WO2023020310A1 - Target gas content control method and semiconductor process device - Google Patents

Abstract

Disclosed in the present application are a target gas content control method and a semiconductor process device. The target gas content control method is used for controlling the content of a target gas in a loading/unloading chamber of the semiconductor process device, and comprises: acquiring a content difference between a target content and a current content of a target gas in a loading/unloading chamber; respectively mapping a pressure value of the loading/unloading chamber and the content difference to a corresponding fuzzy domain of discourse, and determining a fuzzy control parameter according to the mapping results; converting the fuzzy control parameter to a physical domain of discourse to obtain an initial control parameter; and controlling, according to the initial control parameter, the flow of a purge gas purged into the loading/unloading chamber to control the content of the target gas in the loading/unloading chamber. According to the present application, the amount of the purge gas during the control of the content of the target gas can be reduced.

Description

Target gas content control method and semiconductor process equipment technical field

The present application relates to the technical field of semiconductor process control, in particular to a method for controlling the content of a target gas and semiconductor process equipment.

Background technique

Micro-oxygen/micro-positive pressure control of the loading and unloading chamber (LA) is a key performance indicator for semiconductor process equipment. Taking the vertical furnace series equipment as an example, silicon wafers will be affected by oxygen molecules in the atmosphere of the loading and unloading chamber during the process of transportation and lifting boat (in and out of the reaction chamber), resulting in the generation of unnecessary oxide layers. For this, It is usually necessary to control the content of target gases such as oxygen in the loading and unloading chamber. For example, it is necessary to use high-purity nitrogen (PN2) purging means, 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 microoxygen control. In order to avoid the pressure change of the loading and unloading chamber beyond 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 the condition of good micro-oxygen control.

Existing control schemes usually use a fixed flow rate of purge gas (such as high-purity nitrogen) to purge the loading and unloading chamber to achieve the control of the target gas content in the loading and unloading chamber. For example, for the oxygen content in the loading and unloading chamber, the loading and unloading chamber is purged with a certain flow rate of high-purity nitrogen to discharge the oxygen in the loading and unloading chamber, so that the oxygen content meets the process requirements, and at the same time maintain the micron 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 effect of oxygen content control. However, the above solution needs to use purge gas such as high-purity nitrogen gas at a certain flow rate for purge, which may easily cause excessive use of purge gas, resulting in higher costs.

Contents of the invention

In view of this, the present application provides a target gas content control method and semiconductor process equipment to solve the problem of excessive use of purge gas and high cost in existing solutions.

A target gas content control method provided in the present application is used to control the target gas content in the loading and unloading chamber of semiconductor process equipment, including:

Acquiring the content difference between the target content and the current content of the target gas in the loading and unloading chamber;

respectively mapping the pressure value of the loading and unloading chamber and the content difference to the corresponding fuzzy universe, and determining fuzzy control parameters according to each mapping result;

Converting the fuzzy control parameters to the physical domain to obtain initial control parameters;

The flow rate of the purge gas purged into the loading and unloading chamber is controlled according to the initial control parameters, so as to control the content of the target gas in the loading and unloading chamber.

Optionally, mapping the pressure value of the loading and unloading chamber and the content difference to corresponding fuzzy domains respectively, and determining fuzzy control parameters according to each mapping result includes:

Using the first quantization factor to map the pressure value to the first mapping parameter of the first fuzzy domain based on the preset fuzzy mapping formula, and using the second quantization factor to map the content difference based on the preset fuzzy mapping formula Mapping is the second mapping parameter of the second fuzzy domain of discourse;

Acquiring the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe and the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe;

According to the first mapping parameter, the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the second mapping parameter relative to the second The membership degrees of each fuzzy subset in the fuzzy universe determine the fuzzy control parameters.

Optionally, according to the first mapping parameter, the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the second mapping parameter Determining the fuzzy control parameters with respect to the degree of membership of each fuzzy subset of the second fuzzy domain includes:

Identifying the first fuzzy quantity and the corresponding degree of membership represented by each fuzzy subset of the first fuzzy domain where the first mapping parameter is located, and identifying each fuzzy quantity of the second fuzzy domain where the second mapping parameter is located The second fuzzy quantity represented by the subset and the corresponding degree of membership;

Combining each of the first blur quantities and each of the second blur quantities into multiple groups of blur quantities, performing weighted summation on each group of blur quantities and rounding to obtain each initial blur 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 according to each initial fuzzy parameter and its corresponding membership degree.

Optionally, the obtaining the membership degree of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe includes:

Obtaining the fuzzy subsets of the first fuzzy universe where the first mapping parameters are located and the membership functions of each of the fuzzy subsets; using the first mapping parameters and each of the membership functions to calculate each of the fuzzy subsets The degree of membership of the fuzzy quantity represented by the fuzzy subset;

The acquiring the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain includes:

Obtaining the fuzzy subsets of the second fuzzy domain where the second mapping parameters are located and the membership functions of each of the fuzzy subsets; using the second mapping parameters and each of the membership functions to calculate each of the fuzzy subsets The degree of membership of the fuzzy quantity represented by the fuzzy subset.

Optionally, the process of determining the first quantization factor includes:

obtain the physical domain of the pressure value;

Using a preset quantification factor calculation formula to calculate a quantification factor between the physical theoretical domain of the pressure value and the first fuzzy domain as the first quantification factor;

The determination process of the second quantization factor includes:

obtain the physical domain of the content difference;

The quantization factor between the physical domain of the content difference and the second fuzzy domain is calculated by using the preset calculation formula of the quantization factor as the second quantification factor.

Optionally, the physical domain of the content difference includes at least two sub-physical domains;

The quantitative factor between the physical theoretical domain of the content difference and the second fuzzy theoretical domain calculated by using the preset quantitative factor calculation formula is used as the second quantitative factor, including:

Determine the sub-object theoretical domain to which the content difference belongs, and use the preset quantization factor calculation formula to calculate the quantization factor between the sub-object theoretical domain and the second fuzzy domain as the second quantization factor.

Optionally, each of the child theoretical domains has a corresponding second fuzzy domain;

The use of the preset quantization factor calculation formula to calculate the quantization factor between the sub-object theoretical domain and the second fuzzy domain as the second quantization factor includes:

The quantization factor between the sub-physical theoretical domain and the second fuzzy domain corresponding to the sub-physical theoretical domain is calculated by using the preset quantization factor calculation formula as the second quantization factor.

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

The calculation of the quantitative factor between the physical theoretical domain of the pressure value and the first fuzzy theoretical domain by using the preset quantitative factor calculation formula as the first quantitative factor includes:

Determine the sub-physical theoretical domain to which the pressure value belongs, and use the preset quantization factor calculation formula to calculate the quantitative factor between the sub-physical theoretical domain and the first fuzzy domain corresponding to the sub-physical theoretical domain as the first A quantization factor.

Optionally, converting the fuzzy control parameters to the physical domain to obtain initial control parameters includes:

The fuzzy control parameters are transformed into the physical theory domain by using a proportional factor to obtain the initial control parameters.

Optionally, the process of determining the scaling factor includes:

Obtaining the physical theoretical domain of the purge gas flow rate and the corresponding third fuzzy domain;

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

Optionally, the preset quantization factor calculation formula includes: kj=2m/(b-a);

The preset formula for calculating the scaling factor includes: ku=(b-a)/2m;

In the formula, kj represents the quantization factor, ku represents the proportional factor, m represents the upper limit of the fuzzy domain, b represents the upper limit of the physical domain, and a represents the lower limit of the physical domain.

Optionally, the controlling the flow rate of the purge gas purged into the loading and unloading chamber according to the initial control parameters includes:

A discrete filter is used to filter the initial control parameters to obtain flow control parameters, and the flow control parameters are used to control the flow rate of purge gas purged into the loading and unloading chamber.

Optionally, the discrete filter includes:

y(n)= _a1 * _yd (n)+ _a1 *y(n-1)+ _a1 *y(n-2),

In the formula, y(n) represents the flow control parameter at the nth sampling time, y(n-1) represents the flow control parameter at the n-1th sampling time, and y(n-2) represents the n-2th sampling time The flow control parameters at the moment, yd(n) represents the initial control parameter at the nth sampling moment, a1 represents the first filter coefficient, a2 represents the first filter coefficient, 2a1+a2=1, and the symbol * represents multiplication.

The present application also provides a semiconductor process 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 of the unloading chamber and the content difference 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 theoretical domain to obtain initial control parameters; according to the The initial control parameter controls the flow rate of the purge gas purged into the loading and unloading chamber, so as 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 The domain of theory, determine the fuzzy control parameters according to each mapping result, convert the fuzzy control parameters to the physical theoretical domain, and obtain the initial control parameters, and control the flow rate of the purge gas purged into the loading and unloading chamber according to the initial control parameters, so as to control the loading and unloading chamber The target gas content in the chamber realizes the 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 corresponding process quality, the amount of purge gas used can be reduced. Reduce costs in the corresponding control process.

Further, 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 each stage of control, the flow rate of the purge gas purged into the loading and unloading chamber Adjusting with the corresponding content difference can further improve the control accuracy of the target gas content, improve the control efficiency, and reduce the cost in the corresponding control process.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

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;

Fig. 2 is a schematic flow chart of target gas content control in an embodiment of the present application;

Fig. 3 is a schematic diagram of fuzzy subsets of each fuzzy universe in an embodiment of the present application;

Fig. 4 is a schematic diagram of the high-purity nitrogen flow control process in an embodiment of the present application;

Fig. 5 is a schematic diagram of analysis results of high-purity nitrogen flow control in an embodiment of the present application;

FIG. 6 is a schematic structural diagram of a semiconductor device according to an embodiment of the present application.

Detailed ways

Existing control schemes usually use a fixed flow rate of purge gas (such as high-purity nitrogen) to purge the loading and unloading chamber to achieve the control of the target gas content in the loading and unloading chamber. For example, for the oxygen content in the loading and unloading chamber, the hysteresis window control mode is usually adopted: the oxygen control mode of large N2 flow and the oxygen control mode of small N2 flow. In the oxygen control mode of large N2 flow, the gas mass flow controller is usually set to 1000slm/min , open the exhaust valve, under the small N2 flow oxygen control mode, the gas mass flow controller is usually set to 500slm/min, and close the exhaust valve; during the oxygen control process corresponding to each mode, a certain amount of high-purity nitrogen is used to purge loading and unloading The chamber discharges oxygen from the loading and unloading chamber to make the oxygen content meet 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 and ensure the oxygen content control effect. The above-mentioned solution needs to use a certain flow rate of purge gas such as high-purity nitrogen for purging in various modes, which may easily cause excessive use of purge gas and generate 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), The abscissa represents 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, and the oxygen content changes from a large oxygen content to micro oxygen content of 10ppm; The oxygen in the relevant chamber enters the loading and unloading chamber, and the oxygen content changes from less than 10ppm to 800ppm, switch the small N2 flow to control the oxygen; for the ③ area, the oxygen content is greater than 800ppm, switch the large N2 flow to control the oxygen until 10ppm; for the ④ area, The oxygen content is less than or equal to 10ppm, switch to a small N2 flow to control the oxygen, and the oxygen content gradually tends to be around 5ppm. Among them, 10ppm is the target value to meet the process requirements, 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 flow rate of high-purity nitrogen gas is 500slm/min; the oxygen content in the loading and unloading chamber changes from greater than It varies from 800ppm to 10ppm, and the flow rate of high-purity nitrogen gas is 1000slm/min. The above-mentioned high-purity nitrogen flow scheme for the loading and unloading chamber adopts the control method of switching between the large N2 flow control oxygen mode and the small N2 flow control oxygen mode, and when the loading and unloading chamber is well sealed, the target value of the oxygen content is 10ppm ; The high-purity nitrogen of 500slm/min under the small N2 flow control oxygen mode will blow the oxygen content to 5ppm or lower; The coordinates indicate the flow rate of high-purity nitrogen gas (unit slm), the abscissa indicates time (unit s), the dotted line indicates the change of oxygen content with time, the solid line indicates the output change of high-purity nitrogen flow rate with time, when the oxygen content (dashed line) reaches 10ppm , the high-purity nitrogen flow rate (solid line) was switched from 1000 slm/min to 500 slm/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 flow rate of high-purity nitrogen gas required can be less than 500 slm/min. It can be seen that the content control scheme of the target gas such as the traditional control scheme of oxygen content in the loading and unloading chamber is likely to cause blowing of high-purity nitrogen, etc. Excessive use of the sweeping gas results in a waste of the sweeping gas, resulting in high costs.

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

The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, 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 belong to the scope of protection of this application. In the case of no conflict, the following embodiments and technical features thereof can be combined with each other.

The first aspect of the present application provides a method for controlling target gas content, which is used to control the content of target gas in the loading and unloading chamber of semiconductor process equipment. Referring to FIG. 2, the above-mentioned target gas content control method includes:

S110, acquiring a 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 In , the target oxygen content is 10ppm. The current content is the target gas content measured in real time by the gas analyzer set 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 relatively large absolute value; The purge becomes lower and stabilizes at a level near or slightly lower than the target content to ensure the corresponding process quality.

S120, respectively map the pressure value and content difference of the loading and unloading chamber to the corresponding fuzzy universe, and determine 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, and the value ranges corresponding to each physical quantity are physical theoretical domains. Determined by relevant experiments and other analysis methods. For each physical theoretical domain, the corresponding fuzzy domain can be set according to its range, conversion precision and/or required control precision and other factors, and each physical quantity in the physical theoretical domain can be converted into the corresponding fuzzy domain through the process of mapping and corresponding fuzzy processing. According to at least one fuzzy quantity of the domain of discourse, the corresponding fuzzy control parameters can be calculated according to parameters such as each fuzzy quantity and the corresponding membership degree.

S130, transforming the fuzzy control parameters into the physical theoretical domain to obtain initial control parameters.

The fuzzy quantities on each fuzzy domain can be converted to obtain the physical quantities of the corresponding theoretical domain. The fuzzy domains where the above fuzzy control parameters are located can be preset according to the precision required for fuzzy processing, for example, they are all set to [-2, 2] and so on. The physical theoretical domain of the flow range of the initial control parameter can be set according to the flow range of the purge gas corresponding to the purge.

S140. Control the flow rate of the purge gas purged into the loading and unloading chamber according to the initial control parameters, so as 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 results obtained by filtering can be used to control the corresponding purge gas flow rate, so as to make the change process of the control parameters more gentle and avoid the control of the target gas content. The control effect can be improved if the relevant control parameter mutation occurs in the process and other conditions affect 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 the content difference of the loading and unloading chamber are respectively mapped to the corresponding fuzzy universe, and determined according to each mapping result Fuzzy control parameters, transforming the fuzzy control parameters into the physical theoretical domain to obtain the initial control parameters, according to the initial control parameters to control the purge gas flow into the loading and unloading chamber to control the target gas content of the loading and unloading chamber, which can be obtained by The current pressure value and content difference are based on the fuzzy control of the target gas in the loading and unloading chamber. On the basis of ensuring the corresponding process quality, 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 of the loading and unloading chamber are respectively mapped to the corresponding fuzzy domain, and determining the fuzzy control parameters according to each mapping result includes:

Using the first quantization factor based on the preset fuzzy mapping formula to map the pressure value to the first mapping parameter of the first fuzzy domain, using the second quantization factor based on the preset fuzzy mapping formula above to map the content difference to the second fuzzy The second mapping parameter of the domain of discourse;

obtaining the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe and the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe;

According to the first mapping parameter, the membership degree of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the membership degree of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe Control parameters.

These physical quantities of the above-mentioned pressure value and content difference are all in the corresponding physical theoretical domain, and each physical theoretical domain has a corresponding fuzzy domain of discourse, and there are quantitative factors (such as the first quantitative factor and the second quantitative factor) between the physical theoretical domain and the corresponding fuzzy domain of discourse Two quantization factors), each of the physical quantities 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, and the larger the interval range of each fuzzy domain, the higher the corresponding conversion precision. 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 It is also possible to 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. Substituting the mapping parameter into the corresponding membership function for calculation can obtain the corresponding The mapping parameter takes the degree of membership corresponding to the fuzzy quantity. As shown in Fig. 3, the target gas in Fig. 3 is oxygen, the abscissa represents the value of the mapping parameter, and the ordinate represents the degree of membership, which shows the fuzzy universe corresponding to the pressure value, the oxygen content difference and the fuzzy control parameters respectively, The fuzzy subsets of these fuzzy domains are {NB (negative big), NS (negative small), ZO (zero), PS (positive small), PB (positive big)}, and the specific fuzzy quantities include: {-2 , -1, 0, 1, 2}, using triangular membership functions, respectively. Each fuzzy universe shown in Fig. 3 indicates that each mapping parameter obtained 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 to perform fuzzy processing on the first mapping parameter, the second mapping parameter and the corresponding degree of membership respectively to obtain the required fuzzy processing. Control parameters. Here, the oxygen content control process in the loading and unloading chamber is used to illustrate, and the reasoning rules can include: 1. The pressure in the loading and unloading chamber is small, the oxygen content is large, and the flow rate of high-purity nitrogen gas is large; 2. The pressure in the loading and unloading chamber Slightly smaller, slightly larger oxygen content, higher high-purity nitrogen flow; 3. Moderate pressure in the loading and unloading chamber, moderate oxygen content, moderate high-purity nitrogen flow; 4. Slightly higher pressure in the loading and unloading chamber, slightly lower oxygen content , the high-purity nitrogen flow rate is slightly smaller; 5. The loading and unloading chamber has a high pressure, and the oxygen content is small, so the high-purity nitrogen flow rate is very small. The corresponding fuzzy processing rules include: determining each first fuzzy amount corresponding to the first mapping parameter and each second fuzzy amount corresponding to the second mapping parameter, and determining multiple sets of fuzzy amounts including a first fuzzy amount and a second fuzzy amount , use the reasoning formula to calculate the initial fuzzy parameters for each group of fuzzy quantities, determine the membership degree of each initial fuzzy parameter, and perform clear processing on 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 degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe The membership degree determination fuzzy control parameters include:

Identify the first fuzzy quantity represented by each fuzzy subset of the first fuzzy universe where the first mapping parameter is located and the corresponding degree of membership, and identify the second fuzzy quantity represented by each fuzzy subset of the second fuzzy universe 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 on each group of fuzzy quantities and 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 parameters, and the fuzzy control parameters are determined according to each initial fuzzy parameter and the corresponding membership degree of each initial fuzzy parameter.

The fuzzy subsets of the first fuzzy universe and the second fuzzy universe can be referred to as 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 quantity 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, ZO(Xp) represents the membership function of the fuzzy subset ZO, PS(Xp) represents the membership function of the fuzzy subset PS, and Xp represents the mapping parameter (such as the first mapping parameter). When Xp=0.6, get ZO(0.6)=0.4, PS(0.6)=0.6, that is, the first mapping parameter 0.6 corresponds to fuzzy subset ZO and fuzzy subset PS, and the fuzzy quantity represented by fuzzy subset ZO is 0, corresponding to The membership degree of the fuzzy subset PS is 0.4, the fuzzy quantity of the fuzzy subset PS is 1, and the corresponding membership degree is 0.6.

The process of rounding after the above weighted summation includes: u1=<α ₁ N1+(1-α ₁ )N2>, where u1 represents the initial blur parameter, N1 represents the first blur amount, N2 represents the second blur amount, α ₁ represents the first weight (or correction factor), which can be set to a value such as 0.4 or 0.5. <> represents the rounding operator, which means that the absolute value of the value is rounded up. The sign is the same as the sign in <> , eg <-1.3>=-1, <1.7>=2. The above-mentioned first mapping parameter corresponds to a plurality of first blur quantities, and the second mapping parameter corresponds to a plurality of second blur quantities. By combining each first blur quantity and each second blur quantity, multiple groups of non-repeating blur quantities can be obtained, By weighting and summing each group of fuzzy quantities and rounding them up, the initial fuzzy parameters and the membership degree corresponding to each initial fuzzy parameter can be obtained; for example, in a certain group of fuzzy quantities, the first fuzzy quantity N1 is 0, and its membership degree is 0.4, The second fuzzy quantity N2 is 1, its membership degree is 0.8, the first weight α ₁ is 0.5, the corresponding initial fuzzy parameter is u1=<0.5×0+(1-0.5)×1>=1, and its membership degree is 0.4.

In this example, the fuzzy control parameters are determined according to each initial fuzzy parameter and the corresponding degree of membership as the clarification process, in which each initial fuzzy parameter belongs to the fuzzy quantity, and these fuzzy quantities need to be converted into specific fuzzy control parameters, and then the fuzzy control 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 by using the maximum membership average value 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 clearing process of the maximum membership degree average method includes: selecting the initial fuzzy parameter with the largest membership degree among each initial fuzzy parameter as the selected fuzzy parameter, and obtaining the selected membership degree of the fuzzy subset where the selected fuzzy parameter belongs to function, take the maximum membership degree as the function value of the selected membership degree function, obtain multiple fuzzy variable values, and use the average value of each fuzzy variable value 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 clearing process: the initial fuzzy parameter A is 0, and the corresponding membership degree is 0.6; the initial fuzzy parameter B is 1, and the corresponding membership degree is 0.4, the initial fuzzy parameter A with a membership degree of 0.6 is selected as the membership function, and according to the fuzzy subset distribution diagram of the fuzzy control parameters in Figure 3, the fuzzy quantity is 0, that is, the fuzzy subset ZO, so that The function value of the membership function is 0.6, namely:

The solution obtains two fuzzy variable values, Xp'=-0.4, Xp'=0.4, and the first fuzzy control parameter determined by the average value of these two fuzzy variable values is 0. Further, the fuzzy control parameters can be converted to the physical theoretical domain by using the corresponding proportional factor to obtain the initial control parameters; for example, if a certain proportional factor is ku=100, the upper limit b of the physical theoretical domain of the purge gas flow rate is 1000, and the lower limit a is 600, and the fuzzy control parameter x' is 0, then the corresponding initial control parameters can be:

In one embodiment, obtaining the membership degree of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe includes: obtaining the fuzzy subset of the first fuzzy universe where the first mapping parameter is located and the fuzzy subsets of each fuzzy subset A membership function; calculating the membership of the fuzzy quantities represented by each fuzzy subset by using the first mapping parameter and each membership function;

Obtaining the membership degree of the second mapping parameter relative 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; Two mapping parameters and each membership degree function calculate the membership degree of the fuzzy quantities represented by each fuzzy subset.

Optionally, the above-mentioned first mapping parameter and the second mapping parameter may be calculated for corresponding physical quantities by using a fuzzy mapping formula. The above fuzzy mapping formula can be set according to the inference rules corresponding to the loading and unloading chamber, usually using the corresponding quantization factor for mapping, for example, it can be set as y=(x-(a+b)/2)×kj, where kj Represents the quantization factor, b represents the upper limit of the physical theoretical domain, a represents the lower limit of the physical theoretical domain, x represents the physical quantity, and y represents the mapping parameter. Specifically, for a certain mapping parameter, after determining its fuzzy subset and corresponding membership degree function, the mapping parameter can be substituted into each membership degree function to obtain the membership degree of each fuzzy quantity. Taking the pressure value P=2800mtorr as an example, the solution process of the corresponding fuzzy quantity and membership degree is explained. If the corresponding physical theoretical domain (pressure range) is [1500,3500], the first mapping parameter is y=(2800 -(1500+3500)/2)×0.002=0.6, the corresponding fuzzy quantity is 0 (fuzzy subset ZO) and 1 (fuzzy subset PS); according to the membership function of ZO, PS fuzzy subset:

Here Xp=0.6, ZO(0.6)=0.4, PS(0.6)=0.6, that is, the first mapping parameter of the pressure value P=2800mtorr is 0.6, which corresponds to a fuzzy quantity of 0, and the degree of membership is 0.4, which also corresponds to another fuzzy Quantity 1, membership degree is 0.6.

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

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

Optionally, the physical theoretical domain of the above-mentioned content difference includes at least two sub-physical theoretical domains; the quantitative factor between the physical theoretical domain of the content difference and the second fuzzy domain calculated using the preset quantization factor calculation formula is used as the first The second quantization factor includes: determining the sub-object theoretical domain to which the content difference value belongs, and using a preset quantization factor calculation formula to calculate the quantization factor between the sub-object theoretical domain and the second fuzzy domain as the second quantization 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, and each segment is a sub-physics theoretical domain, so as to identify the content difference after obtaining the content difference In the sub-physical theoretical domain, the content difference is mapped from the sub-physical theoretical domain to the corresponding second fuzzy domain, and the difference conversion of the content difference in each sub-physical theoretical domain is realized, so as to meet the requirements of each sub-physical theoretical domain. The desired target gas content control requirements.

Preferably, in consideration of differential control, each sub-object theoretical domain can also have its own second fuzzy domain, that is, different sub-object theoretical domains correspond to different second fuzzy domains, so that refined control can be further realized . Correspondingly, the calculation of the quantitative factor between the sub-physical theoretical domain and the second fuzzy domain as the second quantitative factor using the preset quantitative factor calculation formula includes: using the preset quantitative factor calculation formula to calculate the sub-physical theory The quantization factor between the domain and the second fuzzy domain corresponding to the sub-object theoretical domain is used as the second quantization factor.

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

In other cases, if the target gas accounts for a relatively large proportion of the gas contained in the loading and unloading chamber, the value range of the change in the content difference between the target gas content and the current content of the target gas will affect 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, and the sub-physical theoretical domains of each pressure value can also have corresponding first fuzzy domains, so as to improve the mapping accuracy and corresponding fuzzy processing The accuracy of the process, thereby improving the control effect of the target gas content. At this time, the quantitative factor between the physical theoretical domain and the first fuzzy theoretical domain of the pressure value calculated by the preset quantitative factor calculation formula is used as the first quantitative factor, which may include: determining the sub-physical theoretical domain to which the pressure value belongs, using the preset The quantization factor calculation formula is used to calculate the quantization factor between the sub-physical theoretical domain and the first fuzzy domain corresponding to the sub-physical theoretical domain as the first quantization factor.

In order to make the control process of the target gas content smoother, in an example, the above-mentioned control of the target gas content can be divided into two stages. At this time, the physical theoretical domain of the content difference includes 2 sub-physical theoretical domains, and these two sub-physical theoretical domains pass through The segmentation threshold is determined, that is, the upper limit of one sub-object theoretical domain is the segmentation threshold, and the lower limit of the other sub-object theoretical domain is the segmentation threshold. At this time, according to the relationship between the current content difference and the segmentation threshold, the corresponding sub-physical theoretical domain and the second fuzzy domain can be selected for mapping, and the fuzzy control parameters corresponding to the current content difference can be obtained. Using the fuzzy control parameters to obtain The corresponding initial control parameters control the flow of purge gas that is purged into the loading and unloading chamber. In this way, the entire target gas control process is divided into two stages based on the content difference for fuzzy control, which not only improves the control effect, but also has high control efficiency. The segmentation threshold above can be set according to the target content and the corresponding control accuracy. For example, for the oxygen content adjustment process of the loading and unloading chamber, the segmentation threshold can be set to -5ppm or the like. In some cases, the content difference is less than the segment threshold, indicating that the target gas content in the loading and unloading chamber is high, and the coarse adjustment mode with relatively low control accuracy can be used to control the purge gas flow rate, so that the target gas content in the loading and unloading chamber can be rapidly reduced When the content difference is greater than or equal to the segmentation threshold, it means that the target gas content in the loading and unloading chamber has been reduced to close to the target content. At this time, the fine adjustment mode with relatively high control accuracy can be used to control the purge gas Flow rate, so that the oxygen content of the unloading chamber can further reach the target content, and keep it at this level, so as to ensure the control accuracy.

Further, the data shown in Table 1 and Table 2 are used to illustrate the process of using the segmentation threshold to control the oxygen content in the loading and unloading chamber in two stages. Table 1 shows that when the content difference is less than the segmentation threshold, each physical theoretical domain and corresponding blur The conversion results of the domain of discourse, Table 2 shows the conversion results of each physical domain and the corresponding fuzzy domain of discourse when the content difference is greater than or equal to the segmentation threshold. In Table 1, the first physical theoretical domain where the pressure value is located is [1500,3500], the corresponding first fuzzy domain is [-2,2], and the first quantization factor between the two is 0.002; the content difference The theoretical domain of a sub-object is [-505,-5], the corresponding second fuzzy domain is [-2,2], and the second quantization factor between them is 0.008; the third The fuzzy domain is [-2,2], the physical domain of purge gas flow is [600,1000], and the scaling factor between them is 100. In Table 2, the physical theoretical domain of the pressure value is [1500,3500], the corresponding first fuzzy domain is [-2,2], and the quantization factor between the two is 0.002; The theoretical domain of physics is [-5, 5], the corresponding second fuzzy domain is [-2, 2], and the second quantization factor between them 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 scaling factor between them is 75.

Table 1

physical quantity	physical domain	fuzzy universe	quantization factor
Pressure value	[1500,3500]	[-2,2]	0.002
Content difference	[-505,-5]	[-2,2]	0.008
the	fuzzy universe	physical domain	Scale Factor
initial control parameters	[-2,2]	[600,1000]	100

Table 2

physical quantity	physical domain	fuzzy universe	quantization factor
Pressure value	[1500,3500]	[-2,2]	0.002
Content difference	[-5, 5]	[-2,2]	0.4
the	fuzzy universe	physical domain	Scale Factor
initial control parameters	[-2,2]	[300,600]	75

In an example, converting the fuzzy control parameters to the physical theoretical domain to obtain the initial control parameters includes: converting the fuzzy control parameters to the physical theoretical domain by using a scaling factor to obtain the initial control parameters. The proportional factor can be calculated according to the upper and lower limit characteristics of the third fuzzy domain where the fuzzy control parameters are located and the physical theoretical domain where the initial control parameters are located.

Specifically, the process of determining the scaling factor includes:

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

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

Optionally, the preset quantization factor calculation formula includes: kj=2m/(b-a);

The preset scale factor calculation formula includes: ku=(b-a)/2m;

In the formula, kj represents the quantization factor, ku represents the proportional factor, m represents the upper limit of the fuzzy domain, b represents the upper limit of the physical domain, and a represents the lower limit of the physical domain.

The value ranges corresponding to the above-mentioned physical theoretical domains can be determined according to the characteristics of the specific loading and unloading chambers 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 The physical theoretical domain corresponding to the pressure value can be defined as [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 set to 1000ppm), the oxygen content of the process The target value is usually 10ppm or 5ppm, and 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 Usually it 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 theoretical domain and each fuzzy domain can be set according to the value range and related process characteristics of each physical theoretical domain, and then the quantitative factor calculation formula can be used to calculate the quantitative factors between each physical theoretical domain and the corresponding fuzzy domain. The proportional factor calculation formula is used to calculate the proportional factors between each fuzzy domain and the corresponding theoretical domain. For example, if the upper limit of the first fuzzy universe is 2, the upper limit of the pressure range is 3500, and the lower limit is 1500, then the corresponding first quantization factor is:

In the same way, other quantitative factors can be calculated quickly and accurately. For another example, if the upper limit of the third fuzzy universe is 2, the upper limit of the physical theoretical domain of the purge gas flow rate is 1000, and the lower limit is 600, then the corresponding proportional factor is:

In one embodiment, controlling the flow rate of the purge gas 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 flow control parameters, and using the flow control parameters to control the flow rate of the purge gas into the loading and unloading chamber. Purge gas flow for loading and unloading chambers.

Specifically, discrete filters include:

y(n)= _a1 * _yd (n)+ _a1 *y(n-1)+ _a1 *y(n-2),

In the formula, y(n) represents the flow control parameter at the nth sampling time, y(n-1) represents the flow control parameter at the n-1th sampling time, and y(n-2) represents the n-2th sampling time The flow control parameters at time, yd(n) represents the initial control parameter at the nth sampling time, a1 represents the first filter coefficient, a ₂ represents the first filter coefficient, 2a ₁ +a ₂ =1, and the symbol * represents multiplication.

In this embodiment, a discrete filter is used to filter the initial control parameters, so that the corresponding flow control parameters change more smoothly, which can solve the problem of abrupt changes such as spikes when the purge gas flow output suddenly changes step by step, and can improve the control of blowing. Sweeping gas flow control effect.

In one example, the method for controlling the target gas content provided by this application is described by taking the high-purity nitrogen flow control process when the oxygen content in the loading and unloading chamber is controlled as an example. Referring to Figure 4, the differential pressure gauge is used to measure the 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 the quantization factor 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, the first fuzzy domain, the sub-physical theoretical domain and the second fuzzy domain to convert 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 theoretical domain with the segmentation threshold as the lower limit to Fuzzy controller, so that the fuzzy controller uses the corresponding first physical theoretical domain, first fuzzy theoretical domain, subphysical theoretical domain and second fuzzy theoretical 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 to 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 gas purged to the loading and unloading chamber to control the flow rate of the unloading chamber. oxygen content. Carry out simulation analysis on the high-purity nitrogen flow control process provided in this example. When the process is ready to start, 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 change of oxygen content is shown in Figure 5 . Among them, the left ordinate represents the oxygen content (unit ppm), the right ordinate represents the flow rate of high-purity nitrogen gas (unit slm), the abscissa represents time (unit s), the dotted line represents the change of oxygen content with time, and the solid line represents the flow rate of high-purity nitrogen gas output changes. The target content in Fig. 5 is 11 ppm, and the oxygen content remains at 11 ± 1 ppm after reaching the target content. Through fuzzy control, the final flow rate of high-purity nitrogen gas is stabilized at 310 slm/min. Compared with 500 slm/min in the small N2 flow mode of the traditional solution, the flow rate of high-purity nitrogen gas of 190 slm/min is saved. Point a in the figure corresponds to this embodiment Fuzzy control process, it can be seen that this example can effectively save 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 The fuzzy control parameters are determined by each mapping result, and the fuzzy control parameters are converted to the physical theoretical domain to obtain the initial control parameters. According to the initial control parameters, the flow rate of the purge gas purged into the loading and unloading chamber is controlled to control the target gas in the loading and unloading chamber. It realizes the fuzzy control of the target gas in the loading and unloading chamber based on the current pressure value and the content difference. On the basis of ensuring the corresponding process quality, the amount of purge gas used can be reduced and excessive use of purge gas can be avoided. 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 flow rate of the purge gas purged into the loading and unloading chamber Adjusting with the corresponding content difference can further improve the control accuracy of the target gas content, improve the control efficiency, and reduce the cost in the corresponding control process.

The present application provides a semiconductor process equipment in a second aspect, including a control device, which 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 process equipment; The pressure value and 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 theoretical domain to obtain the initial control parameters; The purge gas flow rate of the loading and unloading chamber is used to control the content of the target gas in the loading and unloading chamber.

For specific limitations on the control device corresponding to the target gas content, please refer to the above-mentioned limitations on the method for controlling the target gas content, which will not be repeated here. The above-mentioned control device can be fully or partially realized by software, hardware and a combination thereof. It can be embedded in or independent of the processor in the computer device in the form of hardware, and can also be stored in the memory of the computer device in the form of software, so that the processor can invoke and execute corresponding operations.

The present application provides a semiconductor device in a third aspect. Referring to FIG. 6, the semiconductor device includes a processor 620 and a storage medium 630; program codes are stored on the storage medium 630; the processor 620 is used to call the program stored in the storage medium codes to execute the target gas content control method of any one of the above embodiments.

The above-mentioned semiconductor equipment adopts the above-mentioned target gas content control method to control the flow rate of the purge gas purged to the loading and unloading chamber in the corresponding process, and then control 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 gas. The cost of gas generation, thereby reducing the corresponding process cost.

Although the application has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art upon the reading and understanding of this specification and the annexed drawings. This application includes all such modifications and variations and is limited only by the scope of the appended claims. With particular regard to the various functions performed by the components described above, terminology used to describe such components is intended to correspond to any component that performs the specified function (eg, which is functionally equivalent) of the component (unless otherwise indicated), even if There are no structural equivalents to the disclosed structures which perform the functions shown herein in the exemplary implementations of the specification.

That is, the above are only embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process conversion made by using the description of the present application and the contents of the accompanying drawings, for example, the mutual technical features between the various embodiments Combination, or direct or indirect application in other related technical fields, are all included in the scope of patent protection of this application.

In addition, in the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front" , "Back", "Left", "Right", "Vertical", "Horizontal", "Top", "Bottom", "Inner", "Outer" and other indications are based on the The orientation or positional relationship is only for the convenience of describing the application and simplifying the description, but does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as limiting the application . In addition, for structural elements with the same or similar characteristics, the present application may use the same or different symbols for identification. In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the present application, "plurality" means two or more, unless otherwise specifically defined.

In this application, the word "exemplary" is used to mean "serving as an example, illustration or illustration". Any one embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The above description is given in order to enable anyone skilled in the art to implement and use the application. In the description above, various details are set forth for purposes of explanation. It should be understood that one of ordinary skill in the art would recognize that the present application may be practiced without these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of the present application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed in this application.

Claims (14)

1. A method for controlling the content of a target gas, used for controlling the content of a target gas in a loading and unloading chamber of semiconductor process equipment, characterized in that the method comprises:

   Acquiring the content difference between the target content and the current content of the target gas in the loading and unloading chamber;

   respectively mapping the pressure value of the loading and unloading chamber and the content difference to the corresponding fuzzy universe, and determining fuzzy control parameters according to each mapping result;

   Converting the fuzzy control parameters to the physical domain to obtain initial control parameters;

   The flow rate of the purge gas purged into the loading and unloading chamber is controlled according to the initial control parameters, so as to control the content of the target gas in the loading and unloading chamber.
2. The target gas content control method according to claim 1, wherein the pressure value of the loading and unloading chamber and the content difference are respectively mapped to the corresponding fuzzy universe, and the fuzzy domain is determined according to each mapping result. Control parameters include:

   Using the first quantization factor to map the pressure value to the first mapping parameter of the first fuzzy domain based on the preset fuzzy mapping formula, and using the second quantization factor to map the content difference based on the preset fuzzy mapping formula Mapping is the second mapping parameter of the second fuzzy domain of discourse;

   Acquiring the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe and the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe;

   According to the first mapping parameter, the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the second mapping parameter relative to the second The membership degrees of each fuzzy subset in the fuzzy universe determine the fuzzy control parameters.
3. The target gas content control method according to claim 2, characterized in that, according to the first mapping parameter, the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe, The second mapping parameter and the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain to determine the fuzzy control parameters include:

   Identifying the first fuzzy quantity and the corresponding degree of membership represented by each fuzzy subset of the first fuzzy domain where the first mapping parameter is located, and identifying each fuzzy quantity of the second fuzzy domain where the second mapping parameter is located The second fuzzy quantity represented by the subset and the corresponding degree of membership;

   Combining each of the first blur quantities and each of the second blur quantities into multiple groups of blur quantities, performing weighted summation on each group of blur quantities and rounding to obtain each initial blur 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 according to each initial fuzzy parameter and its corresponding membership degree.
4. The target gas content control method according to claim 2, characterized in that,

   The acquiring the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe includes:

   Obtaining the fuzzy subsets of the first fuzzy universe where the first mapping parameters are located and the membership functions of each of the fuzzy subsets; using the first mapping parameters and each of the membership functions to calculate each of the fuzzy subsets The degree of membership of the fuzzy quantity represented by the fuzzy subset;

   The acquiring the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain includes:

   Obtaining the fuzzy subsets of the second fuzzy domain where the second mapping parameters are located and the membership functions of each of the fuzzy subsets; using the second mapping parameters and each of the membership functions to calculate each of the fuzzy subsets The degree of membership of the fuzzy quantity represented by the fuzzy subset.
5. The target gas content control method according to claim 2, wherein the process of determining the first quantization factor comprises:

   obtain the physical domain of the pressure value;

   Using a preset quantification factor calculation formula to calculate a quantification factor between the physical theoretical domain of the pressure value and the first fuzzy domain as the first quantification factor;

   The determination process of the second quantization factor includes:

   obtain the physical domain of the content difference;

   The quantization factor between the physical domain of the content difference and the second fuzzy domain is calculated by using the preset calculation formula of the quantization factor as the second quantization factor.
6. The target gas content control method according to claim 5, characterized in that, the physical theoretical domain of the content difference includes at least two sub-physical theoretical domains;

   The quantitative factor between the physical theoretical domain of the content difference and the second fuzzy theoretical domain calculated by using the preset quantitative factor calculation formula is used as the second quantitative factor, including:

   Determine the sub-object theoretical domain to which the content difference belongs, and use the preset quantization factor calculation formula to calculate the quantization factor between the sub-object theoretical domain and the second fuzzy domain as the second quantization factor.
7. The target gas content control method according to claim 6, wherein each of the sub-physical theoretical domains has a corresponding second fuzzy domain;

   The use of the preset quantization factor calculation formula to calculate the quantization factor between the sub-object theoretical domain and the second fuzzy domain as the second quantization factor includes:

   The quantization factor between the sub-physical theoretical domain and the second fuzzy domain corresponding to the sub-physical theoretical domain is calculated by using the preset quantization factor calculation formula as the second quantization factor.
8. The target gas content control method according to claim 7, characterized in that, the physical theoretical domain of the pressure value includes at least two sub-physical theoretical domains, each of the sub-physical theoretical domains of the pressure value has a corresponding first blur Discourse domain;

   The calculation of the quantitative factor between the physical theoretical domain of the pressure value and the first fuzzy theoretical domain by using the preset quantitative factor calculation formula as the first quantitative factor includes:

   Determine the sub-physical theoretical domain to which the pressure value belongs, and use the preset quantization factor calculation formula to calculate the quantitative factor between the sub-physical theoretical domain and the first fuzzy domain corresponding to the sub-physical theoretical domain as the first A quantization factor.
9. The target gas content control method according to claim 1, wherein said converting said fuzzy control parameters into the physical theoretical domain to obtain initial control parameters comprises:

   The fuzzy control parameters are transformed into the physical theory domain by using a proportional factor to obtain the initial control parameters.
10. The target gas content control method according to claim 8, wherein the process of determining the scaling factor comprises:

    Obtaining the physical theoretical domain of the purge gas flow rate and the corresponding third fuzzy domain;

    A proportional factor between the third fuzzy theoretical domain and the physical theoretical domain of the purge gas flow is calculated by using a preset proportional factor calculation formula.
11. The target gas content control method according to claim 5 or 10, characterized in that, the preset quantization factor calculation formula includes: kj=2m/(b-a);

    The preset formula for calculating the scaling factor includes: ku=(b-a)/2m;

    In the formula, kj represents the quantization factor, ku represents the proportional factor, m represents the upper limit of the fuzzy domain, b represents the upper limit of the physical domain, and a represents the lower limit of the physical domain.
12. The target gas content control method according to claim 1, wherein the controlling the flow rate of the purge gas purged into the loading and unloading chamber according to the initial control parameters comprises:

    A discrete filter is used to filter the initial control parameters to obtain flow control parameters, and the flow control parameters are used to control the flow rate of purge gas purged into the loading and unloading chamber.
13. The target gas content control method according to claim 12, wherein the discrete filter comprises:

    y(n)= _a1 * _yd (n)+ _a1 *y(n-1)+ _a1 *y(n-2),

    In the formula, y(n) represents the flow control parameter at the nth sampling time, y(n-1) represents the flow control parameter at the n-1th sampling time, and y(n-2) represents the n-2th sampling time flow control parameters at time, y _d (n) represents the initial control parameters at the nth sampling time, a ₁ represents the first filter coefficient, a ₂ represents the first filter coefficient, 2a ₁ +a ₂ =1, and the symbol * represents the phase take.
14. A semiconductor process equipment, comprising a control device, characterized in that 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 process equipment; The pressure value of the loading and unloading chamber and the content difference 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 into the physical theoretical domain to obtain the initial control parameters; The initial control parameter controls the flow rate of the purge gas purged into the loading and unloading chamber, so as to control the content of the target gas in the loading and unloading chamber.

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