Pressure-type flow control device

Abstract

To provide a pressure type flow volume control device capable of correcting a flow volume on the basis of a self-diagnostic result.SOLUTION: A pressure type flow volume control device 10 includes: a diaphragm 2; a control valve 6 at an upstream side; an upstream-side pressure sensor 3 that detects upstream-side pressure P1; and a control mechanism that has a self-diagnostic function for diagnosis using the pressure reduction data of the upstream-side pressure and reference pressure reduction data, and calculates, using a numerical value prediction calculating formula that acquires a correction value from the pressure reduction data which is composed by calculating a weight from the multiple pieces of pressure reduction data and a corrected value, in-operation correction data from in-operation pressure reduction data obtained at an arbitrary timing in operation so as to control the control valve 6.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a pressure-type flow control device used in semiconductor manufacturing equipment, chemical plants, etc., and particularly relates to a pressure-type flow control device having a self-diagnosis function.

Background Art

[0002] In semiconductor manufacturing equipment and chemical plants, various flow meters and flow control devices are used to control the flow rate of material gases, etching gases, etc. Among these, the pressure-type flow control device is widely used because it can control the mass flow rate of various fluids with high precision by a relatively simple mechanism combining a control valve and a throttle part (e.g., an orifice plate or a critical nozzle). Unlike thermal flow control devices, the pressure-type flow control device has excellent flow control characteristics in that stable flow control can be performed even when the primary supply pressure (the pressure upstream of the control valve) fluctuates greatly.

[0003] Some pressure-type flow control devices adjust the flow rate by controlling the fluid pressure upstream of the throttle part (hereinafter sometimes referred to as the upstream pressure P1). The upstream pressure P1 is usually controlled by adjusting the opening degree of the control valve provided upstream of the throttle part. When the critical expansion condition (upstream pressure P1 / downstream pressure P2 ≥ about 2: in the case of argon gas) is satisfied, the velocity of the gas flowing through the throttle part is fixed at the speed of sound, and the mass flow rate of the gas flowing downstream of the throttle part is known to be proportional to the upstream pressure P1 regardless of the magnitude of the downstream pressure P2 downstream of the throttle part, and the flow rate can be controlled by controlling the upstream pressure P1.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

[0005] However, depending on the type of gas used, especially during prolonged use, corrosion or clogging may occur in the throttle section of a pressure-type flow control device. In this case, the opening area of ​​the throttle section changes, causing a discrepancy between the flow rate calculated from the upstream pressure P1 and the actual flow rate that was initially accurate. Furthermore, since pressure-type flow control devices typically do not have flow meters such as thermal sensors, it is not possible to directly measure the actual flow rate of the gas flowing through the flow path after the opening of the throttle section has changed. For this reason, pressure-type flow control devices have been proposed that utilize the configuration of the pressure-type flow control device to incorporate a self-diagnostic function to determine whether or not there is an abnormality in the throttle section.

[0006] In the pressure-type flow control device described in Patent Document 1, the drop in upstream pressure after closing the control valve is measured during the period in which the critical expansion conditions are satisfied, and self-diagnosis is performed by comparing this with a reference value. Furthermore, it has been found that the relationship ln(P1(t) / P1i)=-α·t (where P1(t) is a function of the upstream pressure P1 with respect to time t, and P1i is the initial upstream pressure) holds during the pressure drop period, and it is disclosed that it is possible to determine whether or not an abnormality has occurred in the throttling section by comparing the slope α obtained from the pressure measurement result with the reference slope α0. The self-diagnosis method described in Patent Document 1 has the advantage that, at the end of a semiconductor manufacturing process, etc., self-diagnosis can be started from any flow rate or any initial upstream pressure, regardless of the flow rate at that time.

[0007] However, even if self-diagnosis reveals blockage or widening of the throttling section, while it is possible to warn the user, it is difficult to continue using the device in that state. Therefore, conventionally, when an abnormality in the throttling section was detected, it was necessary to replace the throttling section, but replacing only the throttling section was not easy. As a practical solution, the entire pressure-type flow control device was often replaced with a new one.

[0008] Replacing pressure-type flow control devices each time significantly increases costs and reduces convenience. This is especially problematic in environments where the diameter of the throttle section is prone to change, such as when the gas used is highly corrosive, as the frequency of replacement of pressure-type flow control devices increases. Therefore, there was a demand for a pressure-type flow control device that could be used stably for a longer period, even when the diameter of the throttle section is prone to change.

[0009] The applicant has developed and commercialized a method for controlling the flow rate by comparing initial reference pressure drop data with pressure drop data during device operation and taking into account errors in machine gun force. In addition, the applicant has focused on a method for calculating the optimal flow rate correction value from pressure drop data during device operation using AI control with a regressive neural network.

[0010] This invention has been made in view of the above, and its main objective is to provide a pressure-type flow control device that can be used more stably and continuously by using AI control. [Means for solving the problem]

[0011] A pressure-type flow control device according to an embodiment of the present invention is The aperture section, A control valve provided on the upstream side of the aforementioned throttling section, An upstream pressure sensor that detects the upstream pressure, which is the pressure in the flow path between the throttling portion and the control valve, A pressure-type flow control device comprising a control mechanism having a self-diagnostic function that diagnoses using pressure drop data of the upstream pressure measured by the upstream pressure sensor, The control mechanism is The initial setting values ​​for determining the opening degree of the control valve, obtained from multiple pressure-type flow control devices, were compared with the measured flow rate error. The aforementioned initial settings were modified.Using correction values ​​and data on the time-dependent change in upstream pressure after closing the control valve from a predetermined pressure, pressure drop data is constructed based on the weight values ​​connecting multiple nodes that constitute a regressive neural network. If the change is determined to be outside the acceptable range when compared with the initial pressure drop data, the initial setting value is modified. It includes a calculation unit that executes a numerical prediction calculation formula to determine the correction value, From the operational pressure drop data, which is the change in upstream pressure over time acquired after closing the control valve at any time during operation, the calculation unit determines: Modify the aforementioned initial settings. We are calculating correction data during operation.

[0012] A calculation formula for AI prediction is constructed from pressure drop data and correction values ​​obtained during pre-shipment inspections. Then, the correction values ​​are recalculated using pressure drop data from the pressure-type flow control device in operation to ensure accurate flow control.

[0013] In the above configuration, a downstream pressure sensor is further provided to detect the downstream pressure, which is the pressure in the downstream flow path of the throttling section. The operating pressure drop data is acquired during the period from when the control valve is closed until the critical expansion condition is satisfied, which indicates that the pressure ratio of the upstream pressure to the downstream pressure is equal to or greater than a specified value.

[0014] Furthermore, in this case, the control mechanism uses the operating pressure drop data and Modify the aforementioned initial settings. The system includes a data recording unit that records correction data during operation, and the calculation unit can further calculate and update the weight values.

[0015] Furthermore, in these cases, the control mechanism uses the operating pressure drop data of other pressure control devices and / or During operation, the aforementioned initial settings are modified. Correction data can also be obtained, and the weight values ​​can be updated.

[0016] Furthermore, in these cases, the control mechanism can also control multiple pressure control devices. [Effects of the Invention]

[0017] According to the pressure-type flow control device of the embodiment of the present invention, by using the calculation formula determined by determining the weight using the pressure drop data and correction values obtained from a plurality of pressure-type flow control devices, the optimal correction value can be calculated from the pressure drop data of the pressure-type flow control device during movement, and it is possible to perform flow control more continuously and accurately.

Brief Description of Drawings

[0018] [Figure 1] It is a schematic diagram showing a pressure-type flow control device according to an embodiment of the present invention. [Figure 2] It is a graph showing the changes in the upstream pressure P1 and the downstream pressure P2 when the valve closing operation is performed from the gas supply state. [Figure 3] It is a graph showing the change in the pressure drop of the upstream pressure when an abnormality occurs in the opening. [Figure 4] Regarding the method of obtaining the calculation formula of the correction value by AI, (a) shows the initial value of the correction for each flow rate setting, (b) shows the actual flow rate with the initial correction value, and (c) shows the correction value of the flow control calculated from the actual flow rate. [Figure 5] It shows a schematic diagram of a neural network. [Figure 6] It is a flowchart showing an embodiment of flow self-diagnosis and correction value determination according to an embodiment of the present invention.

Embodiments for Carrying Out the Invention

[0019] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings, but the present invention is not limited to the embodiments described below.

[0020] Figure 1 shows a pressure-type flow control device 10 according to this embodiment. The pressure-type flow control device 10 includes a throttling section 2 provided in the flow path 1, a control valve 6 provided upstream of the throttling section 2, an upstream pressure sensor 3 for detecting the upstream pressure P1 between the throttling section 2 and the control valve 6, a downstream pressure sensor 4 for detecting the downstream pressure P2 downstream of the throttling section 2, and a temperature sensor 5 for detecting the temperature between the throttling section 2 and the control valve 6.

[0021] For the upstream pressure sensor 3 and the downstream pressure sensor 4, for example, semiconductor piezoresistive diffusion pressure sensors or capacitance manometers are used, and for the temperature sensor 5, for example, resistance thermometers or thermistors are used. For the control valve 6, for example, a piezoelectric element-driven valve (hereinafter sometimes referred to as a piezoelectric valve) is used, in which a metal diaphragm valve body is opened and closed by a piezoelectric actuator. The piezoelectric valve is a valve (proportional valve) that can be opened to any degree by adjusting the driving voltage applied to the piezoelectric element. For the throttling section 2, for example, an orifice plate or a critical nozzle is used, and the opening diameter of the throttling section 2 is set to, for example, 10 to 2000 μm.

[0022] The pressure-type flow control device 10 also includes a control mechanism (or control circuit) 7 connected to each of the sensors 3, 4, and 5 and the control valve 6. The control mechanism 7 is configured to perform a self-diagnostic function that closes the control valve 6 and measures the pressure of the flow pressure when the semiconductor manufacturing equipment process is completed (when the gas supply to the process chamber is stopped). The control mechanism 7 in this embodiment incorporates a CPU, memory, A / D converter, etc., provided on a circuit board, and includes a computer program for performing the operations described later, and is realized by a combination of hardware and software. In this embodiment, an example is shown in which the control mechanism 7 is arranged inside the pressure-type flow control device 10, but some or all of the components of the control mechanism 7 may be provided outside the pressure-type flow control device 10.

[0023] The upstream side of the pressure-type flow control device 10 is connected to a gas supply source (not shown), and the downstream side is connected to the process chamber 12 of a semiconductor manufacturing apparatus via a shut-off valve 11 (typically an on / off valve). A vacuum pump 13 is connected to the process chamber 12, and the inside of the process chamber 12 can be evacuated when gas is supplied from the upstream side of the pressure-type flow control device 10. In the embodiment shown in Figure 1, the shut-off valve 11 is located outside the pressure-type flow control device 10, but the shut-off valve 11 may be built into the pressure-type flow control device 10. For example, an AOV (air-driven valve) or a solenoid valve can be used as the shut-off valve 11.

[0024] In a semiconductor manufacturing process, when supplying gas to the process chamber 12, the control mechanism 7 uses the output of the upstream pressure sensor 3 (and the downstream pressure sensor 4 and temperature sensor 5) to calculate the flow rate and controls the control valve 6 so that the flow rate passing through the throttling section 2 becomes the set flow rate. The calculated flow rate may be displayed as a flow rate output value on the display unit of the external control device.

[0025] More specifically, when the critical expansion condition (P1 / P2 ≥ approximately 2: in the case of argon gas) is met, the calculated flow rate is determined from the output of the upstream pressure sensor 3 according to the flow rate relationship Q = K1P1 (where K1 is a proportionality constant that depends on the type of fluid and the fluid temperature), and the control valve 6 is feedback controlled so that the calculated flow rate is the same as the set flow rate. Also, under noncritical expansion conditions, the flow rate Q = K2P2 m (P1-P2) n The calculated flow rate is determined according to the relationship between (K2 being a proportionality constant dependent on the type of fluid and fluid temperature, and the exponents m and n being values ​​derived from the actual flow rate), and the control valve 6 is feedback controlled so that the calculated flow rate is the same as the set flow rate.

[0026] Furthermore, the pressure-type flow control device 10 of this embodiment is configured to perform a self-diagnosis to acquire pressure drop data at the end of the process when the control valve 6 transitions to a closed state and the gas supply is stopped. At the end of the process, it is also possible to close the shut-off valve 11 in addition to the control valve 6 to perform the self-diagnosis. In this case, the self-diagnosis process can be performed on any gas supply line without affecting semiconductor manufacturing using other gas supply lines.

[0027] The solid line graph in Figure 2 shows the changes in the upstream pressure P1 and downstream pressure P2 when the control valve 6 is closed. As shown in Figure 2, when the control valve 6 receives a closing command at time t1 and remains closed thereafter, the upstream pressure P1 decreases from the initial upstream pressure P1i during gas flow, and the downstream pressure P2 also decreases from the initial downstream pressure P2i.

[0028] In this embodiment, self-diagnosis is performed based on P1(t) measured during the critical expansion period Δt between times t1 and t2, i.e., the period during which P1 / P2 ≥ approximately 2 (in the case of argon gas) satisfies the critical expansion conditions. By performing self-diagnosis while confirming whether critical expansion conditions are actually present, it becomes possible to secure the maximum period for acquiring effective pressure drop data, regardless of the design of the fluid supply control system or the details of the semiconductor manufacturing process. Furthermore, in recent years, the downstream pressure P2 during gas supply (i.e., the initial downstream pressure P2i in Figure 2) is sometimes relatively high, but since self-diagnosis is performed after determining whether critical expansion conditions are actually present, the diagnostic accuracy can be improved. In addition, by closing only the control valve 6, the period during which critical expansion conditions can be maintained can be extended, making self-diagnosis easier to perform.

[0029] On the other hand, the dashed line graph in Figure 2 shows the changes in the upstream pressure P1 and downstream pressure P2 when the shut-off valve 11 is closed simultaneously with the control valve 6. The upstream pressure P1 decreases from the initial upstream pressure P1i during gas flow, and the downstream pressure P2 increases from the initial downstream pressure P2i during gas flow. In other words, pressure fluctuations occur so that the differential pressure between the upstream and downstream sides of the throttling section 2 is eliminated, and the upstream pressure P1 and downstream pressure P2 converge to substantially the same equilibrium pressure value P'' over time. The critical expansion period Δt' between times t1 to t2' that satisfy the critical expansion conditions is shorter than in the case of the solid line, but since the shut-off valve 11 is closed, the self-diagnosis process can be performed without affecting semiconductor manufacturing using other gas supply lines.

[0030] The pressure ratio P1 / P2, which indicates the lower limit of critical expansion conditions, varies depending on the type of gas. For example, it is 2.05 for argon gas, but 1.90 for hydrogen and 1.89 for nitrogen, with each gas having its own specific value. Furthermore, critical expansion conditions also change depending on the upstream gas temperature. For this reason, the control mechanism 7 may be configured to determine the critical expansion condition determination formula during self-diagnosis based on at least one of the gas type and gas temperature.

[0031] The following describes a specific example of self-diagnosis. After closing the control valve 6, the control mechanism 7 checks whether critical expansion conditions are present based on the measured upstream pressure P1 and downstream pressure P2, and compares the pressure drop data P1(t) of the upstream pressure P1 measured during the critical expansion period Δt with the pre-stored reference pressure drop data Y(t). The reference pressure drop data Y(t) is generally measured in advance before leaving the factory and stored in the memory of the control mechanism 7. In this case, the reference pressure drop data Y(t) is the normal pressure drop data measured in advance before leaving the factory, but it may also be measurement data from an abnormal condition, the previous measurement data, or setting data that is not measured.

[0032] Figure 3 shows the pressure drop curve A1 for the upstream pressure P1 under normal conditions after closing the control valve 6, the pressure drop curve A2 when clogging occurs in the throttling section, and the pressure drop curve A3 when opening enlargement occurs in the throttling section due to corrosion or the like.

[0033] As can be seen from Figure 3, if the throttling section 2 is clogged, gas flow becomes more difficult, so the pressure drop curve A2 shifts upward compared to the normal pressure drop curve A1. In this case, the time required for the upstream pressure P1 to drop to a predetermined value becomes longer. Also, the upstream pressure P1 after a predetermined time has elapsed since closing the control valve 6 is higher than in the normal state.

[0034] On the other hand, when the opening of the throttling section 2 is enlarged, the gas flows more easily, so the pressure drop curve A3 is shifted downwards compared to the normal pressure drop curve A1. In this case, the time required for the upstream pressure P1 to drop to a predetermined value is shortened. Also, the upstream pressure P1 after a predetermined time has elapsed after closing the control valve 6 is lower than in the normal state.

[0035] Next, we will explain how to derive the formula for calculating the correction value using AI with a recurrent neural network in this invention.

[0036] Typically, pressure-type flow control devices undergo flow calibration as part of pre-shipment testing. Specifically, this involves correcting the set value for controlling the opening and closing of the control valve 6 to control the upstream pressure. When using a pressure-type flow control device, the maximum flow rate that the device can control is set to 100%, and the control mechanism 7 sets the upstream pressure according to the flow rate setting value requested from an external source (e.g., semiconductor manufacturing equipment) (for example, 50% when 50% of the maximum flow rate is required). For example, when the external request is for a setting of 100%, the calculation unit of the control mechanism 7 (e.g., CPU) sends an opening command for the control valve 6 as 48,000 digits. This value of 48,000 digits is a digital signal value within the control mechanism 7. When the critical expansion condition is met, the flow rate is controlled by controlling the upstream pressure. Although it varies depending on the orifice diameter of the throttling section 2, for example, when flowing at 100% flow rate, this value is conveniently determined as an internal command value to control the control valve 6 to set the upstream pressure to 300 kPa. More specifically, if the control valve 6 is a piezo valve, the 48,000-digit command, for example, instructs it to set the piezo voltage to 80V. Also, as shown in Figure 4(a), when the flow rate is set to 10%, the control valve 6 is controlled by sending a 4,800-digit command, and when the flow rate is 1%, the control valve 6 is controlled by sending a 480-digit command.

[0037] Although it varies depending on the type of pressure-type flow control device, the pressure-type flow control device used in this embodiment is designed to flow at 600 sccm when set to 100% (hereinafter, the designed flow rate value is referred to as the reference value). The control valve 6 is controlled according to the digits in the right column of Figure 4(a) in order to control the upstream pressure by receiving commands from the control mechanism 7 according to the setting values ​​of 1, 3, 5, 10, 20, 40, 60, and 100% shown in the table. When the actual flow rate is measured at this time, an error of about ±0.1 to 0.4 occurs relative to the reference value, as shown in Figure 4(b). This occurs because the orifice diameter, which is the throttling part, differs slightly within the tolerance range of the diameter for each device, and there are differences between devices. Then, the correction value shown in Figure 4(c) is determined by taking into account the errors that occurred for each setting value. Next, in order to understand the characteristics of the orifice, which is the throttling section 2, the control valve is closed when the upstream pressure is 300 kPa (48,000 digits inside the control mechanism), and the pressure drop data of the change in upstream pressure over time is recorded, as shown in Figure 4(d).

[0038] Using this pressure drop data and correction value as a set, for example, 1000 data points are used to calculate the weight values ​​that connect multiple nodes constituting a regressive neural network.

[0039] Figure 5 is a schematic diagram of a recurrent neural network. In this embodiment, the input layer 80 contains pressure drop data, and the output layer 82 contains correction values. There are 49 input layers, and from these 49 pressure drop data points, the 9 output layer values, corresponding to the number of correction values ​​in Figure 4(c), are calculated. The number of hidden layers 81 is not particularly limited, but in this embodiment, an example is shown where layer a has 64 nodes, layer b has 128 nodes, layer c has 256 nodes, layer d has 128 nodes, and layer e has 64 nodes. (The value of n in xn is 48, and the value of n in an is 63, and so on, the number of n changes according to the number of nodes.)

[0040] The number of weights connecting nodes changes depending on the number of nodes in each hidden layer. Increasing the number of weights (number of hidden layers and number of nodes in each layer) improves the expressive power of the neural network, but it requires a great deal of computation time. Recent advancements in computer speed have made it possible to significantly increase the number of hidden layers. In order to input values ​​into the input layer 80 and obtain output values ​​in the output layer 82, it is first necessary to determine the weights. By learning these weight values, good prediction becomes possible. In this embodiment, weights connecting each node are calculated from approximately 1000 pressure drop data points and correction values ​​(values ​​in the input layer (pressure drop data) and values ​​in the output layer (correction values)) obtained in pre-shipment testing, and the numerical prediction calculation formula y=f(x) in the hidden layer is constructed.

[0041] The following describes a specific example of flow rate control in this embodiment. Figure 6 is a flowchart showing the process of deriving a correction value using the numerical prediction calculation formula y=f(x) described above, based on the pressure drop data which is the result of self-diagnosis performed by the pressure-type flow rate control device 10.

[0042] As shown in Figure 6, the pressure-type flow control device 10 performs a self-diagnosis to determine the state of the throttling section 2. First, as shown in step S1, when gas is flowing at a set flow rate, for example 100% of the set flow rate, the control mechanism 7 receives a command to stop the gas supply and issues a closing command (for example, a 0% flow rate command) to the control valve 6.

[0043] Next, as shown in step S2, the pressure drop data of the upstream pressure (operational pressure drop data) is measured based on the output of the upstream pressure sensor 3. Preferably, the operational pressure drop data is obtained by sampling the output of the upstream pressure sensor 3 and the downstream pressure sensor 4, while determining whether or not the critical expansion condition is met based on the outputs of the upstream pressure sensor 3 and the downstream pressure sensor 4. This is because if the critical expansion condition is not met, the pressure will not release easily, and the drop characteristics will change, which may cause problems for the neural network's learning and prediction.

[0044] Next, as shown in step S3, the operating pressure drop data is transmitted to the control mechanism 7 and compared with the initial pressure drop data recorded at the time of shipment (or the updated data if the data has been updated) to determine whether the change is within an acceptable range. If the transmitted pressure drop data P1(t) is determined to be within an acceptable range, as shown in step S7, it is determined that no abnormality has occurred in the throttling section and no correction of the flow control signal is necessary, and the diagnostic process is terminated.

[0045] On the other hand, if it is determined in step S4 that the change is outside the acceptable range, as shown in step S5, 49 P1(t) values ​​(where t is, for example, the upstream pressure value acquired every 2 seconds, with the start of measurement set to 0) are input to x0 to x48 of the input layer 80 of the regressive neural network. A calculation unit executes a numerical prediction calculation formula constructed based on the weight values ​​connecting the multiple nodes constituting the input layer 80, the hidden layer 81, and the output layer 82, and outputs nine correction values ​​y0 to y8 from the output layer 82. The output correction values ​​(operational correction data) update the correction values ​​set at the time of shipment (for example, the correction values ​​shown in Figure 4(c)) and are stored in the memory unit of the control mechanism 7 as new correction values, as shown in step S6.

[0046] In this way, a correction value is obtained from the pressure drop data P1(t), which is the result of the self-diagnosis process. Then, a correction formula is determined for generating the internal flow control signal to be used in subsequent flow control operations.

[0047] Furthermore, the control mechanism 7 includes a data recording unit that records operational pressure drop data and operational correction data, and the calculation unit further calculates and updates the weight values, thereby improving the accuracy of the AI ​​prediction by the neural network. In addition, the accuracy of the AI ​​prediction is further improved by acquiring operational pressure drop data and / or correction data from other pressure control devices connected via the network and updating the weight values. Other pressure control devices connected via the network can use data not only from pressure control devices installed in the same factory, but also from pressure control devices installed in other factories.

[0048] Furthermore, the control mechanism 7 can also utilize a computer system equipped with a high-speed CPU, large-capacity memory, and large-capacity storage device to control multiple pressure control devices. In this case as well, it is possible to centrally manage pressure control devices located not only within the same factory but also in other factories connected via a network. [Industrial applicability]

[0049] The pressure-type flow control device according to an embodiment of the present invention is suitably used for accurately controlling the flow rate of gas supplied in semiconductor manufacturing equipment and the like over a long period of time. [Explanation of Symbols]

[0050] 1. Flow channel 2. Aperture section 3. Upstream pressure sensor 4. Downstream pressure sensor 5. Temperature sensor 6. Control valve 7 Control mechanism 10 Pressure-type flow control device 11 Shut-off valve 12 process chambers 13 Vacuum pump

Claims

1. The aperture section, A control valve provided on the upstream side of the throttling section, An upstream pressure sensor that detects the upstream pressure, which is the pressure in the flow path between the throttled portion and the control valve, A pressure-type flow control device comprising a control mechanism having a self-diagnostic function that diagnoses using pressure drop data of the upstream pressure measured by the upstream pressure sensor, The control mechanism is The system includes a calculation unit that, using initial setting values ​​for determining the opening degree of a control valve obtained from multiple pressure-type flow control devices, corrects these initial setting values ​​obtained from measured flow errors, and uses data on the change in upstream pressure over time after closing the control valve from a predetermined pressure. It then compares this with pressure drop data constructed based on weight values ​​connecting multiple nodes constituting a regressive neural network, and if it determines that the change is outside the acceptable range, it executes a calculation formula to obtain a corrected value by correcting the initial setting values. A pressure-type flow control device that calculates operational correction data for modifying the initial setting value using the calculation unit, based on operational pressure drop data of the change in upstream pressure over time, acquired after closing the control valve at any time during operation.

2. The system further includes a downstream pressure sensor that detects the downstream pressure, which is the pressure in the downstream flow path of the constricted portion. The pressure-type flow control device according to claim 1, wherein the in-operation pressure drop data is acquired during the period after closing the control valve, while satisfying the critical expansion condition that indicates the pressure ratio of the upstream pressure to the downstream pressure is equal to or greater than a specified value.

3. The control mechanism is The pressure-type flow control device according to claim 1 or 2, further comprising a data recording unit that records the operating pressure drop data and the operating correction data for correcting the initial setting value, wherein the calculation unit further calculates and updates the weight value.

4. The pressure-type flow control device according to claim 3, wherein the control mechanism acquires operational pressure drop data and / or operational correction data for correcting the initial setting value of another pressure control device, and updates the weight value.

5. The pressure-type flow control device according to claim 3 or 4, wherein the control mechanism controls a plurality of pressure control devices.

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