Multi-gas mass flow controller and method

Abstract

Mass flow controllers and methods for controlling mass flow controllers are disclosed. One method includes providing a process gas through a flow sensor of a mass flow controller, obtaining a gas-adjusted sensitivity coefficient for the flow sensor, and obtaining gas-adjusted non-linear data for the flow sensor. The method also includes generating gas-adjusted characterization data for the flow sensor using the gas-adjusted sensitivity coefficient and the gas-adjusted non-linear data. A flow value is obtained from the gas-adjusted characterization data using a flow sensor signal from the flow sensor, and a valve of the mass flow controller is controlled using the flow value along with a setpoint signal.

Description

Technical Field

[0001] This invention relates to mass flow sensors and mass flow controllers, and particularly, but not in a limiting sense, to improving the accuracy of mass flow sensors. Background Technology

[0002] A typical mass flow controller (MFC) is a device used in other processes to set, measure, and control the flow rate of gases in industrial processes such as thermal etching and dry etching. A key component of an MFC is a thermal flow sensor used to measure the mass flow rate of the gas flowing through the device.

[0003] Unlike an ideal flow sensor signal (which has a perfectly linear correlation with the mass flow rate of the gas), the flow sensor signal output by a thermal flow sensor is non-linear with respect to the actual flow rate of the fluid: the sensitivity of a thermal flow sensor decreases at higher flow rates. In other words, the sensitivity of the flow sensor signal to flow rate is not constant; it decreases as the flow rate increases. As used herein, sensitivity refers to the ratio of the flow sensor signal to the mass flow rate of the gas being measured.

[0004] In a typical mass flow controller, the nonlinearity of the thermal flow sensor is characterized by a characteristic gas, which is then stored as characteristic data in the MFC's memory in the form of a table. This characteristic data is then used to adjust the flow sensor signal from the thermal flow sensor to provide the measured flow rate.

[0005] When controlling process gases, real-time gas data is used to adjust characteristic data, but this adjustment does not take into account differences (between the thermal flow sensors of various mass flow controllers). For example, many physical aspects (such as sensor construction and voltage regulation) can vary between thermal flow sensors, and applying adjustments to characteristic data can lead to incorrect flow measurements.

[0006] Therefore, there is a need for a method and / or device that provides new and innovative features to address the shortcomings of this approach in the multi-gas nonlinear adjustment of flow sensor signals. Summary of the Invention

[0007] One aspect can be characterized as a method for controlling a mass flow controller, the method comprising: providing process gas through a flow sensor of the mass flow controller, obtaining a gas-adjusted sensitivity coefficient for the flow sensor, and obtaining gas-adjusted nonlinear data for the flow sensor. The method further comprises: using the gas-adjusted sensitivity coefficient and the gas-adjusted nonlinear data to generate gas-adjusted characteristic data for the flow sensor; using a flow sensor signal from the flow sensor, obtaining a flow rate value from the gas-adjusted characteristic data; and using the flow rate value along with a setpoint signal to control a valve of the mass flow controller.

[0008] On the other hand, it can be characterized as a mass flow controller comprising: a main flow path for gas; a valve for controlling the flow rate of gas through the main flow path; and a flow sensor coupled to the main flow path to provide a flow sensor signal indicating the mass flow rate of the gas. A sensitivity adjustment module is configured to adjust a sensitivity coefficient using a conversion factor for process gas to generate a gas-adjusted sensitivity coefficient for the flow sensor. A nonlinearity adjustment module of the mass flow controller is configured to adjust nonlinear data of the flow sensor associated with a characteristic gas using a nonlinearity factor for the process gas to generate gas-adjusted nonlinear data. A characterization module is configured to generate gas-adjusted characteristic data for the flow sensor using the gas-adjusted sensitivity coefficient and the gas-adjusted nonlinear data, and the characterization module is configured to obtain a flow value from the gas-adjusted characteristic data using a flow sensor signal from the flow sensor. A controller of the mass flow controller is configured to control the valve of the mass flow controller using the flow value along with a setpoint signal. Attached Figure Description

[0009] Figure 1 This is a block diagram of a mass flow controller (MFC) that incorporates an improved method for multi-gas nonlinear adjustment of flow sensor signals.

[0010] Figure 2 This is a flowchart depicting an exemplary method that can be traversed in relation to the embodiments disclosed herein.

[0011] Figure 3 It is a description that can be used to achieve Figure 1 The diagram depicts the physical components of MFC.

[0012] Figure 4 It is a graph depicting the characteristic data of a flow sensor related to a specific gas.

[0013] Figure 5 It is a graph depicting the ideal linear signal used by flow sensors in relation to characteristic gases.

[0014] Figure 6 It is a graph depicting the nonlinear data generated by the flow sensor.

[0015] Figure 7 This is a graph depicting exemplary nonlinear data from a flow sensor related to a characteristic gas.

[0016] Figure 8 It is a graph depicting the ideal signal used by flow sensors related to process gases.

[0017] Figure 9 It is a description Figure 7 The graph shows the adjustment of nonlinear data in the process gas flow sensor to generate gas-adjusted nonlinear data.

[0018] Figure 10 It is a graph that depicts the gas-adjusted characteristic data of the flow sensor. Detailed Implementation

[0019] Now refer to the attached diagram, Figure 1 A mass flow controller (MFC) 100 incorporating methods to improve measurement and control accuracy across multiple gas types is shown. The arrangement of these components shown is logical and does not represent an actual hardware diagram. Therefore, components can be combined, further separated, removed, and / or added in actual implementations. As will be understood by those skilled in the art, Figure 1 The components described herein can be implemented in hardware or a combination of hardware and firmware and / or software. Furthermore, the construction of each individual component is well known to those skilled in the art as described in this specification.

[0020] Throughout this disclosure, examples and embodiments are described in terms of controlled gases; however, it should be understood that the examples and embodiments are generally applicable to fluids that may be gases or liquids, and that the fluid may include mixtures and / or compounds of elements. Liquids may be, for example, sulfuric acid, and gases may be nitrogen. Depending on the application, the MFC 100 can deliver gaseous fluids (e.g., nitrogen) and / or liquid fluids (e.g., hydrochloric acid) to tools, for example, in semiconductor facilities. In many embodiments, the MFC 100 is configured to deliver different types of fluids to different types of containers or vessels at varying temperatures and pressures.

[0021] As shown in the figure, the base 105 of the MFC 100 includes a bypass 110 through which gas flows. The bypass 110 directs a constant proportion of gas through the main path 115 and the sensor tube 120. Therefore, the flow rate of the gas through the sensor tube 120 indicates the flow rate of the gas flowing through the main path 115 of the MFC 100.

[0022] In this embodiment, sensor tube 120 is a small-diameter tube that is part of flow sensor 123 of MFC 100. And as shown, sensing elements 125 and 130 are coupled (e.g., wound) to the outside of sensor tube 120. In one illustrative embodiment, sensing elements 125 and 130 are resistance thermometer elements (e.g., wire coils), but other types of sensors (e.g., resistance temperature detectors (RTDs) and thermocouples) may also be used. Furthermore, other embodiments may, of course, utilize different numbers of sensors and different architectures for processing signals from the sensors without departing from the scope of the invention.

[0023] As shown, sensing elements 125 and 130 are electrically connected to sensing element circuit 135. Typically, sensing element circuit 135 is configured to provide a flow sensor signal 150 (in response to signals 146, 148 from sensing elements 125, 130), wherein the flow sensor signal 150 indicates the flow rate through sensor tube 120, and thus indicates the flow rate through main path 115 of MFC 100.

[0024] The flow sensor signal 150 is defined by a temperature distribution along the sensor tube 120, which affects the temperature difference between sensing elements 125 and 130. The flow sensor signal 150 is non-linear with respect to the flow rate through the sensor tube 120 across the entire flow rate range: the sensitivity of the flow sensor signal 150 decreases at higher flow rates (compared to lower flow rates). Brief reference. Figure 4 For example, exemplary characteristic data for the flow sensor 123 are described from the flow sensor signal 150 of a characteristic gas such as nitrogen and the mass flow rate of the fluid passing through the flow sensor 123. Figure 4 As shown, exemplary characteristic data indicates that the sensitivity of flow sensor 123 decreases at higher flow rates (compared to lower flow rates).

[0025] Figure 4The characteristic data shown can be generated during the characterization process before the mass flow controller 100 is released to the customer. The characterization process may include, for example,: flowing gas through the flow sensor 123; measuring the gas flow rate using a precision mass flow meter (not shown) with multiple flow rates within a range of 0% to 100% of the operating range of the flow sensor 123; and obtaining the value of the flow sensor signal 150 for each of the measured flow rates. The characteristic data (used by the flow sensor 123 associated with the characteristic gas) can be represented as: {(f i ,y i f | i = 1, 2, ..., n}, where f i It is the flow value, and y i It is a signal value.

[0026] As described above, in a typical mass flow controller, the nonlinearity of the flow sensor 123 can be characterized by a characteristic gas, which is then stored as characteristic data in the memory of the MFC 100 in the form of a table. This characteristic data can then be used to adjust the flow sensor signal 150 from the flow sensor 123 to provide a measurement of the flow rate. The characteristic data can be adjusted using real-time gas data of the process gas; however, in prior art methods, this adjustment does not take into account the differences between the thermal flow sensors of various mass flow controllers. For example, many physical aspects (such as sensor construction and voltage regulation) may vary between thermal flow sensors, and applying adjustments to the characteristic data can lead to incorrect flow measurements and inaccurate mass flow control.

[0027] One aspect of this disclosure is that the flow sensor 123 is characterized from two operational aspects, and each of these aspects can be adjusted based on the process gas being measured and controlled. More specifically, Figure 4 The characteristic data described is divided into two parts: 1) the ideal signal part (which can be represented as the sensitivity coefficient 162); and 2) the nonlinear part (which can be stored as nonlinear data 167 and represented as the ideal signal data and... Figure 4 (Difference values ​​between characteristic data).

[0028] refer to Figure 2A flowchart illustrating a method for providing improved measurement and control accuracy across multiple gas types is shown. As shown, a sensitivity coefficient 162 for the flow sensor 123 can be generated and stored in the MFC 100 (box 200) as a representation of the ideal signal portion of the characteristic data. Additionally, nonlinear data 167 for the flow sensor 123 can be generated and stored in the MFC 100 (box 202) as a representation of the nonlinear portion of the characteristic data. Figures 5 to 7 An exemplary method for decomposing characteristic data into ideal signal components and nonlinear components is described together. For example... Figure 1 As shown, nonlinear data 167 and sensitivity coefficient 162 are stored as characteristic gas data for flow sensor 123.

[0029] refer to Figure 5 The diagram illustrates a graph depicting an exemplary ideal signal portion of a characteristic gas, where the ideal signal portion represents a linear ideal flow sensor signal. The ideal signal portion (also referred to as the ideal signal) can be represented by a single value as a sensitivity coefficient (SC), where the sensitivity coefficient (SC) is the ratio of the ideal flow sensor signal to the mass flow rate at any point along the line representing the ideal signal. The flow sensor 123 is actually located in (the mass flow controller 100 is designed to control) as... Figure 5 The ideal signal portion is not output within the range of mass flow rates shown, but at very low flow rates, the flow sensor 123 operates linearly. Therefore, the ideal signal portion can be generated by sampling the flow sensor signal 150 at low flow rates to obtain a sensitivity coefficient, and this sensitivity coefficient can be used to calculate the ideal signal value at higher flow rates. The individual ideal signal values ​​of the ideal signal portion can be obtained from s... i =SC*f i This indicates that SC is the sensitivity coefficient, and f i It is the flow rate value, where i = 1, 2, ..., n.

[0030] like Figure 6 As shown, multiple ideal signal values ​​s can be obtained from the ideal signal portion. i The ideal signal values ​​in the data are compared with the corresponding signal values ​​y of the characteristic gas. i The difference between them is used to generate non-linear data. Non-linear data can be represented as: {(f i ,z i f | i = 1, 2, ..., n}, where f i It's the flow value, z i It is a nonlinear value associated with the flow rate of the characteristic gas, where the nonlinear value z i The various nonlinear values ​​in s are equal to s i Subtract yi . Figure 7 This is a depiction of the obtained nonlinear data. Therefore, the flow sensor 123 can be characterized from the sensitivity coefficient 162 and the nonlinear data 167 used in the flow sensor 123.

[0031] According to this method, Figure 1 The stored sensitivity coefficient 162 and stored nonlinear data 167 used by the flow sensor 123 are described. As discussed above, the stored sensitivity coefficient 162 and stored nonlinear data 167 can be generated during the characterization process and stored in the MFC 100 before the MFC 100 is shipped for use.

[0032] The flow sensor 123 of the MFC 100 is characterized by both the ideal signal component (e.g., sensitivity coefficient 162) and the nonlinear component (e.g., nonlinear data 167), which allows these two operational aspects to be individually adjusted based on the type of process gas used.

[0033] Additionally, the conversion factor CF is stored in MFC 100 to generate the stored conversion factor 164 (box 204). Each conversion factor in conversion factor 164 is a ratio of a value used for a specific parameter associated with the characteristic gas to a value used for a specific parameter associated with the process gas. For example, for a specific flow rate value, each conversion factor in the conversion factor can represent the ratio of an ideal signal value for the characteristic gas to an ideal signal value for the specific process gas. Each conversion factor in the conversion factor can represent the ratio of the heat capacity of the characteristic gas to the heat capacity of the process gas with an acceptable level of accuracy.

[0034] Additionally, a gas-specific nonlinear factor (NLF) is stored in the MFC to generate a stored nonlinear factor 168 (box 206). The nonlinear factor can be derived empirically or experimentally, for example, from real-time gas measurements.

[0035] The steps described in reference blocks 200 to 206 can be performed during the characterization process prior to the MFC 100 being sold to end users. During operation, in order to adjust the control of the process gas flow rate, the ideal signal portion of the characteristic data (e.g., the stored sensitivity coefficient 162) and the nonlinear portion of the characteristic data (e.g., nonlinear data 167) are adjusted. More specifically, the stored sensitivity coefficient 162 is adjusted by the sensitivity adjustment module 160 using one of the conversion factors 164 (for the process gas) to obtain a gas-adjusted sensitivity coefficient (GASC) (block 208). As discussed above, the sensitivity coefficient represents the ideal signal portion of the characteristic data used by the flow sensor 123, and the gas-adjusted sensitivity coefficient can be obtained by dividing the sensitivity coefficient by the conversion factor for the process gas (GASC = SC / CF). Figure 8 The representation of the ideal signal portion for characteristic gases and the representation of the ideal signal portion for process gases are shown.

[0036] In addition, the nonlinear adjustment module 166 adjusts the stored nonlinear data 167 using one of the nonlinear factors 168 used for process gas to obtain gas-adjusted nonlinear data (GANL) (box 210). Figure 9 The adjustment of nonlinear data for a characteristic gas to produce gas-adjusted characteristic data is illustrated. The gas-adjusted characteristic data can be represented as a nonlinear value for the process gas.

[0037] As shown in the figure, the characteristic module 169 can use the nonlinear values ​​of the gas-adjusted sensitivity coefficient (GASC) and the gas-adjusted nonlinear data (GANL) to generate the gas-adjusted characteristic data (GACD) for the flow sensor 123 and the process gas (box 212). Figure 10 The diagram illustrates the generation of gas-adjusted characteristic data. Gas-adjusted characteristic data can be generated from {(f i ,s i / CF+NLF*z i Let CF be the conversion factor for process gases and NLF be the nonlinear factor specific to gases.

[0038] In operation, flow sensor 123 outputs flow sensor signal 150 in response to gas flowing through mass flow controller 100, and the flow sensor signal 150 from flow sensor 123 is used to obtain the flow value from gas-adjusted characteristic data (GACD) (box 214). Figure 10For example, a flow sensor signal 150 with a value fs1 corresponds to a flow value with a value fv1. The obtained flow value, along with a setpoint signal 186, is used to control the valve 140 of the mass flow controller 100. Specifically, the flow value is represented by a measured flow signal 161, and the setpoint signal 186 represents the desired mass flow rate; therefore, the controller 170 controls the valve 140 until the flow value equals the desired mass flow rate.

[0039] Although not shown for clarity, it should be understood that the characterization module 169 can amplify and convert the flow sensor signal 150 into a digital representation of the flow sensor signal 150 using an analog-to-digital converter. The digital representation of the flow sensor signal 150 can be used to obtain the flow rate value corresponding to the flow sensor signal 150, and the characterization module 169 can output the measured flow rate signal 161 as a digital signal representing the obtained flow rate.

[0040] Valve 140 can be implemented by a piezoelectric valve or a solenoid valve, and the control signal 180 can be voltage (in the case of a piezoelectric valve) or current (in the case of a solenoid valve).

[0041] Next reference Figure 3 This demonstrates that the depiction can be used to achieve a reference. Figure 1 A block diagram 1100 of the physical components of the described MFC100 is shown. As shown, a display 1112 and non-volatile memory 1120 are coupled to a bus 1122, and the bus 1122 is also coupled to random access memory (“RAM”) 1124, a processing unit (which includes N processing components) 1126, a valve actuator assembly 1128 communicating with an electromagnetic or piezoelectric valve 1130, an interface assembly 1132, a communication assembly 1134, and a mass flow sensor 1136. Although Figure 3 The components depicted in the text represent physical components, but Figure 3 This does not refer to a hardware diagram; therefore, Figure 3 Many of the components described can be implemented through a common construction or distributed across additional physical components. Furthermore, it is certainly conceivable that the reference design could be implemented using other existing and undeveloped physical components and architectures. Figure 3 The described functional components.

[0042] Display 1112 typically operates to present content to a user, and in several implementations, display 1112 is implemented using an LCD or OLED display. For example, display 1112 can provide a graphical or digital representation of the indicated flow rate as a measured flow rate signal 161. Typically, non-volatile memory 1120 is used to store (e.g., permanent storage) data and executable code (including...). Figure 1(The code associated with the functional components depicted). In some embodiments, for example, the non-volatile memory 1120 includes bootloader code, software, operating system code, file system code, and other code that facilitates the implementation of information about... Figure 1 The code of one or more parts of the module being discussed.

[0043] In many implementations, the non-volatile memory 1120 is implemented using flash memory (e.g., NAND or ONENAND memory), but it is certainly conceivable that other memory types could be utilized. Although code from the non-volatile memory 1120 can be executed, the executable code in the non-volatile memory 1120 is typically loaded into RAM 1124 and executed by one or more of the N processing components of the processing unit 1126. As shown, the processing unit 1126 can receive analog temperature and pressure inputs utilized by the functions executed by the controller 170. The N processing components associated with RAM 1124 typically operate to execute instructions stored in the non-volatile memory 1120, thereby implementing… Figure 1 The functional components shown.

[0044] Interface component 1132 typically represents one or more components that enable a user to interact with MFC 100. Interface component 1132 may include, for example, a keypad, a touchscreen, and one or more analog or digital controls, and may be used to translate input from the user into setpoint signals 186. Communication component 1134 typically enables MFC 100 to communicate with external networks and devices, including external processing tools. For example, indicated traffic may be communicated to external devices via communication component 1134. Those skilled in the art will understand that communication component 1134 may include components that enable various wireless (e.g., WiFi) and wired (e.g., Ethernet) communications (e.g., integrated or distributed).

[0045] Figure 3 The mass flow sensor 1136 described herein depicts a method known to those skilled in the art for implementing... Figure 1 The flow sensor 123 shown is a collection of components. These components may include sensing elements, amplifiers, analog-to-digital converters, and filters.

[0046] Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not intended to be limited to the embodiments shown herein, but is to be endowed with the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling a mass flow controller, the method comprising: Process gas is supplied via the flow sensor of the mass flow controller; The sensitivity coefficient is adjusted using a conversion factor for the process gas to generate a gas-adjusted sensitivity coefficient for the flow sensor, the sensitivity coefficient representing the ideal signal of the flow sensor when the flow sensor is used together with the characteristic gas. Obtain gas-adjusted nonlinear data for the flow sensor; The gas-adjusted sensitivity coefficient and the gas-adjusted nonlinear data are used to generate the gas-adjusted characteristic data for the flow sensor. The flow rate value is obtained from the gas-adjusted characteristic data using the flow sensor signal from the flow sensor. as well as The flow rate value, along with the setpoint signal, is used to control the valve of the mass flow controller.

2. The method according to claim 1, wherein, Obtaining the gas-adjusted nonlinear data for the flow sensor includes: Retrieve nonlinear data from the flow sensor associated with the characteristic gas; Retrieve the nonlinear factor for the process gas from the memory of the mass flow controller; and The nonlinear data of the flow sensor is adjusted using a nonlinear factor for the process gas to obtain the gas-adjusted nonlinear data, wherein the nonlinear data is previously generated in relation to the characteristic gas.

3. The method according to claim 1, wherein, The gas-adjusted sensitivity coefficient is generated through the following operation: The sensitivity coefficient for the characteristic gas is obtained, and the sensitivity coefficient for the characteristic gas is divided by the conversion factor for the process gas.

4. The method according to claim 1, wherein, The gas-adjusted characteristic data is composed of {(f i ,s i / CF+NLF*z i The expression is represented by |i = 1, 2, ..., n}, where CF is the conversion factor used for the process gas, NLF is the gas-specific nonlinear factor, and s i =SC*f i , where f i It is the flow rate value, and SC is the sensitivity coefficient of the flow sensor and the characteristic gas, and z i It is equal to s i Subtract y i The nonlinear value, where y i It is the signal value of the characteristic data used for the characteristic gas, the characteristic data being composed of {(f i ,y i Let )|i=1、2、…、n} be used to represent it.

5. A mass flow controller, comprising: The main flow path for gas; A valve for controlling the flow rate of the gas through the main flow path; A flow sensor coupled to the main flow path to provide a flow sensor signal indicating the mass flow rate of the gas; A sensitivity adjustment module is configured to adjust the sensitivity coefficient using a conversion factor for the process gas to generate a gas-adjusted sensitivity coefficient for the flow sensor, the sensitivity coefficient representing the ideal signal of the flow sensor when the flow sensor is used together with a characteristic gas. A nonlinear adjustment module is configured to adjust the nonlinear data of the flow sensor associated with the characteristic gas using a nonlinear factor for the process gas, so as to generate gas-adjusted nonlinear data that was previously generated in relation to the characteristic gas. The characteristic module is configured as follows: The gas-adjusted sensitivity coefficient and the gas-adjusted nonlinear data are used to generate gas-adjusted characteristic data for the flow sensor; and The flow rate value is obtained from the gas-adjusted characteristic data using the flow sensor signal from the flow sensor. as well as A controller configured to use the flow rate value along with a setpoint signal to control the valve of the mass flow controller.

6. The mass flow controller according to claim 5, comprising a non-volatile memory, wherein the non-volatile memory includes the sensitivity coefficient, the nonlinear data, the conversion factor, and the nonlinear factor.

7. The mass flow controller of claim 5, further comprising an interface component configured to receive the conversion factor and the nonlinear factor via a network connection.

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