapping or highly similar regions are input into a classifier to identify stress accumulation regions. The assessment of the quality defects of the quartz machined parts based on the state change parameters of the cleaning fluid includes: assessing the quality defects of the quartz machined parts based on the discrimination results of the stress accumulation area and the state change parameters of the cleaning fluid.

2. The method according to claim 1, characterized in that, The assessment of quality defects in the quartz machined parts based on the state change parameters of the cleaning fluid includes: The abnormal ion release of the quartz workpiece is evaluated based on the state change parameters of the cleaning fluid. The quality defects of the quartz machined parts are assessed based on the abnormal ion release.

3. The method according to claim 1, characterized in that, Also includes: The number and size distribution of particles stripped from the fluid per unit time are recorded in real time, serving as a dynamic response curve for the surface cleanliness of the device. The assessment of quality defects in the quartz machined parts based on the state change parameters of the cleaning fluid includes: The microparticle release mode was used to analyze whether the quartz part had microcracks caused by edge stress accumulation.

4. The method according to claim 1, characterized in that, Also includes: During the drying stage after cleaning, the wake information of the drying airflow after passing through the quartz workpiece is obtained, and the drying airflow is a constant temperature and pressure airflow. The wake flow field is determined based on the wake information in order to evaluate the quality defects of the quartz part according to the wake flow field. The quality defects include the presence of liquid film thickness difference curves and / or surface foreign matter adhesion defects in the quartz part.

5. The method according to any one of claims 1-4, characterized in that, The parameters for the change in the state of the cleaning fluid also include pH and surface tension, and the method further includes: Based on the real-time acquired parameters of the state change of the cleaning fluid in the cleaning tank, the current level of contamination of the cleaning fluid is assessed. The cleaning intensity parameters are adjusted according to the current contamination level of the cleaning fluid. The cleaning intensity parameters include at least one of ultrasonic frequency, cleaning duration, or cleaning temperature.

6. A digital production monitoring system, characterized in that, The system, employing the method as described in any one of claims 1 to 5, comprises: A matching unit is used to automatically match a cleaning formula based on the information of the quartz workpiece during the cleaning stage after heat treatment. The cleaning formula includes the washing solution ratio and the cleaning time-temperature control curve. The acquisition unit is used to acquire the state change parameters of the cleaning fluid in the cleaning tank in real time, including conductivity and redox potential. An evaluation unit is used to evaluate the quality defects of the quartz machined part based on the state change parameters of the cleaning fluid.

7. An electronic device, characterized in that, The electronic device includes at least one processor and at least one memory connected to the processor, wherein the processor is used to call program instructions in the memory to execute the digital production monitoring method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to perform the digital production monitoring method as described in any one of claims 1 to 5.

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