Abstract:
A method for avoiding irregular shutoff of production equipment, includes: measuring regularly time-series data of characteristics of a rotary machine used in the production equipment running for the production; obtaining first failure diagnosis data subjecting the time-series data to a first real-time analysis; obtaining second failure diagnosis data subjecting the first failure diagnosis data to a second real-time analysis; predicting a status of the production equipment several minutes later using the second failure diagnosis data; and shutting off during a production process if the result of the prediction determines that the production equipment will shut off irregularly, and switching to a purge sequence for conducting a gas purge of the production equipment.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS  
         [0001]    This application is based upon and claims the benefit of priority from prior Japanese Patent Application P2001-264286 filed on Aug. 31, 2001; the entire contents of which are incorporated herein by reference.  
         BACKGROUND OF THE INVENTION  
         [0002]    1. Field of the Invention  
           [0003]    The present invention relates to a method for avoiding irregular shutoff of production equipment equipped with a vacuum pump and a system for avoiding irregular shutoff.  
           [0004]    2. Description of the Related Art  
           [0005]    Using failure prediction techniques for a vacuum pump attached to semiconductor production equipment, a life expectancy prediction may be made for a vacuum pump from a long-term perspective in order to perform scheduled maintenance. However, since a thin film deposition is carried out under a plurality of processing conditions having varying pump loads during normal operation of the production equipment, in cases where film deposition is carried out under process conditions where a pump load is high, there are times when the vacuum pump will suddenly shut off during the film deposition.  
           [0006]    If such a situation occurs, in many cases entire product lots in the middle of the film deposition process will be lost. In addition, due to irregular shutoff of the vacuum pump during the film deposition, it is possible that a highly reactive gas and/or a highly toxic gas may be released into the air when the vacuum pump is replaced, deteriorating the work environment and causing problems for employee health maintenance. In particular, in a case of semiconductor production equipment, there are times where a highly toxic gas such as arsine (AsH 3 ), phosphine (PH 3 ), or diborane (B 2 H 6 ) is used. If the vacuum pump suddenly shutoffs while using these gases, the possibility of serious or even life-threatening accidents happening cannot be ruled out. In the case of the vacuum pump for evacuating such highly toxic gases and/or highly reactive gases, there are situations where special measures, such as replacing the vacuum pump in a draft chamber, must be taken. Therefore, depending on the type of the gas used, more labor hours may become necessary than for an ordinary pump exchange.  
           [0007]    In addition, maintenance may be performed after a vacuum pump life expectancy prediction or a prediction of when an irregular shutoff caused by motor current or the temperature of a cooler within the vacuum pump may occur according to experience-based equipment management. However, depending on the gas used, even if these parameters fall within a normal range of values, the vacuum pump may be in trouble. Therefore, whether in a single process or in multiple processes, it is impossible to assess a failure using only these parameters. Furthermore, due to the fact that only pump data is analyzed, in the case where the multiple processes are performed the parameters monitored fluctuate greatly because the pump load varies with each condition, and a life expectancy prediction is impossible.  
           [0008]    As described above, in cases corresponding to production of various kinds of industrial products, when the same production equipment is used to manufacture various kinds of industrial products, the required process conditions are diverse, and there is a problem in that it is impossible to determine a universal threshold or criteria for production equipment failure prediction.  
         SUMMARY OF THE INVENTION  
         [0009]    According to a first aspect of the present invention, a method for avoiding irregular shutoff of production equipment, includes: measuring regularly time-series data of characteristics of a rotary machine used in the production equipment running for the production; obtaining first failure diagnosis data subjecting the time-series data to a first real-time analysis; obtaining second failure diagnosis data subjecting the first failure diagnosis data to a second real-time analysis; predicting a status of the production equipment several minutes later using the second failure diagnosis data; and shutting off during a production process if the result of the prediction determines that the production equipment will shut off irregularly, and switching to a purge sequence for conducting a gas purge of the production equipment.  
           [0010]    According to a second aspect of the present invention, a system for avoiding irregular shutoff of production equipment, includes: a production chamber performing a production process of the production equipment; a rotary machine processing a load of the production process; sensors measuring time-series data of characteristics of the rotary machine and outputting the time-series data in real time; a real-time failure diagnosis module configured to perform a first real-time analysis on the time-series data so as to obtain first failure diagnosis data, to perform a second real-time analysis on the first failure diagnosis data so as to obtain second failure diagnosis data, and to predict future status of production equipment based upon the second failure diagnosis data; and a real-time controller configured to perform and control the production process, and to direct the production equipment to introduce a purge gas if the real-time failure diagnosis module determines that during the production process the production equipment may have an irregular shutoff. 
       
    
    
     BRIEF DESCRIPTION OF DRAWINGS  
       [0011]    [0011]FIG. 1 is schematic diagram of an irregular shutoff avoidance system according to an embodiment of the present invention;  
         [0012]    [0012]FIG. 2 is a block diagram for describing the structure of a real-time failure diagnosis module according to an embodiment of the present invention;  
         [0013]    [0013]FIG. 3 is a block diagram for describing the structure of a pump information analysis module according to an embodiment the present invention; and  
         [0014]    [0014]FIG. 4 is a flowchart for describing an irregular shutoff avoidance method according to an embodiment of the present invention. 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0015]    Various embodiments of the present invention will be described with reference to the accompanying drawings. It is to be noted that the same or similar reference numerals are applied to the same or similar parts and elements throughout the drawings, and the description of the same or similar parts and elements will be omitted or simplified.  
         [0016]    An irregular shutoff avoidance system according to an embodiment of the present invention is described as the system depicted in FIG. 1, which is constructed by a low pressure chemical vapor deposition (LPCVD) system  5  for growing a silicon nitride film (Si 3 N 4  film) and a computer integrated manufacturing (CIM) equipment  1  for performing control/management of the LPCVD system  5 . As shown in FIG. 1, the LPCVD system (production equipment)  5  includes a production chamber  521  having a hermetically sealed structure capable of vacuum pumping. On the exhaust side of the production chamber  521 , a vacuum piping is connected, and on the exhaust side of the vacuum piping, a water-cooled trap  17  with a water-cooled plate for allowing collection of solid reaction by-product is connected. On the exhaust side of the water-cooled trap another vacuum piping 17  is connected, and on the exhaust side of this vacuum piping a pressure control valve  15  is connected. On the exhaust side of the pressure control valve  15  an additional vacuum piping is connected, and on the exhaust side of the additional vacuum piping is connected a vacuum pump system, wherein a mechanical booster pump  18  and a dry pump  19  are connected in series to each other so as to evacuate the inside of the production chamber  521 . The pressure control valve  15  isolates, if necessary, the production chamber  521  from the vacuum pump system ( 18 ,  19 ) so as to adjust exhaust conductance. Meanwhile, a plurality of gas piping are connected to the production chamber  521 , and these gas piping are respectively connected to mass-flow controllers  511 ,  512 ,  513 ,  514 , and so on. A gas supply control system  51  has the mass-flow controllers  511 ,  512 ,  513 ,  514 , . . . , and predetermined gases are supplied to the gas supply control system  51  from a gas supply system  6  of a plant side. More specifically, flow rates of the various source gases and carrier gas to be introduced into the production chamber  521  are respectively controlled by the mass-flow controllers  511 ,  512 ,  513 ,  514 , . . . , in the gas supply control system  51 . The source gases and the like controlled by the gas supply control system  51  pass through gas piping into the production chamber  521 , which maintains a low pressure level. An internal temperature of the production chamber  521  is controlled by a heating unit  522 , which is formed with a heating part and a temperature measurement instrument.  
         [0017]    A film deposition of the silicon nitride film using the LPCVD method includes introducing dichlorosilane (SiH 2 Cl 2 ) gas as a silicon source under low pressure via the mass-flow controller  511 , and introducing an ammonia gas (NH 3 ) as a nitrogen species via the mass-flow controller  512 . These gases then chemically react at around 800° C., depositing a thin film of silicon nitride upon a semiconductor substrate  13 . The mass-flow controller  513  controls the introduction of a nitrogen (N 2 ) gas into the production chamber  521 . The chemical reaction between the dichlorosilane gas and the ammonia gas at 800° C. produces a silicon nitride material, and also causes an ammonium chloride (NH 4 Cl) gas and a hydrogen (H 2 ) gas to develop as reaction by-products. The hydrogen in gas form is evacuated by the vacuum pump system ( 18 ,  19 ) used in the LPCVD system  5 . Meanwhile, the ammonium chloride is in gas form at the time of formation because the inside of the reactive chamber is at a temperature of approximately 800° C. under low pressure conditions of several hundred Pa or less. As shown in FIG. 1, the LPCVD system  5  typically has a water-cooled trap  17 , which collects solid reaction by-product, deployed between the LPCVD system  5  and the vacuum pump system ( 18 ,  19 ). The water-cooled trap  17  plays a role in reducing the amount of ammonium chloride or the by-product material adhered to the pressure adjustment valve  15  or the vacuum pump system ( 18 ,  19 ). The source gas and the reaction by-product gas that pass through the vacuum pump system ( 18 ,  19 ), are removed by a scrubber  7 . The scrubber  7  removes harmful components extracted by the vacuum pump system ( 18 ,  19 ) through absorption or chemical reaction.  
         [0018]    The internal pressure of the production chamber  521  is measured by a pressure gauge  14 , which is connected to the production chamber  521 . A capacitance manometer, Pirani gauge, or the like may be used as the pressure gauge  14 . A pressure control system  16  is connected to the pressure adjustment valve  15 , which adjusts conductance of the evacuation system based on the difference between a measured pressure value, as measured by the pressure gage  14 , and a set pressure value, as fixed by a chamber control system  52 , so that the internal pressure of the production chamber  521  may reach a preset value and maintain the preset value.  
         [0019]    The degree of opening representing the adjustment status of the pressure adjustment valve  15  is output to a LPCVD main control system  53  in real time. In addition, a vibration gauge  31 , a temperature gauge  32 , and an ammeter  33  are connected to the mechanical booster pump  18 , and an exhaust pressure gauge  34  is connected to the exit portion thereof. The ammeter  33  measures current consumed in order to rotate the mechanical booster pump  18 . The values measured by the vibration gauge  31 , temperature gauge  32 , ammeter  33 , and exhaust pressure gauge  34 , respectively, are output to the LPCVD main control system  53 . A vibration gauge  35 , a temperature gauge  36 , and an ammeter  37  are also connected to a dry pump  19 . The ammeter  37  measures consumed current for rotating the dry pump  19 . The measured values from the vibration gauge  35 , temperature gauge  36 , and ammeter  37 , respectively, are output to the LPCVD main control system  53 . The LPCVD main control system  53  contains an LPCVD system real-time controller  531  and a CPU  532 . The LPCVD system real-time controller  531  centrally controls the gas supply control system  51 , the heating unit  522 , and the pressure control system  16 . The CPU  532  includes a real-time failure diagnosis module  533 , and the real-time failure diagnosis module  533  performs calculations of failure diagnosis in real time. The real-time failure diagnosis module  533  stores as time-series data the degree of opening of the pressure adjustment valve  15  from the pressure gauge  14  and the pressure control system  16 , and respective outputs of the vibration gauge  31 , temperature gauge  32 , ammeter  33 , and exhaust pressure gauge  34  connected to the mechanical booster pump  18 , and the vibration gauge  35 , temperature gauge  36 , and ammeter  37  connected to the dry pump (main pump). Moreover, the real-time failure diagnosis module  533  receives in real time outputs from sensors for characteristics such as the pressure control system  16 , the vibration gauges  31 ,  35 , the temperature gauges  32 ,  36 , ammeters  33 ,  37 , or the exhaust pressure gauge  34 , performs calculations regarding these outputs in real time, thus generating a first failure diagnosis data group. Factors such as an average value of time-series data, a standard deviation, a covariance in terms of time, and a covariance in terms of space are calculated for the characteristics. The real-time failure diagnosis module  533  then determines in real time whether the vacuum pump system ( 18 ,  19 ) is in a normal state or at a state just before failure based on the group of first failure diagnosis data. Moreover, based on the determination, a command is given to the LPCVD system real-time controller  531  to initiate either an alarm or a shutoff sequence.  
         [0020]    The LPCVD system  5  shown in FIG. 1 is connected to CIM equipment  1  for performing production management of a plurality of semiconductor production equipment, and operation of the LPCVD system  5  is controlled by the CIM equipment  1 . The CIM equipment  1  includes at least a host computer  101 , a process control information storage unit  102 , and an system information storage unit  103 . The host computer  101 , the process control information storage unit  102 , and the system information storage unit  103  are connected to one another via a bus  105 . In addition, an input/output interface  104  is connected to the bus  105 , and the LPCVD system  5  exchanges information with the CIM equipment  1  via the input/output interface  104 . Although omitted from the illustrations, in actuality the LPCVD system  5  and the CIM equipment  1  are connected to each other via an information network such as the Internet or a local area network (LAN). The process control information storage unit  102  includes a process control database for managing process information such as the process conditions or a film deposition recipe for the semiconductor substrate  13 . Within the process control database, data such as type of product, type of film deposition, process recipe information, temperature/pressure/gas flow rates for the film deposition, and vacuum equipment load testing recipe information are classified and recorded. The LPCVD system real-time controller  531  inputs a film deposition recipe, and a pump load test recipe from the process control information storage unit  102 , and interprets them in real time to centrally control the gas supply control system  51 , the heating unit  522 , and the pressure control system  16 . The system information storage unit  103  includes a system information database wherein the output values of the measuring instruments attached to the LPCVD system  5  and the vacuum pump system ( 18 ,  19 ) are organized by state at each stage under each film deposition condition and recorded. More specifically, time-series data, such as temperature, power consumption and current in the vacuum pump, corresponding statistical data and data forming the Mahalanobis space, time-series data, such as the temperature and pressure of the production chamber  521  and the pressure adjustment valve  15 , corresponding statistical data, and data, such as the thickness of an accumulated film, amount of integrated gas flow (over time), and differences among individual pumps is classified and recorded into the system information database. The measuring instrument output data recorded in the system information storage unit  103  is classified/organized and recorded by the condition, or similar conditions, under which a film corresponding to a product in the process control information storage unit  102  is deposited. In addition, output from each measuring instrument in a reference semiconductor production equipment (LPCVD system), either in the same plant as the LPCVD system  5  or in another, connected to the network is similarly respectively recorded in the system information storage unit  103  via the main control unit of that equipment. The real-time failure diagnosis module  533  obtains statistical characteristics values such as average over time, standard deviation, and auto covariance as well as obtaining the Mahalanobis distance from the Mahalanobis space (reference space) of the multivariate found from the measured values and the characteristics values stored in the system information storage unit  103 , and determining in real time whether the vacuum pump system ( 18 ,  19 ) is in a normal state or at a state just before failure.  
         [0021]    As shown in FIG. 2, the real-time failure diagnosis module  533  includes at least a pump information analysis module  601 , an alarm/shutoff sequence start-up module  602 , a gas flow integrating analysis module  603 , an accumulated deposition information analysis module  604 , a gas consumption analysis module  605 , a loading state analysis module  606 , a system situation analysis module  607 , and a system difference analysis module  608 . Here, if the vacuum pump has an irregular shutoff during the film deposition, the alarm/shutoff sequence start-up module  602  sends a deposition stop signal to the film deposition equipment to cause the deposition process to halt, and switches to a purge sequence. In addition, the gas flow integrating analysis module  603  performs analysis using the integrated amount of inflow gas over time for each type of gas and the inflow time as analytical parameters. Moreover, the accumulated deposition information analysis module  604  performs analysis using accumulated film thickness information as an analytical parameter. The gas consumption analysis module  605  performs analysis using the amount of gas consumption for each type of gas under each process condition as an analytical parameter. Moreover, the loading state analysis module  606  performs analysis using information regarding conditions in the production chamber  521  (i.e. wafer full-charged or an empty boat) as analytical parameters. Moreover, the system situation analysis module  607  uses system situation information (piping length, and pressure) as an analytical parameter. The system difference analysis module  608  performs analysis using the differences in vacuum pumps among the equipment of the system as an analytical parameter.  
         [0022]    Note that the pump information analysis module  601 , as shown in FIG. 3, includes a time-series data analysis module  631 , a statistical data analysis module  632 , and a Mahalanobis distance analysis module  633 . The time-series data analysis module  631  performs a first real-time analysis of the time-series data from the sensors for characteristics such as the pressure control system  16 , the vibration gauges  31 ,  35 , the temperature gauges  32 ,  36 , the ammeters  33 ,  37 , and the exhaust pressure gauge  34  as shown in FIG. 1, generating a first failure diagnosis data group. A group of statistical data, such as the average value, the standard deviation, the auto covariance relating to time, and the auto covariance relating to space for time-series data of characteristics, is calculated as the first failure diagnosis data group. Accordingly, the time-series data analysis module  631  comprises a time average calculation circuit  641 , a time derivation calculation circuit  642  and the like. The statistical data analysis module  632  performs a second real-time analysis of the group of statistical data (the first failure diagnosis data group) generated by the time-series data analysis module  631 , generating a second failure diagnosis data group. Meanwhile, the Mahalanobis distance analysis module  633  reads out the data group for defining the Mahalanobis space from the system information storage unit  103 , and calculates Mahalanobis distances.  
         [0023]    Referring to the flowchart shown in FIG. 4, an irregular shutoff avoidance method, according to an embodiment of the present invention, is described herein.  
         [0024]    (a) To begin with, in step S 101 , information on the vacuum pump system ( 18 ,  19 ) is regularly monitored by the sensors of characteristics such as the pressure control system  16 , vibration gauges  31 ,  35 , temperature gauges  32 ,  36 , ammeters  33 ,  37 , and exhaust pressure gauge  34 . The obtained time-series data for the characteristics is input to the real-time failure diagnosis module  533 . p 1  (b) In step S 102 , the real-time failure diagnosis module  533  performs a first real-time analysis based upon the information obtained in step S 101  from the sensors, such as the pressure control system  16 , vibration gauges  31 ,  35 , temperature gauges  32 ,  36 , ammeters  33 ,  37 , and exhaust pressure gauge  34 , generating a first failure diagnosis data group. For example, the average value, the standard deviation, the auto covariance relating to time, and the auto covariance relating to space for the time-series data of the characteristics are calculated so as to obtain the first failure diagnosis data group. The obtained first failure diagnosis data group is sent to the system information storage unit  103  in the CIM equipment  1 , and recorded in the system information database of the system information storage unit  103 .  
         [0025]    (c) Afterwards, in step S 103 , the real-time failure diagnosis module  533  performs a second real-time analysis based upon the first failure diagnosis data group generated in step S 102 , thus generating a second failure diagnosis data group. In order to generate the second failure diagnosis data group, a group of reference data, such as the Mahalanobis space, may be read out from the system information storage unit  103  and analysis performed based upon the relationship of that group with the reference data. The second failure diagnosis data group that is obtained is then sent to the system information storage unit  103  in the CIM equipment  1 , and recorded in the system information database of the system information storage unit  103 .  
         [0026]    (d) Next, in step S 104 , the real-time failure diagnosis module  533  predicts what the status of the vacuum pump system ( 18 ,  19 ) will be in several minutes based upon the second failure diagnosis data group obtained through the analysis of step S 103 .  
         [0027]    (e) In step S 105 , through comparison to a predetermined threshold it is determined and decided whether or not the vacuum pump system ( 18 ,  19 ) will experience irregular shutoff during the running time of the current process for the film deposition. If it is determined in step S 105  that the vacuum pump will experience irregular shutoff, processing proceeds to step S 121 , in which a film deposition stop signal is sent to the LPCVD system real-time controller  531 . The LPCVD system real-time controller  531  then drives the gas supply control system  51 , and switches to a purge sequence in step S 122 . In step S 122 , the output of the heating unit  522  is reduced in order to start decreasing the temperature of the semiconductor substrate  13  being processed. In the purge sequence of step S 122 , as the temperature of the semiconductor substrate  13  being processed starts to decrease, the flow rates of the respective mass-flow controllers  511 ,  512  becomes zero and the introduction of dichlorosilane gas and ammonia gas into the production chamber  521  is stopped. When the pressure inside the production chamber  521  is reduced to a predetermined pressure, the purge gas of nitrogen (N 2 ) is introduced into the production chamber  521  via the mass-flow controller  514 . During a fixed time period, gas purging is performed and the vacuum pump system ( 18 ,  19 ) is halted. Accordingly, the ‘predetermined threshold’ of step S 105 , which is a reference for determining whether or not irregular shutoff may occur, is set to a value reflecting the time period expected for gas purging.  
         [0028]    (f) In step S 105 , if it is determined that the vacuum pump system ( 18 ,  19 ) will not experience irregular shutoff, processing proceeds to step S 111 . In step S 111 , the real-time failure diagnosis module  533  performs, in real time, the analysis of failure diagnosis of the vacuum pump system ( 18 ,  19 ) using the amount of the integrated inflow of gas for each type of gas and the inflow time period as analytical parameters. While omitted from the illustration of FIG. 4, based upon the amount of integrated inflow of gas and the inflow time period for each type of gas monitored in real time, whether or not the vacuum pump system ( 18 ,  19 ) may experience irregular shutoff is determined through the procedure of steps S 102  to S 105 . If it is determined in step S 105  that the pump will have an irregular shutoff, processing proceeds to step S 121 , and then switches to the purge sequence in step S 122 , as described above.  
         [0029]    (g) At the same time, in step S 112 , the real-time failure diagnosis module  533  uses the accumulated deposition information as an analytical parameter. While omitted from the illustration, based upon the accumulated deposition information monitored in real time, whether or not the vacuum pump system ( 18 ,  19 ) may experience irregular shutoff is determined through the procedure in steps S 102  to S 105 . If it is determined in step S 105  that the pump will experience irregular shutoff, processing proceeds to step S 121 .  
         [0030]    (h) In step S 113 , the real-time failure diagnosis module  533  performs, in real time, the analysis of failure diagnosis of the vacuum pump system ( 18 ,  19 ) using the consumed amount of each type of gas for each process condition as an analytical parameter. Based on the consumed amount of each type of gas for each process condition monitored in real time, whether or not the vacuum pump system ( 18 ,  19 ) may have an irregular shutoff is determined through the procedure of steps S 102  to S 105 .  
         [0031]    (i) In step S 114 , the real-time failure diagnosis module  533  performs analysis for failure diagnosis of the vacuum pump system ( 18 ,  19 ) using loading state information, e.g., information about whether or not a wafer is fully charged, or is an empty boat. Since the loading state information is fixed information, its usage in combination with the characteristics data or the other time-series data enables real time analysis. Therefore, even in this case, based upon the combined information with the other characteristics, whether or not the vacuum pump system ( 18 ,  19 ) will have an irregular shutoff may be determined through the procedure of steps S 102  to S 105 .  
         [0032]    (j) In step S 115 , the real-time failure diagnosis module  533  performs analysis for failure diagnosis of the vacuum pump system ( 18 ,  19 ) using an aspect of the system other than the loading state (e.g., the length of piping, pressure, and the like) as an analytical parameter. Since information regarding system aspect is fixed information, its usage in combination with characteristics data or the other time-series data enables real time analysis. As described above, the information of the system situation combined with the other characteristics is subjected to the procedure in steps S 102  through S 105 .  
         [0033]    (k) In step S 116 , the real-time failure diagnosis module  533  performs analysis for failure diagnosis of the vacuum pump system ( 18 ,  19 ) using the difference in pumps among the equipment in the system as an analytical parameter. Since information regarding such system difference is fixed information, its usage, combined with characteristics data or other time-series data, enables a real time analysis. Accordingly, in this case as well, the combined information is subjected to the procedure of steps S 102  through S 105 .  
         [0034]    (l) When the scheduled film deposition is completed, processing switches to the purge sequence in step S 122  initiating a shutoff operation.  
         [0035]    Continuously executing the aforementioned steps S 101  through S 116  allows avoidance of trouble such as irregular shutoff of the vacuum pump system ( 18 ,  19 ) during film deposition, enabling the product lot being processed to be saved and a pump exchange operation to be performed under safe conditions (i.e., conditions where any non-process gas is enclosed). Note that steps S 111  through S 116  may be executed either at the same time, or at different timings. Moreover, it is not always necessary to execute all of steps S 111  through S 116 ; portions thereof may be omitted.  
         [0036]    Moreover, other than the steps S 111  through S 116 , a step of predicting the amount of by-product material within the vacuum pump system ( 18 ,  19 ), adding the result as a parameter, and performing analysis may be added. With the status of the equipment including the predicted amount of by-product material as a parameter, even if the information given by the vacuum pump system ( 18 ,  19 ) includes fixed values or minute changes, failure prediction and risk prediction may be performed. In the case where a monitored value (e.g., a current value) changes, differing weights for changes may be imposed just after the exchange of the vacuum pump system ( 18 ,  19 ) and at the time where the accumulated film thickness is 10 μm.  
         [0037]    (Other Embodiments)  
         [0038]    While the above embodiment has described the present invention, it should not be taken as meaning that the present invention is limited to the description and the drawings configuring a part of this disclosure. From this disclosure, a variety of substitutable embodiments and operational techniques will become apparent to those with regular skill in the art.  
         [0039]    In the embodiment that is given, LPCVD equipment is described by means of an example; however, naturally, the present invention is not limited to the LPCVD equipment. For example, other semiconductor manufacturing equipment, such as dry etching equipment or ion implantation equipment may also be utilized. Moreover, production equipment or manufacturing equipment used for chemical plants or steel plants other than semiconductor production equipment may be utilized.  
         [0040]    A combination of a mechanical booster pump and a dry pump connected in series is illustrated as a vacuum pump system; however, a vacuum pump system where a mechanical booster pump and an oil-sealed rotary pump are connected in series may also be used. Moreover, a vacuum pump system including only a dry pump or an oil-sealed rotary pump, or a turbo-molecular pump may also be used.  
         [0041]    While the aforementioned embodiment illustrates a configuration where the process control information storage unit  102  and the system information storage unit  103  are integrated in the CIM equipment, the CIM equipment may be omitted by connecting the process control information storage unit  102  and the system information storage unit  103  to the LPCVD system  5  side. In other words, an irregular shutoff avoidance method and system may control a plurality of production equipments as manufacturing steps beginning at the upper level, and may be configured as independent, individual production equipment besides the configuration with a group controller or the CIM equipment using a network, etc.  
         [0042]    Various modifications will become possible for those skilled in the art after receiving the teachings of the present disclosure without departing from the scope thereof. Accordingly, it is natural that the present invention includes a variety of embodiments not described herein. The technical scope of the present invention described should be defined only based upon the following appropriate claims.