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Integration of a particle monitor into the control system for an ion implanter
h!lUMl B
Nuclear Instruments and Methods in Physics Research B74 (1993) 243-247 North-Holland
Beam Interactions with Materials&Atoms
Integration of a particle monitor into the control system for an ion implanter Steven Myers, David McCarron and Julian Blake Eaton Corporation,
108 Cheny Hill D&e,
Beverly, MA 01915, USA
The value of in situ particle monitors for both manufacturing process control and process development in the semiconductor industry is receiving considerable recognition. This paper discusses the integration of a high yield technology (HYT) sensor into the control system of an Eaton high current ion implanter. The automatic triggering of the particle monitor during the various phases of the implant process and the autoclean cycle provides a definite representation of the machine state and the effect of processing over time. Utilizing existing features of the control system, specific thresholds can be associated with each implant process through its process recipe. By regular monitoring of the particle counter, these thresholds are used to anticipate the need for cleaning the process chamber, or if indicated, gracefully bring the current process to an immediate halt. A dedicated history log preserves detailed data for generating summary statistics and the complete data set of a particular process or overall machine performance. Future uses of this. tool with the control system point toward statistical process control applications and intelligent self modifying process cycles. The presentation will include data from a system on which an HYT sensor was employed as a full time process monitor using modified SPC techniques for analysis.
1. Introduction In situ particle
detection
in vacuum
systems
has
been difficult due to the limited sensitivity of available detectors. Because particles can be concentrated by gas flow the first use of monitors was in roughing lines. With more sensitive detectors, surveying particle levels in process chambers under high vacuum has become possible. Historical applications have progressed from: (1) disaster detection where a dramatic increase in particle level causes an immediate alarm, through (2) monitoring of in situ cleaning procedures, (3) process optimization, and finally to (4) statistical process control. In this paper we describe the integration of a Model 20s High Yield Technology in situ monitor into the process chamber of an NV-20A high current ion implanter (fig. l), and give examples from field experience for these four applications.
2. Control system description Particle counts are binned during different stages of the implant process by using triggers from various points of the control software. Although the particle counter itself can be triggered multiple times, it was decided to use it for cumulative counts only and let the control software handle the stage difference counts. A new count cycle is triggered with the process chamber 0168-583X/93/$06.00
roughing sequence which corresponds to the start of the implant cycle. Subsequent totals are saved upon opening the process chamber isolation valve, spinning up the rotary drive, removing the flag faraday from the beam, replacing the faraday, stopping the rotary drive, and venting the process chamber. Additional monitoring occurs at regular intervals, based upon the length of the implant, as often as every two seconds to provide as many as five hundred samples. The data are saved in a log file on the system and are available for future analysis. The triggers are also active during the AutoClean process and can be used to monitor and optimize this process. The operator has an opportunity to utilize the cumulative count data to interactively control the implant through the process recipe. Two thresholds can be associated with each process. A lower value can be used to monitor a gradual build up of particles within the process chamber. When this is exceeded an alarm is posted upon completion of the current implant thus giving the operator the option to perform maintenance. A higher value is also available which can be used to reflect the occurrence of a catastrophic event such as a wafer break. In this event an immediate hold is put on the implant and the operator given the choice to resume the implant or enter manual mode and unload the wafers from the disk. In both cases the alarm is recorded in the data log file. If it is not required to associate alarm thresholds with each recipe, a default
0 1993 - Elsevier Science Publishers B.V. All rights reserved
V. PROCESS CONTROL
S. Myers et al. / Integration of particle monitor into control system
Fig. 1. Configuration of process chamber with Model 20s sensor.
value can be configured for every process run on the machine. The implanter control system for the NV-20A is based on a distributed hardware configuration involving numerous single board computers organized in a hierarchical architecture. Low level input/ output for the implanter is performed on a variety of specialized boards based on the HD64180 processor. These in turn are linked to the cell controller’s communication card, a 68020 based unit through a number of SDLC based fiber optic loops. This board resides in a larger VME based chassis with another 68020 powered board on which all of the control tasks reside, a battery backed up RAM card for storing the database, and an Ether-
net interface card for hooking up to one or more Sun SPARC work stations which present the user interface, including alarm information and access to data files stored on disk. The HYT hardware controller, either a PM150 or PM250, communicates with the cell controller through an RS-232 serial link at 9600 baud (fig. 2). It acts as a slave to the cell controller and will only respond when it receives a command. Each command has an appropriate response associated with it - either data such as a request for the current particle count or the firmware version number, or a return status indicating the success or failure of a command such as setting a new counting interval. To avoid getting out of sync, the communications software will not send a message without having received a response to the previous command. Timeouts are handled by attempting to resend the last request and ultimately by informing the operator that communication with the controller has been lost.
3. Applications
of in situ particle monitors
Two examples illustrate the value of an immediate HYT indication of a disaster [1,2]. In the event of a wafer break there is an immediate increase of large particles. The increase is sufficient to permit halting the implant immediately, thus minimizing damage to implanter components. It is common practice to use dummy wafers to fill out a partial implant load. Inadvertent selection of dummy wafers which had not been cared for properly resulted in a five-fold increase in
SPARC CELL
Implant Triggering
Monitor
t COMM
.
=
Driver Task
Fig. 2. Control system.
245
S. Myers et al. / Integration of particle monitor into control system
HYT counts during the autoclean one can terminate the cleaning immediately as count rates drop to a base level, thus returning the implanter to service with minimum time lost. Fig. 3 shows averages of particles added per wafer pass (PWP) and HYT counts for a series of implants done into photoresist coated wafers. For a given process the average HYT and PWP counts correlate well, though the ratio of HYT to PWP counts can be strongly process dependent. This correlation permits far more rapid process optimization than would be possible if only monitor wafers were used to gather particle data. Fig. 4 shows average HYT counts acquired over many weeks of high volume production runs for a number of different processes. Notice that different processes can have quite different means depending upon energy, dose and specie, and that the mean may well be a number less than 10, borderline for developing SPC controls. Previously published work supports this finding [l-5]. To make proper decisions from this type of noisy data requires careful use of statistics; two recipes have been selected to analyze in greater detail.
HYT Counts and PWP Counts from Photoresist Testing
Number of Photoresist Wafers
Fig. 3. In situ detector counts and particles per wafer pass from photoresist testing.
count. The in situ monitor allowed for immediate detection of this problem whereas the use of monitor wafers would require that the “duty dummies” be part of the monitor batch. Autoclean makes use of repeated venting and roughing while the disk is spinning to remove particles from the process chamber. The HYT monitor has permitted several advances: First, by looking at the rate of decay of particle counts it has been possible to optimize the vent pressure [2]. Second, by monitoring HYT particle
4. Applying statistical process control techniques Deriving control limits that are meaningful and provide the desired level of control without abnormally high “false alarms” requires one to understand the underlying distribution of the data. SPC techniques and Western Electric rules rely heavily on an accurate determination of sample mean and standard deviation. The probabilities associated with these limits assume
Average HYT Counts By Process - Viewport Detector
50
40 Average HYT 30 counts
10
0
I 1
I
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Process
Fig. 4. Average in situ detector counts by process. V. PROCESS CONTROL
S. Myers et al. / Integration of particle monitor into control system
246
Table 1 Summary statistics for HYT counts Transform process number
None 3
Log(x + 1) 3
3
None 15
Log(n + 1) 1.5
Sqrt (x) 15
Average Standard deviation Skewness Kurtosis Number of samples
4.65 5.83 3.24 13.80 165
0.598 0.361 0.121 - 0.193 165
1.81 1.17 1.63 3.01 165
32.10 37.40 2.01 3.39 20
1.320 0.392 0.565 - 0.470 20
5.020 2.690 1.320 0.873 20
Sort (x)
Equations: Average = Xxi /n Standard deviation = C(xi - p)“/(n Skewness = ~‘~;;s!‘, Kurtosis = :(iiGr n
- 1)
equals 0 for a normal distribution - 3
equals 0 for a normal distribution
that the data are normally distributed [6]. The distribution of the data has only a slight effect on the estimate of the sample mean, but has a marked effect on the estimate of sample standard deviation [71. Table 1 provides the summary statistics for the raw data as well as transformed data from process 3 and process 15. These recipes were chosen to illustrate the proper use of transformations and one of the pitfalls most frequently encountered. The values of average and standard deviation are used in developing process control limits. The measures of skewness and kurtosis indicate the shape of the distribution. The skewness is a measure of the symmetry of the distribution and the kurtosis is a measure of the amount of data in the tails
of the distribution. For normally distributed data, both the skewness and kurtosis should approach 0 [7]. In many cases it is possible to convert non-normal data, x, into normally distributed data, y, by applying a transformation, y =f( x). The data was compared against two distributions which are often found in frequency counting, the log-normal and Poisson distributions [8]. The log-normal distribution has a central value, the geometric mean, with values shifted from the mean by various multiplicative factors. The log transformation (data transformed with y = log(x + 1) to account for the 0 values) produces a near normal distribution for process 3. Log and square root transformations fail to provide the desired result when applied to process 15.
SPC Run Chart - Transformed Limits HYT Viewport Counts: Process 3
-
HYT
29
43
51
71
85
99
113
127
141
Implant Number
Fig. 5. SPC run chart for process 3 using transformed limits.
155
DATA
S. Myers et al. / Integration of particle monitor into control system
Two primary factors account for the failure of process 15 to fit any of the tested distributions. The most significant is the small sample size, 20, relative to the large sample standard deviation, 37. The second factor that must be considered is that with mean counts much greater than 10 the data should approximate a normal distribution. It is clear that more data must be collected for process 15 before accurate control limits can be set. Fig. 5 shows the run chart for process 3 using limits derived from the transformed data. The data was transformed with y = log(HYT + l), and control limits derived using standard SPC techniques, but using the values of mean and standard deviation determined for the transformed data. To provide dimensional integrity to the control limits, the antilog.is taken and the data is displayed in units of sensor counts + 1. When transformed limits are used, the process appears under control with only one point falling outside of the upper control limit. Calculation of control limits from data that was not transformed results in 6 counts outside of the upper control limit.
5. Conclusions
In situ particle monitoring capability has been cessfully integrated into semiconductor equipment trol systems. The applications for these sensors varied and the benefits of their use are obvious. data from these sensors can be used for process
succonare The con-
241
trol, but it must be understood that the data may not be normally distributed. Before determining control limits for a piece of equipment one should ensure that the distribution of the data is understood.
Acknowledgements
The authors would like to acknowledge Jennifer Stern, John Lewis and Joe Malenfant of IBM, Essex Junction, Vermont for their guidance and support of this work.
References [l] Real-time particle monitoring of the Eaton NV-20A high
current implanter, SEMATECH Technology Transfer Number 91050524A-ENG (1991). [2] R.W. Burghard, D.J. McCarron, R.J. Marlde and H.K. Nguyen, Inst. of Env. Sci. Proc. 1 (1992) 338. [31 D.J. McCarron, E. Custodio-Williams, Inst. of Env. Sci. Proc. (1991) 689. [41 T.J. Barclay, Microcontamination (November 1985)‘67. [5] P. Borden, Microcontamination (April 1991) 43. [6] D.J. Wheeler and D.S. Chambers, Understanding Statistical Process Control (Statistical Process Control, Inc., Knoxville, 1986). [7] G.E.P. Box, W.G. Hunter and J.S. Hunter, Statistics for Experimenters (Wiley, 1978) p. 89. [81 D.W. Cooper, J.V. Babcock and F. DiPietro, Inst. of Env. Sci. Proc. 1 (1992) 215.
V. PROCESS CONTROL
Nuclear Instruments and Methods in Physics Research B74 (1993) 243-247 North-Holland
Beam Interactions with Materials&Atoms
Integration of a particle monitor into the control system for an ion implanter Steven Myers, David McCarron and Julian Blake Eaton Corporation,
108 Cheny Hill D&e,
Beverly, MA 01915, USA
The value of in situ particle monitors for both manufacturing process control and process development in the semiconductor industry is receiving considerable recognition. This paper discusses the integration of a high yield technology (HYT) sensor into the control system of an Eaton high current ion implanter. The automatic triggering of the particle monitor during the various phases of the implant process and the autoclean cycle provides a definite representation of the machine state and the effect of processing over time. Utilizing existing features of the control system, specific thresholds can be associated with each implant process through its process recipe. By regular monitoring of the particle counter, these thresholds are used to anticipate the need for cleaning the process chamber, or if indicated, gracefully bring the current process to an immediate halt. A dedicated history log preserves detailed data for generating summary statistics and the complete data set of a particular process or overall machine performance. Future uses of this. tool with the control system point toward statistical process control applications and intelligent self modifying process cycles. The presentation will include data from a system on which an HYT sensor was employed as a full time process monitor using modified SPC techniques for analysis.
1. Introduction In situ particle
detection
in vacuum
systems
has
been difficult due to the limited sensitivity of available detectors. Because particles can be concentrated by gas flow the first use of monitors was in roughing lines. With more sensitive detectors, surveying particle levels in process chambers under high vacuum has become possible. Historical applications have progressed from: (1) disaster detection where a dramatic increase in particle level causes an immediate alarm, through (2) monitoring of in situ cleaning procedures, (3) process optimization, and finally to (4) statistical process control. In this paper we describe the integration of a Model 20s High Yield Technology in situ monitor into the process chamber of an NV-20A high current ion implanter (fig. l), and give examples from field experience for these four applications.
2. Control system description Particle counts are binned during different stages of the implant process by using triggers from various points of the control software. Although the particle counter itself can be triggered multiple times, it was decided to use it for cumulative counts only and let the control software handle the stage difference counts. A new count cycle is triggered with the process chamber 0168-583X/93/$06.00
roughing sequence which corresponds to the start of the implant cycle. Subsequent totals are saved upon opening the process chamber isolation valve, spinning up the rotary drive, removing the flag faraday from the beam, replacing the faraday, stopping the rotary drive, and venting the process chamber. Additional monitoring occurs at regular intervals, based upon the length of the implant, as often as every two seconds to provide as many as five hundred samples. The data are saved in a log file on the system and are available for future analysis. The triggers are also active during the AutoClean process and can be used to monitor and optimize this process. The operator has an opportunity to utilize the cumulative count data to interactively control the implant through the process recipe. Two thresholds can be associated with each process. A lower value can be used to monitor a gradual build up of particles within the process chamber. When this is exceeded an alarm is posted upon completion of the current implant thus giving the operator the option to perform maintenance. A higher value is also available which can be used to reflect the occurrence of a catastrophic event such as a wafer break. In this event an immediate hold is put on the implant and the operator given the choice to resume the implant or enter manual mode and unload the wafers from the disk. In both cases the alarm is recorded in the data log file. If it is not required to associate alarm thresholds with each recipe, a default
0 1993 - Elsevier Science Publishers B.V. All rights reserved
V. PROCESS CONTROL
S. Myers et al. / Integration of particle monitor into control system
Fig. 1. Configuration of process chamber with Model 20s sensor.
value can be configured for every process run on the machine. The implanter control system for the NV-20A is based on a distributed hardware configuration involving numerous single board computers organized in a hierarchical architecture. Low level input/ output for the implanter is performed on a variety of specialized boards based on the HD64180 processor. These in turn are linked to the cell controller’s communication card, a 68020 based unit through a number of SDLC based fiber optic loops. This board resides in a larger VME based chassis with another 68020 powered board on which all of the control tasks reside, a battery backed up RAM card for storing the database, and an Ether-
net interface card for hooking up to one or more Sun SPARC work stations which present the user interface, including alarm information and access to data files stored on disk. The HYT hardware controller, either a PM150 or PM250, communicates with the cell controller through an RS-232 serial link at 9600 baud (fig. 2). It acts as a slave to the cell controller and will only respond when it receives a command. Each command has an appropriate response associated with it - either data such as a request for the current particle count or the firmware version number, or a return status indicating the success or failure of a command such as setting a new counting interval. To avoid getting out of sync, the communications software will not send a message without having received a response to the previous command. Timeouts are handled by attempting to resend the last request and ultimately by informing the operator that communication with the controller has been lost.
3. Applications
of in situ particle monitors
Two examples illustrate the value of an immediate HYT indication of a disaster [1,2]. In the event of a wafer break there is an immediate increase of large particles. The increase is sufficient to permit halting the implant immediately, thus minimizing damage to implanter components. It is common practice to use dummy wafers to fill out a partial implant load. Inadvertent selection of dummy wafers which had not been cared for properly resulted in a five-fold increase in
SPARC CELL
Implant Triggering
Monitor
t COMM
.
=
Driver Task
Fig. 2. Control system.
245
S. Myers et al. / Integration of particle monitor into control system
HYT counts during the autoclean one can terminate the cleaning immediately as count rates drop to a base level, thus returning the implanter to service with minimum time lost. Fig. 3 shows averages of particles added per wafer pass (PWP) and HYT counts for a series of implants done into photoresist coated wafers. For a given process the average HYT and PWP counts correlate well, though the ratio of HYT to PWP counts can be strongly process dependent. This correlation permits far more rapid process optimization than would be possible if only monitor wafers were used to gather particle data. Fig. 4 shows average HYT counts acquired over many weeks of high volume production runs for a number of different processes. Notice that different processes can have quite different means depending upon energy, dose and specie, and that the mean may well be a number less than 10, borderline for developing SPC controls. Previously published work supports this finding [l-5]. To make proper decisions from this type of noisy data requires careful use of statistics; two recipes have been selected to analyze in greater detail.
HYT Counts and PWP Counts from Photoresist Testing
Number of Photoresist Wafers
Fig. 3. In situ detector counts and particles per wafer pass from photoresist testing.
count. The in situ monitor allowed for immediate detection of this problem whereas the use of monitor wafers would require that the “duty dummies” be part of the monitor batch. Autoclean makes use of repeated venting and roughing while the disk is spinning to remove particles from the process chamber. The HYT monitor has permitted several advances: First, by looking at the rate of decay of particle counts it has been possible to optimize the vent pressure [2]. Second, by monitoring HYT particle
4. Applying statistical process control techniques Deriving control limits that are meaningful and provide the desired level of control without abnormally high “false alarms” requires one to understand the underlying distribution of the data. SPC techniques and Western Electric rules rely heavily on an accurate determination of sample mean and standard deviation. The probabilities associated with these limits assume
Average HYT Counts By Process - Viewport Detector
50
40 Average HYT 30 counts
10
0
I 1
I
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Process
Fig. 4. Average in situ detector counts by process. V. PROCESS CONTROL
S. Myers et al. / Integration of particle monitor into control system
246
Table 1 Summary statistics for HYT counts Transform process number
None 3
Log(x + 1) 3
3
None 15
Log(n + 1) 1.5
Sqrt (x) 15
Average Standard deviation Skewness Kurtosis Number of samples
4.65 5.83 3.24 13.80 165
0.598 0.361 0.121 - 0.193 165
1.81 1.17 1.63 3.01 165
32.10 37.40 2.01 3.39 20
1.320 0.392 0.565 - 0.470 20
5.020 2.690 1.320 0.873 20
Sort (x)
Equations: Average = Xxi /n Standard deviation = C(xi - p)“/(n Skewness = ~‘~;;s!‘, Kurtosis = :(iiGr n
- 1)
equals 0 for a normal distribution - 3
equals 0 for a normal distribution
that the data are normally distributed [6]. The distribution of the data has only a slight effect on the estimate of the sample mean, but has a marked effect on the estimate of sample standard deviation [71. Table 1 provides the summary statistics for the raw data as well as transformed data from process 3 and process 15. These recipes were chosen to illustrate the proper use of transformations and one of the pitfalls most frequently encountered. The values of average and standard deviation are used in developing process control limits. The measures of skewness and kurtosis indicate the shape of the distribution. The skewness is a measure of the symmetry of the distribution and the kurtosis is a measure of the amount of data in the tails
of the distribution. For normally distributed data, both the skewness and kurtosis should approach 0 [7]. In many cases it is possible to convert non-normal data, x, into normally distributed data, y, by applying a transformation, y =f( x). The data was compared against two distributions which are often found in frequency counting, the log-normal and Poisson distributions [8]. The log-normal distribution has a central value, the geometric mean, with values shifted from the mean by various multiplicative factors. The log transformation (data transformed with y = log(x + 1) to account for the 0 values) produces a near normal distribution for process 3. Log and square root transformations fail to provide the desired result when applied to process 15.
SPC Run Chart - Transformed Limits HYT Viewport Counts: Process 3
-
HYT
29
43
51
71
85
99
113
127
141
Implant Number
Fig. 5. SPC run chart for process 3 using transformed limits.
155
DATA
S. Myers et al. / Integration of particle monitor into control system
Two primary factors account for the failure of process 15 to fit any of the tested distributions. The most significant is the small sample size, 20, relative to the large sample standard deviation, 37. The second factor that must be considered is that with mean counts much greater than 10 the data should approximate a normal distribution. It is clear that more data must be collected for process 15 before accurate control limits can be set. Fig. 5 shows the run chart for process 3 using limits derived from the transformed data. The data was transformed with y = log(HYT + l), and control limits derived using standard SPC techniques, but using the values of mean and standard deviation determined for the transformed data. To provide dimensional integrity to the control limits, the antilog.is taken and the data is displayed in units of sensor counts + 1. When transformed limits are used, the process appears under control with only one point falling outside of the upper control limit. Calculation of control limits from data that was not transformed results in 6 counts outside of the upper control limit.
5. Conclusions
In situ particle monitoring capability has been cessfully integrated into semiconductor equipment trol systems. The applications for these sensors varied and the benefits of their use are obvious. data from these sensors can be used for process
succonare The con-
241
trol, but it must be understood that the data may not be normally distributed. Before determining control limits for a piece of equipment one should ensure that the distribution of the data is understood.
Acknowledgements
The authors would like to acknowledge Jennifer Stern, John Lewis and Joe Malenfant of IBM, Essex Junction, Vermont for their guidance and support of this work.
References [l] Real-time particle monitoring of the Eaton NV-20A high
current implanter, SEMATECH Technology Transfer Number 91050524A-ENG (1991). [2] R.W. Burghard, D.J. McCarron, R.J. Marlde and H.K. Nguyen, Inst. of Env. Sci. Proc. 1 (1992) 338. [31 D.J. McCarron, E. Custodio-Williams, Inst. of Env. Sci. Proc. (1991) 689. [41 T.J. Barclay, Microcontamination (November 1985)‘67. [5] P. Borden, Microcontamination (April 1991) 43. [6] D.J. Wheeler and D.S. Chambers, Understanding Statistical Process Control (Statistical Process Control, Inc., Knoxville, 1986). [7] G.E.P. Box, W.G. Hunter and J.S. Hunter, Statistics for Experimenters (Wiley, 1978) p. 89. [81 D.W. Cooper, J.V. Babcock and F. DiPietro, Inst. of Env. Sci. Proc. 1 (1992) 215.
V. PROCESS CONTROL