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Proceedings of Symposiumin Production and Quality EngineeringKasetsart University, 4-5 June 2002, pp.104-111.

A Simulation Approach for ProductivityImprovement of an IC Factory

Prameth TantivanichPrapaisri Sudasna-na-Ayudthya

Industrial Engineering DepartmentKasetsart University, Bangkok, Thailand

AbstractThe aim of this study is to develop a simulation model to intimate a real world IC

assembly line in order to identify alternatives for productivity improvement. The model isdeveloped under ARENA simulation software. Simulation methodology has been conducted toverify and validate the model before applications to the case study.

IntroductionToday semiconductor manufacturers face worldwide competitions so that they attempt to

reduce capital costs and shorten process cycle time while still ensure quality of their products.Intense competition has resulted in semiconductor manufacturers to initiate drives to improvetheir market responsiveness. The vendor who gets to market before another may grab the shareof the market. For a manufacturing standpoint, productivity improvement is most ofteninterpreted as: faster cycle time, lower cost, maximized machine utilization, and also maximizedfloor space utilization.

Since the need to reduce assembly cost has become significant in the last few years,productivity must continue to improve in the assembly house. So the role of design will becomemore critical. Simulation has become a popular design technique for developing productionschedules and dispatch lists in a manufacturing environment. With simulation we can deal realworld problems with stochastic elements where no analytical solutions are available withsufficient accuracy, estimate the performance under projected operating conditions, comparealternative system designs, maintain experimental conditions well under control, and we canconduct long time frame study.

For manufacturing systems, simulation can determine the bottleneck and optimization, atthe same time it can be used to determine the best alternative by using comparison and sensitivityanalysis. Compared to direct real experimentation, the computer simulation approach has theadvantages, such as lower costs, shorter time, greater flexibility and much small risk.

This research work emphasizes on evaluating the impact of the factors simultaneously onthe productivity by using simulation techniques. The sensitivity analysis and experimental designwill be conducted to investigate the effect in the productivity and cycle time when the

assumptions on factors are changed. In this study, simulation is used to model semiconductorassembly lines and attempt to improve it by using sensitivity analysis.

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Problem StatementEffective method for improving the productivity is one major factor for success in

business. Organization is often faced with decision on the appropriate number of machine, taskassignments, and production plan. By reducing the bottleneck problem in some operations inassembly lines and finding appropriate setting of parameters at each operation in the system, the

total process cycle time will be decreased.Since the operations in assembly lines at this plant are 17 operations and there are a lot ofnumbers of machines in each operation. Scheduling and queuing technique can used to solve thisproblem, but the flexible assembly system and the scheduler has limited freedom in constructingthe schedule to measure equipment utilization. Another technique to solve the problem is usingsimulation. Simulation allows testing the designs without committing resources to acquisition.Simulation also allows understanding the interaction among the variables that make up complexsystem such as the modern factory floor which is impossible to consider all the interactionstaking place at this moment.

Research Objectives

Regarding of the above problem, The objectives of this study are as indicated below.- Determine the significant factors that affect on the productivity of the system. Thismay lead to reduce process cycle time, and operation cost. In addition to thethroughput or productivity of the system can be increased.

- Develop new models of assembly lines and find the appropriate setting for input datathat give the highest productivity within the assumption and limitation of thecompany.

DelimitationThe delimitation of this research for constructing the simulation model are shown below1. The consideration is emphasized only on macro-scope of the system in term of

operation relationship by focusing on twelve IC assembly operations.2. Five main products that are 80% of the company demand are considered.3. Sets of downtime information for each machine and loading plan information are

given as stochastic demand. For processing time of five main products on eachmachine model and transferring time between each operation are given as constantdemand.

4. Schedule of preventive maintenance for each operation is given, and preventivemaintenance time is using an average value among each operation.

5. Sets of downtime information for Die Attach Cure, Post Mold Cure, Third OpticalInspection, and Forth Optical Inspection are not available.

6. There are unlimited of work in process carriers and Urgent lots are out ofconsideration.

7. Rejected products from inspection operation are scrapped.8. Loading procedure for Die Attach Cure, and Post Mold Cure are calculated from the

biggest size of work in process carrier.

Place of the study : This study was conducted at Semiconductor plant located on HiTechIndustry in Ayutthaya and analyzed at Kasetsart University.

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MethodsThe study and development process of this research can be classified into 5 phases as

follows:

1. Information gathering phaseAll information were separated into two groups as the following.1.1 Sets of data for the whole assembly line- Processing time for each product on each operation from the standard processing time

were obtained from the Industrial Engineering department of the study plant.- Loading plan or arrival time for each product from Production Control department.

Every time when the raw materials (Wafer) were sent to the first operation (WaferSaw), the time were recorded as an arrival time which used for the simulation model.

- Average transferring time between each operation.- Schedule of preventive maintenance for machines in each operation, and average

preventive maintenance time.- Number of machines to be used in the system.

1.2 Sets of data for each operationThe data in this group are the time for each action that causes machine stop running(inactive). These data were recorded by the computer system for six months since July toDecember 2000.

2. Data Analysis phaseAfter collecting the relevant raw data from the existing problems by tracking from the

computer system. The sets of data needed to change into the probability distribution formbecause if the average data were used, some variation during the simulation run would occur. Inorder to get the probability distribution form of each data, there are two important activities fromstandard techniques of statistical inference for fitting the appropriate probability distributionform to the data. These are hypothesizing families of distribution, and determining thedistribution. There are two widely used techniques for goodness of fit test. The first one is Chi-square test, and the second one is Kolmogorov-Smirnov tests. From these two methods, we canapply used to fit the distribution with the sets of raw downtime data that tracking from thecomputer system.

3. Constructing Simulation model phaseAfter data collecting, the simulation package that used for modeling the IC assembly

lines was Program Arena 4.0 commercial version. There are two steps for constructing thesimulation model of IC assembly lines. The first one is Structure of creating each product, andthe second one is Structure of each machine operation. The information in detail of each modulewas described as follow:

3.1 Structure of creating each product.In order to generate the structure of creating each product (entity) in the

simulation model, the Create module was used to generate arrivals of each product. TheAssign module was used for the characteristics of each product. The picture of Createmodule which the label is MSOP (products name) and Assign modules picture wereshown in figure 1.

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Figure 1 Create module and Assign module.

MSOP Assign 1

0

3.2 Structure of each machine operationWhen each product reaches each operation, the logic for selecting theavailable machine models was assigned to the Decide module. In order to represent theprocessing of a product at the machine, the Process module is used. The pictures ofDecide module, Process module, and transfer module were shown in figure 2.

Figure 2 Decide module, Process module, and Delay module.

4. Model Verification and Validation phase

Decide 1True

False

WS_MOD1

WS_MOD2

WS TO DA

0

0

0

0

Model verification is a process of determining that a model operates as intended.Throughout the verification process, we try to find and remove unintentional errors in the logicof the model. Steps for model verification are as follows:

1. Walkthrough procedure involves gathering a small group for a review of themodels logic.

2. Using the test cases by modifying experiment list such as replacing sets of data inprobability distribution form with sets of data in constant form.

3. Tracing by using animation to examine in detail the movement of entities throughthe system.

Model validation is the process of reaching an acceptable level of confidence that theinference drawn from the model is correct and applicable to the real-world system beingrepresented. Steps for validating the model are as follows:

1. All model assumptions were reviewed correctly.2. All fitted input probability distributions were tested for correctness.

5. Analysis of Output phaseSince the study plant is a non-terminating system, when analyzing the steady-state

performance, we must deal with two important problems. The first one is the bias from thestarting condition. The second problem involves estimating the variance of the mean response inorder to develop a confidence interval for the mean. For solving the first problem, the besttechnique that is appropriate and widely used is Discarding the data during the initial portion byplotting the graph. This technique necessitates selecting a truncation point which all previousobservations are discarded. The results for the simulation are based only on observationsrecorded after the truncation point.

In order to estimate the variance of the mean, we applied the method of Batch means.Because this method is the easiest and most practical for interpreting output of a non-terminatingsystem. This method is parallel to the method of independent replications used for terminatingsystem. Steps of Batch means method are as follows:

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1. Make a single long run and discard the initial transient phase.2. Divide the remaining observations intonequal each of sizem.3. Define that xj for j =1, , ndenotes the mean of the observations in thejthbatch.Using this notation, we can directly use the results from the terminating case to write the

confidence intervals. The key to make the Batching method work well is to select the appropriatebatch size. If the batches are sufficient large, the means of the two adjacent batches will beapproximately independent regardless of the observations at the end of batchj are correlated withthe observations at the beginning of batchj+1st.

Variance Reduction TechniqueThere are many techniques for variance reduction in non-terminating system which

depends on the analysis approach. If the truncated-replications approach was used, CommonRandom numbers or Antithetic Variates can be applied. For this simulation model, the method ofBatch means was used so that the technique for reducing variance of mean was a single long rununtil the half width of the resulting confidence interval satisfied small criterion. By specifying

the criterion for the termination, the simulation will be run until the condition satisfied. For theshort-term improvement, the condition of less than ten percent of the original half width wasperformed. On the other hand, the long-term improvement was performed with the condition ofless than fifty percent of the original half width.

Result and Discussion1. Bottleneck analysis

The important response being studied in this research is the cycle time of each product,which is impact directly on the productivity. And the objective is to determine the bottleneckoperation that is significantly affecting these responses. In order to solve these problems,sensitivity analysis was conducted by adding a machine in each critical top three operations.Since there are more than one machine model in each operation, the principle for adding amachine was identified by selecting the one that caused the long waiting time.

Solution 1This solution was to add one machine at DTF_MOD2 in DTF operation. The reason for

adding this machine is that this machine model had the highest average waiting time forSC70_5L.

Since this solution was adding the machine at the second model of DTF operation whichcan produce product SC70_5L only so the average cycle time for this product was decreased by10.55% while the cycle time for the other products were similar to the original one. Due to thelower waiting time and lower cycle time, the throughput per year for product SC70_5L wasincreased by 6.56% (139 lots). The result shown that Mold operation had the highest averagewaiting time especially for the MO_MOD3. So the next solution, for solving the bottleneckproblem, was adding a machine at MO_MOD3 in Mold operation.

Solution 2This solution was to add one machine for MO_MOD3 at Mold operation and one

machine at DTF operation. The reason for adding a machine at this machine model in Moldoperation was that this model had the highest average waiting time.

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This solution was adding the machine at the second model of DTF operation, which canproduce only product SC70_5L. This solution also added a machine at the third model of Moldoperation, which can produce SO_JM8L and SOT23_5L. So the average cycle time for productSC70_5L, SO_JM8L were decreased by 10.91%, and 5.8% respectively while the cycle time forthe other products were similar to the original one. Due to the lower waiting time and lower

cycle time, the throughput per year for product SC70_5L, SO_JM8L, SOT23_5L were increasedby 6.56%, 2.92%, and 0.81% respectively. Regarding to the cycle time and throughput forproduct SOT23_5l that was not significantly improved because the adding machine model wasset the first priority to product SO_JM8L. However, the show that Form operation had thehighest average waiting time especially for F_MOD2. So the next solution, for solving thebottleneck problem was to add a machine at F_MOD2 in the Form operation.

Solution 3This solution was to add one machine at Mold operation, one machine at DTF operation,

and one machine for F_MOD2 at Form operation. The reason for adding a machine at thismachine model in Form operation because this model had the highest average waiting time.

This solution was to add the machine at the second model of DTF operation which canproduce only product SC70_5L, one machine at the third model of Mold operation which canproduce SO_JM8L and SOT23_5L, and one machine at the second model of Form operationwhich can produce only SO_JM8L. So the average cycle time for product SC70_5L, andSO_JM8L were decreased by 7.88%, and 12.47% respectively while the cycle time for the otherproducts were similar to the original one. Due to the lower waiting time and lower cycle time,the throughput per year for product SC70_5L, SO_JM8L, and SOT23_5L were increased by6.56%, 7.11%, and 0.81% respectively. Since the consideration is to improve the bottleneckproblem on three critical operations, the sequential effect from the last solution results inincreasing the average waiting time for Forth optical operation by 373.21% (from 8.51 minutesto 40.27 minutes). This operation performs visual inspection for all packages defects. There aretwenty inspection operators per shift. That means we have to prepare more operators for theForth optical operation in case of the last solution is applied so that we can delivery the productquicker.

2. Experimental designIn order to find out which of possibly many parameters have the greatest effect on a

performance measures (cycle time and throughput per year for each product), the experimentaldesign terminology was conducted. In experimental design, the input parameters and structuralassumptions composing a model are called factors, and the output performance measures arecalled response.

The important responses being studied for this step are the cycle time for each productand the average throughput per year of each product. The experiments were performed for allfive products (MSOP8L, PDIP8L, SC70_5L, SO_JM8L, SOT23_5L). The study factors werelisted in Table 1 and their codes were listed in Table 2 and Table 3.

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Table 1 Description of the five factors in the experiments

Coded Variable FactorA Number of machine in DTF operationB Number of machine in Molding operationC Number of machine in Forming operation

D Setup time for each machineE Repair time for each machine

Table 2 Two level factors

Coded Variable Low Level High LevelA Original number of

machinesAdding one DTF machine

B Original number ofmachines

Adding one Molding machine

C Original number ofmachines

Adding one Forming machine

Table 3 Three level factors

CodedVariable

Low Level Medium Level High Level

D Original setup time data 20% decreasing 40% decreasingE Original repair time data 20% decreasing 40% decreasing

The reasons for setting the first three factors at two levels is that these factors concernedwith the number of machine so adding one machine will cost the company a large amount ofinvestment so we try by adding one machine first. The next two factors were set at three levelsbecause these factors concerned with the finished time for the machines. So three level designswere appropriate for studying the behavior of setup and repair time. From these setup, the 32* 23

factorial design was conducted in order to identify factors that have large effects.The response results (cycle time and throughput) from the experiment that run on the

seventy-two different setting were classified by product. In order to evaluate the response resultfrom the simulation model, Analysis of variance (ANOVA) can be conducted and the resultswere analyzed by using Statistica software. The most influence factors concerned with theimprovement of cycle time and throughputs are shown in Table 4.

Table 4 Summary of the influence factors that affect on cycle time and throughput

Product Factors that affect on cycle time Factors that affect on throughputMSOP8L -Setup time for each machine

-Repair time for each machine-Setup time for each machine-Repair time for each machine

PDIP8L -Setup time for each machine-Repair time for each machine

-Setup time for each machine-Repair time for each machine

SC70_5L -Number of machine in DTF operation-Setup time for each machine-Repair time for each machine

-Number of machine in DTF operation andsetup time for each machine-Number of machine in DTF operation and

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repair time for each machineSO_JM8L

-Number of machine in Moldingoperation and Number of machine inForming operation-Number of machine in Molding

operation and setup time for eachmachine-Number of machine in Formingoperation and repair time for eachmachine-Setup time for each machine andrepair time for each machine

-Number of machine in Molding operationand Number of machine in Formingoperation-Number of machine in Molding operation

and setup time for each machine-Number of machine in Molding operationand repair time for each machine-Number of machine in Forming operationand setup time for each machine-Number of machine in Forming operationand repair time for each machine

SOT23_5L

-Setup time for each machine andrepair time for each machine

-Number of machine in Molding operationand setup time for each machine-Number of machine in Molding operationand repair time for each machine

ConclusionThe main purpose for this study was to construct an efficient simulation model for IC

assembly lines and finding the significant factors that affect on the productivity. In other words,the new model will build based on the appropriate setting for input data that gives the highestproductivity and appropriate cycle time within the limitations of the study plant. In order toimprove the productivity and the cycle time within the limitations of the study plant, theappropriate setting factors are 1. Adding one DTF machine 2. Adding one Molding machine 3.Adding one Forming machine 4. 40% decreasing for setup time 5. 40% decreasing for repairtime. However, adding the machines will significantly invest for the company compared to theincrement in productivity. Therefore, the company should do the Benefit-to-Cost Analysis or

the Payback period study in order to implement.

RecommendationsThe simulation model of IC assembly lines can be applied with the other assembly lines.

The result from the simulation model showed that significant factor that should be implementedfirst is to add a machine at Forming operation. And in case of the third solution is applied,preparing more operators for the Forth optical operation is necessary. In order to decrease thesetup time and repair time for machines in each operation, the second recommended action is toimprove the skills of the technicians.

References

Prameth Tantivanich, A Simulation Approach for Improving Productivity in IC Assembly Line,Masters Thesis, International Graduate Program in Industrial Engineering, KasetsartUniversity, Bangkok 10903, Thailand.

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