RETRACTED: The Optimal Decision Combination in Semiconductor Manufacturing

Abstract

Wafer fabrication is a capital-intensive and highly complex manufacturing process. In a wafer fabrication facility (fab), wafers are grouped as a lot to go through repeated sequences of operations to build circuitry. Lot scheduling is an important task for manufacturers in order to improve production efficiency and satisfy customers’ demands of on-time delivery. Cycle time and average work-in-process reduction while meeting customers’ requirements play an important role in improving the competitiveness and sustainability of a semiconductor manufacturer. In this research, we propose the optimal combination rules for lot scheduling problems in wafer fabs, focusing on three complex areas of decision making: lot release control, batch sizing, and dispatching lots to enhance competitiveness and sustainability of a semiconductor facility.

1. Introduction

All sectors of industry are trying to improve their competitiveness and sustainability to secure market share and insure success of their business. This is especially true for highly competitive semiconductor manufacturing companies. Semiconductor fabrication is notorious for heavy use of energy and toxic chemicals. These in turn have adverse influences on both humans and the environment [1,2]. To abate such damages, many companies have researched and implemented environmentally sustainable strategies [3]. Although such an approach is novel and imperative, efforts are still on going and limited due to limitations rising from lack of required technology and insufficient resources [4]. For example, the industry’s shift from batch to single wafer processing has tended to increase energy consumption in fab which is detrimental to the environment. Another promising approach to this issue is improving productivity of semiconductor fabrication [5,6]. Enhanced manufacturing processes will reduce unnecessary rework, cycle time, unnecessary energy use, and limit human exposure to toxic chemicals.

The effective process control in semiconductor industry is challenging due to the process complexity and high level of required precision. It also has a very long production cycle time. Thus, efforts to reduce overall cycle time of fab lines are closely related to improvements of the overall productivity and to enhancements in the competitiveness and sustainability of semiconductor manufacturing. Improving performance measurement in this complicated fab line requires sound judgment and decision-making with regard to lot release policies and scheduling of work on the machines [7,8,9,10,11].

The fundamental architecture of the semiconductor manufacturing environment includes three interesting aspects: re-entrant flow, mixed processing modes, and unbalanced production facilities. The re-entrant characteristics of this environment constitute a complex manufacturing system, whose inherent challenges have attracted the attention of scheduling researchers and practitioners. Bellman et al. [12] classified the semiconductor scheduling problem into a general scheduling problem and a shop-floor scheduling problem. The general scheduling problem includes facility layout scheduling, product inspection scheduling, resource allocation scheduling, and warehouse locating, whereas the shop-floor scheduling deals with the material handling in the workshop. Danping and Lee [13] divided the shop-floor scheduling into a re-entrant job-shop scheduling and flow-shop scheduling problem based on the particular shop’s characteristics. Sarin et al. [14] divided the semiconductor wafer fabrication system (SWFS) scheduling into order release and dispatch rule which is further classified into sequencing and batching. Therefore, our scheduling problem can be regarded as a re-entrant job-shop scheduling problem in the SWFS with a combination of order release rules and dispatching rules.

Since the late 1980s, a number of order release rules which determine the time, the quantity, and the type of jobs that are released and dispatching rules that focus on determining the sequence of which of the lots or batches are dispatched to an idle machine have been developed. Most of these were open-loop release polices and local dispatching rules, which did not consider any current fab information. The key weakness when using local dispatch rules and open-loop order release rules is their inability to handle multiple re-entrant flow to effectively balance the line output [15,16]. Furthermore, given their local nature, they can only improve a single performance measure. For example, the shortest process time (SPT) rule provides the best performance for the average flow time. However, the SPT rule results in increasing the variance of flow time as jobs with long process times simply wait longer. Since manufacturing lead time is affected not only by the average flow time but also by flow time variance and average waiting time, implementation of the SPT rule may lead to unintended lead times.

As solutions to this problem, closed-loop release rules and global dispatching rules that utilize information from both within and outside the domain of the neighborhood of the decision point in time were developed [17,14]. Wein [18] assessed the impact of a combination of closed-loop order release rules and global dispatching rules on the throughput of the fab process. To evaluate these scheduling rules, discrete event simulations of three version of semiconductor wafer fab models with one, two, and four bottlenecks were used. The simulation results brought insights that closed-loop order release rules and global dispatching rules had larger improvements for average throughput rate than open-loop release rules and local dispatching rules. Glassey and Resende [19] developed an input control mechanism, called SA (starvation avoidance) for job shop with the randomness resulting from machine failure and repair. The proposed closed-loop order release rule maximizing the utilization of bottleneck was compared with other input control rules and showed better performance.

In order to validate that closed-loop and global scheduling rules have better performance (effects) than a single and local dispatching rule, Li and Min [20] proposed an adaptive dispatching method (ADM) with parameters determined dynamically by current state information of fab. A frequent job rework and machine failure/repair and other uncertainties are induced by the increment of the complexity in the semiconductor fab process. Consequently, it is necessary to determine appropriate dispatching and order release rules while considering fab process information in accordance with the changes in the environment. Other literature showed that closed-loop and global scheduling rules resulted in the procurement of better performance than other commonly used rules such as UNIF (Uniform), CONTIME (Constant time), FIFO (First in first out), EDD (Earliest due date), CR (Critical ratio), LPT (Longest processing time), and SPT (Shortest processing time) [21,22,23,24].

However, scheduling problems of SWFS are still complex due to the characteristics such as the re-entrant, the batching processes, multi-bottlenecks, and uncertain machine breakdowns [25]. There have been studies developing a new heuristic schedule and combining dispatching rules and order release rules to find appropriate solutions for the scheduling problems of SWFS.

Pickardt et al. [26] demonstrated the effect of combining dispatching rules and meta-heuristics for the effective selection of the appropriate job at the point of dispatching. They utilized GA (genetic algorithm) for creating a composite rule that assigns different dispatching rules to each work station based on the basic job attributes. Then a composite rule from GA was provided to the EA (evolutionary algorithm) such that it then searches for a good assignment of rules (Literature’s rules and a composite rule) to each work center for further improvements. Results showed that combination of a dispatching rule and meta-heuristics was able to generate rules that had lower mean tardiness than any of the other benchmark rules.

Another concept that has been used is scheduling to balance work-in-process (WIP), which attempts to reduce the mean and variance of cycle time by positively correlating arrivals and services at all the machines in the process flow. Priorities are assigned so as to minimize the deviation of the instantaneous inventory level from the average inventory profile. Three such policies are presented in a study by Li et al. [27], with each succeeding policy adopting a more global outlook. As conditions dynamically change in a fab, a dispatching rule that adapts to these conditions is more likely to perform better than a static rule. Hsieh et al. [28] developed an ordinal optimization-based simulation tool in order to select the best combination of rules when significant changes in a fab’s state may occur owing to machine failures or temporary WIP holding due to engineering changes. Qiao and Wu [29] developed the layered scheduling algorithm (LSA) based on the Drum-Buffer-Rope (DBR) mechanism. The proposed algorithm controls the global synchronous pace for the entire production system in the system bottleneck and balances the production load between different layers. It thus helped performances of SWFS through the stability of WIP, the avoidance of bottleneck starvation, and reduction of waiting time.

A simulation-based dispatching rule by Ponsignon and Monch [30] reduced mean flow times while maintaining high-machine utilization. The simulation-based dispatching rule works by downloading the current shop floor machine and WIP status and using them as the initial state for the simulation. Considering WIP balance and due date control, Zhou and Rose [16] presented a framework of operational control strategies for the fab process. The framework consists of four key components that are (1) global and local rules; (2) target WIP estimation; (3) WIP imbalance monitoring and detection; (4) WIP imbalance calibration. They considered various global and local dispatching rules related to the WIP balance, due date control, and the combination of them. Although these rules easily achieve the goal that is balancing WIP level between each of the work stations, none of these rules were able to fully cope with the production uncertainties in the fab process. Consequently, it is necessary to set the target WIP level, monitor and detect the WIP imbalance, and calibrate and handle the work station. They can attain improvement of productivity and efficiency by achieving low WIP level and lead time.

Batch-processing machines in the fab process can process a subset of queued jobs simultaneously. The Apparent tardiness cost (ATC) and Cost over time (COVERT) rules [31] are standard composite rules. Batch processing machines (furnace or ovens), such as those used in thermal oxidation, diffusion, and burn-in processes, are capable of processing several lots simultaneously in a batch. Jia et al. [32] considered the large-scale scheduling problem of re-entrant batch-processing machines based on the rolling horizon control strategy. The scheduling problem considering incompatible job families and re-entrant flow is known to be NP-hard (Non-deterministic polynomial-time hardness) [33]. For simplicity, they divided re-entrant batch-processing machines into smaller ones.

In order to strengthen the competitiveness and sustainability of semiconductor manufacturing lines, this research aims to find the best decision combination for three complex problems; (1) handling lot release, (2) job dispatching at the photo workstation, and (3) batch scheduling for the cleaning and oxidation of workstations, which are the most critical issues in the wafer fab process.

The remainder of this paper is organized as follows. Section 2 introduces the proposed methodology, and simulation experiments are implemented to illustrate the applicability of the proposed methodology in Section 3. In Section 4, simulation results show the optimal combination of rules to achieve the objectives of securing competitiveness and sustainability of semiconductor wafer lines. Conclusions are provided in Section 5.

2. Proposed Methodology

This paper handles three decision-making problems in the wafer fab process. These problems are as follows: (1) lot release problems of when and how many lots of which type should be inputted to the process; (2) batch scheduling problems, or deciding on the size of each batch; and (3) dispatching problems, regarding which lot should be processed first, exist. These three problems are closely connected with the competitiveness and sustainability of a wafer fab. Thus, it is important to find an optimal combination within the given system’s capacity.

2.1. Chaaracteristics of Wafer Fabrication Proess

The wafer fab process has the characteristics of the re-entrant manufacturing system; wafers revisit several identical machines for multiple times until they are completed as finished goods. As shown in Figure 1, the process consists of several loops of imprinting chemical patterns on a single wafer. Each loop requires the sequence of several processes which may be repeated for other loops as well. Of the many processes, photolithography (photo) is considered to be the most critical step. The step is used to define the topology of each layer of thin films which will be later patterned, etched, and coated. Because the photo workstation is revisited for every loop and requires utmost precision, it is regarded as the bottleneck of the wafer fabrication plant [34].

On the other hand, in wafer fabrication, the batch machine allows wafers or lots to be grouped together for processing simultaneously; oxidation furnaces and cleaning equipment are typical batch machines [35]. Because of unavoidable queueing at batch machines, inappropriate production control will incur adverse effects on overall system performance. Therefore, batch processes are also considered to be another type of key processes.

Thus, the key objective of improving production performance is to decide when to release lots, how to dispatch lots at the bottleneck machine, and at the same time determine the batch size for batch machines.

2.2. Lot Release in the Wafer Fab Line

Lot release control is defined as the decision of the optimal release time into the production line and the release conditions. WIP is defined as the average lot number of work-in-process. The representative lot input control rules in the wafer fab process include UNIF, CONWIP, WR (workload regulating), and SA. Among them, CONWIP and SA are generally regarded to outperform other rules. Existing studies assume that lots that are waiting to be input always exist and lot size is always given. Also, the EWR (extended workload regulating) rule, considered important herein, is very similar to the WR rule. Lots are input into the fab line when the workload at the bottleneck process is under the threshold value. The difference lies in the calculation of the workload, namely the expected mask change time is included in the workload. Thus, the application of the EWR rule requires the estimation of the average mask change time by lot. The total time required for mask change, determined by photo workstation scheduling, is defined as ST; if the number of lots that can be treated during the relevant time is Q, the time required for mask change by lot can be estimated by using the following equation.

Time required for mask change per lot = ST/Q

(1)

This job change time per lot is estimated through repeated simulation experiments, and this process is specified as follows. The initial mask change time per lot is calculated in terms of ST/Q after estimating ST and Q using the WR rule. It is also used when the estimated mask change time per lot converges on a specific value.

Step 1. Estimate ST (total time required for mask change in the photo workstation) and Q (the number of treated lots) through simulation.

Step 2. Calculate the required mask change time per lot.

Step 3. Renew the required job change time per lot and then move to Step 1.

2.3. Job Scheduling in the Photo Workstation

It requires a specific setup time to change each machine’s mask at the photo workstation. Mask change greatly influences the wafer fab line performance and should be controlled effectively. The mask scheduling problem herein is defined as the allotment in consideration of the preparation time for mask change in the photo workstation; transportation of jobs to workstations is excluded from this study.

Allotment rules such as FIFO (first-in first-out) and SRPT (shortest remaining processing time), which are widely considered in previous studies, do not consider the delivery time for single products and thus, their types are not diverse. The CWL (cycle workload leveling) rule, on the other hand, is considered important. Under the CWL rule, whenever the machine is idle, workloads for each loop are calculated. Moreover, when the difference between the maximum workload and the minimum workload is over a specific level, the current mask should be changed to the mask for the maximum workload. Likewise, after a machine completes processing a lot, the next concern is whether the mask change can be made, and if so, with what mask should the mask be changed. Notations are defined as follows.

• Notation
• i: Process index
• n_i: Process index of the first bottleneck after process i
• $d_{n_i}$: Process time of n_i
• k_i: Number of lots waiting to perform the ith work
• J_l: Process set belonging to the lth loop
• d_i: ith work process time
• WL_l: lth loop workload

$$W{L}_l=\frac{d_{n_i}\times {{\displaystyle \sum }}_{i\in J}{K}_i}{{{\displaystyle \sum }}_{i\in J}{d}_i}$$

(2)

In Equation (2), the numerator is the value determined by multiplying the number of lots waiting in the workstation at lth repeated work by the relevant lot process time at the photo workstation. The denominator is the sum of the total process time of the relevant loop. Here, when this difference is set as $W{L}^{\mathrm{\Delta }}$, it can be expressed as follows.

If $\left({}_{ma{x}_l}WL_l-{}_{mi{n}_l}WL_l\right)\ge W{L}^{\mathrm{\Delta }}$, the mask for the maximum workload should be changed.

Otherwise, mask change should not be made.

2.4. Batch Scheduling Problem in Cleaning and Oxidation Workstations

The batch scheduling problem is defined as a decision on the size of a batch (the number of lots treated at the same time) and also on the process order of formed batches in the representative workstations, cleaning and oxidation workstations, where machines that are capable of simultaneously processing several lots exist. Given the process characteristics of the two workstations, at the cleaning workstation, a lot waiting in the relevant workstation can be considered as the same lot regardless of the loop it is currently in. However, at the oxidation workstation, types of lots must be distinguished because their processing time and conditions are different according to the number of their visits.

Thus, in this study, in the case of the cleaning workstation, it is assumed that only the decision on the number of lots that can be processed simultaneously is considered. Also, the basic work order between batches should follow the FIFO rule. The BFQL (back and front queues leveling) is designed to equalize the volume of lots waiting for the queue in front of each batch machine and the volume of lots waiting on the queue of the following process so as to facilitate the logistical flow within the process. Also, with various items being considered, wafers differ in processing time according to which order of layer is being processed, and types of these lots should be distinguished. Before detailed description of this rule, notations and expressions are outlined as follows.

• Notation
• Q_ij: Volume of lot type j that is waiting at batch machine group i
• Q_sj: Volume of lot type j that is waiting at batch machines belonging to group i
• B_i: The maximum number of lots that batch machine group i can process at a time
• P_ij: Processing time of lot type j at batch machine group i
• P_sj: Processing time of lot type j at batch machine s belonging to group i
• L_ij: Load of lot type j at batch machine group $i=\left|\frac{Q_{ij}}{B_i}\right|\times P_{ij}$
• L_ik: Load of lot type k at batch machine group $i=\left|\frac{Q_{ij}}{B_i}\right|\times P_{ij}$
• L_sk: Load of lot type k at batch machine s belonging to group i
• $\succ$(i): The machine followed by batch machine group i
• Step 1. If $\exists$j s.t. max _s∊≻(i) (Q_sj − P_sj) ≤ 0, load lot type j and stop. Otherwise, go to Step 2.
• Step 2. If L_ij < P_ij $\forall$j, wait and go to Step 1.
• Otherwise, for j s.t. L_ij > P_ij load lot type k
• s.t. max _k∊≻(j) (L_ik − L_sk)

3. Simulation Experiments

In this study, it was assumed that the wafer fab process produces only three types of wafers, because these three types of wafers alone are sufficient to consider the diverse characteristics of the process. The experiment is composed of three key processes and two major non-bottlenecks, which include photo process requiring mask change, two batch processes; cleaning and oxidation, and two non-bottleneck processes; etching and deposition. The photo operation was included to review the application of the proposed rules for the purpose of determining the effective mask change at the bottleneck workstation. Two batch processes, cleaning and oxidation, were also included to assess the effectiveness of the proposed batch rule. Although the etching and deposition are neither bottlenecks nor batch processes, they are included to represent non-bottlenecks. The two processes are chosen because (1) they are considered to be one of the important processes in fabrication line and (2) they are included in Wein’s model [18].

We used production ratio, cycle time, and average WIP as the performance measures in order to identify the dominant combination rule among several alternatives. Based on the Little’s law, the expected average WIP is calculated by multiplying cycle time and throughput. Figure 2a shows the case of applying two different alternatives with a similar cycle. If the average WIP of two alternatives is similar, we can conclude that alternative 1 with higher throughput has better performance than alternative 2.

In the same manner, Figure 2b describes the case of the relationship between cycle time and throughput under controlled average WIP. We can assert that alternative 1, with a higher throughput, is better than alternative 2.

Test Bed

The experimented wafer process herein is the Hewlett Packard’s TRC (technology research center) process introduced by Wein [18]. As shown in

Table 1. Technology research center (TRC) Process.

Workstation Number	Workstation Name	No. of Machines	Frequency of Visit by Lot	Process Type
1	CLEAN	2	19	CLEANING
2	TMGOX	2	5	OXIDATION
3	TMNOX	2	5	OXIDATION
4	TMFOX	1	3	OXIDATION
5	TU11	1	1	DEPOSITION
6	TU43	1	2	DEPOSITION
7	TU72	1	1	DEPOSITION
8	TU73	1	3	DEPOSITION
9	TU74	1	2	DEPOSITION
10	PLM5L	1	3	DEPOSITION
11	PLM5U	1	1	DEPOSITION
12	SPUT	1	2	DEPOSITION
13	PHPPS	4	13	PHOTO
14	PHGCA	3	12	PHOTO
15	PHHB	1	15	PHOTO
16	PHBI	2	11	PHOTO
17	PHFI	1	10	PHOTO
18	PHJPS	1	4	PHOTO
19	PLM6	2	2	ETCHING
20	PLM7	1	2	ETCHING
21	PLM8	2	4	ETCHING
22	PHWET	2	21	ETCHING
23	PHPLO	2	23	RESIST STRIP
24	IMP	2	8	ION IMPLANT

, according to Wein’s paper, there are a total of 24 wafer fab process workstations, and as a research wafer process, engineering hold time exists in which many types of wafers are produced while researchers observe wafer status during the process. However, in this study, it was assumed that there is no hold time to avoid the impact of engineering holding time for inspection to the overall productivity.

It was assumed that the average process time by workstation follows a normal distribution, and the variance of process time was modeled into the dispersion of a normal distribution. Also, it was assumed that inside a workstation, the process time distributions of all machines follow normal distribution with a common parameter. This means that lots reaching a workstation always have a consistent process time regardless of equipment.

Each process time used herein was estimated on the basis of information on the process time in Wein’s model as well as in the actual wafer fab process. Also, it was assumed that with the exception of the photo workstation, the process time of other workstations included work preparation time and workers’ loading/unloading times. At the photo workstation, the process time and the mask change time were separately modeled. Here, the mask change time in the photo workstation was assumed to be 2 h, which was estimated on the basis of the mask change time in the general wafer fab process.

In addition, as explained above, lot transfer time between workstations was not considered, and the size of a lot was set at 24 sheets of wafers. Moreover, the machine failure time included unexpected machine failure and maintenance time.

The recipe shown in

Table 2. TRC Process recipe.

Loop	Process Sequence
1	Enter→1→2→13→14
2	23→15→20→22→23→22→17→13→14
3	15→23→16→24→23→22→17→1→8→4→22→2→2→1→2→8→13→14
4	18→23→15→16→23→18→22→1→1→13→14
5	23→15→16→24→23→11→17→1→2→8→9→21→22→4→22→22→1→2→13→14
6	23→15→16→24→24→23→22→17→13→14
7	18→23→15→16→20→23→1→17→1→1→3→13→14
8	16→24→23→22→17→9→21→1→3→13→14
9	15→23→15→16→24→23→22→17→1→3→13→14
10	23→15→16→23→15→16→24→23→22→17→1→3→10→22→12→6→22→6→1→1→4→10→19→23→1→10→13→14
11	16→21→12→13→14
12	18→23→15→15→15→6→16→19→23→17→11→13→14→15→21→23→5→Exit

shows the series of workstations that each lot input into the wafer fab process should undergo. Wafers produced in the wafer fab process have 12 layers (or loops) each, and layers that undergo workstations are greatly different. For instance, the first loop undergoes workstations 1, 2, 13, and 14, whereas the second loop undergoes workstations 23, 15, 20, 22, 23, 22, 17, 13, and 14. Also, loops are shown with a focus on workstation 14 (PHGCA), which is responsible for the photo process. Each loop shows processes necessary for forming a layer, as well as each process workstation.

In this experiment, with regard to the above-explained TRC process, two types of wafer fab process were modeled according to the process time for workstation and bottleneck levels at the photo workstation in

Table 3. Wafer fab process with heavy bottleneck.

Workstation No.	MPT	MTBF	MTTR	% UTIL **
1	0.48	142.18	1.22	35.8
2	1.17	101.11	10	21.3
3	1.27	113.25	5.21	17.8
4	1.8	103.74	2.56	16.2
5	0.79	100.55	4.33	22.3
6	0.99	113.25	5.21	49.9
7	0.8	116.78	4.38	21.9
8	0.56	113.22	3.43	41.3
9	0.6	100.59	3.74	31.3
10	0.52	97.53	2.71	38.4
11	1.01	52.67	3.78	29.8
12	0.78	72.57	3.43	40.4
13	0.54	82.37	1.15	41.8
14 *	0.99	142.76	1.81	90.2
15	0.11	387.2	12.8	41.6
16	0.38	No Failure		47.9
17	0.2	119.2	1.57	47.2
18	0.46	No Failure		42.2
19	1.77	146.38	7.42	45.6
20	0.69	136.58	4.49	35
21	0.97	136.58	5.49	48.5
22	0.13	118.92	1.08	33
23	0.14	No Failure		36.9
24	0.47	155.18	7.86	48.1

*: Bottleneck workstation. **: % Utilization: [(AR) × (NV/L) × (MPT)/(#Machines) + (MTTR)/(MTBF + MTTR)] × 100(%). AR: average arrival rate; NV/L: the frequency of visit by lot; # Machines: number of machines by workstation; MPT: mean processing time; MTBF: mean time between failures; MTTR: mean time to repair.

and

Table 4. Wafer fab process with equal workload.

Workstation No.	MPT	MTBF	MTTR	% UTIL
1	0.87	142.18	1.22	64.2
2	7.11	251.11	0.94	82.5
3	7.23	173.25	1.21	83.8
4	10.94	133.74	2.56	85.8
5	2.79	150.55	4.33	67
6	1.43	143.25	5.21	69.3
7	3.21	136.78	4.38	76.9
8	1.13	163.22	3.43	80
9	1.6	130.59	3.74	76.5
10	1.02	197.53	2.71	71.7
11	3.47	152.67	3.78	82.2
12	1.78	172.57	3.43	83.8
13	0.89	182.37	1.15	67.2
14	1.42	142.76	2.38	94.6
15	0.23	387.2	2.8	80.1
16	0.62	No Failure		78.4
17	0.34	119.2	1.57	79.5
18	0.85	No Failure		78.2
19	3.47	146.38	1.42	80.8
20	1.69	136.58	3.49	80.3
21	1.76	136.58	2.49	82.8
22	0.32	118.92	1.08	78.2
23	0.29	No Failure		76.7
24	0.87	155.18	7.86	84.9

.

The wafer fab process in Table 3 has a heavy bottleneck, i.e., the photo workstation. The wafer fab process in Table 4 has a comparatively equalized workload. Thus, the impact of the bottleneck is lessened when compared to wafer fab process 1. The process time, machine failure interval, and repair time by each wafer fab process are outlined in Table 3 and Table 4.

MPT (mean processing time) refers to the average process time: this is the process time distribution parameter by workstation; MTBF (mean time between failures) is defined as the average operating time between malfunctions; MTTR (mean time to repair) refers to the average repair time for malfunctioned machines; and % utilization refers to the rough average operation ratio by workstation, and by this value, each workstation’s workload can be determined. It is assumed that MPT follows normal distribution, and both MTBF and MTTR follow exponential distribution. If it is assumed that lot arrival at the process follows the Poisson distribution, the value is determined after fixing the average arrival ratio as a specific value.

4. Simulation Results

In this simulation experiment, with regard to the two types of wafer fab process as explained above, combinations of five lot input rules, three mask scheduling rules, and two batch scheduling rules performances were compared. Each rule is outlined as shown in

Table 5. List of considered rules.

Type	Rule
Lot input rules	UNIF (Uniform), CONWIP (Constant WIP), WR (Workload regulating), SA (Starvation aviodance)
Mask scheduling rules	FIFO (First in first out), SRPT (Shortest remaining process time), CWL (Cycle workload leveling)
Batch scheduling rules	FIFO (First in first out), BFQL (Back and front queues leveling)

.

4.1. Result in Wafer Fab Line with Heavy Bottleneck

First, in the wafer fab process with the heavy bottleneck at the photo workstation, the best rule was determined by comparing the combinations of 24 rules including the existing four lot input rules (UNIF, CONWIP, WR, SA), three mask scheduling rules (FIFO, SRPT, CWL), and two batch scheduling rules (FIFO, BFQL).

Table 6. Results for combinations of rules under the UNIF rule in a wafer fab process.

Combination of Rules	Interarrival Time	Production Ratio	Cycle Time	WIP
UNIF-FIFO-FIFO	30	0.8	206.5	6.9
	20	1.19	176.2	8.8
	15	1.6	167.5	11.2
	10	2.4	177.6	17.8
	9	2.59	713.3	79.3
	8	2.35	3959.4	494.4
UNIF-FIFO-BFQL	30	0.8	206.6	6.9
	20	1.19	176.3	8.8
	15	1.6	167.5	11.2
	10	2.4	181.5	18.1
	9	2.6	713.3	79.3
	8	2.34	3996.6	499.9
UNIF-SRPT-FIFO	30	0.8	207.4	6.9
	20	1.2	178.2	8.9
	15	1.58	167.8	11.1
	10	2.4	181.2	18.1
	9	2.67	222.4	24.7
	8	2.85	1346.9	168.5
UNIF-SRPT-BFQL	30	0.8	207.4	6.9
	20	1.2	177	88
	15	1.6	166.2	11.2
	10	2.4	182.8	18.3
	9	2.67	225.9	25.1
	8	2.83	1372.8	171.9
UNIF-CWL-FIFO	30	0.8	208.2	6.9
	20	1.2	176.9	8.8
	15	1.6	166.6	11.1
	10	2.4	164.5	16.5
	9	2.67	179.7	19.9
	8	3.44	276.3	39.5
UNIF-CWL-BFQL	30	0.8	208.2	6.9
	20	1.2	176.9	8.8
	15	1.6	166.8	11.1
	10	2.4	166.6	16.7
	9	2.67	180.1	20
	8	3.41	807.2	115.3

,

Table 7. Results of combinations of rules under the CONWIP rule in a wafer fab process with a heavy bottleneck.

Combination of Rules	Production Ratio	Cycle Time	WIP
CONWIP-FIFO-FIFO	2.67	270.2	30
	2.73	441.8	50
	2.71	617.8	70
	2.72	789.9	90
	2.72	882.2	100
CONWIP-FIFO-BFQL	2.65	272.1	30
	2.68	448.2	50
	2.69	620.9	70
	2.74	782.5	90
	2.73	877.8	100
CONWIP-SRPT-FIFO	2.75	262.1	30
	2.77	433.2	50
	2.78	603.5	70
	2.81	759.1	90
	2.78	863.5	100
CONWIP-SRPT-BFQL	2.75	261.7	30
	2.76	434.4	50
	2.78	603.4	70
	2.81	767.4	90
	2.8	858.8	100
CONWIP-CWL-FIFO	3.58	201.2	30
	4.14	290.8	50
	4.26	393.4	70
	4.3	500.7	90
	4.29	558.6	100
	4.16	867.1	150
CONWIP-CWL-BFQL	3.56	201.5	30
	4.09	292.5	50
	4.29	392.3	70
	4.33	500.6	90
	4.32	557.9	100
	4.17	865.6	150

,

Table 8. Results of combinations of rules under the WR rule in a wafer fab process with a heavy bottleneck.

Combination of Rules	Bottleneck Time	Production Ratio	Cycle Time	WIP
WR-FIFO-FIFO	100	2.25	169.7	15.9
	200	2.62	285	31.1
	400	2.62	558.4	60.9
	600	2.63	824.9	90.5
	700	2.63	964.1	105.6
WR-FIFO-BFQL	100	2.25	169.8	15.6
	200	2.61	286.9	31.2
	400	2.61	559.2	60.9
	600	2.63	828.7	90.5
	700	2.63	964	105.7
WR-SRPT-FIFO	100	2.1	173	15.1
	200	2.7	233.2	26.3
	400	2.77	381.4	44.1
	600	2.77	529.5	61.4
	700	2.78	606.3	70.2
WR-SRPT-BFQL	100	2.13	171.6	15.2
	200	2.67	234.8	26.2
	400	2.78	380	43.9
	600	2.79	527.8	61.2
	700	2.79	602.6	70.1
WR-CWL-FIFO	100	2.34	163.8	16
	200	3.09	242.6	31.3
	400	4.13	356.4	61.4
	600	4.35	496.9	90.4
	700	4.32	582.1	104.9
WR-CWL-BFQL	100	2.34	164.2	16
	200	3.09	243.9	31.4
	400	4.15	356.5	61.4
	600	4.31	495.3	89.5
	700	4.34	579	104.8

and

Table 9. Results of combinations of rules under the SA rule in a wafer fab process with a heavy bottleneck.

Combination of Rules	α-Value	Production Ratio	Cycle Time	WIP
SA-FIFO-FIFO	5	2.92	317.7	38.8
	10	3.03	544.8	68.4
	15	3.02	493.1	61.9
	20	3.05	568.9	71.7
	25	2.87	651.1	96.2
SA-FIFO-BFQL	5	3.01	344.4	38.8
	10	2.95	422.6	52
	15	2.98	469.5	58.6
	20	3.12	616.1	72.3
	25	3.03	638.6	81.4
SA-SRPT-FIFO	5	2.78	310.5	35.2
	10	2.83	511.9	60.7
	15	2.8	582.9	67.9
	20	2.83	761.5	89.6
	25	2.77	766.9	88.5
SA-SRPT-BFQL	5	2.78	286.3	34.2
	10	2.84	574.5	67.7
	15	2.83	577.9	68.7
	20	2.82	772.9	91.7
	25	2.81	816.6	96.3
SA-CWL-FIFO	5	3.87	248.1	72.2
	10	4.14	310.8	55.7
	15	4.18	412.1	72.1
	20	4.27	457.3	81.1
	25	4.2	539.9	96.2
SA-CWL-BFQL	5	3.89	271.3	43.8
	10	4.13	353.3	60.8
	15	4.22	407.4	71.8
	20	4.23	436.7	77.4
	25	4.3	459.6	82.3

show the experiment results of comparing the combination of proposed rules.

In this study, parameters for various experiments were estimated based on preliminary experiments. For instance, in the case of the CWL rule, the best value was selected based on experiments using various levels of parameters by the combination of each rule. With the batch scheduling rules, the minimum batch size was determined as three based on preliminary experiments. This value was also equally applied to the BFQL rule. In the wafer fab process with the heavy bottleneck at the workstation, experiments of combinations of various rules revealed the following results.

These experiments revealed the following results in shown in Figure 3 and Figure 4. First, the lot release rule, UNIF, which does not consider the system status, offers lower performance than CONWIP, WR, and SA rules. As for the production ratio, CONWIP, WR, and SA rules, overall, are higher than the UNIF rule. The UNIF rule shows a sharp rise in cycle time and average WIP at a specific production ratio. However, the CONWIP, WR, and SA rules show a gentle rise in cycle time and WIP. These results suggest that closed loop lot input mechanisms based on the wafer fab process status—namely, CONWIP, WR, and SA rules—offer better performance than the open loop lot input mechanism UNIF.

Specifically, when comparing the experiment results of the WR and SA rules, WR offers a slightly higher production ratio than SA, but at a specific production ratio, and the SA rule shows a low cycle time and WIP. For instance, the combined rules of WR-CWL-FIFO show a cycle time of 356.4 and a WIP of 61.4 at the hourly production ratio of 4.13, whereas the combined rules of SA-CWL-FIFO show a cycle time of 310.8 and a WIP of 55.7 at the production ratio of 4.14. These results suggest that the SA rule outperforms other rules. Glassey and Resende [19] conducted a similar simulation experiment in various wafer fab processes and they proposed that the SA rule was superior to other rules; however, there is no big difference in wafer fab process situations when the mask change time is considered, as was the case in this study. Also, as explained by Glassey and Resende [19], the SA rule has shortcomings of being sensitive to fluctuations in the delivery period.

Second, as for the mask scheduling rules experiment results, the CWL rule offers better performance than FIFO and SRPT rules. These results are due to the fact that, in this study, the modeled wafer fab process considers the mask change time, and that the CWL rule, compared with other rules, well reflects the impact of mask change time to the overall fabrication process. Specifically, the existing FIFO and SRPT rules simply consider only work time, whereas the CWL rule considers both work time and preparation time. In addition, while FIFO and SRPT have focused on the local dispatch rule, CWL focuses on the whole loop. It is supported by the fact that global optimization is generally better than local optimization when looking at the whole process.

Lastly, the batch rules, FIFO and BFQL, were not significantly different performance-wise. These results are due to the fact that in the first experiment type, with the heavy bottleneck at the photo workstation, the amount of lots waiting in the cleaning and oxidation workstations was small, thus lessening allotment influence between batches. This leads to the performance of the entire wafer fab process not being greatly influenced.

As such, in the first wafer fab process, with the heavy bottleneck, lot input rules of CONWIP, WR, and SA are superior and the mask scheduling rule of CWL is the best of the three rules. Also, in the batch scheduling rules, there was no noticeable performance difference between FIFO and BFQL.

Based on the results in shown in Figure 5 and Figure 6, in the situation of the wafer fab process with a heavy bottleneck, EWR, WR, and SA rules were again compared. In this experiment, CWL was selected as the allotment rule, and BFQL was selected as the batch rule. The results of these experiments are outlined in

Table 10. Results of experiments under EWR-CWL-BFQL rules in a wafer fab process with a heavy bottleneck.

Combination of Rules	Bottleneck Time	Production Ratio	Cycle Time	WIP
EWR-CWL-BFQL	200	2.84	194.2	23
	300	3.27	250.9	34.2
	400	3.72	294.6	45.7
	500	3.99	340.2	56.4
	600	4.2	378.2	66.3
	700	4.35	424.8	76.4
	800	4.38	479.5	87
	1000	4.34	601.8	108.6

.

When comparing the above results, EWR, overall, is slightly superior to CONWIP, WR, and SA. The EWR rule is an effective way to maintain the workload consistently. The workload of the overall processes is greatly influenced by the workload in the bottleneck process. The EWR rule recommends job input when the workload falls below a specific level. When calculating the workload of the bottleneck, deciding whether to consider the setup time (here, mask changing time) is critical. In the case where frequent mask change in a bottleneck process occurs, the EWR rule is very effective compared to other candidate rules in determining when to release a job.

In the second wafer fab process, with the relatively equalized workload, the performance of combinations of lot input rules and scheduling rules (the mask scheduling rule was fixed as CWL) was compared. Lot input rules considered in this experiment were WR, SA, and EWR, and as with the above experiment, necessary parameters were estimated through preliminary experiments.

4.2. Result in the Wafer Fab Line with the Equal Workload

In the wafer fab process with the relatively equalized workload, the experiments of batch scheduling rules revealed the following results, as shown in Figure 7 and Figure 8.

First, in the batch scheduling rules experiment, WR and SA were used as lot input rules. As shown in the above table, at similar production ratios, BFQL offers smaller cycle time and average WIP than FIFO. Also, at a specific cycle time and average WIP, BFQL offers a lower production ratio than FIFO. These results are due to the fact that in the situation with a fixed batch size, FIFO determines the process order based on when the order arrives and BFQL determines the process order while considering the number of waiting lots before and after relevant workstations. Therefore, when frequently creating allotment situations between the batches in less bottlenecked situations at the photo workstation, BFQL more accurately reflects the overall wafer fab process situation.

Next, in the EWR rule experiment, CWL was used as the mask scheduling rule, and BFQL was fixed as the batch scheduling rule, using the results from the previous experiment. This experiment was concerned with how EWR, with superior performance in the first wafer fab process, would perform in the second wafer fab process; the results are outlined as follows.

First, SA offered lower performance than WR. Specifically, SA, overall, offered lower production ratios, and showed higher cycle time with WIP levels at a specific production ratio. This is due to the fact that SA experienced a heavy change in delivery period due to less serious workstation bottleneck under the second wafer fab process situation compared to the first wafer fab process situation. However, it failed to reflect this situation well.

Second, EWR offered a slightly higher production ratio than WR, yet, there was no significant difference in cycle time or WIP between these two rules. However, in the case of considering work preparation time at workstations with high operation ratios other than the photo workstation, the two rules are expected to show a great difference in performance. This is because EWR considers mask change time and thus more accurately reflects wafer fab process situations.

As such, in the experiments of the second wafer fab process, with less serious bottleneck, similar to the results from the first wafer fab process, EW offered a slightly better performance than WR and SA, whose performance is known to be excellent. Also, as for the batch scheduling rules, BFQL, which considers the priority of batches, offered better performance than FIFO.

5. Conclusions

This paper deals with the wafer fab process, namely, work input control problems; mask scheduling problems at the photo workstation; and batch scheduling problems for cleaning and oxidation workers.

As such, in order to evaluate and compare diverse rules, two process models were determined according to the bottleneck levels using the TRC process, and simulation models were created. Using these two wafer fab process models, with regard to the aforementioned three problems, the performances of different combinations of rules were evaluated based on three evaluation criteria: production ratio, cycle time, and average WIP. Results of the experiments showed that in the wafer fab process with the heavy bottleneck, WR and SA, which are already known as good lot input rules, offered good results, and in particular, SA was slightly more superior. The EWR rule, which has further expanded the WR rule in consideration of mask change time at the photo workstation, offered a slightly better performance than the existing rules. In the wafer fab process with the less serious bottleneck, WR offered a slightly better performance than SA. This is because the delivery period underwent heavy change due to less serious bottleneck at the photo workstation. Regarding mask scheduling problems, CWL, which considers mask change time, offered much better performance than SRPT. This is because existing rules did not consider mask change time. With regard to batch scheduling problems in cleaning and oxidation workstations, BFQL offered a slightly better performance than FIFO.

The solutions to problems considered herein are expected to be utilized in the wafer fab processes which do not consider delivery time. They are also expected to improve production ratio, shorten cycle time, curtail the delivery time of finished products, enhance wafer yield, reduce WIP, and thus, cut product costs.

While our research has presented novel methodology with promising results, there is still a great deal of work to be done. First, the application of our methodology in actual on-the-ground settings using real data may lead to more accurate results. Second, identifying and quantifying the impacts that productivity increases have on the environment will contribute to a flexible extension of our research.