KR101560968B1 - real time monitoring trouble prediction and diagnosis apparatus in direct connected type equipment and thereof trouble diagnosis and prediction method - Google Patents

real time monitoring trouble prediction and diagnosis apparatus in direct connected type equipment and thereof trouble diagnosis and prediction method Download PDF

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KR101560968B1
KR101560968B1 KR1020140022618A KR20140022618A KR101560968B1 KR 101560968 B1 KR101560968 B1 KR 101560968B1 KR 1020140022618 A KR1020140022618 A KR 1020140022618A KR 20140022618 A KR20140022618 A KR 20140022618A KR 101560968 B1 KR101560968 B1 KR 101560968B1
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value
data
error
sensing
facility
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KR1020140022618A
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KR20150101206A (en
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채영덕
동석근
이창락
김현섭
옥진우
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(주)우광에스디에스
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Abstract

The present invention discloses an equipment direct coupled type real-time monitoring failure diagnosis apparatus and a failure time prediction diagnosis apparatus. A diagnostic method in such a fault diagnosis apparatus comprises the steps of generating a digital sensing data by receiving a sensing signal provided from at least two or more sensors for sensing a working state of a working part of a faulty manufacturing facility, The data obtained from the operating parts interrelated on the recipe of the manufacturing facility are compared and analyzed for each real-time monitoring cycle, the fault diagnosis data including the faulty operation part information is generated through the result of the comparison analysis, Or outside And < / RTI >

Description

Technical Field [0001] The present invention relates to a real-time monitoring fault diagnosis and fault diagnosis apparatus, and a fault diagnosis and diagnosis method,
The present invention relates to an equipment field for manufacturing semiconductor devices and the like. In particular, the present invention relates to an apparatus for accurately diagnosing a failure of operating components constituting a facility, The present invention relates to a real-time monitoring fault diagnosis and fault diagnosis apparatus, and a diagnosis method and a fault diagnosis method for the fault diagnosis.
Industrial manufacturing facilities consist of a variety of complex operating components. The ability to detect and diagnose faults in such operating parts is very important.
For example, semiconductor processing facilities require regular monitoring. The processing conditions vary with time, with very minor variations of important process parameters that produce undesirable results. Small changes can easily occur in the composition or pressure of the etching gas, the process chamber, or the wafer temperature. In many cases, changes in process data that reflect the degradation of process characteristics can not be detected simply by reference to the displayed process data. It is difficult to detect abnormalities and characteristic deterioration of a process early.
Generally, a semiconductor process is performed in a vacuum chamber, in which a semiconductor substrate is placed in a chamber, and then a plasma is generated on the top of the deposited substrate to form a thin film on the substrate or perform etching. An RF source, a bias power, a CKD valve, a controller, and the like, which constitute a semiconductor device during the process of the substrate, such as an MFC (Mass Flow Controller), an RF source The critical factors related to various production such as the change of the amount of the gas to be injected, the pressure change, the state of the plasma and the like are changed, which causes a problem that the deposition or etching characteristics are changed. In order to solve this problem, a separate in-situ diagnostic apparatus is used to monitor the plasma, but various sensors such as a gas amount and a CKD valve can not provide a cause of failure. Alternatively, there is a method of modeling the relationship between electrical signals and in-situ of a working part to a neural network, or diagnosing the cause of failure by utilizing an information storage system such as an FDC. In this case, it is required to install expensive in-situ system and large-capacity server system such as FDC, and it is difficult to generate a specific failure pattern in advance, and a method in which the user manually confirms FDC information after an accident, It is applied as a cause analysis concept after the accident. This makes it impossible to provide the cause of the failure in case of a previously unknown failure.
In addition, when the active component sensor information collected from the equipment is collected and processed at a single processing site through a remote host, it takes a considerable time to collect and transmit a large amount of sensor information, and a large amount of sensor information The algorithm for fault diagnosis becomes complicated, which causes a problem that the accuracy of fault diagnosis of the system is lowered.
On the other hand, when the active component sensor information collected from a plurality of plasma equipment is collected and processed at a single processing site through a remote host, it takes a considerable amount of time to collect and transmit a large amount of sensor information, Information becomes complicated due to the complexity of the algorithm for predictive diagnosis of the failure, which leads to a problem that the accuracy of the diagnosis of the failure prediction is lowered.
There are devices that provide information that users can judge using various sensor information in semiconductor production equipment. The most commonly used systems are FDS and EES. These systems provide approximate information rather than a detailed representation of the various sensors, so that they can judge and verify the state according to the user's senses or their ability. In addition, these systems do not accurately represent actual values in providing information of various sensors but rather provide information to the user by processing the information provided by the sensor, I can not see exactly what the state is.
For example, in the case of flow sensors, the information they provide is shown as 0-5 V in the data distributed by the manufacturers providing these sensors. In this case, the FDS and EES, which transfer the existing information to the user, limit the voltage information supplied from the flow controller to 0-5V, which is provided by the manufacturer of the flow controller. These flow controllers are not only providing accurate information of 0-5V, but also information that exceeds the voltage and allowable voltage range of 5V. In the conventional system, the flow controller has a minus value. However, since the value delivered to the actual user carries information of the OV, the actual flow controller has a problem but the user can not recognize it. This is true for + values, not just - values.
As described above, the conventional system can not transmit the information provided by the actual sensor to the user as it is, so that it is impossible to maintain the same characteristics of the production system in a precise system such as semiconductor production, However, since the defects occur in the semiconductor after the various steps, the loss can be increased.
An object of the present invention is to provide an equipment direct connection type real-time monitoring trouble diagnosis apparatus which can directly diagnose a failure of operating parts constituting a facility and judge a mounting position of a failed operating part.
SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is therefore an object of the present invention to provide a method and apparatus for accurately estimating a failure occurrence time of operating parts constituting a facility, The present invention provides a direct-coupled type real-time monitoring failure-time prediction diagnosis apparatus that provides a real-time detection and comparison of the current state and the past state of constituent operating parts to detect when a failure of the corresponding operating part can occur.
According to one aspect of the present invention, there is provided an equipment direct coupled type real-
At least two sensors for sensing operating parts of the manufacturing facility;
A sensor input processing unit for processing the sensing signals provided from the sensors to generate digital sensing data;
A controller for comparing the data obtained from the operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data for each real-time monitoring cycle to generate the fault diagnosis data including the faulty operation part information; And
So that communication between the control unit and the outside is performed, And a communication unit for transmitting the data.
 According to one aspect of the present invention, a facility-connected real-
Receiving sensing signals provided from at least two or more sensors for sensing operating components of the manufacturing facility to generate digital sensing data;
Comparing data obtained from operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data for each real-time monitoring cycle;
Generate fault diagnosis data including the faulty operation part information through the result of the comparison and analysis;
The fault diagnosis data is displayed or the data is transmitted to the outside using the Internet, USB communication, or various standardized communication systems. send.
According to an aspect of the present invention, there is provided an equipment direct coupled type real-time monitoring failure prediction diagnostic apparatus,
At least two sensors for sensing operating parts of the manufacturing facility;
A sensor input processing unit for processing the sensing signals provided from the sensors to generate digital sensing data;
The data obtained from the operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data is compared with the error of the setting value and the error set value for each real time monitoring prediction cycle, A control unit for generating data; And
And a communication unit for performing communication between the control unit and the outside and transmitting the predictive diagnostic data to the outside.
 According to still another aspect of the present invention, there is provided a method of directly diagnosing a facility-
Processing each of the sensing signals provided from at least two sensors for sensing operating components of the manufacturing facility to generate digital sensing data;
The data obtained from the operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data is compared with the error of the setting value and the error set value for each real time monitoring prediction cycle, Generate data;
The predictive diagnosis data is displayed or transmitted to the outside in the vicinity of the corresponding manufacturing facility.
According to the facility direct connection type real-time monitoring failure diagnosis apparatus of the present invention as described above, it is possible to accurately diagnose the failure of the operating components constituting the facility and promptly determine the installation position of the failed operating component. As a result, maintenance time due to failure of semiconductor production equipment can be shortened, which improves utilization rate and increases operation time.
As the equipment utilization rate of these equipments is improved and the time for failure is shortened, it is more effective for the increase of the semiconductor production quantity rather than the advantage of the production equipment, so that the enormous cost reduction of the manufacturer can be achieved.
In addition, according to the equipment direct connection type real-time monitoring failure prediction diagnosis apparatus, it is possible to precisely predict the occurrence possibility of failure of the operating parts constituting the equipment and to deliver the same to the user, thereby preventing the loss of the semiconductor wafer being processed due to sudden stoppage during normal operation It is possible to reduce the maintenance cost and the maintenance time by informing the user of the necessity of replacement of each constituent operating part at the time of maintenance.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram showing the configuration of an equipment direct connection type real-time monitoring failure diagnosis apparatus according to an embodiment of the present invention;
Figure 2 is an exemplary detailed configuration block diagram of the sensor input processor of Figure 1;
3 is a flowchart of a real-time monitoring failure diagnosis according to the present invention.
4 is an exemplary detail flow chart according to Fig.
5 is a waveform diagram of a steady state signal used for fault diagnosis in the operation of FIG.
FIG. 6 is a waveform diagram of an abnormal state signal used for fault diagnosis in the operation of FIG. 4; FIG.
Figure 7 is another exemplary detail flow chart according to Figure 3;
8 is a detailed flowchart of the branching operation in Fig. 7;
FIG. 9 is a waveform diagram of a pressure valve signal used for fault diagnosis in the operation of FIG. 7; FIG.
10 is a waveform diagram of a pressure sensing signal used for fault diagnosis in the operation of FIG.
Fig. 11 is a view showing an example of installation of operating parts mounted on a facility to which the present invention is applied; Fig.
12 is a flow chart for detecting a power supply abnormality according to the present invention.
13 is a flowchart of an input connector connection and an operation part replacement operation according to the present invention.
14 is a flowchart of a control operation of tuning and calibration according to the present invention.
15 is a flowchart of a comparative analysis control operation of the same operating part according to the present invention.
Fig. 16 is a signal waveform diagram used in Fig. 15; Fig.
17 is a flowchart of an operation of providing corresponding channel status information according to the present invention.
18 is a flowchart of a real-time monitoring failure time prediction diagnosis according to another embodiment of the present invention.
Figures 19A-19C illustrate utility illustrations used in the prediction algorithm of Figure 18;
20A and 20B are other illustrative application drawings used in the prediction algorithm of FIG. 18. FIG.
BRIEF DESCRIPTION OF THE DRAWINGS The above and other objects, features, and advantages of the present invention will become more apparent from the following description of preferred embodiments with reference to the attached drawings. However, the present invention is not limited to the embodiments described herein but may be embodied in other forms. Rather, the embodiments disclosed herein are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art, without intention other than to provide an understanding of the present invention.
In this specification, when it is mentioned that some element or lines are connected to a target element block, it also includes a direct connection as well as a meaning indirectly connected to the target element block via some other element.
In addition, the same or similar reference numerals shown in the drawings denote the same or similar components as possible. In some drawings, the connection relationship of elements and lines is shown for an effective explanation of the technical contents, and other elements or functional blocks may be further provided.
Each of the embodiments described and exemplified herein may include complementary embodiments thereof, and basic operations and internal mechanism details of input / output valves, gas flow controllers, etc., mounted in semiconductor manufacturing facilities, etc., Please note that it is not described in detail for the sake of brevity.
First, FIG. 1 is a block diagram of a facility-direct-coupled real-time monitoring failure diagnosis apparatus according to an embodiment of the present invention.
Referring to FIG. 1, an equipment direct connection type real-time monitoring fault diagnosis apparatus includes a display unit 30, a power source unit 40, an auxiliary power source unit 42, a memory unit 50, a sensor input processing unit 80, And a communication unit 110.
The control unit 100 may include a CPU 102, a GPU 104, and a memory 106.
An application program operating in the kernel area of the operating system can be executed by the CPU 102. [ The GPU 104 can process graphic data exclusively under the control of the CPU 102. [ The GPU 104 processes graphics data by executing a device driver that is a program. The memory 106 may be a non-volatile memory such as a volatile memory such as a DRAM or a flash memory, and functions as a main memory of the controller 100.
The display unit 30 may be connected to a touch unit 32 for receiving a user touch input. The touch unit 32 may sense a user input by depressurizing or electrostatic operation.
The power supply unit 40 receives an AC power or a DC power through an input terminal Pin. When the AC power is received at the input terminal Pin, the power supply unit 40 may convert AC power to DC power and regulate the converted output voltage to a constant voltage. When the DC power is received at the input terminal (Pin), the power supply unit 40 may perform an operation of converting the received DC voltage to an internal required voltage level.
The auxiliary power supply unit 42 may be charged with power supplied from the power supply unit 40. That is, the auxiliary power unit 42 may be implemented as a supercap or a rechargeable battery as a means for power backup. For example, when the switch 44 is closed during a power-down or a power failure, the power of the sub power source 42 may be provided to the controller 100. [ Although only the power source is applied to the controller 100 through the line L50 in the figure, each functional block of FIG. 1 includes a communication unit 110, a display unit 30, a sensor input processing unit 80, and the like.
The sensor input processing unit 80 may include a plurality of sensor input units 81, 82, and 83. The plurality of sensor input units 81, 82, and 83 may be correspondingly connected to sensors that detect the operation state of the operating parts of the manufacturing facility. For example, the first sensor input S1 is applied to the sensor input 81, the second sensor input S2 is applied to the sensor input 82, and the third sensor input S3 is applied to the sensor input 83 ). ≪ / RTI > However, this is merely an example, and a plurality of sensor inputs may be applied to one sensor input portion.
The sensor input processing unit 80 processes the sensing signals provided from the sensors to generate digital sensing data. Here, the sensing signal may be an analog voltage signal, and the digital sensing data may be generated by sampling and quantizing the analog voltage signal.
The control unit 100 receives the digital sensing data through the line L10.
The control unit 100 compares and analyzes data obtained from the operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data for each real-time monitoring cycle to generate the failure diagnosis data including the failed operation part information.
The communication unit 110 communicates with the control unit 100 in a variety of ways (Internet, 232, 485, USB, etc.), receives the fault diagnosis data via the line L40, send. A host or a server may be located outside.
The display unit 30 is connected to the controller 100 through a line L20. The display unit 30 transmits the user input data received through the touch unit 32 to the control unit 100 or displays the display data applied from the control unit 100 on the screen.
The memory unit 50 may be connected to the controller 100 through a line L30 to perform data communication.
The real-time monitoring trouble diagnosis apparatus of FIG. 1 is installed in a facility direct connection type. That is, in the case where the facility is a semiconductor manufacturing apparatus, the sensor input processing unit 80 is connected to various sensors of the apparatus immediately located on the site, and the failure diagnosis apparatus can be installed in the vicinity of the semiconductor manufacturing apparatus . Therefore, the operator or manager on the spot can take immediate action when confirming the fault diagnosis data of the real-time monitoring fault diagnosis apparatus.
Fig. 2 is an exemplary detailed configuration block diagram of the sensor input processing unit of Fig. 1. Fig.
2, the sensor input processing unit includes an electrostatic discharge protection / surge protection unit 80-1 for performing ESD protection or surge protection with respect to the sensing signal SI, A plurality of amplifiers 80-3 and 80-4 for amplifying the level of the sensing signal SI at a predetermined amplification rate, a high impedance matching unit 80-2 for performing impedance matching for the amplified sensing signal, An A / D converter 50-5 for converting the input SI into digital sensing data, a plug-in play 80-6 for performing a plug-in play for receiving the power input PI to sense the connection state of the active components, A plurality of regulators 80-7 and 80-8 for providing a constant voltage, and a signal converter 80-9 for outputting the digital sensing data.
3 is a flowchart of a real-time monitoring failure diagnosis according to the present invention.
First, in step S30, under the control of the control unit 100 of FIG. 1, the sensor input processing unit 80 receives sensing signals provided from at least two or more sensors that detect the operation parts of the manufacturing facility, And generates data.
In step S31, the control unit 100 compares and analyzes the data obtained from the operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data for each real-time monitoring period.
In step S32, the control unit 100 generates the failure diagnostic data including the failed operation part information through the result of the comparison analysis.
In step S33, under control of the control unit 100, the communication unit 110 displays the above-described failure diagnosis data, send.
The operation of FIG. 3 will become more apparent with reference to FIG.
4 is an exemplary detailed flowchart according to FIG. FIG. 5 is a waveform diagram of a normal state signal used for fault diagnosis in the operation of FIG. 4, and FIG. 6 is a waveform diagram of an abnormal state signal used for fault diagnosis in the operation of FIG.
Assuming that there is a first valve as the input side valve at the front end of the MFC and a second valve as the output side valve at the rear end of the MFC,
In step S400, a system initial value check operation is performed, and in step S401, it is checked whether there is an MFC control operation command. In this case, assume that the initial control output value is normal. If the initial control output value is abnormal, it will be determined that the MFC, which is the gas flow controller, is faulty.
If it is the MFC control, it is checked in step S402 whether the first and second valves are turned on.
When the first and second valves are turned on, it is checked in step S403 whether or not the control output value is equal to or greater than the ON valve value of the first valve. If the control output value is less than the ON valve value of the first valve, it is determined that the first valve is defective in step S404. That is, the first valve error.
When the control output value is equal to or greater than the ON valve value of the first valve, the step S406 is checked through the first time delay in step S405. Here, the first time delay means a given constant time delay, and may be several tens of microseconds to several seconds.
Step S406 is a step of checking whether the output holding time is equal to or greater than a set value. If the output hold time is not equal to or greater than the preset value, the first valve error in step S404 is determined.
 If the output holding time is equal to or greater than the set value in step S406, the step S408 is checked through the second time delay in step S407. Here, the second time delay means a given constant time delay, and may be several tens of microseconds to several seconds.
Step S408 is a step of checking whether the control output value is equal to or larger than zero. If the control output value is not 0 or more, it is determined to be the second valve error in step S409.
In step S408, it is checked whether the control output value is equal to or less than the error value in step S410 if the control output value is 0 or more. If the control output value is equal to or greater than the error value in step S410, it is determined to be an MFC error in step S411.
If the control output value is equal to or less than the error value in step S410, the step S412 is determined to be normal.
By comparing and analyzing the data obtained from the operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data for each real-time monitoring cycle, it is possible to accurately diagnose the failure of the operating parts constituting the equipment by directly comparing with the equipment, It is possible to determine the mounting position of the operating part.
As a result, the present state of various sensors and control devices in the semiconductor equipment is checked, and when the error occurs, the cause and the location of the accident are diagnosed as to what cause of the error is caused by which sensor and which device.
In the case of FIG. 4, the first valve may correspond to gas input valve 2 or gas input valve 4 in FIG. The second valve may correspond to gas output valve 10 or gas output valve 12 in Fig. In this case, the gas flow controller may correspond to MFC 6 or MFC 8 in FIG.
The status of various sensors and operating parts in semiconductor manufacturing equipment is monitored in real time (200 times per second). The data of the constituent units of various operating parts (sensors, independent controllers, etc.) linked to the recipe of the equipment (operation sequence of the equipment) are collected and analyzed. Therefore, in the event of an error, quick cause analysis and fault location are provided to the user, so that the maintenance time (PM) and the maintenance cost can be minimized.
In an existing semiconductor manufacturing process, an error may occur in a specific operating part, which may be a problem of the operating part, but there may be more cases of errors occurring due to the failure of other operating parts. This means that it is impossible to diagnose the failure of the whole system depending on the error occurrence signal of a specific operating part in devices performing various functions due to the characteristics of semiconductor manufacturing equipment. In the case of monitoring equipment used in the past, only an error is recognized when an error occurs, but there is no judgment as to whether or not a problem occurs internally. Eventually, the user will be notified of the error through the experience. In the case of semiconductor manufacturing equipment, even if one operating part fails, all products will have an abnormal effect. Therefore, judging what is the problem is quite complex and difficult.
In the present invention, detailed data of various sensors constituting the manufacturing process are stored. In addition, when a problem occurs, what kind of problem is displayed in accordance with the recipe (operation sequence of the equipment) of the semiconductor manufacturing equipment is memorized which waveform is output from each sensor. In addition, basic state information, which must necessarily occur in the peripheral operating parts during the operation of the specific operating part, is stored, and it is configured to recognize whether or not the basic operating state information is generated. Therefore, if a specific waveform does not occur in a specific abnormal recipe, it is the cause analysis of the subject causing the action. Therefore, it is possible to find the point where the problem occurs.
For example, whether or not the system is started can be determined by checking a change in a set value of a specific operating part in the control device. The MFC-Mass flow controller detects an input of a SET value, And the ON signal of the gas input / output valve corresponding to the ON / At this time, when the ON signal of the gas input / output valve (Solenoid Valve) is detected, the normal gas flow proceeds. At this time, since the input and output valves of the MFC are both open, in order to follow SET value, . At this time, since a high pressure difference occurs between the input and output of the gas flow controller, the output value (ACT) of the gas flow controller instantaneously generates a pulse-like waveform as shown in the graph G2 of FIG. 5, the horizontal axis represents time and the vertical axis represents the voltage of the sensing signal. Graph G1 is the gas flow control value of the MFC, and graph G2 is the sensing signal that appears during normal operation of the gas input / output valve. That is, when the first and second valves are turned on, the gas pressure at the rear end of the valve suddenly increases from zero, and is followed by the gas flow rate control value.
If the instantaneous peak value of the output value of the gas flow controller is not outputted, the gas input / output valve is turned ON but the valve is not opened normally. Therefore, it can be judged that the gas injection valve is abnormal.
On the other hand, if the output waveform of the instantaneous gas flow controller is sensed but the SET value can not be followed after a predetermined time and a specific deviation between the SET value (input value) and the ACT (output value) Is greater than " 0 "). This means that all of the gas input / output valves are opened, but it can be judged that the characteristics of the gas flow controller are changed and defective.
At the same time, a normal signal appearing when the gas input / output valve is opened is measured, and the output value ACT converges to a value "0" after a specified time although the command value (set) If it has an output type, it can be judged that the gas input valve and the gas flow controller are normal but the gas output valve is abnormal.
In the case of FIG. 6, an example of a waveform for determining an error of the gas output valve corresponding to the second valve is shown as a graph G3. In Fig. 6, the horizontal axis represents time and the vertical axis represents the voltage of the sensing signal. Graph G1 is the gas flow control value of the MFC and graph G2 is the output signal that appears when the first valve, i.e., the gas input valve, is opened. In the case of FIG. 6, the graph G1 is determined as the failure of the second valve since the normal output value or the output value of the graph G3 converges to a value of 0 "after a specific time. When the first valve is opened normally, the same sensing signal as the normal state is generated through the output of the MFC. However, if the second valve fails or goes into an abnormal state, the internal pressure of the first valve and the MFC becomes equal after a certain time. Eventually, the gas will no longer flow normally and the output signal of the MFC will disappear. Therefore, if it is monitored that the output signal disappears within a certain time domain after the first and second valves are opened, the error of the second valve can be accurately confirmed.
If there is no change in the state where there is no signal generated when the first and second valves are opened when monitoring the output signal of the MFC, it can be determined that the MFC problem is the case of the present invention. That is, in the embodiment of the present invention, the maximum value and the minimum value of the normal output voltage of the currently connected MFC are basically stored. Therefore, it can be regarded as a problem of the MFC itself when a value other than the maximum value and the minimum value is completely different (for example, an MFC having a 5v output signal), for example, a value exceeding 10V or a negative value is monitored. In addition, if the MFC is not completely damaged, the output value is higher than the command value or frequent hunting occurs. However, if the output value outside the specific region is monitored, the probability of failure of the MFC itself is higher.
In this way, it is possible to measure and analyze accurate anomalous points by monitoring and comparing signals generated when various components are interlocked with each other in real time. Through the diagnosis, It is possible to judge the specific characteristics of the system in progress and to use the storage and management of basic phenomena (error, hunting, abnormality phenomenon, etc.) so that it is possible to judge whether or not the problem is certain.
Figure 7 is another exemplary detailed flowchart according to Figure 3;
In the case of Fig. 7, it is judged whether there is an abnormality in the pressure valve or in the first and second sensors for sensing the pressure based on the data obtained in the operating parts interrelated on the recipe of the corresponding manufacturing facility Flow chart.
The pressure valve used in Fig. 7 may correspond to the pressure valve 24 of Fig. In addition, the first and second sensors may correspond to the first and second pressure sensors 18 and 20, respectively.
In addition, the first and second valves may correspond to the input valve 2 and the output valve 10, respectively, in Fig.
First, the initial value confirmation in step S710 is performed, and in step S711, it is checked whether the mode is the pressure control mode. In this case, it is assumed that the initial pressure value is a normal condition. If the initial pressure value is not in the normal condition, the operation is performed according to the initial pressure value.
If the first and second valves are all turned on in step S712 and the pressure valve command value is " 0 " or more, the pressure checking is performed in step S713. If the pressure valve value does not become 0 or more even when both the first and second valves are in the ON state, it is determined in step S714 that the pressure valve is abnormal.
If the pressure valve value is equal to or greater than 0 in step S713, the first time delay in step S715 is followed by checking the output value of the first sensor in step S716. If the pressure value of the first sensor is not equal to or lower than the set value (within the set value range), it is determined that the first sensor is abnormal in step S717.
If the pressure value of the first sensor is within the set value range, the output value of the second sensor is checked in step S719 after the second time delay in step S718. If the pressure value of the second sensor is not equal to or smaller than the set value (within the set value range), the abnormality of the second sensor is determined in step S720.
If the pressure value of the second sensor is within the set value range, it is determined in step S721 that the pressure control is normal.
FIG. 8 is a detailed flowchart of the branching operation in FIG. 7; FIG.
Referring to FIG. 8, in step S810, it is checked whether the command value of the pressure control is zero. If the command value is not 0, it is checked in step S816 whether or not the valve is forcibly closed, and if it is forcibly closed, it is determined in step S813 that the pressure valve is abnormal.
If the command value is 0, it is checked in step S812 whether the output value is 0 or not. If the output value is not 0, it is determined in step S813 that the pressure valve is abnormal.
If the output value is 0, it is checked whether the pressure sensing value is decreased in step S815 through the third time delay in step S814. If the pressure sensing value is increased, it is a leakage error in the piping line. If the pressure sensing value is decreased, it is a leakage error of the pressure valve. Therefore, it is determined as a leakage error in step S816.
If the pressure sensing value is neither decreased nor increased, it is determined as a normal state in step S817.
FIG. 9 is a waveform diagram of a pressure valve signal used for fault diagnosis in the operation of FIG. 7, and FIG. 10 is a waveform diagram of a pressure sensing signal used for fault diagnosis in the operation of FIG. In the drawings, the horizontal axis represents time and the vertical axis represents voltage.
In the case of FIG. 9, the graph CON indicates the open command value of the pressure valve, and the graph OUT indicates the sensing output value of the pressure valve.
In the case of FIG. 10, the graph SC represents the sensing output change value of the pressure sensor.
As a result, when the command value for controlling the pressure in the pipe is applied at a specific pressure value, the open state of the pressure valve and the output value of the pressure sensing change can be monitored to determine whether the normal operation is performed. For example, if the pressure valve is open but there is no change in the sensor value, it is determined to be a sensor problem. If it is monitored that the open command value is applied to the pressure valve but the degree of opening of the pressure valve is not maintained at all, it is determined that the pressure valve itself is faulty.
In addition, when the command to close the pressure valve is delivered and the case where the pressure sensor value is gradually raised while the gas inlet / outlet valves are all locked is detected, it is not a problem of the specific operating parts but a valve between the gas inlet valve and the valve (LEAK: a state in which a separation and an assembling failure occurred on the piping and the connection portion) occurred somewhere. If the command to close the pressure valve is applied but the value indicating the degree of opening of the pressure valve is not "0" but more than a specific voltage, it is determined that the pressure valve is not completely blocked due to foreign matter.
Fig. 11 is a view showing an example of the installation of operating parts mounted on a facility to which the present invention is applied.
Referring to FIG. 11, an example of the installation operating components of the semiconductor manufacturing equipment is shown schematically.
11, a gas flow controller, MFC 6, is installed between the gas input valve 2 and the gas output valve 10. Between the gas input valve 4 and the gas output valve 12, a gas flow controller MFC 8 is installed. A pressure valve 24 provided in a pipe 16 extending in a furnace 14 having a chamber for processing semiconductor wafers is controlled by a pressure valve controller 22 and the first and second pressure sensors 18, .
Fig. 12 is a flow chart at the time of detection of power supply abnormality according to the present invention.
Referring to FIG. 12, if an abnormal power supply is detected in step S120, the sub power unit is driven in step S121. During the operation of the auxiliary power unit, various data and failure diagnostic data stored in the memory 106 are transmitted to the host through the communication unit 110 in step S122. In step S123, the data is stored in an internal nonvolatile memory such as a flash memory or an external storage memory such as an SSD or an HDD for data backup.
It is checked whether or not the set off time has elapsed in step S124 and the power-off mode is entered in step S125 when elapsed.
In this way, the maintenance of the system during power failure and the recording of the equipment shutdown status are maintained without loss. The power of the apparatus of the present invention can be maintained for a predetermined period of time even if a phenomenon occurs in which all the systems are down due to an instantaneous power failure and an accident of the power supply system. Therefore, it is possible to store and manage the cause of the stationary state of the semiconductor equipment and various information at the time. Information that can be used to determine how the system has progressed and developed in the event of an incident is obtained. In the event of a power failure or a power failure, it is difficult to understand what abnormal conditions are maintained in a shutdown state because all information is simultaneously transmitted in the case of a general device. In such a case, difficulties may arise in the recovery of the initial system. In order to minimize the initial recovery time, the operation of FIG. 13 is performed in the case of the present invention. Accordingly, the power state is detected to check the power failure and the power failure state, and when this signal is detected, the time information of the detected time is recorded and the sensing information of all the operating parts is stored within the set time.
13 is a flowchart of an input connector connection and an operation part replacement operation according to the present invention.
Referring to FIG. 13, when an active component is connected to the input connector in step S130, a channel search is performed in step S131, and connection channel information is displayed in step S132. In step S133, the connection information is stored. In step S134, it is checked whether the operating part connected to the input connector is off. In the case of the active component off, it is checked in step S135 whether or not the operating component is replaced. Otherwise, it is determined in step S136 that the power source of the connector is error. In the case of the operation part exchange, it is checked whether the operation part setting is set in step S137, and if not, the existing setting value is maintained in step S138.
In the case of the active component setting, the exchange history information is stored and the initialization of the existing setting value is performed in step S139.
If step S139 is performed, the operation part setting screen is displayed in step S140, and setting of the inputted operation part is performed by receiving the setting input.
13 is a control flow chart for automatic detection of an operating part connection, abnormality determination, and execution of a function for managing the parts replacement history. When connected to any input connector when connecting various sensors, the user must confirm what number of the corresponding channel is in the conventional case. However, in the present invention, when the sensor and other devices are connected, the system automatically detects how many times the channel is connected to the user and transmits the detected signal to the user.
Thus, the connection failure (error) and set-up time can be minimized. It is possible to check whether or not the specific operating parts are exchanged and to store and manage the details thereof, thereby providing the user with the overall situation of the system. In order to determine whether the operation part is replaced or not, it is necessary to confirm whether or not the operation part is exchanged, and if there is no user confirmation for a certain period of time, a total abnormal alarm of the equipment is generated. And automatically stores the replacement date and time of the corresponding operating part. Initialize the set values at the initial system installation and save the initial settings for the replaced active parts.
14 is a flowchart of a control operation of tuning and calibration according to the present invention.
Referring to FIG. 14, after storing the initial test data in step S1400, the change information for tuning and calibration is stored in step S1410. Comparisons and error displays are performed in step S1420, and a correction position is provided in step S1430.
Various information of various operating parts is stored in the first operation state after the equipment is completely installed for the first time, and the corresponding information is stored when the command value changes. Such change information is then digitized and stored for later use in tuning and calibration.
When the calibration command is executed, the standard data of the corresponding operating part is displayed on the screen, and the actual measured output value is displayed on the standard data screen to check the error information. The error information is transmitted to the user by comparing and analyzing the difference with the measurement information by a predetermined time unit. By using this information, the user can check which part of the tuning of the active parts is to be calibrated, and it can be precisely calibrated by repeatedly performing it.
Fig. 15 is a flowchart of a comparative analysis control operation of the same operating part according to the present invention; Fig.
Referring to FIG. 15, in operation S1430, a function of comparing and analyzing each other in order to minimize a characteristic error of the same operating parts in the production equipment is entered. This minimizes the error of the entire system of the production equipment, so that the performance of the equipment can always be optimized.
A method is employed which is performed in order to allow the user to compare the states of the same operating parts with each other. When the operating parts to be compared are selected, they are switched to the analysis screen required for the comparative analysis and the comparison analysis algorithm is performed. When the analysis program is automatically performed for a predetermined time, various information such as the error rate between channels and the difference in the control speed in the transient phenomenon are extracted in step S1510. Error information is displayed in step S1520. By using this function, it is possible to accurately compare and analyze the states of the same operating parts during installation, after replacement, and periodic maintenance, thus contributing to improvement of equipment performance.
16 is a signal waveform diagram used in Fig.
Referring to the drawings, the horizontal axis indicates time and the vertical axis indicates voltage. Graph P10 is the sensing output related to the first operating part. Graph P20 is the sensing output related to the second operating component compared with the first operating component. It can be seen that although the command value is changed from 3.2V to 0V for the same type of A.B operating parts, the response times between the respective operating parts are different.
It is possible to select the same operating parts and simultaneously measure the output value when the command value changes or when a specific signal is input, so that the user can diagnose the difference between the respective operating parts. Based on this, management that minimizes the error of each operating part can be performed on a system-by-system basis.
17 is an operation flow chart of providing corresponding channel status information according to the present invention.
When the user does not see the information of various operating parts immediately on site and only the remote transmitted information is confirmed through the host, the operation is not preferable and the maintenance time may be increased. FIG. 17 provides a function of extracting stored information so that the information of the operating parts can be confirmed. Therefore, it is possible to minimize the operation and maintenance time of equipment by providing basic information directly to the user in the field.
In step S1710, it is checked whether the user has requested the information of the corresponding operating part. In step S1720, data of the corresponding operating part is extracted from the entire data and the data is read so that only information of the corresponding channel can be checked. In step S1730, data conversion and screen output are formatted. Then, in step S1740, status information such as various error information, error rate information, and an exchange period including time and the like is displayed on the screen.
Meanwhile, the following functions may be additionally provided in the embodiment of the present invention.
By storing and managing the generated errors and the information of the exchanged parts list, it is possible to add a function of statistical conversion so that the user can view the information more conveniently. According to this, more accurate and convenient information retrieval and judgment information can be provided to the user.
On the other hand, when the system is initialized or activated, the initial value of the current system is confirmed and the current state information is stored in the internal temporary storage 1. When the storage capacity of the internal temporary storage 1 is exceeded, it is switched to the internal temporary storage 2 and the storage is again performed.
Here, the temporary storage can be implemented by a large capacity hard disk driver or an SSD. This is because it is necessary to overcome the limit of the capacity of the limited hardware storage and to extract the information of a specific range without any loss in case of a problem. In case of abnormal voltage or a specific problem, it checks the time information and date information, and stores information about 1 minute before and after 1 minute in the external storage . At this time, when an abnormality is detected in the operating part of A, the data of the operating part of A is not stored. This is because, in the case of semiconductor production equipment and system-based equipment, it is not merely a problem of an operating part in which an error is detected because an error is detected in one operating part. As a result, all of the operating parts are interlocked and operated, so that only the operating parts in which an error is detected can not be suspected. In addition to the operating parts in which the error is detected, it is possible to store information of all operating parts interlocked with each other, so that it is possible to confirm how the actual operating parts are actually operated. In addition, if the host is connected through the communication unit, time information on the occurrence of the abnormality is additionally transmitted, so that it is possible to accurately determine at what point the problem occurs even in a system for remotely monitoring the status of the equipment.
From now on, predictive failure diagnosis will be explained.
1, the facility-connected real-time monitoring failure diagnosis diagnostic apparatus includes a display unit 30, a power supply unit 40, an auxiliary power supply unit 42, a memory unit 50, a sensor input processing unit 80, (100), and a communication unit (110).
The control unit 100 receives the digital sensing data through the line L10.
The controller 100 compares and analyzes the data obtained from the operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data for each real-time monitoring prediction cycle with the error of the setting value and the error set value, And generates predictive diagnostic data including information.
The communication unit 110 performs communication between the controller 100 and the outside and receives the failure diagnosis diagnostic data through the line L40 and transmits the diagnosis diagnosis data to the outside. A host or a server may be located outside.
The display unit 30 is connected to the controller 100 through a line L20. The display unit 30 transmits the user input data received through the touch unit 32 to the control unit 100 or displays the display data applied from the control unit 100 on the screen.
The memory unit 50 may be connected to the controller 100 through a line L30 to perform data communication.
The real-time monitoring failure prediction diagnostic apparatus of FIG. 1 is installed in a facility direct connection type. That is, in the case where the facility is a semiconductor manufacturing apparatus, the sensor input processing unit 80 is connected to various sensors of the apparatus immediately located on the site, and the failure prediction predictive diagnosis apparatus is installed near the semiconductor manufacturing apparatus . Therefore, the operator or manager on the spot can take immediate action when confirming the failure diagnosis diagnosis data of the real-time monitoring failure prediction diagnostic apparatus.
An exemplary detailed configuration of the sensor input processing unit in Fig. 1 may be configured as the block of Fig. 2 described above.
That is, the sensor input processing unit 80 includes an electrostatic discharge protection / surge protection unit 80-1 for performing ESD protection and surge protection with respect to the sensing signal SI, A plurality of amplifiers 80-3 and 80-4 for amplifying the level of the sensing signal SI at a predetermined amplification rate, a high-impedance matching unit 80-2 for performing impedance matching, An A / D converter 50-5 for converting a digital signal SI into digital sensing data, a plug-in play 80-6 for receiving a power input PI to perform plug-in play, a plurality of regulators (80-7, 80-8) for outputting the digital sensing data, and a signal converter (80-9) for outputting the digital sensing data.
18 is a flowchart for real-time monitoring failure prediction diagnosis according to another embodiment of the present invention.
First, the controller 100 of FIG. 1 performing initialization in step S300 checks whether it is a failure prediction diagnosis mode in step S301. In the case of the failure prediction diagnosis mode, the reference value is set by measuring the sensing signal of the operating component during the set time in step S302. The reference value is used to determine an error with respect to the set value.
 In step S303, it is checked whether or not the sampling time has elapsed. If elapsed, the digital sensing data is read.
In step S304, the current sensing data is compared with the reference value. In step S305, an error is extracted to generate unit predictive diagnostic data (predictive time data).
In step S306, error accumulation value, real-time management of elapsed time, and tolerance management are performed to generate predictive diagnostic data.
In step S307, failure diagnosis predictive diagnosis data indicating a maintenance and replacement cycle is generated.
As a result, by performing steps S302 to S307, predictive diagnostic data including predicted operating component failure timing information is generated. The predictive diagnostic data for predicting a failure is obtained by comparing and analyzing data obtained from operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data for each real-time monitoring prediction period with an error and an error set value for the set value .
In step S308, the failure diagnosis predictive diagnostic data is displayed on the screen or provided to the communication unit 110. [ The communication unit 110 transmits the failure diagnosis prediction data to the outside.
By predicting the time that an equipment or equipment can be accidents, productivity is improved if an error or an accidental defective part is prevented. In addition, during the Preventive Maintenance (PM) cycle, unnecessary exchange of expensive sensor and control device operating parts is minimized. Therefore, the cost reduction effect due to the reduction of the maintenance cost is obtained.
In the case of the embodiment of the present invention, the actual failure and the time prediction up to the point at which the problem is possible can be realized by monitoring the real time information and storing the error information between the SET value (command value) and ACT An error rate generated at each change point of the value, an increase in the error variation, a tilt phenomenon, and the like.
In the case of conventional monitoring systems, the user extracts only some of the necessary information from the vast amount of data stored after the system is operated. The user graphs the extracted information to infer an approximate maintenance cycle. Therefore, the failure prediction is diagnosed based on subjective and conventional information and phenomena such as user management information and facility experience, and reliability is degraded. Also, characteristics of various operating parts can be changed as certain foreign substances are generated by mixed operation of various gases among characteristics of operating parts of semiconductor manufacturing equipment. On the other hand, in some cases, these foreign substances are removed due to the use of a specific gas. As a result, in the past, it is difficult to accurately recognize such abnormal internal factors.
In the case of the present invention, the state of various operating parts is monitored in real time, and it is accurately determined whether the error is changed through analysis. This management allows you to accurately identify the internal conditions that may shorten or extend the life of specific operating parts. It is very important to accurately recognize the internal situation because unnecessary maintenance time is increased and unnecessary maintenance costs are incurred if the usable operating parts are exchanged according to experience and subjective judgment.
According to the present invention, it is possible to detect an error increase rate of the SET value and the ACT value of various sensors and to know the time at which a real problem may occur. During the Preventive Maintenance (PM) cycle, this information will also be used to determine if the items need to be replaced, maintained, or are still available.
Figs. 19A-19C are exemplary utilization diagrams used in the prediction algorithm of Fig.
The first method of predictive failure diagnosis predicts the replacement and maintenance cycle using the difference between the command value (set value) and the output value (ACT value) of various sensors and the error value range set in the system.
Basically, data is measured after a certain time delay at the point where the variation of the values of various sensors occurs, and the error value is stored. For example, if there is no change in the command value of the sensor value (the drive command value applied to the active component connected to the sensor, eg on / off signal), the unit of measurement is the unit of 10 minutes and the average value is calculated every hour. On the other hand, the change in the command value of the sensor value is also measured step by step and the average value is calculated in units of one hour. The measurement is the command value and output value of each sensor. In the case of average use, the command value and the output value accumulate 125 data read every time the command value changes every 10 minutes or so, average it, and then average it again. When it is 1 hour, it is averaged again and saved. At this time, the difference between the two values is stored and managed as the initial reference value ref. The second and third also use the same method to store and manage information.
When more than one value is stored, the prediction algorithm is executed using this information. The prediction algorithm operates according to a time schedule as shown in FIG. 4A.
FIG. 19B shows a graph of predictive failure diagnosis per unit section. In the drawing, the horizontal axis represents time and the vertical axis represents the operation time of the equipment in units of time.
In Fig. 19A, the error set value is assumed to be 1%. The reference value REF interval and average calculation are as follows.
Create averages of 16 times in 225-second intervals over a period of time. It also generates an average of 8 times every 20 seconds within 225 seconds. Calculate one value (REF value). If there is no change of more than 10% of the set value within the 225 second unit time, it averages once every 225 seconds. If the set value is changed by 10% or more, even if it is not 225 seconds, the average is created after 5 seconds, and one cycle of 225 seconds is not recognized but it is recognized as one cycle. At this time, if there is a lot of change in set value for one hour and 16 is completed, skip for the remaining time.
When the fault diagnosis flow starts, the value of the sensing signal is measured for 1 hour and the error value generated is set as the reference value. The reference value generated in 1H in Fig. 19A is stored as the CNT1 value for management. Then, if the second value is generated in 2H, it is compared with the first value to generate another changed value. The gradient is calculated using the difference between the second value thus generated and the reference REF value to generate the prediction time as shown in FIG. 19B. In FIG. 19B, a graph G1 calculates the predicted failure time for the reference value of CNT1. On the other hand, as shown at time point t2, if the value of 0.1% is changed for 2 hours and the error setting value is given as 1%, the expected time for an error to occur after 20 hours can be obtained as G2.
If the third data comes out again, it is also compared with the reference value. Since it changes by 0.2% for 3 hours, the prediction value of 15 hours can be obtained as shown in the graph G3. Basically, it can be seen that it is possible to predict by using the three values including the driving frequency, the error value and the error simultaneously. If the value of 0.3% is changed from the reference value after the fifth measurement, the prediction time of 30 hours is obtained because 0.2% has changed for 6 hours. On the other hand, as can be seen from the change from G4 to G5, the prediction function that can follow the internal environmental change is performed basically while continuously increasing.
The second method of diagnosing failure prediction timing is to predict maintenance and replacement cycles through continuous hunting detection.
Various sensors and operating parts follow the target value through control internally based on the given command value (target value). In case of normal sensor, the output value of the actual control state is close to DC because all the systems are stabilized internally.
However, in the case of an abnormal sensor, the control value excessively changes in the follow-up of the target value, and the output value such as bounce fluctuates. This hunting amount and cycle can be used to follow the abnormality of the system and the life span.
Basically, the error value and the output control value set based on the command value (target value) of various sensors are compared and used. In this case, the fluctuation value of the output value is counted by the width and the number of occurrences for a certain period of time.
If the first hunting occurs in a normal operation state (when the hunting criterion is maintained for 10 seconds or more - the time for judging hunting here is an example value for explaining the present invention), the time information and hunting Store the value. If a second hunting occurs, it stores the information again and calculates the time until the first and second hunting occur and obtains the first predicted time. If the third hunting occurs again, the first and third times may be calculated again to obtain the changed tracking time.
The prediction algorithm according to the second method operates according to the time table in FIG. 19C. FIG. 19C shows an example in which an alarm occurs when hunting occurs more than 12 times per hour. If the hunting lasts more than 2 minutes, the prediction algorithm is not used separately because the failure has already occurred.
When the system starts up, it monitors until the first hunting occurs.
If the first hunting occurred after one hour, the time information, voltage width and error width should be stored and managed. When the second hunting occurs, the first hunting is saved as the first hunting. Then, the number of hunting occurrences is counted and stored. The stored information is used to predict the number of hunting times and the time from when the system starts up to when it occurs. If hunting occurs as shown in FIG. 19C, the first hunting (C1) occurs after 2 hours and the second hunting (C2) occurs after 3 hours. Therefore, hunting occurred twice over the entire 3-hour period and 30% of the first occurrence frequency was increased, so that the slope was increased by 30% per hour. Based on this, if the 30% increase per hour is continuously occurring, the estimated diagnosis time for the failure that can occur 12 times per hour will be about 10 hours. In this way, the failure prediction time is inferred, and if the rate of increase decreases, the new value is set based on the decrease rate. This is because it is an algorithm that can follow the environmental change.
The third method of diagnosing the failure prediction timing is to monitor the error of the command value and the output value unlike the first method and to continuously detect the increase of the output voltage of the sensors or the operating parts when the command value is constant, It is to predict the exchange cycle.
20A and 20B are illustrative utilization drawings used in the third method. Various operating parts and sensors are controlled based on the given command value, so they follow the command value. In other words, in the case of a normal operating part or sensor, all the systems are internally stabilized, so the output of the actual control state is close to DC, and the error is within the error range provided by the manufacturers. Also, the error rate is equal to zero or has a value smaller than the command value, that is, a minus (-) error.
In the case of the gas flow controller (MFC), the internal flow valve is controlled according to the command value and the gas is discharged to the target amount. If foreign matter is generated in the internal piping, the gas flow controller opens the valve more to discharge the gas to follow the command value. This will increase the output value.
Thus, while the command value is kept constant, the third method of the predicted failure time diagnosis can be utilized.
If the system is activated and certain valve signals and command values are changed (100 mV or more for 10 seconds), this point is recorded as the starting point of the third method at time point t1 in Fig. 5A. At this time, recording the starting point is to utilize the third method of predictive failure diagnosis at the same time and again at the next restart because the semiconductor equipment repeats a series of corresponding processes indefinitely. That is, the prediction information generated in the previous step is used again at the time of restart.
In the third method, only the output value is stored, not the difference between the command value and the output value. In this case, the first output value may be generated by using the method of obtaining the ref value as it is, as in the first method of the predictive failure diagnosis. The graph G10 of FIG. 19B indicates the ref value. For example, the output value of 100 or more times can be sensed with a delay time of 10 seconds, and the average value thereof can be stored as a ref.
If the command value is not changed after the first value is generated in this way, the average value is stored by measuring the output value several times at regular time intervals. If the command value has not changed in this way, it will repeat indefinitely. Here, it means a target value to control the command value.
Here, if two or more data are generated, the increment is stored as shown in the graph G11 of FIG. 20B by using the difference between the ref of the first generated output value and the second generated data.
The value of this increment is converted to%, and then compared with the preset error limit (assuming 1%), if the first generated prediction information increases 0.1% of output value during 1 hour, Can be calculated. In this way, when the third and fourth information are generated, it is possible to calculate another prediction time information by comparing the difference between the ref of the first output value and the difference.
If the third method is performed and the command value changes and the various valves and signals have changed, the execution of this method is stopped. In FIG. 20A, the interval between the time point t2 and the time point t3 means a period in which the execution of the third method is suspended.
Then, when various valves and command values corresponding to the start point of the third method come, this method proceeds from what has been performed in the previous step.
Continuous monitoring in this manner can detect an increase in foreign matter in the internal piping of the gas flow controller. Therefore, according to this method, it is possible to predict the maintenance and cleaning cycle of the operating parts and piping of the equipment as shown in Fig.
As described above, an optimal embodiment has been disclosed in the drawings and specification. Although specific terms have been employed herein, they are used for purposes of illustration only and are not intended to limit the scope of the invention as defined in the claims or the claims. Therefore, those skilled in the art will appreciate that various modifications and equivalent embodiments are possible without departing from the scope of the present invention. For example, without departing from the spirit of the present invention, various configurations and modifications may be made to the internal structure, the detailed structure and the shape of the apparatus, and the diagnosis-related flow charts, if the matters are different.
80: Sensor input processing unit
100:
110:

Claims (22)

  1. At least two sensors for sensing operating parts of the manufacturing facility;
    A sensor input processing unit including an A / D converter for processing sensing signals provided from the sensors and generating a digital sensing data, and a signal converter for receiving an output of the A / D converter;
    And a controller for receiving the power supplied from the power supply unit or the auxiliary power supply unit, and for diagnosing the cause and location of an accident when an error occurs in the operation of the operation part of the manufacturing facility, A controller for comparing and analyzing the obtained data for each real-time monitoring cycle to generate and store fault diagnosis data including faulty operation part information; And
    So that communication between the control unit and the outside is performed, And a communication unit for transmitting,
    Wherein when the gas flow rate controller is installed between the input valve, the output valve, and the input / output valves among the operating parts of the manufacturing facility, the control unit controls the gas flow controller Is diagnosed that the input valve has failed if it is not monitored as an instantaneous peak maximum value.
  2. delete
  3. The apparatus of claim 1, wherein the controller diagnoses that the gas flow controller is malfunctioning when the sensing signal of the gas flow controller is higher than a set value of the gas flow controller.
  4. delete
  5. The apparatus of claim 1, wherein the manufacturing facility is a semiconductor manufacturing facility.
  6. delete
  7. The apparatus of claim 1, wherein the communication unit is connected to an external host and transmits facility shutdown state data to the host when the power supply is abnormal and abnormal power is input to the manufacturing facility.
  8. A sensor for receiving a sensing signal provided from at least two or more sensors for sensing the operating state of the operating part of the manufacturing facility and for receiving the output of the A / D converter and the digital sensing data, By an input processing unit;
    The control unit compares data obtained from operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data by a control unit at every real-time monitoring period to diagnose the cause and location of an accident when an error occurs in the operation of the operating part of the manufacturing facility. Analyze;
    Generating the failure diagnosis data including the failed operation part information by the control unit through the result of the comparison and analysis;
    The failure diagnosis data is displayed on the display unit or transmitted to the outside through the communication unit ≪ / RTI &
    Wherein when the gas flow controller is installed between the input valve, the output valve and the input / output valves among the operating components of the manufacturing facility, the sensing signal of the gas flow controller immediately after application of the operating component drive command has an instantaneous peak And if the input value is not monitored as the maximum value, the input valve is diagnosed by the control unit.
  9. delete
  10. The method of claim 8, wherein the gas flow controller diagnoses that the gas flow controller has failed when the sensing signal of the gas flow controller is higher than a set value of the gas flow controller.
  11. delete
  12. The method as claimed in claim 8, wherein, when the pressure valves are installed in the first and second sensors and the first and second sensors,
    When the pressure value of the pressure valve is normal and the output value of the first sensor is not within the set value range,
    A facility-connected real-time monitoring trouble diagnosis method for diagnosing a fault with an abnormality of the second sensor when the pressure value of the pressure valve and the output value of the first sensor are normal and the output value of the second sensor is not within the set value range .
  13. The method as claimed in claim 8, further comprising the steps of: detecting a power state to check a power failure and a power failure state; recording time information at a time when a power failure or a power failure state is detected; and sensing information of all the operating parts Wherein the fault diagnosis method further comprises the steps of:
  14. 9. The method of claim 8,
    And performing a function of automatically detecting, determining an abnormality, and managing a working part exchange history through a connector.
  15. The method of claim 8, further comprising performing a function of extracting stored information so that the information of the operating parts can be confirmed in the field.
  16. At least two sensors for sensing operating parts of the manufacturing facility;
    A sensor input processing unit including an A / D converter for processing sensing signals provided from the sensors and generating a digital sensing data, and a signal converter for receiving an output of the A / D converter;
    Wherein the control unit receives power from a power supply unit or an auxiliary power supply unit and compares data obtained from operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data with an error and an error set value of the set value every real- And generating predictive diagnostic data including the predicted operating part failure information; And
    And a communication unit for performing communication between the control unit and the outside and transmitting the predictive diagnostic data to the outside,
    Wherein,
    Setting a reference value by obtaining an error between a sensing setting value and a sensing output value of the sensing signal during a set time;
    Thereafter, when the first sampling time period is reached, the second sensing output value is read to calculate a first difference value from the reference value, an error ratio is calculated by subtracting the calculated first difference value from the error setting value, Generating a first error prediction time that is predicted in the cycle;
    A third sensing output value is read at a second sampling time period after the first sampling time period to calculate a second difference value from the reference value, and the calculated second difference value is subtracted from the error setting value, Generating a second error prediction time predicted in a second sampling time period after determining the ratio;
    (N is a natural number equal to or greater than 3) sampling period after the second sampling time period to calculate an n-th difference value from the reference value, and the n-th sensing output value is calculated from the calculated n-th sampling time period Subtracting the difference value to obtain an error ratio, and generating an n-th error prediction time predicted in an n-th sampling time period;
    Wherein the predicted diagnosis data is displayed or alerted when the generated first to nth error prediction times reach a pre-alarm time.
  17. delete
  18. And a signal converter for processing the sensing signals provided from at least two or more sensors for sensing operating parts of the manufacturing facility to receive digital sensing data from an A / D converter and an output from the A / D converter, By an input processing unit;
    The control part compares and analyzes the data obtained from the operating parts interrelated on the recipe of the corresponding manufacturing facility among the digital sensing data with the error and the error set value of the setting value by the real time monitoring prediction cycle, Generate the predictive diagnostic data included by the control unit;
    The predictive diagnosis data generated by the control unit is displayed on the display unit in the vicinity of the corresponding manufacturing facility or transmitted to the outside through the communication unit,
    Wherein the error and error set values are measured by the controller as an average value.
  19. delete
  20. 19. The diagnostic method of claim 18, wherein the generation of the predictive diagnostic data is generated by obtaining a slope using a difference between the command value and the output value of the sensors under a given error set value.
  21. 19. The method of claim 18, wherein the generation of the predictive diagnostic data is generated by counting the hunting frequency of the command value and the output value of the sensors under a given error set value to obtain the occurrence frequency.
  22. 19. The method of claim 18, wherein, in a state where the manufacturing facility is controlled to a predetermined command value, the generation of the predictive diagnostic data is performed by continuously analyzing and detecting an output increment of the sensing data, Diagnostic method.
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