A Review on Control Strategies of Batch Processing Machines in Semiconductor Manufacturing

7th IFAC Conference on Manufacturing Modelling, Management, and Control International Federation of Automatic Control June 19-21, 2013. Saint Petersburg, Russia

A Review on Control Strategies of Batch Processing Machines in Semiconductor Manufacturing Pyung-Hoi Koo*. Dug Hee Moon** *Department of Systems Management and Engineering, Pukyong National University, Busan, Korea, (Tel: +82-51-629-6485; e-mail: [email protected]) * Department of Industrial and Systems Engineering, Changwon National University, Changwon, Korea (e-mail: [email protected]) Abstract: Batch processing machines process several jobs simultaneously as a batch. When a batch machine completes the service of a batch and there is at least one product waiting in queue, a realtime control decision is made to decide whether to start a job with a partial batch or wait until future job arrivals occur. This paper provides a review of research works on real-time control strategies of batch processing machines in semiconductor manufacturing. We relate the research works with some distinct characteristics of semiconductor manufacturing and discuss the issues for future works. Keywords: batch processing machines, realtime control strategies, dispatching, semiconductor manufacturing

1. INTRODUCTION Production scheduling problems encountered in semiconductor manufacturing systems have several features that make them difficult, such as a variety of processes involved, re-entrant product flow, and rapidly changing products. Machines in semiconductor manufacturing process may be grouped into discrete processing machines (DPMs) and batch processing machines (BPMs). DPMs process wafers individually while BPMs process several jobs simultaneously as a batch. Some of the BPMs in the semiconductor manufacturing include oxidation, diffusion and deposition. The batch processing leads to waiting for jobs to form a batch. In addition, on completion of batch processes, multiple jobs are released to downstream operations, resulting in non-smooth product flow with high variability. Hence, efficient and effective use of BPMs is important for high system performance. There are two decision approaches to assign resources to the jobs: scheduling and real-time control. Scheduling is the task of allocating jobs to available production resources over time, assuming all data such as job arrivals and process related data are known in advance and deterministic. A number of research works have addressed the BPM scheduling problems from a variety of different perspectives. Lee et al. (1992) pioneered the study on BPM scheduling problems. Existing research works on BPM scheduling are comprehensively reviewed in Potts et al. (2000), Mathirajan and Sivakumar (2006), and Monch and Fowler (2011). One of the characteristics in semiconductor manufacturing is high uncertainty due to a variety of processes involved, long lead time, urgent orders and so on. The uncertainty reduces the performance of the scheduling decisions or sometimes makes the schedules infeasible. Therefore, in most real-world semiconductor manufacturing systems, the production line is controlled in realtime by considering current system status.

978-3-902823-35-9/2013 © IFAC

This paper provides a review of research works on realtime control strategies of batch processing machines in semiconductor manufacturing systems. 2. PROBLEM DESCRIPTION Fig. 1 shows a graphical representation of batch process machines. In general, the batch processing times are long compared to the discrete processing times, roughly ten hours as opposed to one or two hours for most other processes (Uzsoy, 1995; Mathirajan and Sivakumar, 2006).

Product arrival

batching

Batch To next process processing

Fig. 1 Schematic representation of a batch processing There are two types of BPMs in semiconductor manufacturing: diffusion type and burn-in type. In both BPM types, once processing begins on a batch, no lot can be added or removed from the machine until the processing of the entire batch is completed. However, there are some differences between these two BPMs. In diffusion type BPMs, a number of wafers are heated and filled with a carrier gas for changes of their electrical and chemical characteristics. The batching machines can accommodate a number of wafer lots usually equivalent to between six and 12 standard lots. Due to the chemical nature of the process, it may not be possible to process jobs with different recipes together in the same batch (incompatible job families). The jobs that require the same recipes can be viewed as a job family and all jobs within a job family requires the same processing time. Burnin type BPMs test the IC chips electrically and thermally to identify and scrap the weak or fragile devices. Each IC chip

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has a pre-specified minimum burn-in time, which may depend on its type. Since IC chips can stay in the oven for a period longer than their minimum burn-in time, it is possible to place different products in the oven simultaneously (compatible job families). The processing time of each batch is defined by the longest processing time required by any lot in the batch. Real-time control is generally performed in a event basis with local information only. There are two situations in which realtime control decisions are made: machine-initiated and product-initiated. When a machine completes the service of a batch and there is at least one product waiting in queue, a decision is made whether to start a job with a partial batch or wait until some number of future arrivals occur (machineinitiated control decision). Here, selecting the next batch for the BPM involves decisions on both which operation to process next (dispatching decision) and how many lots to put in the batch (loading decision). The dispatching decision refers to the prioritization of the lots that are put together in a batch. The loading decision considers a trade-off between starting the batch immediately and waiting for more lots to arrive. If the total number of lots in the buffer is less than the capacity of the furnace, starting a batch immediately underutilizes the furnace. However, delaying the initiation of processing until more lots arrive increases the queueing time for the lots that are currently waiting for processing. The term, realtime control, will be used for both dispatching and loading in this paper. The realtime control decision is also made when a new product arrives at the BPM which is being idle (product-initiated control decision). When a product arrives at the BPM and finds at least one BPM idle, the decision on whether the job family including the new product is loaded immediately should be made. 3. REALTIME CONTROL STRATEGY Earlier works on realtime control strategies on batch processing machines are reviewed in van der Zee (2003). He indicates that re-entrant flows are rarely seen in the literature, and most realtime control strategies adopt a lead time criterion. Recently, Sarin et al. (2011) provide a review of dispatching rules in wafer fabrication. Their survey covers dispatching for serial machines, bottleneck machine, batch machines and automated material handling systems, all together. Our paper focuses on realtime control problems for batch machines. We classify realtime control strategies of BPMs into two policies, threshold policy and look-ahead policy, according to the use of knowledge on future arrivals of products. The threshold policy is applied when there is no information available while look-ahead policies use information on near future system status. 3.1 Threshold Control Strategy A server capable of processing several jobs simultaneously is called a bulk server in queueing terminology, and there have been extensive works in queueing theory on bulk processing. Most of this literature, however, focuses on performance evaluation rather than control (Neale and Duenyas, 2003). Minimum batch size (MBS) control strategy introduced by

Neuts (1967) is the most basic control strategy of threshold policies. In this control strategy, processing of a batch is started when the number of products waiting in the queue becomes greater than or equal to the predetermined minimum batch size. The determination of the optimal MBS in different manufacturing environments is a main research topic in the threshold strategy. Table 1 lists an overview of literature on threshold control policies. Table 1: literature on threshold control strategy Product type

Literature

Single product

Neuts(1967), Deb & Serfozo(1973), Neuts & Nadarajan(1982), Makis(1985), Grunani et al.(1992), Avramidis(1998),

Multiple product

Akcali et al. (2000), Xia et al.(2002), Park & Banerjee (2011), Flower et al. (2002), Phojanamongkolki et al. (2002)

Deb and Serfozo (1973) address the systems with a single batch processing machine serving a single product. They provide a stochastic dynamic program to determine the optimal MBS size under Poisson arrival assumption with the objective of minimizing costs consisting of service costs and waiting costs. They prove that the optimal control policy is a threshold policy when product arrivals follow Poisson process and the batch service times are independent and identically distributed (IID) and independent of the arrival process. Neut and Nadarajan (1982) extend the previous work by considering multiple batch machines. Makis (1985) extends the work by considering bounded waiting time where customers waiting times cannot exceed a given constant value. Grunani et al. (1992) consider a reentrant two-stage system where the first stage is a discrete processing system and the second is a batch processing system. (We will express this network as d à b where d and b denote discrete and batch process, respectively). They use a renewal approximation to determine MBS that minimizes the expected long-run average cost that is the sum of set-up costs, holding costs and capacity utilization costs. Avramidis et al. (1998) present an algorithm to determine the optimal MBS for minimizing the expected long-run number of customers in the system for both diffusion type and burn-in type batch machines subject to Poisson job arrivals. They argue that the MBS size should be determined by considering system utilization and service time distribution. For multiple product cases, Akcali et al. (2000) examine the performance of control policies for the batch processing machines. For loading decision, they present two threshold policies: variable-threshold-by-product policy where threshold value for a product is determined based on its demand volume and variable-threshold-by-recipe policy where threshold value is determined based on the relationship between inter-arrival time and processing time. Through the simulation experiments, they conclude that the loading policies have more significant effect than the dispatching policies on the performance. Xia et al. (2002) include buffer sizes of products in the dispatching decision. Their objective is to maximize the throughput and (in case of finite buffers)

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minimize the loss flow due to buffer overflow. Park and Banerjee (2011) present MBS-SEU model where tardiness and earliness are included in MBS-based decision. They introduce a stochastic evaluation utility (SEU) function which depends on lead time, lateness and earliness. When a job is expected on time, the MBS-SEU model behaves the same as general MBS model. However, when a job is expected late or early, the results of MBS-SEU model varies with the stochastic information of arrival such as types of arrival distribution and rate, traffic intensity, etc. A few works have addressed the MBS policy for the systems with multiple products and multiple machines. Flower et al. (2002) analyze the batch processing machine by using queueing models and provide an analytical model to determine MBS size for multiple machines with multiple products with unequal batch sizes to minimize the cycle time. They present closed-form formula to approximate the steady-state performance measures including cycle time and work-inprocess. To find the near-optimal batch sizes for different product families, a genetic algorithm based batching decision is introduced. Phojanamongkolkij et al. (2002) extends the work of Flower et al. (2002) by introducing product priorities. 3.2 Look-Ahead Control Strategy The threshold policies discussed above do not consider future product arrivals. Today, in most wafer fabs, production information systems such as MES (manufacturing execution system) are installed in the plant and shop floor data can be gathered in realtime. Look-ahead strategy in BPM control decisions considers near future informations. Table 2 lists an overview of research works on look-ahead control policy. The system configuration, decision criteria, and some distinct characteristics are specified for each literature Table 2. Look-ahead strategies Literature Glassey & Weng(1991) Flower et al.(1992) Flower et al.(2000) Weng& Leachman(1993) Robinson et al. (1995) Duenyes and Neal(1997) Neale & Duenyas(2000) Neale & Duenyas(2003) Cigolini (2002) Murray et al (2008) Van der Zee et al.(1997) Van der Zee et al.(2001) Van der Zee (2002) Van der Zee (2004) Van der Zee (2007) Tajan et al.(2011) Kim et al.(1998) Kim et al.(2001) Kim et al.(2010) Monch & Habenicht (2003) Monch & Zimmermann (2004) Gupta&Sivakumar(2006) Gupta&Sivakumar(2007) Sha et al. (2007) Tu (2010)

Prod Stat- CriteExtra notes4 -uct1 ion2 rion3 FT DBH s s FT NACH s,m s FT NACHM s,m m FT MCR s,m s FT RHCR, b-d m s FT H,HA m s FT TCLH, d-b-d m s FT WPRC; burn-in type, d-b-d m s FT WNLTT,reentrant with b-d m m FT TLNA, sequence dependent m m FT DJAH m m FT DSH;non-ident. machine m m FT DJAH-F, b-d m s FT DJAH-C, burn-in type m m FT DJAH-C-S, different job size m m FT MPCH, optimization based m s FT BFQL m m TD MMBS, MDBH, PUCH m m TD PRA, PRALC m m TD SDBH, DBDH,GABH m m TD sequence dependent m m TD LAB, static weight s s s s TD,ER LAB2,dynamic weight TD LBCR m/s s m m QT,RT DBCTC, d-b-d

Cerekci & Banerjee (2010) Park & Banerjee (2011) Ham et al. (2011)

m m m

TD NACH-T, NARCH-T, d-b s s TD,ER,FTMBS-DBU, NACH-DBU m Cmax i-RTD

1,2 s: single product type (machine), m: multiple product types (machines) 3 FT: flow time, TD: tardiness, ER: earliness, QT: queueing time, RT: running time, Cmax: makespan. 4 b: batch machine, d: discrete machine

Glassey and Weng (1991) may be among the first to use nearfuture information for realtime BPM control in semiconductor manufacturing. They present dynamic batching heuristic (DBH) assuming a single batch machine and a single job family with processing time T. At any time instance t that the batch machine is available and only a partial batch is available, DBH is activated. Given the forecasted job arrivals during planning horizon (t, t+T), DBH makes batching decision to minimize the total delay time. The amount of delay time for products in queue that are waiting for future arrivals is compared to the amount of delay time that can be saved for the future arrivals by waiting until the arrivals occur. If the result is a gain, then the machine is kept idle for that duration. Otherwise, the machine starts processing the partial batch immediately. Simulation results show that DBH outperforms threshold policies in moderate traffic intensity between 30% and 70% even with some errors in the arrival times. Fowler et al. (1992) present a control heuristic named NACH (Next Arrival Control Heuristic) for both single product family and multiple product families. These heuristics only consider the first future job arrival and determines if it is more efficient to start the batch process immediately or at the next arrival time. They show that their heuristics are robust in the sense that they perform well even with prediction errors. Fowler et al. (2000) extend their previous work for multiple batch processing machines case. Weng and Leachman (1993) present Minimum Cost Rate (MCR) heuristic for single machine case either with single and multiple products where the holding cost per unit time is used for the control decision. MCR requires a large amount of data to realize the improvement in system performance and is less robust in forecast error compared to NACH. Identifying the weakness of MCR, Robinson et al. (1995) present Rolling Horizon Cost Rate (RHCR) heuristic which is a combination of the cost rate calculations of MCR and the rolling horizon decision of NACH. RHCR addresses the control of a tandem b àd network. They argue that some additional improvements can be gained at low to moderate traffic intensity by considering downstream machine status. Duenyas and Neale (1997) present two realtime control heuristics for furnace type BPMs, one without next arrival information (H) and the other with next arrival information (HA). For HA, only the first future arrival is considered like in NACH. The decisions are made in three steps. The first step uses queuing model to determine eligible job families for the next service. Then one of the eligible job families is selected as the best candidate for service. Finally, it is decided whether to serve the selected job family or to be idle until the next decision epoch. Neale and Duenyas (2000) address control problem for a tandem dàb àd network. They present a control heuristic, TCLH(Two Control Limit Heuristic), in which the states of upstream and downstream machines are considered. Through experiments, they argue

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that the upstream information is more beneficial than the downstream information. Neale and Duenyas (2003) extend their work for burn-in type BPMs and propose a heuristic, named WPRC (Weighted Processing Rate for Compatible), to minimize the lead time. Cigolini et al. (2002) present a dynamic look-ahead approach based on the wait-no-longerthan-time (WNLTT) to minimize the flow time. They examine the performance of the new method by using simulation model of a re-entrant flow manufacturing environment with a tandem bàd network. Van der Zee et al. (1997) introduce the Dynamic Job Assignment Heuristic (DJAH) where the performance criterion is the minimization of logistic costs per part on a long term. Logistic costs associated with a job consist of linear waiting costs and a fixed amount of setup costs. Later, van der Zee et al. (2001) study batch processing system with multiple non-identical machines, and develop a new strategy called DSH (Dynamic Scheduling Heuristic) to choose a machine from different types of machines based on the required processing condition, product characteristics, or operating cost. DJAH has subsequently been modified by considering downstream processing stage in van der Zee (2002) where DJAH-F is presented for a flow shop consisting of a tandem b àd network. Van der Zee (2004) also studies the realtime control problems for burn-in type BPMs. Later, he extends his work by considering product families of non-identical size and present new control strategy called DJAH-C-S (van der Zee, 2007). Tajan (2011) presents a heuristic based on model predictive control (MPCH). At each decision instance, MPC optimally solves a related deterministic problem with truncated horizon. Given the sequence of decisions from the simpler problem, only the first control is implemented and the rest are discarded. Kim et al. (1998) devises a rule called BFQL (back and front queues levelling) in which scheduling decisions are made in such a way that the workloads of batch processing machines and their downstream machines are well balanced. The research works discussed above attempt to improve system related performance, lead time or waiting time, to develop their control rules. Recently some researchers deal with the realtime BPM control considering due date. Kim et al. (2001) study the control problems in a semiconductor fab with multiple product types with different due dates and different process flows. Three decision problems, lot release control for system input, lot scheduling for serial machine, and batch scheduling for batch machines, are addressed. For the batching decision, three rules, MMBS, MDBH and PUCH are introduced. MMBS and MDBH are modified from MBS and DBH, respectively, by considering slack time for each product. The idea is that the job family with least slack time is selected for the next operation, and whether to start processing the selected job family is determined based on MBS and DBH, respectively. PUCH (Processing Urgency Classification Heuristic) defines a new criterion namely processing urgency and gives the priority to the jobs with highest urgency for the next process. Kim et al (2010) present list scheduling heuristics including PRA (priority rule based algorithm) and PRALC (PRA with look-ahead check) where a job with the highest priority among jobs is selected and scheduled on the machines over time. Sequence dependent

setup time is considered in their models. In PRA, the priority is calculated based on weighted due date. In PRALC, future information is added in PRA procedure. Monch and Habenicht (2003) present batching heuristics called SBDH (static batch dispatching heuristic) and DBDH (dynamic batch dispatching heuristics) based on the existing ATC (apparant tardiness cost) dispatching rule to minimize total weighted tardiness. DBDH considers future lot arrivals while SBDH does not. Slack times are considered to select the lots to form a batch in both heuristics. They also present a genetic algorithm based batching heuristic (GABH) for assigning formed batches to parallel batch machines and sequencing them on each single machine separately. In their later studies, Monch and Zimmermann (2004) include the sequence dependent setup times in batching decisions. One important finding is that batch loading decision has a greater impact on the performance than the batch dispatching decisions, which is parallel to the discussion in Akcali et al. (2000). Gupta et al. (2006) present a control heuristic called LAB (Look Ahead Batching) for single product single batch machine system in which decisions are made considering the arrival epoch and due dates of incoming lots. LAB uses conjunctive simulated scheduling approach and attempt to minimize the variation of tardiness of jobs in a batch as well as average tardiness. Later, Gupta et al (2007) improve LAB by resolving conflicting objectives related to earliness and tardiness. Sha et al. (2007) develop a due-date oriented look-ahead batch control rule (LBCR) that considers the due date and processing time and expects to raise delivery rates and reduce the average tardiness. LBCR combines with the methodologies of the dispatching rule, critical ratio(CR), and a batch dispatching rule, NACHM. Cerekci and Banerjee (2010) propose two look-ahead batch control strategies for a two-stage production unit (dàb) to minimize the mean tardiness. The first control strategy, NACH-T(next arrival control heuristic for tardiness objective) is a rolling horizon decision making approach that incorporates arrival times and due dates of the upcoming products. The second strategy, NARCH-T(next arrival resequencing based control heuristic for tardiness objective), employs a re-sequencing approach at the serial processor station to improve the batching decisions. Re-sequencing takes place as changing the sequence of the serial processor’s queue by pulling an urgent product to the front if there is a benefit in doing so. Park and Banerjee (2011) introduce the use of stochastic utility evaluation (SUE) function that captures the weight for the tardiness and earliness. The SUE function is incorporated in an existing model, NACH, to develop new control policies, NACH-SUE, to solve tricriteria (lead time, tardiness, and earliness). In some manufacturing processes in a wafer fab, the jobs are required to be processed in the time range because of the undesirable copper film oxidation or fluorine precipitation on wafer surfaces. This time constraint is often found in the buffer between wet etch and furnace operations in the wafer fab. Tu and Chen (2010) address the shop floor control problems in dàbàd processes with time constraints. They present dynamic batch job control with time constraints (DBCTC) where the concept of inventory policy, (S,s) policy, is employed to control the WIP level to avoid machines being idle and wafers exceeding time constraints simultaneously.

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Ham et al. (2011) present IP(integer programming)-based realtime dispatching heuristic called i-RTD for two-station (bàb) batching flow shop consisting wet etch and furnace with time window constraints. Here, an IP optimization model, whose objective is to minimize makespan, is utilized iteratively to construct a near-optimal schedule for batch processors. 4. CONCLUDING REMARKS For control decisions on BPMs, it is important to select an appropriate performance measure. Lead time is one of the critical performance measures in semiconductor manufacturing. Minimizing lead time is equivalent, by Little’s law, to minimizing the average time in system. Most previous research works attempts to minimize the flow time and holding cost of WIP. However, due-date satisfaction is also an important performance measure. Only a few recent researches consider due-date related performance. In a wafer fab, wafer lots which have gone through reentrant loops can be considered as different job types. Typically, the lithography process is the bottleneck and has a predefined production schedule. This gives rise to pseudo due dates for jobs in the BPMs. The late arrival of lots at subsequent stations may lead to starvation of the system bottleneck and thus lose output. Tardiness is a common measure for the duedate related performance. As indicated by Park and Banerjee (2011), minimizing earliness is also an important performance measure especially in lean manufacturing environment. Most of the control rules, except Kim et al. (1998, 2001), developed so far address the isolated problem of optimizing the local performance of batch processing machines. We insist that the decision making at batch machines should consider overall system performance. Sometimes, locally optimized decisions may deteriorate the system-wide performance. For example, lead time minimization at the batch machines may lead to unbalanced and excessive WIP level throughout the manufacturing system, resulting in increased system flow time. The relationship between local performance measures and the system-wide performance measures is an interesting topic. In addition, relationship between order release/lot scheduling and batch machine dispatching is of also interest. Even though the look-ahead policies mostly provide better performance than the threshold policies, most real-world wafer fabs use threshold policies for batch decisions (Leachmann et al. 2002). This is because the threshold policies are easy to implement and future information is often incorrect. For this reason, more efforts should be made to develop simple and robust look-ahead control rules for realworld applications. A combination of the two control strategies can be a good strategy. Finally, the ideas behind the dispatching decisions on discrete processing machines may be applied for batch processing machines. For example, work load balancing among re-entrant loops and the theory of constraints are among the concepts possibly applicable for batch processing machines.

ACKNOWLEDGEMENT This research was supported by Basic Science research program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (2012R1A1A4A01014897). REFERENCES Akcali, E., Uzsoy, R., Hiscock, D., Moser, A. and Teyner, T. (2000). Alternative loading and dispatching policies for furnace operations in semiconductor manufacturing: a comparison by simulation, Proc. of Winter Simulation Conf., 1428-1435. Avramidis, A.N., Healy, K.J. and Uzsoy, R. (1998). Control of a batch-processing machine: A computational approach', Int. Journal of Production Research, 36(11), 3167-3181. Cerekci, A. and Banerjee, A. (2010). Dynamic control of the batch processor in a serial-batch processor system with mean tardiness performance, Int. Journal of Production Research, 48(5), 1339-1359. Cigolini, R, Perona, M., Portioli, A. and Zambelli, T., (2002). A new dynamic look-ahead scheduling procedure for batching machines, Journal of Scheduling, 5, 185–204. Deb, R. K. and Serfozo, R. F. (1973). Optimal control of batch service queues, Advances in Applied Probability, 5, 340-361. Duenyas, I. and Neale, J. (1997). Stochastic scheduling of a batch processing machine with incompatible job families, Annals of Operations Research, 70, 191-220. Fowler, J.W., Phillips, D.T. and Hogg, G.L. (1992), RealTime Control of Multiproduct Bulk-Service Semiconductor Manufacturing Processes, IEEE Transactions on Semiconductor Mfg., 5(2), 158-163. Fowler, J.W. Hogg, G.L. and Phillips, D.T. (2000). Control of multiproduct bulk service diffusion/oxidation processes. part 2: Multiple servers," IIE Transactions, 32, 167-176. Fowler, J.W., Phojanamongkolkij, N., Cochran, J.K. and Montgomery, D.C. (2002). Optimal batching in a wafer fabrication facility using a multiproduct G/G/c model with batch processing, Int. Journal of Production Research, 40(2), 275-292. Glassey, C. and Weng, W. (1991), Dynamic batching heuristic for simultaneous processing, IEEE Transactions on Semiconductor Mfg., 4(2), 77-82. Gurnani, H., Anupindi, R. and Akella, R. (1992). Control of Batch Processing Systems in Semiconductor Wafer Fabrication Facilities, IEEE Transactions on Semiconductor Mfg., 5(4), 319-328. Gupta, A., and Sivakumar, A. (2006). Optimization of duedate objectives in scheduling semiconductor batch manufacturing, Int. Journal of Machine Tools and Manufacture, 46, 1671-1679. Gupta, A., and Sivakumar, A. (2007). Controlling delivery performance in semiconductor manufacturing using look-ahead batching, Int. Journal of Production Research, 45(3), 591-613.

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2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia

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