Intelligent monitoring system applied to HCI conveying pipeline of epitaxy process

Abstract

This invention discloses an intelligent monitoring system for HCl delivery pipelines in epitaxial processes, belonging to the field of intelligent control technology for epitaxial processes. The invention includes a multi-source data acquisition module, a multi-level linkage control module, a nitrogen purging module, a pressure relief protection module, a weight monitoring module, and an intelligent control module. The data acquisition range is constructed based on the connection area between the CHI tank truck and the delivery pipeline within the plant area. This invention relies on graded pressure relief deviation to achieve pipeline pressure stabilization and safety protection, and uses real-time HCl inventory calculation to achieve real-time monitoring of tank truck remaining capacity and seamless intelligent switching of gas sources. Simultaneously, based on pressure and temperature temporal fluctuation analysis, it achieves early identification and emergency protection of minor leaks, solving the shortcomings of traditional systems such as incomplete purging, delayed leak detection, and reliance on manual gas source switching. This comprehensively improves the safety, continuity, and intelligence level of HCl delivery, reducing production safety risks and manual maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for epitaxial processes, specifically to an intelligent monitoring system applied to HCI delivery pipelines in epitaxial processes. Background Technology

[0002] Epitaxial growth is a core step in semiconductor chip manufacturing, enabling the growth of single-crystal thin films. Hydrogen chloride (HCl) is a key special gas source used for etching and cleaning in epitaxial growth. Its delivery pressure, gas temperature, supply continuity, and purity stability directly determine the uniformity of the epitaxial layer and the final yield of semiconductor devices. The HCl gas source required for epitaxial growth is usually supplied centrally by tank trucks. The tank trucks are connected to the epitaxial process equipment through dedicated pipelines. Multiple control operations, such as pressure regulation, temperature maintenance, pipeline purging, and pressure relief protection, need to be completed during the transportation process to meet the high precision, high safety, and high continuity requirements of the special gas source in epitaxial growth.

[0003] Existing HCl tank truck transport control technology has significant application defects. A method and system for automated switching based on gas supply equipment, published in CN119179314A, relies solely on single-point pressure and temperature data from the tank truck to achieve automated gas source switching, without optimization for multi-stage heating and series transport scenarios in epitaxial processes. During multi-stage heating, HCl experiences volume expansion and pressure accumulation; each stage of pressure relief device operates only according to independent thresholds, lacking coordinated linkage between stages, making it highly susceptible to issues where the pressure at the pipeline end exceeds the process set value. HCl is a highly corrosive and toxic gas; even minor leaks are difficult to detect early using traditional single-point detection methods, often triggering alarms only after the leak has expanded, easily leading to pipeline corrosion and safety risks. Furthermore, the purging gas source configuration is chaotic, the pressure relief pathway linkage is poor, and residual HCl in the pipeline is not thoroughly cleaned during tank truck replacement. Simultaneously, the lack of real-time monitoring and intelligent predictive switching mechanisms for HCl weight in the tank truck makes it impossible to guarantee a continuous and stable gas supply for epitaxial processes, and the overall control accuracy and safety protection capabilities are insufficient to meet the needs of high-end semiconductor epitaxial production.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent monitoring system for HCI delivery pipelines in epitaxial processes, in order to solve the problems mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent monitoring system for HCI delivery pipelines in epitaxial processes, comprising a multi-source data acquisition module, a multi-level linkage control module, a nitrogen purging module, a pressure relief protection module, a weight monitoring module, and an intelligent control module;

[0007] The data acquisition range is constructed based on the connection area between the CHI tank truck and the conveying pipeline in the plant area. The multi-source data acquisition module acquires the original data of the entire conveying process based on the data acquisition range and verifies and generates a standard basic dataset.

[0008] The multi-level linkage control module generates heating and pressure stabilization control commands based on the standard basic dataset; the nitrogen purging module retrieves some data from the standard basic dataset and performs linkage analysis to generate pipeline residual equivalent values ​​and purging mode adaptation signals.

[0009] The pressure relief protection module generates graded pressure relief deviation and graded pressure stabilization control signals based on the standard basic dataset. The weight monitoring module analyzes the standard basic dataset as the source data to obtain the real-time HCl inventory calculation value. The intelligent control module integrates all received data and signals, summarizes and generates a full-process control command set to realize delivery regulation, purging and cleaning, pressure relief and stabilization, residual monitoring and intelligent gas source switching.

[0010] Furthermore, the multi-source data acquisition module acquires the raw data of the front-end pressure, middle-end temperature, and end-end pressure and temperature of the delivery pipeline according to the data acquisition range. The raw data is compared and verified with the preset normal range, and abnormal data is removed and integrated into a standard basic dataset, providing a unique and valid basis for subsequent modules and ensuring that all analysis data is true and usable.

[0011] Furthermore, the multi-level linkage control module obtains the heating demand determination value through linkage analysis between the intermediate temperature and the preset process temperature, and adjusts the heating power according to the determination value.

[0012] Furthermore, the pressure compensation calculation value is obtained by calculating the difference between the front-end pressure and the end-end pressure. Combined with the pressure compensation calculation value, the pressure is accurately compensated to eliminate the transport fluctuations caused by the heating of HCl.

[0013] Furthermore, the nitrogen purging module calls the standard basic dataset, preset pipeline volume data, and HCl pipeline pressure residual volume conversion coefficient. By using the unit time decay rate of the front-end pressure and the end pressure, and combining the pipeline volume and pipeline pressure residual volume conversion coefficient, it progressively calculates the pipeline residual equivalent value. The pipeline residual equivalent value is compared with the preset purging threshold to obtain the purging mode adaptation signal. Based on the purging mode adaptation signal, the purging mode is automatically selected and the start and stop are controlled.

[0014] Furthermore, the nitrogen purging module uses the pipeline residual equivalent value as the criterion, and compares and analyzes the high and low level thresholds with the pipeline residual equivalent value: when the pipeline residual equivalent value is higher than the high-level purging threshold among the high and low level thresholds, a high-pressure purging adaptation signal is generated; when the pipeline residual equivalent value is within the high and low range of the high and low level thresholds, a low-pressure purging adaptation signal is generated; when the pipeline residual equivalent value is lower than the low-level purging threshold among the high and low level thresholds, a purging stop signal is generated, thereby realizing adaptive matching of the purging mode.

[0015] Furthermore, the pressure relief protection module calls the standard basic dataset and temporarily collects the intermediate auxiliary pressure data. It calculates the front-end pressure relief deviation by comparing the front-end pressure with the preset safety pressure upper limit, and calculates the back-end pressure stabilization deviation by comparing the end pressure with the preset process pressure standard value, thus completing the two-stage pressure stabilization control and generating a graded pressure stabilization control signal.

[0016] The weight monitoring module calls the standard basic dataset and preset empty tanker weight data, performs difference analysis between the real-time weight of the tanker and the empty weight to obtain the real-time HCl inventory calculation value, and uploads the real-time HCl inventory calculation value to the control platform in real time, providing accurate data support for gas source switching.

[0017] Furthermore, the intelligent control module compares the real-time HCl inventory calculation value with the preset minimum supply threshold to obtain a gas source switching trigger signal. Based on the gas source switching trigger signal, it automatically starts the backup tanker and shuts down the tanker in use, thereby realizing uninterrupted intelligent switching of the epitaxial process gas source.

[0018] Furthermore, the intelligent control module calls the standard basic dataset to perform time-series analysis on the continuously collected effective pressure data and effective temperature data, calculates the pressure fluctuation rate and temperature fluctuation rate, and then obtains the leakage safety judgment value through weighted calculation. The leakage safety judgment value is compared with the preset leakage threshold to obtain the safety protection action signal, and the emergency shutdown and safety alarm are automatically executed based on the signal.

[0019] The beneficial effects of this invention are:

[0020] 1. This invention constructs a standard basic dataset through multi-source data acquisition and validity verification. Based on the heating demand judgment value and pressure compensation calculation value, it realizes the linkage control of heating power and delivery pressure, which completely solves the technical problems of pressure accumulation, lack of pressure relief coordination, and large temperature and pressure fluctuations in the multi-stage heating and delivery of HCl in epitaxial processes. It ensures that the HCl delivery pressure and temperature continuously and stably match the requirements of epitaxial processes, guarantees the uniformity of epitaxial layer growth from the gas supply level, and effectively improves the product quality and production yield of semiconductor epitaxial wafers.

[0021] 2. This invention achieves adaptive matching of nitrogen purging mode by calculating the residual equivalent value of pipelines, completes pipeline pressure stabilization and safety protection by relying on the graded pressure relief deviation, realizes real-time monitoring of tank truck balance and seamless intelligent switching of gas source by calculating the real-time HCl inventory value, and realizes early identification and emergency protection of trace leaks based on pressure and temperature time-series fluctuation analysis. It solves the defects of traditional systems such as incomplete purging, delayed leak detection and reliance on manual gas source switching, comprehensively improves the safety, continuity and intelligence level of HCl transportation, and reduces production safety risks and manual operation and maintenance costs. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a system flowchart of the present invention;

[0024] Figure 2 This is a schematic diagram of the delivery pipeline of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1: Please refer to Figure 1 - Figure 2 As shown, this embodiment is an intelligent monitoring system applied to the HCI delivery pipeline of epitaxial process, including a multi-source data acquisition module, a multi-level linkage control module, a nitrogen purging module, a pressure relief protection module, a weight monitoring module and an intelligent control module;

[0027] The multi-source data acquisition module first performs raw data acquisition operations. Based on the connection area between the CHI tank truck and the conveying pipeline in the plant area, the data acquisition range is constructed. The multi-source data acquisition module obtains the raw pressure data of the front end of the HCl tank truck outlet and the connection of the conveying pipeline, the raw temperature data of the middle part of the multi-stage heating area in the conveying pipeline, the raw pressure data of the end of the connection between the conveying pipeline and the inlet area of ​​the process equipment, and the raw temperature data of the end of the area. The module then transmits the raw data throughout the process and verifies and generates a standard basic dataset.

[0028] Then, a data validity verification operation is performed: the intelligent control system presets the normal range of each raw data, of which the normal range of front-end pressure is 0.8MPa-1.2MPa, and the normal range of middle and end temperature is 35℃-45℃; each raw data collected is compared with the corresponding preset normal range one by one, and data that exceeds the normal range is judged as abnormal data and directly removed, and only valid data that meets the range requirements is retained.

[0029] Finally, the standard basic dataset generation operation is performed: the valid front-end pressure data, mid-end temperature data, terminal pressure data, and terminal temperature data that have passed verification are integrated and packaged to generate a standard basic dataset, which is stored in the memory area of ​​the intelligent control system. This dataset serves as the sole basis for analysis of all subsequent modules, ensuring data validity from the source and preventing invalid data from interfering with subsequent calculations.

[0030] The process by which the multi-level linkage control module generates heating and voltage stabilization control commands based on a standard basic dataset:

[0031] The first step is to calculate the heating demand determination value: calculate the difference between the effective mid-range temperature data and the preset process temperature data, according to the formula. ,in, This represents the heating demand determination value, and the heating power adjustment requirement; when the heating demand determination value... A value greater than 0 indicates that the temperature is too high and power needs to be reduced; when the heating demand is determined... A value less than 0 indicates that the temperature is too low and power needs to be increased; when the heating demand is determined... A value of 0 indicates that the temperature meets the standard and no adjustment is needed.

[0032] The second step involves adaptive adjustment of heating power: based on the heating demand value. The operating power of the primary heating unit A2 and the secondary heating unit A4 in the conveying pipeline is automatically adjusted, such as... Figure 2 As shown, the intermediate temperature is stably controlled within the preset process temperature range;

[0033] The third step is to calculate the pressure compensation value: calculate the difference between the effective pressure data at the front end and the effective pressure data at the back end, according to the formula. ,in, This represents the pressure compensation calculation value, and the adjustment range for pressure compensation; when the pressure compensation calculation value... A value greater than 0 indicates insufficient end pressure requiring compensation. When the pressure compensation calculation value is... A value less than 0 indicates that the pressure at the front end is too high and needs to be released.

[0034] The fourth step is to perform fluctuation elimination: simultaneously complete the heating power adjustment and pressure compensation operations to completely eliminate the pressure and temperature fluctuations caused by the thermal expansion of HCl, output heating and pressure stabilization control commands, and ensure the stability of the gas source parameters for the epitaxial process.

[0035] After the nitrogen purging module starts, it first calls the standard basic dataset, and simultaneously retrieves the preset pipeline volume data and the conversion factor for the residual pressure volume of the HCl pipeline. The preset pipeline volume data is a fixed value of the total volume of the delivery pipeline built into the intelligent control system.

[0036] The first step is to calculate the residual equivalent value of the pipeline: by combining the decay rate of effective pressure data at the front end and the effective pressure data at the end of the pipeline with the preset pipeline volume data, the value is calculated according to the formula. ,in, It is expressed as the pressure decay rate, and as the total amount of HCl remaining in the pipeline; This represents the valid front-end pressure data acquired at the first sampling time; This represents the valid front-end pressure data acquired at the second sampling time; This is expressed as the sampling time interval; then according to the formula... ,in, This is expressed as the equivalent residual pressure in the pipeline; then, according to the formula... ,in, It is expressed as the pipeline residual equivalent value, and as the total amount of HCl residual gas in the pipeline, which can be directly used to determine the purging level. This is represented as preset pipeline volume data; This is expressed as the HCl pipeline pressure residual volume conversion factor;

[0037] The second step is to generate a purging mode adaptation signal: retrieve the system's built-in preset high and low level thresholds, which include a high-level purging threshold and a preset low-level purging threshold, and then set the pipeline residual equivalent value. Comparison with preset high-level purging threshold and preset low-level purging threshold:

[0038] If the pipeline residual equivalent value >A high-level purging threshold is preset, and it is determined that the residual HCl in the pipeline is at a high level, requiring rapid and powerful cleaning. A high-pressure purging adaptation signal is generated as the only purging mode command.

[0039] If the preset low-level purging threshold is less than the pipeline residual equivalent value If the preset high-level purging threshold is set, and the HCl residue in the pipeline is determined to be at a medium level, requiring gentle cleaning, a low-pressure purging adaptation signal will be generated as the only purging mode instruction.

[0040] If the pipeline residual equivalent value If the preset low-level purging threshold is set, it is determined that the residual HCl in the pipeline has been cleaned up and no further purging is needed. A purging stop signal is generated as the only purging mode command.

[0041] The generated high-pressure purging adaptation signal, low-pressure purging adaptation signal, and purging stop signal are summarized into a purging mode adaptation signal and output to the nitrogen purging execution units HPN2 and LPN2 to control the start, switching, or stop of the corresponding purging mode.

[0042] The third step is to perform purging start-stop control: based on the purging mode adaptation signal, automatically control the start-up, operation mode switching and stop actions of the nitrogen purging unit to realize the automatic cleaning of residual HCl in the pipeline.

[0043] Example 2: After the pressure relief protection module is started, it first calls the standard basic dataset and temporarily collects intermediate auxiliary pressure data. This data is only used to assist in pressure relief control and is not included in the standard basic dataset.

[0044] The first step is to calculate the pre-heating pressure relief deviation: For the pre-heating pipeline pressure, the difference between the effective front-end pressure data and the preset safe pressure data is calculated. The pre-heating pressure relief deviation = effective front-end pressure data - preset safe pressure upper limit, which represents the extent to which the pre-heating pipeline pressure exceeds the standard. When the pre-heating pressure relief deviation is greater than 0, it indicates that primary pressure relief is required, and primary pressure relief control is performed to stabilize the pre-heating pressure within the safe range. When the pre-heating pressure relief deviation is less than or equal to 0, it indicates that no pressure relief is required.

[0045] The second step is to calculate the post-stage pressure stabilization deviation: For the pipeline pressure after heating, the difference between the effective end pressure data and the preset process pressure data is calculated. The post-stage pressure stabilization deviation = effective end pressure data - preset process pressure standard value, which represents the extent to which the pipeline pressure exceeds the standard after heating. When the post-stage pressure stabilization deviation is greater than 0, it indicates that secondary pressure stabilization is required. The pressure relief valve on the middle end is opened to perform secondary pressure stabilization and control, stabilizing the pressure after heating within the process requirement range. When the post-stage pressure stabilization deviation is less than or equal to 0, it indicates that pressure stabilization is not required.

[0046] The third step is to execute the graded pressure stabilization and control signal output: Based on the calculation results of the two-level deviation, the graded pressure stabilization and control signal is generated to realize graded pressure auxiliary adjustment on both sides of the middle pipeline, so as to avoid pressure accumulation leading to pipeline damage.

[0047] After the weight monitoring module starts, it first calls the standard basic dataset, which contains real-time valid weight data of tank trucks. At the same time, it retrieves the preset empty weight data of tank trucks, which is the fixed empty weight value of tank trucks built into the system.

[0048] The first step is to calculate the real-time HCl inventory: the difference between the real-time effective weight data of the tank truck and the preset empty weight data of the tank truck is calculated. The real-time HCl inventory is calculated as follows: real-time effective weight data of the tank truck minus preset empty weight data of the tank truck. The real-time HCl inventory is calculated as follows, which represents the real-time remaining total amount of HCl in the tank truck and provides a basis for gas source switching.

[0049] The second step is to perform data upload: The real-time HCl inventory calculation value is uploaded to the plant's intelligent management and control platform. The platform simultaneously displays the calculation value, gas consumption rate, remaining usage time, and other information, providing intuitive, accurate, and unique data support for intelligent gas source switching.

[0050] When the intelligent control module executes gas source switching control, it first calls the real-time HCl inventory calculation value generated by the weight monitoring module, and at the same time retrieves the preset minimum supply threshold. The preset minimum supply threshold is the minimum HCl inventory requirement for stable supply of the epitaxial process, and the fixed value can be 100kg.

[0051] The first step is to generate a gas source switching trigger signal: the real-time HCl inventory is accurately compared with the preset minimum supply threshold. When the real-time HCl inventory is lower than the preset minimum supply threshold, a gas source switching trigger signal is generated, indicating the execution requirement for gas source switching; when the real-time HCl inventory is greater than the preset minimum supply threshold, no action is taken.

[0052] The second step is to execute the gas source switching command output: Based on the gas source switching trigger signal, the gas source switching sub-command in the full control command set is generated. According to the logic of "starting the standby first and then closing the active tanker", the outlet valve of the standby tanker is automatically opened. After the pressure of the standby tanker is stable and meets the standard, the outlet valve of the active tanker is closed, realizing seamless intelligent switching of the gas source for the epitaxial process, with no supply interruption and no manual intervention throughout the process.

[0053] When the intelligent control module executes safety protection control, it first calls the standard basic dataset to perform time-series linkage analysis on the continuously collected effective data of front-end pressure, effective data of terminal pressure, effective data of intermediate temperature, and effective data of terminal temperature.

[0054] The first step is to calculate the fluctuation rate: obtain the average total pressure of the pipeline at time 1 and time 2, and then obtain the pressure fluctuation rate by the ratio of the difference between the two average values ​​to the sampling interval, which is used to characterize the abnormal change rate of pipeline pressure.

[0055] The second step is to generate the temperature fluctuation rate: obtain the average total temperature of the pipeline at time 1 and time 2, and then obtain the temperature fluctuation rate by the ratio of the difference between the average values ​​at the two times to the sampling interval, which is used to characterize the abnormal change rate of pipeline temperature.

[0056] The third step is to calculate the leakage safety discrimination value: the pressure fluctuation rate and temperature fluctuation rate are analyzed according to the formula: leakage safety discrimination value = (pressure fluctuation rate × 0.6) + (temperature fluctuation rate × 0.4), where 0.6 and 0.4 are the preset weights for the safety of the extended process. Pressure fluctuation is more sensitive to leakage and has a higher weight, thus obtaining the leakage safety discrimination value.

[0057] The fourth step involves generating safety protection action signals: comparing and analyzing the preset leakage threshold with the leakage safety judgment value.

[0058] If the leakage safety judgment value is greater than the preset leakage threshold, it is determined that there is a minor leak in the pipeline, and a safety protection action signal is generated.

[0059] If the leakage safety judgment value is less than or equal to the preset leakage threshold, the pipeline is deemed to be in normal condition and no protective action is taken.

[0060] Based on the safety protection action signals, the system automatically shuts off the tank truck outlet valve, activates audible and visual alarms, and pushes alarms to the platform, enabling early identification and rapid protection against minor leaks.

[0061] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0062] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

Claims

1. An intelligent monitoring system for HCI delivery pipelines in epitaxial processes, characterized in that, It includes a multi-source data acquisition module, a multi-level linkage control module, a nitrogen purging module, a pressure relief protection module, a weight monitoring module, and an intelligent control module; The data acquisition range is constructed based on the connection area between the CHI tank truck and the conveying pipeline in the plant area. The multi-source data acquisition module acquires the original data of the entire conveying process based on the data acquisition range and verifies and generates a standard basic dataset. The multi-level linkage control module generates heating and pressure stabilization control commands based on the standard basic dataset; the nitrogen purging module retrieves some data from the standard basic dataset and performs linkage analysis to generate pipeline residual equivalent values ​​and purging mode adaptation signals. The pressure relief protection module generates graded pressure relief deviation and graded pressure stabilization control signals based on the standard basic dataset. The weight monitoring module analyzes the standard basic dataset as the source data to obtain the real-time HCl inventory calculation value. The intelligent control module integrates all received data and signals, summarizes and generates a full-process control command set to realize delivery regulation, purging and cleaning, pressure relief and stabilization, residual monitoring and intelligent gas source switching.

2. The intelligent monitoring system for HCI delivery pipelines in epitaxial processes according to claim 1, characterized in that, The multi-source data acquisition module acquires the raw data of the front-end pressure, middle-end temperature, and end-end pressure and temperature of the delivery pipeline according to the data acquisition range. The raw data is compared and verified with the preset normal range, and abnormal data is removed and integrated into a standard basic dataset.

3. The intelligent monitoring system for HCI delivery pipelines in epitaxial processes according to claim 2, characterized in that, The multi-level linkage control module obtains the heating demand determination value through linkage analysis between the intermediate temperature and the preset process temperature, and adjusts the heating power according to the determination value.

4. The intelligent monitoring system for HCI delivery pipelines in epitaxial processes according to claim 3, characterized in that, The pressure compensation calculation value is obtained by calculating the difference between the front-end pressure and the end-end pressure. The pressure compensation calculation value is combined to complete the accurate pressure compensation and eliminate the transmission fluctuation caused by the heating of HCl.

5. The intelligent monitoring system for HCI delivery pipelines in epitaxial processes according to claim 2, characterized in that, The nitrogen purging module calls the standard basic dataset, preset pipeline volume data, and HCl pipeline pressure residual volume conversion coefficient. It calculates the pipeline residual equivalent value by progressively verifying the unit time decay rate of the front-end pressure and the end-end pressure, combined with the pipeline volume and pipeline pressure residual volume conversion coefficient. The pipeline residual equivalent value is compared with the preset purging threshold to obtain the purging mode adaptation signal. Based on the purging mode adaptation signal, the purging mode is automatically selected and the start and stop are controlled.

6. The intelligent monitoring system for HCI delivery pipelines in epitaxial processes according to claim 5, characterized in that, The nitrogen purging module uses the pipeline residual equivalent value as the criterion, and compares and analyzes the high and low level thresholds with the pipeline residual equivalent value: when the pipeline residual equivalent value is higher than the high-level purging threshold of the high and low level thresholds, a high-pressure purging adaptation signal is generated; when the pipeline residual equivalent value is within the high and low range of the high and low level thresholds, a low-pressure purging adaptation signal is generated; when the pipeline residual equivalent value is lower than the low-level purging threshold of the high and low level thresholds, a purging stop signal is generated, thus realizing adaptive matching of the purging mode.

7. The intelligent monitoring system for HCI delivery pipelines in epitaxial processes according to claim 2, characterized in that, The pressure relief protection module calls the standard basic dataset and temporarily collects the intermediate auxiliary pressure data. It calculates the front-end pressure relief deviation by comparing the front-end pressure with the preset safety pressure upper limit, and calculates the back-end pressure stabilization deviation by comparing the end pressure with the preset process pressure standard value, thus completing the two-stage pressure stabilization control and generating a graded pressure stabilization control signal.

8. The intelligent monitoring system for HCI delivery pipelines in epitaxial processes according to claim 2, characterized in that, The weight monitoring module calls the standard basic dataset and preset empty tanker weight data, performs difference analysis between the real-time weight of the tanker and the empty weight to obtain the real-time HCl inventory calculation value, and uploads the real-time HCl inventory calculation value to the control platform in real time, providing accurate data support for gas source switching.

9. The intelligent monitoring system for HCI delivery pipelines in epitaxial processes according to claim 8, characterized in that, The intelligent control module compares the real-time HCl inventory calculation value with the preset minimum supply threshold to obtain a gas source switching trigger signal. Based on the gas source switching trigger signal, it automatically starts the backup tank car and shuts down the tank car in use, realizing uninterrupted intelligent switching of the epitaxial process gas source.

10. The intelligent monitoring system for HCI delivery pipelines in epitaxial processes according to claim 2, characterized in that, The intelligent control module calls the standard basic dataset to perform time-series analysis on the continuously collected effective pressure and temperature data, calculates the pressure fluctuation rate and temperature fluctuation rate, and then obtains the leakage safety judgment value through weighted calculation. The leakage safety judgment value is compared with the preset leakage threshold to obtain the safety protection action signal, and the emergency shutdown and safety alarm are automatically executed based on the signal.

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