Gas mixing cabinet dynamic ratio adjusting method and system for electronic special gas

Abstract

The invention discloses a gas mixing cabinet dynamic ratio adjusting method and system for electronic special gas, and belongs to the technical field of electronic special gas. Inputting the electronic special gas into a mixing device according to an initial proportion through a mass flow controller; monitoring the component concentration of the mixed gas in real time by using a tunable semiconductor laser absorption spectrum sensor; a model prediction control algorithm is adopted, the future trend is predicted according to the real-time concentration deviation, an optimized flow set value is dynamically generated, and the calculation frequency is reduced through a sampling verification mechanism; based on the optimized set value, the opening degree of the mass flow controller is adjusted through a compensation algorithm in combination with the gas flow rate, temperature and air pressure data collected in real time, so that the matching deviation caused by various factors is compensated. According to the invention, the gas mixing precision is obviously improved, the mixing time is shortened, and the harsh requirements of high-end processes such as semiconductor manufacturing on the ratio of electronic special gas are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic special gas, and particularly relates to a dynamic proportioning adjustment method and system for a mixed gas cabinet of electronic special gas. BACKGROUND

[0002] Electronic special gas is a key raw material for semiconductor, flat panel display, photovoltaic and other microelectronic industries, and is known as the "food" of the electronic industry. These industries have extremely strict requirements for the cleanliness, purity and mixing accuracy of electronic special gas. Large-scale integrated circuit manufacturing often requires gas purity of 6N (99.9999%) or above, and impurity content of less than 10⁻ 7 orders of magnitude. In the core processes of wafer manufacturing such as photolithography, chemical vapor deposition (CVD), etching and doping, mixed gas accurately mixed by multiple electronic special gases is often used, and the accuracy of the proportion directly determines the performance, yield and production safety of integrated circuits.

[0003] At present, the commonly used gas mixing methods in industry mainly include the weight method and the traditional flow control method. The weight method calculates the content of each component by stepwise charging and weighing the gas cylinder, which has acceptable accuracy, but low efficiency, cannot realize continuous production, and has the problem of proportioning deviation caused by gas residue. The continuous flow mixing method based on mass flow controller (MFC) is a more common online mixing technology. However, in actual application, especially in the scene of mixing multiple components of electronic special gas, the existing technology still faces significant challenges: 1. Diffusion difference leads to proportioning distortion: There are differences in molecular weight and diffusion coefficient of different gas components. In the mixing device, it is difficult to achieve instantaneous uniform mixing of each component gas. The difference in density will cause the gas to stratify, and the difference in diffusion coefficient will cause the time of each component to reach the monitoring point to be inconsistent during the transportation of the mixed gas, thereby producing dynamic proportioning deviation. This problem is particularly prominent for low-concentration components (such as doping gas), and the actual concentration may deviate from the target value by more than 10%, which is difficult to meet the stringent requirements of advanced processes such as EUV lithography.

[0004] 2. Environmental fluctuations affect control accuracy: Temperature fluctuations in the semiconductor workshop environment and the process itself will change the gas density and volume flow, and the traditional MFC is calibrated based on constant temperature conditions. When the working condition temperature changes, the measured value of the MFC will deviate from the actual mass flow. At the same time, fluctuations in gas source pressure or downstream pressure will also interfere with the stable control of flow. The existing system lacks an effective mechanism for real-time, joint compensation of temperature, pressure and other working condition parameters, resulting in difficulty in maintaining stable proportioning accuracy over a long period of time or when the environment changes.

[0005] 3. Control response lag and lack of intelligence: Most gas mixing cabinets use single closed-loop feedback control, that is, adjusting the MFC according to the concentration monitoring results. However, due to the delay in gas mixing, delivery and concentration analysis, this control method has inherent lag, and it is difficult to quickly correct the proportioning deviation. In addition, the existing control strategy usually only adjusts for the current error, lacks the ability to predict future system behavior (such as concentration change trend determined by diffusion process), and cannot achieve forward-looking and optimal control, resulting in long mixing time and easy overshoot or oscillation during adjustment.

[0006] To solve the above problems, although some technical solutions propose to improve the structure of the mixing device or use a quantitative pre-storage method, they often fail to fundamentally solve the online real-time control problem caused by the difference in multi-component diffusion and dynamic working condition fluctuations. Therefore, there is an urgent need for a high-precision gas mixing method that can real-time perceive the mixing state, intelligently predict the change trend and dynamically compensate for various interference factors to meet the increasing precision, efficiency and stability requirements of high-end electronic manufacturing for special gas proportioning. SUMMARY

[0007] The purpose of the present application is to overcome the shortcomings of the prior art and provide a dynamic proportioning adjustment method and system for a gas mixing cabinet for electronic special gases.

[0008] The purpose of the present application is achieved by the following technical solutions: The present application provides a dynamic proportioning adjustment method for a gas mixing cabinet for electronic special gases, comprising the following steps: Flow pre-setting and gas input stage: the input flow of multiple electronic special gases is controlled by multiple mass flow controllers of the gas mixing cabinet, so that they enter the mixing device according to the initial input proportion; Real-time spectral analysis stage: a tunable diode laser absorption spectrum sensor integrated in the mixing device is used to monitor the concentration of each component of the mixed gas in real time and obtain real-time concentration data; Dynamic optimization stage: compare the real-time concentration data with the preset target concentration value at the current time node to obtain the real-time concentration deviation, and use the model predictive control algorithm to predict the concentration deviation trend at the subsequent time nodes according to the initial input proportion, real-time concentration data and real-time concentration deviation; then dynamically generate the optimized flow set value of each component gas at the subsequent time nodes according to the concentration deviation trend, and reduce the number of calculations by sampling and verifying the optimization effect according to the preset interval time; if the real-time concentration deviation of the sampling time node is less than or equal to the predicted concentration deviation of the time node in the concentration deviation trend obtained by the last optimization, continue to use the optimized flow set value generated by the last optimization; if the real-time concentration deviation of the sampling time node is greater than the predicted concentration deviation of the time node in the concentration deviation trend obtained by the last optimization, re-optimize dynamically; Flow compensation stage: based on the optimized flow set value, combined with the real-time collected gas flow rate, gas temperature and gas pressure data to adjust the opening of the corresponding mass flow controller, to compensate for the ratio deviation caused by the difference of gas diffusion coefficient, temperature fluctuation and pressure fluctuation.

[0009] Preferably, two-stage pressure reducing valve and check valve are arranged on each electronic special gas pipeline of the mixing cabinet; the flow control of the mass flow controller is based on the initial input ratio and the initial calibration of the gas molecular weight, and the pressure deviation of each pipeline is controlled by the two-stage pressure reducing valve to prevent gas backflow and pressure fluctuation.

[0010] Preferably, the inner walls of all pipelines, valves and mixing devices in contact with electronic special gas are treated with electrolytic polishing to prevent metal ion pollution.

[0011] Preferably, the laser beam path of the tunable semiconductor laser absorption spectrum sensor passes through an inert gas purged protective window and the outlet gas flow channel of the mixing device, and the wavelength is tuned to the isolated peak of the molecular absorption spectrum of the low concentration component gas, so as to capture the concentration transient change in real time.

[0012] Preferably, the protective window and the mixing device are sealed with perfluoroether rubber to prevent external air from entering the analysis light path while ensuring the cleanliness of the electronic special gas.

[0013] Preferably, the model predictive control algorithm constructs a prediction model based on the gas diffusion dynamics model and the thermodynamic state equation, and the objective function is to minimize the integral square sum of concentration deviation in a future period of time, and the optimized flow set value sequence of each component gas is calculated through rolling optimization, and the first element of the sequence is taken as the immediate control instruction.

[0014] Preferably, the gas diffusion dynamics model includes a diffusion coefficient difference compensation term of each component gas, and the diffusion coefficient difference compensation term is dynamically adjusted according to the residence time of the gas in the mixing device, wherein the diffusion compensation weight of the low concentration component is higher than that of the high concentration component.

[0015] Preferably, the flow compensation stage further comprises the following steps: The real-time collected gas flow rate, gas temperature and gas pressure data are input into the ideal gas state equation, and the diffusion coefficient compensation factor of the target electronic special gas is introduced to construct a flow compensation model with flow rate, temperature and pressure as variables; the comprehensive compensation coefficient under the current working condition is calculated through the flow compensation model, and the optimized flow set value is multiplied by the comprehensive compensation coefficient to generate the actual control instruction to adjust the opening of the corresponding mass flow controller.

[0016] Preferably, for different electronic special gases, the diffusion coefficient compensation factor of the target electronic special gas under various flow rate, temperature and pressure combination conditions is obtained through experimental calibration in advance and stored in a compensation factor database; when the flow compensation model is constructed, the diffusion coefficient compensation factor of the target electronic special gas under the current condition is queried from the compensation factor database based on the real-time collected gas flow rate, gas temperature and gas pressure data.

[0017] Preferably, the mixing device comprises a mixing tank with a multi-hole gas injection pipe, wherein the injection holes of the low-density gas are distributed in the lower part of the mixing tank, the injection holes of the high-density gas are distributed in the upper part of the mixing tank, and the sizes of the injection holes are designed according to the density gradient of the gas to promote natural diffusion and shorten the mixing time.

[0018] Preferably, a stirring member is arranged in the mixing tank, which adjusts the working state and movement amplitude according to the density of the mixed gas, and is used to enhance the turbulent mixing effect.

[0019] The second aspect of the present application provides: a dynamic proportioning adjustment system of a gas mixing cabinet for electronic special gases, which is used to realize any one of the above-mentioned dynamic proportioning adjustment methods of the gas mixing cabinet for electronic special gases, and comprises: a flow preset and gas input module, which is used to control the input flow of various electronic special gases through a plurality of mass flow controllers of the gas mixing cabinet, so that the electronic special gases enter the mixing device according to the initial input proportion; a spectrum real-time analysis module, which is used to monitor the concentration of each component of the mixed gas in real time online by using a tunable semiconductor laser absorption spectrum sensor integrated on the mixing device, and obtain real-time concentration data; a dynamic optimization module, which is used to compare the real-time concentration data with the preset target concentration value at the current time node to obtain a real-time concentration deviation, and predict the concentration deviation trend of subsequent time nodes according to the initial input proportion, the real-time concentration data and the real-time concentration deviation by using a model predictive control algorithm; then, the optimized flow set value of each component gas in the subsequent time nodes is dynamically generated according to the concentration deviation trend, and the optimization effect is verified according to the preset interval time sampling to reduce the number of calculations; if the real-time concentration deviation of the sampling time node is less than or equal to the predicted concentration deviation of the time node in the concentration deviation trend obtained by the last optimization, the optimized flow set value generated last is continued to be used; if the real-time concentration deviation of the sampling time node is greater than the predicted concentration deviation of the time node in the concentration deviation trend obtained by the last optimization, dynamic optimization is re-performed; a flow compensation module, which is used to adjust the opening of the corresponding mass flow controller based on the optimized flow set value and in combination with the real-time collected gas flow rate, gas temperature and gas pressure data, so as to compensate the proportioning deviation caused by the difference in gas diffusion coefficient, temperature fluctuation and pressure fluctuation.

[0020] The third aspect of the present application provides a dynamic proportioning adjustment device for a mixed gas cabinet of electronic special gas, comprising a memory and a processor, the memory stores a computer program executable on the processor, and the processor implements any of the above-mentioned dynamic proportioning adjustment methods for the mixed gas cabinet of electronic special gas when executing the computer program.

[0021] The fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium stores computer executable instructions, and the computer executable instructions are loaded and executed by a processor to implement any of the above-mentioned dynamic proportioning adjustment methods for the mixed gas cabinet of electronic special gas.

[0022] The fifth aspect of the present application provides a computer program product comprising instructions, the computer program product is executed on a terminal to make the terminal execute any of the above-mentioned dynamic proportioning adjustment methods for the mixed gas cabinet of electronic special gas.

[0023] The beneficial effects of the present application are: 1) The present application greatly improves the proportioning accuracy of the mixed gas, especially effectively solves the problem of proportioning deviation of low-concentration electronic special gas components caused by the difference in diffusion coefficient. By introducing a TDLAS (tunable diode laser absorption spectroscopy) sensor for real-time online monitoring, the real concentration change of each component can be captured with low delay, providing a data basis for accurate control. Through the model predictive control (MPC) algorithm, the future trend is predicted based on the real-time concentration deviation, and the low-concentration component is given a higher diffusion compensation weight for forward-looking flow regulation, thereby reducing the proportioning error of low-concentration components and meeting the stringent requirements of EUV lithography, advanced chip manufacturing and other high-end processes on gas proportioning.

[0024] 2) The present application realizes high intelligence and efficiency improvement of the mixing process. The traditional "blind tuning" and repeated test emptying method is replaced by the dynamic optimization and sampling verification mechanism based on MPC, the system can make autonomous decisions and respond quickly, reducing the mixing time and greatly reducing the gas waste. The MPC algorithm generates an optimal flow set value sequence through rolling optimization, and combines interval sampling verification to reduce unnecessary computational overhead while ensuring accuracy, improving the response speed and economy of the system.

[0025] 3) The system is enhanced in adaptability and long-term reliability in many aspects to meet the specific requirements of electronic specialty gas applications. At the hardware level, all gas-contacting pipelines and components are required to be electrolytically polished and sealed with perfluoroether rubber to fundamentally prevent metal ion contamination and volatile organic compound release, ensuring ultra-high cleanliness of electronic specialty gas. At the control level, a multi-dimensional compensation algorithm based on gas flow rate, temperature and pressure is introduced to correct density and flow rate changes caused by operating condition fluctuations in real time, overcoming the measurement errors of traditional mass flow controllers (MFC) under temperature and pressure changes, and ensuring the extreme stability of the mixture under long-term operation. BRIEF DESCRIPTION OF DRAWINGS

[0026] Fig. 1 A flow chart of the method of the present application; Fig. 2 A flow chart of real-time spectral analysis; Fig. 3 A principle block diagram of the system of the present application. DETAILED DESCRIPTION

[0027] The technical solutions of the present application will be described in detail below with reference to the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] Referring to Figs. 1-3 , the first aspect of the present application provides: a dynamic mixture ratio adjustment method for electronic specialty gas mixing cabinets, comprising the following steps: Flow rate presetting and gas input stage: the input flow rates of multiple electronic specialty gases are controlled by multiple mass flow controllers of the mixing cabinet to enter the mixing device according to the initial input ratio; Real-time spectral analysis stage: a tunable semiconductor laser absorption spectrum sensor integrated on the mixing device is used to monitor the concentrations of each component of the mixed gas in real time and online, and real-time concentration data is obtained; Dynamic optimization stage: compare the real-time concentration data with the preset target concentration value at the current time node to obtain the real-time concentration deviation, and use the model predictive control algorithm to predict the concentration deviation trend of each subsequent time node according to the initial input ratio, real-time concentration data and real-time concentration deviation; then dynamically generate the optimized flow set value of each component gas in each subsequent time node according to the concentration deviation trend, and reduce the number of calculations according to the preset interval time sampling to verify the optimization effect; if the real-time concentration deviation of the sampling time node is less than or equal to the predicted concentration deviation of the time node in the concentration deviation trend obtained by the last optimization, the optimized flow set value generated last time is continued to be used; if the real-time concentration deviation of the sampling time node is greater than the predicted concentration deviation of the time node in the concentration deviation trend obtained by the last optimization, dynamic optimization is performed again; Flow compensation stage: based on the optimized flow set value, adjust the opening of the corresponding mass flow controller combined with the real-time collected gas flow rate, gas temperature and gas pressure data to compensate for the proportioning deviation caused by the difference in gas diffusion coefficient, temperature fluctuation and pressure fluctuation.

[0029] In this embodiment, the specific implementation of the present application is described in detail by taking the preparation of neon / krypton / fluorine mixed gas for EUV lithography process as an example. The mixed gas requires neon gas as the background gas, accounting for about 98%, krypton gas and fluorine gas as low-concentration key components, accounting for about 1.5% and 0.5% respectively, and the proportioning accuracy requirement is very high (low-concentration component error ≤ ± 2%), and the mixing needs to be completed quickly to adapt to the production line rhythm.

[0030] The mixed gas cabinet system mainly includes: a gas path unit: three independent electronic special gas supply pipelines corresponding to neon gas, krypton gas and fluorine gas respectively. A first-stage pressure reducing valve and a second-stage pressure reducing valve are arranged on the upstream of each pipeline to form two-stage pressure stabilization, so as to ensure that the inlet gas pressure is stabilized at 0.7 MPa ± 0.5%. A check valve (to prevent gas backflow) and a high-precision mass flow controller (MFC) are installed in sequence after the pressure reducing valve, the range of the MFC is selected according to the preset proportion of each gas, and the initial calibration is carried out based on the molecular weight.

[0031] Mixing and detection unit: including a vertical mixing tank. The outlets of three gas lines are extended into the mixing tank through multi-hole nozzles. To achieve efficient mixing, the nozzles of the low-density neon line are mainly distributed in the lower part of the mixing tank, while the nozzles of the high-density krypton and fluorine lines are mainly distributed in the upper part of the mixing tank, and the nozzle sizes are differentiated according to the gas density. The outlet pipeline of the mixing tank is integrated with a TDLAS (Tunable Diode Laser Absorption Spectroscopy) sensor. The laser emission and reception ends of the sensor are connected to the mixing gas flow channel through a perfluoroether rubber sealed inert gas purging protection window, ensuring that the optical components are clean and not affected by the ambient air. The TDLAS sensor is tuned to the characteristic absorption spectrum of krypton and fluorine (especially its isolated absorption peak) with a sampling frequency of 20 Hz to capture the transient concentration changes of low-concentration components in real time. Control unit: the core is an industrial programmable logic controller (PLC) or industrial computer (IPC) with built-in model predictive control (MPC) algorithm and temperature-pressure compensation algorithm. The PLC / IPC is electrically connected to all MFCs, TDLAS sensors, and temperature and pressure sensors installed in the mixing tank.

[0032] In some embodiments, each electronic special gas line of the mixing cabinet is equipped with two-stage pressure reducing valves and check valves; the flow control of the mass flow controller is based on the initial input ratio and the molecular weight of the gas, and the pressure control deviation of each line is controlled by the two-stage pressure reducing valves to prevent gas backflow and pressure fluctuations.

[0033] In some embodiments, all pipelines, valves, and inner walls of the mixing device that come into contact with electronic special gases are treated with electrolytic polishing to prevent metal ion contamination.

[0034] In this embodiment, the flow preset and gas input: the operator inputs the target ratio (Ne: 98%, Kr: 1.5%, F2: 0.5%) through the human-machine interface (HMI). The control system calculates the initial flow set value of each gas according to the ratio and the calibration parameters of the MFC, and sends instructions to the corresponding MFC. The MFC accurately controls the valve opening to allow the three electronic special gases to enter the mixing tank at the initial set flow rate. The two-stage pressure reducing valves and check valves on each line work together to ensure stable gas inlet pressure and prevent pressure fluctuations and gas cross-contamination.

[0035] In some embodiments, the laser beam path of the tunable diode laser absorption spectroscopy sensor passes through an inert gas purging protection window and the outlet gas flow channel of the mixing device, and is tuned to the isolated peak of the molecular absorption spectrum of the low-concentration component gas, thereby capturing the transient concentration changes in real time.

[0036] In some embodiments, the protective window and the mixing device are sealed with perfluoroelastomer to prevent external air from entering the analysis light path while ensuring the cleanliness of the electronic special gas.

[0037] In the present embodiment, the real-time spectral analysis stage further comprises the following steps: Signal acquisition and baseline correction: the laser beam emitted by the tunable diode laser absorption spectroscopy (TDLAS) sensor passes through the gas flow in the mixing device, and the receiving end of the sensor obtains the original spectral signal containing the characteristic absorption information of the target gas; first, the original spectral signal is corrected by selecting a specific wavelength region without the target gas absorption peak as the reference baseline to eliminate the influence of background noise and instrument drift on the measurement results; Feature extraction and peak identification: analyze the baseline-corrected spectral signal to identify the characteristic absorption peaks corresponding to different electronic special gas components, and record the peak intensity and position information of each characteristic absorption peak; Concentration inversion calculation: input the peak intensity and position information of the characteristic absorption peaks into the pre-trained multi-output least squares support vector machine regression (MLS-SVR) algorithm model, which is trained based on standard sample spectral data of different electronic special gas components. The model can simultaneously process the concentration and interference effect of multi-component gas, and calculate the real-time concentration data of each component gas through the least squares support vector machine regression (MLS-SVR) algorithm model.

[0038] In the present embodiment, signal acquisition: the laser is scanned with a low-frequency triangular wave, while superimposing a high-frequency sinusoidal wave for wavelength modulation. The light intensity signal received by the detector is sampled by a high-precision analog-to-digital converter to obtain the original second harmonic signal containing gas absorption information. Baseline correction: the system pre-scans in a pure nitrogen background without target gas and stores a standard baseline signal. In actual measurement, the system identifies the characteristic wavelength region far from the gas absorption peak in each scanning signal. For example, in the scanning range of 1390 nm to 1395 nm, select 1390.5 nm to 1391 nm and 1394.5 nm to 1394.8 nm as the reference baseline region which does not contain absorption peaks. By least squares method, the response of the current scanning signal in these regions is fitted with the stored standard baseline to calculate a baseline offset and slope, and then the baseline is deducted from the original signal in the entire scanning range, effectively eliminating the low-frequency noise caused by optical device drift and background fluctuations. Feature extraction and peak recognition are performed on the baseline-corrected signal: The system has an internal database of electronic special gas feature absorption peak, which stores the standard absorption peak position and shape characteristics of different gas components at a specific wavelength. The algorithm will traverse the corrected signal, calculate its first derivative, and identify the inflection point in the signal through a sliding window peak detection algorithm, which corresponds to the peak value position of the absorption peak. For each peak value identified, the system records the center wavelength and peak intensity. For example, in the detection of neon / krypton / fluorine mixed gas, the system will identify the characteristic peaks corresponding to krypton and fluorine at the same time, and record their data respectively. For overlapping or adjacent absorption peaks, the algorithm will use Gaussian fitting or Lorentz fitting for peak deconvolution to accurately separate the contributions of each component.

[0039] Concentration inversion calculation is the core of this stage, and its specific implementation depends on the pre-trained multi-output least squares support vector machine regression model: Model training: Under laboratory conditions, a large number of spectral scans are performed on a variety of electronic special gas standard sample mixtures with known accurate concentrations at various temperatures and pressures to construct a spectral database containing tens of thousands of samples. Each sample data includes the peak intensity, position information of each feature absorption peak, and the corresponding true concentration value. The MLS-SVR model is trained using this database. The model selects the radial basis function as the kernel function and optimizes the regularization parameter and kernel function width through cross-validation to ensure the generalization ability and prediction accuracy of the model. Real-time calculation: The extracted real-time peak intensity and position information (as a multi-dimensional feature vector) are input into the trained MLS-SVR model. The advantage of this model is its multi-output characteristic, which can simultaneously process the concentration information of multiple gas components and effectively learn the mutual interference relationship between different component absorption peaks. After receiving the input vector, the model performs internal calculations and directly outputs a vector containing the concentration values of each component, such as [Ne concentration, Kr concentration, F2 concentration]. This process realizes the nonlinear mapping from spectral features to concentration values, with high computational efficiency and strong anti-cross interference ability.

[0040] In the embodiment of preparing neon / krypton / fluorine mixed gas for EUV lithography process, when the mixed gas flows through the detection area of the TDLAS sensor, the specific wavelength laser beam emitted by the sensor passes through the gas. Krypton and fluorine molecules will absorb light of a specific wavelength, causing the light intensity to decay. The TDLAS sensor detects the transmitted light intensity signal in real time and performs concentration inversion calculation based on the Beer-Lambert Law. To obtain high-precision data, the system performs real-time condition filtering on the original spectral signal, such as subtracting background noise through low-pass filtering and filtering out invalid spectral signals by setting a threshold. The real-time concentration data of krypton and fluorine calculated by inversion is uploaded to the control unit at a frequency of 20Hz.

[0041] In some embodiments, the model predictive control algorithm constructs a prediction model based on a gas diffusion kinetics model and a thermodynamic state equation, and the objective function is to minimize the integral square sum of concentration deviation in a future period of time, and the optimization flow rate set value sequence of each component gas is calculated by rolling optimization, and the first element of the sequence is taken as the immediate control instruction.

[0042] In some embodiments, the gas diffusion kinetics model contains a diffusion coefficient difference compensation term of each component gas, which dynamically adjusts according to the residence time of the gas in the mixing device, wherein the diffusion compensation weight of the low-concentration component is higher than that of the high-concentration component.

[0043] In this embodiment, dynamic optimization (MPC control): the control unit compares the real-time concentration data fed back by the TDLAS (such as Kr: 1.52%, F2: 0.48%) with the target concentration to obtain the real-time concentration deviation. The built-in model predictive control (MPC) algorithm starts to work. The algorithm is based on a prediction model containing gas diffusion kinetics and thermodynamic state equation. The model takes into account the dynamic mixing behavior of different gases (especially low-concentration components Kr and F2) in the mixing tank due to the difference in diffusion coefficient, and sets a higher compensation weight for low-concentration components. The MPC algorithm also predicts the concentration deviation trend in a future period of time (the specific time is set according to the actual working environment, and the longer the set time, the greater the prediction error at the subsequent time node, which needs to be considered comprehensively). Based on this prediction, the algorithm dynamically generates an optimized flow rate set value sequence for each component gas in a future period of time. In order to reduce the calculation burden of the controller, the system sets a sampling verification every preset time (not limited to a specific time value, which also depends on the actual situation and needs to be balanced between calculation resources and precision requirements). If the actual concentration deviation at the sampling point is less than or equal to the predicted value, the current optimized set value is maintained; if the actual deviation is greater than the predicted value, a new round of MPC optimization calculation is immediately performed to ensure the rapidity and accuracy of the control.

[0044] In some embodiments, the flow compensation stage further comprises the following steps: The real-time collected gas flow rate, gas temperature and gas pressure data are input into the ideal gas state equation, and the diffusion coefficient compensation factor of the target electronic special gas is introduced to construct a flow compensation model with flow rate, temperature and gas pressure as variables; the comprehensive compensation coefficient under the current working condition is calculated by the flow compensation model, and the actual control instruction is generated by multiplying the optimized flow rate set value by the comprehensive compensation coefficient to adjust the opening of the corresponding mass flow controller.

[0045] In some embodiments, the diffusion coefficient compensation factor of the target electronic special gas under various combinations of flow rate, temperature and pressure is obtained by pre-experiment calibration, and is stored in a compensation factor database; when constructing the flow compensation model, the diffusion coefficient compensation factor of the target electronic special gas under the current working condition is queried from the compensation factor database based on the real-time collected gas flow rate, gas temperature and pressure data.

[0046] In the present embodiment, the system will pre-experimentally establish a compensation factor database for different electronic special gases. The database records the compensation values corresponding to each gas under various combinations of gas flow rate, temperature and working pressure.

[0047] Real-time parameter acquisition: During operation, the system real-time monitors three key parameters: the gas flow rate flowing through the mass flow controller (MFC), the gas temperature in the mixing device and the pressure data.

[0048] Coefficient calculation: The system calculates the final comprehensive compensation coefficient (i.e. the diffusion coefficient compensation factor of the target electronic special gas under the current working condition) based on the real-time collected flow rate, temperature and pressure data through a weighted geometric mean model. The weighted geometric mean model will assign a weight factor to each parameter (flow rate, temperature, pressure), and these weight factors are related to the diffusion characteristics of the target electronic special gas. The calculation process is essentially a weighted comprehensive operation of the ratio of the real-time value of each parameter to its standard reference value. The multi-dimensional compensation with real-time flow rate v, real-time temperature T and real-time pressure P as variables includes the following core steps: Compensation factor matching step: The system has a compensation factor database for different electronic special gases. The database is established by pre-experiment calibration, and stores the compensation values of the target electronic special gas under different combinations of flow rate, temperature and pressure.

[0049] Comprehensive compensation coefficient calculation: A compensation model based on weighted geometric mean is used to calculate the comprehensive compensation coefficient K_comp under the current working condition: K_comp = α * (v / v0) k_v + β * (T / T0) k_T + γ * (P / P0) k_P Wherein, v0, T0 and P0 are the standard reference values of flow rate, temperature and pressure, which are pre-set, and are usually the benchmark values measured under ideal and stable calibration conditions in the laboratory (such as a specific flow rate, 25°C temperature, standard atmospheric pressure), or the standard working condition points specified by the process; as the benchmark for calculating the relative change ratio, the formula calculates the change ratio of the real-time value relative to the standard value (such as v / v0), thereby quantifying the degree of deviation of the current working condition from the standard working condition.

[0050] a, b and g are weight factors assigned to the three compensation terms of flow rate, temperature and pressure respectively (a+b+g=1); used to measure the relative importance of the three factors on the mixture deviation under different physical properties of the gas (mainly the diffusion coefficient); for example: for the gas with slow diffusion speed and susceptible to flow state, the weight a of flow rate will be set higher; for the gas sensitive to temperature, the weight b of temperature will be higher, the weight factors are determined through gas property analysis and experimental calibration.

[0051] k_v, k_T and k_P are the sensitivity indexes of flow rate, temperature and pressure respectively determined by fitting the compensation factor database, which are the indexes reflecting the sensitivity of the changes of each working condition parameter on the final flow compensation; since the effects of flow rate, temperature and pressure on gas flow and mixing behavior are not simple linear relationships, these indexes are used to characterize their nonlinear effects. For example, k_v is close to 1 (linear effect), while k_T is greater than 1, indicating that the temperature change has a more sensitive and significant effect on the mixture.

[0052] Command generation: finally, the optimized flow set value is multiplied by the calculated comprehensive compensation coefficient to generate the actual control command sent to the MFC, realizing accurate adjustment of the flow valve.

[0053] In some embodiments, the mixing device includes a mixing tank with a multi-hole gas injection pipe, wherein the injection holes of the low-density gas are distributed in the lower part of the mixing tank, the injection holes of the high-density gas are distributed in the upper part of the mixing tank, and the sizes of the injection holes are designed according to the density gradient of the gas to promote natural diffusion and shorten the mixing time.

[0054] In this embodiment, the core purpose of injecting the high-density gas from the upper part of the mixing tank and the low-density gas from the lower part of the mixing tank is to accelerate the mixing by utilizing the natural diffusion characteristics of the gas itself and shorten the mixing time. The solutions adopting this design idea are within the protection scope of the present application, which are not limited to a specific mixing tank structure.

[0055] In some embodiments, a stirring member is arranged in the mixing tank, which adjusts the working state and movement amplitude according to the density of the mixed gas, for enhancing the turbulent mixing effect.

[0056] In this embodiment, further for the application scenarios where the mixing time is too long in specific cases, a stirring member can be optionally arranged in the mixing tank to enhance the mixing effect; further, the mixing effect can also be enhanced by accelerating the gas flow rate of the gas of different densities from the injection holes, and the way of accelerating the gas flow rate can be used alone or together with the scheme of arranging the stirring member, which is determined according to the specific engineering situation.

[0057] For example, the mixing tank is a cylindrical tank with a diameter of 300 mm and a height of 400 mm, and the gas injection pipe is a cylindrical pipe with a diameter of 50 mm and a height of 300 mm, and the injection holes of the low-density gas are arranged in the lower part of the pipe, and the injection holes of the high-density gas are arranged in the upper part of the pipe. Fig. 3As shown, the second aspect of the present application provides a dynamic proportioning adjustment system for a gas mixing cabinet for electronic special gases, which is used to implement any of the above-mentioned dynamic proportioning adjustment methods for a gas mixing cabinet for electronic special gases, and comprises: a flow pre-setting and gas input module, which is used to control the input flow of each electronic special gas through a plurality of mass flow controllers of the gas mixing cabinet, so that each electronic special gas enters the mixing device according to an initial input proportion; a real-time spectrum analysis module, which is used to monitor the concentration of each component of the mixed gas in real time and online by using a tunable semiconductor laser absorption spectrum sensor integrated on the mixing device, and obtain real-time concentration data; a dynamic optimization module, which is used to compare the real-time concentration data with preset target concentration values at current time nodes to obtain real-time concentration deviations, and predict the concentration deviation trend at subsequent time nodes according to the initial input proportion, the real-time concentration data and the real-time concentration deviations by using a model predictive control algorithm; then, the dynamic optimization module is used to dynamically generate optimized flow set values of each component gas at subsequent time nodes according to the concentration deviation trend, and reduce the number of calculations by sampling and verifying the optimization effect according to a preset interval time; if the real-time concentration deviation at the sampling time node is less than or equal to the predicted concentration deviation of the time node in the concentration deviation trend obtained by the last optimization, the optimized flow set values generated by the last optimization are continuously used; if the real-time concentration deviation at the sampling time node is greater than the predicted concentration deviation of the time node in the concentration deviation trend obtained by the last optimization, dynamic optimization is performed again; a flow compensation module, which is used to adjust the opening degree of the corresponding mass flow controller based on the optimized flow set values in combination with the real-time collected gas flow rate, gas temperature and gas pressure data, so as to compensate for the proportioning deviation caused by the difference in gas diffusion coefficient, temperature fluctuation and pressure fluctuation.

[0058] The third aspect of the present application provides a dynamic proportioning adjustment device for a gas mixing cabinet for electronic special gases, which comprises a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements any of the above-mentioned dynamic proportioning adjustment methods for a gas mixing cabinet for electronic special gases when executing the computer program.

[0059] The fourth aspect of the present application provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions are loaded and executed by a processor to implement any of the above-mentioned dynamic proportioning adjustment methods for a gas mixing cabinet for electronic special gases.

[0060] The fifth aspect of the present application provides a computer program product comprising instructions, which, when executed on a terminal, causes the terminal to perform any of the above-mentioned dynamic proportioning adjustment methods for a gas mixing cabinet for electronic special gases.

[0061] The foregoing is considered as illustrative only of the principles of the application. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the application to the exact construction and operation described. Accordingly, all such variations are intended to be included within the scope of the present application as defined in the claims below and their equivalents.

Claims

1. A method for dynamic proportioning adjustment of a mixing chamber for electronic specialty gases, characterized in that: Includes the following steps: Flow preset and gas input stage: The input flow of various electronic special gases is controlled by multiple mass flow controllers in the mixing cabinet, so that they enter the mixing device according to the initial input ratio; Real-time spectral analysis stage: Using a tunable semiconductor laser absorption spectral sensor integrated on the mixing device, the concentration of each component of the mixed gas is monitored online in real time, and real-time concentration data is obtained; Dynamic optimization phase: The real-time concentration data is compared with the preset target concentration value at the current time node to obtain the real-time concentration deviation. A model predictive control algorithm is used to predict the concentration deviation trend at subsequent time nodes based on the initial input ratio, real-time concentration data, and real-time concentration deviation. Then, the optimized flow rate setpoints for each component gas at subsequent time nodes are dynamically generated based on the concentration deviation trend. The optimization effect is verified by sampling at preset intervals to reduce the number of calculations. If the real-time concentration deviation at the sampling time node is less than or equal to the predicted concentration deviation at that time node in the concentration deviation trend obtained in the previous optimization, the optimized flow rate setpoints generated in the previous optimization are used. If the real-time concentration deviation at the sampling time node is greater than the predicted concentration deviation at that time node in the concentration deviation trend obtained in the previous optimization, dynamic optimization is performed again. Flow compensation stage: Based on the optimized flow setpoint, the opening of the corresponding mass flow controller is adjusted in combination with the real-time collected gas flow rate, gas temperature and gas pressure data to compensate for the ratio deviation caused by differences in gas diffusion coefficient, temperature fluctuation and pressure fluctuation.

2. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to claim 1, characterized in that: Each electronic special gas pipeline in the gas mixing cabinet is equipped with a two-stage pressure reducing valve and a check valve; The mass flow controller's flow control is initially calibrated based on the initial input ratio and gas molecular weight, and the pressure in each pipeline is controlled by two-stage pressure reducing valves to prevent gas backflow and pressure fluctuations.

3. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to claim 2, characterized in that: All pipes, valves, and mixing devices that come into contact with electronic specialty gases have their inner walls electropolished to prevent metal ion contamination.

4. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to claim 1, characterized in that: The laser beam path of the tunable semiconductor laser absorption spectroscopy sensor passes through an inert gas-purged protective window and the outlet gas flow channel of the mixing device, and the wavelength is tuned to select isolated peaks of the molecular absorption spectrum of the low-concentration component gas, thereby capturing transient changes in concentration in real time.

5. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to claim 4, characterized in that: The protective window and the mixing device are sealed with perfluoroether rubber to prevent outside air from entering the analytical optical path and to ensure the cleanliness of the electronic special gas.

6. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to claim 1, characterized in that: The model predictive control algorithm described above is based on a gas diffusion dynamics model and a thermodynamic equation of state to construct a predictive model. Its objective function is to minimize the integral sum of squares of the concentration deviation over a future period of time. It calculates the optimal flow setpoint sequence for each component gas through rolling optimization and uses the first element of the sequence as an immediate control command.

7. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to claim 6, characterized in that: The gas diffusion kinetics model includes a diffusion coefficient difference compensation term for each component gas. This diffusion coefficient difference compensation term is dynamically adjusted according to the residence time of the gas in the mixing device, wherein the diffusion compensation weight of the low-concentration component is higher than that of the high-concentration component.

8. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to claim 1, characterized in that: The traffic compensation phase also includes the following steps: The real-time collected gas velocity, gas temperature, and gas pressure data are input into the ideal gas equation of state, and a diffusion coefficient compensation factor for the target electronic special gas is introduced to construct a flow compensation model with velocity, temperature, and gas pressure as variables. The comprehensive compensation coefficient under the current operating condition is calculated through the flow compensation model, and the optimized flow setpoint is multiplied by the comprehensive compensation coefficient to generate an actual control command to adjust the opening degree of the corresponding mass flow controller.

9. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to claim 8, characterized in that: For different electronic specialty gases, the diffusion coefficient compensation factor of the target electronic specialty gas under various combinations of flow rate, temperature and pressure is obtained in advance through experimental calibration and stored in the compensation factor database. When constructing the flow compensation model, the diffusion coefficient compensation factor of the target electronic specialty gas under the current operating condition is queried from the compensation factor database based on the real-time collected gas flow rate, gas temperature and pressure data.

10. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to any one of claims 1-9, characterized in that: The mixing device includes a mixing tank with a porous jet pipe, wherein the nozzles for low-density gas are distributed in the lower part of the mixing tank and the nozzles for high-density gas are distributed in the upper part of the mixing tank, and the nozzle size is designed according to the gas density gradient to promote natural diffusion and shorten the mixing time.

11. The method for dynamic proportioning adjustment of a mixing chamber for electronic special gases according to claim 10, characterized in that: The mixing tank is equipped with a stirring element, which adaptively adjusts its working state and movement amplitude according to the density of the mixed gas to enhance the turbulent mixing effect.

12. A dynamic proportioning adjustment system for a mixing chamber used for electronic special gases, characterized in that: The method for dynamically adjusting the mixing ratio of a gas mixing chamber for electronic special gases as described in any one of claims 1-11 includes: The flow preset and gas input module is used to control the input flow of various electronic special gases through multiple mass flow controllers in the mixing cabinet, so that they enter the mixing device according to the initial input ratio. The real-time spectral analysis module is used to monitor the concentration of each component of the mixed gas in real time and obtain real-time concentration data using a tunable semiconductor laser absorption spectral sensor integrated on the mixing device. The dynamic optimization module compares real-time concentration data with the preset target concentration value at the current time node to obtain the real-time concentration deviation. It then uses a model predictive control algorithm to predict the concentration deviation trend for subsequent time nodes based on the initial input ratio, real-time concentration data, and real-time concentration deviation. Based on this trend, it dynamically generates optimized flow rate setpoints for each gas component at subsequent time nodes and verifies the optimization effect by sampling at preset intervals to reduce the number of calculations. If the real-time concentration deviation at the sampling time node is less than or equal to the predicted concentration deviation for that time node in the previous optimization trend, the previously generated optimized flow rate setpoint is used. If the real-time concentration deviation at the sampling time node is greater than the predicted concentration deviation for that time node in the previous optimization trend, dynamic optimization is performed again. The flow compensation module is used to adjust the opening of the corresponding mass flow controller based on the optimized flow setpoint and the real-time collected gas flow rate, gas temperature and pressure data, in order to compensate for the ratio deviation caused by differences in gas diffusion coefficient, temperature fluctuation and pressure fluctuation.

13. A dynamic proportioning adjustment device for a mixing chamber of electronic specialty gases, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that: When the processor executes the computer program, it implements the dynamic proportioning adjustment method for the mixing cabinet of electronic special gases as described in any one of claims 1-11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the dynamic proportioning adjustment method for a mixing chamber for electronic special gases as described in any one of claims 1-11.

15. A computer program product containing instructions, characterized in that: When the computer program product is run on a terminal, the terminal executes the dynamic proportioning adjustment method for a mixing chamber for electronic special gases as described in any one of claims 1-11.

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