TWI830326B - Target gas content control method and a semiconductor process equipment - Google Patents

Abstract

A target gas content control method and a semiconductor process equipment, wherein the target gas content control method is used to control the content of the target gas in the loading and unloading chamber of the semiconductor process equipment, including: obtaining the content difference between the target content and the current content of the target gas in the loading and unloading chamber; The pressure value and content difference of the loading and unloading chamber are mapped to the corresponding fuzzy universe respectively, and the fuzzy control parameters are determined according to the mapping results; The fuzzy control parameters are converted to the physical domain to obtain the initial control parameters; According to the initial control parameters, the purge gas flow into the loading and unloading chamber is controlled to control the content of the target gas in the loading and unloading chamber.

Description

Target gas content control method and semiconductor process equipment

This application relates to the technical field of semiconductor process control, and specifically relates to a target gas content control method and semiconductor process equipment.

Micro-oxygen/micro-positive pressure control of the loading and unloading chamber (LA) is a key performance indicator of semiconductor process equipment. Taking the vertical furnace series equipment as an example, during the transportation process of silicon wafers and the lifting boat (in and out of the reaction chamber), they will be affected by oxygen molecules in the atmosphere of the loading and unloading chamber, resulting in the generation of unnecessary oxide layers. In this regard, It is usually necessary to control the target gas content such as oxygen in the loading and unloading chamber. For example, it is necessary to use high-purity nitrogen (PN2) purging method, combined with oxygen (O2) analyzer and gas mass flow controller (MFC) for closed-loop control, to reduce and control the oxygen content in the loading and unloading chamber (LA) , this process is called microaerobic control. In order to prevent the pressure change in the loading and unloading chamber from exceeding the safe range during the micro-oxygen control process, it is necessary to control the pressure in the loading and unloading chamber to ensure the reliable operation of the micro-positive pressure system under good micro-oxygen control.

Existing control solutions usually use a fixed flow of purge gas (such as high-purity nitrogen) to purge the loading and unloading chamber to achieve control of the target gas content in the loading and unloading chamber. For example, in view of the oxygen content in the loading and unloading chamber, a certain flow of high-purity nitrogen gas is used to purge the loading and unloading chamber to discharge the oxygen in the loading and unloading chamber, so that the oxygen content meets the process requirements while maintaining the micron temperature of the loading and unloading chamber. Positive pressure; slightly positive pressure can effectively prevent outside air from entering the loading and unloading chamber, ensuring the oxygen content control effect. However, the above solution requires the use of a certain flow rate of purge gas such as high-purity nitrogen, which can easily lead to excessive use of purge gas and result in higher costs.

In view of this, this application provides a target gas content control method and semiconductor process equipment to solve the problem that existing solutions easily cause excessive use of purge gas and result in higher costs.

This application provides a method for controlling target gas content, which is used to control the content of target gas in a loading and unloading chamber of semiconductor processing equipment, including: obtaining the content difference between the target content and the current content of the target gas in the loading and unloading chamber. value; map the pressure value and the content difference value of the loading and unloading chamber to the corresponding fuzzy domain respectively, and determine the fuzzy control parameters according to each mapping result; convert the fuzzy control parameters to the physical domain to obtain the initial control parameters; The purge gas flow rate purged into the loading and unloading chamber is controlled according to the initial control parameter to control the content of the target gas in the loading and unloading chamber.

Optionally, mapping the pressure value and the content difference value of the loading and unloading chamber to the corresponding fuzzy universe respectively, and determining the fuzzy control parameters according to each mapping result includes: using the first quantization factor based on a preset fuzzy mapping formula Map the pressure value to a first mapping parameter of the first fuzzy domain, use a second quantization factor to map the content difference to a second mapping parameter of the second fuzzy domain based on the preset fuzzy mapping formula; obtain the The membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain and the membership degree of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain; According to the first mapping parameter, the third The membership degree of a mapping parameter relative to each fuzzy subset of the first fuzzy domain, the second mapping parameter, and the membership degree of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain determine the fuzzy control parameter. .

Optionally, according to the first mapping parameter, the membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the second mapping parameter with respect to the second fuzzy Determining the fuzzy control parameter by the membership degree of each fuzzy subset of the universe includes: identifying the first fuzzy quantity and the corresponding membership degree represented by each fuzzy subset of the first fuzzy universe where the first mapping parameter is located, identifying the second The second fuzzy quantity represented by each fuzzy subset of the second fuzzy domain where the mapping parameter is located and the corresponding membership degree; each first fuzzy quantity and each second fuzzy quantity are combined into multiple groups of fuzzy quantities, and each group is The fuzzy quantities are weighted and summed and then rounded to obtain each initial fuzzy parameter; the minimum membership degree corresponding to each group of fuzzy quantities is determined as the membership degree of the corresponding initial fuzzy parameter, and the value is determined based on each initial fuzzy parameter and its corresponding membership degree. Fuzzy control parameters.

Optionally, obtaining the membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain includes: obtaining the fuzzy subset of the first fuzzy domain where the first mapping parameter is located and each of the fuzzy subsets. The membership function of the fuzzy subset; using the first mapping parameter and each membership function to calculate the membership degree of each fuzzy quantity represented by the fuzzy subset; obtaining the second mapping parameter relative to each of the second fuzzy domain The membership degree of the fuzzy subset includes: obtaining the fuzzy subset of the second fuzzy universe where the second mapping parameter is located and the membership functions of each fuzzy subset; using the second mapping parameter and each membership function Calculate the membership degree of the fuzzy quantity represented by each fuzzy subset.

Optionally, the determination process of the first quantization factor includes: obtaining the physical theoretical domain of the pressure value; using a preset quantization factor calculation formula to calculate the quantization between the physical theoretical domain of the pressure value and the first fuzzy theory domain. factor as the first quantification factor; the determination process of the second quantification factor includes: obtaining the physical theoretical domain of the content difference; using the preset quantification factor calculation formula to calculate the physical theoretical domain of the content difference and the second The quantization factor between fuzzy universes is used as the second quantization factor.

Optionally, the physical theoretical domain of the content difference includes at least two sub-physical theoretical domains; the preset quantification factor calculation formula is used to calculate the relationship between the physical theoretical domain of the content difference and the second fuzzy theoretical domain. The quantization factor as the second quantification factor includes: determining the sub-physical theoretical domain to which the content difference belongs, and using the preset quantification factor calculation formula to calculate the quantization factor between the sub-physical theoretical domain and the second fuzzy theory domain. as the second quantization factor.

Optionally, each sub-physics domain has a corresponding second fuzzy domain; the preset quantification factor calculation formula is used to calculate the quantization factor between the sub-physics domain and the second fuzzy domain as the The second quantization factor includes: using a preset calculation formula of the quantization factor to calculate the quantization factor between the sub-physics domain and the second fuzzy domain corresponding to the sub-physics domain as the second quantization factor.

Optionally, the physical theory domain of the pressure value includes at least two sub-physical theory domains, and each sub-physical theory domain of the pressure value has a corresponding first fuzzy theory domain;

Calculating the quantization factor between the physical theory domain of the pressure value and the first fuzzy theory domain using a preset quantification factor calculation formula as the first quantification factor includes: determining the sub-physical theory domain to which the pressure value belongs, using The preset quantization factor calculation formula calculates the quantization factor between the sub-physics domain and the first fuzzy domain corresponding to the sub-physics domain as the first quantization factor.

Optionally, converting the fuzzy control parameters to the physical theoretical domain to obtain the initial control parameters includes: using a proportional factor to convert the fuzzy control parameters to the physical theoretical domain to obtain the initial control parameters.

Optionally, the determination process of the proportional factor includes: obtaining the physical domain of the purge gas flow rate and the corresponding third fuzzy domain; using a preset proportional factor calculation formula to calculate the third fuzzy domain and the purge Scale factor between physical domains of gas flow.

Optionally, the preset calculation formula of the quantization factor includes: ; The default calculation formula of the proportion factor includes: ; In the formula, represents the quantization factor, represents the proportion factor, Represents the upper limit of the fuzzy domain, represents the upper limit of the physical domain, Represents the lower limit of the physical domain.

Optionally, controlling the purge gas flow purged into the loading and unloading chamber according to the initial control parameter includes: using a discrete filter to filter the initial control parameter to obtain a flow control parameter, and using the flow control parameter to control The flow of purge gas purged into the loading and unloading chamber.

Optionally, the discrete filter includes: , in the formula, Indicates the first Flow control parameters at each sampling time, Indicates the first Flow control parameters at each sampling time, Indicates the first Flow control parameters at each sampling time, Indicates the first Initial control parameters at each sampling moment, represents the first filter coefficient, represents the first filter coefficient, , symbol Represents multiplication.

The application also provides a semiconductor processing equipment, including a control device, the control device is used to obtain the content difference between the target content and the current content of the target gas in the loading and unloading chamber of the semiconductor processing equipment; The pressure value and the content difference are mapped to the corresponding fuzzy domain respectively, and the fuzzy control parameters are determined according to each mapping result; the fuzzy control parameters are converted to the physical domain to obtain the initial control parameters; the purge is controlled based on the initial control parameters The purge gas flow into the loading and unloading chamber is used to control the content of the target gas in the loading and unloading chamber.

The above target gas content control method and semiconductor process equipment map the pressure value and content difference in the loading and unloading chamber to the corresponding fuzzy values by obtaining the content difference between the target content and the current content of the target gas in the loading and unloading chamber. Domain of discussion, determine the fuzzy control parameters according to each mapping result, convert the fuzzy control parameters to the physical theory domain, obtain the initial control parameters, and control the purge gas flow purged into the loading and unloading chamber according to the initial control parameters to control the loading and unloading chamber. The target gas content in the chamber realizes fuzzy control of the target gas in the loading and unloading chamber based on the current pressure value and content difference. On the basis of ensuring the quality of the corresponding process, the amount of purge gas used can be reduced. Reduce costs in the corresponding control process.

Furthermore, it can also divide the physical theoretical domain of the content difference into multiple sub-physical theoretical domains to perform multi-stage control on the content difference. During the control process of each stage, the purge gas flow rate purged into the loading and unloading chamber is increased. With the adjustment of the corresponding content difference, the control accuracy of the target gas content can be further improved, the control efficiency can be improved, and the cost in the corresponding control process can be reduced.

The following disclosure provides many different embodiments or examples of different means for implementing the disclosure. Specific examples of components and configurations are described below to simplify the present disclosure. Of course, these are examples only and are not intended to be limiting. For example, the following description in which a first member is formed over or on a second member may include embodiments in which the first member and the second member are formed in direct contact, and may also include embodiments in which additional members Embodiments may be formed between the first member and the second member such that the first member and the second member may not be in direct contact. Additionally, the present disclosure may repeat reference numbers and/or letters in various instances. This repetition is for simplicity and clarity and does not inherently indicate a relationship between the various embodiments and/or configurations discussed.

In addition, for ease of description, spatially relative terms such as “below,” “below,” “lower,” “above,” “upper,” and the like may be used herein to describe one element or component in relation to another(s). The relationship between components or components, as illustrated in the figure. Spatially relative terms are intended to cover different orientations of the device in use or operation other than the orientation depicted in the figures. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.

Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the disclosure are approximations, the values stated in the specific examples are reported as precisely as possible. Any numerical value, however, inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Furthermore, as used herein, the term "about" generally means within 10%, 5%, 1% or 0.5% of a given value or range. Alternatively, the term "approximately" means within one acceptable standard error of the mean when considered by one of ordinary skill in the art. Except in operating/working examples, or unless otherwise expressly specified, all numerical ranges such as quantities, durations of time, temperatures, operating conditions, ratios of quantities, and the like for materials disclosed herein, Quantities, values and percentages should be understood to be modified in all instances by the term "approximately". Accordingly, unless indicated to the contrary, the numerical parameters set forth in the patent claims of this disclosure and accompanying invention claims are approximations that may vary as necessary. At a minimum, each numerical parameter should be interpreted in light of the number of reported significant digits and by applying ordinary rounding techniques. Ranges may be expressed herein as from one endpoint to the other endpoint or between two endpoints. All ranges disclosed herein include endpoints unless otherwise specified.

Existing control solutions usually use a fixed flow of purge gas (such as high-purity nitrogen) to purge the loading and unloading chamber to achieve control of the target gas content in the loading and unloading chamber. For example, for the oxygen content in the loading and unloading chamber, hysteresis window control modes are usually used: large N2 flow oxygen control mode and small N2 flow oxygen control mode. In the large N2 flow oxygen control mode, the gas mass flow controller is usually set to 1000slm/min. , open the exhaust valve, in the small N2 flow oxygen control mode, the gas mass flow controller is usually set to 500slm/min, close the exhaust valve; during the oxygen control process corresponding to each mode, a certain amount of high-purity nitrogen is purged for loading and unloading Chamber, the oxygen is discharged from the loading and unloading chamber, so that the oxygen content meets the process requirements, while maintaining a slight positive pressure in the loading and unloading chamber; the slight positive pressure can effectively prevent outside air from entering the loading and unloading chamber, ensuring the oxygen content control effect. The above solutions require the use of a certain flow rate of purge gas such as high-purity nitrogen in various modes, which can easily lead to excessive use of purge gas and result in higher costs.

Taking the oxygen control scheme of the loading and unloading chamber as an example to further illustrate the problems described in the background technology, the existing oxygen control logic can be referred to as shown in Figure 1a. In Figure 1a, the ordinate represents the oxygen content of the loading and unloading chamber (LA), and the horizontal axis represents the oxygen content of the loading and unloading chamber (LA). The coordinates represent time, and the curve represents the relationship between oxygen content and time: for area ①, a large N2 flow rate is used to control oxygen, changing from a large oxygen content to a micro-oxygen content of 10ppm; for area ②, because the film transfer port is open, the film cassette or related The oxygen in the chamber enters the loading and unloading chamber, and the oxygen content changes from less than 10ppm to 800ppm. Switch to a small N2 flow rate to control oxygen; for the ③ area, the oxygen content is greater than 800ppm, switch to a large N2 flow rate to control oxygen until 10ppm; for the ④ area, the oxygen If the content is less than or equal to 10ppm, switch to a small N2 flow rate to control oxygen, and the oxygen content will gradually tend to be around 5ppm. Among them, 10ppm is the target value to meet the process requirements, and 800ppm is the upper limit of the small N2 flow window, that is, the oxygen content in the loading and unloading chamber changes from 10ppm to 800ppm, and the high-purity nitrogen flow is 500slm/min; the oxygen content in the loading and unloading chamber changes from greater than 800ppm to 10ppm changes, high purity nitrogen flow is 1000slm/min. The above-mentioned high-purity nitrogen flow scheme for the loading and unloading chamber adopts a control method of switching between a large N2 flow oxygen control mode and a small N2 flow oxygen control mode. When the loading and unloading chamber is well sealed, its oxygen content target value is 10ppm. ; In the small N2 flow oxygen control mode, 500slm/min high-purity nitrogen will blow the oxygen content to 5ppm or lower; as shown in Figure 1b, the left ordinate represents the oxygen content (unit ppm) of the loading and unloading chamber, and the right ordinate represents the oxygen content of the loading and unloading chamber (unit ppm). The coordinates represent the high-purity nitrogen flow rate (unit slm), the abscissa represents time (unit s), the dotted line represents the change of oxygen content over time, the solid line represents the output change of high-purity nitrogen flow rate over time, when the oxygen content (dashed line) reaches 10ppm , the high-purity nitrogen flow rate (solid line) was switched from 1000slm/min to 500slm/min and remained unchanged, and the oxygen content was finally maintained at about 3ppm. To maintain the 10ppm oxygen content required by the process, the actual required high-purity nitrogen flow rate can be less than 500slm/min. It can be seen that the traditional loading and unloading chamber oxygen content control scheme, this type of target gas content control scheme, can easily cause high-purity nitrogen and other purges. Excessive use of gas leads to the problem of wasting purge gas, resulting in high corresponding costs.

In view of the problem that the traditional target gas content control scheme can easily cause excessive use of purge gas, this application provides a target gas content control method and semiconductor process equipment, using fuzzy control, which can reduce the amount of purge gas used in the control process. , reducing the cost of target gas content control solutions.

The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of this application. The following embodiments and their technical features may be combined with each other without conflict.

The first aspect of the present application provides a target gas content control method for controlling the target gas content in the loading and unloading chamber of semiconductor processing equipment. Referring to Figure 2, the above target gas content control method includes:

S110: Obtain the content difference between the target content and the current content of the target gas in the loading and unloading chamber.

The above-mentioned target gases include oxygen and other gases in the loading and unloading chamber that will affect the process effect. The target content is the target value (or ideal value) of the target gas content in the loading and unloading chamber, which can be set according to the corresponding process requirements. For example, in some processes, the target oxygen content in the loading and unloading chamber is 5ppm, and in other processes , the target oxygen content is 10ppm. The current content is the target gas content measured in real time by the gas analyzer installed at the loading and unloading chamber. When the current content is high, the content difference between the target content and the current content is a negative value with a large absolute value; at this time, if the purge gas is blown into the loading and unloading chamber, the target content will increase with the purge gas. The purge becomes low and stabilizes at a level near the target content or slightly less than the target content to ensure the corresponding process quality.

S120: Map the pressure value and content difference value of the loading and unloading chamber to the corresponding fuzzy domain respectively, and determine the fuzzy control parameters according to each mapping result.

This application adopts the theory of fuzzy control. The above-mentioned pressure value and content difference are physical quantities. The value range corresponding to each physical quantity is a physical theoretical domain. Each physical theoretical domain can be based on the characteristics of the loading and unloading chamber in various processes, and through Relevant experiments and other analysis methods are determined. For each physical theoretical domain, the corresponding fuzzy domain can be set according to factors such as its range, conversion accuracy and/or required control accuracy. Each physical quantity in the physical theoretical domain can be converted into the corresponding fuzzy domain through processes such as mapping and corresponding fuzzy processing. At least one fuzzy quantity in the domain of discussion, the corresponding fuzzy control parameters can be calculated based on each fuzzy quantity and corresponding membership degree and other parameters.

S130: Convert the fuzzy control parameters to the physical theoretical domain to obtain initial control parameters.

By converting the fuzzy quantities in each fuzzy domain, the physical quantities corresponding to the physical domain can be obtained. The fuzzy universe in which the above fuzzy control parameters are located can be preset according to the accuracy required for fuzzy processing, such as [-2, 2] and so on. The physical domain of the flow range where the initial control parameters are located can be set according to the flow range of the purge gas corresponding to the purge.

S140: Control the flow of purge gas purged into the loading and unloading chamber according to the initial control parameters to control the content of the target gas in the loading and unloading chamber.

In the above step S140, the initial control parameters can also be filtered by discrete filtering or smoothing filtering, and the filtered results are used to control the corresponding purge gas flow, so as to make the change process of the control parameters smoother and avoid target gas content control. Situations that affect the control effect, such as sudden changes in relevant control parameters during the process, can improve the control effect.

In this embodiment, by obtaining the content difference between the target content and the current content of the target gas in the loading and unloading chamber, the pressure value and content difference of the loading and unloading chamber are respectively mapped to the corresponding fuzzy domain, and the determination is determined based on each mapping result. Fuzzy control parameters are converted to the physical theory domain to obtain initial control parameters. According to the initial control parameters, the purge gas flow purged into the loading and unloading chamber is controlled to control the target gas content of the loading and unloading chamber. The target gas in the loading and unloading chamber is fuzzy controlled based on the current pressure value and content difference. On the basis of ensuring the quality of the corresponding process, the amount of purge gas used can be reduced and excessive use of purge gas can be avoided, thereby reducing Costs in the target gas content control process.

In one embodiment, the pressure value and content difference value of the loading and unloading chamber are mapped to the corresponding fuzzy universe respectively, and determining the fuzzy control parameters according to each mapping result includes:

The first quantization factor is used to map the pressure value to the first mapping parameter of the first fuzzy universe based on the preset fuzzy mapping formula, and the second quantization factor is used to map the content difference to the second fuzzy value based on the preset fuzzy mapping formula. The second mapping parameter of the domain of discourse;

Obtain the membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain and the membership degree of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain;

The fuzzy determination is based on the first mapping parameter, the membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain, the second mapping parameter and the membership degree of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain. control parameters.

The above physical quantities such as pressure value and content difference are in the corresponding physical theory domain. Each physical theory domain has a corresponding fuzzy theory domain. There are quantification factors between the physical theory domain and the corresponding fuzzy theory domain (such as the first quantification factor and the third quantization factor). (two quantization factors), each physical quantity in it can be converted to the corresponding fuzzy domain through the quantization factor. The above-mentioned first mapping parameter and second mapping parameter are preliminary mapping parameters for mapping corresponding physical quantities to corresponding fuzzy domains. The larger the interval range of each fuzzy domain, the higher the corresponding conversion accuracy. Each fuzzy domain can correspond to different fuzzy intervals. For example, the fuzzy interval of the first fuzzy domain can be [-2,2], and the fuzzy interval of the second fuzzy domain can be [-3,3]; each fuzzy domain can It can also have the same fuzzy interval, for example, both are set to [-2,2].

Each fuzzy domain includes multiple fuzzy subsets, and each fuzzy subset has a corresponding membership function. A certain mapping parameter usually belongs to multiple fuzzy subsets. By substituting the mapping parameters into the corresponding membership function for calculation, the corresponding The mapping parameter takes the membership degree of the corresponding fuzzy quantity. Referring to Figure 3, the target gas in Figure 3 is oxygen. The abscissa represents the value of the mapping parameter, and the ordinate represents the degree of membership. It shows the fuzzy universe corresponding to the pressure value, oxygen content difference and fuzzy control parameters respectively. The fuzzy subsets of these fuzzy domains are {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}. The specific fuzzy quantities include: {-2 , -1, 0, 1, 2}, using triangular membership functions respectively. Each fuzzy domain shown in Figure 3 shows that each mapping parameter taken on the abscissa is covered by at least two fuzzy subsets.

This embodiment can combine the actual characteristics of the loading and unloading chamber to determine the inference rules, and then determine the corresponding fuzzy processing rules, so as to perform fuzzy processing on the first mapping parameters, the second mapping parameters and the corresponding membership degrees to obtain the required fuzzy processing. control parameters. The oxygen content control process in the loading and unloading chamber is explained here. The reasoning rules can include: 1. The pressure of the loading and unloading chamber is very small, and the oxygen content is large, so the flow rate of high-purity nitrogen is very large; 2. The pressure of the loading and unloading chamber Slightly smaller, the oxygen content is slightly larger, and the high-purity nitrogen flow rate is slightly larger; 3. The loading and unloading chamber pressure is moderate, the oxygen content is moderate, and the high-purity nitrogen flow rate is moderate; 4. The loading and unloading chamber pressure is slightly larger, and the oxygen content is slightly smaller , the flow rate of high-purity nitrogen is slightly smaller; 5. The pressure in the loading and unloading chamber is very high, and the oxygen content is very small, so the flow rate of high-purity nitrogen is very small. The corresponding fuzzy processing rules include: determining each first fuzzy quantity corresponding to the first mapping parameter and each second fuzzy quantity corresponding to the second mapping parameter, and determining multiple sets of fuzzy quantities including one first fuzzy quantity and one second fuzzy quantity. , use the inference formula to calculate each group of fuzzy quantities to obtain each initial fuzzy parameter, determine the membership degree of each initial fuzzy parameter, and clarify each initial fuzzy parameter according to each membership degree to determine the fuzzy control parameters.

In one example, according to the first mapping parameter, the membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain, the second mapping parameter and the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain. The membership degree determines the fuzzy control parameters including:

Identify the first fuzzy quantity represented by each fuzzy subset of the first fuzzy domain where the first mapping parameter is located and the corresponding membership degree, and identify the second fuzzy quantity represented by each fuzzy subset of the second fuzzy domain where the second mapping parameter is located. and the corresponding degree of membership;

Combining each first fuzzy quantity and each second fuzzy quantity into multiple groups of fuzzy quantities, performing weighted summation of each group of fuzzy quantities and then rounding to obtain each initial fuzzy parameter;

The minimum membership degree corresponding to each group of fuzzy quantities is determined as the membership degree corresponding to the initial fuzzy parameter, and the fuzzy control parameters are determined based on each initial fuzzy parameter and the membership degree corresponding to each initial fuzzy parameter.

The fuzzy subsets of the first fuzzy domain and the second fuzzy domain can be shown in Figure 3. Each fuzzy subset represents the corresponding fuzzy quantity and has a corresponding membership function. For example, the fuzzy quantity represented by the fuzzy subset NB is: -2, the fuzzy quantity represented by the fuzzy subset NS is -1, the fuzzy quantity represented by the fuzzy subset ZO is 0, the fuzzy quantity represented by the fuzzy subset PS is 1, and the fuzzy quantity represented by the fuzzy subset PB is 2. Taking the first mapping parameter 0.6 corresponding to the pressure value of the loading and unloading chamber as an example to illustrate the above process, according to the fuzzy subset distribution diagram of the pressure value in Figure 3, the first fuzzy amount corresponding to 0.6 is 0 (fuzzy subset ZO) and 1 (fuzzy subset PS); the membership functions of fuzzy subset ZO and fuzzy subset PS are as follows:

,

,

In the formula, Represents the membership function of the fuzzy subset ZO, represents a fuzzy subset The membership function of Represents a mapping parameter (such as the first mapping parameter). time, get it , , that is, the first mapping parameter 0.6 corresponds to the fuzzy subset ZO and the fuzzy subset PS. The fuzzy quantity represented by the fuzzy subset ZO is 0, and the corresponding membership degree is 0.4. The fuzzy quantity represented by the fuzzy subset PS is 1, and the corresponding membership degree is 0.4. is 0.6.

The above process of weighted summing and rounding includes: , in the formula, represents the initial fuzzy parameters, represents the first fuzzy quantity, represents the second fuzzy quantity, Indicates the first weight (or correction factor), which can be set to 0.4 or 0.5, etc. Represents the rounding operation element, which indicates that the absolute value of the value is rounded to an integer. The positive and negative signs are the same as The positive and negative signs in are the same, for example, <-1.3>=-1, <1.7>=2. The above-mentioned first mapping parameters correspond to multiple first blur quantities, and the second mapping parameters correspond to multiple second blur quantities. By combining each first blur quantity and each second blur quantity, multiple sets of non-overlapping blur quantities can be obtained. By performing weighted summation of each group of fuzzy quantities and then rounding, the initial fuzzy parameters and the membership degrees corresponding to each initial fuzzy parameter can be obtained; for example, in a certain group of fuzzy quantities, the first fuzzy quantity is 0, its membership degree is 0.4, and the second fuzzy quantity is 1, its membership degree is 0.8, and the first weight is 0.5, and the corresponding initial fuzzy parameter is , its membership degree is 0.4.

In this example, determining the fuzzy control parameters based on each initial fuzzy parameter and the corresponding degree of membership is a clarifying process. Each initial fuzzy parameter belongs to a fuzzy quantity. These fuzzy quantities need to be converted into specific fuzzy control parameters, and then the fuzzy control The parameters are converted into physical quantities (such as initial control parameters) and sent to the control mechanism for control. Optionally, this process can be clarified using the maximum membership average method to determine the fuzzy control parameters. On the basis of meeting the actual control requirements, it can reduce the amount of calculation, stabilize the output, and solve the problem of frequent control. . The process of clarifying by the maximum membership average method includes: selecting the initial fuzzy parameter with the largest membership among the various initial fuzzy parameters as the selected fuzzy parameter, and obtaining the selected membership function of the fuzzy subset where the selected fuzzy parameter is located, so as to The maximum membership degree is used as the function value of the selected membership function, multiple fuzzy variable values are obtained, and the average value of each fuzzy variable value is used as the fuzzy control parameter. The following is an example of determining the corresponding fuzzy control parameters based on two initial fuzzy parameters to illustrate the above clarifying process: the initial fuzzy parameter A is 0, the corresponding membership degree is 0.6, the initial fuzzy parameter B is 1, and the corresponding membership degree is 0.4, taking the initial fuzzy parameter A with a membership degree of 0.6 as the selected membership function, and then according to the fuzzy subset distribution diagram of the fuzzy control parameters in Figure 3, the fuzzy quantity is 0, which is the fuzzy subset ZO. Let the membership of the ZO fuzzy subset The function value of the function is 0.6, which is:

,

Solving to get two fuzzy variable values, , , the first fuzzy control parameter determined by the average value of these two fuzzy variables is 0. Furthermore, the corresponding scaling factor can be used to convert the fuzzy control parameters into the physical theoretical domain to obtain the initial control parameters; for example, a certain scaling factor is , the upper limit of the physical domain of the purge gas flow rate is 1000, the lower limit is 600, fuzzy control parameters is 0, then the corresponding initial control parameters can be:

.

In one embodiment, obtaining the membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain includes: obtaining the fuzzy subset of the first fuzzy domain in which the first mapping parameter is located and the values of each fuzzy subset. Membership function; use the first mapping parameter and each membership function to calculate the membership degree of the fuzzy quantity represented by each fuzzy subset;

Obtaining the membership degree of the second mapping parameter with respect to each fuzzy subset of the second fuzzy universe includes: obtaining the fuzzy subset of the second fuzzy universe where the second mapping parameter is located and the membership function of each fuzzy subset; using Two mapping parameters and each membership function calculate the membership degree of the fuzzy quantity represented by each fuzzy subset.

Optionally, the above-mentioned first mapping parameter and second mapping parameter may be calculated using fuzzy mapping formulas for corresponding physical quantities. The above fuzzy mapping formula can be set according to the inference rules corresponding to the loading and unloading chamber. The corresponding quantization factor is usually used for mapping. For example, it can be set to , in the formula, represents the quantization factor, represents the upper limit of the physical domain, represents the lower limit of the physical domain, represents a physical quantity, Represents mapping parameters. Specifically, for a certain mapping parameter, after determining the fuzzy subset it belongs to and the corresponding membership function, the mapping parameter can be substituted into each membership function to obtain the membership degree of each fuzzy quantity. Taking the pressure value P=2800mtorr as an example, the corresponding fuzzy quantity and membership degree solution process will be explained. If the corresponding physical theory domain (pressure range) is [1500, 3500], the first mapping parameter is , the corresponding fuzzy quantities are 0 (fuzzy subset ZO) and 1 (fuzzy subset PS); according to the membership functions of ZO and PS fuzzy subsets:

,

,

here ,get , , that is, the first mapping parameter of the pressure value P=2800mtorr is 0.6, which corresponds to a fuzzy quantity 0 with a membership degree of 0.4, and also corresponds to another fuzzy quantity 1 with a membership degree of 0.6.

Specifically, the determination process of the first quantization factor includes: obtaining the physical theory domain of the pressure value; using a preset quantization factor calculation formula to calculate the quantization factor between the physical theory domain of the pressure value and the first fuzzy theory domain as the first quantification factor;

The determination process of the second quantification factor includes: obtaining the physical theory domain of the content difference; using the preset above-mentioned quantification factor calculation formula to calculate the quantification factor between the physical theory domain of the content difference and the second fuzzy theory domain as the second quantification factor.

Optionally, the physical domain of the content difference includes at least two sub-physical domains; the quantification factor between the physical domain of the content difference and the second fuzzy domain is calculated using a preset quantification factor calculation formula as The second quantification factor includes: determining the sub-physical theory domain to which the content difference belongs, and using a preset quantification factor calculation formula to calculate the quantization factor between the sub-physics theory domain and the second fuzzy theory domain as the second quantification factor. Here, the entire physical theoretical domain of the content difference can be divided into multiple segments according to the target gas content control requirements corresponding to different content differences. Each segment is a sub-physical theoretical domain, so that after the content difference is obtained, the content difference can be identified. The sub-physics theory domain in which it is located maps the content difference from the sub-physics theory domain to the corresponding second fuzzy theory domain, realizing the differential conversion of the content difference in each sub-physics theory domain, thereby satisfying the requirements of each sub-physics theory domain. The required target gas content control needs.

Preferably, for the purpose of differential control, each sub-physics domain can also have its own second fuzzy domain, that is, different sub-physics domains correspond to different second fuzzy domains, thereby further achieving refined control. . Correspondingly, the above-mentioned calculation of the quantization factor between the sub-physics theory domain and the second fuzzy theory domain as the second quantization factor using the preset quantization factor calculation formula includes: using the preset quantization factor calculation formula to calculate the sub-physics theory. The quantization factor between the domain and the second fuzzy domain corresponding to the sub-physical theory domain is used as the second quantization factor.

In some cases, if the target gas accounts for a relatively small proportion of the gas included in the loading and unloading chamber, the change in the content difference between the target content and the current content of the target gas has no impact on the range of the pressure value in the loading and unloading chamber. When the influence or influence is very small, each sub-physical theory domain of the content difference can correspond to the physical theory domain of the same pressure value, so that the pressure value fuzzy processing can be performed using the physical theory domain of the pressure value and the corresponding first fuzzy theory domain to improve Fuzzy processing efficiency, thereby improving target gas content control efficiency.

In other cases, if the target gas accounts for a relatively large proportion of the gas included in the loading and unloading chamber, the change in the content difference between the target content and the current content of the target gas will affect the range of the pressure value in the loading and unloading chamber. When there is a certain influence, the physical theoretical domain of the pressure value can also be divided into multiple sub-physical theoretical domains. Each sub-physical theoretical domain of the pressure value can also have a corresponding first fuzzy domain to improve the mapping accuracy and corresponding fuzzy processing. process accuracy, thereby improving the target gas content control effect. At this time, the above-mentioned calculation of the quantification factor between the physical theory domain of the pressure value and the first fuzzy theory domain using the preset quantification factor calculation formula as the first quantification factor may include: determining the sub-physical theory domain to which the pressure value belongs, using the preset Set the quantization factor calculation formula to calculate the quantization factor between the sub-physics domain and the first fuzzy domain corresponding to the sub-physics domain as the first quantization factor.

In order to make the control process of the target gas content smoother, in one example, the above target gas content control can be divided into two stages. At this time, the physical theoretical domain of the content difference includes two sub-physical theoretical domains. These two sub-physical theoretical domains It is determined by the segmentation threshold, that is, the upper limit of one sub-physics domain is the segmentation threshold, and the lower limit of the other sub-physics domain is the segmentation threshold. At this time, the corresponding sub-physics domain and the second fuzzy domain can be selected for mapping based on the relationship between the current content difference and the segmentation threshold, to obtain the fuzzy control parameters corresponding to the current content difference, and use the fuzzy control parameters to obtain The corresponding initial control parameters control the purge gas flow rate purged into the loading and unloading chamber. In this way, the entire target gas control process is divided into two stages for fuzzy control based on the content difference. On the basis of improving the control effect, it also has high control efficiency. The above-mentioned segmentation threshold can be set according to the target content and corresponding control accuracy. For example, for the oxygen content adjustment process of the loading and unloading chamber, the segmentation threshold can be set to a value such as -5ppm. In some cases, the content difference is less than the segmentation threshold, which indicates that the target gas content in the loading and unloading chamber is high. The coarse adjustment mode with relatively low control accuracy can be used to control the purge gas flow to quickly reduce the target gas content in the loading and unloading chamber. to close to the target content to ensure control efficiency; the content difference is greater than or equal to the segmentation threshold, indicating that the target gas content in the loading and unloading chamber has been reduced to close to the target content. At this time, the purge gas can be controlled in a fine-tuning mode with relatively high control accuracy. flow, so that the oxygen content in the unloading chamber further reaches the target content and is maintained at this level to ensure control accuracy.

Further, the data shown in Table 1 and Table 2 are used to illustrate the process of dividing the oxygen content in the loading and unloading chamber into two sections using segmentation thresholds. Table 1 shows each physical theoretical domain and the corresponding fuzzy when the content difference is less than the segmentation threshold. Conversion results of the domain of discourse. Table 2 shows the conversion results of each physical domain and the corresponding fuzzy domain when the content difference is greater than or equal to the segmentation threshold. In Table 1, the first physical domain where the pressure value is located is [1500, 3500], the corresponding first fuzzy domain is [-2, 2], and the first quantification factor between the two is 0.002; the content difference The sub-physics domain it is located in is [-505,-5], the corresponding second fuzzy domain is [-2,2], and the second quantization factor between the two is 0.008; the third quantization factor shown in Table 1 The fuzzy domain is [-2,2], the physical domain of purge gas flow is [600,1000], and the scale factor between the two is 100. In Table 2, the physical domain of the pressure value is [1500, 3500], the corresponding first fuzzy domain is [-2, 2], and the quantification factor between the two is 0.002; The physical domain is [-5, 5], the corresponding second fuzzy domain is [-2, 2], and the second quantization factor between the two is 0.4; the third fuzzy domain shown in Table 2 is [- 2,2], the physical domain of purge gas flow is [300,600], and the proportional factor between the two is 75. Table 1 physical quantity physical domain fuzzy domain quantization factor Pressure value [1500,3500] [-2,2] 0.002 Content difference [-505,-5] [-2,2] 0.008 fuzzy domain physical domain scale factor initial control parameters [-2,2] [600,1000] 100 Table 2 physical quantity physical domain fuzzy domain quantization factor Pressure value [1500,3500] [-2,2] 0.002 Content difference [-5,5] [-2,2] 0.4 fuzzy domain physical domain scale factor initial control parameters [-2,2] [300,600] 75

In one example, converting the fuzzy control parameters into the physical theoretical domain to obtain the initial control parameters includes: using a proportional factor to convert the fuzzy control parameters into the physical theoretical domain to obtain the initial control parameters. The proportional factor can be calculated based on the upper and lower limit characteristics of the third fuzzy domain where the fuzzy control parameters are located and the physical domain where the initial control parameters are located.

Specifically, the determination process of the scale factor includes:

Obtain the physical domain of purge gas flow and the corresponding third fuzzy domain;

The proportional factor between the third fuzzy domain and the physical domain of purge gas flow is calculated using a preset proportional factor calculation formula.

Optionally, the preset quantization factor calculation formula includes: ;

The default scaling factor calculation formulas include: ;

In the formula, represents the quantization factor, represents the proportion factor, Represents the upper limit of the fuzzy domain, represents the upper limit of the physical domain, Represents the lower limit of the physical domain.

The value range corresponding to each of the above physical theoretical domains can be determined based on the characteristics of the specific loading and unloading chamber in various processes and through relevant experiments and other analysis methods; for example, the ideal pressure range of the loading and unloading chamber is 2.5±1torr. At this time, it can The physical theoretical domain corresponding to the defined pressure value is [1500, 3500], and the unit is mtorr; the oxygen content feedback value range of the loading and unloading chamber is usually 0-1000ppm (greater than 1000ppm, all are set to 1000ppm). The oxygen content target of the process The value is usually 10ppm or 5ppm. The physical theoretical domain of the oxygen content difference e can be [-505, 5] (values less than -505ppm are regarded as -505ppm), and the unit is ppm; the range of the gas mass flow controller is usually is 1000slm. At this time, the physical theoretical domain corresponding to the initial control parameters can be set to [300, 1000], and the unit is slm.

In this example, each physical theory domain and each fuzzy theory domain can be set according to the value range of each physical theory domain and related process characteristics, and then the quantization factor calculation formula is used to calculate the quantization factor between each physical theory domain and the corresponding fuzzy theory domain. The proportional factor calculation formula is used to calculate the proportional factors between each fuzzy domain and the corresponding physical theoretical domain. For example, if the upper limit of the first fuzzy domain is 2, the upper limit of the pressure range is 3500, and the lower limit is 1500, then the corresponding first quantization factor is: ;Similarly, other quantitative factors can be calculated quickly and accurately. For another example, if the upper limit of the third fuzzy domain is 2, the upper limit of the physical domain of purge gas flow is 1000, and the lower limit is 600, then the corresponding scaling factor is: .

In one embodiment, controlling the purge gas flow purged into the loading and unloading chamber according to the initial control parameters includes: using a discrete filter to filter the initial control parameters to obtain the flow control parameters, and using the flow control parameters to control the purge gas flow into the loading and unloading chamber. Purge gas flow rate for loading and unloading chambers.

Specifically, discrete filters include:

,

In the formula, Indicates the first Flow control parameters at each sampling time, Indicates the first Flow control parameters at each sampling time, Indicates the first Flow control parameters at each sampling time, Indicates the first Initial control parameters at each sampling moment, represents the first filter coefficient, represents the first filter coefficient, , symbol Represents multiplication.

This embodiment uses a discrete filter to filter the initial control parameters, so that the corresponding flow control parameters change more gently, which can solve the problem of sudden changes such as spikes when the purge gas flow output suddenly changes in steps, and can improve the control of the purge gas. Sweep gas flow control effect.

In one example, the target gas content control method provided by this application is explained by taking the high-purity nitrogen flow control process when controlling the oxygen content of the loading and unloading chamber as an example. As shown in Figure 4, a differential pressure meter is used to measure the loading and unloading chamber. The current pressure value of the chamber. The oxygen analyzer is used to measure the current content of oxygen in the loading and unloading chamber. When the content difference between the target content and the current content is less than the segmentation threshold, the control selector will use the segmentation threshold as the upper limit. The control parameters such as quantization factors corresponding to the sub-physical theoretical domain are uploaded to the fuzzy controller, so that the fuzzy controller uses the corresponding first physical theoretical domain, first fuzzy domain, sub-physical theoretical domain and second fuzzy domain to calculate the current pressure value. Convert with the current content difference to obtain the corresponding fuzzy control parameters; when the content difference is greater than or equal to the segmentation threshold, the control selector will upload control parameters such as quantization factors corresponding to the sub-physical theory domain with the segmentation threshold as the lower limit to The fuzzy controller uses the corresponding first physical theory domain, the first fuzzy theory domain, the sub-physical theory domain and the second fuzzy theory domain to convert the current pressure value and the current content difference to obtain the corresponding fuzzy control parameters. ; In this way, the fuzzy controller can convert the above fuzzy control parameters to the corresponding physical theoretical domain and obtain the initial control parameters. The discrete filter filters the initial control parameters to obtain the corresponding flow control parameters, so that the gas mass flow controller uses the corresponding flow control parameters to control the flow of high-purity nitrogen purged to the loading and unloading chamber to control the flow of the unloading chamber. oxygen content. Simulate and analyze the high-purity nitrogen flow control process provided in this example. When preparing to start the process, the oxygen content in the loading and unloading chamber needs to be controlled at the target content. According to the control method provided in this example, the oxygen content changes as shown in Figure 5. . The left ordinate represents oxygen content (unit ppm), the right ordinate represents high-purity nitrogen flow (unit slm), the abscissa represents time (unit s), the dotted line represents the change of oxygen content over time, and the solid line represents high-purity nitrogen flow output changes. The target content in Figure 5 is 11ppm. After reaching the target content, the oxygen content is maintained at 11±1ppm. Through fuzzy control, the final high-purity nitrogen flow rate is stabilized at 310 slm/min. Compared with the traditional solution of 500 slm/min in the small N2 flow mode, the high-purity nitrogen flow rate is saved by 190 slm/min. The point after point a in the figure is this implementation. The example corresponds to the fuzzy control process. It can be seen that this example can effectively save the high-purity nitrogen used in the control process.

The above target gas content control method, by obtaining the content difference between the target content and the current content of the target gas in the loading and unloading chamber, maps the pressure value and content difference of the loading and unloading chamber to the corresponding fuzzy domain respectively. According to Each mapping result determines the fuzzy control parameters, converts the fuzzy control parameters into the physical theory domain, and obtains the initial control parameters. According to the initial control parameters, the purge gas flow purged into the loading and unloading chamber is controlled to control the target gas in the loading and unloading chamber. content, realizing fuzzy control of the target gas in the loading and unloading chamber based on the current pressure value and content difference. On the basis of ensuring the quality of the corresponding process, the amount of purge gas used can be reduced and excessive use of purge gas can be avoided. Purge gas; in addition, the physical theoretical domain of the content difference can be divided into multiple sub-physical theoretical domains to perform multi-stage control on the content difference. During the control process of each stage, the purge gas flow rate purged into the loading and unloading chamber can be With the adjustment of the corresponding content difference, the control accuracy of the target gas content can be further improved, the control efficiency can be improved, and the cost in the corresponding control process can be reduced.

In a second aspect, the present application provides a semiconductor processing equipment, including a control device, the control device is used to obtain the content difference between the target content and the current content of the target gas in the loading and unloading chamber of the semiconductor processing equipment; The pressure value and content difference of the chamber are mapped to the corresponding fuzzy domain respectively, and the fuzzy control parameters are determined according to each mapping result; the fuzzy control parameters are converted to the physical domain to obtain the initial control parameters; the purge is controlled according to the initial control parameters. The purge gas flow of the unloading chamber is used to control the target gas content in the loading and unloading chamber.

For specific limitations on the control device corresponding to the target gas content, please refer to the above limitations on the target gas content control method, which will not be described again here. The above control device can be implemented in whole or in part by software, hardware and combinations thereof. It can be embedded in the processor of the computer device in the form of hardware or independent of it, or can be stored in the memory of the computer device in the form of software so that the processor can call and perform corresponding operations.

This application provides a semiconductor device to a third party, as shown in Figure 6. The semiconductor device includes a processor 620 and a storage medium 630; program code is stored on the storage medium 630; the processor 620 is used to call the program code stored in the storage medium. , to execute the target gas content control method of any of the above embodiments.

The above-mentioned semiconductor equipment uses the above-mentioned target gas content control method to control the purge gas flow purged to the loading and unloading chamber during the corresponding process, and then controls the content of the target gas in the loading and unloading chamber, which can reduce the amount of purge gas and reduce the use of purge. The cost of gas generation thereby reducing the corresponding process cost.

The foregoing content summarizes the features of several embodiments so that those skilled in the art may better understand the aspects of the present disclosure. Those skilled in the art should appreciate that they may readily use the present disclosure as a basis for designing or modifying other processes and structures for carrying out the same purposes and/or achieving the same advantages of the embodiments described herein. Those skilled in the art should also understand that such equivalent constructions do not depart from the spirit and scope of the disclosure, and that they can be variously changed, replaced, and altered herein without departing from the spirit and scope of the disclosure.

620: Processor 630:Storage media S110-S140: Steps

The present disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. It should be noted that in accordance with standard practice in the industry, the various components are not drawn to scale. In fact, the dimensions of the various components may be arbitrarily increased or reduced for clarity of discussion. Figure 1a is a schematic diagram of the oxygen control logic of the existing scheme; Figure 1b is a schematic diagram of the control result analysis of the existing scheme; Figure 2 is a schematic flow chart of target gas content control in an embodiment of the present application; Figure 3 is a schematic diagram of fuzzy subsets of each fuzzy domain in an embodiment of the present application; Figure 4 is a schematic diagram of the high-purity nitrogen flow control process in an embodiment of the present application; Figure 5 is a schematic diagram of analysis of high-purity nitrogen flow control results in one embodiment of the present application; FIG. 6 is a schematic structural diagram of a semiconductor device according to an embodiment of the present application.

S110-S140: Steps

Claims (14)

A target gas content control method for controlling the content of a target gas in a loading and unloading chamber of a semiconductor process equipment. The method includes: Obtain the content difference between the target content and the current content of the target gas in the loading and unloading chamber; Map the pressure value and the content difference value of the loading and unloading chamber to the corresponding fuzzy universe respectively, and determine a fuzzy control parameter according to each mapping result; Convert the fuzzy control parameters to the physical domain to obtain an initial control parameter; A purge gas flow rate purged into the loading and unloading chamber is controlled according to the initial control parameter to control the content of the target gas in the loading and unloading chamber. The target gas content control method as described in claim 1, wherein mapping a pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy universes respectively, and determining the fuzzy control parameters according to each mapping result includes: A first quantization factor is used to map the pressure value to a first mapping parameter of a first fuzzy domain based on a preset fuzzy mapping formula, and a second quantization factor is used to map the content difference based on the preset fuzzy mapping formula. The value mapping is a second mapping parameter of a second fuzzy universe; Obtain the membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain and the membership degree of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain; According to the first mapping parameter, the membership degree of the first mapping parameter relative to each fuzzy subset of the first fuzzy domain, the second mapping parameter and the membership degree of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain The membership degree of the set determines the fuzzy control parameters. The target gas content control method as described in claim 2, wherein the first mapping parameter, the membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain, the second mapping parameter and The membership degree of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe determines the fuzzy control parameters including: Identify a first fuzzy quantity and corresponding membership degree represented by each fuzzy subset of the first fuzzy domain where the first mapping parameter is located, and identify each fuzzy subset representation of the second fuzzy domain where the second mapping parameter is located. A second fuzzy quantity and the corresponding membership degree; Combining each of the first fuzzy quantities and each of the second fuzzy quantities into multiple groups of fuzzy quantities, performing weighted summation of each group of fuzzy quantities and then rounding to obtain each initial fuzzy parameter; The minimum membership degree corresponding to each group of fuzzy quantities is determined as the membership degree corresponding to the initial fuzzy parameter, and the fuzzy control parameter is determined based on each initial fuzzy parameter and its corresponding membership degree. The target gas content control method as described in claim 2, wherein obtaining the membership degree of the first mapping parameter with respect to each fuzzy subset of the first fuzzy universe includes: Obtain the fuzzy subset of the first fuzzy domain where the first mapping parameter is located and the membership functions of each fuzzy subset; use the first mapping parameter and each membership function to calculate the representations of each fuzzy subset. Membership degree of fuzzy quantity; Obtaining the membership degree of the second mapping parameter with respect to each fuzzy subset of the second fuzzy universe includes: Obtain the fuzzy subset of the second fuzzy domain where the second mapping parameter is located and the membership functions of each fuzzy subset; use the second mapping parameter and each membership function to calculate the representations of each fuzzy subset. Membership degree of fuzzy quantity. The target gas content control method as described in claim 2, wherein the determination process of the first quantification factor includes: Obtain the physical domain of the pressure value; Using a preset quantization factor calculation formula to calculate the quantization factor between the physical domain of the pressure value and the first fuzzy domain as the first quantization factor; The determination process of the second quantization factor includes: Obtain the physical domain of the content difference; The preset quantization factor calculation formula is used to calculate the quantization factor between the physical theory domain of the content difference and the second fuzzy theory domain as the second quantization factor. The target gas content control method as described in claim 5, wherein the physical domain of the content difference includes at least two sub-physical domains; Calculating the quantization factor between the physical theory domain of the content difference and the second fuzzy theory domain using the preset quantization factor calculation formula as the second quantization factor includes: Determine the sub-physics theoretical domain to which the content difference belongs, and use the preset quantification factor calculation formula to calculate the quantization factor between the sub-physics theory domain and the second fuzzy theory domain as the second quantization factor. The target gas content control method as described in claim 6, wherein each sub-physics domain has a corresponding second fuzzy domain; Calculating the quantization factor between the sub-physical theory domain and the second fuzzy theory domain as the second quantization factor using the preset quantization factor calculation formula includes: The preset quantization factor calculation formula is used to calculate the quantization factor between the sub-physics domain and the second fuzzy domain corresponding to the sub-physics domain as the second quantization factor. The target gas content control method as described in claim 7, wherein the physical domain of the pressure value includes at least two sub-physical theoretical domains, and each sub-physical domain of the pressure value has a corresponding first fuzzy domain; The use of a preset quantization factor calculation formula to calculate the quantization factor between the physical theory domain of the pressure value and the first fuzzy theory domain as the first quantization factor includes: Determine the sub-physics domain to which the pressure value belongs, and use the preset quantification factor calculation formula to calculate the quantification factor between the sub-physics domain and the first fuzzy domain corresponding to the sub-physics domain as the first quantification factor. . The target gas content control method as described in claim 1, wherein the fuzzy control parameters are converted into the physical domain to obtain the initial control parameters including: A proportional factor is used to convert the fuzzy control parameters into the physical theoretical domain to obtain the initial control parameters. The target gas content control method as described in claim 8, wherein the determination process of the proportional factor includes: Obtain the physical domain of the purge gas flow rate and the corresponding third fuzzy domain; A preset proportional factor calculation formula is used to calculate the proportional factor between the third fuzzy domain and the physical domain of the purge gas flow rate. The target gas content control method as described in claim 5 or 10, wherein the preset calculation formula of the quantification factor includes: ; The default calculation formula of the proportion factor includes: ; In the formula, represents the quantization factor, represents the proportion factor, Represents the upper limit of the fuzzy domain, represents the upper limit of the physical domain, Represents the lower limit of the physical domain. The target gas content control method according to claim 1, wherein controlling the purge gas flow rate purged into the loading and unloading chamber according to the initial control parameter includes: A discrete filter is used to filter the initial control parameter to obtain a flow control parameter, and the flow control parameter is used to control the flow of purge gas purged into the loading and unloading chamber. The target gas content control method as described in claim 12, wherein the discrete filter includes: , in the formula, Indicates the first Flow control parameters at each sampling time, Indicates the first Flow control parameters at each sampling time, Indicates the first Flow control parameters at each sampling time, Indicates the first Initial control parameters at each sampling moment, represents the first filter coefficient, represents the first filter coefficient, , symbol Represents multiplication. A semiconductor processing equipment, including a control device, wherein the control device is used to obtain the content difference between a target content and the current content of a target gas in a loading and unloading chamber of the semiconductor processing equipment; A pressure value and the content difference of the chamber are respectively mapped to the corresponding fuzzy domain, and the fuzzy control parameters are determined according to each mapping result; the fuzzy control parameters are converted to the physical domain to obtain an initial control parameter; according to the initial control parameters Control a purge gas flow rate purged into the loading and unloading chamber to control the content of the target gas in the loading and unloading chamber.