Patent Publication Number: US-2021190575-A1

Title: Multi-gas mass flow controller and method

Description:
BACKGROUND 
     Field 
     The present invention relates to mass flow sensors and mass flow controllers, and in particular, but not by way of limitation, the present invention relates to improving an accuracy of mass flow sensors. 
     Background 
     A typical mass flow controller (MFC) is a device that sets, measures, and controls the flow of a gas in industrial processes such as thermal and dry etching among other processes. An important part of an MFC is a thermal flow sensor that measures the mass flow rate of the gas flowing through the device. 
     As opposed to an idealized flow sensor signal (that has a perfect linear dependence upon a mass flow rate of the gas) a flow sensor signal that is output by a thermal flow sensor is non-linear relative to an actual flow rate of the fluid: a sensitivity of the thermal flow sensor drops at higher flow rates. In other words, sensitivity of the flow sensor signal to the flow is not constant—it decreases with increasing flow. As used herein, sensitivity refers to the ratio of the flow sensor signal to the mass flow rate of the gas being measured. 
     In a typical mass flow controller, the nonlinearity of the thermal flow sensor is characterized with a characterization gas, and then stored as characterization data in a memory of the MFC in the form of a table. Then, a flow signal from the thermal flow sensor is adjusted using the characterization data to provide a measured flow rate. 
     When a process gas is controlled, the characterization data is adjusted with live gas data for the process gas, but the adjustment does not account for differences (between the thermal flow sensors of each mass flow controller). For example, many physical aspects such as sensor construction and voltage adjustment may vary between thermal flow sensors, and applying the adjustment to the characterization data results in incorrect flow measurements. 
     Accordingly, a need exists for a method and/or apparatus to provide new and innovative features that address the shortfalls of present methodologies in multi-gas nonlinearity adjustment to a flow signal. 
     SUMMARY 
     An aspect may be characterized as a method for controlling a mass flow controller that includes providing a 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 nonlinearity data for the flow sensor. The method also includes producing gas-adjusted characterization data for the flow sensor using the gas-adjusted sensitivity coefficient and the gas-adjusted nonlinearity data. A flow value from the gas-adjusted characterization data is obtained using a flow sensor signal from the flow sensor, and the flow value is used along with a setpoint signal to control a valve of the mass flow controller. 
     Another aspect may be characterized as a mass flow controller that includes a main flow path for a gas, a control valve to control a flow rate of the gas through the main flow path, and a flow sensor coupled to the main flow path to provide a flow sensor signal indicative of a mass flow rate of the gas. A sensitivity adjustment module is configured to adjust a sensitivity coefficient with a conversion factor for a process gas to produce a gas-adjusted sensitivity coefficient for the flow sensor. A nonlinearity adjustment module of the mass flow controller is configured to adjust nonlinearity data associated with a characterization gas for the flow sensor with a nonlinearity factor for the process gas to produce gas-adjusted nonlinearity data. A characterization module is configured to produce gas-adjusted characterization data for the flow sensor using the gas-adjusted sensitivity coefficient and the gas-adjusted nonlinearity data, and the characterization module is configured to obtain a flow value from the gas-adjusted characterization data using a flow sensor signal from the flow sensor. A controller of the mass flow controller is configured to use the flow value along with a setpoint signal to control a valve of the mass flow controller. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a mass flow controller (MFC) that incorporates improved methodologies for multi-gas nonlinearity adjustment to a flow sensor signal; 
         FIG. 2  is a is a flowchart depicting an exemplary method that may be traversed in connection with embodiments disclosed herein; 
         FIG. 3  is a block diagram depicting physical components of an MFC that may be used to realize aspects of the MFC depicted in  FIG. 1 ; 
         FIG. 4  is a graph depicting characterization data for a flow sensor in connection with a characterization gas; 
         FIG. 5  is a graph depicting an ideal linear signal for the flow sensor in connection with a characterization gas; 
         FIG. 6  is a graph depicting creation of nonlinearity data for the flow sensor; 
         FIG. 7  is a graph depicting exemplary nonlinearity data for the flow sensor in connection with a characterization gas; 
         FIG. 8  is a graph depicting an ideal signal for the flow sensor in connection with a process gas; 
         FIG. 9  is a graph depicting an adjustment of the nonlinearity data in  FIG. 7  to produce gas-adjusted nonlinearity data for the flow sensor in connection with a process gas; 
         FIG. 10  is a graph depicting creation of gas-adjusted characterization data for the flow sensor. 
     
    
    
     DETAILED DESCRIPTION 
     Referring now to the drawings,  FIG. 1  illustrates a mass flow controller (MFC)  100  that incorporates methodologies to improve measurement and control accuracy across multiple gas types. The illustrated arrangement of these components is logical and not meant to be an actual hardware diagram. Thus, the components can be combined, further separated, deleted and/or supplemented in an actual implementation. As one of ordinary skill in the art will appreciate, the components depicted in  FIG. 1  may be implemented in hardware, or hardware in combination with firmware and/or software. Moreover, in light of this specification, the construction of each individual component will be well known within the skill of those of ordinary skill in the art. 
     Throughout this disclosure, examples and embodiments are described in terms of gases being controlled, but it should be recognized that the examples and embodiments are generally applicable to fluids that may be gases or liquids, and the fluids may include a mixture of elements and/or compounds. A liquid for example may be sulfuric acid and a gas may be nitrogen. Depending upon the application, the MFC  100  may deliver a fluid in a gaseous state (e.g., nitrogen) and/or a liquid state (e.g., hydrochloric acid) to, for example, a tool in a semiconductor facility. The MFC  100  in many embodiments is configured to deliver different types of fluids under varying temperatures and pressures to different types of containers or vessels. 
     As depicted, a base  105  of the MFC  100  includes bypass  110  through which a gas flows. Bypass  110  directs a constant proportion of gas through a main path  115  and sensor tube  120 . As a consequence, the flow rate of the gas through the sensor tube  120  is indicative of the flow rate of the gas flowing through the main path  115  of the MFC  100 . 
     In this embodiment, the sensor tube  120  is a small-bore tube that is part of a flow sensor  123  of the MFC  100 . And as shown, sensing elements  125  and  130  are coupled to (e.g., wound around) the outside of sensor tube  120 . In one illustrative embodiment, sensing elements  125  and  130  are resistance-thermometer elements (e.g., coils of conductive wire), but other types of sensors (e.g., resistance temperature detectors (RTD) and thermocouples) may also be utilized. Moreover, other embodiments may certainly utilize different numbers of sensors and different architectures for processing the signals from the sensors without departing from the scope of the present invention. 
     As depicted, sensing elements  125  and  130  are electrically connected to a sensing-element circuit  135 . In general, the sensing-element circuit  135  is configured (responsive to signals  146 ,  148  from the sensing elements  125 ,  130 ) to provide a flow sensor signal  150 , which is indicative of the flow rate through the sensor tube  120 , and hence, indicative of the flow rate through the main path  115  of the MFC  100 . 
     The flow sensor signal  150  is defined by a temperature profile along the sensor tube  120  that affects a temperature difference between the sensing elements  125 ,  130 . The flow sensor signal  150  is non-linear relative to the flow rate through the sensor tube  120  across a range of flow rates: the sensitivity of the flow sensor signal  150  decreases at higher flow rates (as compared to lower flow rates). Referring briefly to  FIG. 4  for example, depicted is exemplary characterization data for the flow sensor  123  in terms of the flow sensor signal  150  and a mass flow rate of a fluid through the flow sensor  123  for a characterization gas such as nitrogen. As shown in  FIG. 4 , the exemplary characterization data indicates that the sensitivity of the flow sensor  123  decreases at higher flow rates (as compared to lower flow rates). 
     The characterization data depicted in  FIG. 4  may be produced during a characterization process before the mass flow controller  100  is released to customers. The characterization process may include, for example, causing a gas to flow through the flow sensor  123 ; measuring the flow rate of the gas with a precision mass flow meter (not shown) for multiple flow rates across a range of flow rate values from 0% to 100% of the operating range of the flow sensor  123 ; and obtaining values of the flow sensor signal  150  for each of the measured flow rates. The characterization data (for the flow sensor  123  in connection with a characterization gas) may be represented by: {(f i , y i )|i=1, 2, . . . , n} where f i  are flow values and y i  are signal values. 
     As discussed above, in a typical mass flow controller, the nonlinearity of the flow sensor  123  may be characterized with a characterization gas, and then stored as the characterization data in a memory of the MFC  100  in the form of a table. Then, the flow sensor signal  150  from the flow sensor  123  may be adjusted using the characterization data to provide a measurement of the flow rate. The characterization data may be adjusted with live gas data for the process gas, but in prior art approaches, the adjustment does not account for differences between the thermal flow sensors of each different mass flow controller. For example, many physical aspects such as sensor construction and voltage adjustment may vary between thermal flow sensors, and applying the adjustment to the characterization data results in incorrect flow measurements and inaccurate mass flow control. 
     An aspect of the present disclosure is that the flow sensor  123  is characterized in terms of two operational aspects of the flow sensor  123 , and each of these two aspects may be adjusted based upon the process gas that is measured and controlled. More specifically, the characterization data depicted in  FIG. 4  is separated into two portions: 1) an ideal signal portion (which may be represented as a sensitivity coefficient  162 ); and 2) a non-linear portion (which may be stored as nonlinearity data  167  and represented as difference values between ideal signal data and the characterization data of  FIG. 4 ). 
     Referring to  FIG. 2 , shown is a flowchart depicting a method for providing improved measurement and control accuracy across multiple gas types. As shown, the sensitivity coefficient  162  for the flow sensor  123  may be produced and stored in the MFC  100  as a representation of the ideal signal portion of the characterization data (Block  200 ). In addition, the nonlinearity data  167  for the flow sensor  123  may be produced and stored in the MFC  100  as a representation of the nonlinear portion of the characterization data (Block  202 ).  FIGS. 5-7  collectively depict an exemplary approach to breaking the characterization data into the ideal signal portion and the nonlinear portion. As shown in  FIG. 1 , the nonlinearity data  167  and sensitivity coefficient  162  are stored as characterization-gas data for the flow sensor  123 . 
     Referring to  FIG. 5  shown is a graph depicting an exemplary ideal signal portion for a characterization gas that represents an ideal flow sensor signal that is linear. The ideal signal portion (also referred to as an ideal signal) may be represented by a single value as a sensitivity coefficient (SC) that is a ratio of the ideal flow sensor signal and the mass flow rate at any point along the line that represents the ideal signal. The flow sensor  123  does not actually output the ideal signal portion in a range of mass flow values (that the mass flow controller  100  is designed to control) as shown in  FIG. 5 , but at very low flow values, the flow sensor  123  does operate in a linear manner. As a consequence, the ideal signal portion may be created by sampling the flow sensor signal  150  at low flow values to obtain the sensitivity coefficient, and the sensitivity coefficient may be used to calculate ideal signal values at higher flow rates. Each ideal signal value of the ideal signal portion may be represented by s i =SC*f i  where SC is the sensitivity coefficient and f i  are the flow values where i=1, 2, . . . , n. 
     As shown in  FIG. 6 , nonlinearity data may be created by obtaining a difference between each of multiple ideal signal values, s i , of the ideal signal portion and corresponding signal values of the characterization data, y i , for the characterization gas. The nonlinearity data may be represented as: {(f i , z i )|i=1, 2, . . . , n} where f i  are flow values, z i  are nonlinearity values associated with flow values for the characterization gas, where each of the nonlinearity values, z i , is equal to s i  minus y i .  FIG. 7  is a depiction the resultant non-linearity data. Thus, the flow sensor  123  may be characterized in terms of the sensitivity coefficient  162  and nonlinearity data  167  for the flow sensor  123 . 
     Consistent with this approach,  FIG. 1  depicts a stored sensitivity coefficient  162  and stored nonlinearity data  167  for the flow sensor  123 . As discussed above, the stored sensitivity coefficient  162  and the stored nonlinearity data  167  may be created during the characterization process and stored in the MFC  100  before the MFC  100  is released for use. 
     Characterizing the flow sensor  123  of the MFC  100  in terms of an ideal signal portion (e.g., the sensitivity coefficient  162 ) and a non-linear portion (e.g., the nonlinearity data  167 ) enable these two operational aspects to be separately adjusted based upon the type of process gas that is used. 
     In addition, conversion factors, CFs, are stored in the MFC  100  to produce stored conversion factors  164  (Block  204 ). Each of the conversion factors  164  is a ratio of a value for some particular parameter associated with the characterization gas to a value for the particular parameter associated with a process gas. For example, each of the conversion factors may represent, for a particular flow value, a ratio of an ideal signal value for a characterization gas to an ideal signal value for a particular process gas. With an accepted degree of accuracy, each of the conversion factors may represent a ratio of a heat capacity of the characterization gas to a heat capacity for the processing gas. 
     In addition, gas-specific nonlinearity factors (NLFs) are stored in the MFC to produce stored nonlinearity factors  168  (Block  206 ). The nonlinearity factors may be derived empirically or experimentally (e.g., from live gas measurements). 
     The steps described with reference to Blocks  200  to  206  may be performed during a characterization process that is carried out before the MFC  100  is released for use to the end user. During operation, to adjust for the control of a flow rate of a process gas, the ideal signal portion of the characterization data (e.g., the stored sensitivity coefficient  162 ) and the nonlinear portion (e.g., the nonlinearity data  167 ) of the characterization data are adjusted. More specifically, the stored sensitivity coefficient  162  is adjusted by the sensitivity adjustment module  160  with one of the conversion factors (for the processing gas)  164  to obtain a gas-adjusted sensitivity coefficient (GASC) (Block  208 ). As discussed above, the sensitivity coefficient represents an ideal signal portion of the characterization data for the flow sensor  123 , and the gas-adjusted sensitivity coefficient may be obtained by dividing the sensitivity coefficient by the conversion factor for the process gas (GASC=SC/CF).  FIG. 8  shows a representation of the ideal signal portion for the characterization gas and a representation of the ideal signal portion for a process gas. 
     In addition, a nonlinearity adjustment module  166  adjusts the stored nonlinearity data  167  with one of the nonlinearity factors  168  for the process gas to produce gas-adjusted nonlinearity data (GANL) (Block  210 ).  FIG. 9  shows the adjustment of nonlinearity data for the characterization gas to produce the gas-adjusted characterization data. The gas-adjusted characterization data may be represented as nonlinearity values for the process gas. 
     As shown, gas-adjusted characterization data (GACD) for the flow sensor  123  and the process gas may be produced by the characterization module  169  using the gas-adjusted sensitivity coefficient (GASC) and the nonlinearity values of the gas-adjusted nonlinearity data (GANL) (Block  212 ).  FIG. 10  graphically depicts the production of the gas-adjusted characterization data. The gas-adjusted characterization data may be represented by: {(f i , s i/ CF+NLF*z i )|i=1, 2, . . . , n} where CF is the conversion factor for the process gas and NLF is the gas-specific nonlinearity factor. 
     In operation, the flow sensor  123  outputs the flow sensor signal  150  in response to a gas flowing through the mass flow controller  100 , and a flow value from the gas-adjusted characterization data (GACD) is obtained using the flow sensor signal  150  from the flow sensor  123  (Block  214 ). In  FIG. 10 , for example, a flow sensor signal  150  with a value of fs1 corresponds to a flow value with a value of fv1. The obtained flow value along with a setpoint signal  186  is used to control the valve  140  of the mass flow controller  100 . In particular, the flow value is represented by the measured flow signal  161 , and the setpoint signal  186  represents a desired mass flow rate, so the controller  170  controls the valve  140  until the flow value is equal to the desired mass flow rate. 
     Although not shown for clarity, it should be recognized that the characterization module  169  may amplify and convert, using an analog to digital converter, the flow sensor signal  150  to a digital representation of the flow sensor signal  150 . The digital representation of the flow sensor signal  150  may be used to obtain the flow value corresponding to the flow sensor signal  150 , and the characterization module  169  may output the measured flow signal  161  as a digital signal that represents the obtained flow rate. 
     The valve  140  may be realized by a piezoelectric valve or solenoid valve, and the control signal  180  may be a voltage (in the case of a piezoelectric valve) or current (in the case of a solenoid valve). 
     Referring next to  FIG. 3 , shown is a block diagram  1100  depicting physical components that may be utilized to realize the MFC  100  described with reference to  FIG. 1 . As shown, a display  1112  and nonvolatile memory  1120  are coupled to a bus  1122 , and the bus  1122  is also coupled to random access memory (“RAM”)  1124 , a processing portion (which includes N processing components)  1126 , a valve driver component  1128  that is in communication with a solenoid or piezo type valve  1130 , an interface component  1132 , a communication component  1134 , and a mass flow sensor  1136 . Although the components depicted in  FIG. 3  represent physical components,  FIG. 3  is not intended to be a hardware diagram; thus, many of the components depicted in  FIG. 3  may be realized by common constructs or distributed among additional physical components. Moreover, it is certainly contemplated that other existing and yet-to-be developed physical components and architectures may be utilized to implement the functional components described with reference to  FIG. 3 . 
     The display  1112  generally operates to provide a presentation of content to a user, and in several implementations, the display is realized by an LCD or OLED display. For example, the display  1112  may provide an indicated flow as a graphical or numeric representation of the measured flow signal  161 . In general, the nonvolatile memory  1120  functions to store (e.g., persistently store) data and executable code including code that is associated with the functional components depicted in  FIG. 1 . In some embodiments for example, the nonvolatile memory  1120  includes bootloader code, software, operating system code, file system code, and code to facilitate the implementation of one or more portions of the modules discussed in connection with  FIG. 1 . 
     In many implementations, the nonvolatile memory  1120  is realized by flash memory (e.g., NAND or ONENAND memory), but it is certainly contemplated that other memory types may be utilized. Although it may be possible to execute the code from the nonvolatile memory  1120 , the executable code in the nonvolatile memory  1120  is typically loaded into RAM  1124  and executed by one or more of the N processing components in the processing portion  1126 . As shown, the processing portion  1126  may receive analog temperature and pressure inputs that are utilized by the functions carried out by the controller  170 . The N processing components in connection with RAM  1124  generally operate to execute the instructions stored in nonvolatile memory  1120  to effectuate the functional components depicted in  FIG. 1 . 
     The interface component  1132  generally represents one or more components that enable a user to interact with the MFC  100 . The interface component  1132 , for example, may include a keypad, touch screen, and one or more analog or digital controls, and the interface component  1132  may be used to translate an input from a user into the setpoint signal  186 . And the communication component  1134  generally enables the MFC  100  to communicate with external networks and devices including external processing tools. For example, an indicated flow may be communicated to external devices via the communication component  1134 . One of ordinary skill in the art will appreciate that the communication component  1134  may include components (e.g., that are integrated or distributed) to enable a variety of wireless (e.g., WiFi) and wired (e.g., Ethernet) communications. 
     The mass flow sensor  1136  depicted in  FIG. 3  depicts a collection of components known to those of ordinary skill in the art to realize the flow sensor  123  shown in  FIG. 1 . These components may include sensing elements, amplifiers, analog-to-digital conversion components, and filters. 
     Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.