State determination system and liquid manufacturing / supplying system

Abstract

The present invention has: a signal intensity detection device (200) that continuously detects signal intensity associated with impurities in an inspection target liquid; and an information processing device (100) that determines the state of the liquid quality of the inspection target liquid on the basis of the signal intensity detected by the signal intensity detection device (200).

Description

Condition determination system and liquid production and supply system

[0001] The present invention relates to a state determination system and a liquid manufacturing and supply system.

[0002] In a system for analyzing impurities in a test liquid using an adsorbent that adsorbs the impurities, a technique has been proposed for switching between passing the test liquid through the adsorbent and passing an eluent that elutes the impurities adsorbed to the adsorbent through the adsorbent (see, for example, Patent Document 1). Another proposed method analyzes particles in the test liquid by passing the test liquid through a particle capture membrane, recovering the particle capture membrane through which the test liquid has been passed, and observing the recovered particle capture membrane with a scanning electron microscope (SEM) (see, for example, Patent Document 2).

[0003] JP 2022-120536 A JP 2021-162564 A

[0004] The above-mentioned techniques have a problem in that it is not easy to continuously monitor the state of the liquid quality of the liquid to be tested.

[0005] An object of the present invention is to provide a state determination system and a liquid manufacturing and supply system that can continuously and easily grasp the state of the liquid quality of a liquid to be inspected.

[0006] The state determination system of the present invention includes a signal strength detection device that continuously detects signal strength associated with impurities in a liquid to be tested, and an information processing device that determines the state of the liquid quality of the liquid to be tested based on the signal strength detected by the signal strength detection device.

[0007] The liquid manufacturing and supply system of the present invention also includes a signal intensity detection device that continuously detects signal intensity associated with impurities in the liquid under test; an information processing device that determines the liquid quality state of the liquid under test based on the signal intensity detected by the signal intensity detection device; a valve unit that controls the supply of the liquid under test from a liquid manufacturing and supply facility that produces and / or supplies the liquid under test to a use point that uses the liquid under test; and a control device that controls the valve unit based on the liquid quality state of the liquid under test determined by the information processing device.

[0008] In the present invention, the state of the liquid quality of the liquid to be tested can be continuously and easily monitored.

[0009] 6 is a diagram showing a first embodiment of a state determination system of the present invention. FIG. 6 is a diagram showing an example of components provided in the information processing device shown in FIG. 1. FIG. 6 is a flowchart for explaining an example of an information processing method in the information processing device shown in FIG. 1. FIG. 6 is a flowchart for explaining another example of an information processing method in the information processing device shown in FIG. 1. FIG. 6 is a diagram for explaining a method of determining the state of a test object liquid in the determination unit shown in FIG. 2. FIG. 6 is a diagram showing a second embodiment of an impurity acquisition system of the present invention. FIG. 6 is a diagram showing an example of components provided in the information processing device shown in FIG. 6. FIG. 6 is a diagram showing an example of input / output of the learning model shown in FIG. 6. FIG. 6 is a flowchart for explaining an example of an information processing method in the information processing device shown in FIG. 6. FIG. 6 is a diagram showing an example of a liquid manufacturing and supply system to which the state determination system of the present invention is applied. FIG. 6 is a diagram showing another example of a liquid manufacturing and supply system to which the state determination system of the present invention is applied.

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the accompanying drawings.

[0011] FIG. 1 is a diagram showing a first embodiment of a condition determination system according to the present invention. As shown in FIG. 1, the condition determination system according to this embodiment includes an information processing device 100, a signal intensity detection device 200, a metering device 300, a flowmeter 310, and an on-off valve 400. A branch path 20 branches off from a main path 10, which carries a test liquid from an ultrapure water production facility to a point of use. Here, the ultrapure water production facility produces ultrapure water to be supplied to a semiconductor cleaning device, which is a point of use, and supplies the ultrapure water to the semiconductor cleaning device. In the following description, this ultrapure water will be referred to as the test liquid (test liquid), and the test liquid refers to the ultrapure water supplied from the ultrapure water production facility.

[0012] The flow meter 310 measures the flow rate of the test liquid passing through the main path 10. The flow meter 310 outputs the measured flow rate values ​​in chronological order to the information processing device 100. The flow meter 310 in this embodiment is included in the metering device 300. Note that a pressure gauge may be provided instead of the flow meter 310.

[0013] The on-off valve 400 is an on-off valve that controls the flow of the test liquid branching from the main path 10 to the branch path 20. The on-off valve 400 may be an on-off valve whose opening degree is adjustable. The on-off valve 400 controls opening and closing and the opening degree based on a control signal from the information processing device 100 or an external instruction.

[0014] The signal strength detection device 200 is a device that continuously detects (e.g., at time intervals shorter than a predetermined time, such as 1 ms intervals) the signal strength of impurities in the test liquid passing through the branch path 20. The detected signal strength is an index that corresponds to the amount of impurities contained in the test liquid passing from the branch path 20 to the signal strength detection device 200. The impurities may be, for example, ionized impurities. Examples of the signal strength detection device 200 include an analyzer using ICP mass spectrometry (ICP / ICP-MS), an analyzer using single particle inductively coupled plasma mass spectrometry (spICPMS), an analyzer using liquid chromatography mass spectrometry (LC-MS), and an analyzer using an ion chromatograph or gas chromatograph (GC-MS). When an ICP-MS is used as the signal intensity detection device 200, the test liquid is introduced into argon plasma using the ICP-MS, elements contained in the test liquid are ionized, and the number of ions at the m / z (mass-to-charge ratio) of the ionized elements is measured as the signal intensity using a mass spectrometer. The predetermined time here may be, for example, the measurement time interval in the quantification device 300, or a preset time. The signal intensity detection device 200 outputs time-series data of the signal intensity of the detected impurities to the information processing device 100.

[0015] The quantification apparatus 300 is an apparatus that quantitatively measures impurities (e.g., metals, ions, fine particles, organic matter, etc.) contained in the test liquid passing through the branch path 20. For example, the quantification apparatus 300 measures the particle size and number of fine particles contained in the test liquid passing through the branch path 20. Furthermore, the quantification apparatus 300 may measure (calculate) the concentration of fine particles using the measured number of fine particles. In this case, examples of the quantification apparatus 300 include a particle meter and an apparatus that performs measurement using a method of trapping fine particles in a membrane (e.g., the method described in JP 2021-162564 A). Furthermore, for example, the quantification apparatus 300 measures the concentration of metal ions in the test liquid passing through the branch path 20. In this case, examples of the quantification apparatus 300 include an apparatus or method that performs analysis using metal concentration (e.g., the method described in WO 2019-221186 A or the system described in JP 2022-120536 A). In this case, the quantification device 300 may be equipped with, for example, an ion adsorption membrane or monolithic organic porous material that adsorbs impurities (ionic metal impurities or fine particles) contained in the test liquid passing through the branch path 20, or an ion exchanger unit filled with ion exchange resin. When the quantification device 300 is equipped with an ICP / ICP-MS, spICPMS, LC-MS, GC-MS, or the like, these may also be used as the signal intensity detection device 200. The quantification device 300 outputs time-series data of the measurement results to the information processing device 100. It is preferable that the quantification device 300 be a particle counter from the viewpoint of outputting time-series data of quantitative information.

[0016] The information processing device 100 determines the state of the liquid quality of the test target liquid based solely on the time series data of signal strength output from the signal strength detection device 200. Alternatively, the information processing device 100 may determine the state of the liquid quality of the test target liquid based on the time series data of signal strength output from the signal strength detection device 200 and the time series data of the measurement results output from the quantification device 300. Alternatively, the information processing device 100 may determine the state of the liquid quality of the test target liquid based on the time series data of signal strength output from the signal strength detection device 200, the time series data of the measurement results output from the quantification device 300, and the time series data of the flow rate values ​​output from the flowmeter 310.

[0017] Fig. 2 is a diagram showing an example of components included in the information processing device 100 shown in Fig. 1. As shown in Fig. 2, the information processing device 100 shown in Fig. 1 has an intensity information acquisition unit 110, a quantitative information acquisition unit 120, a determination unit 130, and an output unit 140. Note that Fig. 2 shows only the main components related to this embodiment among the components included in the information processing device 100 shown in Fig. 1.

[0018] The strength information acquisition unit 110 acquires strength information indicating time-series data of signal strength output from the signal strength detection device 200. The strength information acquisition unit 110 outputs the acquired strength information to the determination unit 130.

[0019] The quantitative information acquisition unit 120 acquires quantitative information indicating time-series data of measurement results output from the quantification apparatus 300. The quantitative information acquisition unit 120 also acquires quantitative information indicating time-series data of flow rate values ​​output from the flow meter 310. The quantitative information acquisition unit 120 outputs the acquired quantitative information to the determination unit 130. The quantitative information acquisition unit 120 may also acquire time-series data of operation management information that manages the flow of the test liquid as quantitative information. The operation management information is information indicating the operating status of a water treatment device (hereinafter referred to as an upstream water treatment device) and a control device (hereinafter referred to as an upstream control device) that are provided upstream of the position of the flow meter 310 on the main path 10. Examples of the operation management information include information indicating the operating status (operation / stop) of the upstream water treatment device, information indicating the open / close state of an on-off valve provided as an upstream control device, and information indicating the operating status (operation / stop) of a pump provided as an upstream control device for pumping the test liquid from a storage tank or a coagulation tank. When the quantitative information acquisition unit 120 acquires the time series data of the operation management information, the quantitative information acquisition unit 120 outputs the acquired time series data of the operation management information to the determination unit 130 .

[0020] The determination unit 130 determines the state of the test target liquid based on the time-series data of signal intensity indicated by the intensity information output from the intensity information acquisition unit 110. The determination unit 130 also determines the state of the test target liquid based on the time-series data of signal intensity indicated by the intensity information acquisition unit 110, and the time-series data and particle size of the metal ion concentration and the number of particle, indicated by the quantitative information acquisition unit 120. In this case, the determination unit 130 can also identify impurities contained in the test target liquid. The determination unit 130 also determines the state of the test target liquid based on the time-series data of signal intensity indicated by the intensity information acquisition unit 110, the time-series data and particle size of the metal ion concentration and the number of particle, indicated by the quantitative information acquisition unit 120, and the time-series data of the flow rate value indicated by the quantitative information acquisition unit 120. Furthermore, when the time-series data of the operation management information is output from the quantitative information acquisition unit 120, the determination unit 130 identifies the cause of the change in the state of the test liquid based on the time-series data of the signal intensity indicated by the intensity information output from the intensity information acquisition unit 110, the time-series data of the metal ion concentration and number of fine particles and particle size indicated by the quantitative information output from the quantitative information acquisition unit 120, and the time-series data of the operation management information. Specific determination (identification) methods will be described later. The determination unit 130 outputs the determination result to the output unit 140. Furthermore, the determination unit 130 compares the signal intensity indicated by the intensity information output from the intensity information acquisition unit 110 with a preset threshold. If the signal intensity indicated by the intensity information output from the intensity information acquisition unit 110 exceeds the threshold, the determination unit 130 notifies the output unit 140 of this fact. Furthermore, the determination unit 130 compares the particle concentration based on the number of fine particles indicated by the quantitative information output from the quantitative information acquisition unit 120 with a preset threshold. If the concentration of the particulate matter exceeds the threshold value, the determining unit 130 notifies the output unit 140 to that effect.

[0021] The output unit 140 outputs the determination result output from the determination unit 130. This output method may be displaying on a display or the like, printing using a printing device, or transmitting to another device. Furthermore, the output unit 140 performs a predetermined output when notified by the determination unit 130 that the signal strength has exceeded a threshold. Furthermore, the output unit 140 performs a predetermined output when notified by the determination unit 130 that the particle concentration has exceeded a threshold. These output methods may be displaying on a display or the like, emitting a warning sound, turning on a warning light, or transmitting to another device.

[0022] The following describes an information processing method in the information processing device 100 shown in Fig. 1. Fig. 3 is a flowchart for explaining an example of an information processing method in the information processing device 100 shown in Fig. 1.

[0023] The intensity information acquisition unit 110 acquires intensity information indicating time-series data of the signal intensity of impurities contained in the test liquid from the signal intensity detection device 200 (step S1). The quantitative information acquisition unit 120 acquires quantitative information indicating time-series data of the measurement results output from the quantification device 300 (step S2). The processing of step S1 or the processing of step S2 can be performed either first. Subsequently, the determination unit 130 determines the state of the test liquid based on the time-series data of the signal intensity indicated by the intensity information acquired by the intensity information acquisition unit 110 in step S1, and the time-series data and particle size of the metal ion concentration and number of fine particles indicated by the quantitative information acquisition unit 120 in step S2 (step S3).

[0024] Fig. 4 is a flowchart for explaining another example of the information processing method in the information processing device 100 shown in Fig. 1. Here, the explanation will be given taking as an example a case where the quantitative measurement device 300 is a device equipped with an ion exchanger unit that concentrates the test liquid.

[0025] The intensity information acquisition unit 110 acquires intensity information indicating time-series data of the signal intensity of impurities contained in the test liquid from the signal intensity detection device 200 (step S11). The information processing device 100 then determines whether it is time for the quantification device 300 to perform quantification (step S12). The timing for quantification is the timing for acquiring impurities contained in the test liquid during the elution process following the concentration process of the test liquid and calculating the concentration of the acquired impurities. Because quantification cannot be performed during the concentration process, the information processing device 100 must determine whether it is time for the quantification device 300 to perform impurity quantification. The information processing device 100 may also determine the timing for performing the concentration process and elution process based on a pre-planned operation schedule.

[0026] When the information processing device 100 determines that it is time for the quantification device 300 to perform quantification, the quantitative information acquisition unit 120 acquires quantitative information indicating time-series data of the measurement results output from the quantification device 300 (step S13). The determination unit 130 then determines the state of the test liquid based on the time-series data of signal intensity indicated by the intensity information acquired by the intensity information acquisition unit 110 in step S11, and the time-series data and particle size of the metal ion concentration and number of fine particles indicated by the quantitative information acquisition unit 120 in step S13 (step S14).

[0027] The following describes a method for determining the state of the test liquid in the determination unit 130 shown in FIG. 2 . FIG. 5 is a diagram for explaining the method for determining the state of the test liquid in the determination unit 130 shown in FIG. 2 . The upper graph in FIG. 5 is time-series data showing fluctuations in water quality over time as determined from the signal strength of impurities detected by the signal strength detection device 200 shown in FIG. 1 in the test liquid passing through the branch path 20. The middle graph in FIG. 5 is time-series data showing the impurity concentration / particle count measured over time in the test liquid passing through the branch path 20 by the quantification device 300 shown in FIG. 1 . The lower graph in FIG. 5 is time-series data showing the flow rate of the test liquid passing through the main path 10 as measured by the flowmeter 310 shown in FIG. 1 . Here, the quantification device 300 is equipped with an ion exchanger unit, and during the concentration period in which the ion exchanger unit concentrates the test liquid, the quantification device 300 cannot measure the impurity concentration. Therefore, the sampling intervals for the impurity concentration / particle count shown in the middle graph of Figure 5 are longer than the sampling intervals for the water quality fluctuations (signal strength) shown in the upper graph of Figure 5 and the sampling intervals for the flow rate shown in the lower graph of Figure 5.

[0028] Referring to the data enclosed by the dashed-dotted line in the measurement results shown in Figure 5, it can be seen that the timing when the water quality fluctuations significantly fluctuated and the timing when the flow rate increased were almost the same. It can also be seen that during the concentration period of the test liquid, when the impurity concentration / particle count increased, the water quality fluctuations significantly fluctuated and the flow rate increased. This indicates that there is a correlation between the water quality fluctuations, changes in flow rate, and changes in impurity concentration / particle count at the timing enclosed by the dashed-dotted line. In this case, the determination unit 130 determines that the fluctuations in the water quality of the test liquid and the changes in the impurity concentration / particle count are caused by changes in the flow rate of the test liquid.

[0029] Furthermore, when a particle meter is used as the quantification device 300, if the number of particles measured by the quantification device 300 does not change during a time (period) when the fluctuations in water quality determined from the signal strength detected by the signal strength detection device 200 fluctuate significantly, the determination unit 130 determines that the increase in the number of ions is caused by ions, not particles. Furthermore, when a particle meter is used as the quantification device 300, if the number of particles measured by the quantification device 300 increases, the particle size of the particles measured by the quantification device 300 can be used to identify the particles causing the increase.

[0030] Furthermore, if the timing of a large change in the water quality fluctuation determined from the signal strength detected by the signal strength detection device 200 corresponds to the timing of a change in the operating status of the upstream water treatment device or the upstream control device indicated in the operation management information, the determination unit 130 determines that the cause of the water quality fluctuation determined from the signal strength detected by the signal strength detection device 200 is the change in the operating status of the upstream water treatment device or the upstream control device. If the measurement value measured by the quantification device 300 has changed, the determination unit 130 determines that the change in the measurement value measured by the quantification device 300 is due to the change in the operating status of the upstream water treatment device or the upstream control device.

[0031] Generally, for the signal strength detection device 200 to measure ion concentration, it is necessary to convert the number of detected ions into ion concentration using a pre-created calibration curve. Furthermore, the signal strength detection device 200 does not provide high analytical accuracy for ion concentration, etc. On the other hand, when using an ion exchanger unit capable of measuring ion concentration, such as the one described above, pretreatment such as concentration is required, and sampling intervals become longer. Therefore, continuous analysis is difficult to perform with the quantification device 300. While the techniques described in Patent Documents 1 and 2 above can perform analysis (quantification) with appropriate accuracy, they are unable to continuously grasp the state of water quality. Furthermore, even if the quantification device 300 uses only the flowmeter 310 or only time-series data from operational management information, it is difficult to analyze trends in impurities.

[0032] Therefore, in this embodiment, the determination unit 130 determines the state of the test liquid using the detection results from the signal strength detection device 200, which detects continuous changes in signal strength, and the measurement results from the quantification device 300, which performs analysis with appropriate accuracy. This makes it possible to perform determinations that compensate for the inconvenient aspects of the signal strength detection device 200 and the quantification device 300 described above. As a result, analysis of the test liquid with appropriate accuracy can be performed continuously and easily. (Second embodiment)

[0033] FIG. 6 is a diagram illustrating a second embodiment of a status determination system according to the present invention. As shown in FIG. 6, the status determination system according to this embodiment includes an information processing device 101, a signal strength detection device 200, a metering device 300, a flowmeter 310, an on-off valve 400, and a learning model 500. A branch path 20 branches off from a main path 10, which carries a test liquid from an ultrapure water production facility to a point of use. Here, the ultrapure water production facility produces ultrapure water to be supplied to a semiconductor cleaning device, which is a point of use, and supplies the ultrapure water to the semiconductor cleaning device. In the following description, this ultrapure water is referred to as the test liquid (test liquid), and the test liquid refers to the ultrapure water supplied from the ultrapure water production facility. The signal strength detection device 200, the metering device 300, the flowmeter 310, and the on-off valve 400 are the same as those in the first embodiment.

[0034] The information processing device 101 acquires time-series data of signal strength output from the signal strength detection device 200, time-series data of measurement results output from the quantification device 300, and time-series data of operation management information. During the learning phase of the learning model 500, the information processing device 101 inputs intensity information indicating the acquired time-series data of signal strength, quantitative information indicating the time-series data of measurement results, and time-series data of operation management information to the learning model 500. During the inference phase of the learning model 500, the information processing device 101 inputs the intensity information indicating the acquired time-series data of signal strength and the time-series data of operation management information to the learning model 500, and acquires status information indicating the status of the test liquid output from the learning model 500. This status information is, for example, information indicating quantitative values ​​(e.g., particle size, number of particles, particle concentration, metal ion concentration) and flow rate values ​​of impurities (e.g., metals, ions, particles, organic matter, etc.) contained in the test liquid passing through the branch path 20. In addition, the information processing device 101 may input the time series data of signal strength output from the signal strength detection device 200, the time series data of measurement results output from the quantification device 300, and the time series data of flow rate values ​​output from the flow meter 310 into the learning model 500, and obtain a status signal indicating the state of the liquid to be tested output from the learning model 500.

[0035] Fig. 7 is a diagram showing an example of components included in the information processing device 101 shown in Fig. 6. As shown in Fig. 7, the information processing device 101 shown in Fig. 6 has an intensity information acquisition unit 110, a quantitative information acquisition unit 120, a determination unit 131, an output unit 140, and a learning model generation unit 151. The intensity information acquisition unit 110, the quantitative information acquisition unit 120, and the output unit 140 are the same as those included in the information processing device 100 in the first embodiment. Note that Fig. 7 shows only the main components related to this embodiment among the components included in the information processing device 101 shown in Fig. 6.

[0036] The learning model generation unit 151 generates a learning model 500 using time series data of signal strength output as strength information from the strength information acquisition unit 110, time series data of measurement results output as quantitative information from the quantitative information acquisition unit 120, and time series data of operation management information output as quantitative information from the quantitative information acquisition unit 120.

[0037] The learning model 500 is a learning model generated by performing machine learning in advance on quantitative values ​​of impurities contained in the test liquid according to time-series data of signal strength and time-series data of operation management information. The learning model 500 may be provided within the information processing device 101. The learning model 500 may have, for example, a neural network structure in which multiple neurons are interconnected. A neuron is an element that performs a predetermined calculation on multiple inputs and outputs a single value as the calculation result. The learning method for the learning model 500 may be a general method for generating a learning model, that is, a method in which quantitative values ​​of impurities contained in the test liquid are learned based on time-series data of signal strength and time-series data of operation management information.

[0038] Fig. 8 is a diagram showing an example of input and output of the learning model 500 shown in Fig. 6. As shown in Fig. 8, when time-series data of signal intensity indicated by the intensity information and time-series data of operation management information indicated by the quantitative information are input, the learning model 500 shown in Fig. 6 outputs status information including information indicating impurities contained in the test target liquid and information indicating the quantitative values ​​of the impurities contained in the test target liquid, based on a learning model generated in advance by machine learning.

[0039] The determination unit 131 determines the state of the test target liquid based on the time-series data of signal intensity indicated by the intensity information output from the intensity information acquisition unit 110. Specifically, the determination unit 131 inputs the intensity information (time-series data of signal intensity) output from the intensity information acquisition unit 110 and the quantitative information (time-series data of operation management information) output from the quantitative information acquisition unit 120 into the learning model 500 and acquires the state information output from the learning model 500. The determination unit 131 also determines the state of the test target liquid based on the time-series data of signal intensity indicated by the intensity information output from the intensity information acquisition unit 110 and the time-series data and particle size of the metal ion concentration and the number of fine particles indicated by the state information output from the learning model 500. The determination unit 131 outputs the determination result to the output unit 140. Furthermore, the determination unit 131 compares the signal intensity indicated by the intensity information output from the intensity information acquisition unit 110 with a preset threshold. If the signal strength indicated by the strength information output from the strength information acquisition unit 110 exceeds the threshold, the determination unit 131 notifies the output unit 140 to that effect.

[0040] The following describes an information processing method in the information processing device 101 shown in Fig. 6. Fig. 9 is a flowchart for explaining an example of an information processing method in the information processing device 101 shown in Fig. 6.

[0041] The intensity information acquisition unit 110 acquires intensity information indicating time-series data of the signal intensity of impurities contained in the test liquid from the signal intensity detection device 200 (step S21). The quantitative information acquisition unit 120 acquires quantitative information indicating time-series data of operation management information output from the quantification device 300 (step S22). The process of step S21 or the process of step S22 can be performed either first. Next, the determination unit 131 inputs the intensity information acquired by the intensity information acquisition unit 110 in step S21 and the quantitative information acquired by the quantitative information acquisition unit 120 in step S22 into the learning model 500 (step S23). The determination unit 131 then acquires the state information output from the learning model 500 (step S24).

[0042] As described above, in this embodiment, the determination unit 131 inputs the detection results from the signal strength detection device 200 and the operation management information output from the quantification device 300 into a learning model 500 that outputs the state of the test liquid, and acquires the state information output from the learning model 500. The determination unit 131 determines the state of the test liquid based on the acquired state information. This allows for a determination that compensates for the inconvenient aspects of the signal strength detection device 200 and the quantification device 300. As a result, analysis of the test liquid with appropriate accuracy can be performed continuously and easily. Furthermore, by using the pre-trained learning model 500, a determination based on statistical trends can be made.

[0043] The following describes an embodiment in which the above-described state determination system is used. FIG. 10 is a diagram showing an example of a liquid manufacturing and supply system to which the state determination system of the present invention is applied. The embodiment shown in FIG. 10 is a system in which ultrapure water is supplied to a semiconductor cleaning device (point of use) via a non-regenerative ion exchange device CP1000 and an ultrafiltration device UF1100 in an ultrapure water production facility. The ultrapure water (liquid to be tested) supplied to the CP1000 is supplied from a liquid manufacturing and supply facility installed upstream. The liquid manufacturing and supply facility is also a facility for producing ultrapure water. The dashed lines in FIG. 10 indicate the water flow path or the path of a control signal for testing the water quality of the ultrapure water, which is the liquid to be tested.

[0044] There are two flow paths through which ultrapure water is supplied to the semiconductor cleaning apparatus. One of the flow paths is provided with an impurity removal unit 1200, and the ultrapure water is supplied to the semiconductor cleaning apparatus via the impurity removal unit 1200. An on-off valve 2000 is provided between the CP 1000 and the UF 1100. An on-off valve 2300 is provided to control the recovery of ultrapure water from the CP 1000 to the ultrapure water recovery tank. An on-off valve 2400 is provided to control the recovery of ultrapure water from the UF 1100 to the ultrapure water recovery tank. On-off valves 2100 and 2200 are provided on each of the two flow paths for supplying ultrapure water to the semiconductor cleaning apparatus.

[0045] 1 and 6. The state determination system 1 performs the processing described in the first and second embodiments on the ultrapure water from the CP 1000 or the ultrapure water from the UF 1100, which is the liquid to be tested. The control device 1500 is a second control device that controls the opening and closing of the on-off valves 2000, 2100, 2200, 2300, and 2400 based on the state of the liquid to be tested determined by the state determination system 1.

[0046] When the state determination system 1 determines that the concentration of particles contained in the test liquid exceeds the threshold, the control device 1500 controls the on-off valve 2000 to a closed state. At this time, the control device 1500 controls the on-off valve 2300 to an open state. Furthermore, when the state determination system 1 determines that the concentration of particles contained in the test liquid is equal to or lower than the threshold, the control device 1500 opens the on-off valve 2000. At this time, the control device 1500 closes the on-off valve 2300. Furthermore, when the state determination system 1 determines that the concentration of particles contained in the test liquid exceeds the threshold, the control device 1500 controls the on-off valves 2100 and 2200 to a closed state. At this time, the control device 1500 controls the on-off valve 2400 to an open state. Furthermore, when the state determination system 1 determines that the concentration of particles contained in the test liquid is equal to or lower than the threshold, the control device 1500 opens the on-off valves 2100 and 2200. At this time, the control device 1500 controls the on-off valve 2400 to a closed state. Note that the control device 1500 may control the on-off valve 2100 to an open state when the state determination system 1 determines that the concentration of impurities contained in the test liquid is equal to or lower than the first concentration threshold, control the on-off valve 2200 to an open state and control the on-off valve 2100 to a closed state when the state determination system 1 determines that the concentration of impurities contained in the test liquid exceeds the first concentration threshold and is equal to or lower than the second concentration threshold, and control the on-off valves 2100 and 2200 to a closed state when the state determination system 1 determines that the concentration of impurities contained in the test liquid exceeds the second concentration threshold. This utilizes the fact that even if the impurity concentration in the flow path provided with the impurity removal unit 1200 is somewhat high, the impurities contained in the ultrapure water are removed by the impurity removal unit 1200, thereby reducing the impurity concentration of the ultrapure water supplied to the semiconductor cleaning apparatus. In addition, the control device 1500 may control the open / closed state of the on-off valves 2000, 2100, 2200, 2300, and 2400 based on the signal strength acquired by the state determination system 1, the type of impurity identified by the state determination system 1, and the cause of the state change identified by the state determination system 1.For example, if the signal strength acquired by the state determination system 1, the type of impurity identified by the state determination system 1, or the cause of the state change identified by the state determination system 1 does not allow the liquid to be tested to be supplied to the semiconductor cleaning equipment, which is the point of use, the control device 1500 controls the on-off valve 2000 to a closed state.

[0047] FIG. 11 is a diagram showing another example of a liquid manufacturing and supply system to which the state determination system of the present invention is applied. In the application example shown in FIG. 11 , the state determination system 1, CP1000, UF1100, control device 1500, and on-off valve 2400 are the same as the state determination system 1, CP1000, UF1100, control device 1500, and on-off valve 2400 shown in FIG. 10 . Ultrapure water, which is the outlet water from the UF1100, is distributed into multiple flow paths and supplied to multiple semiconductor cleaning devices connected to each flow path. Each of the multiple flow paths branches into a flow path leading to the state determination system 1. The ultrapure water flowing in each branched flow path is subjected to the processing described in the first and second embodiments in the state determination system 1, using each ultrapure water as the test liquid. The control device 1500 selects which flow path to process the ultrapure water flowing in by controlling the opening and closing of on-off valves 2500-1 to 2500-4 provided in each branched flow path. Similarly to the above-described process, control device 1500 controls the opening and closing of on-off valves 2100-1 to 2100-4 provided in each flow path based on the state of the liquid under test determined by state determination system 1. Note that control device 1500 may have conditions corresponding to each of the multiple semiconductor cleaning devices, and control the opening and closing of on-off valves 2100-1 to 2100-4 based on the state of the liquid under test determined by state determination system 1 and the conditions corresponding to each semiconductor cleaning device.

[0048] In this way, if the condition of the test liquid determined by the state determination system 1 is such that the test liquid is not suitable for supply to the semiconductor cleaning equipment, which is the point of use, the system controls the on-off valve to prevent ultrapure water from being supplied to the semiconductor cleaning equipment. This prevents contamination of semiconductor devices and components within the ultrapure water facility. The liquid (water) to be measured is not limited to ultrapure water; it may also be a chemical solution such as IPA (isopropyl alcohol), PGMA (polyglycerol methacrylate), or PGMEA (propylene glycol monomethyl ether acetate). While the embodiment using a bottle to collect the eluent has been described, the collected eluent may also be directly sprayed into an analytical device for quantitative analysis. The concentration of metal impurities measured by the state determination system 1 is not particularly limited, but is desirably 100 ng / L or less, preferably 1 ng / L or less, and more preferably 0.1 ng / L or less.

[0049] Although the above description has been given by allocating each function (process) to each component, this allocation is not limited to the above. Furthermore, the configuration of the components is also not limited to the above-described embodiments, which are merely examples. Furthermore, each embodiment may be combined.

[0050] The processes performed by the information processing devices 100 and 101 may be performed by logic circuits that are individually manufactured for each purpose. Alternatively, computer programs (hereinafter referred to as programs) that describe the processing procedures may be recorded on recording media that can be read by the information processing devices 100 and 101, and the programs recorded on the recording media may be read and executed by the information processing devices 100 and 101. Recording media readable by each of the information processing devices 100 and 101 include removable recording media such as floppy (registered trademark) disks, magneto-optical disks, DVDs (Digital Versatile Discs), CDs (Compact Discs), Blu-ray (registered trademark) Discs, USB (Universal Serial Bus) memories, and SD cards, as well as memories such as ROMs (Read Only Memory) and RAMs (Random Access Memory) and HDDs (Hard Disc Drives) built into each of the information processing devices 100 and 101. The programs recorded on these recording media are read by a CPU provided in each of the information processing devices 100 and 101, and the same processing as described above is performed under the control of the CPU. Here, the CPU operates as a computer that executes a program read from a recording medium on which the program is recorded.

[0051] Some or all of the above embodiments can be described as, but are not limited to, the following supplementary notes: (Supplementary Note 1) An information processing device having an intensity information acquisition unit that acquires, from a signal intensity detection device, intensity information indicating signal intensity associated with continuously detected impurities in a test target liquid, and a determination unit that determines the state of liquid quality of the test target liquid based on the signal intensity indicated by the intensity information acquisition unit. (Supplementary Note 2) An information processing method that performs a process of acquiring, from a signal intensity detection device, intensity information indicating signal intensity associated with continuously detected impurities in a test target liquid, and a process of determining the state of liquid quality of the test target liquid based on the signal intensity indicated by the acquired intensity information. (Supplementary Note 3) A program that causes a computer to execute a procedure of acquiring, from a signal intensity detection device, intensity information indicating signal intensity associated with continuously detected impurities in the test target liquid, and a procedure of determining the state of liquid quality of the test target liquid based on the signal intensity indicated by the acquired intensity information.

[0052] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0053] This application claims priority based on Japanese Patent Application No. 2024-114580, filed July 18, 2024, the disclosure of which is incorporated herein in its entirety by reference.

[0054] 1 Status determination system 10 Main path 20 Branch path 100, 101 Information processing device 110 Intensity information acquisition unit 120 Quantitative information acquisition unit 130, 131 Determination unit 140 Output unit 151 Learning model generation unit 200 Signal intensity detection device 300 Quantitative device 310 Flow meter 400, 2000, 2100, 2100-1 to 2100-4, 2200, 2300, 2400, 2500-1 to 2500-4 Opening and closing valve 500 Learning model 1000 CP 1100 UF 1200 Impurity removal unit 1500 Control device

Claims

1. A state determination system having a signal strength detection device that continuously detects signal strength associated with impurities in a liquid to be tested, and an information processing device that determines the state of the liquid quality of the liquid to be tested based on the signal strength detected by the signal strength detection device.

2. A condition determination system as claimed in claim 1, further comprising a quantitative measurement device that quantitatively measures impurities contained in the test liquid, and the information processing device determines the state of the liquid quality of the test liquid based on the signal strength detected by the signal strength detection device and the measurement results of the quantitative measurement device.

3. A condition determination system as described in claim 2, wherein the information processing device identifies impurities contained in the test liquid based on the signal strength detected by the signal strength detection device and the measurement results of the quantitative determination device.

4. A condition determination system according to claim 2 or claim 3, wherein the information processing device acquires time series data of operational management information that manages the flow of the test subject liquid, and identifies the cause of a change in the liquid quality state of the test subject liquid based on the signal strength detected by the signal strength detection device, the measurement results of the quantification device, and the time series data of the operational management information.

5. A condition determination system as described in claim 4, wherein the information processing device performs machine learning in advance on the quantitative values ​​of impurities contained in the test liquid according to the time series data of the signal strength and the time series data of the operation management information to generate a learning model.

6. A condition determination system as described in claim 5, wherein the information processing device inputs the signal strength detected by the signal strength detection device and the time series data of the operation management information into the learning model, and determines the liquid quality state of the test target liquid based on the condition information indicating the state of the test target liquid output from the learning model.

7. A status determination system according to claim 1 or claim 2, wherein the information processing device performs a predetermined output when the signal strength detected by the signal strength detection device exceeds a preset threshold.

8. A condition determination system according to claim 1 or claim 2, wherein the signal intensity detection device is an ICP-MS, which ionizes elements contained in the test liquid and measures the number of ions at the m / z (mass-to-charge ratio) of the ionized elements as signal intensity.

9. A liquid manufacturing and supply system comprising: the state determination system according to claim 1; a valve unit that controls the supply of the liquid to be tested from a liquid manufacturing and supply facility that performs at least one of the manufacturing and supply of the liquid to be tested to a use point that uses the liquid to be tested; and a control device that controls the valve unit based on the liquid quality state of the liquid to be tested determined by the information processing device.

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