Que no se contemplen ni hagan cambios al Libro Primero, capítulos 1 a 10, del Texto Básico desde la CSM 2004 hasta el comienzo de la CSM 2014

Complete validation implies that the developed simulation model is behaving just like the real-world system. The Segment used in this work is designed to represent the reality of operations in a semiconductor and has been developed by a factory engineer with many years’ experience in the field, so a full validation against data from a real fab is not possible. However, as mentioned in the literature review, three types of model validation can be applied, face validation, validation of model assumptions, and input/output transformation validation. Partial validation of the model based on the first two types was only possible in this work.

Face validation was the first goal of this simulation model, where, the constructed model appeared to be reasonable on its face. This was approved by consulting a representative of the ICMR who was knowledgeable about the system behaviour under study. This validation took place without deep investigation and was carried out using the animation capabilities of the simulation model, to confirm that the lots were being processed in the sequence mandated by the Segment, for example batching rules at RWash and IEtch stations, where, two lots were batched before processing, also sampling at the measurement stations (TCheck, LAlign, and LDim) is followed as intended, in addition to that machines were subjected to the different breakdowns as exposed.

Validation of model assumptions fall into two general classes: structural assumptions and data assumptions.

The structural assumptions took place by validating the number of stations with their machines. For example, in MDep station there are 2 machines, in TCheck station there are 2 machines, and so on.

The data assumptions were done by investigating the input model variables that were generated randomly in the model and made sure that they represent the actual variables, like the availability of the stations. The total down times of all the machines were reported from the model, then the average of each station was calculated, and finally the availability was computed using Equation 6-2, where, the total time is the simulation run time.

p…h6h†6:‡ = (1 −‰„$ :ˆ _{„:h6 :ˆ) × 100} Equation 6-2 As mentioned previously the model reports the total down time of each machine at the “Shutdown” block of the shutdown module (see Figure 6-3). Therefore, some calculations were carried out in order to calculate the availability of each station based on the reported values as shown in Table 6-4.

Table 6-4: Calculated availability based on down times reported.

Station Average Station Down Time (minutes) Computed Availability (%) Target Availability (%) No. Type 1 MDep 238,609 77.24 76 2 TCheck 29,669 97.17 97 3 LPat 157,575 84.97 84 4 LAlign 28,211 97.31 97 5 LDim 44,385 95.77 96 6 MEtch 223,636 78.67 77 7 RWash 44,116 95.79 96 8 IDep 179,722 82.86 83 9 IPol 86,399 91.76 92 10 IEtch 268,270 74.41 75 11 VDep 186,300 82.23 82 12 VPol 223,300 78.70 79

First the average down time of each station was computed as presented in the third column, then the availability was calculated using Equation 6-2, and the results were given in the fourth column to be compared to the last column which

was the target availability. It was clear that the results of the computed availability are around the same values of the target availability.

Further data assumptions validation was achieved by comparing the provided data about stations utilisation “Target U” to that reported from the developed model “Reported U” as presented in Table 6-5.

Table 6-5: Target and reported U.

Station Type Target U Reported U Station Type Target U Reported U

MDep 0.601 0.573 RWash 0.271 0.263 TCheck 0.290 0.191 IDep 0.636 0.618 LPat 0.718 0.708 IPol 0.430 0.411 LAlign 0.510 0.378 IEtch 0.704 0.671 LDim 0.491 0.325 VDep 0.644 0.626 MEtch 0.734 0.717 VPol 0.501 0.463

It was clear that the utilisations of all the stations reported from the model were around the values of the target utilisation calculated using Equation 5-6 to give the values presented in Table 5-6, except TCheck, LAlign, and LDim stations because these are sampling stations.

Also, adding the non-sampling rate of each station to the reported utilisation to match the target utilisation calculated using Equation 5-6 and give the values presented in Table 5-6 as shown in Table 6-6.

Table 6-6: Reported, calculated and target U of sampling stations.

Station Type Non-sampling

rate Reported U Calculated U Target U

TCheck 1/3 0.191 0.255 0.636

LAlign 1/4 0.378 0.473 0.704

LDim 1/3 0.325 0.433 0.644

Finally, it should be noted that although the representative of the ICMR who provided all the needed data to develop the Segment and who was familiar with the system modelled and knowledgeable about its behaviour ensured a supportive collaboration during the model validation; yet, the developed model can only be claimed to be partially valid.

After the conceptual model has been translated, implemented in ExtendSimTM _Suite

v8.0.2, verified and validated to the extent possible; different experiments were carried out. This is discussed in details in the next chapter.

7 SEGMENT EXPERIMENTATION, RESULTS, AND

ANALYSIS

So far, a full description of the Segment has been presented with all the important details in chapter 6. With the validation completed as far as possible, experiments to improve the performance of the Segment by applying different lot release control strategies can be undertaken. In this chapter the performance measures used in this work, along with the list of assumptions and the simulation parameters used for the simulation experiments are addressed. Then a preliminary analysis of the base model is presented followed by a number of scenarios that are classified in to two groups.

Group I scenarios: These use the same methodologies applied earlier for the Minifab (discussed in Chapter 4), which starts with testing the effect of applying different Push behaviours, then evaluating the effect of the CONWIP application, and afterwards testing the impact of applying ICONWIP on the performance of the Segment that targets reducing the variability of arrivals that is induced by CONWIP as mentioned earlier.

Group II scenarios: These aim to balance the distribution of WIP across the stations of the Segment, which is the second issue addressed in this work that appeared at further analysis of CONWIP results. It begins with evaluating the effect of applying DBR, then a combination CONWIP and DBR is tested that results in a hybrid CONWIP/DBR lot release control strategy , and finally a developed lot release control strategy named LCONWIP is proposed.

7.1 PERFORMANCE MEASURES

As mentioned earlier, the performance measures that are evaluated in this work include:

Throughput rate, which is the number of finished lots per day. This is reported as the mean and variance of the daily throughput rate reported

from the model averaged based on the outcomes of the number of replications ( ZZZZ, m_no).

Cycle time, which is the time spent to produce one lot starting from entering the fab to begin with step 1 (S1) and ending with leaving the fab after

finishing step 46 (S46). This is reported as mean and variance of the cycle

time reported for each lot over the run averaged based on the outcomes of the number of replications ( ZZZZ,m_Šn).

Work in process, this is the number of lots that entered the fab and still being processed. Which is reported as the mean and variance of the instantaneous WIP level reported throughout the run averaged based on the outcomes of the number of replications (ZZZZZZ,m_{‹}).

Utilisation of the resources (machines and stations), which is the percentage of time these resources are busy. This is reported as the mean of the weekly instantaneous monitored utilisation over the whole run averaged based on the outcomes of the number of replications (vŒ).

The objective of this research is to achieve the target THas well as to minimize the cycle time and work in process while keeping the utilisation of all stations below the Umax of each station. Moreover, the variances of these performance measures are to be minimized.

7.2 LIST OF ASSUMPTIONS

Some revisions and assumptions have been made to the fab, these include:

Processing times are deterministic.
Travel times are triangularly distributed.

Random failure and random repair times are exponentially distributed.
While the machines process wafer by wafer, WIP is delivered to each

Cascaded batching is not modelled explicitly for stations 7 and 10 (RWash and IEtch); however, it is factored in the processing times of those stations.

The time units are minutes.

Sampling is modelled; however, no rework is considered, as, it is too low to be considered.