CN115708191A - Target gas content control method and semiconductor process equipment - Google Patents
Target gas content control method and semiconductor process equipment Download PDFInfo
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Abstract
The application discloses a target gas content control method and semiconductor process equipment, wherein the target gas content control method is used for controlling the content of target gas in a loading and unloading chamber of the semiconductor process equipment, and comprises the following steps: acquiring a content difference value between the target content and the current content of the target gas in the loading and unloading chamber; respectively mapping the pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy domain, and determining a fuzzy control parameter according to each mapping result; converting the fuzzy control parameter into a physical discourse domain to obtain an initial control parameter; and controlling the flow of the purge gas purged into the loading and unloading cavity according to the initial control parameters so as to control the content of the target gas in the loading and unloading cavity. The method and the device can reduce the consumption of the purge gas during the control of the content of the target gas.
Description
Technical Field
The application relates to the technical field of semiconductor process control, in particular to a target gas content control method and semiconductor process equipment.
Background
Micro-oxygen/micro-positive pressure control of load and unload chambers (LA) is a key performance indicator for semiconductor processing equipment, such as vertical furnace series equipment. Taking the vertical furnace series equipment as an example, the silicon wafer is affected by oxygen molecules in the atmosphere of the loading and unloading chamber during the transportation process and the process of lifting the boat (entering and exiting the reaction chamber), so that an unnecessary oxide layer is generated, and therefore, in some semiconductor process processes, the content of target gases such as oxygen in the loading and unloading chamber is generally required to be controlled. For example, a high-purity nitrogen (PN 2) purge under the closed-loop control of an oxygen (O2) analyzer and a gas Mass Flow Controller (MFC) is required to reduce and control the oxygen content in the load and unload chamber (LA); in order to prevent the pressure change of the loading and unloading chamber from exceeding a safety range in the micro-oxygen control process, the pressure in the loading and unloading chamber needs to be controlled, and the reliable operation of the micro-positive pressure system under the condition of good micro-oxygen control is ensured.
The existing control scheme usually adopts a mode of blowing high-purity nitrogen and other blowing gases at a fixed flow rate to blow a loading and unloading chamber so as to control target gases in the loading and unloading chamber. For example, a hysteresis window control mode is typically employed for oxygen content in the loadlock chamber: a large N2 flow oxygen control mode and a small N2 flow oxygen control mode, wherein in the large N2 flow oxygen control mode, the gas mass flow controller is usually set to 1000slm/min, an exhaust valve is opened, and in the small N2 flow oxygen control mode, the gas mass flow controller is usually set to 500slm/min, and the exhaust valve is closed; in the oxygen control process corresponding to each mode, the loading and unloading chamber is purged by certain high-purity nitrogen, oxygen is discharged out of the loading and unloading chamber, the oxygen content meets the process requirement, and meanwhile, the micro-positive pressure of the loading and unloading chamber is maintained; the micro-positive pressure can effectively resist the external air entering the loading and unloading cavity, and the oxygen content control effect is ensured. According to the scheme, the purging gas such as high-purity nitrogen with a certain flow rate is required to be purged in various modes, so that the purging gas is easily used excessively, and high cost is generated.
Disclosure of Invention
In view of this, the present application provides a method for controlling a target gas content and a semiconductor processing apparatus, so as to solve the problem that the existing solution is easy to cause excessive use of the purge gas, resulting in higher cost.
The application provides a target gas content control method for controlling the content of a target gas in a loading and unloading chamber of semiconductor process equipment, which comprises the following steps:
acquiring a content difference value between the target content and the current content of the target gas in the loading and unloading chamber;
respectively mapping the pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy domain, and determining a fuzzy control parameter according to each mapping result;
converting the fuzzy control parameter into a physical discourse domain to obtain an initial control parameter;
and controlling the flow of the purge gas purged into the loading and unloading cavity according to the initial control parameters so as to control the content of the target gas in the loading and unloading cavity.
Optionally, the mapping the pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy domain respectively, and the determining fuzzy control parameters according to each mapping result includes:
mapping the pressure value to a first mapping parameter of a first fuzzy domain by adopting a first quantization factor based on a preset fuzzy mapping formula, and mapping the content difference value to a second mapping parameter of a second fuzzy domain by adopting a second quantization factor based on the preset fuzzy mapping formula;
obtaining the membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy domain and the membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain;
and determining the fuzzy control parameter 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.
Optionally, the determining the fuzzy control parameter according to the first mapping parameter, the degree of membership of the first mapping parameter to each fuzzy subset of the first fuzzy domain, the degree of membership of the second mapping parameter to each fuzzy subset of the second fuzzy domain comprises:
identifying a first fuzzy quantity and a corresponding membership degree of each fuzzy subset representation of the first fuzzy domain where the first mapping parameter is located, and identifying a second fuzzy quantity and a corresponding membership degree of each fuzzy subset representation of the second fuzzy domain where the second mapping parameter is located;
combining each first fuzzy quantity and each second fuzzy quantity into a plurality of groups of fuzzy quantities, performing weighted summation on each group of fuzzy quantities, and then rounding to obtain each initial fuzzy parameter;
and determining the minimum membership degree corresponding to each group of fuzzy quantities as the membership degree corresponding to the initial fuzzy parameter, and determining the fuzzy control parameter according to each initial fuzzy parameter and the corresponding membership degree.
Optionally, the obtaining the degree of membership of the first mapping parameter to each fuzzy subset of the first universe of ambiguity comprises:
acquiring fuzzy subsets of the first fuzzy domain where the first mapping parameter is located and membership functions of the fuzzy subsets; calculating the membership degree of the fuzzy quantity represented by each fuzzy subset by adopting the first mapping parameter and each membership function;
the obtaining the degree of membership of the second mapping parameter to each fuzzy subset of the second ambiguity domain comprises:
acquiring fuzzy subsets of the second fuzzy domain where the second mapping parameter is located and membership functions of the fuzzy subsets; and calculating the membership of the fuzzy quantity represented by each fuzzy subset by adopting the second mapping parameter and each membership function.
Optionally, the determining of the first quantization factor includes:
acquiring a physical domain of the pressure value;
calculating a quantization factor between the physics discourse domain of the pressure value and the first fuzzy discourse domain by adopting a preset quantization factor calculation formula to serve as the first quantization factor;
the determination of the second quantization factor comprises:
acquiring a physical discourse domain of the content difference;
and calculating the quantization factor between the physical discourse domain of the content difference value and the second fuzzy discourse domain by adopting a preset quantization factor calculation formula to serve as the second quantization factor.
Optionally, the physical discourse domain of the content difference comprises at least two sub-physical discourse domains;
the calculating, by using the preset quantization factor calculation formula, the quantization factor between the physics discourse domain of the content difference value and the second fuzzy discourse domain as the second quantization factor includes:
and determining a sub-physics discourse domain to which the content difference value belongs, and calculating a quantization factor between the sub-physics discourse domain and the second fuzzy discourse domain by adopting a preset quantization factor calculation formula to serve as the second quantization factor.
Optionally, each of the sub-physics discourse domains has a corresponding second fuzzy discourse domain;
the calculating the quantization factor between the sub-physics discourse domain and the second fuzzy discourse domain by adopting the preset quantization factor calculation formula as the second quantization factor comprises the following steps:
and calculating the quantization factor between the sub-physics discourse domain and a second fuzzy discourse domain corresponding to the sub-physics discourse domain by adopting a preset quantization factor calculation formula to serve as the second quantization factor.
Optionally, the physics discourse domain of the pressure value comprises at least two sub physics discourse domains, and the sub physics discourse domains of the pressure values respectively have corresponding first fuzzy discourse domains;
the calculating the quantization factor between the physics theory domain and the first fuzzy theory domain of the pressure value by adopting a preset quantization factor calculation formula as the first quantization factor comprises the following steps:
and determining a sub-physics discourse domain to which the pressure value belongs, and calculating a quantization factor between the sub-physics discourse domain and a first fuzzy discourse domain corresponding to the sub-physics discourse domain by adopting a preset quantization factor calculation formula to serve as the first quantization factor.
Optionally, the converting the fuzzy control parameter into a physical discourse domain to obtain an initial control parameter includes:
and converting the fuzzy control parameter into a physical discourse domain by adopting a scale factor to obtain the initial control parameter.
Optionally, the determining of the scale factor includes:
acquiring a physical universe of flow of the purge gas and a corresponding third universe of ambiguity;
and calculating a scaling factor between the third ambiguity domain and the physics of the purge gas flow using a preset scaling factor calculation formula.
Optionally, the preset quantization factor calculation formula includes: kj =2 m/(b-a);
the preset calculation formula of the scale factor comprises: ku = (b-a)/2 m;
in the formula, kj represents a quantization factor, ku represents a scale factor, m represents an upper limit of a fuzzy domain, b represents an upper limit of a physical domain, and a represents a lower limit of the physical domain.
Optionally, the controlling the flow of purge gas into the loadlock chamber according to the initial control parameter comprises:
and performing filtering processing on the initial control parameters by adopting a discrete filter to obtain flow control parameters, and controlling the flow of the purge gas which is purged into the loading and unloading chamber by adopting the flow control parameters.
Optionally, the discrete filter comprises:
y(n)＝a 1 *y d (n)+a 2 *y(n-1)+a 1 *y(n-2)，
wherein y (n) represents the flow control parameter at the nth sampling time, y (n-1) represents the flow control parameter at the (n-1) th sampling time, y (n-2) represents the flow control parameter at the (n-2) th sampling time, and y d (n) denotes an initial control parameter at the nth sampling instant, a 1 Represents a first filter coefficient, a 2 Representing a first filter coefficient, 2a 1 +a 2 (= 1), symbol = multiplication).
The application also provides semiconductor process equipment which comprises a control device, a loading chamber and a unloading chamber, wherein the control device is used for acquiring a content difference value between the target content and the current content of target gas in the loading chamber and the unloading chamber of the semiconductor process equipment; respectively mapping the pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy domain, and determining a fuzzy control parameter according to each mapping result; converting the fuzzy control parameter into a physical discourse domain to obtain an initial control parameter; and controlling the flow of the purge gas purged into the loading and unloading cavity according to the initial control parameters so as to control the content of the target gas in the loading and unloading cavity.
According to the target gas content control method and the semiconductor process equipment, the content difference value between the target content and the current content of the target gas in the loading and unloading cavity is obtained, the pressure value and the content difference value in the loading and unloading cavity are respectively mapped to the corresponding fuzzy domains, the fuzzy control parameter is determined according to each mapping result, the fuzzy control parameter is converted to the physical domain to obtain the initial control parameter, the flow of the purge gas which is purged into the loading and unloading cavity is controlled according to the initial control parameter to control the target gas content of the loading and unloading cavity, the fuzzy control of the target gas in the loading and unloading cavity is realized by taking the current pressure value and the content difference value as the basis, the amount of the purge gas which is used can be reduced on the basis of ensuring the corresponding process quality, and the cost in the corresponding control process is reduced.
Furthermore, the method can also divide the physical discourse domain of the content difference into a plurality of sub-physical discourse domains so as to carry out multi-section control on the content difference, and the flow of the purging gas which is purged into the loading and unloading cavity is adjusted along with the corresponding content difference in the control process of each section, thereby further improving the control precision of the content of the target gas, improving the control efficiency and reducing the cost in the corresponding control process.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1a is a schematic of the oxygen control logic of a prior art scheme;
FIG. 1b is a schematic diagram of analysis of control results of a prior art scheme;
FIG. 2 is a schematic diagram of a target gas content control flow in an embodiment of the present application;
FIG. 3 is a diagram of fuzzy subsets of fuzzy universes in one embodiment of the present application;
FIG. 4 is a schematic illustration of a high purity nitrogen flow control process in an embodiment of the present application;
FIG. 5 is a schematic diagram showing the analysis of the flow control result of high purity nitrogen 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.
Detailed Description
Taking the oxygen control scheme of the load and unload chamber as an example to further illustrate the problems described in the background art, the conventional oxygen control logic can be referred to as shown in fig. 1a, the ordinate represents the oxygen content of the load and unload chamber, the abscissa represents time, and the curve represents the relationship between the oxygen content and the time: for the area (1), large N2 flow is adopted to control oxygen, and the oxygen content is changed from atmospheric oxygen content to micro oxygen content of 10ppm; for the area (2), oxygen of the wafer box or a related chamber enters the loading and unloading chamber due to the opening of a wafer transfer port or the lifting boat, the oxygen content is changed from less than 10ppm to 800ppm, and small N2 flow is adopted for controlling oxygen; for the area (3), the oxygen content is more than 800ppm, and the oxygen is controlled by adopting a large N2 flow until the oxygen content reaches 10ppm; for the region (4), the oxygen content is less than or equal to 10ppm, the oxygen content gradually approaches about 5ppm by switching the small N2 flow to control the oxygen. Wherein 10ppm is the target value for meeting the process requirement, 800ppm is the upper limit value of a small N2 flow window, namely the oxygen content of a loading and unloading chamber is changed from 10ppm to 800ppm, and the flow of high-purity nitrogen is 500slm/min; the loadlock chamber oxygen content varied from greater than 800ppm to 10ppm, with a high purity nitrogen flow of 1000slm/min. The high-purity nitrogen flow scheme of the loading and unloading chamber adopts a control mode of switching a large N2 flow oxygen control mode and a small N2 flow oxygen control mode, and under the condition of good sealing of the loading and unloading chamber, the target value of the oxygen content is 10ppm; high purity nitrogen at 500slm/min in a small N2 flow oxygen control mode will blow the oxygen content to 5ppm or less; as shown in fig. 1b, the left ordinate represents the oxygen content (in ppm) of the load/unload chamber, the right ordinate represents the high-purity nitrogen flow rate (in slm), the abscissa represents time (in s), the broken line represents the change of the oxygen content with time, the solid line represents the output change of the high-purity nitrogen flow rate, when the oxygen content (broken line) reaches 10ppm, the high-purity nitrogen flow rate (solid line) is switched from 1000slm/min to 500slm/min and is maintained, and finally the oxygen content is maintained at about 3 ppm. The actually required high-purity nitrogen flow can be less than 500slm/min to maintain the 10ppm oxygen content required by the process, and thus the traditional content control scheme of target gases such as loading and unloading chamber oxygen content control scheme is easy to cause excessive use of purge gases such as high-purity nitrogen, the problem of purge gas waste exists, and the corresponding cost is high.
Aiming at the problem that the traditional content control scheme of the target gas easily causes excessive use of the purge gas, the application provides the content control method of the target gas and semiconductor process equipment, which adopt a fuzzy control mode and can reduce the use amount of the purge gas in the control process and reduce the cost of the content control scheme of the target gas.
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. The following embodiments and their technical features may be combined with each other without conflict.
In a first aspect, the present application provides a method for controlling a content of a target gas in a loading and unloading chamber of a semiconductor processing apparatus, and referring to fig. 2, the method for controlling a content of a target gas includes:
s110, acquiring a content difference value between the target content and the current content of the target gas in the loading and unloading chamber.
The target gas includes oxygen and other gases which affect the process effect in the loading and unloading chamber. The target level is a target (or desired) level of the target gas level in the load lock chamber and may be set according to the process requirements, for example, a target oxygen level of 5ppm in some processes and 10ppm in other processes. The current content is the target gas content obtained by real-time measurement of a gas analyzer arranged at the loading and unloading chamber. When the current content is higher, the content difference value between the target content and the current content is a negative value with a larger absolute value; if the purge gas is blown into the loading and unloading chamber, the target content becomes lower along with the purge of the purge gas and is stabilized near the target content or slightly lower than the target content, so as to ensure the corresponding process quality.
And S120, respectively mapping the pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy domain, and determining fuzzy control parameters according to each mapping result.
The fuzzy control theory is adopted, the pressure values and the content difference values are physical quantities, the value ranges corresponding to the physical quantities are physical universes, and the physical universes can be determined according to the characteristics of the loading and unloading chamber in various processes through analysis modes such as related experiments. For each physical discourse domain, corresponding fuzzy discourse domains can be respectively set according to factors such as the range, the conversion precision and/or the required control precision, each physical quantity of the physical discourse domain can be converted into at least one fuzzy quantity of the corresponding fuzzy discourse domain through the processes of mapping, corresponding fuzzy processing and the like, and corresponding fuzzy control parameters can be calculated according to each fuzzy quantity and corresponding parameters such as membership degree.
And S130, converting the fuzzy control parameter into a physical discourse domain to obtain an initial control parameter.
The fuzzy quantity on each fuzzy domain can obtain the physical quantity corresponding to the physical domain through conversion. The fuzzy domain in which the fuzzy control parameter is located can be preset according to the precision required by the fuzzy processing, such as [ -2,2] and the like. The physical domain of the flow range in which the initial control parameter is located may be set according to the flow range in which the purge corresponds to the purge gas.
S140, controlling the flow of the purge gas purged into the loading and unloading cavity according to the initial control parameters so as to control the content of the target gas in the loading and unloading cavity.
In the step S140, discrete filtering or smooth filtering may be performed on the initial control parameter, and the result obtained by filtering is used to control the corresponding flow rate of the purge gas, so that the change process of the control parameter is smoother, the situation that the control effect is affected by sudden change of the related control parameter in the control process of the target gas content is avoided, and the control effect can be improved.
According to the method, the content difference value between the target content and the current content of the target gas in the loading and unloading chamber is obtained, the pressure value and the content difference value of the loading and unloading chamber are respectively mapped to the corresponding fuzzy domain, the fuzzy control parameter is determined according to each mapping result, the fuzzy control parameter is converted into the physical domain to obtain the initial control parameter, the flow of the purge gas blown into the loading and unloading chamber is controlled according to the initial control parameter to control the target gas content of the loading and unloading chamber, the target gas in the loading and unloading chamber can be subjected to fuzzy control by taking the current pressure value and the content difference value as the basis, on the basis of ensuring the corresponding process quality, the used purge gas amount can be reduced, the excessive use of the purge gas is avoided, and the cost in the target gas content control process is reduced.
In one embodiment, the mapping the pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy domains respectively, and the determining fuzzy control parameters according to each mapping result includes:
mapping the pressure value into a first mapping parameter of a first fuzzy domain by adopting a first quantization factor based on a preset fuzzy mapping formula, and mapping the content difference value into a second mapping parameter of a second fuzzy domain by adopting a second quantization factor based on a preset fuzzy mapping formula;
obtaining the membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy domain and the membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain;
and determining the fuzzy control parameter according to the first mapping parameter, the degree of membership of the first mapping parameter to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the degree of membership of the second mapping parameter to each fuzzy subset of the second fuzzy universe.
The physical quantities of the pressure value and the content difference value are all in corresponding physical discourse domains, each physical discourse domain is provided with a corresponding fuzzy discourse domain, quantization factors (such as a first quantization factor and a second quantization factor) are arranged between the physical discourse domains and the corresponding fuzzy discourse domains, and each physical quantity can be converted into the corresponding fuzzy discourse domain through the quantization factors. The first mapping parameter and the 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 is, the higher the corresponding conversion precision is. Each ambiguity domain can correspond to different ambiguity intervals, for example, the ambiguity interval of the first ambiguity domain can be [ -2,2], and the ambiguity interval of the second ambiguity domain can be [ -3,3]; each ambiguity domain can also have the same ambiguity interval, such as all set to [ -2,2].
Each fuzzy domain comprises a plurality of fuzzy subsets, each fuzzy subset has a corresponding membership function, a certain mapping parameter generally belongs to the fuzzy subsets, and the mapping parameter is substituted into the corresponding membership function for calculation, so that the membership of the corresponding fuzzy quantity of the corresponding mapping parameter can be obtained. Referring to fig. 3, in fig. 3, the target gas is oxygen, the abscissa represents the value of the mapping parameter, and the ordinate represents the membership degree, which shows fuzzy domains corresponding to the pressure value, the oxygen content difference value and the fuzzy control parameter, respectively, where fuzzy subsets of the fuzzy domains are { NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large) }, and the specific fuzzy quantities include: { -2, -1,0,1,2}, and adopting triangular membership functions respectively. The respective ambiguity fields shown in fig. 3 indicate that the respective mapping parameters taken on the abscissa are covered by at least two ambiguity subsets.
The embodiment can determine the inference rule by combining the actual characteristics of the loading and unloading chamber, and further determine the corresponding fuzzy processing rule so as to perform fuzzy processing on the first mapping parameter, the second mapping parameter and the corresponding membership degree respectively to obtain the required fuzzy control parameter. The oxygen content control process in the loading and unloading chamber is explained here, and the inference rule can include: 1. the pressure of the loading and unloading chamber is very small, the oxygen content is very large, and the flow of high-purity nitrogen is very large; 2. the pressure of the loading and unloading chamber is slightly small, the oxygen content is slightly large, and the flow of high-purity nitrogen is slightly large; 3. the pressure of the loading and unloading chamber is moderate, the oxygen content is moderate, and the flow of high-purity nitrogen is moderate; 4. the pressure of the loading and unloading chamber is slightly larger, the oxygen content is slightly smaller, and the flow of high-purity nitrogen is slightly smaller; 5. the loading and unloading chamber has large pressure, small oxygen content and small high-purity nitrogen flow. 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, determining a plurality of groups of fuzzy quantities comprising one first fuzzy quantity and one second fuzzy quantity, calculating each group of fuzzy quantities by adopting an inference formula to obtain each initial fuzzy parameter, determining the membership degree of each initial fuzzy parameter, and performing sharpening processing on each initial fuzzy parameter according to each membership degree to determine a fuzzy control parameter.
In one example, the determining the fuzzy control parameters according to the first mapping parameter, the degree of membership of the first mapping parameter to each fuzzy subset of the first fuzzy domain, the second mapping parameter, and the degree of membership of the second mapping parameter to each fuzzy subset of the second fuzzy domain comprises:
identifying a first fuzzy quantity and a corresponding membership degree of each fuzzy subset characterization of a first fuzzy domain where the first mapping parameter is located, and identifying a second fuzzy quantity and a corresponding membership degree of each fuzzy subset characterization of a second fuzzy domain where the second mapping parameter is located;
combining each first fuzzy quantity and each second fuzzy quantity into a plurality of groups of fuzzy quantities, carrying out weighted summation on each group of fuzzy quantities, and then rounding to obtain each initial fuzzy parameter;
and determining the minimum membership degree corresponding to each group of fuzzy quantities as the membership degree corresponding to the initial fuzzy parameters, and determining the fuzzy control parameters according to each initial fuzzy parameter and the membership degree corresponding to each initial fuzzy parameter.
The fuzzy subsets of the first and second fuzzy domains may be referred to in fig. 3, each fuzzy subset characterizing a corresponding fuzzy quantity and having a corresponding membership function, e.g., the fuzzy subset NB characterizing the fuzzy quantity is-2, the fuzzy subset NS characterizing the fuzzy quantity is-1, the fuzzy subset ZO characterizing the fuzzy quantity is 0, the fuzzy subset PS characterizing the fuzzy quantity is 1, and the fuzzy subset PB characterizing the fuzzy quantity 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 fig. 3, the first fuzzy amount corresponding to 0.6 is 0 (fuzzy subset ZO) and 1 (fuzzy subset PS); membership functions of the fuzzy subset ZO and the fuzzy subset PS are as follows:
where ZO (Xp) represents a membership function of the fuzzy subset ZO, PS (Xp) represents a membership function of the fuzzy subset PS, and Xp represents a mapping parameter (e.g., a first mapping parameter). If Xp =0.6, ZO (0.6) =0.4 and PS (0.6) =0.6 are obtained, 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, the corresponding degree of membership is 0.4, the fuzzy quantity of the fuzzy subset PS is 1, and the corresponding degree of membership is 0.6.
The process of rounding after weighted summation includes: u1= < alpha 1 N1+(1-α 1 )N2>Wherein u1 represents an initial blur parameter, N1 represents a first blur amount, N2 represents a second blur amount, and α 1 Representing a first weight (or correction factor), may be set to the equivalent of 0.4 or 0.5,<>expressing the rounding operator, expressing the rounding of the absolute value of the value therein, the sign and<>of the same sign, e.g.<-1.3>＝-1，<1.7>And (2). The first mapping parameter corresponds to a plurality of first fuzzy quantities, the second mapping parameter corresponds to a plurality of second fuzzy quantities, each first fuzzy quantity and each second fuzzy quantity are combined to obtain a plurality of groups of non-repeated fuzzy quantities, and each group of fuzzy quantities is weighted to obtainAnd then rounding to obtain initial fuzzy parameters and membership degrees corresponding to the initial fuzzy parameters; for example, in a certain set of fuzzy quantities, the first fuzzy quantity N1 is 0 and its degree of membership is 0.4, the second fuzzy quantity N2 is 1 and its degree of membership is 0.8, and the first weight α 1 0.5, corresponding initial blur parameter u1=<0.5×0+(1-0.5)×1>=1, with a degree of membership of 0.4.
In this example, the fuzzy control parameters are determined as a sharpening process according to each initial fuzzy parameter and the corresponding membership degree, where each initial fuzzy parameter belongs to a fuzzy quantity, and the fuzzy quantities need to be converted into specific fuzzy control parameters, and then the fuzzy control parameters are converted into physical quantities (such as the initial control parameters) and sent to the control mechanism for control. Optionally, the process can adopt a maximum membership mean value method for carrying out sharpening processing to determine fuzzy control parameters, so that the calculation amount can be reduced on the basis of meeting the actual control requirement, the output is stable, and the problem of frequent control is solved. The process of carrying out the clarification processing by the maximum membership mean value method comprises the following steps: selecting the initial fuzzy parameter with the maximum membership degree from the initial fuzzy parameters as a selected fuzzy parameter, obtaining a selected membership degree function of a fuzzy subset where the selected fuzzy parameter is located, taking the maximum membership degree as a function value of the selected membership degree function, obtaining a plurality of fuzzy variable values, and taking the average value of each fuzzy variable value as a fuzzy control parameter. The following describes the sharpening process by taking the determination of the corresponding fuzzy control parameter according to the two initial fuzzy parameters as an example: the initial fuzzy parameter a is 0, the corresponding membership degree is 0.6, the initial fuzzy parameter B is 1, the corresponding membership degree is 0.4, the initial fuzzy parameter a with the membership degree of 0.6 is taken as the selected membership degree function, and then according to the fuzzy subset distribution diagram of the fuzzy control parameter in fig. 3, the fuzzy quantity is 0, namely the fuzzy subset ZO, the function value of the membership function of the fuzzy subset ZO is made to be 0.6, namely:
and solving to obtain two fuzzy variable values, wherein Xp '= -0.4, and Xp' =0.4, and the first fuzzy control parameter determined by the average value of the two fuzzy variable values is 0. Further, the fuzzy control parameters can be converted into a physical discourse domain by adopting corresponding scale factors to obtain initial control parameters; for example, if the scaling factor is ku =100, the upper limit b of the physical range of the purge gas flow is 1000, the lower limit a is 600, and the fuzzy control parameter x' is 0, the corresponding initial control parameter may be:
in one embodiment, obtaining the degree of membership of the first mapping parameter to each fuzzy subset of the first universe of ambiguity comprises: acquiring fuzzy subsets of the first fuzzy domain where the first mapping parameter is located and membership functions of the fuzzy subsets; calculating the membership degree of the fuzzy quantity represented by each fuzzy subset by adopting the first mapping parameter and each membership function;
obtaining the membership of the second mapping parameter to each fuzzy subset of the second fuzzy domain comprises: acquiring fuzzy subsets of the second fuzzy domain where the second mapping parameter is located and membership functions of the fuzzy subsets; and calculating the membership of the fuzzy quantity represented by each fuzzy subset by adopting the second mapping parameter and each membership function.
Alternatively, the first mapping parameter and the second mapping parameter may be calculated for the corresponding physical quantities by using fuzzy mapping formulas, respectively. The fuzzy mapping equation may be set according to an inference rule corresponding to the loading and unloading chamber, and the mapping may be performed by using a corresponding quantization factor, for example, y = (x- (a + b)/2) × kj, where kj represents the quantization factor, b represents an upper limit of a physical universe of relations, a represents a lower limit of the physical universe of relations, x represents a physical quantity, and y represents a mapping parameter. Specifically, after determining the fuzzy subset where a certain mapping parameter is located and the corresponding membership function, the mapping parameter may be substituted into each membership function to obtain the membership of each fuzzy quantity. Taking the pressure value P =2800mtorr as an example, describing the solving process of the corresponding fuzzy quantity and membership degree, if the corresponding physical universe (pressure range) is [1500,3500], the first mapping parameter is y = (2800- (1500 + 3500)/2) × 0.002=0.6, and the corresponding fuzzy quantity is 0 (fuzzy subset ZO) and 1 (fuzzy subset PS); according to the membership functions of ZO and PS fuzzy subsets:
where Xp =0.6, ZO (0.6) =0.4, ps (0.6) =0.6, i.e., the pressure value P =2800mtorr has a first mapping parameter of 0.6 corresponding to one fuzzy 0 and a membership of 0.4, and another fuzzy 1 and a membership of 0.6.
Specifically, the determining process of the first quantization factor includes: acquiring a physical discourse domain of the pressure value; calculating a quantization factor between the physics discourse domain of the pressure value and the first fuzzy discourse domain by adopting a preset quantization factor calculation formula to serve as the first quantization factor;
the determination process of the second quantization factor comprises: acquiring a physical discourse domain of the content difference; and calculating the quantization factor between the physical discourse domain of the content difference value and the second fuzzy discourse domain by adopting a preset quantization factor calculation formula to serve as the second quantization factor.
Optionally, the physical domain of content difference comprises at least two sub-physical domains; the calculating, as the second quantization factor, the quantization factor between the physics discourse domain of the content difference and the second fuzzy discourse domain by using the preset quantization factor calculation formula includes: and determining a sub-physics discourse domain to which the content difference value belongs, and calculating a quantization factor between the sub-physics discourse domain and the second fuzzy discourse domain by adopting a preset quantization factor calculation formula to serve as the second quantization factor. The whole physical discourse domain of the content difference value can be divided into a plurality of sections according to target gas content control requirements corresponding to different content difference values, each section is a sub-physical discourse domain, so that after the content difference value is obtained, the sub-physical discourse domain where the content difference value is located is identified, the content difference value is mapped to a corresponding second fuzzy discourse domain from the sub-physical discourse domain, the difference conversion of the content difference value on each sub-physical discourse domain is realized, and the target gas content control requirements required by each sub-physical discourse domain are met.
Preferably, in consideration of the difference control, each sub-physics discourse domain can also have a respective second fuzzy domain, that is, different sub-physics discourse domains correspond to different second fuzzy domains, so that the refined control can be further realized. Correspondingly, the calculating, by using the preset quantization factor calculation formula, the quantization factor between the sub-domain of physics and the second domain of ambiguity as the second quantization factor includes: and calculating the quantization factor between the sub-physics theory domain and the second fuzzy theory domain corresponding to the sub-physics theory domain by adopting a preset quantization factor calculation formula to serve 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, and the change of the content difference between the target content and the current content of the target gas has no influence or little influence on the value range of the pressure value in the loading and unloading chamber, each sub-physical discourse domain of the content difference can correspond to the physical discourse domain of the same pressure value, so that the pressure value fuzzy processing is performed by adopting the physical discourse domain of the pressure value and the corresponding first fuzzy discourse domain, the fuzzy processing efficiency is improved, and the control efficiency of the content of the target gas is improved. In other cases, if the proportion of the target gas in the gas contained in the loading and unloading chamber is relatively large, and the change of the content difference between the target content and the current content of the target gas has a certain influence on the value range of the pressure value in the loading and unloading chamber, the physical universe of the pressure value may also be divided into a plurality of sub-physical universes, and the sub-physical universes of each pressure value may also have corresponding first fuzzy universes, so as to improve the mapping accuracy and the accuracy of the corresponding fuzzy processing process, thereby improving the control effect of the target gas content. At this time, the calculating, as the first quantization factor, a quantization factor between the physics universe of pressure values and the first universe of ambiguity by using a preset quantization factor calculation formula may include: and determining a sub-physics discourse domain to which the pressure value belongs, and calculating a quantization factor between the sub-physics discourse domain and a first fuzzy discourse domain corresponding to the sub-physics discourse domain by adopting a preset quantization factor calculation formula to serve as the first quantization factor.
In order to make the control process of the target gas content smoother, in one example, the target gas content control may be divided into 2 segments, where the physical domain of the content difference includes 2 sub-physical domains, and the two sub-physical domains are determined by a segmentation threshold, that is, the upper limit of one sub-physical domain is the segmentation threshold, and the lower limit of the other sub-physical domain is the segmentation threshold. At this time, the corresponding sub-physics discourse domain and the second discourse domain can be selected for mapping according to the relation between the current content difference value and the segmentation threshold value, so as to obtain the fuzzy control parameter corresponding to the current content difference value, and the corresponding initial control parameter is obtained by adopting the fuzzy control parameter to control the flow of the sweeping gas swept into the loading and unloading cavity. Therefore, the whole target gas control process is divided into 2 sections for fuzzy control on the basis of the content difference, and the control efficiency is higher on the basis of improving the control effect. The segment threshold may be set according to the target content and the corresponding control accuracy, for example, the segment threshold may be set to be equal to-5 ppm for the oxygen content adjustment process of the load and unload chamber. In some cases, the content difference value is smaller than a segmentation threshold value, the target gas content in the loading and unloading cavity is represented, the flow rate of the purge gas can be controlled by adopting a coarse adjustment mode with relatively low control precision, so that the target gas content in the loading and unloading cavity is quickly reduced to be close to the target content, and the control efficiency is ensured; and when the content difference value is greater than or equal to the segmentation threshold value, representing that the content of the target gas in the loading and unloading chamber is reduced to be close to the target content, and controlling the flow of the purge gas by adopting a fine adjustment mode with relatively high control precision, so that the oxygen content of the unloading chamber further reaches the target content and is kept at the level, and the control precision is ensured.
Further, the data shown in tables 1 and 2 are used for explaining the process of controlling the oxygen content in the loading and unloading chamber by dividing the segmented threshold into two segments, wherein the table 1 shows the conversion results of each physical discourse domain and the corresponding fuzzy domain when the content difference is smaller than the segmented threshold, and the table 2 shows the conversion results of each physical discourse domain and the corresponding fuzzy domain when the content difference is larger than or equal to the segmented threshold. In Table 1, the pressure value is in the first physical universe of discourse [1500,3500], the corresponding first fuzzy universe is [ -2,2], and the first quantization factor between the two is 0.002; a sub-physics theory domain where the content difference value is located is [ -505, -5], a corresponding second ambiguity domain is [ -2,2], and a second quantization factor between the two is 0.008; the third ambiguity shown in Table 1 is [ -2,2], the physical argument for purge gas flow is [600,1000], and the scaling factor between the two is 100. In Table 2, the physical universe of pressure values is [1500,3500], the corresponding first universe of ambiguity is [ -2,2], and the quantization factor between the two is 0.002; the other sub-domain of physics where the content difference value is located is [ -5,5], the corresponding second domain of ambiguity is [ -2,2], and the second quantization factor between the two is 0.4; the third ambiguity range shown in Table 2 is [ -2,2], the physical argument for the purge gas flow is [300,600], and the scaling factor between the two is 75.
TABLE 1
TABLE 2
| Physical quantity | Universe of physics | Universe of fuzzy discourse | Quantization factor |
| Pressure value | [1500,3500] | [-2,2] | 0.002 |
| Difference in content | [-5，5] | [-2,2] | 0.4 |
| Domain of fuzzy discourse | Domain of physics | Scaling factor | |
| Initial control parameter | [-2,2] | [300,600] | 75 |
In one example, converting the fuzzy control parameter to a physical discourse domain, and obtaining the initial control parameter comprises: and converting the fuzzy control parameter into a physical discourse domain by adopting a scale factor to obtain an initial control parameter. The scale factor can be obtained by calculation according to the upper and lower limit characteristics of a third fuzzy domain where the fuzzy control parameter is located and a physical domain where the initial control parameter is located.
Specifically, the process of determining the scale factor includes:
acquiring a physical universe of flow of the purge gas and a corresponding third universe of ambiguity;
and calculating a scaling factor between the third ambiguity domain and the physics of the purge gas flow using a preset scaling factor calculation formula.
Optionally, the preset quantization factor calculation formula includes: kj =2 m/(b-a);
the predetermined scaling factor calculation includes: ku = (b-a)/2 m;
in the formula, kj represents a quantization factor, ku represents a scale factor, m represents an upper limit of a fuzzy domain, b represents an upper limit of a physical domain, and a represents a lower limit of the physical domain.
The value range corresponding to each physical discourse domain can be determined by analysis modes such as related experiments according to the characteristics of a specific loading and unloading chamber in various processes; for example, the ideal pressure range of the loading and unloading chamber is 2.5 ± 1torr, and at this time, the physical universe corresponding to the pressure value may be defined as [1500,3500], and the unit is mtorr; the feedback value of the oxygen content of the loading and unloading chamber is usually in the range of 0-1000ppm (more than 1000ppm, all set as 1000 ppm), the target value of the oxygen content of the process is usually 10ppm or 5ppm, the physical range of the oxygen content difference e can be [ -505,5] (the value less than-505 ppm, all set as-505 ppm), and the unit is ppm; the range of a gas mass flow controller is usually 1000slm, and at this time, the physical range corresponding to the initial control parameter can be set to [300, 1000], and the unit is slm.
In the example, each physics theory domain and each fuzzy theory domain can be set according to the value range of each physics theory domain and relevant process characteristics, then the quantization factor between each physics theory domain and the corresponding fuzzy theory domain is calculated by adopting a quantization factor calculation formula, and the scale factor between each fuzzy theory domain and the corresponding physics theory domain is calculated by adopting a scale factor calculation formula. For example, if the upper limit of the first ambiguity 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:
other quantization factors can be calculated quickly and accurately by the same method. For another example, if the upper limit of the third ambiguity domain is 2, the upper limit of the physical domain of the purge gas flow is 1000, and the lower limit is 600, then the corresponding scaling factor is:
in one embodiment, said controlling the flow of purge gas into said loadlock chamber in accordance with said initial control parameter comprises: and performing filtering processing on the initial control parameters by adopting a discrete filter to obtain flow control parameters, and controlling the flow of the purge gas which is purged into the loading and unloading chamber by adopting the flow control parameters.
Specifically, the discrete filter includes:
y(n)＝a 1 *y d (n)+a 2 *y(n-1)+a 1 *y(n-2)，
wherein y (n) represents the flow control parameter at the nth sampling time, y (n-1) represents the flow control parameter at the (n-1) th sampling time, y (n-2) represents the flow control parameter at the (n-2) th sampling time, and y d (n) denotes an initial control parameter at the nth sampling instant, a 1 Representing a first filter coefficient, a 2 Representing a first filter coefficient, 2a 1 +a 2 =1, the symbol h denotes multiplication.
In this embodiment, the discrete filter is used to perform filtering processing on the initial control parameter, so that the corresponding flow control parameter change is more gradual, the problem of sudden change such as peak generated when the flow output of the purge gas suddenly changes in a step manner can be solved, and the control effect of controlling the flow of the purge gas can be improved.
In an example, a target gas content control method provided by the present application is described by taking a high-purity nitrogen flow control process during loading and unloading chamber oxygen content control as an example, referring to fig. 4, a differential pressure gauge is used for measuring a current pressure value of a loading and unloading chamber, an oxygen analyzer is used for measuring a current content of oxygen in the loading and unloading chamber, and when a content difference between the target content and the current content is smaller than a segmented threshold, a control selector is controlled to upload control parameters such as quantization factors corresponding to sub-physical discourse domains taking the segmented threshold as an upper limit to a fuzzy controller, so that the fuzzy controller converts the current pressure value and the current content difference to obtain corresponding fuzzy control parameters by using corresponding first physical discourse domains, first fuzzy discourse domains, sub-physical discourse domains and second fuzzy discourse domains; when the content difference value is greater than or equal to the segmentation threshold value, the control selector uploads control parameters such as quantization factors corresponding to a sub-physical discourse domain taking the segmentation threshold value as a lower limit to the fuzzy controller, so that the fuzzy controller converts the current pressure value and the current content difference value by adopting a corresponding first physical discourse domain, a corresponding first fuzzy discourse domain, a corresponding sub-physical discourse domain and a corresponding second fuzzy discourse domain to obtain corresponding fuzzy control parameters; thus, the fuzzy controller can convert the fuzzy control parameters into corresponding physical discourse domain to obtain initial control parameters. And the discrete filter carries out filtering processing on the initial control parameters to obtain corresponding flow control parameters, so that the gas mass flow controller adopts the corresponding flow control parameters to control the flow of the high-purity nitrogen blown to the loading and unloading cavity so as to control the oxygen content of the unloading cavity. The simulation analysis of the high purity nitrogen flow control process provided in this example requires controlling the oxygen content of the loadlock chamber to a target level when the process is ready to begin, and the oxygen content changes as shown in fig. 5 according to the control scheme provided in this example. Wherein the left ordinate represents the oxygen content (in ppm), the right ordinate represents the high purity nitrogen flow (in slm), the abscissa represents time (in s), the broken line represents the change in oxygen content with time, and the solid line represents the change in output of the high purity nitrogen flow. The target level in FIG. 5 is 11ppm, and the oxygen level remains at 11. + -.1 ppm after the target level is reached. Through fuzzy control, the flow of the high-purity nitrogen is stabilized at 310slm/min, compared with 500slm/min in a small N2 flow mode in a traditional scheme, the flow of the high-purity nitrogen of 190slm/min is saved, and after a point a in the figure, the fuzzy control process corresponds to the embodiment, so that the high-purity nitrogen used in the control process can be effectively saved.
According to the target gas content control method, the content difference value between the target content and the current content of the target gas in the loading and unloading cavity is obtained, the pressure value and the content difference value of the loading and unloading cavity are respectively mapped to the corresponding fuzzy domains, the fuzzy control parameter is determined according to each mapping result, the fuzzy control parameter is converted into the physical domain to obtain the initial control parameter, the flow of the purge gas blown into the loading and unloading cavity is controlled according to the initial control parameter to control the target gas content of the loading and unloading cavity, the fuzzy control of the target gas in the loading and unloading cavity based on the current pressure value and the content difference value is realized, the amount of the purge gas used can be reduced on the basis of ensuring the corresponding process quality, and the excessive use of the purge gas is avoided; in addition, the physical discourse domain of the content difference can be divided into a plurality of sub-physical discourse domains so as to carry out multi-section control on the content difference, and the flow of the purging gas which is purged into the loading and unloading cavity is adjusted along with the corresponding content difference in the control process of each section, so that the control precision of the content of the target gas can be further improved, the control efficiency is improved, and the cost in the corresponding control process is reduced.
The present application provides in a second aspect a semiconductor processing apparatus comprising a control device for obtaining a content difference between a target content and a current content of a target gas in a loading and unloading chamber of the semiconductor processing apparatus; respectively mapping the pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy domain, and determining fuzzy control parameters according to each mapping result; converting the fuzzy control parameter into a physical discourse domain to obtain an initial control parameter; and controlling the flow of the purge gas purged into the loading and unloading cavity according to the initial control parameters so as to control the content of the target gas in the loading and unloading cavity.
For the specific definition of the control device corresponding to the target gas content, reference may be made to the definition of the target gas content control method above, and details are not repeated here. The control means described above may be implemented wholly or partly by software, hardware or a combination thereof. The method can be embedded in hardware or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute corresponding operations.
The present application provides in a third aspect a semiconductor device, shown with reference to fig. 6, comprising a processor 620 and a storage medium 630; the storage medium 630 has program code stored thereon; the processor 620 is configured to call the program code stored in the storage medium to execute the target gas content control method according to any of the above embodiments.
The semiconductor equipment adopts the target gas content control method to control the flow of the purge gas purged to the loading and unloading cavity in the corresponding process, so that the content of the target gas in the loading and unloading cavity is controlled, the consumption of the purge gas can be reduced, the cost of using the purge gas is reduced, and the corresponding process cost is reduced.
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 based upon a reading and understanding of this specification and the annexed drawings. The present application includes all such modifications and alterations, and is limited only by the scope of the appended claims. In particular regard to the various functions performed by the above described components, the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the specification.
That is, the above description is only an embodiment of the present application, and not intended to limit the scope of the present application, and all equivalent structures or equivalent flow transformations made by using the contents of the specification and the drawings, such as mutual combination of technical features between various embodiments, or direct or indirect application to other related technical fields, are included in the scope of the present application.
In addition, in the description of the present application, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, merely for convenience of description and simplification of the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present application. In addition, the present application may be identified by the same or different reference numerals for structural elements having the same or similar characteristics. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The previous description is provided to enable any person skilled in the art to make and use the present application. In the foregoing description, various details have been set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes are not shown 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 herein.
Claims (14)
1. A target gas content control method for controlling the content of a target gas in a loading and unloading chamber of semiconductor processing equipment is characterized by comprising the following steps:
acquiring a content difference value between the target content and the current content of the target gas in the loading and unloading chamber;
respectively mapping the pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy domain, and determining fuzzy control parameters according to each mapping result;
converting the fuzzy control parameter into a physical discourse domain to obtain an initial control parameter;
and controlling the flow of the purge gas purged into the loading and unloading cavity according to the initial control parameters so as to control the content of the target gas in the loading and unloading cavity.
2. The method of claim 1, wherein the mapping the pressure value and the content difference value of the load and unload chamber to corresponding fuzzy domains, respectively, and determining fuzzy control parameters according to the mapping results comprises:
mapping the pressure value to a first mapping parameter of a first fuzzy domain by adopting a first quantization factor based on a preset fuzzy mapping formula, and mapping the content difference value to a second mapping parameter of a second fuzzy domain by adopting a second quantization factor based on the preset fuzzy mapping formula;
obtaining the membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy domain and the membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain;
and determining the fuzzy control parameter according to the first mapping parameter, the degree of membership of the first mapping parameter to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the degree of membership of the second mapping parameter to each fuzzy subset of the second fuzzy universe.
3. The method of claim 2, wherein said determining the fuzzy control parameter based on the first mapping parameter, the degree of membership of the first mapping parameter to each fuzzy subset of the first fuzzy domain, the second mapping parameter, and the degree of membership of the second mapping parameter to each fuzzy subset of the second fuzzy domain comprises:
identifying a first fuzzy quantity and a corresponding membership degree of each fuzzy subset representation of the first fuzzy domain where the first mapping parameter is located, and identifying a second fuzzy quantity and a corresponding membership degree of each fuzzy subset representation of the second fuzzy domain where the second mapping parameter is located;
combining each first fuzzy quantity and each second fuzzy quantity into a plurality of groups of fuzzy quantities, carrying out weighted summation on each group of fuzzy quantities, and then rounding to obtain each initial fuzzy parameter;
and determining the minimum membership degree corresponding to each group of fuzzy quantities as the membership degree corresponding to the initial fuzzy parameters, and determining the fuzzy control parameters according to each initial fuzzy parameter and the corresponding membership degree thereof.
4. The target gas content control method according to claim 2,
the obtaining the membership of the first mapping parameter to each fuzzy subset of the first fuzzy domain comprises:
acquiring fuzzy subsets of the first fuzzy domain where the first mapping parameter is located and membership functions of the fuzzy subsets; calculating the membership degree of the fuzzy quantity represented by each fuzzy subset by adopting the first mapping parameter and each membership function;
the obtaining the membership degree of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain comprises:
acquiring fuzzy subsets of the second fuzzy domain where the second mapping parameter is located and membership functions of the fuzzy subsets; and calculating the membership of the fuzzy quantity represented by each fuzzy subset by adopting the second mapping parameter and each membership function.
5. The target gas content control method according to claim 2,
the determination of the first quantization factor comprises:
acquiring a physical domain of the pressure value;
calculating a quantization factor between the physics discourse domain of the pressure value and the first fuzzy discourse domain by adopting a preset quantization factor calculation formula to serve as the first quantization factor;
the determination of the second quantization factor comprises:
acquiring a physical discourse domain of the content difference;
and calculating the quantization factor between the physical discourse domain of the content difference value and the second fuzzy discourse domain by adopting a preset quantization factor calculation formula to serve as the second quantization factor.
6. The method of claim 5, wherein the physical universe of content differences includes at least two sub-physical universes;
the calculating, by using the preset quantization factor calculation formula, a quantization factor between the physics discourse domain of the content difference and the second fuzzy discourse domain as the second quantization factor includes:
and determining a sub-physics discourse domain to which the content difference value belongs, and calculating a quantization factor between the sub-physics discourse domain and the second fuzzy discourse domain by adopting a preset quantization factor calculation formula to serve as the second quantization factor.
7. The method of claim 6, wherein each of said sub-domains of physics has a corresponding second domain of ambiguity;
the calculating the quantization factor between the sub-physics theory domain and the second fuzzy theory domain by adopting the preset quantization factor calculation formula as the second quantization factor comprises the following steps:
and calculating the quantization factor between the sub-physics discourse domain and a second fuzzy discourse domain corresponding to the sub-physics discourse domain by adopting a preset quantization factor calculation formula to serve as the second quantization factor.
8. The method of claim 7, wherein the physical universe of pressure values includes at least two sub-physical universes, each sub-physical universe of pressure values having a corresponding first fuzzy universe;
the calculating, by using a preset quantization factor calculation formula, a quantization factor between the physics discourse domain of the pressure value and the first fuzzy discourse domain as the first quantization factor includes:
and determining a sub-physics discourse domain to which the pressure value belongs, and calculating a quantization factor between the sub-physics discourse domain and a first fuzzy discourse domain corresponding to the sub-physics discourse domain by adopting a preset quantization factor calculation formula to serve as the first quantization factor.
9. The method of claim 1, wherein the converting the fuzzy control parameter to a physical universe of discourse to obtain an initial control parameter comprises:
and converting the fuzzy control parameter into a physical discourse domain by adopting a scale factor to obtain the initial control parameter.
10. The target gas content control method according to claim 8, wherein the determination process of the scaling factor includes:
acquiring a physical universe of flow of the purge gas and a corresponding third universe of ambiguity;
and calculating a scaling factor between the third ambiguity domain and the physics domain of the purge gas flow using a preset scaling factor calculation formula.
11. The target gas content control method according to claim 5 or 10, wherein the preset quantization factor calculation formula includes: kj =2 m/(b-a);
the preset calculation formula of the scale factor comprises: ku = (b-a)/2 m;
in the formula, kj represents a quantization factor, ku represents a scale factor, m represents an upper limit of a fuzzy domain, b represents an upper limit of a physical domain, and a represents a lower limit of the physical domain.
12. The method of claim 1, wherein the controlling the flow of purge gas into the loadlock chamber based on the initial control parameter comprises:
and performing filtering processing on the initial control parameters by adopting a discrete filter to obtain flow control parameters, and controlling the flow of the purge gas which is purged into the loading and unloading chamber by adopting the flow control parameters.
13. The target gas content control method according to claim 12, wherein the discrete filter includes:
y(n)＝a 1 *y d (n)+a 2 *y(n-1)+a 1 *y(n-2)，
wherein y (n) represents the flow control parameter at the nth sampling time, y (n-1) represents the flow control parameter at the (n-1) th sampling time, y (n-2) represents the flow control parameter at the (n-2) th sampling time, yd (n) represents the initial control parameter at the nth sampling time, and a 1 Representing a first filter coefficient, a 2 Represents the first filter coefficient, 2a1+ a 2 (= 1), symbol = multiplication).
14. The semiconductor processing equipment comprises a control device, and is characterized in that the control device is used for acquiring a content difference value between a target content and a current content of a target gas in a loading and unloading chamber of the semiconductor processing equipment; respectively mapping the pressure value and the content difference value of the loading and unloading chamber to corresponding fuzzy domain, and determining a fuzzy control parameter according to each mapping result; converting the fuzzy control parameter into a physical discourse domain to obtain an initial control parameter; and controlling the flow of the purge gas purged into the loading and unloading cavity according to the initial control parameters so as to control the content of the target gas in the loading and unloading cavity.
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