Method and device for monitoring silicon-oxygen impurities in a source furnace, electronic device and storage medium

Abstract

The application belongs to the technical field of impurity monitoring, and discloses a source furnace silicon-oxygen impurity monitoring method and device, electronic equipment and storage medium, the method comprises the following steps: acquiring the real-time main source mass spectrum signal of the main source material and the real-time impurity original mass spectrum signal of the silicon-oxygen impurity in the source furnace heating process in real time, according to the real-time main source mass spectrum signal and the real-time impurity original mass spectrum signal, combining the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurity when the source furnace starts heating, the actual impurity signal of the silicon-oxygen impurity is calculated, based on the real-time main source mass spectrum signal and the actual impurity signal, the evaporation rate data of the main source material and the change trend information of the silicon-oxygen impurity are calculated respectively, to determine the dynamic early warning threshold, monitor the actual impurity signal, when the actual impurity signal is greater than or equal to the dynamic early warning threshold, it is determined that the release of the silicon-oxygen impurity is too high; through the above method, the production efficiency of the epitaxial film is improved.

Description

Technical Field

[0001] This application relates to the technical field of impurity monitoring, and more specifically, to a method, apparatus, electronic device, and storage medium for monitoring silicon-oxygen impurities in a source furnace. Background Technology

[0002] In molecular beam epitaxy (MBE) technology, the source furnace is a key component for providing high-purity source materials, and its core boron nitride (PBN) crucible must possess high purity, chemical inertness, and thermal stability. In actual long-term production, after repeated high-temperature growth and thermal cycling, the source furnace may face the problem of impurity release that is difficult to detect through conventional monitoring.

[0003] Although boron nitride crucibles are certified as high-purity at the time of manufacture, they are not absolutely flawless. During the baking and degassing stage, after the boron nitride crucible has been running at high temperatures for a long time, trace amounts of silicon and oxygen impurities inside may diffuse to the surface and form volatile silicon oxides (such as SiO / SiOH) under the action of high temperature and local reducing atmosphere. This results in new impurity peaks in the RGA spectrum detected by the residual gas analyzer (RGA).

[0004] Existing residual gas analyzers primarily monitor common volatiles such as water vapor and hydrogen during the baking stage. Their sensitivity to these specific impurities is limited, making early identification of such problems difficult. The continuous release of impurities gradually affects the purity and performance of epitaxial films. Currently, there is a lack of effective technical solutions for identifying, predicting early, and actively suppressing the release of silicon-oxygen impurities induced by prolonged high temperatures in the crucible. This poses a potential risk to batch consistency of epitaxial films and long-term device reliability.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this application is to provide a method, apparatus, electronic device, and storage medium for monitoring silicon-oxygen impurities in a source furnace. By using the real-time main source mass spectrum signal of the main source material and the real-time raw impurity mass spectrum signal of the silicon-oxygen impurities, the actual impurity signal of the silicon-oxygen impurities is identified. A dynamic early warning threshold determined by the real-time main source mass spectrum signal and the actual impurity signal is used to monitor the silicon-oxygen impurities. This solves the problem that existing impurity monitoring methods struggle to identify silicon-oxygen impurities released induced by long-term high temperatures in the crucible, which can affect epitaxial thin film production. The method accurately identifies the actual impurity signal of silicon-oxygen impurities and monitors them through a dynamic early warning threshold, thereby improving the production efficiency of epitaxial thin films.

[0007] In a first aspect, this application provides a method for monitoring silicon-oxygen impurities in a source furnace, used to monitor silicon-oxygen impurities during the heating process of a source furnace, including:

[0008] Real-time acquisition of the main source mass spectrum signal of the main source material and the real-time raw mass spectrum signal of silicon-oxygen impurities during the heating process of the source furnace;

[0009] Based on the real-time main source mass spectrum signal and the real-time impurity original mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurity when the source furnace starts heating, the actual impurity signal of the silicon-oxygen impurity is calculated.

[0010] Based on the real-time main source mass spectrometry signal and the actual impurity signal, the evaporation rate data of the main source material and the change trend information of the silicon-oxygen impurities are calculated respectively to determine the dynamic early warning threshold.

[0011] The actual impurity signal is monitored, and when the actual impurity signal is greater than or equal to the dynamic warning threshold, it is determined that the release of silicon-oxygen impurities is too high.

[0012] The source furnace silicon-oxygen impurity monitoring method provided in this application can monitor silicon-oxygen impurities during the source furnace heating process. By using the real-time main source mass spectrum signal of the main source material and the real-time raw impurity mass spectrum signal of the silicon-oxygen impurities, the actual impurity signal of the silicon-oxygen impurities can be identified. The silicon-oxygen impurities are monitored by a dynamic early warning threshold determined by the real-time main source mass spectrum signal and the actual impurity signal. This solves the problem that existing impurity monitoring methods are unable to identify silicon-oxygen impurities released by long-term high temperature in the crucible, which affects the production of epitaxial thin films. It can accurately identify the actual impurity signal of silicon-oxygen impurities and monitor them through a dynamic early warning threshold, thereby improving the production efficiency of epitaxial thin films.

[0013] Optionally, based on the real-time main source mass spectrum signal and the real-time impurity raw mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurity when the source furnace starts heating, the actual impurity signal of the silicon-oxygen impurity is calculated, including:

[0014] When the source furnace starts heating, the initial source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurities are recorded to calculate the noise contribution rate of the silicon-oxygen impurities.

[0015] Based on the real-time main source mass spectrometry signal and the noise contribution rate, the real-time noise mass spectrometry signal of the silicon-oxygen impurities is calculated.

[0016] The actual impurity signal of the silicon-oxygen impurity is calculated by subtracting the real-time noise mass spectrum signal from the real-time impurity raw mass spectrum signal.

[0017] The source furnace silicon-oxygen impurity monitoring method provided in this application can monitor silicon-oxygen impurities during the source furnace heating process. By calculating the noise contribution rate, the interference of background noise on the silicon-oxygen impurity signal is effectively removed, making the real-time impurity signal more accurate and improving the monitoring precision.

[0018] Optionally, when the source furnace starts heating, the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurities are recorded to calculate the noise contribution rate of the silicon-oxygen impurities, including:

[0019] When the source furnace starts heating, the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurities are obtained.

[0020] The noise contribution rate of the silicon-oxygen impurities is calculated based on the ratio of the initial impurity mass spectrum signal to the initial main source mass spectrum signal.

[0021] Optionally, based on the real-time main source mass spectrometry signal and the actual impurity signal, the evaporation rate data of the main source material and the changing trend information of the silicon-oxygen impurities are calculated respectively to determine the dynamic early warning threshold, including:

[0022] The evaporation rate data of the main source material is calculated based on the real-time change rate of the main source mass spectrometry signal.

[0023] Based on the change in the actual impurity signal within each preset period, the change trend information of the silicon-oxygen impurities is calculated;

[0024] Based on the evaporation rate data and the trend information, a dynamic early warning threshold is determined.

[0025] The source furnace silicon-oxygen impurity monitoring method provided in this application can monitor silicon-oxygen impurities during the source furnace heating process. By combining the evaporation rate of the main source material with the changing trend of silicon-oxygen impurities, the warning threshold can be dynamically adjusted, making it more adaptable to the complex changes in the actual production process and avoiding false alarms and missed alarms.

[0026] Optionally, the dynamic early warning threshold includes a dynamic early warning threshold for the amount of change and a dynamic early warning threshold for the rate of change.

[0027] Optionally, based on the evaporation rate data and the trend information, a dynamic early warning threshold is determined, including:

[0028] Based on the evaporation rate data and the preset change coefficient threshold, the dynamic early warning threshold for the change is determined.

[0029] Based on the change trend information and the preset change rate coefficient threshold, the change rate dynamic early warning threshold is determined.

[0030] Optionally, monitoring the actual impurity signal to determine that the release of silicon-oxygen impurities is too high when the actual impurity signal is greater than or equal to the dynamic warning threshold includes:

[0031] Based on the actual impurity signal, the real-time change amount of the silicon-oxygen impurities and the real-time change rate of the silicon-oxygen impurities within the preset period are calculated respectively.

[0032] The actual impurity signal is monitored by determining whether the real-time change amount is greater than or equal to the change amount dynamic warning threshold and whether the real-time change rate is greater than or equal to the change rate dynamic warning threshold.

[0033] When the real-time change is greater than or equal to the dynamic warning threshold for the change, or when the real-time change rate is greater than or equal to the dynamic warning threshold for the change rate, it is determined that the release of silicon-oxygen impurities is too high, and a warning is issued.

[0034] Secondly, this application provides a source furnace silicon-oxygen impurity monitoring device for monitoring silicon-oxygen impurities during the source furnace heating process, including:

[0035] The acquisition module is used to acquire the real-time main source mass spectrum signal of the main source material and the real-time raw mass spectrum signal of silicon-oxygen impurities during the heating process of the source furnace.

[0036] The calculation module is used to calculate the actual impurity signal of the silicon-oxygen impurity based on the real-time main source mass spectrum signal and the real-time impurity original mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurity when the source furnace starts heating.

[0037] The determination module is used to calculate the evaporation rate data of the main source material and the change trend information of the silicon-oxygen impurities based on the real-time main source mass spectrometry signal and the actual impurity signal, respectively, in order to determine the dynamic early warning threshold.

[0038] The monitoring module is used to monitor the actual impurity signal, and to determine that the release of silicon-oxygen impurities is too high when the actual impurity signal is greater than or equal to the dynamic warning threshold.

[0039] This source furnace silicon-oxygen impurity monitoring device identifies the actual impurity signal of silicon-oxygen impurities by using the real-time main source mass spectrum signal of the main source material and the real-time raw impurity mass spectrum signal of the silicon-oxygen impurities. It then monitors the silicon-oxygen impurities by using a dynamic early warning threshold determined by the real-time main source mass spectrum signal and the actual impurity signal. This solves the problem that existing impurity monitoring methods are unable to identify silicon-oxygen impurities released by long-term high temperature in the crucible, which affects the production of epitaxial thin films. The device can accurately identify the actual impurity signal of silicon-oxygen impurities and monitor them through a dynamic early warning threshold, thereby improving the production efficiency of epitaxial thin films.

[0040] Thirdly, this application provides an electronic device including a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, it performs the steps in the source furnace silicon oxide impurity monitoring method described above.

[0041] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the source furnace silicon oxide impurity monitoring method described above.

[0042] Beneficial effects: The source furnace silicon-oxygen impurity monitoring method, device, electronic equipment, and storage medium provided in this application identify the actual impurity signal of silicon-oxygen impurities by using the real-time main source mass spectrum signal of the main source material and the real-time raw impurity mass spectrum signal of the silicon-oxygen impurities. By using a dynamic early warning threshold determined by the real-time main source mass spectrum signal and the actual impurity signal, the silicon-oxygen impurities are monitored. This solves the problem that existing impurity monitoring methods are unable to identify silicon-oxygen impurities released by long-term high temperature in the crucible, which affects the production of epitaxial thin films. It can accurately identify the actual impurity signal of silicon-oxygen impurities and monitor them by using a dynamic early warning threshold, thereby improving the production efficiency of epitaxial thin films. Attached Figure Description

[0043] Figure 1 A flowchart of a source furnace silicon-oxygen impurity monitoring method provided in an embodiment of this application.

[0044] Figure 2 This is a schematic diagram of the source furnace silicon-oxygen impurity monitoring device provided in an embodiment of this application.

[0045] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0046] Labeling Explanation: 1. Acquisition Module; 2. Calculation Module; 3. Determination Module; 4. Monitoring Module; 301. Processor; 302. Memory; 303. Communication Bus. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0048] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0049] Please refer to Figure 1 , Figure 1 This application discloses a method for monitoring silicon-oxygen impurities in a source furnace, as described in some embodiments. The method is used to monitor silicon-oxygen impurities during the heating process of a source furnace and includes the following steps:

[0050] Step S101: Real-time acquisition of the main source mass spectrum signal of the main source material and the real-time raw mass spectrum signal of silicon-oxygen impurities during the heating process of the source furnace.

[0051] Step S102: Based on the real-time main source mass spectrum signal and the real-time impurity original mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities when the source furnace starts heating, the actual impurity signal of silicon-oxygen impurities is calculated.

[0052] Step S103: Based on the real-time main source mass spectrometry signal and the actual impurity signal, calculate the evaporation rate data of the main source material and the changing trend information of silicon-oxygen impurities, respectively, to determine the dynamic early warning threshold.

[0053] Step S104: Monitor the actual impurity signal, and determine that the release of silicon-oxygen impurities is too high when the actual impurity signal is greater than or equal to the dynamic warning threshold.

[0054] This method for monitoring silicon-oxygen impurities in a source furnace identifies the actual impurity signal of silicon-oxygen impurities by using the real-time main source mass spectrum signal of the main source material and the real-time raw impurity mass spectrum signal of the silicon-oxygen impurities. It then monitors the silicon-oxygen impurities using a dynamic warning threshold determined by the real-time main source mass spectrum signal and the actual impurity signal. This method solves the problem of existing impurity monitoring methods failing to identify silicon-oxygen impurities released induced by long-term high temperatures in the crucible, which can affect epitaxial film production. It accurately identifies the actual impurity signal of silicon-oxygen impurities and monitors them through a dynamic warning threshold, thereby improving the production efficiency of epitaxial films.

[0055] Specifically, in step S101, the real-time main source mass spectrum signal of the main source material and the real-time raw mass spectrum signal of silicon-oxygen impurities are acquired in real time during the heating process of the source furnace. That is, starting from the baking and degassing stage of the source furnace, two quadrupole mass spectrometers (QMS) are calibrated. One is used to monitor the signal of the main source material (such as gallium Ga) and its isotopes (e.g., adjusting the quadrupole mass spectrometer to focus on the gallium atom signal with mass numbers of 69 and 71), and the other is used to detect the signal of silicon-oxygen compounds (i.e., silicon-oxygen impurities, such as silicon oxide SiO and silicon hydroxide SiOH) (e.g., adjusting the quadrupole mass spectrometer to focus on the silicon oxide signal with mass number of 44 and the silicon hydroxide signal with mass number of 45, as well as the oxygen atom signal with mass number of 16 and the water atom signal with mass number of 18). This allows the two quadrupole mass spectrometers to capture the main source material and silicon-oxygen impurities in the source furnace gas information more sensitively and accurately, so as to obtain the real-time main source mass spectrum signal of the main source material and the real-time raw mass spectrum signal of silicon-oxygen impurities in real time during the heating process of the source furnace.

[0056] The main source material is the primary material that needs to be evaporated and deposited onto the substrate during MBE growth, such as gallium (Ga), aluminum (Al), and indium (In). Silicon-oxygen impurities are compounds containing silicon and oxygen that may be released from the crucible or source material during the source furnace heating process, such as SiO and SiOH. These impurities pose a potential threat to film quality.

[0057] Specifically, in step S102, based on the real-time main source mass spectrum signal and the real-time original impurity mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurities when the furnace starts heating, the actual impurity signal of the silicon-oxygen impurities is calculated, including:

[0058] When the source furnace starts heating, the initial source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities are recorded to calculate the noise contribution rate of silicon-oxygen impurities.

[0059] Based on the real-time main source mass spectrometry signal and noise contribution rate, the real-time noise mass spectrometry signal of silicon-oxygen impurities is calculated.

[0060] The actual impurity signal of silicon-oxygen impurities is calculated by subtracting the real-time noise mass spectrum signal from the real-time impurity raw mass spectrum signal.

[0061] Specifically, in step S102, when the source furnace starts heating, the initial source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities are recorded to calculate the noise contribution rate of silicon-oxygen impurities, including:

[0062] When the source furnace starts heating, the initial source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities are obtained.

[0063] The noise contribution rate of silicon-oxygen impurities is calculated based on the ratio of the initial impurity mass spectrum signal to the initial main source mass spectrum signal.

[0064] It should be noted that the noise contribution rate refers to the background interference of silicon-oxygen impurities caused by the evaporation of the main source material. It reflects the inherent influence of background noise on the mass spectrum signal of silicon-oxygen impurities and can be calculated based on the ratio of the initial impurity mass spectrum signal of silicon-oxygen impurities to the initial main source mass spectrum signal of the main source material.

[0065] In step S102, at the initial stage when the source furnace begins heating, i.e., before the main source material has evaporated in large quantities or before silicon-oxygen impurities have been significantly released, the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurities are acquired by a mass spectrometer. These initial signals reflect the mass spectrometry information under stable background conditions.

[0066] The noise contribution rate of silicon-oxygen impurities is calculated by comparing the initial impurity mass spectrum signal with the initial main source mass spectrum signal. This noise contribution rate characterizes the degree of background interference generated by the main source material on the silicon-oxygen impurity mass spectrum signal before the main source material has evaporated significantly. This method avoids interference caused by background noise from the evaporation of the main source material, thus enabling a more accurate acquisition of the true signal of silicon-oxygen impurities.

[0067] In step S102, based on the product of the real-time main source mass spectrum signal and the pre-calculated noise contribution rate, the interference part caused by background noise and superimposed on the original mass spectrum signal of silicon-oxygen impurities is calculated, and the real-time noise mass spectrum signal of silicon-oxygen impurities is obtained.

[0068] The real-time noise mass spectrometry signal is subtracted from the real-time impurity raw mass spectrometry signal to separate the signal generated purely by the evaporation of silicon-oxygen impurities from the original impurity signal, which may contain noise, thereby obtaining a more accurate true signal and obtaining the real-time impurity signal of silicon-oxygen impurities.

[0069] Specifically, in step S103, based on the real-time main source mass spectrometry signal and the actual impurity signal, the evaporation rate data of the main source material and the changing trend information of silicon-oxygen impurities are calculated respectively to determine the dynamic early warning threshold, including:

[0070] The evaporation rate data of the main source material are calculated based on the real-time change rate of the main source mass spectrometry signal.

[0071] Based on the change in the actual impurity signal within each preset period, the change trend information of silicon-oxygen impurities is calculated;

[0072] Based on evaporation rate data and trend information, dynamic early warning thresholds are determined.

[0073] In step S103, the evaporation rate data of the main source material is quantified based on the rate of change of the mass spectrometry signal of the main source material over time (i.e., the real-time rate of change, such as the rate of change of the real-time main source mass spectrometry signal per second or per minute). For example, if the real-time main source mass spectrometry signal of the main source material increases by 5 units in the past minute, the evaporation rate data of the main source material is calculated to be 5 units / minute.

[0074] Based on the numerical fluctuations of the actual impurity signals corresponding to silicon-oxide impurities within a preset period (i.e., the amount of change in each preset period), the trend information of silicon-oxide impurities is calculated. This information can characterize the release dynamics of silicon-oxide impurities during the heating process. For example, if the actual impurity signal increases by 5, 7, 7, and 8 units every 5 minutes in the last 20 minutes, respectively, then the calculated trend information of silicon-oxide impurities is an increase of 6.5 units / 5 minutes. The preset period can be set according to actual needs.

[0075] Specifically, the dynamic early warning thresholds include a change-amount dynamic early warning threshold and a change-rate dynamic early warning threshold. The change-amount dynamic early warning threshold measures whether the release of silicon-oxygen impurities has reached a warning level and can be determined based on the evaporation rate data of the main source material. For example, when the evaporation rate of the main source material is high, the release of silicon-oxygen impurities may also increase accordingly; in this case, the change-amount dynamic early warning threshold can be adjusted to adapt to this change. The change-rate dynamic early warning threshold measures whether the release rate of silicon-oxygen impurities has reached a warning level and can be determined based on the changing trend information of silicon-oxygen impurities. For example, when the real-time impurity signal of silicon-oxygen impurities changes significantly within a preset period, indicating that its release rate is rapidly increasing, adjusting the change-rate dynamic early warning threshold can reflect this rapid changing trend.

[0076] Specifically, in step S103, based on evaporation rate data and trend information, a dynamic early warning threshold is determined, including:

[0077] Based on evaporation rate data and preset change coefficient thresholds, the dynamic early warning threshold for change is determined.

[0078] Based on the trend information and the preset rate of change coefficient threshold, the dynamic early warning threshold for the rate of change is determined.

[0079] In step S104, based on the evaporation rate data and a preset change coefficient threshold, a dynamic warning threshold for the change amount is calculated. This dynamic warning threshold is a dynamic reference value used to measure whether the release of silicon-oxygen impurities is too high. For example, based on a given basic change threshold, the dynamic warning threshold for the change amount is calculated by adding the product of the evaporation rate data and the preset change coefficient threshold. The basic change threshold and the preset change coefficient threshold can be set based on experience, experimental data, or specific process requirements. The purpose is to provide a flexible benchmark to adapt to monitoring needs under different main source materials, different heating stages, or different product quality requirements.

[0080] Based on trend information and a preset rate of change coefficient threshold, a dynamic early warning threshold for the rate of change is calculated. This threshold serves as a dynamic reference value to measure whether the release rate of silicon-oxide impurities is excessively high. For example, the dynamic early warning threshold is calculated by multiplying the trend information by the preset rate of change coefficient threshold. The preset rate of change coefficient threshold can be adjusted based on factors such as impurity characteristics, process sensitivity, experience, and experimental data. Its purpose is to detect sudden or persistent abnormal growth of silicon-oxide impurities, thereby achieving earlier early warning.

[0081] Specifically, in step S104, the actual impurity signal is monitored to determine that the release of silicon-oxygen impurities is too high when the actual impurity signal is greater than or equal to the dynamic warning threshold, including:

[0082] Based on the actual impurity signal, the real-time change of silicon-oxygen impurities and the real-time change rate of silicon-oxygen impurities within the preset period are calculated respectively.

[0083] The system determines whether the real-time change is greater than or equal to the dynamic warning threshold for change and whether the real-time change rate is greater than or equal to the dynamic warning threshold for change rate, in order to monitor the actual impurity signal.

[0084] When the real-time change is greater than or equal to the dynamic warning threshold for change, or the real-time change rate is greater than or equal to the dynamic warning threshold for change rate, it is determined that the release of silicon-oxygen impurities is too high, and a warning is issued.

[0085] In step S104, at consecutive monitoring time points (such as the start and end time points of each preset cycle), the absolute value changes of real-time impurity signals for silicon-oxygen impurities are acquired to calculate the real-time change in silicon-oxygen impurities. For example, the difference between the real-time impurity signal at the current moment and the real-time impurity signal at the previous moment or the previous N moments can be calculated to reflect the instantaneous or short-term increase or decrease in the release amount of silicon-oxygen impurities. The purpose is to capture the absolute change in the release amount of silicon-oxygen impurities.

[0086] The average rate of change of the real-time impurity signal within a preset period is obtained to calculate the real-time rate of change of silicon-oxygen impurities, which aims to reflect the urgency of the release trend of silicon-oxygen impurities.

[0087] Since the release of silicon-oxygen impurities is reflected not only in their absolute concentration, but also in the magnitude and rate of their concentration change, the actual impurity signal is monitored by judging whether the real-time change is greater than or equal to the dynamic warning threshold for the change amount and whether the real-time change rate is greater than or equal to the dynamic warning threshold for the change rate.

[0088] When the real-time change is less than the dynamic warning threshold for change and the real-time change rate is less than the dynamic warning threshold for change rate, it is determined that the release of silicon-oxygen impurities is within an acceptable range, and the real-time change continues to be monitored.

[0089] When the real-time change is greater than or equal to the dynamic warning threshold for the change amount, or the real-time change rate is greater than or equal to the dynamic warning threshold for the change rate, it is determined that the release of silicon oxide impurities is too high, and an early warning is issued to alert operators of the abnormal release of silicon oxide impurities, thus providing operators with more reaction time. This dual dynamic monitoring mechanism effectively avoids the problem of delayed or missed warnings that may be caused by a single threshold judgment, greatly reducing the risk of product contamination due to excessive release of silicon oxide impurities, and improving the stability of the production process and product quality.

[0090] As can be seen from the above, this method for monitoring silicon-oxygen impurities in a source furnace acquires the real-time main source mass spectrum signal of the main source material and the real-time raw impurity mass spectrum signal of silicon-oxygen impurities during the furnace heating process. Based on the real-time main source mass spectrum signal and the real-time raw impurity mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurities at the start of furnace heating, the actual impurity signal of silicon-oxygen impurities is calculated. Based on the real-time main source mass spectrum signal and the actual impurity signal, the evaporation rate data of the main source material and the changing trend information of silicon-oxygen impurities are calculated respectively to determine the dynamic early warning threshold and monitor the actual impurity signal so that when the actual impurity... When the mass spectrometry signal is greater than or equal to the dynamic warning threshold, it is determined that the release of silicon-oxygen impurities is too high. Therefore, by using the real-time main source mass spectrometry signal of the main source material and the real-time raw impurity mass spectrometry signal of the silicon-oxygen impurities, the actual impurity signal of the silicon-oxygen impurities can be identified. The dynamic warning threshold determined by the real-time main source mass spectrometry signal and the actual impurity signal is used to monitor the silicon-oxygen impurities. This solves the problem that existing impurity monitoring methods are unable to identify silicon-oxygen impurities released by long-term high temperature in the crucible, which affects the production of epitaxial thin films. It can accurately identify the actual impurity signal of silicon-oxygen impurities and monitor them through the dynamic warning threshold, thereby improving the production efficiency of epitaxial thin films.

[0091] refer to Figure 2 This application provides a source furnace silicon-oxygen impurity monitoring device for monitoring silicon-oxygen impurities during the source furnace heating process, including:

[0092] Module 1 is used to acquire the real-time main source mass spectrum signal of the main source material and the real-time raw mass spectrum signal of silicon-oxygen impurities during the heating process of the source furnace.

[0093] Calculation module 2 is used to calculate the actual impurity signal of silicon-oxygen impurities based on the real-time main source mass spectrum signal and the real-time original impurity mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities when the source furnace starts heating.

[0094] Module 3 is used to calculate the evaporation rate data of the main source material and the changing trend information of silicon-oxygen impurities based on the real-time main source mass spectrometry signal and the actual impurity signal, respectively, in order to determine the dynamic early warning threshold.

[0095] Monitoring module 4 is used to monitor the actual impurity signal, so as to determine that the release of silicon-oxygen impurities is too high when the actual impurity signal is greater than or equal to the dynamic warning threshold.

[0096] This source furnace silicon-oxygen impurity monitoring device identifies the actual impurity signal of silicon-oxygen impurities by using the real-time main source mass spectrum signal of the main source material and the real-time raw impurity mass spectrum signal of the silicon-oxygen impurities. It then monitors the silicon-oxygen impurities by using a dynamic early warning threshold determined by the real-time main source mass spectrum signal and the actual impurity signal. This solves the problem that existing impurity monitoring methods are unable to identify silicon-oxygen impurities released by long-term high temperature in the crucible, which affects the production of epitaxial thin films. The device can accurately identify the actual impurity signal of silicon-oxygen impurities and monitor them through a dynamic early warning threshold, thereby improving the production efficiency of epitaxial thin films.

[0097] Specifically, during execution, module 1 acquires the real-time main source mass spectrum signal of the main source material and the real-time raw mass spectrum signal of silicon-oxygen impurities during the heating process of the source furnace. That is, starting from the baking and degassing stage of the source furnace, two quadrupole mass spectrometers (QMS) are calibrated. One is used to monitor the signal of the main source material (such as gallium Ga) and its isotopes (e.g., adjusting the quadrupole mass spectrometer to focus on the gallium atom signal with mass numbers of 69 and 71), and the other is used to detect the signal of silicon-oxygen compounds (i.e., silicon-oxygen impurities, such as silicon oxide SiO and silicon hydroxide SiOH) (e.g., adjusting the quadrupole mass spectrometer to focus on the silicon oxide signal with a mass number of 44 and the silicon hydroxide signal with a mass number of 45, as well as the oxygen atom signal with a mass number of 16 and the water atom signal with a mass number of 18). This allows the two quadrupole mass spectrometers to more sensitively and accurately capture the main source material and silicon-oxygen impurities in the source furnace gas information, so as to obtain the real-time main source mass spectrum signal of the main source material and the real-time raw mass spectrum signal of silicon-oxygen impurities during the heating process of the source furnace.

[0098] The main source material is the primary material that needs to be evaporated and deposited onto the substrate during MBE growth, such as gallium (Ga), aluminum (Al), and indium (In). Silicon-oxygen impurities are compounds containing silicon and oxygen that may be released from the crucible or source material during the source furnace heating process, such as SiO and SiOH. These impurities pose a potential threat to film quality.

[0099] Specifically, when calculation module 2 calculates the actual impurity signal of silicon-oxygen impurities based on the real-time main source mass spectrum signal and the real-time original impurity mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities at the start of heating of the source furnace, it performs the following:

[0100] When the source furnace starts heating, the initial source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities are recorded to calculate the noise contribution rate of silicon-oxygen impurities.

[0101] Based on the real-time main source mass spectrometry signal and noise contribution rate, the real-time noise mass spectrometry signal of silicon-oxygen impurities is calculated.

[0102] The actual impurity signal of silicon-oxygen impurities is calculated by subtracting the real-time noise mass spectrum signal from the real-time impurity raw mass spectrum signal.

[0103] Specifically, when the source furnace starts heating, calculation module 2 records the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities. When calculating the noise contribution rate of silicon-oxygen impurities, it executes the following:

[0104] When the source furnace starts heating, the initial source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities are obtained.

[0105] The noise contribution rate of silicon-oxygen impurities is calculated based on the ratio of the initial impurity mass spectrum signal to the initial main source mass spectrum signal.

[0106] It should be noted that the noise contribution rate refers to the background interference of silicon-oxygen impurities caused by the evaporation of the main source material. It reflects the inherent influence of background noise on the mass spectrum signal of silicon-oxygen impurities and can be calculated based on the ratio of the initial impurity mass spectrum signal of silicon-oxygen impurities to the initial main source mass spectrum signal of the main source material.

[0107] When the calculation module 2 is executed, at the initial stage when the source furnace begins heating—that is, before the main source material has evaporated in large quantities or before silicon-oxygen impurities have been significantly released—it acquires the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurities using a mass spectrometer. These initial signals reflect the mass spectrometry information under stable background conditions.

[0108] The noise contribution rate of silicon-oxygen impurities is calculated by comparing the initial impurity mass spectrum signal with the initial main source mass spectrum signal. This noise contribution rate characterizes the degree of background interference generated by the main source material on the silicon-oxygen impurity mass spectrum signal before the main source material has evaporated significantly. This method avoids interference caused by background noise from the evaporation of the main source material, thus enabling a more accurate acquisition of the true signal of silicon-oxygen impurities.

[0109] When the calculation module 2 is executed, it calculates the interference part caused by background noise and superimposed on the original mass spectrum signal of silicon-oxygen impurities based on the product of the real-time main source mass spectrum signal and the pre-calculated noise contribution rate, and obtains the real-time noise mass spectrum signal of silicon-oxygen impurities.

[0110] The real-time noise mass spectrometry signal is subtracted from the real-time impurity raw mass spectrometry signal to separate the signal generated purely by the evaporation of silicon-oxygen impurities from the original impurity signal, which may contain noise, thereby obtaining a more accurate true signal and obtaining the real-time impurity signal of silicon-oxygen impurities.

[0111] Specifically, when determining the dynamic early warning threshold, module 3 calculates the evaporation rate data of the main source material and the changing trend information of silicon-oxygen impurities based on the real-time main source mass spectrometry signal and the actual impurity signal, respectively, and performs the following:

[0112] The evaporation rate data of the main source material are calculated based on the real-time change rate of the main source mass spectrometry signal.

[0113] Based on the change in the actual impurity signal within each preset period, the change trend information of silicon-oxygen impurities is calculated;

[0114] Based on evaporation rate data and trend information, dynamic early warning thresholds are determined.

[0115] When module 3 is executed, it quantifies the evaporation rate data of the main source material based on the rate of change of the mass spectrometry signal of the main source material over time (i.e., the real-time rate of change, such as the rate of change of the real-time main source mass spectrometry signal per second or per minute). For example, if the real-time main source mass spectrometry signal of the main source material increases by 5 units in the past minute, the calculated evaporation rate data of the main source material is 5 units / minute.

[0116] Based on the numerical fluctuations of the actual impurity signals corresponding to silicon-oxide impurities within a preset period (i.e., the amount of change in each preset period), the trend information of silicon-oxide impurities is calculated. This information can characterize the release dynamics of silicon-oxide impurities during the heating process. For example, if the actual impurity signal increases by 5, 7, 7, and 8 units every 5 minutes in the last 20 minutes, respectively, then the calculated trend information of silicon-oxide impurities is an increase of 6.5 units / 5 minutes. The preset period can be set according to actual needs.

[0117] Specifically, the dynamic early warning thresholds include a change-amount dynamic early warning threshold and a change-rate dynamic early warning threshold. The change-amount dynamic early warning threshold measures whether the release of silicon-oxygen impurities has reached a warning level and can be determined based on the evaporation rate data of the main source material. For example, when the evaporation rate of the main source material is high, the release of silicon-oxygen impurities may also increase accordingly; in this case, the change-amount dynamic early warning threshold can be adjusted to adapt to this change. The change-rate dynamic early warning threshold measures whether the release rate of silicon-oxygen impurities has reached a warning level and can be determined based on the changing trend information of silicon-oxygen impurities. For example, when the real-time impurity signal of silicon-oxygen impurities changes significantly within a preset period, indicating that its release rate is rapidly increasing, adjusting the change-rate dynamic early warning threshold can reflect this rapid changing trend.

[0118] Specifically, when determining the dynamic early warning threshold based on evaporation rate data and trend information, module 3 executes the following:

[0119] Based on evaporation rate data and preset change coefficient thresholds, the dynamic early warning threshold for change is determined.

[0120] Based on the trend information and the preset rate of change coefficient threshold, the dynamic early warning threshold for the rate of change is determined.

[0121] When module 3 is executed, it calculates a dynamic warning threshold for the amount of change based on the evaporation rate data and a preset change coefficient threshold. This dynamic warning threshold serves as a dynamic reference value to measure whether the release of silicon-oxygen impurities is excessive. For example, the dynamic warning threshold is calculated by adding the product of a given basic change threshold, the evaporation rate data, and the preset change coefficient threshold. The basic change threshold and the preset change coefficient threshold can be set based on experience, experimental data, or specific process requirements. The purpose is to provide a flexible benchmark to adapt to monitoring needs under different main source materials, different heating stages, or different product quality requirements.

[0122] Based on trend information and a preset rate of change coefficient threshold, a dynamic early warning threshold for the rate of change is calculated. This threshold serves as a dynamic reference value to measure whether the release rate of silicon-oxide impurities is excessively high. For example, the dynamic early warning threshold is calculated by multiplying the trend information by the preset rate of change coefficient threshold. The preset rate of change coefficient threshold can be adjusted based on factors such as impurity characteristics, process sensitivity, experience, and experimental data. Its purpose is to detect sudden or persistent abnormal growth of silicon-oxide impurities, thereby achieving earlier early warning.

[0123] Specifically, when monitoring module 4 detects an excessive release of silicon-oxygen impurities by monitoring the actual impurity signal and determining that the actual impurity signal is greater than or equal to the dynamic warning threshold, it executes the following:

[0124] Based on the actual impurity signal, the real-time change of silicon-oxygen impurities and the real-time change rate of silicon-oxygen impurities within the preset period are calculated respectively.

[0125] The system determines whether the real-time change is greater than or equal to the dynamic warning threshold for change and whether the real-time change rate is greater than or equal to the dynamic warning threshold for change rate, in order to monitor the actual impurity signal.

[0126] When the real-time change is greater than or equal to the dynamic warning threshold for change, or the real-time change rate is greater than or equal to the dynamic warning threshold for change rate, it is determined that the release of silicon-oxygen impurities is too high, and a warning is issued.

[0127] During execution, monitoring module 4 acquires the absolute value changes of real-time impurity signals for silicon-oxide impurities at continuous monitoring time points (such as the start and end times of each preset cycle) to calculate the real-time change in silicon-oxide impurities. For example, it can calculate the difference between the current real-time impurity signal and the real-time impurity signal from the previous moment or the previous N moments to reflect the instantaneous or short-term increase or decrease in the release amount of silicon-oxide impurities. Its purpose is to capture the absolute change in the release amount of silicon-oxide impurities.

[0128] The average rate of change of the real-time impurity signal within a preset period is obtained to calculate the real-time rate of change of silicon-oxygen impurities, which aims to reflect the urgency of the release trend of silicon-oxygen impurities.

[0129] Since the release of silicon-oxygen impurities is reflected not only in their absolute concentration, but also in the magnitude and rate of their concentration change, the actual impurity signal is monitored by judging whether the real-time change is greater than or equal to the dynamic warning threshold for the change amount and whether the real-time change rate is greater than or equal to the dynamic warning threshold for the change rate.

[0130] When the real-time change is less than the dynamic warning threshold for change and the real-time change rate is less than the dynamic warning threshold for change rate, it is determined that the release of silicon-oxygen impurities is within an acceptable range, and the real-time change continues to be monitored.

[0131] When the real-time change is greater than or equal to the dynamic warning threshold for the change amount, or the real-time change rate is greater than or equal to the dynamic warning threshold for the change rate, it is determined that the release of silicon oxide impurities is too high, and an early warning is issued to alert operators of the abnormal release of silicon oxide impurities, thus providing operators with more reaction time. This dual dynamic monitoring mechanism effectively avoids the problem of delayed or missed warnings that may be caused by a single threshold judgment, greatly reducing the risk of product contamination due to excessive release of silicon oxide impurities, and improving the stability of the production process and product quality.

[0132] As can be seen from the above, this source furnace silicon-oxygen impurity monitoring device acquires the real-time main source mass spectrum signal of the main source material and the real-time raw impurity mass spectrum signal of silicon-oxygen impurities during the source furnace heating process. Based on the real-time main source mass spectrum signal and the real-time raw impurity mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurities at the start of heating, the actual impurity signal of silicon-oxygen impurities is calculated. Based on the real-time main source mass spectrum signal and the actual impurity signal, the evaporation rate data of the main source material and the changing trend information of silicon-oxygen impurities are calculated respectively to determine the dynamic early warning threshold and monitor the actual impurity signal so that when the actual impurity... When the mass spectrometry signal is greater than or equal to the dynamic warning threshold, it is determined that the release of silicon-oxygen impurities is too high. Therefore, by using the real-time main source mass spectrometry signal of the main source material and the real-time raw impurity mass spectrometry signal of the silicon-oxygen impurities, the actual impurity signal of the silicon-oxygen impurities can be identified. The dynamic warning threshold determined by the real-time main source mass spectrometry signal and the actual impurity signal is used to monitor the silicon-oxygen impurities. This solves the problem that existing impurity monitoring methods are unable to identify silicon-oxygen impurities released by long-term high temperature in the crucible, which affects the production of epitaxial thin films. It can accurately identify the actual impurity signal of silicon-oxygen impurities and monitor them through the dynamic warning threshold, thereby improving the production efficiency of epitaxial thin films.

[0133] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device includes a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other via a communication bus 303 and / or other forms of connection mechanisms (not shown). The memory 302 stores a computer program executable by the processor 301. When the electronic device is running, the processor 301 executes the computer program to perform the source furnace silicon-oxygen impurity monitoring method in any optional implementation of the above embodiments, to achieve the following function: real-time acquisition of the main source material during the source furnace heating process. The real-time main source mass spectrum signal and the real-time raw mass spectrum signal of silicon-oxygen impurities are used to calculate the actual impurity signal of silicon-oxygen impurities. Based on the real-time main source mass spectrum signal and the real-time raw mass spectrum signal of impurities, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities when the furnace starts heating, the evaporation rate data of the main source material and the change trend information of silicon-oxygen impurities are calculated respectively to determine the dynamic warning threshold. The actual impurity signal is monitored so that when the actual impurity signal is greater than or equal to the dynamic warning threshold, it is determined that the release of silicon-oxygen impurities is too high.

[0134] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the source furnace silicon-oxygen impurity monitoring method in any optional implementation of the above embodiments to achieve the following functions: real-time acquisition of the real-time main source mass spectrum signal of the main source material and the real-time raw impurity mass spectrum signal of silicon-oxygen impurities during the source furnace heating process; calculation of the actual impurity signal of silicon-oxygen impurities based on the real-time main source mass spectrum signal and the real-time raw impurity mass spectrum signal, combined with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of silicon-oxygen impurities when the source furnace starts heating; calculation of the evaporation rate data of the main source material and the change trend information of silicon-oxygen impurities based on the real-time main source mass spectrum signal and the actual impurity signal to determine the dynamic warning threshold; monitoring the actual impurity signal to determine that the release of silicon-oxygen impurities is too high when the actual impurity signal is greater than or equal to the dynamic warning threshold. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0135] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0136] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0137] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0138] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0139] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring silicon-oxygen impurities in a source furnace, used to monitor silicon-oxygen impurities during the heating process of a source furnace, characterized in that, The method comprises the steps of: obtaining a real-time main source mass spectrum signal of a main source material and a real-time impurity original mass spectrum signal of a silicon-oxygen impurity in a source furnace heating process in real time; calculating an actual impurity signal of the silicon-oxygen impurity according to the real-time main source mass spectrum signal and the real-time impurity original mass spectrum signal, in combination with an initial main source mass spectrum signal of the main source material and an initial impurity mass spectrum signal of the silicon-oxygen impurity at the beginning of heating of the source furnace; calculating evaporation rate data of the main source material and change trend information of the silicon-oxygen impurity based on the real-time main source mass spectrum signal and the actual impurity signal, to determine a dynamic early warning threshold; calculating evaporation rate data of the main source material and change trend information of the silicon-oxygen impurity based on the real-time main source mass spectrum signal and the actual impurity signal, to determine a dynamic early warning threshold, comprising: calculating the evaporation rate data of the main source material according to a real-time change rate of the real-time main source mass spectrum signal; calculating the change trend information of the silicon-oxygen impurity according to a change amount of the actual impurity signal in each preset period; determining a dynamic early warning threshold based on the evaporation rate data and the change trend information; the dynamic early warning threshold comprises a change amount dynamic early warning threshold and a change rate dynamic early warning threshold; determining a dynamic early warning threshold based on the evaporation rate data and the change trend information, comprising: determining the change amount dynamic early warning threshold based on the evaporation rate data and a preset change amount coefficient threshold; determining the change rate dynamic early warning threshold based on the change trend information and a preset change rate coefficient threshold; monitoring the actual impurity signal to determine that the silicon-oxygen impurity is released too much when the actual impurity signal is greater than or equal to the dynamic early warning threshold; monitoring the actual impurity signal to determine that the silicon-oxygen impurity is released too much when the actual impurity signal is greater than or equal to the dynamic early warning threshold, comprising: calculating a real-time change amount of the silicon-oxygen impurity and a real-time change rate of the silicon-oxygen impurity in the preset period based on the actual impurity signal, respectively; judging whether the real-time change amount is greater than or equal to the change amount dynamic early warning threshold and whether the real-time change rate is greater than or equal to the change rate dynamic early warning threshold, respectively, to monitor the actual impurity signal; determining that the silicon-oxygen impurity is released too much when the real-time change amount is greater than or equal to the change amount dynamic early warning threshold or the real-time change rate is greater than or equal to the change rate dynamic early warning threshold, and issuing a warning prompt.

2. The source furnace silicon oxygen impurity monitoring method according to claim 1, characterized by, calculating an actual impurity signal of the silicon-oxygen impurity according to the real-time main source mass spectrum signal and the real-time impurity original mass spectrum signal, in combination with an initial main source mass spectrum signal of the main source material and an initial impurity mass spectrum signal of the silicon-oxygen impurity at the beginning of heating of the source furnace, comprising: recording the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the silicon-oxygen impurity at the beginning of heating of the source furnace, to calculate a noise contribution rate of the silicon-oxygen impurity; Based on the real-time main source mass spectrum signal and the noise contribution rate, a real-time noise mass spectrum signal of the siloxane impurity is calculated; The real-time impurity original mass spectrum signal is subtracted by the real-time noise mass spectrum signal to calculate an actual impurity signal of the siloxane impurity.

3. The source furnace silicon oxygen impurity monitoring method according to claim 2, characterized by, When the source furnace starts heating, the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the siloxane impurity are recorded to calculate the noise contribution rate of the siloxane impurity, including: When the source furnace starts heating, the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the siloxane impurity are recorded to calculate the noise contribution rate of the siloxane impurity, including: According to the ratio of the initial impurity mass spectrum signal and the initial main source mass spectrum signal, the noise contribution rate of the siloxane impurity is calculated.

4. A source furnace silicon oxygen impurity monitoring apparatus for monitoring silicon oxygen impurities during a source furnace heating process, characterized by, Including: The acquisition module is used to acquire the real-time main source mass spectrum signal of the main source material and the real-time impurity original mass spectrum signal of the siloxane impurity in the source furnace heating process in real time; The calculation module is used to calculate the actual impurity signal of the siloxane impurity according to the real-time main source mass spectrum signal and the real-time impurity original mass spectrum signal, in combination with the initial main source mass spectrum signal of the main source material and the initial impurity mass spectrum signal of the siloxane impurity when the source furnace starts heating; The determination module is used to calculate the evaporation rate data of the main source material and the change trend information of the siloxane impurity based on the real-time main source mass spectrum signal and the actual impurity signal, to determine a dynamic early warning threshold value; The determination module is used to calculate the evaporation rate data of the main source material and the change trend information of the siloxane impurity based on the real-time main source mass spectrum signal and the actual impurity signal, to determine a dynamic early warning threshold value, including: According to the real-time change rate of the real-time main source mass spectrum signal, the evaporation rate data of the main source material is calculated; According to the change amount of the actual impurity signal in each preset period, the change trend information of the siloxane impurity is calculated; Based on the evaporation rate data and the change trend information, a dynamic early warning threshold value is determined; The dynamic early warning threshold value includes a change amount dynamic early warning threshold value and a change rate dynamic early warning threshold value; Based on the evaporation rate data and the change trend information, a dynamic early warning threshold value is determined, including: Based on the evaporation rate data and a preset change amount coefficient threshold value, a change amount dynamic early warning threshold value is determined; Based on the change trend information and a preset change rate coefficient threshold value, a change rate dynamic early warning threshold value is determined; The monitoring module is used to monitor the actual impurity signal to determine that the siloxane impurity is released too high when the actual impurity signal is greater than or equal to the dynamic early warning threshold value; The monitoring module is used to monitor the actual impurity signal to determine that the siloxane impurity is released too high when the actual impurity signal is greater than or equal to the dynamic early warning threshold value, including: Based on the actual impurity signal, the real-time change amount of the siloxane impurity and the real-time change rate of the siloxane impurity in the preset period are calculated respectively; respectively, to monitor the actual impurity signal; when the real-time change amount is greater than or equal to the change amount dynamic early warning threshold, or the real-time change rate is greater than or equal to the change rate dynamic early warning threshold, it is determined that the silicon-oxygen impurity release is too high, and an early warning prompt is issued.

5. An electronic device, comprising: A computer device comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor executes the computer program to run the steps of the source furnace silicon-oxygen impurity monitoring method according to any one of claims 1-3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to run the steps of the source furnace silicon-oxygen impurity monitoring method according to any one of claims 1-3.

Images