Title: Apparatus for predicting life of rotary machine and equipment using the same
Abstract: An apparatus for predicting life expectancy of a rotary machine includes: a load recipe input module acquiring loading conditions of a rotary machine; a characterizing feature input module obtaining characterizing feature data of a rotary machine; and a life expectancy prediction module calculating life expectancy of the rotary machine in conformity with the loading conditions and the characterizing feature data.
Patent Number: 6,944,572 Issued on 09/13/2005 to Ushiku, et al.
| Inventors:
|
Ushiku; Yukihiro (Yokohama, JP);
Arikado; Tsunetoshi (Tokyo, JP);
Samata; Shuichi (Yokohama, JP);
Nakao; Takashi (Kawasaki, JP);
Mikata; Yuuichi (Yokohama, JP)
|
| Assignee:
|
Kabushiki Kaisha Toshiba (Tokyo, JP)
|
| Appl. No.:
|
752816 |
| Filed:
|
January 8, 2004 |
Foreign Application Priority Data
| Mar 23, 2001[JP] | P2001-85736 |
| Current U.S. Class: |
702/181; 702/34; 702/179; 702/182; 702/183; 702/184; 702/185; 714/31 |
| Intern'l Class: |
G06F 101/21 |
| Field of Search: |
702/181,184,34,179,182,183,185
714/31
|
References Cited [Referenced By]
U.S. Patent Documents
| 5210704 | May., 1993 | Husseiny.
| |
| 5406502 | Apr., 1995 | Haramaty et al.
| |
| 5610339 | Mar., 1997 | Haseley et al.
| |
| 5710723 | Jan., 1998 | Hoth et al.
| |
| 6208953 | Mar., 2001 | Milek et al.
| |
| 6226597 | May., 2001 | Eastman et al.
| |
| 6260004 | Jul., 2001 | Hays et al.
| |
| 6619111 | Sep., 2003 | Soneda et al.
| |
| Foreign Patent Documents |
| 5-195980 | Aug., 1993 | JP.
| |
| 08-055145 | Feb., 1996 | JP.
| |
| 8-261886 | Oct., 1996 | JP.
| |
| 09-189290 | Jul., 1997 | JP.
| |
| 10-228309 | Aug., 1998 | JP.
| |
| 10-335193 | Dec., 1998 | JP.
| |
| 11-070445 | Mar., 1999 | JP.
| |
| 11-288856 | Oct., 1999 | JP.
| |
| 2000/-283056 | Oct., 2000 | JP.
| |
Other References
Konishi et al., "Diagnostic system to determine the in-service life of dry vacuum
pumps," IEEE Proc. Sci. Meas. Technol. (1999), 146: 270-276.
|
Primary Examiner: Nghiem; Michael
Assistant Examiner: Cherry; Stephen J.
Attorney, Agent or Firm: Finnegan, Henderson, Farabow, Garrett & Dunner, L.L.P.
Parent Case Text
This is a continuation of application Ser. No. 10/101,720, now U.S. Pat. No.
6,865,513, filed Mar. 21, 2002, which claims priority from application Ser. No.
P2001-85736, filed on Mar. 23, 2001 in Japan, both of which are incorporated herein
by reference.
CROSS REFERENCE TO RELATED APPLICATIONS
This application is based upon and claims the benefit of priority from prior
Japanese Patent Application 2001-085736 filed on Mar. 23, 2001; the entire contents
of which are incorporated by reference herein.
Claims
1. An apparatus for predicting a life of a rotary machine comprising:
a process condition recipe input module configured to acquire loading conditions
of a rotary machine according to a target process recipe from among a plurality
of process recipes provided by a process controller that controls the loading conditions,
the process recipes including various kinds of gases;
a characterizing feature input module configured to obtain characterizing feature
data of the rotary machine; and
a life expectancy prediction module configured to calculate life expectancy of
the rotary machine in conformity with a determination reference corresponding to
the target process recline and the characterizing feature data, the determination
reference determined by referencing failure data of the rotary machine.
2. The apparatus of claim 1, wherein the loading conditions are defined by process
conditions of a chamber where gas is introduced and a semiconductor manufacturing
process is performed, and the rotary machine is a vacuum pump.
3. The apparatus of claim 2, wherein the characterizing feature data include
one of voltage, current, power, temperature, vibration, and sound of the vacuum pump.
4. The apparatus of claim 2, wherein calculation of the life expectancy uses
time series data of the characterizing feature data.
5. The apparatus of claim 4, wherein calculation of the life expectancy uses
any one of average value, standard deviation, auto correlation coefficient, lag
width of an auto correlation coefficient, and statistical analysis value of a Mahalanobis-Taguchi
distance of a limited time segment in the time series data for characterizing feature data.
6. The apparatus of claim 4, wherein the calculation of life expectancy is derived
from speed of change of the statistical analysis value, a simple regression or
multiple regression analysis.
7. The apparatus of claim 2, wherein the calculated life expectancy is fed to
a local area network.
8. The apparatus of claim 2, wherein based on the calculated life expectancy,
an emergency shutoff signal for the process is provided.
9. The apparatus of claim 2, wherein the process conditions include kind of the
gas and flow rate of the gas.
10. The apparatus of claim 1, wherein the determination reference is updated
in conformity with a change in the characterizing feature data.
11. Manufacturing equipment comprising:
a process controller configured to control a production process based on a target
process recipe from among a plurality of process recipes, the process recipes including
various kinds of gases;
a rotary machine configured to process a load of the production process; and
a life expectancy prediction controller configured to calculate life expectancy
of the rotary machine in conformity with a determination reference corresponding
to the target process recipe obtained by the process controller and characterizing
feature data obtained from the rotary machine, the determination reference determined
by referencing failure data of the rotary machine.
12. The manufacturing equipment of claim 11, wherein the process recipe defines
process conditions of a chamber where gas is introduced and a semiconductor manufacturing
process is performed, and the rotary machine is a vacuum pump.
13. The manufacturing equipment of claim 12, wherein the process conditions include
kind of the gas and flow rate of the gas.
14. The manufacturing equipment of claim 12, wherein the characterizing feature
data include one of voltage, current, power, temperature, vibration, and sound
of the vacuum pump.
15. The manufacturing equipment of claim 12, wherein calculation of the life
expectancy uses time series data of the characterizing feature data.
16. The manufacturing equipment of claim 15, wherein the calculation of the life
expectancy uses any one of average value, standard deviation, auto correlation
coefficient, lag width of an auto correlation coefficient, and statistical analysis
value of a Mahalanobis-Taguchi distance of a limited time segment in the time series
data for the characterizing feature data.
17. The manufacturing equipment of claim 15, wherein the calculation of life
expectancy is derived from speed of change of the statistical analysis value, a
simple regression or multiple regression analysis.
18. The manufacturing equipment of claim 12, wherein the calculated life expectancy
is fed to a local area network.
19. The manufacturing equipment of claim 12, wherein based on the calculated
life expectancy, an emergency shutoff signal for the process is provided.
20. The manufacturing equipment of claim 12, wherein the process controller and
the life expectancy prediction controller are installed in a same case.
21. The manufacturing equipment of claim 11, wherein the life expectancy prediction
controller is supplied with a plurality of process recipes.
22. The manufacturing equipment of claim 11, wherein the determination reference
is updated in conformity with a change in the characterizing feature data.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a rotary machine life expectancy prediction
method, which measures life expectancy of a rotary machine, and a rotary machine
repair timing determination method, which determines most appropriate repair timing
for a rotary machine based on the life expectancy thereof.
2. Description of the Related Art
Failure diagnosis has become important for the sake of efficient semiconductor
device manufacturing. Especially as the trend towards large item/small volume production
of system LSI grows, an efficient yet highly adaptable semiconductor device manufacturing
method has become necessary. It is possible to use a small-scale production line
for efficient production of semiconductor devices. However, if the production line
is merely reduced, the capacity utilization of manufacturing equipments drops.
Accordingly, there are problems such as investment efficiency falling in comparison
with large-scale production lines. To rectify this situation, there is method where
a plurality of manufacturing processes is performed on one semiconductor manufacturing
equipment. For example, in a low-pressure chemical vapor deposition (LPCVD) system,
reactive gases introduced and reaction products differ depending on the types of
film depositions. These are evacuated from the LPCVD chamber using a vacuum pump.
Accordingly, the film deposition requirements differ and the formation situations
for reaction products within the vacuum pump differ depending on the types of manufacturing
process. Therefore, life expectancy of the vacuum pump is affected by the process history.
Normally, sensors for monitoring currents, temperatures, etc. during operation
are attached to the vacuum pump. By doing so, whether a vacuum pump is malfunctioning
can be observed by an operator, either by directly viewing them or from information
on plotted graphs. However, since the currents and the temperatures change for
the vacuum pump depending on the various process conditions, it is extremely difficult
to measure life expectancy of the vacuum pump from these values, which change with
every process.
If the vacuum pump should have an irregular shutdown during film deposition in
LPCVD, then the lot being processed becomes defective. Moreover, excessive maintenance
of the LPCVD system may become necessary due to microscopic dust caused by residual
reactive gases, within the chamber and the piping used for gas introduction or
vacuum evacuation. Implementation of such excessive maintenance causes manufacturing
efficiency of the semiconductor device to drop dramatically.
If regular maintenance is scheduled with a margin of safety as a measure to prevent
such sudden irregular shutdowns during the manufacturing process, the frequency
for maintaining the vacuum pump may become astronomical. Not only does this increase
maintenance cost, but it also invites a decrease in capacity utilization of the
LPCVD system due to changing the vacuum pump, causing the manufacturing efficiency
of the semiconductor device to drastically decline. To commonly use semiconductor
manufacturing equipment for a plurality of processes, which is required for an
efficient small-scale production line, it is desirable to accurately diagnose vacuum
pump life expectancy and to operate the vacuum pump without having any waste in
terms of time.
Previously, some methods of diagnosing vacuum pump life expectancy have
been proposed. In Japanese Patent Application Laid-open No. 2000-283056, vacuum
pump failure forecasting using a plurality of physical quantities such as amount
of current, temperature or vibration for the vacuum pump is disclosed. In addition,
it has been disclosed that operating conditions of the semiconductor manufacturing
equipment such as operating time versus stand-by time must be considered to forecast
vacuum pump failure. However, it is impossible for this to accommodate historical
results of vacuum pump life expectancy in the case where a common semiconductor
manufacturing equipment is used for a plurality of processes. It is noted that
the objective of Japanese Patent Application Laid-open No. 2000-283056 lies in
observing abnormalities of a vacuum pump, and not in forecasting life expectancy.
Therefore, demands have been made for development of an apparatus and method for
predicting vacuum pump life expectancy.
SUMMARY OF THE INVENTION
An apparatus for predicting life expectancy of a rotary machine includes: a load
recipe input module configured to acquire loading conditions of a rotary machine;
a characterizing feature input module configured to obtain characterizing feature
data of a rotary machine; and a life expectancy prediction module calculating life
expectancy of the rotary machine in conformity with the loading conditions and
the characterizing feature data.
A manufacturing equipment using a rotary machine includes: a process controller
configured to a production process; a rotary machine configured to process load
of the production process; and a life expectancy prediction controller configured
to calculate life expectancy of the rotary machine in conformity with the process
recipe obtained by the process controller and characterizing feature data obtained
from the rotary machine.
A method is provided comprising: reading a load recipe of loading conditions
of
a rotary machine; determining whether changes exist for the loading conditions
by comparing the load recipe with an already existing load recipe for the process;
employing an already existing determination reference if no changes exist for the
load conditions, and reading in and employing a determination reference accommodating
the process conditions if changes exist for the loading conditions instead of the
already existing determination reference; processing time series data by reading
in detected characterizing feature data for the rotary machine, which correspond
to the determination reference; and calculating life expectancy of the rotary machine
in conformity with the time series data and the determination reference.
A method is provided comprising: reading a load recipe of loading conditions
of
a rotary machine; determining whether changes exist for the loading conditions
by comparing the load recipe with an already existing load recipe for the process;
employing an already existing determination reference if no changes exist for the
loading conditions, and reading in and employing a determination reference accommodating
the process conditions if changes exist for the loading conditions instead of the
already existing determination reference; processing time series data by reading
in detected characterizing feature data for the rotary machine, which correspond
to the determination reference; calculating life expectancy of the rotary machine
in conformity with the time series data and the determination reference; finding
stand-by times of the process in a time period until the calculated life expectancy
is reached, by a semiconductor production simulator; and determining a stand-by
time, of the found stand-by times, which least affects the process or a time including
this stand-by time, to be the replacement or repair time of rotary machine.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 is a schematic block diagram showing a configuration of a semiconductor
manufacturing equipment according the first embodiment of the present invention;
FIG. 2 is a schematic block diagram showing a configuration of a block of each
controller composing an apparatus for predicting life expectancy according the
first embodiment of the present invention;
FIG. 3 is a graph showing an example of time varying current of a dry pump during
process operation according the first embodiment of the present invention;
FIG. 4 is a graph showing another example of time varying current of a dry pump
during process operation according the first embodiment of the present invention;
FIG. 5 is a flowchart showing a method of predicting dry pump life expectancy
according the first embodiment of the present invention;
FIG. 6 is a block diagram showing a configuration of an apparatus for predicting
life expectancy according a modified example of the first embodiment of the present invention;
FIG. 7 is a graph showing an example of time varying current of a dry pump until
failure according the second embodiment of the present invention;
FIG. 8 is a graph showing auto covariance analysis results for the time varying
current shown in FIG. 6;
FIG. 9 is a flowchart showing a method of predicting dry pump life expectancy
according the third embodiment of the present invention;
FIG. 10 is a flowchart showing a method of predicting dry pump life expectancy
according the fourth embodiment of the present invention;
FIG. 11 is a block diagram showing a structural example of a semiconductor production
system for a method of determining dry pump repair timing according the fifth embodiment
of the present invention; and
FIG. 12 is a flowchart showing a method of determining dry pump repair timing
according the fifth embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
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 components throughout the
drawings, and the description of the same or similar parts and components will
be omitted or simplified.
(First Embodiment)
As shown in FIG. 1, an LPCVD system used as a semiconductor manufacturing equipment
according to the present invention encompasses a chamber
1, which has an
air-tight structure capable of being evacuated, and is connected on the evacuation
side of the chamber
1 to a dry pump
5, which is used as a vacuum
pump, through a trap
2 and a gate valve
3 by a vacuum piping
4.
A pump controller
12 controls the operation by outputting pump control signals
23 in the dry pump
5, and obtains pump operation information
24
of the dry pump
5. A plurality of gas piping are connected to the upstream
side of the chamber
1, and this gas piping is connected to mass flow controllers
7,
8, and
9, respectively. The mass flow controllers
7,
8, and
9 are connected to a gas supply system
6, which supplies
predetermined gases to be introduced to the chamber
1. A process controller
11 performs control and verification of, for example, pressures, temperatures
and amounts of gas flow inside the chamber
1, in conformity with process
control signals
21 and process information
22. A life expectancy
prediction controller
13 is connected to the process controller
11
and the pump controller
12. Life expectancy of the dry pump
5 is
predicted by reading process conditions for the chamber
1, for example a
process recipe
25, which includes process conditions such as types of gases,
gas flow rates, pressures, and substrate temperatures, from the process controller
11, and characterizing feature data
26, which includes, for example,
characterizing feature amounts for the dry pump
5 such as currents, temperatures,
and vibrations, from the pump controller
12.
Each controller consists of function blocks as shown in FIG.
2. The process
controller
11 has a gas supply control unit
31, a pressure control
unit
32, a temperature control unit
33, and the pump controller
12
has a pump control unit
35, a current/voltage monitor
36, a temperature
monitor
37, a vibration monitor
38, a pressure monitor
39.
The life expectancy prediction controller
13 has a process condition recipe
input module
41, a characterizing feature data input module
42, a
life expectancy prediction module
43, an output module
44, and a
storage unit
45. The life expectancy prediction module
43 performs
calculation of life expectancy of the dry pump
5, by reading a determination
reference corresponding to the process recipe
25 from the storage unit
45,
and processing statistically the characterizing feature data
26 for the
dry pump
5. The process recipe
25 and the characterizing feature
data
26 are read by the process condition recipe input module
41
and the characterizing feature data input module
42, respectively. The output
module
44 outputs calculated pump life expectancy information
28
by the life expectancy prediction module
43 to a server
15. In addition,
if it has become clear that the dry pump
5 is on the verge of failure, the
output module alerts and sends emergency shutoff signals
29a and
29b to the process controller
11 and the pump controller
12,
respectively. The storage unit
45 stores process information
22 of
the process recipe
25, a corresponding life expectancy determination reference,
and, also a calculated prediction life expectancy.
Including such an LPCVD system, various semiconductor manufacturing equipments
are integrated as a computer integrated manufacturing system (CIM) through a local
area network (LAN)
14 and administered in accordance with the CIM-based
server (or host computer)
15.
The life expectancy prediction controller
13 can transmit life expectancy
results to the CIM-based server
15 as pump life expectancy information
28
through the LAN
14. Here, in addition to the dry pump life expectancy information
28, the server
15 can read in film deposition process conditions
as lot processing information
27 from the process controller
11.
In addition, the life expectancy prediction controller
13 can send the emergency
shutoff signals
29a,
29b to the process controller
11 and the pump controller
12 immediately before failure.
The life expectancy prediction controller
13, in response to the process
conditions obtained from the process controller
11 can verify the operating
conditions of the dry pump
5, and therefore is able to perform calculations
for predicting life expectancy of the dry pump
5 during the process operation.
In addition, the amount of precipitated material accumulated inside the dry pump
5 can be estimated by referencing the lot processing information
27,
which is held in the server
15, and the life expectancy determination reference
value can be updated through the process history. Moreover, the life expectancy
prediction can also be used by constructively utilizing differences in process
conditions, which are described later.
Using the LPCVD system shown in FIG. 1, in the case where depositing a silicon
nitride (Si
3N
4) film, dichlorosilane (SiH
2Cl
2)
gas and ammonia (NH
3) gas are respectively introduced via mass flow
controllers
7 and
8 into the chamber
1 under low pressure
conditions of approximately several 100 Pa. Inside the chamber
1, a silicon
(Si) substrate is heated to approximately 650° C., and through the chemical
reaction of the dichlorosilane and ammonia, a silicon nitride film is deposited
upon the silicon substrate. In addition to generating the silicon nitride film,
this reaction produces reaction by-products of ammonium chloride (NH
4Cl)
gas and hydrogen gas. A reaction equation can be expressed as follows;
Since hydrogen is a gaseous body, it can be easily purged from the dry pump
5. On the other hand, since the temperature of the silicon substrate within
the chamber
1 is approximately 650° C. and it is under low pressure
of approximately several 100 Pa at the time of formation, the ammonium chloride
is also in a gas phase. Generally, the LPCVD system has the trap
2 for collecting
solid reaction by-product material disposed between the chamber
1 and the
dry pump
5. With this trap
2, it is impossible to completely collect
the by-product material from the reaction under conditions of low pressure. Therefore,
the reaction by-product that escapes from the trap
2 without being collected
reaches the dry pump
5. Meanwhile, the not-reacted gas and the by-product
material are cooled down. The pressure in the dry pump
5 suddenly increases
from the low pressure conditions to normal atmospheric pressure due to the compression
of the gas. While the by-product ammonium chloride is a gaseous body under high
temperatures and low pressure, it solidifies as it cools and the pressure increases.
Inside the dry pump
5, since the evacuated gas is subjected to repeated
compression, cooled gaseous ammonium chloride throughout the evacuated gas begins
to solidify and precipitate within the dry pump
5. There are cases where
the precipitated material adheres and accumulates, and there are cases where the
precipitated material falls off after a certain amount of it has precipitated,
which depends upon portions inside the dry pump
5 where the ammonium chloride
precipitates. In addition, the precipitation inside the pump, in particular between
the rotor, which is a rotating body, and the casing thereof, causes reduction in
clearance and clogging. In this case, an increase in the amount of a current and
a power to the dry pump
5, and an increase in a temperature of the dry pump
5, or development of a vibration start to occur instantaneously. However,
since smoothing and separation of the precipitated material is continuously occurring,
the current level or the power level and the temperature return just as quickly
to substantially normal levels and the vibration decreases. The dry pump
5
repeatedly has the reaction by-product materials precipitated as described above,
which ultimately leads to its failure.
FIG. 3 is the time series data of the normal current levels in the early stage
of utilization of the dry pump
5 when the number of times the LPCVD has
been performed is still low. It can be understood from this that precipitation
affecting the operation of the dry pump
5 has not developed. On the other
hand, as the dry pump
5 develops wear and tear, just before failure, humps
51 and spikes
52 indicating abnormal increases in the current can
be seen in the current level time log, as shown in FIG.
4. This shows that
the precipitation is frequently developing over a wide range inside the dry pump
5. Such abnormal increases in the current begin to occur frequently as the
amount of precipitated material accumulated inside the dry pump
5 increases.
The precipitated material continues to increase, and immediately before of the
dry pump
5, a temporal increase in the current level or the power level,
the temperature, the vibrations, etc. can be observed due to the accumulation of
precipitated material. For example, the average level and the standard deviation
of the current in the dry pump
5, which are calculated for a certain time
period, continue to increase in accordance with the increase in precipitated material.
Accordingly, by monitoring this level as a determination reference, life expectancy
can be predicted by finding the increasing speed. The life expectancy determination
reference of the dry pump
5 is determined by referencing failure data. However,
as described hereto, in the case where a single semiconductor manufacturing equipment
is applied to various processes, the determination reference of the life expectancy
such as the average level and standard deviation of the current varies with every
process condition. In addition, the increasing speed of characterizing feature
data such as the average level, the standard deviation of the current, etc. depends
on the process condition history. Therefore, in the first embodiment, a respective
determination reference level is set for the characterizing feature data of every
process condition, and life expectancy is calculated through the increasing speed
of the characterizing feature data during the process. Moreover, in the case where
the increasing speed of the characterizing feature data incidentally changes, not
only is life expectancy recalculated, but the life expectancy determination reference
level is also updated in line with this. Thus, it becomes possible for the life
expectancy prediction to accommodate various process conditions, and also take
the process history into consideration.
The case where the average level and the standard deviation of the current in
the dry pump
5 are used as the characterizing feature data
26 for
dry pump
5, the life expectancy prediction is described forthwith with reference
to FIGS. 2 and 5. The determination reference of the dry pump life expectancy and
the predicted life expectancy value are stored for every process condition in the
storage unit
45 of the life expectancy prediction controller
13 as
shown in FIG.
2.
(a) In Step S
201 in FIG. 5, the process condition recipe input module
41 in the life expectancy prediction controller
13 reads in the process
recipe
25 from the process controller
11, and discerns present process
conditions of chamber
1, such as types of gases or flow rates of gases,
pressures, and temperatures.
(b) In Step S
202, it is determined whether there is any change in these
present process conditions compared to earlier process conditions and if it is
judged that there are no changes, then the presently set determination reference
can be used without modification.
(c) When there has been a change in the process conditions, in Step S
203,
the life expectancy determination reference value set for every process condition
is renewedly read in from the storage unit
45.
(d) In Step S
204, the characterizing feature data input module
42
read in current levels from the characterizing feature data
26 of the dry
pump
5.
(e) In Step S
205, the life expectancy prediction module
43 calculates
the average level and the standard deviation over a predetermined time interval,
e.g. 10 seconds, so as to smooth out incidental changes.
(f) In Step S
206, the life expectancy prediction module
43 calculates
the increasing speed in conformity with the obtained average level and standard
deviation of the current, and estimates the length of time until each determination
reference is reached.
(g) In Step S
207, it is determined whether, the predicted life expectancy
is normal.
(h) When the predicted life expectancy is normal, it is determined whether the
present process is finished, in Step S
208. If it is not finished, the procedure
is repeated to return in Step S
204. If it is finished, then the dry pump
5 becomes in stand-by state until next process (Step S
211).
(i) When it has been discerned, in Step S
207, that the predicted life
expectancy is not normal and the dry pump
5 is on the verge of failure,
in Step S
209, the emergency shutoff signals
29a,
29b
are sent from the output module
44 to the process controller
11
and the pump controller
12. The process controller
11 and the pump
controller
12, having received the emergency shutoff signals
29a,
29b, execute shutoff sequences of the chamber
1 and the dry
pump
5.
(j) In Step S
210, repair or replacement of the dry pump
5 is performed.
Thereafter, the dry pump
5 will be in stand-by state until next process
(Step S
211).
The life expectancy prediction controller
13 can transfer the predicted
length of time until the determination reference of the dry pump
5 is reached,
as pump life expectancy information for every process condition to the server
15
via LAN
14, in Step S
206. Based on the transferred data in the server
15, the pump life expectancy information for the dry pump
5 is updated;
moreover, if the life expectancy determination reference is corrected in conformity
with changes in the increasing speed of the characterizing feature data, the updated
determination reference is returned to the restore unit
45. Naturally, instead
of the server
15, storage and processing of this data may be performed on
a separate host computer used as a database upon the LAN
14.
Moreover, when the average level and standard deviation of the current
of the dry pump
5 during a process in the chamber
1, increases beyond
expectation and it has become clear that the dry pump
5 is on the verge
of failure, the emergency shutoff signal
29a is sent from the life
expectancy prediction controller
13 to the process controller
11.
The process controller
11, having received the emergency shutoff signal
29a, issues instructions to stop the supply of reactive gases to
the chamber
1 and close the gate valve
3, and halts the process.
This function allows the chamber
1 to be protected from contamination resulting
from a sudden shutdown of the dry pump
5.
According to the first embodiment, since the determination reference for
the characterizing feature data
26 of the dry pump
5 is prescribed
for every process condition, the history throughout the life expectancy of the
dry pump
5 can be analyzed, and response to changes in the determination
reference according the process condition history is possible.
It is noted here that the life expectancy prediction controller
13 reads
in the process recipe
25 for the type of gas, the flow rate of gas, etc.
from the process controller
11, and discerns the process conditions; however,
it is also possible for this reading in to be from the server
15 via LAN
14. Alternatively, a host computer used as a database can be used in place
of the server
15. Here., the average and the standard deviation of the current
level over time are used as the statistical method for the life expectancy prediction
calculation in the life expectancy prediction controller
13; however, besides
this, auto correlation coefficient according to auto covariance analysis, the lag
width of the auto correlation coefficient, or likewise can be used. In addition,
comprehensive determination using not only current levels but a plurality of characterizing
feature data is also effective. In this case, if a Mahalanobis-Taguchi (MT) distance
is used, the prediction can be made with even greater accuracy. Methods, which
apply simple regression or multiple regression analysis to the increasing speed
of the characterizing feature data, are effective in raising efficiency of the prediction.
(Modification)
In the first embodiment, the life expectancy prediction controller
13
is
shown as an independent apparatus; however, in the alternate example, as shown
in FIG. 6, a process/life expectancy prediction control apparatus
18 includes
a process controller
11a and a life expectancy prediction controller
13a, together. Besides this, it is similar to the first embodiment
and the repetitive description is thus abbreviated.
The process controller
11a has a gas supply control unit
31a,
a pressure control unit
32a, a temperature control unit
33a.
The life expectancy prediction controller
13a has a process condition
recipe input module
41a, a characterizing feature data input module
42a, a life expectancy prediction module
43a, an output
module
44a, and a storage unit
45a. The life expectancy
prediction module
43a performs calculation of life expectancy of
the dry pump
5, by reading a determination reference corresponding to the
process recipe in the process controller
11a from the storage unit
45a, and processing statistically the characterizing feature data
26 for the dry pump
5. The process recipe and the characterizing
feature data
26 are read by the process condition recipe input module
41a
and the characterizing feature data input module
42a, respectively.
The life expectancy prediction results can be transmitted as lot processing information
27a together with the process conditions to a server
15 via
LAN
14 by an output module
44a. The storage unit
45a
also stores the calculated life expectancy data. Process control signals
21a
include an emergency shutoff signal from the output module
44a.
In response to the process conditions, the process/life expectancy prediction
control apparatus
18, which is installed both the process controller
11a
and the life expectancy prediction controller, can discern the operating conditions
of the dry pump
5, and therefore is able to perform calculations for predicting
life expectancy of the dry pump
5 during operation. In addition, the amount
of precipitated material accumulated inside the dry pump
5, can be estimated
by referencing the lot processing information
27a, which is held
in the server
15, and the life expectancy determination reference value
can be updated throughout the process history.
According to the modified example of the first embodiment, since the determination
reference for the characterizing feature data
26 of the dry pump
5
is prescribed for every process condition, the history throughout the life expectancy
of the dry pump
5 can be analyzed, and response to changes in the determination
reference in conformity with the process condition history is possible.
In this modified example, the life expectancy prediction controller
13a
is combined with the process controller
11a; however, a similar
combination may also naturally be possible with the pump controller
12 attached
to the dry pump
5.
(Second Embodiment)
An example where auto covariance analysis of the dry pump current is used as a
method of predicting life expectancy of a semiconductor manufacturing equipment
according the second embodiment of the present invention is described forthwith.
With the semiconductor manufacturing equipment life expectancy prediction method
according to the second embodiment of the present invention, time series data of
characterizing feature data such as currents, powers, inner pressures, vibrations,
and temperatures obtained from the dry pump are analyzed, and stochastic techniques
are used to predict dry pump failure. For example, if a relationship such as "if
dry pump current is high at a certain point in time, current increases even after
a predetermined lag width τ (data interval)" can be found, it is useful in
dry pump life expectancy prediction.
To begin with, in order to analyze time series data of the characterizing feature
data obtained from the dry pump, an assumption of constancy must be made. Simply
put, constancy indicates that time series data at each time are realized with the
same stochastic process, or the statistical properties of a stochastic process
do not change over time. To have this constancy, conditions must be met where expected
value E[x(t)]=μ remains unchanged over time, expected value E[x(t)
2]=μ
2
remains unchanged over time, or in short the dispersion of x(t) over time
should not change, and further, expected value E[x(t)x(τ)] for an arbitrary
t, τ is dependent on only the function of t-τ, or in other words expected
value E[x(t)x(τ)] is dependent solely on the difference in time. Namely,
expected value E[x(t)x(t+τ)] becomes a function of lag width τ, and
expected value E[x(t)]=μ becomes fixed.
Therefore, the degree to which variable x(t) and the variable x(t+τ)
after lag width τ operate together, or the covariance of x(t) and x(t+τ):
is a function of only lag width (data interval) τ. This is because
This is called auto covariance function C(τ), and is defined as:
Moreover, autocorrelation coefficient ρ
xx(τ) is
defined as:
C(τ) represents the strength of the connection between the data
separated by lag width τ.
In other words, when this amount is positively large, variable x(t) and the variable
x(t+τ) after lag width τ tend to behave in the same manner; on the
other hand, if it is negatively large, it shows that variable x(t) and variable
x(t+τ) tend to behave in opposite manners. Also, if this amount is 0, it
can be understood that variable x(t) and variable x(t+τ) behave independent
of each other.
Further by dividing C(τ) by C(o), which is the normal dispersion, the
value of ρ
xx(τ) can be standardized to be:
Since the normal dispersion of C (0) represents the strength of the relationship
with itself, and not a correlation stronger than itself,
Eventually, as this auto correlation coefficient ρ
xx(τ)
approaches 1, it can be determined that there is a strong relationship between
variable x(t) and variable x(t+τ), allowing the life expectancy of the semiconductor
manufacturing equipment to be predicted. More specifically, the time series data
for the characterizing feature data of the initial, non-deteriorated state of the
dry pump is measured and made the reference time series data. The reference auto
covariance function can be obtained from this reference time series data. Next,
the time series data for the characterizing feature data of the dry pump during
the process is measured, and from this the auto covariance function during the
process can be obtained. The auto correlation coefficient can be found from the
process and reference auto covariance function. If the auto correlation coefficient
is near |1|, it can be determined that, regardless of whether the value is positive
or negative, there is a strong relationship between the normal characterizing feature
data of the dry pump; if it is near 0, then it can be determined that the correlation
is weak and near to the end of its life expectancy.
As shown in FIG. 7, from the beginning to the two months period of the dry pump
5 usage, there are few temporary spikes in the current, but as the usage
period progresses over two months until just before failure, as described above
(refer to FIG.
4), large spikes in the current can be seen. On the other
hand, steady changes in the current in the dry pump
5 are so small that
it is almost humanly impossible to detect. Auto covariance analysis carried out
based on this data gives the results shown in FIG.
8. Large, periodic changes
in the auto correlation coefficient become manifest while the dry pump
5
is in normal working order, but as the dry pump
5 wears out as the usage
period has become longer, these periodic changes become smaller and approach zero.
Accordingly, if these periodic changes are tracked, the condition of the dry pump
5 can be diagnosed. The life expectancy prediction controller
13
performs diagnosis on the dry pump
5 based on this signal, and calculates
the number of lots that can be processed during the lifespan of the dry pump
5
and registers this result in the server
15.
In the second embodiment, the current level is used as the characterizing feature
data for the dry pump
5; however, other physical properties such as a power
level, a temperature, a vibration, or a sound spectrum may be used. In addition,
it is also effective to predict the life expectancy of the dry pump
5 by
using not only just the one physical property of current level, but various physical
properties comprehensively as the determination reference for the dry pump
5.
(Third Embodiment)
Description is made with reference to the example of the LPCVD system
used in the first embodiment, depicted in FIGS. 2 and 9. In the case of using not
only just the one physical property of current level, but various physical properties
comprehensively as the characterizing feature data for the dry pump
5, life
expectancy of the dry pump
5 can be effectively predicted utilizing a Mahalanobis-Taguchi
(MT) distance.
It is necessary to find an inverse matrix obtained from the reference data during
normal conditions, or a reference Mahalanobis space, in order to find a MT distance
with the life expectancy prediction controller
13. For example, the auto
correlation coefficient of the auto covariance with respect to the time series
data of the current, the temperature, and the vibration of the dry pump
5
may be used as the data forming the reference space. The inverse matrix of the
correlation matrix derived from the current, the temperature, and the vibration
data is then found. Calculation for finding the inverse matrix from this correlation
matrix can be performed in the life expectancy prediction controller
13;
alternatively, it may be performed in the server
15 or another computer
in the CIM system. This reference Mahalanobis space may be set beforehand for every
process condition; however, there is also a chance it may change depending on the
history of the various process conditions.
(a) In Step S
501 in FIG. 9, the process condition recipe input module
41 in the life expectancy prediction controller
13 reads in the process
recipe
25 from the process controller
11, and discerns present process
conditions of the chamber
1, such as types of gases or flow rates of gases,
pressures, and temperatures.
(b) In Step S
502, it is determined whether there has been a change in
the process conditions. When there is no change in the process conditions found,
the inverse matrix of the present reference (Mahalanobis) space continues to be used.
(c) In Step S
503a, when a change in the process conditions has
been discerned from the process recipe
25 in Step S
502, current,
temperature, and vibration data of the dry pump
5 is obtained for a predetermined
number of rotations, for example 20 rotations, and with it the reference data is
reconfigured to find a new inverse matrix in Step S
503b.
(d) Thereafter, in Step S
504, the characterizing feature data input module
42 read in the characterizing feature data
26 of the current levels,
the temperatures and the vibrations of the dry pump
5, which are obtained
during processing, for a predetermined number of rotations.
(e) In Step S
505, the life expectancy prediction module
43 calculates
the inverse matrix from the characterizing feature data
26 of the current
levels, the temperatures and the vibrations of the dry pump
5, which is
set as the verified Mahalanobis space. The MT distance is then found from this
verified Mahalanobis space and the reference space found earlier, and calculation
of the life expectancy of the dry pump
5 is performed.
(f) In Step S
506, the life expectancy prediction module
43 performs
the life expectancy prediction for the dry pump
5. When the dry pump
5
is normal, the verified Mahalanobis space is analogous to the reference space and
the MT distance shows a value of around 1. A larger value for the MT distance shows
that the verified space and the reference space have deviated, and usually an MT
distance of approximately 10 is determined to be abnormal. Accordingly, if an MT
distance of 10 is made the life expectancy determination reference for the dry
pump
5, the dry pump life expectancy can be predicted from the MT distance
calculated at each measurement point or the speed of the increase in the MT distance.
The predicted results are stored in the storage unit
45 by the output
module
44, and also, registered as the pump life expectancy information
28 for each process condition in the server
15 via LAN
14.
According to the third embodiment, when predicting the life expectancy
of the dry pump
5, a correlation matrix of the various physical properties
is obtained by taking into consideration conditions of the dry pump
5 and
the MT distance can be used to determine life expectancy of the dry pump
5.
(Fourth Embodiment)
Since the average value and the standard deviation of the characterizing feature
data such as currents, powers, temperatures, vibrations, and sounds change correspond
to the various process conditions, a method that accommodates this has been described
in the above-mentioned embodiment. In the life expectancy prediction method according
to the fourth embodiment, a method, which is simplified further, is described.
If the semiconductor manufacturing equipment were to be roughly divided, it would
be said to have two states: the operational state where the manufacturing process
is being performed and the stand-by state between when a lot is taken out and the
next lot is inserted. The above-mentioned first through third embodiments are examples
where the life expectancy prediction of the dry pump
5 is performed during
operation of the semiconductor manufacturing equipment. During operation of the
semiconductor manufacturing equipment, the characterizing feature data such as
the current level is taken during an active process since the not-reacted gas and
the reaction by-products are being carried from the chamber
1 to the dry
pump
5. On the other hand, during the stand-by state, since the chamber
1 is being purged by inactive gas such as nitrogen (N
2) gas,
the load on the dry pump
5 is low, and amount of abnormal material being
attached is low, processing is relatively static. The fourth embodiment is an example
where the life expectancy prediction of the dry pump
5 is performed while
the semiconductor manufacturing equipment is on stand-by. In this description,
FIGS. 10 and 2 are used and the current level of the dry pump
5 is used
as the characterizing feature data for the life expectancy determination reference.
(a) In Step S
601, the process condition recipe input module
41
in the life expectancy prediction controller
13 reads in the process recipe
25 from the process controller
11, and discerns the present process
conditions of the chamber
1 such as types of gases, flow rates of gases,
chamber temperatures, and pressures.
(b) In Step S
602, it is discerned whether the LPCVD system is in an operational
state or a stand-by state.
(c) When it has been discerned that the LPCVD system is in a stand-by state,
in Step. S
604, the characterizing feature data input module
42 read
in the current level from the characterizing feature data
26 of the dry
pump
5.
(d) In Step S
605, the life expectancy prediction module
43 calculates
the increasing speed in conformity with the average level and the standard deviation
of the current obtained, and estimates the length of time until each the life expectancy
determination reference read in from the storage unit
45 is reached.
(e) In Step S
606, the life expectancy prediction module
43 predicts
the life expectancy from the calculated length of time until the determination
reference values of the dry pump
5 are reached. The predicted results are
stored in the storage unit
45 by the output module
44, and also,
registered as the pump life expectancy information
28 for each process condition
in the server
15 via LAN
14. Based on the transferred data in the
server
15, the pump life expectancy information
28 for the dry pump
5 is updated, and moreover, if the life expectancy determination reference
is corrected in conformity with changes in the increasing speed of the life expectancy,
the updated life expectancy determination reference is returned to the storage
unit
45 of the life expectancy prediction controller
13.
Even if the dry pump
5 is on stand-by, the characterizing feature data
26 of the dry pump
5 changes for every process due to the historical
results of the internally precipitated material. Even if on standby, the level
of current in the dry pump
5 increases for every process in conformity with
these historical results, and it is possible to measure the life expectancy of
the dry pump
5 through analysis of the increase speed of the characterizing
feature data
26.
Here, as a stand-by life expectancy prediction for the dry pump
5, instead
of the constantly flowing purge with nitrogen gas, it is also effective to change
the nitrogen gas flow rate. The nitrogen gas flow rate is changed and the amount
of change in the average level and the standard deviation of the current in the
dry pump
5 corresponding to the change in the gas flow rate is measured.
When almost at failure, the authors have found that the change in the average level
and the standard deviation of the current tends to decrease in comparison with
the change in the flow rate of nitrogen gas. Accordingly, it is possible to predict
the life expectancy of the dry pump
5 from the change in the average level
and the standard deviation of the current compared to the change in the nitrogen
gas flow rate. In this manner, it is easier to accurately determine the life expectancy
since life expectancy prediction testing during stand-by can be performed under
the conditions that are not applicable during operation.
The life expectancy prediction can be performed by intermittently introducing
inactive gas and measuring the change in load features for the dry pump 5.
In this case, the ease with which the attached material is smoothed and detached,
can be studied. As usage of the dry pump 5 gets longer, the ability to smooth
and detach the attached material drops. This change can be picked up as changes
in the current level of the dry pump 5, allowing the life expectancy prediction
to be made for the dry pump 5.
In addition, a cleaning gas is sometimes used to remove attached material within
the chamber 1 during stand-by. The change of the current level in this procedure
can also be use for prediction. Moreover, even more accurate prediction can be
attained by intermittently introducing and halting inactive gas after introducing
the cleaning gas, and measuring the change in the load features of the dry pump 5.
Effective testing can also be performed by introducing reactive gas, instead
of inactive gas. Generally, when introducing inactive gas, changes in the average
level and the standard deviation of the load thereof may become difficult to measure
since the load of the dry pump 5 is small; in such a case, the use of a
reactive gas is effective.
Unlike during process operation, the same conditions can be employed to simplify
the life expectancy prediction in the case where the life expectancy prediction
is performed for the dry pump 5 while the semiconductor manufacturing equipment
is on stand-by. In addition, since this stand-by is controlled through CIM, the
sequence for performing life expectancy testing can be compiled into an operating
program for semiconductor manufacturing equipment and executed.
(Fifth Embodiment)
As shown in FIG. 11, a semiconductor production system comprises a configuration
where a plurality of semiconductor manufacturing equipments 71, 72,
. . . are connected to a LAN 14, which is connected to a server 15,
and a semiconductor production simulator 16 is further connected to the
server 15.
Here, assuming a small-scale production line where approximately 100 lots are
produced per month, there will be somewhere around 50 semiconductor manufacturing
equipments. The semiconductor production simulator 16 con
