Target gas content control method and semiconductor process apparatus

Abstract

The application discloses a target gas content control method and a semiconductor process equipment. The target gas content control method is used for controlling the content of a target gas in a load / unload chamber of the semiconductor process equipment. The method comprises the following steps: obtaining a content difference between a target content and a current content of the target gas in the load / unload chamber; mapping a pressure value of the load / unload chamber and the content difference to corresponding fuzzy domains respectively, and determining a fuzzy control parameter according to each mapping result; converting the fuzzy control parameter to a physical domain to obtain an initial control parameter; and controlling the flow of a purge gas purged into the load / unload chamber according to the initial control parameter, so as to control the content of the target gas in the load / unload chamber. The application can reduce the amount of purge gas used in the control of the target gas content.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor process control, in particular to a target gas content control method and a semiconductor process equipment. BACKGROUND

[0002] Micro-oxygen / micro-positive pressure control of a load / unload chamber (LA) is a key performance indicator of a semiconductor process equipment, such as a vertical furnace series equipment. For example, in the vertical furnace series equipment, the silicon wafer is affected by oxygen molecules in the atmosphere of the load / unload chamber during the transmission process and the lifting boat (into and out of the reaction chamber) process, resulting in the generation of unnecessary oxide layers. Therefore, in some semiconductor process, it is necessary to control the content of target gases such as oxygen in the load / unload chamber. For example, high-purity nitrogen (PN2) purging means under the closed-loop control of an oxygen (O2) analyzer and a gas mass flow controller (MFC) is needed to reduce and control the oxygen content in the load / unload chamber (LA). In order to avoid the pressure change of the load / unload chamber exceeding the safe range during micro-oxygen control, the pressure in the load / unload chamber needs to be controlled to ensure the reliable operation of the micro-positive pressure system under the condition of good micro-oxygen control.

[0003] The existing control scheme usually adopts the method of purging the load / unload chamber with a fixed flow of purging gas such as high-purity nitrogen to control the target gas in the load / unload chamber. For example, the oxygen content in the load / unload chamber is usually controlled by a hysteresis window control mode: large N2 flow oxygen control mode and small N2 flow oxygen control mode. In the large N2 flow oxygen control mode, the gas mass flow controller is usually set to 1000 slm / min, and the exhaust valve is opened. In the small N2 flow oxygen control mode, the gas mass flow controller is usually set to 500 slm / min, and the exhaust valve is closed. In the oxygen control process of each mode, the load / unload chamber is purged with a certain amount of high-purity nitrogen to exhaust the oxygen from the load / unload chamber, so that the oxygen content meets the process requirements, and the micro-positive pressure of the load / unload chamber is maintained. The micro-positive pressure can effectively prevent external air from entering the load / unload chamber, thereby ensuring the oxygen content control effect. The above scheme needs to use a certain amount of purging gas such as high-purity nitrogen for purging in various modes, which is easy to cause excessive use of purging gas and high cost. SUMMARY

[0004] In view of this, the present application provides a target gas content control method and a semiconductor process equipment to solve the problem of excessive use of purging gas and high cost in the existing scheme.

[0005] The target gas content control method provided by the present application is used for controlling the content of a target gas in a load / unload chamber of a semiconductor process equipment, and includes the following steps:

[0006] obtaining a content difference between a target content and a current content of the target gas in the load / unload chamber;

[0007] mapping the pressure value of the load-unload chamber and the content difference value to corresponding fuzzy domains respectively, and determining a fuzzy control parameter according to each mapping result;

[0008] converting the fuzzy control parameter to a physical domain to obtain an initial control parameter;

[0009] controlling the flow of purge gas purged into the load-unload chamber according to the initial control parameter to control the content of the target gas in the load-unload chamber.

[0010] Optionally, the mapping the pressure value of the load-unload chamber and the content difference value to corresponding fuzzy domains respectively, and determining a fuzzy control parameter according to each mapping result comprises:

[0011] mapping the pressure value to a first mapping parameter of a first fuzzy domain based on a preset fuzzy mapping formula using a first quantization factor, and mapping the content difference value to a second mapping parameter of a second fuzzy domain based on the preset fuzzy mapping formula using a second quantization factor;

[0012] obtaining the membership degrees of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain and the membership degrees of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain;

[0013] determining the fuzzy control parameter according to the first mapping parameter, the membership degrees of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain, the second mapping parameter, and the membership degrees of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain.

[0014] Optionally, the determining the fuzzy control parameter according to the first mapping parameter, the membership degrees of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain, the second mapping parameter, and the membership degrees of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain comprises:

[0015] identifying a first fuzzy quantity and a corresponding membership degree represented by each fuzzy subset of the first fuzzy domain where the first mapping parameter is located, and identifying a second fuzzy quantity and a corresponding membership degree represented by each fuzzy subset of the second fuzzy domain where the second mapping parameter is located;

[0016] combining each first fuzzy quantity and each second fuzzy quantity into a plurality of groups of fuzzy quantities, and obtaining each initial fuzzy parameter by weighted summing and rounding each group of fuzzy quantities;

[0017] The minimum membership degrees of the fuzzy quantities in each group are determined as the membership degrees of the corresponding initial fuzzy parameters, and the fuzzy control parameters are determined according to the initial fuzzy parameters and the corresponding membership degrees.

[0018] Optionally, the obtaining of the membership degrees of the first mapping parameter with respect to the fuzzy subsets of the first fuzzy universe comprises:

[0019] The fuzzy subsets of the first fuzzy universe where the first mapping parameter is located and the membership degree functions of the fuzzy subsets are obtained, and the membership degrees of the fuzzy quantities represented by the fuzzy subsets are calculated by using the first mapping parameter and the membership degree functions.

[0020] The obtaining of the membership degrees of the second mapping parameter with respect to the fuzzy subsets of the second fuzzy universe comprises:

[0021] The fuzzy subsets of the second fuzzy universe where the second mapping parameter is located and the membership degree functions of the fuzzy subsets are obtained, and the membership degrees of the fuzzy quantities represented by the fuzzy subsets are calculated by using the second mapping parameter and the membership degree functions.

[0022] Optionally, the determination of the first quantization factor comprises:

[0023] The physical universe of the pressure value is obtained.

[0024] The quantization factor between the physical universe of the pressure value and the first fuzzy universe is calculated as the first quantization factor by using a preset quantization factor calculation formula.

[0025] The determination of the second quantization factor comprises:

[0026] The physical universe of the content difference value is obtained.

[0027] The quantization factor between the physical universe of the content difference value and the second fuzzy universe is calculated as the second quantization factor by using a preset quantization factor calculation formula.

[0028] Optionally, the physical universe of the content difference value comprises at least two sub-physical universes.

[0029] The calculation of the quantization factor between the physical universe of the content difference value and the second fuzzy universe as the second quantization factor by using a preset quantization factor calculation formula comprises:

[0030] The sub-physical universe to which the content difference value belongs is determined, and the quantization factor between the sub-physical universe and the second fuzzy universe is calculated as the second quantization factor by using a preset quantization factor calculation formula.

[0031] Optionally, each of the sub-physical domains has a corresponding second fuzzy domain;

[0032] The preset quantization factor calculation formula is used to calculate a quantization factor between the sub-physical domain and the second fuzzy domain as the second quantization factor.

[0033] The preset quantization factor calculation formula is used to calculate a quantization factor between the sub-physical domain and the second fuzzy domain corresponding to the sub-physical domain as the second quantization factor.

[0034] Optionally, the physical domain of the pressure value includes at least two sub-physical domains, and each of the sub-physical domains of the pressure value has a corresponding first fuzzy domain.

[0035] The preset quantization factor calculation formula is used to calculate a quantization factor between the physical domain of the pressure value and the first fuzzy domain as the first quantization factor.

[0036] The sub-physical domain to which the pressure value belongs is determined, and the preset quantization factor calculation formula is used to calculate a quantization factor between the sub-physical domain and the first fuzzy domain corresponding to the sub-physical domain as the first quantization factor.

[0037] Optionally, the conversion of the fuzzy control parameter to the physical domain to obtain the initial control parameter includes:

[0038] The fuzzy control parameter is converted to the physical domain by using a proportional factor to obtain the initial control parameter.

[0039] Optionally, the determination process of the proportional factor includes:

[0040] The physical domain of the purge gas flow and a corresponding third fuzzy domain are obtained.

[0041] A preset proportional factor calculation formula is used to calculate a proportional factor between the third fuzzy domain and the physical domain of the purge gas flow.

[0042] Optionally, the preset quantization factor calculation formula includes: kj=2m / (b-a);

[0043] The preset proportional factor calculation formula includes: ku=(b-a) / 2m;

[0044] In the formula, kj represents a quantization factor, ku represents a proportional 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.

[0045] Optionally, the control of the purge gas flow purged into the loading / unloading chamber according to the initial control parameter includes:

[0046] The initial control parameter is filtered by a discrete filter to obtain a flow control parameter, and the flow control parameter is used to control the flow of purge gas purged into the load / unload chamber.

[0047] Optionally, the discrete filter comprises:

[0048] y(n) = a1*y d (n) + a2*y(n-1) + a1*y(n-2),

[0049] wherein y(n) represents the flow control parameter at the n th 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, y d (n) represents the initial control parameter at the n th sampling time, a1 represents a first filter coefficient, a2 represents a first filter coefficient, 2a1+a2=1, and the symbol * represents multiplication.

[0050] The application also provides a semiconductor process equipment comprising a control device configured to obtain a content difference between a target content and a current content of a target gas in a load / unload chamber of the semiconductor process equipment, map a pressure value of the load / unload chamber and the content difference to corresponding fuzzy domains respectively, determine a fuzzy control parameter according to each mapping result, convert the fuzzy control parameter to a physical domain to obtain an initial control parameter, and control the flow of purge gas purged into the load / unload chamber according to the initial control parameter to control the content of the target gas in the load / unload chamber.

[0051] The above target gas content control method and semiconductor process equipment obtain the content difference between the target content and the current content of the target gas in the load / unload chamber, map the pressure value of the load / unload chamber and the content difference to corresponding fuzzy domains respectively, determine the fuzzy control parameter according to each mapping result, convert the fuzzy control parameter to the physical domain to obtain the initial control parameter, and control the flow of purge gas purged into the load / unload chamber according to the initial control parameter to control the content of the target gas in the load / unload chamber, thereby realizing fuzzy control of the target gas in the load / unload chamber based on the current pressure value and the content difference, reducing the amount of purge gas used and lowering the cost in the corresponding control process while ensuring the corresponding process quality.

[0052] Further, the physical domain of the content difference can be divided into multiple sub-physical domains to control the content difference in multiple stages, and the flow of purge gas purged into the load / unload chamber is adjusted according to the corresponding content difference in each stage of control, which can further improve the control accuracy of the target gas content, improve the control efficiency, and reduce the cost in the corresponding control process. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1a This is a schematic diagram of the oxygen control logic in the existing scheme;

[0055] Figure 1b This is a schematic diagram illustrating the control results analysis of the existing scheme;

[0056] Figure 2 This is a schematic diagram of the target gas content control process in one embodiment of this application;

[0057] Figure 3 This is a schematic diagram of fuzzy subsets of each fuzzy domain in one embodiment of this application;

[0058] Figure 4 This is a schematic diagram of the high-purity nitrogen flow control process in one embodiment of this application;

[0059] Figure 5 This is a schematic diagram illustrating the analysis of high-purity nitrogen flow control results in one embodiment of this application;

[0060] Figure 6 This is a schematic diagram of a semiconductor device structure according to an embodiment of this application. Detailed Implementation

[0061] 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 Figure 1a As shown, Figure 1aIn the figure, the ordinate represents the oxygen content of the loading and unloading chamber, the abscissa represents time, and the curve represents the relationship between oxygen content and time: for region ①, oxygen is controlled by using a large N2 flow rate, and the oxygen content changes from atmospheric oxygen content to micro-oxygen content of 10 ppm; for region ②, oxygen enters the loading and unloading chamber from the wafer cassette or related chambers due to the opening of the wafer transfer port or the lifting of the boat, and the oxygen content changes from less than 10 ppm to 800 ppm, and oxygen is controlled by using a small N2 flow rate; for region ③, the oxygen content is greater than 800 ppm, and oxygen is controlled by using a large N2 flow rate until 10 ppm; for region ④, the oxygen content is less than or equal to 10 ppm, and the small N2 flow rate is switched, and the oxygen content gradually tends to about 5 ppm. 10 ppm is the target value required to reach the process requirement, and 800 ppm is the upper limit value of the small N2 flow rate window, that is, the oxygen content of the loading and unloading chamber changes from 10 ppm to 800 ppm, and the high-purity nitrogen flow rate is 500 slm / min; the oxygen content of the loading and unloading chamber changes from greater than 800 ppm to 10 ppm, and the high-purity nitrogen flow rate is 1000 slm / min. The high-purity nitrogen flow rate scheme of the above loading and unloading chamber uses a control mode in which the large N2 flow rate control mode and the small N2 flow rate control mode are switched, and the target value of the oxygen content is 10 ppm under the condition that the loading and unloading chamber is well sealed; under the small N2 flow rate control mode, the high-purity nitrogen flow rate of 500 slm / min can blow the oxygen content to 5 ppm or lower; as shown in Figure 1b The left ordinate represents the oxygen content (unit: ppm) of the loading and unloading chamber, the right ordinate represents the high-purity nitrogen flow rate (unit: slm), the abscissa represents time (unit: s), the dashed line represents the change of the oxygen content with time, and the solid line represents the output change of the high-purity nitrogen flow rate. When the oxygen content (dashed line) reaches 10 ppm, the high-purity nitrogen flow rate (solid line) is switched from 1000 slm / min to 500 slm / min and remains unchanged, and the oxygen content finally remains at about 3 ppm. To maintain the process requirement of 10 ppm oxygen content, the actual required high-purity nitrogen flow rate can be less than 500 slm / min. It can be seen that the conventional oxygen content control scheme of the loading and unloading chamber of this type of target gas content control scheme is prone to overuse of the purge gas, and there is a problem of waste of the purge gas, which causes high cost.

[0062] In view of the problem that the conventional target gas content control scheme is prone to overuse of the purge gas, the present application provides a target gas content control method and a semiconductor process equipment, which uses a fuzzy control mode and can reduce the amount of purge gas used in the control process and reduce the cost of the target gas content control scheme.

[0063] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort are within the protection scope of the present application. In the case of no conflict, each of the described embodiments and the technical features thereof can be combined with each other.

[0064] The first aspect of the present application provides a target gas content control method for controlling the content of a target gas in a load / unload chamber of a semiconductor process equipment, referring to Figure 2 The target gas content control method comprises the following steps.

[0065] In S110, a content difference value between a target content and a current content of the target gas in the load / unload chamber is obtained.

[0066] The target gas includes a gas which can affect the process effect in the load / unload chamber, such as oxygen. The target content is a target value (or ideal value) of the content of the target gas in the load / unload chamber, which can be set according to the corresponding process requirement. For example, in some process, the target oxygen content in the load / unload chamber is 5ppm, and in another process, the target oxygen content is 10ppm. The current content is the content of the target gas measured by a gas analyzer arranged in the load / unload chamber. When the current content is high, the content difference value between the target content and the current content is a large negative absolute value. At this time, if a purge gas is blown into the load / unload chamber, the target content will decrease with the blowing of the purge gas and stabilize at a level close to or slightly lower than the target content, so as to ensure the corresponding process quality.

[0067] In S120, a pressure value of the load / unload chamber and the content difference value are mapped to corresponding fuzzy domains respectively, and a fuzzy control parameter is determined according to each mapping result.

[0068] The present application adopts the theory of fuzzy control. The pressure value and the content difference value are physical quantities, and the value range of each physical quantity is a physical domain. Each physical domain can be determined according to the characteristics of the load / unload chamber in various processes and by analysis methods such as relevant experiments. For each physical domain, a corresponding fuzzy domain can be set according to its range, conversion accuracy and / or required control accuracy. Each physical quantity of the physical domain can be converted into at least one fuzzy quantity of the corresponding fuzzy domain through mapping and corresponding fuzzy processing. According to each fuzzy quantity and corresponding membership degree and other parameters, the corresponding fuzzy control parameter can be calculated.

[0069] In S130, the fuzzy control parameter is converted to the physical domain to obtain an initial control parameter.

[0070] The fuzzy quantity on each fuzzy field can be converted to obtain the physical quantity corresponding to the physical field. The fuzzy fields of the above-mentioned fuzzy control parameters can be pre-set according to the required accuracy of the fuzzy processing, such as being set as [-2, 2] and the like. The physical field of the flow range of the initial control parameter can be set according to the flow range of the corresponding purge gas.

[0071] In S140, the flow of the purge gas purged into the load / unload chamber is controlled according to the initial control parameter, so as to control the content of the target gas in the load / unload chamber.

[0072] In the above-mentioned step S140, the initial control parameter can also be subjected to filtering processing such as discrete filtering or smoothing filtering, and the result obtained by filtering is used to control the corresponding flow of the purge gas, so as to make the change process of the control parameter more gentle and avoid the situation that the control parameter suddenly changes in the control process of the target gas content, which can improve the control effect.

[0073] In the embodiment, the content difference between the target content and the current content of the target gas in the load / unload chamber is obtained, the pressure value of the load / unload chamber and the content difference are respectively mapped to corresponding fuzzy fields, the fuzzy control parameter is determined according to each mapping result, the fuzzy control parameter is converted to the physical field to obtain the initial control parameter, and the flow of the purge gas purged into the load / unload chamber is controlled according to the initial control parameter, so as to control the target gas content of the load / unload chamber. The target gas in the load / unload chamber can be controlled based on the current pressure value and the content difference, the amount of the purge gas used can be reduced on the basis of ensuring the corresponding process quality, the excessive use of the purge gas can be avoided, and thus the cost in the control process of the target gas content is reduced.

[0074] In one embodiment, the mapping of the pressure value of the load / unload chamber and the content difference to corresponding fuzzy fields and the determination of the fuzzy control parameter according to each mapping result include:

[0075] The pressure value is mapped to a first mapping parameter of a first fuzzy field by using a first quantization factor based on a pre-set fuzzy mapping formula, and the content difference is mapped to a second mapping parameter of a second fuzzy field by using a second quantization factor based on the pre-set fuzzy mapping formula;

[0076] The membership degrees of the first mapping parameter with respect to each fuzzy subset of the first fuzzy field and the membership degrees of the second mapping parameter with respect to each fuzzy subset of the second fuzzy field are obtained;

[0077] The fuzzy control parameter is determined according to the first mapping parameter, the membership of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain, the second mapping parameter and the membership of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain.

[0078] The physical quantities of the pressure value and the content difference value are in corresponding physical domains, each physical domain has a corresponding fuzzy domain, and the physical domain and the corresponding fuzzy domain have a quantization factor (such as a first quantization factor and a second quantization factor). Each physical quantity can be converted to the corresponding fuzzy domain through the quantization factor. The first mapping parameter and the second mapping parameter are preliminary mapping parameters for mapping the corresponding physical quantity to the corresponding fuzzy domain. The larger the interval range of each fuzzy domain is, the higher the conversion accuracy is. Each fuzzy domain can correspond to different fuzzy intervals, such as the fuzzy interval of the first fuzzy domain can be [-2, 2], and the fuzzy interval of the second fuzzy domain can be [-3, 3]. Each fuzzy domain can also have the same fuzzy interval, such as [-2, 2].

[0079] Each fuzzy domain includes a plurality of fuzzy subsets, each fuzzy subset has a corresponding membership function, and a mapping parameter usually belongs to a plurality of fuzzy subsets. By substituting the mapping parameter into the corresponding membership function, the membership of the corresponding fuzzy quantity taken by the corresponding mapping parameter can be obtained. Figure 3 As shown in the figure, Figure 3 In the figure, the abscissa represents the value of the mapping parameter, and the ordinate represents the membership. It shows the fuzzy domains corresponding to the pressure value, the oxygen content difference value and the fuzzy control parameter 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 fuzzy quantities taken include: {-2, -1, 0, 1, 2}, and triangular membership functions are used respectively. Figure 3 The figure shows that each mapping parameter taken on the abscissa is covered by at least two fuzzy subsets.

[0080] The embodiment can determine the inference rule according to the actual characteristics of the loading and unloading chamber, and then determine the corresponding fuzzy processing rule to respectively perform fuzzy processing on the first mapping parameter, the second mapping parameter and the corresponding membership to obtain the required fuzzy control parameter. Here, the oxygen content control process in the loading and unloading chamber is described, 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 high-purity nitrogen flow is very large; 2. The pressure of the loading and unloading chamber is slightly small, the oxygen content is slightly large, and the high-purity nitrogen flow is slightly large; 3. The pressure of the loading and unloading chamber is moderate, the oxygen content is moderate, and the high-purity nitrogen flow is moderate; 4. The pressure of the loading and unloading chamber is slightly large, the oxygen content is slightly small, and the high-purity nitrogen flow is slightly small; 5. The pressure of the loading and unloading chamber is very large, the oxygen content is very small, and the high-purity nitrogen flow is very small. The corresponding fuzzy processing rule includes: 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 fuzzy quantities each including a first fuzzy quantity and a second fuzzy quantity, calculating each initial fuzzy parameter for each group of fuzzy quantities using the inference formula, determining the membership of each initial fuzzy parameter, and performing defuzzification processing on each initial fuzzy parameter according to each membership to determine the fuzzy control parameter.

[0081] In one example, the determining the fuzzy control parameter according to the first mapping parameter, the membership of the first mapping parameter with respect to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the membership of the second mapping parameter with respect to each fuzzy subset of the second fuzzy universe includes:

[0082] identifying a first fuzzy quantity and a corresponding membership represented by each fuzzy subset of the first fuzzy universe where the first mapping parameter is located, and identifying a second fuzzy quantity and a corresponding membership represented by each fuzzy subset of the second fuzzy universe where the second mapping parameter is located;

[0083] combining each first fuzzy quantity and each second fuzzy quantity into a plurality of fuzzy quantities, and performing weighted summation on each group of fuzzy quantities to obtain each initial fuzzy parameter;

[0084] determining the minimum membership corresponding to each group of fuzzy quantities as the membership of the corresponding initial fuzzy parameter, and determining the fuzzy control parameter according to each initial fuzzy parameter and the membership corresponding to each initial fuzzy parameter.

[0085] The fuzzy subsets of the first fuzzy universe and the second fuzzy universe can refer to Figure 3As shown, each fuzzy subset represents a corresponding fuzzy quantity and has a corresponding membership function, for example, fuzzy subset NB represents a fuzzy quantity of -2, fuzzy subset NS represents a fuzzy quantity of -1, fuzzy subset ZO represents a fuzzy quantity of 0, fuzzy subset PS represents a fuzzy quantity of 1, and fuzzy subset PB represents a fuzzy quantity of 2. Taking the first mapping parameter 0.6 corresponding to the loading and unloading chamber pressure value as an example, according to the fuzzy subset distribution diagram of the medium pressure value, 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: Figure 3

[0086]

[0087] In the formula, ZO(Xp) represents the membership function of fuzzy subset ZO, PS(Xp) represents the membership function of fuzzy subset PS, and Xp represents a mapping parameter (such as a first mapping parameter). When 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 fuzzy subset ZO and fuzzy subset PS, the fuzzy quantity represented by fuzzy subset ZO is 0, and the corresponding membership degree is 0.4; the fuzzy quantity of fuzzy subset PS is 1, and the corresponding membership degree is 0.6.

[0088] The above process of weighted summation and rounding includes u1 = <α1N1 + (1-α1)N2>, in which u1 represents an initial fuzzy parameter, N1 represents a first fuzzy quantity, N2 represents a second fuzzy quantity, α1 represents a first weight (or a correction factor), which can be set to 0.4 or 0.5 or the like, and < > represents a rounding operator, which means rounding the absolute value of the number in it to the nearest integer, and the sign is the same as that in < >, for example, <-1.3> = -1 and <1.7> = 2. The above first mapping parameter corresponds to a plurality of first fuzzy quantities, and the second mapping parameter corresponds to a plurality of second fuzzy quantities. Combining each first fuzzy quantity and each second fuzzy quantity can obtain a plurality of groups of non-repeating fuzzy quantities. Weighted summation and rounding of each group of fuzzy quantities can obtain an initial fuzzy parameter and a membership degree corresponding to each initial fuzzy parameter; for example, in a certain group of fuzzy quantities, the first fuzzy quantity N1 is 0, the membership degree is 0.4, the second fuzzy quantity N2 is 1, the membership degree is 0.8, and the first weight α1 is 0.5. The corresponding initial fuzzy parameter is u1 = <0.5x0 + (1-0.5)x1> = 1, and the membership degree is 0.4.

[0089] ​In the example, the fuzzy control parameter is determined according to each initial fuzzy parameter and the corresponding membership degree, and the fuzzy control parameter is converted into a physical quantity (such as an initial control parameter) to control the control mechanism. Alternatively, the process can be de-fuzzified by using the maximum membership degree average method to determine the fuzzy control parameter, which can reduce the calculation amount, stabilize the output, and solve the problem of frequent control while meeting the actual control requirements. The process of de-fuzzification by using the maximum membership degree average method includes: selecting the initial fuzzy parameter with the maximum membership degree as the selected fuzzy parameter from each initial fuzzy parameter, obtaining the selected membership function of the fuzzy subset where the selected fuzzy parameter is located, taking the maximum membership degree as the function value of the selected membership function, obtaining a plurality of fuzzy variable values, and taking the average value of each fuzzy variable value as the fuzzy control parameter. The following is an example of determining the corresponding fuzzy control parameter according to two initial fuzzy parameters: the initial fuzzy parameter A is 0, the corresponding membership degree is 0.6, the initial fuzzy parameter B is 1, and the corresponding membership degree is 0.4. The initial fuzzy parameter A with the membership degree of 0.6 is taken as the selected membership function, and then the first fuzzy control parameter is determined according to the membership function of the fuzzy subset where the selected fuzzy parameter is located and the membership degree of the selected fuzzy parameter. Figure 3 The membership function of the fuzzy subset of the fuzzy control parameter is shown in the figure, the fuzzy quantity is 0, that is, the fuzzy subset ZO, and the function value of the membership function of the ZO fuzzy subset is 0.6, that is:

[0090]

[0091] The two fuzzy variable values Xp' = -0.4 and Xp" = 0.4 are obtained, and the average value of the two fuzzy variable values determines the first fuzzy control parameter as 0. Further, the fuzzy control parameter can be converted to the physical domain by using the corresponding scale factor to obtain the initial control parameter; for example, a scale factor ku = 100, the upper limit b of the physical domain of the purge gas flow is 1000, the lower limit a is 600, and the fuzzy control parameter x' is 0, then the corresponding initial control parameter can be:

[0092]

[0093] In one embodiment, obtaining the membership degrees of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain includes: obtaining the fuzzy subset of the first fuzzy domain where the first mapping parameter is located and the membership degree function of each fuzzy subset; and calculating the membership degrees of the fuzzy quantities represented by each fuzzy subset by using the first mapping parameter and each membership degree function.

[0094] The acquisition of the membership degrees of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain includes: acquiring the fuzzy subset of the second fuzzy domain where the second mapping parameter is located and the membership degree function of each fuzzy subset; and calculating the membership degrees of the fuzzy quantities represented by each fuzzy subset by using the second mapping parameter and each membership degree function.

[0095] Optionally, the first mapping parameter and the second mapping parameter can be calculated by using a fuzzy mapping formula for the corresponding physical quantity. The fuzzy mapping formula can be set according to the inference rule corresponding to the loading and unloading chamber, and is usually mapped by using a corresponding quantization factor, such as y=(x-(a+b) / 2)×kj, where kj represents the quantization factor, b represents the upper limit of the physical domain, a represents the lower limit of the physical domain, x represents the physical quantity, and y represents the mapping parameter. Specifically, after determining the fuzzy subset where the mapping parameter is located and the corresponding membership degree function, the mapping parameter can be substituted into each membership degree function to obtain the membership degrees of the mapping parameter with respect to each fuzzy quantity. Taking the pressure value P=2800mtorr as an example, the solving process of the corresponding fuzzy quantity and the membership degree is described. If the corresponding physical domain (pressure range) is [1500, 3500], the first mapping parameter is y=(2800-(1500+3500) / 2)×0.002=0.6, and the corresponding fuzzy quantities are 0 (fuzzy subset ZO) and 1 (fuzzy subset PS); according to the membership functions of the ZO and PS fuzzy subsets:

[0096]

[0097] Here, Xp=0.6, ZO(0.6)=0.4, and PS(0.6)=0.6, that is, the first mapping parameter of the pressure value P=2800mtorr is 0.6, corresponding to a fuzzy quantity 0 with a membership degree of 0.4, and another fuzzy quantity 1 with a membership degree of 0.6.

[0098] Specifically, the determination process of the first quantization factor includes: acquiring the physical domain of the pressure value; and calculating the quantization factor between the physical domain of the pressure value and the first fuzzy domain as the first quantization factor by using a preset quantization factor calculation formula.

[0099] The determination process of the second quantization factor includes: acquiring the physical domain of the content difference value; and calculating the quantization factor between the physical domain of the content difference value and the second fuzzy domain as the second quantization factor by using a preset quantization factor calculation formula.

[0100] Optionally, the physical domain of the content difference value includes at least two sub-physical domains; and the calculating the quantization factor between the physical domain of the content difference value and the second fuzzy domain as the second quantization factor by using the preset quantization factor calculation formula includes: determining the sub-physical domain to which the content difference value belongs, and calculating the quantization factor between the sub-physical domain and the second fuzzy domain as the second quantization factor by using the preset quantization factor calculation formula. Here, the whole physical domain of the content difference value can be divided into multiple segments according to the target gas content control requirements corresponding to different content difference values, each segment being a sub-physical domain, so that after obtaining the content difference value, the sub-physical domain in which the content difference value is located is identified, the content difference value is mapped from the sub-physical domain to the corresponding second fuzzy domain, the differential conversion of the content difference value in each sub-physical domain is realized, and the target gas content control requirements of each sub-physical domain are met.

[0101] Preferably, in consideration of differential control, each sub-physical domain can also have its own second fuzzy domain, that is, different sub-physical domains correspond to different second fuzzy domains, so that further refined control can be realized. Correspondingly, the calculating the quantization factor between the sub-physical domain and the second fuzzy domain as the second quantization factor by using the preset quantization factor calculation formula includes: calculating the quantization factor between the sub-physical domain and the second fuzzy domain corresponding to the sub-physical domain as the second quantization factor by using the preset quantization factor calculation formula.

[0102] In some cases, if the target gas accounts for a relatively small proportion of the gas included in the load / unload chamber, the content difference between the target content and the current content of the target gas has no effect or little effect on the range of values of the pressure value in the load / unload chamber, and each sub-physical domain of the content difference can correspond to the same physical domain of the pressure value, so as to perform fuzzy processing on the pressure value by using the physical domain of the pressure value and the corresponding first fuzzy domain, improve the fuzzy processing efficiency, and thus improve the target gas content control efficiency. In other cases, if the target gas accounts for a relatively large proportion of the gas included in the load / unload chamber, the content difference between the target content and the current content of the target gas has a certain effect on the range of values of the pressure value in the load / unload chamber, and the physical domain of the pressure value can also be divided into a plurality of sub-physical domains, and each sub-physical domain of the pressure value can also have a corresponding first fuzzy domain, so as to improve the mapping accuracy and the accuracy of the corresponding fuzzy processing process, and thus improve the target gas content control effect. At this time, the above-mentioned calculation of the quantization factor between the physical domain of the pressure value and the first fuzzy domain as the first quantization factor by using the preset quantization factor calculation formula can include: determining the sub-physical domain to which the pressure value belongs, and calculating the quantization factor between the sub-physical domain and the first fuzzy domain corresponding to the sub-physical domain as the first quantization factor by using the preset quantization factor calculation formula.

[0103] In order to make the target gas content control process more smooth, in an example, the above target gas content control can be divided into two sections, and the physical domain of the content difference value includes two sub-physical domains, and the two sub-physical domains are determined by a section threshold, that is, the upper limit of one sub-physical domain is the section threshold, and the lower limit of the other sub-physical domain is the section threshold. At this time, the corresponding sub-physical domain and the second fuzzy domain can be selected according to the relationship between the current content difference value and the section threshold to map the fuzzy control parameter corresponding to the current content difference value, and the initial control parameter corresponding to the fuzzy control parameter is used to control the flow of the purge gas purged into the loading and unloading chamber. In this way, the entire target gas control process is divided into two sections based on the content difference value for fuzzy control, which not only improves the control effect but also has high control efficiency. The above section threshold 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 section threshold can be set to -5 ppm. In some cases, the content difference value is less than the section threshold, indicating that the target gas content in the loading and unloading chamber is high, and the control accuracy of the coarse adjustment mode can be used to control the flow of the purge gas, so that the target gas content in the loading and unloading chamber is quickly reduced to close to the target content, ensuring the control efficiency; the content difference value is greater than or equal to the section threshold, indicating that the target gas content in the loading and unloading chamber has been reduced to close to the target content, and the fine adjustment mode with relatively high control accuracy can be used to control the flow of the purge gas, so that the oxygen content in the loading and unloading chamber reaches the target content and is maintained at this level, ensuring the control accuracy.

[0104] Further, the data shown in Table 1 and Table 2 are used to illustrate the process of controlling the oxygen content in the loading and unloading chamber by dividing into two segments using the segment threshold. Table 1 shows the conversion results of each physical domain and the corresponding fuzzy domain when the content difference is less than the segment threshold, and Table 2 shows the conversion results of each physical domain and the corresponding fuzzy domain when the content difference is greater than or equal to the segment threshold. In Table 1, the first physical domain of the pressure value 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 is in a sub-physical domain [-505, -5], the corresponding second fuzzy domain is [-2, 2], and the second quantization factor between the two is 0.008; the third fuzzy domain shown in Table 1 is [-2, 2], the physical domain of the purge gas flow is [600, 1000], and the proportional factor between the two is 100. In Table 2, the physical domain of the pressure value is [1500, 3500], the corresponding first fuzzy domain is [-2, 2], and the quantization factor between the two is 0.002; the content difference is in another sub-physical domain [-5, 5], the corresponding second fuzzy domain is [-2, 2], and the second quantization factor between the two is 0.4; the third fuzzy domain shown in Table 2 is [-2, 2], the physical domain of the purge gas flow is [300, 600], and the proportional factor between the two is 75.

[0105] Table 1

[0106]

[0107]

[0108] Table 2

[0109]

[0110] In one example, converting the fuzzy control parameter to the physical domain to obtain the initial control parameter includes: converting the fuzzy control parameter to the physical domain using a proportional factor to obtain the initial control parameter. The proportional factor can be calculated according to the upper and lower limits of the third fuzzy domain in which the fuzzy control parameter is located and the physical domain in which the initial control parameter is located.

[0111] Specifically, the determination process of the proportional factor includes:

[0112] obtaining the physical domain of the purge gas flow and the corresponding third fuzzy domain;

[0113] calculating the proportional factor between the third fuzzy domain and the physical domain of the purge gas flow using a preset proportional factor calculation formula.

[0114] Optionally, the preset quantization factor calculation formula includes: kj=2m / (b-a);

[0115] The preset proportional factor calculation formula includes: ku=(b-a) / 2m;

[0116] In the formula, kj represents a quantization factor, ku represents a proportional 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.

[0117] The value range corresponding to each physical domain can be determined according to the characteristics of the specific load / unload chamber in various processes and through relevant experiments and other analysis methods. For example, the ideal pressure range of the load / unload chamber is 2.5±1 torr, at which time the pressure value corresponding to the physical domain can be defined as [1500, 3500] in units of mtorr. The oxygen content feedback value range of the load / unload chamber is usually 0-1000 ppm (more than 1000 ppm is all set as 1000 ppm), and the oxygen content target value of the process is usually 10 ppm or 5 ppm. The physical domain of the oxygen content difference e can be [-505, 5] (values less than -505 ppm are all considered as -505 ppm) in units of ppm. The range of the gas mass flow controller is usually 1000 slm, at which time the initial control parameter corresponding to the physical domain can be set as [300, 1000] in units of slm.

[0118] The present example can set each physical domain and each fuzzy domain according to the value range of each physical domain and the relevant process characteristics, calculate the quantization factor between each physical domain and the corresponding fuzzy domain using the quantization factor calculation formula, and calculate the proportional factor between each fuzzy domain and the corresponding physical domain using the proportional factor calculation formula. For example, if the upper limit of the first fuzzy domain is 2, the upper limit of the pressure range is 3500, and the lower limit is 1500, the corresponding first quantization factor is: Similarly, other quantization factors can be quickly and accurately calculated. For another example, if the upper limit of the third fuzzy domain is 2, the upper limit of the physical domain of the purge gas flow is 1000, and the lower limit is 600, the corresponding proportional factor is:

[0119] In one embodiment, the controlling the purge gas flow purged into the load / unload chamber according to the initial control parameter includes: filtering the initial control parameter using a discrete filter to obtain a flow control parameter, and controlling the purge gas flow purged into the load / unload chamber using the flow control parameter.

[0120] Specifically, the discrete filter includes:

[0121] y(n)=a1*y d (n)+a2*y(n-1)+a1*y(n-2),

[0122] 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-1)th sampling time, and y(n-2) represents the flow control parameter at the (n-2)th sampling time. d (n) represents the initial control parameters at the nth sampling time, a1 represents the first filter coefficient, a2 represents the first filter coefficient, 2a1+a2=1, and the symbol * represents multiplication.

[0123] This embodiment uses a discrete filter to filter the initial control parameters, making the corresponding flow control parameters change more smoothly. This can solve the problem of sudden changes such as spikes when the purge gas flow output changes abruptly, and improve the control effect of controlling the purge gas flow.

[0124] In one example, the high-purity nitrogen flow control process during oxygen content control in the loading and unloading chamber is used as an example to illustrate the target gas content control method provided in this application. (Refer to...) Figure 4 As shown, the differential pressure gauge is used to measure the current pressure value of the loading and unloading chamber, and the oxygen analyzer is used to measure the current oxygen content in the loading and unloading chamber. When the content difference between the target content and the current content is less than the segmented threshold, the control selector uploads control parameters such as the quantization factor corresponding to the sub-physics domain with the segmented threshold as the upper limit to the fuzzy controller. The fuzzy controller then uses the corresponding first physics domain, first fuzzy domain, sub-physics domain, and second fuzzy domain to convert the current pressure value and the current content difference to obtain the corresponding fuzzy control parameters. When the content difference is greater than or equal to the segmented threshold, the control selector uploads control parameters such as the quantization factor corresponding to the sub-physics domain with the segmented threshold as the lower limit to the fuzzy controller. The fuzzy controller then uses the corresponding first physics domain, first fuzzy domain, sub-physics domain, and second fuzzy 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 physics domain to obtain the initial control parameters. A discrete filter filters the initial control parameters to obtain the corresponding flow control parameters. The gas mass flow controller then uses these parameters to control the flow rate of high-purity nitrogen purging into the loading / unloading chamber, thereby controlling the oxygen content in the unloading chamber. A simulation analysis of the high-purity nitrogen flow control process provided in this example shows that when preparing to start the process, the oxygen content in the loading / unloading chamber needs to be controlled at the target level. Based on the control method provided in this example, the oxygen content changes as follows: Figure 5 As shown in the figure, the left vertical axis represents oxygen content (ppm), the right vertical axis represents high-purity nitrogen flow rate (slm), and the horizontal axis represents time (s). The dashed line represents the change of oxygen content over time, and the solid line represents the change of high-purity nitrogen flow rate. Figure 5The target content of the target gas is 11 ppm, and the oxygen content is maintained at 11±1 ppm after reaching the target content. Through fuzzy control, the final high-purity nitrogen flow is stabilized at 310 slm / min, which is 190 slm / min less than the 500 slm / min of the traditional small N2 flow mode. The point a in the figure corresponds to the fuzzy control process of the present example, and it can be seen that the present example can effectively save the high-purity nitrogen used in the control process.

[0125] 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, mapping the pressure value of the loading and unloading chamber and the content difference to the corresponding fuzzy domain respectively, determining the fuzzy control parameter according to each mapping result, converting the fuzzy control parameter to the physical domain to obtain the initial control parameter, and controlling the flow of the purge gas purged into the loading and unloading chamber according to the initial control parameter to control the content of the target gas in the loading and unloading chamber, realizes the fuzzy control of the target gas in the loading and unloading chamber based on the current pressure value and the content difference, and can reduce the amount of purge gas used and avoid excessive use of purge gas while ensuring the corresponding process quality. In addition, the physical domain of the content difference can be divided into multiple sub-physical domains to control the content difference in multiple stages, and the flow of the purge gas purged into the loading and unloading chamber is adjusted according to the corresponding content difference in each control process, which can further improve the control accuracy of the target gas content, improve the control efficiency, and reduce the cost in the corresponding control process.

[0126] The present application provides a semiconductor process equipment in the second aspect, including control device, the control device is used for obtaining 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 mapped to the corresponding fuzzy domain respectively, and 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;According to the initial control parameter, the flow of the purge gas purged into the loading and unloading chamber is controlled to control the content of the target gas in the loading and unloading chamber.

[0127] The specific limitation of the control device corresponding to the target gas content can be referred to the limitation of the target gas content control method in the above, which will not be repeated here. The above control device can be realized by software, hardware and their combination. It can be embedded in the processor in the computer device in hardware form or independent of the processor in the computer device, or stored in the memory in the computer device in software form, so that the processor calls and executes the corresponding operation.

[0128] The present application provides a semiconductor equipment in the third aspect, referring to Figure 6As shown, the semiconductor device includes a processor 620 and a storage medium 630; the storage medium 630 stores program code; the processor 620 is configured to invoke the program code stored in the storage medium to execute the target gas content control method described in any of the above embodiments.

[0129] The semiconductor device described above controls the flow of purge gas purged to the load / unload chamber in the corresponding process by using the target gas content control method described above, thereby controlling the content of the target gas in the load / unload chamber, which can reduce the amount of purge gas used, reduce the cost of using the purge gas, and thus reduce the corresponding process cost.

[0130] Although the present 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 on the foregoing description and accompanying drawings. The present application includes all such modifications and alterations and is limited only by the scope of the following claims. In particular, with respect to the various functions described above, the terminology used is intended to describe software that performs the specified function (e.g., is functionally equivalent), unless otherwise indicated, even if the software is not structurally equivalent to the disclosed structure that performs the function in the exemplary implementations shown herein.

[0131] That is, the above description is merely exemplary of the application, and is not intended to limit the scope of the application, which is defined solely by the appended claims, along with the full range of equivalents to which such claims are entitled. It being therefore intended that all such technically equivalent structures or processes not specifically described herein fall within the scope of the present application.

[0132] 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", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are merely for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, for structural elements with the same or similar properties, the present application can use the same or different reference numerals to identify them. Furthermore, the terms "first", "second", are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features limited by "first", "second" can explicitly or implicitly include one or more features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0133] In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration." Any implementation described as exemplary in this application is not necessarily to be construed as preferred or advantageous over other implementations. The application is susceptible to various modifications and alternative forms, and specific embodiments there of are shown by way of example in the drawings and are described in detail herein. It should be understood, however, that the application is not to be limited to the particular forms or methods disclosed. Rather, the application is to cover all modifications, equivalents, and alternatives falling within the scope of the application as defined by the appended claims.

Claims

1. A target gas content control method for controlling a content of a target gas in a load / unload chamber of a semiconductor process apparatus, characterized by, The method comprises: obtaining a content difference between a target content and a current content of the target gas in the load / unload chamber; mapping the pressure value of the load / unload chamber and the content difference to corresponding fuzzy domains respectively, determining a fuzzy control parameter according to each mapping result, the pressure value and the content difference being in corresponding physical domains, each of the physical domains having a corresponding fuzzy domain, the physical domain and the corresponding fuzzy domain having a quantization factor, each physical quantity of the physical domain being converted to the corresponding fuzzy domain through the corresponding quantization factor; converting the fuzzy control parameter to a physical domain to obtain an initial control parameter; controlling the flow of purge gas purged into the load / unload chamber according to the initial control parameter to control the content of the target gas in the load / unload chamber and reduce the amount of purge gas used.

2. The target gas content control method according to claim 1, characterized by, The method further comprises: mapping the pressure value to a first mapping parameter of a first fuzzy domain using a first quantization factor based on a preset fuzzy mapping formula, and mapping the content difference to a second mapping parameter of a second fuzzy domain using a second quantization factor based on the preset fuzzy mapping formula; obtaining the membership degrees of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain and the membership degrees of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain; determining the fuzzy control parameter according to the first mapping parameter, the membership degrees of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain, the second mapping parameter, and the membership degrees of the second mapping parameter with respect to each fuzzy subset of the second fuzzy domain.

3. The target gas content control method according to claim 2, characterized by, The method further comprises: identifying the first fuzzy quantity and the corresponding membership degree represented by each fuzzy subset of the first fuzzy domain where the first mapping parameter is located, and identifying the second fuzzy quantity and the corresponding membership degree represented by each fuzzy subset of the second fuzzy domain where the second mapping parameter is located; combining each of the first fuzzy quantities and the second fuzzy quantities into a plurality of groups of fuzzy quantities, performing weighted summation on each group of fuzzy quantities and taking an integer to obtain each initial fuzzy parameter; determining the minimum membership degree corresponding to each group of fuzzy quantities as the membership degree of the corresponding initial fuzzy parameter, and determining the fuzzy control parameter according to each initial fuzzy parameter and its corresponding membership degree.

4. The target gas content control method according to claim 2, wherein the obtaining of the membership degrees of the first mapping parameter with respect to each fuzzy subset of the first fuzzy domain comprises: obtaining the membership functions of the fuzzy subsets of the first fuzzy universe and the membership functions of the fuzzy subsets of the second fuzzy universe; and calculating the membership degrees of the fuzzy quantities represented by the fuzzy subsets of the first fuzzy universe and the fuzzy subsets of the second fuzzy universe by using the first mapping parameter and the membership functions. The obtaining of the membership degrees of the fuzzy subsets of the second fuzzy universe with respect to the second mapping parameter comprises: obtaining the membership functions of the fuzzy subsets of the second fuzzy universe and the membership functions of the fuzzy subsets; and calculating the membership degrees of the fuzzy quantities represented by the fuzzy subsets of the second fuzzy universe by using the second mapping parameter and the membership functions. 5.The target gas content control method according to claim 2, wherein The determining process of the first quantization factor comprises: obtaining the physical universe of the pressure value; calculating the quantization factor between the physical universe of the pressure value and the first fuzzy universe as the first quantization factor by using a preset quantization factor calculation formula; The determining process of the second quantization factor comprises: obtaining the physical universe of the content difference value; calculating the quantization factor between the physical universe of the content difference value and the second fuzzy universe as the second quantization factor by using a preset quantization factor calculation formula.

6. The target gas content control method according to claim 5, characterized by, The physical universe of the content difference value comprises at least two sub-physical universes; The calculating of the quantization factor between the physical universe of the content difference value and the second fuzzy universe as the second quantization factor by using a preset quantization factor calculation formula comprises: determining the sub-physical universe to which the content difference value belongs, and calculating the quantization factor between the sub-physical universe and the second fuzzy universe as the second quantization factor by using a preset quantization factor calculation formula.

7. The target gas content control method according to claim 6, characterized by, Each of the sub-physical universes has a corresponding second fuzzy universe; The calculating of the quantization factor between the sub-physical universe and the second fuzzy universe as the second quantization factor by using a preset quantization factor calculation formula comprises: calculating the quantization factor between the sub-physical universe and the second fuzzy universe corresponding to the sub-physical universe as the second quantization factor by using a preset quantization factor calculation formula.

8. The target gas content control method according to claim 7, characterized by, The physical universe of the pressure value comprises at least two sub-physical universes, and each of the sub-physical universes of the pressure value has a corresponding first fuzzy universe; The calculating of the quantization factor between the physical universe of the pressure value and the first fuzzy universe as the first quantization factor by using a preset quantization factor calculation formula comprises: determining the sub-physical universe to which the pressure value belongs, and calculating the quantization factor between the sub-physical universe and the first fuzzy universe corresponding to the sub-physical universe as the first quantization factor by using a preset quantization factor calculation formula.

9. The target gas content control method according to claim 1, characterized by, The converting of the fuzzy control parameter to the physical universe to obtain the initial control parameter comprises: converting the fuzzy control parameter to the physical universe by using a proportional factor to obtain the initial control parameter.

10. The target gas content control method according to claim 8, characterized by, The determining process of the proportional factor comprises: obtaining the physical universe of the purge gas flow and a corresponding third fuzzy universe; calculating the proportional factor between the third fuzzy universe and the physical universe of the purge gas flow by using a preset proportional factor calculation formula.

11. The target gas content control method according to claim 5 or 10, characterized by, The preset quantization factor calculation formula includes: kj=2m / (b-a); The preset proportion factor calculation formula includes: ku=(b-a) / 2m; In the formula, kj represents a quantization factor, ku represents a proportion 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 target gas content control method according to claim 1, characterized by, The controlling the flow rate of the purge gas purged into the load / unload chamber according to the initial control parameter includes: The initial control parameter is filtered by a discrete filter to obtain a flow control parameter, and the flow control parameter is used to control the flow rate of the purge gas purged into the load / unload chamber.

13. The target gas content control method according to claim 12, characterized by, The discrete filter includes: y(n) = a1 * y d (n) + a2 * y(n-1) + a1 * y(n-2), In the formula, y(n) represents a flow control parameter at the n th sampling moment, y(n-1) represents a flow control parameter at the (n-1) th sampling moment, y(n-2) represents a flow control parameter at the (n-2) th sampling moment, y d (n) represents an initial control parameter at the n th sampling moment, a1 represents a first filter coefficient, a2 represents a first filter coefficient, 2a1+a2=1, and the symbol * represents multiplication.

14. A semiconductor process apparatus comprising a control device, characterized in that The control device is used to obtain a content difference between a target content and a current content of a target gas in the load / unload chamber of the semiconductor process equipment, map a pressure value of the load / unload chamber and the content difference to corresponding fuzzy domains respectively, determine fuzzy control parameters according to mapping results, the pressure value and the content difference are in corresponding physical domains, each of the physical domains has a corresponding fuzzy domain, each physical quantity of the physical domain is converted to the corresponding fuzzy domain through a corresponding quantization factor, the physical domain and the corresponding fuzzy domain have the quantization factor, and the fuzzy control parameters are converted to a physical domain to obtain initial control parameters; the initial control parameters are used to control the flow rate of the purge gas purged into the load / unload chamber, so as to control the content of the target gas in the load / unload chamber and reduce the amount of the purge gas used. ​

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