IEE 572Design of Experiments
Experimental Design in Simulation of Semiconductor
Manufacturing
Dr. Douglas Montgomery
Date: May 8, 2023
Team:
Chanettre Rasmidatta
Ching I Tseng
Aditya Rastogi
Design of Experiments Project Report
Executive Summary
Due to the competitive market today in the semiconductor industry, ABC Co. wants to
investigate the factors, which affect the average cycle time and throughput. The
objective is to minimize the average cycle time and maximize throughput. Since the
model has eight factors and two levels each, we want to identify the factors that have
large effect. By doing this, 28-4 fractional factorial design is demonstrated and single
replication with 6 runs at the center is also used in this experiment. We have emphasized
the use of these designs in screening experiments to quickly and efficiently identify the
subset of factors that are active and to provide some information on interaction. Half-
Normal plot is used in the ANOVA, residual analysis and model adequacy checking,
regression analysis and contour plots to help the engineer to have the better interpretation
of the experiment as well to examine the active factors in more details.
The results from the experiment suggest that only two out of eight factors were
significant, which are release rate and dispatching rule. The model passed the tests for
normality and independence assumptions. In additions, the validity of the model was
performed based on the regression models to verify the two responses, average cycle time
and throughput. The model was verified using the confirmation run and the error was less
than one percent. The predicted values were very close to the actual values and thus
supporting the design.
Based on the results, we recommend that SSU dispatching rule should be used at release
rate of 19.5K wafers per month is the best combination to yield a higher throughput and
lower average cycle time.
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 2
Design of Experiments Project Report
TABLE OF CONTENTS
1. Experimental Design in Simulation of Semiconductor Mfg.4
1.1 Problem Statement1.2 Description of the Model
2. Choice of Factors Levels and Range 63. Selection Response Variable 84. Choice of Experimental Design 95. Performing the Experiment 106. Statistical Analysis of the data 11
6.1 Analysis of Variances (ANOVA)6.2 Model Adequacy Checking
6.2.1 Normality Assumption6.2.2 Residual Analysis6.2.3 Box-Cox Transformation
6.3 Regression Analysis6.3.1 Average Cycle Time6.3.2 Throughput
6.4 Interaction Graph of Factors A and G6.5 Optimal Designs
7. Conclusions 247.1 Confirmation Testing 7.2 Recommendations
APPENDIX 27
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 3
Design of Experiments Project Report
1. Experimental Design in Simulation of Semiconductor Manufacturing
1.1 Problem StatementABC Co. is a leading semiconductor manufacturing company. Lately they have
discovered that modeling the semiconductor manufacturing and simulating it for various
conditions would save lot of time and resources. The manager of the ABC Co. wants to
investigate the factors, which affect the average cycle time and throughput. The
objective is to minimize the average cycle time and maximize throughput. The lesser the
cycle time, the lesser the work-in-process, which means lesser investment in inventory.
The shorter cycle time also provides market responsiveness. With this goal in mind he
wants to plan an experiment or sequence of experiments designed to take him in the
direction of that goal.
1.2 Description of the ModelThe model represents a 300mm DRAM facility with approximately 450 process steps and
398 process tools providing 1709 total tool ports, WIP positions, handlers, etc. that are
grouped into 80 tool groups. There are 15 operators in 8 different types and the
maximum designed capacity was 20,000 wafers/month. Only one type of DRAM part,
which processes through one routing, is released into the system. The flow is a highly re-
entrant, i.e. jobs feedback through sequences of the tool-groups many times. A lot of 25
parts is released at a fixed interval depending upon the maximum designed capacity.
Twenty-one types of reticles, generic resources, with a capacity of two each, are used.
Process tool downtimes for both preventative and unexpected maintenance are
incorporated, along with employee lunches and breaks. AutoSched AP, a commercial
simulation software package was used to model this system.
This model simulates the manual material handling system and the various assumptions
for this system are listed below:
There is no operator’s traveling time to the front of stocker when an inter-bay
movement was requested.
Gaining access to stockers in a bay is considered as resource contingent.
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 4
Design of Experiments Project Report
Load and unload times are 1 minute each.
The average operator’s traveling speed is assumed to be 2 miles/hr, which is a
reasonably slow walking speed, considering the weight of the AGV (Automate Guide
Vehicle).
To compensate for safety precautions and other human factors in the Fab, travel times
used are equal to [distance/speed]*, where is equal to 1.5.
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 5
Design of Experiments Project Report
2. Choice of Factors Levels and RangeFrom the previous experiment and experience, the Potential Design Factors and the
Nuisance factors can be identified. The potential design factors are number of operators,
release rate, dispatching rule, stocker quantity, and number of reticles, of which number
of reticle is held-constant factor and the design factors are:
1) For operators, there are 5 factors and two levels each. The operator in this model
is responsible for loading and unloading the wafers on the machines and they are
also responsible for transportation of wafers within the Fab. Varying the number
of operators would possibly affect the performance of the system.
2) Release Rate, i.e. the rate at which the wafers are released into the factory, has
two levels. The release rate is measured by the number of wafers scheduled to
release into the Fab per month. The release rate affects the machine utilization,
specially the batching machine that in turn affects the system performance.
3) The dispatching rule for the bottleneck workstations has two levels. The
bottleneck machines were identified from the previous experiments. According to
the theory of constraints, the bottleneck machine determines the capacity of the
Fab that determines the throughput.
4) Stocker Quantity, which has two levels. In this model the stockers are treated as
stations and there is one stocker at each bay. The shortage of stockers can cause
blocking which can severely delay the manufacturing processes.
The details of the factors, level and range are given in the table below
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 6
Design of Experiments Project Report
Table 1 Design Factors and their Levels
Factor Levels Range
Operator
OP_DIFF 2 2 4
OP_PHOTO 2 2 4
OP_ETCH 2 3 5
OP_WET 2 2 4
OP_MOVE 2 35 55
Release Rate 2 18K 19K
Dispatching Rules 2 FIFOSame
Setup
Stockers Qty. 2 2 4
The Nuisance Factors are the various distributions for the processing time and the down
time, which are uncontrollable or the noise factors. The controllable nuisance factor that
can be identified is the random number stream that is to be used for the simulation. We
intend to keep the random number stream constant throughout the experiment.
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 7
Design of Experiments Project Report
3. Selection Response VariableThe purpose of the study is to determine the time taken for a lot of wafers to be produced.
This factor is best represented by the average cycle time and thus the average cycle time
happens to be one of our response variables.
Various other parameters are necessary to determine the proper running of the factory,
one of which is the throughput. Thus the two-response variables for our project are:
1) Average cycle time and
2) Throughput
Cycle time is defined as total elapsed time from lot creation to lot completion that include
process time, move time, queue time, and hold time.
The average output of a production process (machine, workstation, line, and plant) per
unit time is defined as the system’s throughput.
These response variables can be obtained from the simulation output report. The
simulation would run for a period of time at steady state. The steady state would be
determined by a long initial run and the statistics collected during this warm-up period
would be eliminated from the simulation output.
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 8
Design of Experiments Project Report
4. Choice of Experimental DesignSince the model has eight factors and two levels each, we need to identify the factors that
have large effects. To do so, screening experiments will be used at the initial stage of the
experiment. We will use the 2k Fractional Factorial Design for this screening experiment.
We choose 28-4 Fractional Factorial Design, single replication with 6 runs at the center
point as shown in Table 2. We determine the number of runs from the results of the
Design Expert software.
The alias for this design is a bit different for factor A. The possible reason for this could
be the use of center points and also that the factor A is a categorical factor. The defining
relation and the aliases is shown in Appendix 1.
Table 2: Design Matrix
Std Run Block
Factor1: DISPATC
HING RULE
Factor2: OP_DIFF
Factor3: OP_PHOTO
Factor4: OP_ETCH
Factor5: OP_WET
Factor6: OP_MOVE
Factor7: RELEASE
RATE
Factor8: STOCKER
S QTY.
R1: AVG.
CYCLE TIME
R2: THROUGHPUT
Hours Lots1 21 Block 1 {-1} -1 -1 -1 -1 -1 -1 -12 14 Block 1 {1} -1 -1 -1 -1 1 1 13 17 Block 1 {-1} 1 -1 -1 1 -1 1 14 22 Block 1 {1} 1 -1 -1 1 1 -1 -15 3 Block 1 {-1} -1 1 -1 1 1 1 -16 12 Block 1 {1} -1 1 -1 1 -1 -1 17 9 Block 1 {-1} 1 1 -1 -1 1 -1 18 20 Block 1 {1} 1 1 -1 -1 -1 1 -19 10 Block 1 {-1} -1 -1 1 1 1 -1 110 2 Block 1 {1} -1 -1 1 1 -1 1 -111 8 Block 1 {-1} 1 -1 1 -1 1 1 -112 13 Block 1 {1} 1 -1 1 -1 -1 -1 113 11 Block 1 {-1} -1 1 1 -1 -1 1 114 19 Block 1 {1} -1 1 1 -1 1 -1 -115 6 Block 1 {-1} 1 1 1 1 -1 -1 -116 18 Block 1 {1} 1 1 1 1 1 1 117 16 Block 1 {-1} 0 0 0 0 0 0 018 1 Block 1 {1} 0 0 0 0 0 0 019 7 Block 1 {-1} 0 0 0 0 0 0 020 15 Block 1 {1} 0 0 0 0 0 0 021 4 Block 1 {-1} 0 0 0 0 0 0 022 5 Block 1 {1} 0 0 0 0 0 0 0
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 9
Design of Experiments Project Report
5. Performing the ExperimentThe experiments were performed using the AutoSched AP simulation software package.
In the initial stage, one long run was made to determine the warm-up period. The warm-
up period is the time taken by the simulation model to reach a steady state, where no
statistics is collected. Cycle time was plotted against time (in days) and the period was
determined to be 94 days as shown in Figure 1.
Fig.1 Warm-up Period Determination
The run length was determined to be 3 years and only single replication was made at each
run due to the limited resources. A single run took about two and half hours on a fast
machine (PIII, 800Mhz). Refers to Appendix 2 and see the Result Matrix.
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 10
Warm-Up Period Determination
0
5
10
15
2025
30
35
1 29 57 85 113
141
169
197
225
253
281
309
337
365
393
421
449
477
Days
Cycl
e Ti
me
(Day
s)
Design of Experiments Project Report
6. Statistical Analysis of the dataUpon completion of the runs, the results were fed to the Design Expert software and
the results were analyzed. As mentioned earlier, the design chosen was a resolution
IV, 28-4 fraction factorial design. The analysis includes ANOVA, residual analysis and
model adequacy checking, regression analysis, and contour plots. These analyses are
discussed in detail below.
6.1 Analysis of Variances (ANOVA)Figure 2 below shows the half-normal plot, which shows the effects of various
factors. Based on this graph, where the response variable is average cycle time, the
factors that lie along the line are negligible and three factors seem to be significant.
The two main effects from this analysis are A and G and a two-factor interaction AG.
Fig.2 Half-Normal Plot of Average Cycle Time
The similar analysis was performed for response variable, throughput, as shown in the
Figure 3 below.
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 11
Design of Experiments Project Report
Fig.3 Half-Normal Plot of Throughput
Table 3 shows the results of analysis of variance. Based on the response variable, average
cycle time, it shows that the factors that we chose are significant and their interaction is
significant, and that there is no evidence of second-order curvature in the response.
Table 3 ANOVA for Avg. Cycle Time
The Table 4 supplements ANOVA table. The high value of R-Squared indicates that the
major proportion of variability is included in the model.
Table 4 Supplementary Data of Avg. Cycle Time
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 12
SourceSum of
Squares DFMean
Square F Value Prob > FModel 3094696.40 3 1031565.47 2238.82 < 0.0001 significantA 2440544.15 1 2440544.15 5296.75 < 0.0001G 510956.81 1 510956.81 1108.94 < 0.0001AG 143195.44 1 143195.44 310.78 < 0.0001Curvature 852.64 1 852.64 1.85 0.1915 not significantResidual 7832.96 17 460.76Lack of Fit 5791.66 13 445.51 0.872996454 0.6215 not significantPure Error 2041.30 4 510.33Cor Total 3103382 21
Design of Experiments Project Report
The similar analysis was performed for response variable, throughput, as shown in the
Table5&6 below.
Table 5 ANOVA for Throughput
Table 6 Supplementary Data of Avg. Throughput
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 13
SourceSum of
Squares DF Mean Square F Value Prob > FModel 14050525.55 2 7025262.77 4574.10 < 0.0001 significantA 13028404.55 1 13028404.55 8482.71 < 0.0001AG 1022121.00 1 1022121.00 665.50 < 0.0001Curvature 560.48 1 560.48 0.36 0.5533 not significantResidual 27645.79 18 1535.88Lack of Fit 17847.12 14 1274.79 0.52 0.8373 not significantPure Error 9798.67 4 2449.67Cor Total 14078731.82 21
Std. Dev. 21.4654Mean 1288.3230C.V. 1.6661PRESS 12992.7900R-Squared 0.9975Adj R-Squared 0.9970Pred R-Squared 0.9958Adeq Precision 100.0214
Design of Experiments Project Report
6.2 Model Adequacy Checking6.2.1 Normality Assumption
The adequacy of the underlying model should be checked before the conclusions from
the analysis of variance are adopted. Violation of the basic assumptions and model
adequacy can be easily investigated by the examination of residuals. For example, if the
model is adequate, the residuals should be structure less and that is, they should contain
no obvious patterns. In Figure 4, presents a normal probability plot of the residuals for
average cycle time. There is no severe indication of non-normality, nor is there any
evidence pointing to possible outliers and the equality of variance assumption does not
seem to be violated. Figure 5, Normal Plot of Residuals for Throughput shown below is
also normally distributed and it resembles a straight line.
Fig 4 Normal Plot of Residuals for Avg. Cycle Time
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 14
Design of Experiments Project Report
Fig 5 Normal Plot of Residuals for Throughput
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 15
Design of Experiments Project Report
6.2.2 Residual AnalysisVarious residual plots are shown in this section below. Figure 6-10 show diagnostic plots
of the model. The residuals are normally distributed and the equality of variance does not
seem to be violated.
Fig 6 Residual vs. Predicted Plot for Cycle Time
Fig.7 Residual vs. Predicted Plot for Throughput
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 16
Design of Experiments Project Report
Fig.8 Residual vs. Run Number for Avg. Cycle Time
Fig.9 Residual vs. Run Number for Throughput
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 17
Design of Experiments Project Report
Fig.10 Residuals vs. Significant Factor
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 18
Design of Experiments Project Report
6.2.3 Box-Cox TransformationThe model was tested for any transformations that could have been applied, but the Box-
Cox Plot did not suggest any new transformations for both response variables, namely
Average Cycle Time and Throughput as shown in Figure 11 and 12.
Fig.11 Box Cox Plot for Cycle Time
Fig.12 Box Cox Plot for Throughput
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 19
Design of Experiments Project Report
6.3 Regression AnalysisThe equations below are fitted regression model representations of the two-factor
factorial experiments for both responses.
6.3.1 Average Cycle TimeFinal Equation in Terms of Coded Factors:Avg Cycle Time = 1292.14 - 333.07 * A +178.70 * G - 94.60 * A * G
Final Equation in Terms of Actual Factors:
Dispatching Rule FIFOAvg Cycle Time = -5207.44845 + 0.36441 * RELEASE RATE
Dispatching Rule SSUAvg Cycle Time = -1143.43634 + 0.11213 * RELEASE RATE
6.3.2 ThroughputFinal Equation in Terms of Coded Factors:
Throughput = 8004.00 + 769.55 * A + 14.62 * G + 252.75 * A * G
Final Equation in Terms of Actual Factors:
Dispatching Rule FIFOThroughput = 13187.57955 - 0.31750 * RELEASE RATE
Dispatching Rule SSUThroughput = 2089.17045 + 0.35650 * RELEASE RATE
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 20
Design of Experiments Project Report
6.4 Interaction Graph of Factors A and G Figure 13 shows the interaction effect of factors A and G. The average cycle time
decreases when changing from FIFO to SSU and from 19.5K wafers per month to 18K
wafers per month. The other factors do not have significant effect on the responses. The
contour plot was constructed by converting the type of factors from categorical to
numeric as shown in Appendix 3.
Fig.13 Interaction Graph of Avg. Cycle Time
Figure 14 below shows the interaction effect of factors A and G. Using the dispatching
rule FIFO, throughput is higher at 18K release rate than at 19.5K release rate compared to
the dispatching rule SSU where the throughput is higher at 19.5K than 18K release rate.
The other factors do not have significant effect on the responses. The contour plot was
constructed by converting the type of factors from categorical to numeric as shown in
Appendix 3
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 21
Design of Experiments Project Report
Fig.14 Interaction Graph of Throughput
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 22
Design of Experiments Project Report
6.5 Optimal DesignsThe Design Expert provides optimal designs with the desirability factor of 0.852, which
determines the optimal level for each factor as shown in the Figure 15. The “circle” mark
and two ends on the line represent the current operating condition and its ranges. The last
two boxes show the ranges of two responses. Cycle time follows the hierarchical
principle, while throughput follows the linear relationship. The alternative solutions are
shown in the Appendix 4. The constraints used are the high and the low level of each
factor, where the objective used is to maximize the Throughput and minimize the Avg.
Cycle Time.
Fig. 15 Ramps for various factors
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 23
Design of Experiments Project Report
7. ConclusionsThe 28-4 fractional factorial designs were run and analyzed to determine the effect of
various factors on average cycle time and throughput. The following conclusions were
made:
Only two factors and their interaction were significant: release rate and
dispatching rule
The model was tested for its adequacy and found that the assumption of normality
and independency are not violated
R2 value was very high, that suggesting that model accounted for most of the
variability
Box-Cox Plot did not suggest any transformation
The graphs for the significant factors were analyzed and the best value is obtained
at high value of release rate and using dispatching rule as SSU, which is in
conjunction with our intuition.
The optimal value with specified desirability was calculated using the software.
Since only two factors have been identified as significant, more detailed
experiment can be designed to study the effects are various levels of these factors.
7.1 Confirmation TestingBased on the regression models, runs were made to verify the results obtained for both
responses, average cycle time and throughput. Table 7 shows the predicted and actual
results.
Table 7 Confirmation Testing values
The table indicates that the predicted values are very close to the actual values and thus
supporting the design.
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 24
Design of Experiments Project Report
7.2 RecommendationsBased on the conclusions and the validity of the model, we recommend:
To use SSU as a dispatching policy
To operate at a release rate of 19.5K to yield a higher throughput and lower
average cycle time.
To design a new experiment to take into consideration more levels of these
significant factors, such as 32 or higher
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 25
Design of Experiments Project Report
APPENDIX 1
Defining Relation and Aliases
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 26
Design of Experiments Project Report
APPENDIX 2
Result Matrix
Std Run BlockFactor1: OP_DIFF
Factor2: OP_PHOTO
Factor3: OP_ETCH
Factor4: OP_WET
Factor5: OP_MOVE
Factor6:
RELEASE RATE
Factor7: DISPATC
HING RULE
Factor8: STOCKER
S QTY.
R1: AVG.
CYCLE TIME
R2: THROUGHPUT
Hours Lots1 16 Block 1 2 2 3 2 35 18000 FIFO 2 1358.95 74762 5 Block 1 4 2 3 2 35 19500 SSU 4 1034.83 90603 10 Block 1 2 4 3 2 55 18000 SSU 4 1894.73 69984 8 Block 1 4 4 3 2 55 19500 FIFO 2 877.549 85035 11 Block 1 2 2 5 2 55 19500 SSU 2 1895.17 70106 2 Block 1 4 2 5 2 55 18000 FIFO 4 890.351 84977 15 Block 1 2 4 5 2 35 19500 FIFO 4 1347.63 74538 18 Block 1 4 4 5 2 35 18000 SSU 2 1081.39 89789 20 Block 1 2 2 3 4 55 19500 FIFO 4 1337.59 749010 21 Block 1 4 2 3 4 55 18000 SSU 2 1068.04 899511 22 Block 1 2 4 3 4 35 19500 SSU 2 1895.25 699012 3 Block 1 4 4 3 4 35 18000 FIFO 4 888.456 849513 7 Block 1 2 2 5 4 35 18000 SSU 4 1884.61 701414 13 Block 1 4 2 5 4 35 19500 FIFO 2 867.791 850315 12 Block 1 2 4 5 4 55 18000 FIFO 2 1339.14 749816 1 Block 1 4 4 5 4 55 19500 SSU 4 1012.69 910417 6 Block 1 3 3 4 3 45 18750 FIFO 3 1642.32 719718 14 Block 1 3 3 4 3 45 18750 SSU 3 934.913 878919 9 Block 1 3 3 4 3 45 18750 FIFO 3 1591.25 730720 19 Block 1 3 3 4 3 45 18750 SSU 3 927.695 880721 17 Block 1 3 3 4 3 45 18750 FIFO 3 1648.65 718022 4 Block 1 3 3 4 3 45 18750 SSU 3 924.109 8812
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 27
Design of Experiments Project Report
APPENDIX 3
Appendix 3A Contour Plot for Cycle Time
Appendix 3B Contour Plot for Throughput
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 28
Design of Experiments Project Report
APPENDIX 4
Optimal Solution
Ching-I Tseng, Chanettre Rasmidatta, Aditya Rastogi Page 29
ConstraintsLower Upper Lower Upper
Name Goal Limit Limit Weight Weight ImportanceDispatching Rule is in range FIFO SSU 1 1 5OP_DIFF is in range 2 4 1 1 3OP_ETCH is in range 3 5 1 1 3OP_MOVE is in range 35 55 1 1 3OP_PHOTO is in range 2 4 1 1 3OP_WET is in range 2 4 1 1 3RELEASE RATE is in range 18000 19500 1 1 3Stocker Qty. is in range 2 4 1 1 3Avg Cycle Time minimize 867.7908 1895.247 1 1.620733 3Throughput maximize 6990 9104 1 1 3
SolutionsNumber Dispatching RuleOP_DIFF* OP_ETCH*OP_MOVE*OP_PHOTO*OP_WET* RELEASE RATEStocker Qty.*Avg Cycle TimeThroughputDesirability
1 SSU 3.74 4.87 51.00 2.51 2.89 18658.56 2.58 948.814 8742.73 0.8519152 SSU 2.09 3.85 40.57 2.74 3.77 18666.88 2.74 949.748 8745.53 0.8519153 SSU 2.27 3.74 42.06 2.53 3.31 18667.96 2.36 949.869 8745.9 0.8519154 SSU 2.21 4.76 39.23 3.50 3.33 18655.67 2.79 948.49 8741.75 0.8519155 SSU 3.41 4.27 36.23 3.75 2.35 18669.91 2.42 950.088 8746.56 0.8519156 SSU 2.11 3.23 42.56 3.48 3.72 18654.61 3.33 948.372 8741.4 0.8519147 SSU 3.81 3.44 42.65 3.91 2.30 18671.45 3.71 950.26 8747.07 0.8519148 SSU 3.73 4.50 50.49 2.39 3.34 18653.72 3.76 948.272 8741.1 0.8519149 SSU 2.21 4.84 51.25 2.15 3.67 18653.53 2.21 948.251 8741.04 0.851914
10 SSU 3.65 4.67 42.91 2.33 2.18 18672.18 2.83 950.341 8747.32 0.85191411 FIFO 3.35 4.60 40.95 2.44 2.38 18000.00 3.10 1351.9 7487.2 0.28940112 FIFO 3.72 3.39 40.82 2.66 2.50 18000.00 3.19 1351.9 7487.2 0.28940113 FIFO 2.39 3.74 44.41 2.88 2.67 18000.00 3.25 1351.9 7487.2 0.28940114 FIFO 3.96 4.97 53.93 3.47 2.16 18000.00 2.01 1351.9 7487.2 0.28940115 FIFO 2.63 4.40 45.84 2.65 3.09 18000.00 2.82 1351.9 7487.2 0.28940116 FIFO 3.68 3.27 43.19 3.23 2.60 18000.00 2.94 1351.9 7487.2 0.28940117 FIFO 3.29 4.65 38.93 3.25 2.35 18000.00 3.01 1351.9 7487.2 0.28940118 FIFO 3.93 4.22 54.59 3.52 3.91 18000.00 3.35 1351.9 7487.2 0.28940119 FIFO 2.32 4.74 45.67 2.20 2.30 18000.00 2.80 1351.9 7487.2 0.28940120 FIFO 3.05 3.45 48.95 3.12 3.21 18000.00 3.67 1351.9 7487.2 0.289401