TW202309990A - Target gas content control method and a semiconductor process equipment - Google Patents

Abstract

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

Description

Target gas content control method and semiconductor process equipment

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

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

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

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

A target gas content control method provided by the present application is used to control the content of a target gas in a loading and unloading chamber of semiconductor processing equipment, comprising: obtaining the content difference between the target content and the current content of the target gas in the loading and unloading chamber value; the pressure value of the loading and unloading chamber and the content difference are respectively mapped to the corresponding fuzzy domain, and the fuzzy control parameters are determined according to each mapping result; the fuzzy control parameters are converted to the physical theoretical domain to obtain the initial control parameters; The flow rate of the purge gas purged into the loading and unloading chamber is controlled according to the initial control parameter, so as to control the content of the target gas in the loading and unloading chamber.

Optionally, the pressure value of the loading and unloading chamber and the content difference are respectively mapped to the corresponding fuzzy universe, and determining the fuzzy control parameters according to each mapping result includes: using the first quantization factor based on the preset fuzzy mapping formula The pressure value is mapped to the first mapping parameter of the first fuzzy domain, and the content difference is mapped to the second mapping parameter of the second fuzzy domain based on the preset fuzzy mapping formula by using the second quantization factor; obtaining the The degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe and the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe; according to the first mapping parameter, the second The fuzzy control parameter is determined by the membership degree of a mapping parameter relative to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the membership degree of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe .

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

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

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

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

Optionally, each of the sub-physical domains has a corresponding second fuzzy domain; the quantization factor between the sub-physical domain and the second fuzzy domain is calculated using the preset quantization factor calculation formula as the The second quantization factor includes: calculating the quantization factor between the sub-physical theoretical domain and the second fuzzy domain corresponding to the sub-physical theoretical domain by using the preset quantization factor calculation formula as the second quantization factor.

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

The quantization factor between the physical theoretical domain of the pressure value and the first fuzzy domain is calculated by using the preset quantization factor calculation formula as the first quantization factor, including: determining the sub-physical theoretical domain to which the pressure value belongs, using The preset quantization factor calculation formula calculates the quantization factor between the sub-physical theoretical domain and the first fuzzy domain corresponding to the sub-physical theoretical domain as the first quantization factor.

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

Optionally, the process of determining the scaling factor includes: obtaining the physical theoretical domain of the purge gas flow rate and the corresponding third fuzzy domain; calculating the third fuzzy domain and the purging Scale factor between physical domains of gas flow.

； 預設的該比例因數計算式包括：

； 式中，

表示量化因數，

表示比例因數，

表示模糊論域的上限，

表示物理論域的上限，

表示物理論域的下限。 Optionally, the preset quantization factor calculation formula includes:

; The preset formula for calculating the scaling factor includes:

; where,

Indicates the quantization factor,

represents the scale factor,

represents the upper limit of the fuzzy universe,

represents the upper limit of the theoretical domain of physics,

Indicates the lower limit of the theoretical domain of physics.

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

，式中，

表示第

個採樣時刻的流量控制參數，

表示第

個採樣時刻的流量控制參數，

表示第

個採樣時刻的流量控制參數，

表示第

個採樣時刻的初始控制參數，

表示第一濾波係數，

表示第一濾波係數，

，符號

表示相乘。 Optionally, the discrete filter includes:

, where,

Indicates the first

The flow control parameters at each sampling time,

Indicates the first

The flow control parameters at each sampling time,

Indicates the first

The flow control parameters at each sampling time,

Indicates the first

The initial control parameters at each sampling time,

represents the first filter coefficient,

represents the first filter coefficient,

,symbol

Indicates multiplication.

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

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

Further, it can also divide the physical theoretical domain of the content difference into multiple sub-physical theoretical domains to perform multi-stage control on the content difference. During each stage of control, the flow rate of the purge gas purged into the loading and unloading chamber Adjusting with the corresponding content difference can further improve the control accuracy of the target gas content, improve the control efficiency, and reduce the cost in the corresponding control process.

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

In addition, for ease of description, spatially relative terms such as "below", "below", "under", "above", "upper" and the like may be used herein to describe the relationship between one element or member and another(s) The relationship between elements or components, as illustrated in the figure. Spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and thus the spatially relative descriptors used herein should be interpreted similarly.

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

Existing control schemes usually use a fixed flow rate of purge gas (such as high-purity nitrogen) to purge the loading and unloading chamber to achieve the control of the target gas content in the loading and unloading chamber. For example, for the oxygen content in the loading and unloading chamber, the hysteresis window control mode is usually adopted: the oxygen control mode of large N2 flow and the oxygen control mode of small N2 flow. In the oxygen control mode of large N2 flow, the gas mass flow controller is usually set to 1000slm/min , open the exhaust valve, under the small N2 flow oxygen control mode, the gas quality flow controller is usually set to 500slm/min, and close the exhaust valve; during the oxygen control process corresponding to each mode, a certain amount of high-purity nitrogen is used to purge loading and unloading The chamber discharges oxygen from the loading and unloading chamber to make the oxygen content meet the process requirements while maintaining a slight positive pressure in the loading and unloading chamber; the slight positive pressure can effectively prevent outside air from entering the loading and unloading chamber and ensure the oxygen content control effect. The above-mentioned solution needs to use a certain flow rate of purge gas such as high-purity nitrogen for purging in various modes, which may easily cause excessive use of purge gas and generate higher costs.

Taking the oxygen control scheme of the loading and unloading chamber as an example to further illustrate the problems mentioned in the background technology, the existing oxygen control logic can refer to Figure 1a. In Figure 1a, the vertical axis represents the oxygen content of the loading and unloading chamber (LA), and the horizontal axis The coordinates represent time, and the curve represents the relationship between oxygen content and time: for the ① area, a large N2 flow is used to control the oxygen, and the oxygen content changes from a large oxygen content to a micro oxygen content of 10ppm; Oxygen in the chamber enters the loading and unloading chamber, and the oxygen content changes from less than 10ppm to 800ppm, switch the small N2 flow to control the oxygen; for the ③ area, the oxygen content is greater than 800ppm, switch the large N2 flow to control the oxygen until 10ppm; for the ④ area, the oxygen The content is less than or equal to 10ppm, switch to small N2 flow to control oxygen, and the oxygen content gradually tends to about 5ppm. Among them, 10ppm is the target value to meet the process requirements, 800ppm is the upper limit of the small N2 flow window, that is, the oxygen content in the loading and unloading chamber changes from 10ppm to 800ppm, and the flow rate of high-purity nitrogen gas is 500slm/min; the oxygen content in the loading and unloading chamber changes from greater than It varies from 800ppm to 10ppm, and the flow rate of high-purity nitrogen gas is 1000slm/min. The above-mentioned high-purity nitrogen flow scheme for the loading and unloading chamber adopts the control method of switching between the large N2 flow control oxygen mode and the small N2 flow control oxygen mode, and when the loading and unloading chamber is well sealed, the target value of the oxygen content is 10ppm ; The high-purity nitrogen of 500slm/min under the small N2 flow control oxygen mode will blow the oxygen content to 5ppm or lower; The coordinates represent the high-purity nitrogen flow rate (unit slm), the abscissa represents time (unit s), the dotted line represents the change of oxygen content with time, the solid line represents the output change of high-purity nitrogen flow rate with time, when the oxygen content (dashed line) reaches 10ppm , the high-purity nitrogen flow (solid line) was switched from 1000slm/min to 500slm/min and remained unchanged, and the oxygen content was finally maintained at about 3ppm. To maintain the 10ppm oxygen content required by the process, the actual flow rate of high-purity nitrogen gas required can be less than 500 slm/min. It can be seen that the content control scheme of the target gas such as the traditional loading and unloading chamber oxygen content control scheme is easy to cause purging of high-purity nitrogen, etc. Excessive use of gas, there is a problem of wasting purge gas, which makes the corresponding cost high.

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

The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of this application. In the case of no conflict, the following embodiments and technical features thereof can be combined with each other.

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

S110, acquiring a content difference between the target content and the current content of the target gas in the loading and unloading chamber.

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

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

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

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

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

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

In the above step S140, the initial control parameters can also be filtered by discrete filtering or smoothing filtering, and the results obtained by filtering can be used to control the corresponding purge gas flow rate, so as to make the change process of the control parameters more gentle and avoid the control of the target gas content. The control effect can be improved if the relevant control parameter mutation occurs in the process and other conditions affect the control effect.

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

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

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

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

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

The physical quantities of the above-mentioned pressure value and content difference are all in the corresponding physical theoretical domain, each physical theoretical domain has a corresponding fuzzy domain, and there are quantization factors (such as the first quantization factor and the second Two quantization factors), in which each physical quantity can be converted to the corresponding fuzzy domain by quantization factors. The above-mentioned first mapping parameter and second mapping parameter are preliminary mapping parameters for mapping corresponding physical quantities to corresponding fuzzy domains, and the larger the interval range of each fuzzy domain, the higher the corresponding conversion precision. Each fuzzy domain can correspond to different fuzzy intervals, for example, the fuzzy interval of the first fuzzy domain can be [-2,2], and the fuzzy interval of the second fuzzy domain can be [-3,3]; each fuzzy domain It is also possible to have the same fuzzy interval, for example, both are set to [-2,2].

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

This embodiment can combine the actual characteristics of the loading and unloading chamber to determine the inference rules, and then determine the corresponding fuzzy processing rules to perform fuzzy processing on the first mapping parameter, the second mapping parameter and the corresponding degree of membership respectively to obtain the required fuzzy processing. Control parameters. Here, the oxygen content control process in the loading and unloading chamber is used to illustrate, and the reasoning rules can include: 1. The pressure in the loading and unloading chamber is small, the oxygen content is large, and the flow rate of high-purity nitrogen gas is large; 2. The pressure in the loading and unloading chamber Slightly smaller, slightly larger oxygen content, higher high-purity nitrogen flow; 3. Moderate pressure in the loading and unloading chamber, moderate oxygen content, moderate high-purity nitrogen flow; 4. Slightly higher pressure in the loading and unloading chamber, slightly lower oxygen content , the high-purity nitrogen flow rate is slightly smaller; 5. The loading and unloading chamber has a high pressure, and the oxygen content is small, so the high-purity nitrogen flow rate is very small. The corresponding fuzzy processing rules include: determining each first fuzzy amount corresponding to the first mapping parameter and each second fuzzy amount corresponding to the second mapping parameter, and determining multiple sets of fuzzy amounts including a first fuzzy amount and a second fuzzy amount , use the reasoning formula to calculate the initial fuzzy parameters for each group of fuzzy quantities, determine the membership degree of each initial fuzzy parameter, and perform clear processing on each initial fuzzy parameter according to each membership degree to determine the fuzzy control parameters.

In one example, according to the first mapping parameter, the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe, the second mapping parameter and the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe The membership degree determination fuzzy control parameters include:

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

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

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

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

，

,

，

,

表示模糊子集ZO的隸屬函數，

表示模糊子集

的隸屬函數，

表示映射參數（如第一映射參數）。

時，求得

，

，即第一映射參數0.6對應模糊子集ZO和模糊子集PS，模糊子集ZO表徵的模糊量為0，對應的隸屬度為0.4，模糊子集PS的模糊量為1,對應的隸屬度為0.6。 In the formula,

Represents the membership function of the fuzzy subset ZO,

Represents a fuzzy subset

The membership function of

Indicates a mapping parameter (such as the first mapping parameter).

when, get

,

, that is, the first mapping parameter 0.6 corresponds to the fuzzy subset ZO and the fuzzy subset PS, the fuzzy quantity represented by the fuzzy subset ZO is 0, and the corresponding membership degree is 0.4, the fuzzy quantity of the fuzzy subset PS is 1, and the corresponding membership degree is 0.6.

，式中，

表示初始模糊參數，

表示第一模糊量，

表示第二模糊量，

表示第一權重（或修正因數），可以設置0.4或者0.5等值，

表示取整運運算元，表示將其中數值的絕對值四捨五入取整，正負號與

中的正負號相同，例如＜-1.3＞=-1，＜1.7＞=2。上述第一映射參數對應多個第一模糊量，第二映射參數對應多個第二模糊量，對各個第一模糊量和各個第二模糊量進行組合，可以得到多組不重複的模糊量，對各組模糊量進行加權求和後取整，可以得到初始模糊參數以及各個初始模糊參數對應的隸屬度；例如，某組模糊量中，第一模糊量

為0，其隸屬度為0.4，第二模糊量

為1，其隸屬度為0.8，第一權重

為0.5，對應的初始模糊參數為

，其隸屬度為0.4。 The process of rounding after the above weighted summation includes:

, where,

represents the initial fuzzy parameters,

represents the first blurring amount,

represents the second blur amount,

Represents the first weight (or correction factor), which can be set to 0.4 or 0.5, etc.

Indicates the rounding operator, which means that the absolute value of the value in it is rounded to an integer, and the positive and negative signs are the same as

The positive and negative signs in are the same, for example <-1.3>=-1, <1.7>=2. The above-mentioned first mapping parameter corresponds to a plurality of first blur quantities, and the second mapping parameter corresponds to a plurality of second blur quantities. By combining each first blur quantity and each second blur quantity, multiple groups of non-repeating blur quantities can be obtained, The weighted sum of each group of fuzzy quantities is rounded to get the initial fuzzy parameters and the degree of membership corresponding to each initial fuzzy parameter; for example, in a certain group of fuzzy quantities, the first fuzzy quantity

is 0, its degree of membership is 0.4, and the second fuzzy amount

is 1, its membership degree is 0.8, and the first weight

is 0.5, and the corresponding initial fuzzy parameter is

, whose degree of membership is 0.4.

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

，

,

，

，這兩個模糊變數值平均值所確定的第一模糊控制參數為0。進一步地，可以採用對應的比例因數將模糊控制參數轉換至物理論域，得到初始控制參數；比如，某比例因數為

，吹掃氣體流量的物理論域的上限

為1000，下限

為600，模糊控制參數

為0，則對應的初始控制參數可以為： Solve to get two fuzzy variable values,

,

, the first fuzzy control parameter determined by the average value of these two fuzzy variables is 0. Further, the fuzzy control parameters can be converted to the physical theoretical domain by using the corresponding proportional factor to obtain the initial control parameters; for example, a certain proportional factor is

, the upper limit of the physical domain of purge gas flow

is 1000, the lower limit

is 600, fuzzy control parameter

is 0, the corresponding initial control parameters can be:

。

.

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

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

，式中，

表示量化因數，

表示物理論域的上限，

表示物理論域的下限，

表示物理量，

表示映射參數。具體地，對於某映射參數，確定其所處的模糊子集和對應的隸屬度函數之後，可以將該映射參數代入各個隸屬度函數，求得其取各個模糊量的隸屬度。以壓力值P=2800mtorr為例，對其對應的模糊量和隸屬度的求解過程進行說明，若對應的物理論域（壓力範圍）為[1500,3500]，第一映射參數為

，對應的模糊量為0（模糊子集ZO）和1（模糊子集PS）；根據ZO、PS模糊子集的隸屬函數： Optionally, the above-mentioned first mapping parameter and the second mapping parameter may be calculated for corresponding physical quantities by using a fuzzy mapping formula. The above fuzzy mapping formula can be set according to the inference rules corresponding to the loading and unloading chamber, usually using the corresponding quantization factor for mapping, for example, it can be set as

, where,

Indicates the quantization factor,

represents the upper limit of the theoretical domain of physics,

represents the lower limit of the theoretical domain of physics,

represents a physical quantity,

Represents a mapping parameter. Specifically, for a certain mapping parameter, after determining its fuzzy subset and corresponding membership degree function, the mapping parameter can be substituted into each membership degree function to obtain the membership degree of each fuzzy quantity. Taking the pressure value P=2800mtorr as an example, the solution process of the corresponding fuzzy quantity and membership degree is explained. If the corresponding physical theoretical domain (pressure range) is [1500,3500], the first mapping parameter is

, the corresponding fuzzy quantities are 0 (fuzzy subset ZO) and 1 (fuzzy subset PS); according to the membership functions of ZO and PS fuzzy subsets:

，

,

，

,

，得到

，

，即壓力值P=2800mtorr的第一映射參數為0.6，對應一個模糊量0，隸屬度為0.4，還對應另一個模糊量1，隸屬度為0.6。 here

,get

,

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

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

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

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

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

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

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

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

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

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

Specifically, the process of determining the scaling factor includes:

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

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

； Optionally, the preset quantization factor calculation formula includes:

;

； The preset scale factor calculation formula includes:

;

表示量化因數，

表示比例因數，

表示模糊論域的上限，

表示物理論域的上限，

表示物理論域的下限。 In the formula,

Indicates the quantization factor,

represents the scale factor,

represents the upper limit of the fuzzy universe,

represents the upper limit of the theoretical domain of physics,

Indicates the lower limit of the theoretical domain of physics.

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

；同理可以快速準確計算其他量化因數。又比如若第三模糊論域的上限為2，吹掃氣體流量的物理論域的上限為1000，下限為600，此時對應的比例因數為：

。 In this example, each physical theoretical domain and each fuzzy domain can be set according to the value range of each physical theoretical domain and the relevant process characteristics, and then the quantitative factor calculation formula is used to calculate the quantization factor between each physical theoretical domain and the corresponding fuzzy domain. The proportion factor between each fuzzy domain of theory and the corresponding domain of theory is calculated respectively by using the calculation formula of scale factor. For example, if the upper limit of the first fuzzy universe is 2, the upper limit of the pressure range is 3500, and the lower limit is 1500, then the corresponding first quantization factor is:

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

.

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

Specifically, discrete filters include:

，

,

表示第

個採樣時刻的流量控制參數，

表示第

個採樣時刻的流量控制參數，

表示第

個採樣時刻的流量控制參數，

表示第

個採樣時刻的初始控制參數，

表示第一濾波係數，

表示第一濾波係數，

，符號

表示相乘。 In the formula,

Indicates the first

The flow control parameters at each sampling time,

Indicates the first

The flow control parameters at each sampling time,

Indicates the first

The flow control parameters at each sampling time,

Indicates the first

The initial control parameters at each sampling time,

represents the first filter coefficient,

represents the first filter coefficient,

,symbol

Indicates multiplication.

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

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

The above target gas content control method, by obtaining the content difference between the target content and the current content of the target gas in the loading and unloading chamber, maps the pressure value and content difference of the loading and unloading chamber to the corresponding fuzzy domain respectively, according to The fuzzy control parameters are determined by each mapping result, and the fuzzy control parameters are converted to the physical theoretical domain to obtain the initial control parameters. According to the initial control parameters, the flow rate of the purge gas purged into the loading and unloading chamber is controlled to control the target gas in the loading and unloading chamber. content, the fuzzy control of the target gas in the loading and unloading chamber based on the current pressure value and content difference is realized, and on the basis of ensuring the quality of the corresponding process, the amount of purge gas used can be reduced and excessive use of purge gas can be avoided. In addition, the physical theoretical domain of the content difference can be divided into multiple sub-physical theoretical domains to perform multi-stage control on the content difference. During the control process of each stage, the flow rate of the purge gas purged into the loading and unloading chamber Adjusting with the corresponding content difference can further improve the control accuracy of the target gas content, improve the control efficiency, and reduce the cost in the corresponding control process.

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

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

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

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

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

620: Processor 630: storage medium S110-S140: Steps

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

S110-S140: Steps

Claims (14)

A method for controlling the content of a target gas, used to control the content of a target gas in a loading and unloading chamber of a semiconductor process equipment, the method comprising: Obtain 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 universe respectively, and determining a fuzzy control parameter according to each mapping result; Converting the fuzzy control parameters to the physical domain to obtain an initial control parameter; According to the initial control parameter, the flow rate of a purge gas purged into the loading and unloading chamber is controlled, so as to control the content of the target gas in the loading and unloading chamber. The target gas content control method as described in Claim 1, wherein the pressure value of the loading and unloading chamber and the content difference are respectively mapped to the corresponding fuzzy domain, and determining the fuzzy control parameters according to each mapping result includes: Using a first quantization factor based on the preset fuzzy mapping formula to map the pressure value into a first mapping parameter of a first fuzzy domain, using a second quantization factor based on the preset fuzzy mapping formula to the content difference values are mapped to a second mapping parameter of a second fuzzy universe; obtaining the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe and the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe; According to the first mapping parameter, the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy domain, the second mapping parameter and the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy domain The membership degree of the set determines the fuzzy control parameters. The target gas content control method according to claim 2, wherein, according to the first mapping parameter, the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy universe, the second mapping parameter and Determining the fuzzy control parameters with respect to the degree of membership of each fuzzy subset of the second fuzzy universe with respect to the second mapping parameter includes: identifying a first fuzzy quantity and a corresponding degree of membership represented by each fuzzy subset of the first fuzzy domain where the first mapping parameter is located, and identifying each fuzzy subset representation of the second fuzzy domain where the second mapping parameter is located A second fuzzy quantity and corresponding degree of membership; Combining each of the first blur quantities and each of the second blur quantities into multiple groups of blur quantities, performing weighted summation on each group of blur quantities and rounding to obtain each initial blur parameter; The minimum membership degree corresponding to each group of fuzzy quantities is determined as the membership degree corresponding to the initial fuzzy parameter, and the fuzzy control parameter is determined according to each initial fuzzy parameter and its corresponding membership degree. The target gas content control method as described in Claim 2, wherein the acquisition of the degree of membership of the first mapping parameter relative to each fuzzy subset of the first fuzzy domain includes: Obtaining the fuzzy subsets of the first fuzzy domain where the first mapping parameters are located and the membership functions of each of the fuzzy subsets; using the first mapping parameters and each of the membership functions to calculate the representation of each of the fuzzy subsets The degree of membership of the fuzzy quantity; The acquisition of the degree of membership of the second mapping parameter relative to each fuzzy subset of the second fuzzy universe includes: Obtaining the fuzzy subsets of the second fuzzy universe where the second mapping parameters are located and the membership functions of each of the fuzzy subsets; using the second mapping parameters and each of the membership functions to calculate the representation of each of the fuzzy subsets The degree of membership of the fuzzy quantity. The target gas content control method as described in Claim 2, wherein the process of determining the first quantization factor includes: Get the physical domain of the pressure value; Using a preset quantization factor calculation formula to calculate the quantization factor between the physical domain of the pressure value and the first fuzzy domain as the first quantization factor; The determination process of the second quantization factor includes: Obtain the physical domain of the content difference; The quantization factor between the physical domain of the content difference and the second fuzzy domain is calculated by using the preset calculation formula of the quantization factor as the second quantization factor. The target gas content control method as claimed in claim 5, wherein the physical domain of the content difference includes at least two sub-physical domains; The quantitative factor between the physical theoretical domain of the content difference and the second fuzzy domain using the preset quantitative factor calculation formula is used as the second quantitative factor, including: Determine the sub-object theoretical domain to which the content difference belongs, and use the preset quantization factor calculation formula to calculate the quantization factor between the sub-object theoretical domain and the second fuzzy domain as the second quantization factor. The target gas content control method as described in Claim 6, wherein each sub-physics domain has a corresponding second fuzzy domain; The use of the preset quantization factor calculation formula to calculate the quantization factor between the sub-object theoretical domain and the second fuzzy domain as the second quantization factor includes: The quantization factor between the sub-physical theoretical domain and the second fuzzy domain corresponding to the sub-physical theoretical domain is calculated by using the preset quantization factor calculation formula as the second quantization factor. The target gas content control method according to claim 7, wherein the physical domain of the pressure value includes at least two sub-physical domains, and each sub-physical domain of the pressure value has a corresponding first fuzzy domain; The quantization factor between the physical theoretical domain of the pressure value and the first fuzzy domain is calculated using the preset quantization factor calculation formula as the first quantization factor, including: Determine the sub-physical theoretical domain to which the pressure value belongs, and use the preset quantization factor calculation formula to calculate the quantization factor between the sub-physical theoretical domain and the first fuzzy domain corresponding to the sub-physical theoretical domain as the first quantization factor . The target gas content control method as described in Claim 1, wherein the fuzzy control parameters are converted to the physical domain, and the initial control parameters obtained include: The fuzzy control parameters are transformed into physical theory domain by adopting a proportional factor to obtain the initial control parameters. The target gas content control method as described in Claim 8, wherein the process of determining the scaling factor includes: Obtain the physical theoretical domain of the purge gas flow rate and the corresponding third fuzzy domain; A proportional factor between the third fuzzy theoretical domain and the physical theoretical domain of the purge gas flow is calculated by using a preset proportional factor calculation formula. The target gas content control method as described in Claim 5 or 10, wherein the preset quantization factor calculation formula includes:

; The preset formula for calculating the scaling factor includes:

; where,

Indicates the quantization factor,

represents the scale factor,

represents the upper limit of the fuzzy universe,

represents the upper limit of the theoretical domain of physics,

Indicates the lower limit of the theoretical domain of physics. The target gas content control method according to claim 1, wherein the controlling the flow rate of the purge gas purged into the loading and unloading chamber according to the initial control parameters includes: A discrete filter is used to filter the initial control parameter to obtain a flow control parameter, and the flow control parameter is used to control the flow rate of the purge gas purged into the loading and unloading chamber. The target gas content control method as claimed in claim 12, wherein the discrete filter includes:

, where,

Indicates the first

The flow control parameters at each sampling time,

Indicates the first

The flow control parameters at each sampling time,

Indicates the first

The flow control parameters at each sampling time,

Indicates the first

The initial control parameters at each sampling time,

represents the first filter coefficient,

represents the first filter coefficient,

,symbol

Indicates multiplication. A semiconductor process equipment, comprising a control device, wherein the control device is used to obtain the content difference between a target content and the current content of a target gas in a loading and unloading chamber of the semiconductor processing equipment; the loading and unloading chamber A pressure value and the content difference of the chamber are respectively mapped to the corresponding fuzzy domain, and the fuzzy control parameters are determined according to each mapping result; the fuzzy control parameters are converted into the physical theoretical domain to obtain an initial control parameter; according to the initial control parameter The flow of a 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.

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