Vacuum furnace hearth structure and simulation optimization method

Abstract

The application discloses a vacuum furnace hearth structure and a simulation optimization method, relates to the technical field of vacuum furnace control, and realizes real-time acquisition of pressure values of each chamber at different process stages through a distributed sensor array, calculates a pressure gradient and identifies an abnormal point by using a sliding window method, generates a target pumping speed by using a PID control algorithm based on a vacuum degree deviation sequence, adjusts a valve opening degree by using a servo motor to optimize the vacuum degree, optimizes a pressure difference between chambers in combination with a computational fluid dynamics model and a fuzzy control algorithm, predicts a pressure gradient change trend by using time series analysis, and dynamically allocates a pumping speed of each chamber. The application can effectively solve the technical problems that, in the existing series connection type multi-chamber vacuum furnace, a traditional pumping system is difficult to adapt to dynamic requirements of different process stages on the vacuum degree and the gas flow, pressure gradient and gas flow characteristics are not well controlled, and process stability, efficiency and product quality are affected.

Description

Technical Field

[0001] This invention relates to the field of vacuum furnace lining structure optimization technology, specifically to a vacuum furnace lining structure and simulation optimization method. Background Technology

[0002] Vacuum furnace technology plays a crucial role in fields such as materials heat treatment and semiconductor manufacturing. Its core lies in achieving high-quality processes through precise control of the vacuum environment. Series-type multi-chamber vacuum furnaces have attracted much attention due to their ability to meet complex process requirements. They complete preheating, heating, and cooling steps in stages through multiple chambers, ensuring the stability and consistency of material properties.

[0003] Chinese patent application CN114492079B discloses an optimization method for a silicon carbide vacuum sintering furnace based on computational heat transfer simulation. The method includes the following steps: establishing a model of thermal radiation and heat conduction during the heating process of the vacuum sintering furnace based on its body parameters; performing computational heat transfer simulation to obtain the temperature rise curve of the vacuum sintering furnace throughout the heating process; and optimizing the original furnace design by calculating and comparing the workpiece temperature rise curves, temperature differences between the apex and center workpieces, and temperature differences between the surface and core of the same workpiece under different heating rates, different graphite heating tube diameters, and different distances between the graphite heating tubes and the effective heating zone. This invention studies the effects of different heating rates, different graphite heating tube diameters, and different distances between the graphite heating tubes and the effective heating zone on the heating efficiency and temperature uniformity of the vacuum sintering furnace, providing a reference for performance optimization of the vacuum sintering furnace. It meets the process requirements of silicon carbide vacuum sintering furnaces and improves the heating efficiency and temperature uniformity of the heating system.

[0004] The invention patent with publication number CN102586723A discloses an automatic vacuum pressure control method and device for heat treatment equipment. It establishes a closed-loop control system comprising a vacuum pressure transmitter, a gas flow transmitter, a frequency converter, and a rotary vane mechanical vacuum pump motor for an ion chemical heat treatment furnace. The system includes a frequency converter, a vacuum pressure transmitter, a mechanical vacuum pump, a gas flow transmitter, a programmable controller, and a control computer. The gas flow transmitter is installed on the vacuum furnace's gas supply pipeline. The data acquisition end of the vacuum pressure transmitter directly contacts the pressure inside the heat treatment furnace. The mechanical vacuum pump is installed at the furnace's exhaust port. The programmable controller is connected to the frequency converter, the vacuum pressure transmitter, and the gas flow transmitter. The frequency converter is connected to the mechanical vacuum pump motor circuit. The control computer is connected to the programmable controller.

[0005] In series-connected multi-chamber vacuum furnaces, the core challenge lies in achieving independent gas extraction control for each chamber to meet the vacuum requirements of different process stages. The primary technical factor is the pressure gradient distribution between chambers. Due to variations in process stages, the required vacuum levels differ significantly between chambers. For example, the preheating stage requires a lower vacuum to promote gas flow, while the heating stage requires a higher vacuum to reduce the influence of impurities. Improper pressure gradient control can lead to irregular gas flow between chambers, causing pressure fluctuations in some chambers. In a certain semiconductor heat treatment process, excessively high pressure in the preheating chamber can cause gas to flow back into the heating chamber, affecting the surface quality of the material.

[0006] Meanwhile, the instability of the pressure gradient directly affects the dynamic adjustment capability of the gas extraction rate. Existing systems lack intelligent extraction strategies, making it difficult to adjust the extraction rate of each chamber according to real-time process requirements, resulting in the vacuum environment being difficult to maintain stability during dynamic processes. Summary of the Invention

[0007] The purpose of this invention is to provide a vacuum furnace chamber structure and simulation optimization method, which can effectively solve the technical problems in existing series multi-chamber vacuum furnaces where the traditional gas extraction system is difficult to adapt to the dynamic requirements of vacuum degree and gas flow rate at different process stages in each chamber, and the pressure gradient and gas flow characteristics are poorly controlled, resulting in the impact on process stability, efficiency and product quality.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A method for simulating and optimizing the structure of a vacuum furnace lining includes:

[0010] S101: Real-time acquisition of pressure gradient distribution data for each chamber of the vacuum furnace at different process stages;

[0011] S102: Based on the pressure gradient distribution data and the trend of pressure gradient distribution data, determine whether each chamber meets the vacuum requirements of the corresponding process stage, and generate a sequence of deviation values ​​to characterize the deviation.

[0012] S103: Based on the deviation value sequence, determine the gas extraction rate adjustment requirements of each chamber, and use a PID control algorithm to calculate the required gas extraction rate adjustment for each chamber to obtain the target gas extraction rate.

[0013] S104: Adjust the valve opening of the independent pumping system according to the target pumping rate to control the gas flow rate and obtain the real-time vacuum level of each chamber.

[0014] S105: Obtain the deviation between the real-time vacuum level and the preset process requirements, and determine whether the gas flow characteristics meet the heat and mass transfer uniformity requirements based on the deviation, and obtain flow characteristic deviation data.

[0015] S106: Based on the flow characteristic deviation data, a fuzzy control algorithm is used to dynamically optimize the pressure difference between each chamber, and a stable vacuum environment parameter is obtained by adjusting the pumping rate.

[0016] S107: Extract real-time process monitoring data from the stable vacuum environment parameters, use time series analysis algorithm to predict future pressure gradient change trends, and generate pumping control commands for the next stage.

[0017] S108: According to the pumping control command, update the operating parameters of the independent pumping system and dynamically allocate the pumping rate of each chamber to achieve optimized vacuum environment stability.

[0018] Furthermore, S101 is specifically as follows: real-time acquisition of pressure values ​​of each chamber during the preheating, heating, and cooling stages, and calculation of pressure gradient of each chamber at different process stages based on the acquired pressure values; if the pressure gradient exceeds a preset threshold, identification of abnormal chambers and process stages; and prediction of pressure gradient change trends in future process stages based on historical pressure data.

[0019] Furthermore, in S103, the PID control algorithm calculates the pumping rate adjustment based on the deviation value sequence and preset proportional, integral, and derivative parameters.

[0020] Furthermore, S104 includes: acquiring real-time vacuum data of each chamber, calculating the deviation between the real-time vacuum and the preset vacuum threshold corresponding to the target pumping rate, calculating the valve opening adjustment amount based on the deviation and controlling the servo motor to perform the adjustment; acquiring the adjusted gas flow rate, evaluating its matching degree with the target pumping rate, and making fine adjustments if necessary to obtain an optimized chamber vacuum level.

[0021] Furthermore, S105 includes: comparing the real-time vacuum degree with the preset process parameters to determine the deviation; analyzing the gas flow field distribution using a computational fluid dynamics model based on the deviation; evaluating whether the heat transfer uniformity and mass transfer uniformity meet the process requirements based on the gas flow field distribution; and generating the flow characteristic deviation data by comprehensively considering the evaluation results of heat transfer uniformity and mass transfer uniformity.

[0022] Furthermore, S106 includes: generating pressure difference adjustment amounts for each chamber based on the flow characteristic deviation data using a fuzzy control algorithm; dynamically adjusting the pumping speed of the corresponding chamber if the pressure difference adjustment amount exceeds a preset threshold; monitoring the adjusted chamber pressure value and optimizing pressure fluctuations using a PID control algorithm to obtain a smooth pressure curve; and integrating the parameters to obtain the final stable vacuum environment parameters when the smooth pressure curve meets the preset stable vacuum standard.

[0023] Furthermore, S107 includes: using a time series analysis algorithm to analyze the historical change pattern of pressure data and predict the pressure gradient change trend; if the predicted trend exceeds a preset threshold, calculating the rate and direction of pressure change; based on the trend analysis results, using a PID control algorithm to generate the air extraction control command; and optimizing the air extraction control command based on the feedback after the device executes the control command.

[0024] Furthermore, S108 includes: generating an initial pumping rate allocation scheme based on the pumping control command; monitoring the vacuum degree change trend of each chamber; adjusting the pumping rate allocation scheme if the vacuum degree change trend deviates from the target value; updating the operating parameters of the independent pumping system; evaluating the stability of the vacuum environment; and optimizing the operating parameters based on the deviation and readjusting the pumping rate again until a stable vacuum environment is obtained.

[0025] In addition, the present invention also discloses a vacuum furnace chamber structure, which is designed and manufactured using the above-described vacuum furnace chamber structure simulation optimization method.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] This invention primarily addresses the problem of uneven pressure distribution and difficulty in precisely controlling vacuum levels in multi-chamber systems at different process stages (such as preheating, heating, and cooling). It utilizes sensors to collect real-time pressure data from each chamber, analyzes the dynamic trends of pressure gradient changes, and uses preset thresholds to determine vacuum deviations. Based on the deviation sequence, this invention employs a PID control algorithm to calculate the target pumping rate, precisely adjusts the valve openings of independent pumping systems, and controls gas flow through servo motors to achieve real-time vacuum optimization. Simultaneously, a fuzzy control algorithm dynamically adjusts the pressure difference between chambers to optimize gas flow characteristics and ensure uniform heat and mass transfer. By predicting pressure gradient trends through time series analysis, pumping control commands are generated, and pumping system parameters are dynamically updated, ultimately achieving a stable multi-chamber vacuum environment that precisely matches process requirements. This invention significantly improves the stability and process consistency of multi-chamber vacuum systems, reduces energy consumption, and increases production efficiency. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0029] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0030] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive. Embodiments of the invention will now be described in detail with reference to the accompanying drawings.

[0031] Example 1:

[0032] See Figure 1 This embodiment discloses a simulation optimization method for the furnace chamber structure of a vacuum furnace, including:

[0033] S101: Real-time acquisition of pressure gradient distribution data for each chamber of the vacuum furnace at different process stages;

[0034] S102: Based on the pressure gradient distribution data and the trend of pressure gradient distribution data, determine whether each chamber meets the vacuum requirements of the corresponding process stage, and generate a sequence of deviation values ​​to characterize the deviation.

[0035] S103: Based on the deviation value sequence, determine the gas extraction rate adjustment requirements of each chamber, and use a PID control algorithm to calculate the required gas extraction rate adjustment for each chamber to obtain the target gas extraction rate.

[0036] S104: Adjust the valve opening of the independent pumping system according to the target pumping rate to control the gas flow rate and obtain the real-time vacuum level of each chamber.

[0037] S105: Obtain the deviation between the real-time vacuum level and the preset process requirements, and determine whether the gas flow characteristics meet the heat and mass transfer uniformity requirements based on the deviation, and obtain flow characteristic deviation data.

[0038] S106: Based on the flow characteristic deviation data, a fuzzy control algorithm is used to dynamically optimize the pressure difference between each chamber, and a stable vacuum environment parameter is obtained by adjusting the pumping rate.

[0039] S107: Extract real-time process monitoring data from the stable vacuum environment parameters, use time series analysis algorithm to predict future pressure gradient change trends, and generate pumping control commands for the next stage.

[0040] S108: According to the pumping control command, update the operating parameters of the independent pumping system and dynamically allocate the pumping rate of each chamber to achieve optimized vacuum environment stability.

[0041] In practical applications, by acquiring real-time pressure gradient distribution data and combining various control algorithms such as PID control, fuzzy control, and time series analysis, a complete closed-loop control system is constructed, enabling independent gas extraction control for each chamber. It can accurately capture the dynamic changes in vacuum levels in each chamber at different process stages, and perform multi-dimensional regulation to address issues such as abnormal pressure gradients and poor gas flow characteristics. Compared to traditional unified pumping systems, it improves the control accuracy of vacuum levels in each chamber, reduces pressure gradient fluctuations, and solves the core technical problem of traditional systems' inability to adapt to the differentiated vacuum requirements of each chamber.

[0042] Furthermore, S101 specifically includes: real-time acquisition of pressure values ​​for each chamber during the preheating, heating, and cooling stages; calculation of the pressure gradient for each chamber at different process stages based on the acquired pressure values; identification of abnormal chambers and process stages if the pressure gradient exceeds a preset threshold; and prediction of future pressure gradient trends based on historical pressure data. This allows for timely and accurate monitoring of the pressure gradient in each chamber at different process stages, rapid identification of anomalies, and facilitating timely intervention. Furthermore, the predicted trends provide a basis for subsequent control, further enhancing process stability.

[0043] Furthermore, in S103, the PID control algorithm calculates the pumping rate adjustment amount based on the deviation value sequence and preset proportional, integral, and derivative parameters. Calculating the pumping rate adjustment amount based on preset parameters and the deviation value sequence makes the adjustment more precise and faster, better meeting the pumping rate adjustment needs of each chamber and ensuring vacuum stability.

[0044] Furthermore, S104 includes: acquiring real-time vacuum data for each chamber, calculating the deviation between the real-time vacuum level and a preset vacuum threshold corresponding to the target pumping rate, calculating the valve opening adjustment amount based on the deviation and controlling the servo motor to perform the adjustment; acquiring the adjusted gas flow rate, evaluating its matching degree with the target pumping rate, and making fine adjustments if necessary to obtain an optimized chamber vacuum level. By accurately calculating the valve opening adjustment amount and evaluating the gas flow rate matching degree, precise control of the gas flow rate is achieved, ensuring that each chamber can reach an optimized vacuum level and improving the reliability of the process.

[0045] Furthermore, S105 includes: comparing the real-time vacuum level with preset process parameters to determine the deviation; analyzing the gas flow field distribution using a computational fluid dynamics model based on the deviation; evaluating whether the heat transfer uniformity and mass transfer uniformity meet the process requirements based on the gas flow field distribution; and generating the flow characteristic deviation data by comprehensively evaluating the heat transfer uniformity and mass transfer uniformity. Utilizing a computational fluid dynamics model to analyze the gas flow field distribution and comprehensively evaluate heat transfer and mass transfer uniformity results in more accurate flow characteristic deviation data, providing a reliable basis for subsequent pressure difference optimization and helping to ensure product quality consistency.

[0046] Furthermore, S106 includes: generating pressure difference adjustment amounts for each chamber based on the flow characteristic deviation data using a fuzzy control algorithm; dynamically adjusting the pumping speed of the corresponding chamber if the pressure difference adjustment amount exceeds a preset threshold; monitoring the adjusted chamber pressure values ​​and optimizing pressure fluctuations using a PID control algorithm to obtain a smooth pressure curve; and integrating the parameters to obtain the final stable vacuum environment parameters when the smooth pressure curve meets a preset stable vacuum standard. The combination of fuzzy control and PID control algorithms can dynamically optimize the pressure difference between chambers, effectively suppress pressure fluctuations, obtain stable vacuum environment parameters, and improve the stability of the vacuum environment and the reliability of the process.

[0047] Furthermore, S107 includes: analyzing historical change patterns of pressure data using a time series analysis algorithm to predict pressure gradient change trends; if the predicted trend exceeds a preset threshold, calculating the rate and direction of pressure change; generating the extraction control command using a PID control algorithm based on the trend analysis results; and optimizing the extraction control command based on feedback from the equipment after executing the control command. By using a time series analysis algorithm to predict pressure gradient change trends, generate and optimize extraction control commands in advance, enabling the system to proactively adapt to future process requirements, reduce pressure fluctuations, and improve the system's intelligence and foresight.

[0048] Furthermore, S108 includes: generating an initial pumping rate allocation scheme based on the pumping control command; monitoring the vacuum level change trend of each chamber; adjusting the pumping rate allocation scheme if the vacuum level change trend deviates from the target value; updating the operating parameters of the independent pumping system; evaluating the stability of the vacuum environment; and optimizing the operating parameters based on the deviation and readjusting the pumping rate until a stable vacuum environment is obtained. By dynamically adjusting the pumping rate allocation scheme and optimizing the operating parameters, the stability of the vacuum environment is ensured to meet the standards, enabling the entire vacuum furnace system to operate continuously and stably, meeting long-term process requirements.

[0049] Furthermore, this invention also discloses a vacuum furnace chamber structure, which is designed and manufactured using the aforementioned vacuum furnace chamber structure simulation optimization method. The structure designed and manufactured using the above optimization method possesses the characteristics of independent control of each chamber, stable vacuum environment, and high process reliability, and can be better applied to fields such as material heat treatment and semiconductor manufacturing.

[0050] To facilitate a better understanding of the invention by those skilled in the art, the invention will be further described below in conjunction with a series-connected multi-chamber vacuum furnace.

[0051] The vacuum furnace has a series multi-chamber structure, including three chambers: chamber A, chamber B, and chamber C. The process stages are preheating, heating, and cooling, respectively.

[0052] S101: Real-time acquisition of pressure values ​​in each chamber at different process stages. The preset pressure range is 100-300 Pa for the preheating stage, 1-10 Pa for the heating stage, and 50-150 Pa for the cooling stage. The pressure value of chamber A during the preheating stage is 100 Pa, the pressure value of chamber B during the heating stage is 1 Pa, and the pressure value of chamber C during the cooling stage is 50 Pa.

[0053] Calculate the pressure gradient: The pressure gradient between chamber A and chamber B is 99 Pa, and the pressure gradient between chamber B and chamber C is 49 Pa, both within the preset threshold of 100 Pa.

[0054] Based on historical pressure data, the predicted trend of pressure gradient change in future process stages is that it will gradually increase, but will not exceed the preset threshold.

[0055] S102: Based on the pressure gradient distribution data and its changing trend, it is determined that each chamber meets the vacuum requirements of the corresponding process stage, and the generated deviation value sequence is 0.

[0056] S103: Since the deviation value sequence is 0, it is determined that there is no need to adjust the gas extraction rate of each chamber, and the target extraction rate remains at its current value. The proportional parameter of the PID control algorithm is 0.5, the integral parameter is 0.2, and the derivative parameter is 0.1.

[0057] S104: The target pumping rate remains unchanged, and the valve opening remains in its current state. The real-time vacuum level is consistent with the preset threshold, and the gas flow rate matches the target pumping rate, requiring no fine-tuning.

[0058] S105: The real-time vacuum degree is consistent with the preset process parameters. The gas flow field distribution is uniform, and the heat transfer and mass transfer uniformity meet the process requirements. The flow characteristic deviation data is 0.

[0059] S106: The flow characteristic deviation data is 0, and the pressure difference adjustment of each chamber generated by the fuzzy control algorithm is 0, so there is no need to adjust the pumping speed. The pressure curve is smooth and meets the preset stable vacuum standard. The stable vacuum environment parameters are the current pressure values ​​and related parameters of each chamber.

[0060] S107: If the time series analysis algorithm predicts that the future pressure gradient change trend is within the preset threshold, there is no need to generate a new pumping control command.

[0061] S108: The pumping rate distribution scheme remains unchanged, and the vacuum environment stability assessment meets the standards.

[0062] 200 pieces of 316 stainless steel sheet, measuring 300mm × 200mm × 5mm, were randomly divided into two groups of 100 pieces each. One group underwent heat treatment using a conventional method, while the other used the method of this invention. The laboratory temperature was maintained at 25±2℃, the relative humidity at 45±5%, and the power supply voltage at a stable 380±5V. A series-connected three-chamber vacuum furnace was used: chamber A (preheating chamber) with a volume of 0.5m³, chamber B (heating chamber) with a volume of 0.5m³, and chamber C (cooling chamber) with a volume of 0.5m³. The heating power for each chamber was 15kW, and the cooling method was water cooling with an adjustable cooling rate. The preheating stage temperature was 400℃, with a holding time of 30min; the heating stage temperature was 1050℃, with a holding time of 60min; and the cooling stage used forced water cooling to below 100℃. Pressure was monitored in real time using a vacuum gauge with an accuracy of ±0.1 Pa, temperature was measured using thermocouples with a resolution of 0.01℃, gas flow rate was determined using a laser Doppler flow meter, and the surface and internal structure of the material were observed using a metallographic microscope. Testing was conducted continuously for 10 days, with 10 batches tested each day, and each data set represents the average of 100 batches.

[0063] The experimental data comparison results of Example 1 are shown in Table 1 below:

[0064] Table 1:

[0065] index Traditional methods Example 1 Method Process stability (fluctuation coefficient) 0.15 0.09 Product qualification rate 92.3% 99.5% Pressure gradient fluctuation amplitude ±35Pa ±14Pa Gas flow field uniformity 82% 98% Heat transfer uniformity 85% 97%

[0066] In this embodiment, the synergistic effect of each step achieves precise control of the vacuum level of each chamber at different process stages. Traditional unified pumping systems often encounter vacuum level mismatches when dealing with different process stages in multiple chambers. However, in this method, the pressure gradient of each chamber is stabilized within a preset threshold, the gas flow field is uniformly distributed, and the heat and mass transfer uniformity is good, thus solving the technical problem that traditional systems cannot adapt to the differentiated vacuum requirements of each chamber.

[0067] Example 2:

[0068] In this embodiment, the vacuum furnace also has 3 chambers, and the process stages are basically the same as in Embodiment 1, except that:

[0069] S101: The pressure value of chamber A during the preheating stage is 350 Pa, which exceeds the preset pressure range of 100-300 Pa during the preheating stage. The calculated pressure gradient between chamber A and chamber B (pressure value of 10 Pa during the heating stage) is 340 Pa, which exceeds the preset threshold of 100 Pa. The abnormal chamber is identified as chamber A, and the process stage is the preheating stage.

[0070] Based on historical data, the pressure in chamber A is expected to continue to rise during the preheating phase.

[0071] S102: Chamber A does not meet the vacuum requirements of the preheating stage, and the generated deviation value sequence is 50 (350-300=50), while the deviation values ​​of other chambers are 0.

[0072] S103: Based on the deviation value sequence, it is determined that the gas extraction rate of chamber A needs to be reduced. The proportional parameter of the PID control algorithm is 0.5, the integral parameter is 0.2, and the derivative parameter is 0.1. The calculated gas extraction rate adjustment is 50×0.5+0×0.2+0×0.1=25L / s, and the target gas extraction rate is the current rate minus 25L / s.

[0073] S104: The real-time vacuum level of chamber A deviates from the preset threshold by 50 Pa. The valve opening adjustment is calculated to be 10%. The servo motor is controlled to reduce the valve opening by 10%. After adjustment, the gas flow rate matches the target pumping rate by 95%. A 5% fine-tuning is performed to achieve a 100% match, and the vacuum level of chamber A drops to 300 Pa.

[0074] S105: The real-time vacuum degree of chamber A is 300Pa, which is consistent with the preset process parameters. There are no deviations in other chambers. The gas flow field distribution is uniform, and the heat transfer and mass transfer uniformity meet the requirements. The flow characteristic deviation data is 0.

[0075] S106: The flow characteristic deviation data is 0, no need to adjust the pressure difference and pumping speed, the pressure curve is smooth, and the stable vacuum environment parameters are determined.

[0076] S107: If the predicted future pressure gradient change trend is within the preset threshold, there will be no new air extraction control command.

[0077] S108: The pumping rate distribution scheme is stable, and the vacuum environment stability assessment meets the standards.

[0078] 200 silicon wafers, 200mm in diameter and 0.725mm thick, were randomly divided into two groups of 100 wafers each for semiconductor thermal processing testing. The testing was conducted in a Class 10 cleanroom with temperature controlled at 23±1℃ and relative humidity at 40±3% to avoid the influence of external dust and temperature / humidity fluctuations. Chamber A (preheating chamber) was equipped with an independent quartz heating tube, while chamber B (heating chamber) used a graphite heating element. The leakage rate of the inter-chamber isolation valve was ≤1×10⁻⁶. -8 Pa·m³ / s. The preheating stage temperature was 200℃, with a holding time of 20 min; the heating stage temperature was 800℃, with a holding time of 40 min; and the cooling stage involved natural cooling to room temperature. A molecular pump assembly was used to maintain vacuum, equipped with a capacitive thin-film vacuum gauge (accuracy ±0.05 Pa), a surface roughness meter (resolution 0.01 nm) to detect the silicon wafer surface quality, and a helium mass spectrometer to detect leakage rate. Five batches were tested daily for 20 consecutive days, and the average value of data from 100 batches per group was taken.

[0079] The experimental data comparison results of Example 2 are shown in Table 2 below:

[0080] Table 2:

[0081] index Traditional methods Example 2 Method Increase Pressure Anomaly Adjustment Time 12 minutes 4.5 minutes 62.5% Adjusted pressure stability ±8Pa ±2Pa 75% Product surface defect rate 3.2% 0.9% 71.9% Gas reflux rate 5.6% 0.3% 94.6% Valve adjustment accuracy ±3% ±0.5% 83.3%

[0082] In this embodiment, when the pressure in chamber A exceeds the limit, the anomaly is promptly identified in step S101, a deviation value sequence is generated in step S102, and the pumping rate and valve opening are precisely adjusted in steps S103-S104 to restore the vacuum level of chamber A to the preset range. Traditional systems, lacking precise deviation calculation and adjustment mechanisms, often require a long time to stabilize the vacuum level in such situations and are prone to repeated fluctuations. This method solves the problem of irregular gas flow caused by improper pressure gradient control in traditional systems, reducing the adjustment time to less than 5 minutes.

[0083] Example 3:

[0084] In this embodiment, the vacuum furnace has four chambers, and the process stages are preheating, heating, heat preservation, and cooling.

[0085] S101: The pressure value of chamber D during the cooling stage is 200Pa, which exceeds the preset pressure range of 50-150Pa for the cooling stage. The pressure gradient with chamber C (pressure value of 5Pa during the heat preservation stage) is 195Pa, which exceeds the preset threshold of 100Pa. The abnormal chamber is identified as chamber D, and the process stage is the cooling stage.

[0086] Historical data shows that the pressure in chamber D fluctuates and increases during the cooling phase.

[0087] S102: The deviation value sequence of chamber D is 50 (200-150=50), other chambers are normal. S103: The pumping rate of chamber D needs to be reduced. The PID control algorithm parameters are proportional 0.6, integral 0.3, derivative 0.2. The pumping rate adjustment is 50×0.6+0×0.3+0×0.2=30L / s. The target pumping rate is the current rate minus 30L / s.

[0088] S104: The real-time vacuum degree of chamber D deviates from the preset threshold by 50 Pa. The valve opening adjustment is calculated to be 15%. After the adjustment is reduced, the gas flow rate matches the target pumping rate by 90%. After a 5% fine adjustment, the matching degree meets the standard, and the vacuum degree drops to 150 Pa.

[0089] S105: The real-time vacuum level of chamber D is 150 Pa, consistent with the preset parameter. However, the real-time vacuum level of chamber B (heating stage) is 8 Pa, which deviates by 3 Pa from the preset threshold of 5 Pa. Computational fluid dynamics analysis shows slight non-uniformity in the gas flow field distribution, with heat transfer uniformity at 90% and mass transfer uniformity at 88%, both slightly lower than the process requirement of 95%. The resulting flow characteristic deviation data is 10%.

[0090] S106: Based on a 10% deviation in flow characteristics, the fuzzy control algorithm generates a pressure difference adjustment of 4 Pa ​​between chamber B and its adjacent chambers, exceeding the preset threshold of 3 Pa. The pumping rate of chamber B is then adjusted. After adjustment, the pressure value is monitored, and a PID control algorithm is used to optimize pressure fluctuations, obtaining a smooth pressure curve that meets stable vacuum standards. Stable vacuum environment parameters are then integrated. S107: A time series analysis algorithm predicts that the pressure gradient in chamber B will increase during the future heating phase, exceeding the preset threshold of 1 Pa. The calculated rate of change is 0.5 Pa / min, and the direction is upward. Based on this, a PID control algorithm is used to generate a pumping control command, requiring an increase of 5 L / s in the pumping rate of chamber B. According to equipment feedback, the optimized command is an increase of 4 L / s.

[0091] S108: Based on the optimized pumping control commands, an initial pumping rate allocation scheme is generated, increasing the pumping rate of chamber B by 4 L / s. Monitoring shows that the vacuum level is gradually approaching the target value, and the operating parameters of the independent pumping system are updated. The stability of the vacuum environment is assessed as meeting the standards.

[0092] Twenty-one ceramic substrates (150mm × 150mm × 2mm) were randomly divided into two groups of 100 each for sintering tests at multiple process stages. The test was conducted in a constant temperature and humidity laboratory (24±1℃, 50±3% relative humidity), free from significant vibration and electromagnetic interference. A series-connected four-chamber vacuum furnace was used, with chambers A (preheating), B (heating), C (holding), and D (cooling) each having a volume of 0.6m³. Each chamber was equipped with an independent evacuation system (including a Roots pump and a mechanical pump), and the evacuation rate was independently adjustable. The preheating stage was conducted at 300℃ for 15 minutes; the heating stage at 1200℃ for 50 minutes; the holding stage at 1200℃ for 30 minutes; and the cooling stage using inert gas to cool to below 200℃. High-temperature thermocouples (measurement range 0-1600℃, accuracy ±1℃) and infrared thermal imagers (resolution 640×512) are used to monitor the temperature field distribution. Gas chromatographs are used to analyze the gas composition inside the furnace, and universal testing machines are used to test the mechanical properties of the materials. Each batch is tested at a 1-hour interval, with continuous testing for 100 hours. Data from each group of 100 batches is statistically analyzed.

[0093] The experimental data comparison results of Example 3 are shown in Table 3 below:

[0094] Table 3:

[0095] index Traditional methods Example 3 Method Increase Heat transfer uniformity 90% 96.5% 7.2% Mass transfer uniformity 88% 97.3% 9.3% Process efficiency 50 pieces / hour 70 pieces / hour 40% Consistency of product quality 85% 98% 13% Accuracy of stress trend prediction 65% 92% 27% Multi-chamber collaborative control accuracy ±5Pa ±1.2Pa 76%

[0096] In this embodiment, not only is the pressure exceeding the limit in chamber D resolved, but the flow characteristic deviation in chamber B is also optimized through steps S105-S106. Furthermore, steps S107-S108 enable proactive control of future pressure changes. Traditional systems lack accurate assessment and dynamic optimization capabilities for gas flow characteristics and cannot predict pressure change trends, resulting in poor product quality consistency. This method, through computational fluid dynamics model analysis and fuzzy control algorithms, improves both heat transfer uniformity and mass transfer uniformity to over 96%, resolving the problem of uneven heat and mass transfer. Simultaneously, the application of time series analysis algorithms allows the system to anticipate pressure changes, avoiding the impact of pressure fluctuations on the process, improving process efficiency by 40%, and achieving a product quality consistency of 98%.

[0097] Example 4:

[0098] This embodiment is basically the same as embodiment 2, except that in this embodiment, step S105a is added between steps S105 and S106. S105a specifically includes:

[0099] S1: Calculate the vacuum fluctuation frequency f in real time and compare it with the critical frequency fo of material phase transformation;

[0100] S2: If |f - fo| / fo > 5%, then activate the plasma flow field intervention module to inject argon ion flow into the abnormal chamber;

[0101] S3: Argon ion current density controlled at 10 15 -10 16 m -3 Range, injection duration ≤ 5s.

[0102] In practical applications, based on the relationship between the real-time vacuum fluctuation frequency f and the material phase transformation critical frequency fo, fo is obtained through the preset process parameters in S105:

[0103] If |f-fo| / fo > 5%, then the plasma flow field active intervention module is activated to inject an argon ion flow into the abnormal chamber at an ion density of 10. 15 -10 16 m -3 It is used to correct the non-compliant heat and mass transfer uniformity in S105.

[0104] In this embodiment, the object being processed is a silicon wafer. Similar to Embodiment 2, pressure oscillations occur during the heating stage, with a frequency f = 0.6 Hz and a critical material phase transition frequency fo = 0.5 Hz.

[0105] Example 2 serves as a control group: the pressure difference is adjusted using only the fuzzy control algorithm in S106 of Example 2;

[0106] This embodiment serves as the experimental group: plasma intervention is initiated at S105a with an argon ion current density of 1.2 × 10⁻⁶. 16 m -3 Then, perform pressure difference optimization in S106.

[0107] The specific results are shown in Table 4 below:

[0108] Table 4:

[0109]

[0110] Argon ion flow neutralizes charged impurities found in the flow field analysis of S105, and ion wind suppresses eddies, creating a stable adjustment environment for the fuzzy control algorithm of S106.

[0111] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0112] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for simulating and optimizing the structure of a vacuum furnace lining, characterized in that, include: S101: Real-time acquisition of pressure gradient distribution data for each chamber of the vacuum furnace at different process stages; S102: Based on the pressure gradient distribution data and the trend of pressure gradient distribution data, determine whether each chamber meets the vacuum requirements of the corresponding process stage, and generate a sequence of deviation values ​​to characterize the deviation. S103: Based on the deviation value sequence, determine the gas extraction rate adjustment requirements of each chamber, and use a PID control algorithm to calculate the required gas extraction rate adjustment for each chamber to obtain the target gas extraction rate. S104: Adjust the valve opening of the independent pumping system according to the target pumping rate to control the gas flow rate and obtain the real-time vacuum level of each chamber. S105: Obtain the deviation between the real-time vacuum level and the preset process requirements, and determine whether the gas flow characteristics meet the heat and mass transfer uniformity requirements based on the deviation, and obtain flow characteristic deviation data. S106: Based on the flow characteristic deviation data, a fuzzy control algorithm is used to dynamically optimize the pressure difference between each chamber, and a stable vacuum environment parameter is obtained by adjusting the pumping rate. S107: Extract real-time process monitoring data from the stable vacuum environment parameters, use time series analysis algorithm to predict future pressure gradient change trends, and generate pumping control commands for the next stage. S108: According to the pumping control command, update the operating parameters of the independent pumping system and dynamically allocate the pumping rate of each chamber to achieve optimized vacuum environment stability.

2. The method for simulating and optimizing the structure of a vacuum furnace lining according to claim 1, characterized in that, S101 is as follows: Real-time acquisition of pressure values ​​of each chamber during the preheating, heating and cooling stages, and calculation of pressure gradient of each chamber at different process stages based on the acquired pressure values; if the pressure gradient exceeds the preset threshold, abnormal chambers and process stages are identified; based on historical pressure data, the trend of pressure gradient change in future process stages is predicted.

3. The method for simulating and optimizing the structure of a vacuum furnace lining according to claim 1, characterized in that: In S103, the PID control algorithm calculates the pumping rate adjustment based on the deviation value sequence and preset proportional, integral and derivative parameters.

4. The method for simulating and optimizing the structure of a vacuum furnace lining according to claim 1, characterized in that, S104 includes: acquiring real-time vacuum data of each chamber, calculating the deviation between the real-time vacuum and the preset vacuum threshold corresponding to the target pumping rate, calculating the valve opening adjustment amount based on the deviation and controlling the servo motor to perform the adjustment; acquiring the adjusted gas flow rate, evaluating its matching degree with the target pumping rate and fine-tuning it to obtain an optimized chamber vacuum level.

5. The method for simulating and optimizing the structure of a vacuum furnace lining according to claim 1, characterized in that, S105 includes: comparing the real-time vacuum degree with the preset process parameters to determine the deviation; analyzing the gas flow field distribution using a computational fluid dynamics model based on the deviation; evaluating whether the heat transfer uniformity and mass transfer uniformity meet the process requirements based on the gas flow field distribution; and generating the flow characteristic deviation data by comprehensively considering the evaluation results of heat transfer uniformity and mass transfer uniformity.

6. The method for simulating and optimizing the structure of a vacuum furnace lining according to claim 5, characterized in that: S106 includes: generating pressure difference adjustment amounts for each chamber based on the flow characteristic deviation data using a fuzzy control algorithm; dynamically adjusting the pumping speed of the corresponding chamber if the pressure difference adjustment amount exceeds a preset threshold; monitoring the adjusted chamber pressure value and optimizing pressure fluctuations using a PID control algorithm to obtain a smooth pressure curve; and integrating the parameters to obtain the final stable vacuum environment parameters when the smooth pressure curve meets the preset stable vacuum standard.

7. The method for simulating and optimizing the structure of a vacuum furnace lining according to claim 5, characterized in that, S107 includes: using a time series analysis algorithm to analyze the historical change pattern of pressure data and predict the pressure gradient change trend; if the predicted trend exceeds a preset threshold, calculating the rate and direction of pressure change; based on the trend analysis results, using a PID control algorithm to generate the air extraction control command; and optimizing the air extraction control command based on the feedback after the device executes the control command.

8. The method for simulating and optimizing the structure of a vacuum furnace lining according to claim 1, characterized in that, S108 includes: generating an initial pumping rate allocation scheme based on the pumping control command; monitoring the vacuum degree change trend of each chamber; adjusting the pumping rate allocation scheme if the vacuum degree change trend deviates from the target value; updating the operating parameters of the independent pumping system; evaluating the stability of the vacuum environment; and optimizing the operating parameters based on the deviation and readjusting the pumping rate until a stable vacuum environment is obtained if the standard is not met.

9. A vacuum furnace chamber structure, characterized in that: The method includes designing and fabricating the vacuum furnace lining structure using the simulation optimization method described in any one of claims 1-8.

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