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 con- trol. 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 experi- ence 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

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 monitor- ing 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 Auto- Clean process and can be used to monitor and opti- mize this process.

The operator has an opportunity to utilize the cu- mulative 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 re- sume 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

0168-583X/93/$06.00 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 involv- ing 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-

CELL

COMM

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 appro- priate 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 suc- cess 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 with- out having received a response to the previous com- mand. Timeouts are handled by attempting to resend the last request and ultimately by informing the opera- tor 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. Inad- vertent selection of dummy wafers which had not been cared for properly resulted in a five-fold increase in

Implant Triggering

Monitor

SPARC

t Driver = Task .

Fig. 2. Control system.

HYT Counts and PWP Counts from Photoresist Testing

S. Myers et al. / Integration of particle monitor into control system 245

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 mini- mum time lost.

Number of Photoresist Wafers

Fig. 3. In situ detector counts and particles per wafer pass from photoresist testing.

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 pro- cess 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 develop- ing 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 particle count. The in situ monitor allowed for immediate detection of this problem whereas the use of monitor wafers would require that the “duty dum- mies” be part of the monitor batch.

4. Applying statistical process control techniques

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

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 I

1 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.

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Table 1

S. Myers et al. / Integration of particle monitor into control system

Summary statistics for HYT counts

Transform None Log(x + 1) Sort (x) None Log(n + 1) Sqrt (x) process number 3 3 3 15 1.5 15

Average 4.65 0.598 1.81 32.10 1.320 5.020 Standard deviation 5.83 0.361 1.17 37.40 0.392 2.690 Skewness 3.24 0.121 1.63 2.01 0.565 1.320 Kurtosis 13.80 - 0.193 3.01 3.39 - 0.470 0.873 Number of samples 165 165 165 20 20 20

Equations: Average = Xxi /n

Standard deviation = C(xi - p)“/(n - 1)

Skewness = ~‘~;;s!‘, equals 0 for a normal distribution

Kurtosis = :(iiGr - 3 equals 0 for a normal distribution n

that the data are normally distributed [6]. The distribu- tion 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 trans- formation, 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 - DATA

29 43 51 71 85 99 113 127 141 155

Implant Number

Fig. 5. SPC run chart for process 3 using transformed limits.

S. Myers et al. / Integration of particle monitor into control system 241

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 col- lected 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 trans- formed 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 suc- cessfully integrated into semiconductor equipment con- trol systems. The applications for these sensors are varied and the benefits of their use are obvious. The data from these sensors can be used for process con-

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 Stern, John Lewis and Junction, Vermont for this work.

References

like to acknowledge Jennifer Joe Malenfant of IBM, Essex their guidance and support of

[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 Statisti-

cal 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.

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