Dynamic Wet-Furnace Dispatching/Scheduling in Wafer Fab Myoungsoo Ham
Michael Raiford
Frank Dillard
Wayne Risner
Samsung Austin Semi. Austin, Texas, USA
[email protected]
Samsung Austin Semi. Austin, Texas, USA
[email protected]
Samsung Austin Semi. Austin, Texas, USA
[email protected]
Samsung Austin Semi. Austin, Texas, USA
[email protected]
Meghan Knisely
James Harrington
Tom Murtha
Samsung Austin Semi. Samsung Austin Semi. Samsung Austin Semi. Austin, Texas, USA Austin, Texas, USA Austin, Texas, USA
[email protected] [email protected] [email protected]
at the next step within a certain amount of time. If queue time is exceeded, wafer quality can be jeopardized or the wafers might require a reclean. The scheduler must guarantee that queue time is honored. Similarly, queue time restrictions with upstream steps feeding wet clean steps must also be considered.
Abstract The pre-cleaning equipment (or wet etch) in semiconductor manufacturing can load more than one dozen lots at one time. Because of the large capacity, the wet etch plays a key role in keeping the line balanced. After the wet etch operation, the wafers go mainly to furnaces, and the remaining goes to dry etch, cvd, photo or back to wet etch. If the wet etch produces the wafers in unbalanced fashion, the downstream equipment can be starved for inventory. Furthermore, there are often queue time restrictions between the wet etch and the downstream step and vice versa. This paper shows the success story of developing and implementing the wet etch scheduler which enables us to insure the proper inventory level at downstream steps and meet the queue time restriction at Samsung Austin Semiconductor
•
The scheduler needs to consider the batch size of downstream steps. For example, some of furnaces run with 6 lot batch size. Then, ideally, wet etch needs to clean 6 lots with the same recipe sequentially in order to minimize the waiting time of the entire batch. This is especially important in cases where there is a queue time involved.
•
When considering batch size, the scheduler must also consider the minimum and maximum batch sizes at the furnaces when scheduling wet cleans. When scheduling at the wet clean, we must schedule up to the maximum furnace batch size and no less than the minimum furnace batch size.
Keywords Wet, Furnace, Dispatching, Scheduling, Queue time restriction, Pull
In addition to the above interactions in the flow, the wet clean has its own restrictions:
INTRODUCTION Scheduling the wet etch area presents a huge challenge because of the complicated flows among upstream steps, wet etch steps, and downstream steps. The main challenges I found are outlined below. •
•
The pre-cleaning equipment also known as wet etch equipment or wet hoods have high capacity. Therefore, the scheduling order at the wet hoods is extremely important to downstream steps and line balance. If wet etch over-produces at a certain step, the lots have to wait longer at downstream steps. Likewise, if wet etch under-produces at a certain step, the downstream step can be starved for inventory.
•
Wet etch typically runs in two lot batches with the same recipe per robot loading optimization. So, the scheduler needs to insure that they are scheduled in batches of two.
•
There are also cases when certain wet equipment shows higher yield than others.
LITERATURE REVIEW Glassey and Weng [1] initially brought the idea of using the future arrival information called look-ahead. Fowler at el. [2] expanded the concept to multiple products and created the heuristic method called Next Arrival Control Heuristic (NACH). The additional improvement opportunities in coordinating of the scheduling of batch processing with
Another main factor which makes the wet etch scheduling more difficult is the queue time restriction between wet etch and downstream steps. Once the wafers finish processing at wet etch, the wafers must process
1-4244-0255-07/06/$20.00©2006 IEEE
HyungTae Park Samsung Austin Semi. Austin, Texas, USA
[email protected]
144
2006 IEEE/SEMI Advanced Semiconductor Manufacturing Conference
both upstream and downstream were discussed by Glassey and Weng [1] and Robinson at el [3]. Choung et al. [4] worked on diffusion scheduling system. Sun et al. [5] worked on scheduling problem with delay time constraint. Zee [6] did the literature overview of look-ahead strategies for batching operations. Ibrahim at al. [7] worked on batching system for furnace operation. They seemed to optimize the batching schedule at furnace step only.
The objective of the scheduler is to produce wafers more efficiently while ensuring the various scheduling restrictions are met. I. FURNACE SCHEDULER The furnace scheduler is comprised of 3 parts: Categorizing, Predicting, and Assigning. CATEGORIZING The scheduler categorizes the wafers by current step, waiting/processing status, and queue-time.
WET/FURNACE OPERATION Multiple areas are feeding the wet operation. And the wet operation feeds multiple areas. Furnace Dry Etch
Wet etch
CMP Imp Sput
Queue time
Queue time
PREDICTING Then, the scheduler predicts when the furnace would unload the current batch.
Furnace Dry Etch
ASSIGNING type of job j (if the lot waits at furnace with queue typej time, 1. if the lot waits at furnace without queue time, 2. if the lot runs at wet with queue time, 3. if the lot runs at wet without queue time, 4. if the lot waits at wet with queue time, 5. if the lot waits at wet without queue time, 6.)
CVD Photo Wet Etch
Wet Etch
Figure 1. Wet-Furnace Operation WET-FURNACE DISPATCHER/SCHEDULER The target of furnace scheduler includes the waiting lots at furnace step, the running lots at wet step, and the waiting lots at wet step. Then, the wet scheduler ensures that we load the lots on the wet tool in a timely manner and in the right order so that we can meet the furnace needs while honoring at least the minimum furnace batch size and cleaning for maximum furnace batch size when there is sufficient inventory. Since there are strict queue time restrictions, we utilize a pull methodology to feed inventory.
rdtik
remaining delay time of job j = how much time left before the delay time is expired.
dtik
if the delay time of job j when the job is loaded into the furnace is negative, the delay time of job j o/w 0
fwj
fab-wide weight of the job j
W_rptj remaining processing time of the job j at wet operation The scheduler sorts the lots by category, queue time, lot priority, and step priority. Then, the scheduler checks the batch size and assigns each group of lots with the same recipe into the first available furnace one by one until it assigns all lots. The available furnace time is continuously updated as the scheduler assigns the batch to the furnace. The furnace queue selection procedure sorts the jobs with their modified weight (=wj) as follows:
SCHEDULER STARTS
FURNACE SCHEDULER - Generate wish-list from the furnace view point which covers inventory at furnace and inventory at
wj =
WET SCHEDULER - Mix the furnace scheduler output and the inventory not going for furnace. - Generate best sequence of loading order for wet.
4*M - rdtik
for the jobs at type 1
3*M + fwj
for the jobs at type 2
2*M - W_rptj
for the jobs at type 3, 4
1*M + fwj
for the jobs at type 5, 6
Algorithm 1. Queue Selection Procedure (Furnace) Initialize Set V ={}, Vc ={1, …, j} While (until set Vc is empty)
SCHEDULER ENDS
{ - Sort the set Vc by minimum wj, * - Pick fist top jobj
Figure 2. Wet-Furnace Scheduler Flow
145
-
-
-
}
-
Create subset v* with other top n highest weighed jobs up to maximum batch size at the same recipe with the selected jobj*. Calculate ρ-factor of the selected batch (=v*).
With given inventory, wet scheduler generates best sequences with valid reason. •
What if wet has a choice of multiple steps and they are all going for the same furnace?
∑ nj*=1 w j
•
What if lots cannot meet the queue time restrictions?
∑nj*=1 p j
•
What if some lots are going to a furnace and some lots are NOT going to a furnace?
Remove set v* out of Vc Set V = V U v*
The wet scheduler answers those questions. We consider predicted waiting time at the furnace, daily move target, lot priority, queue time restrictions, and other factors in order to generate the job sequence for wet. Ranking the importance of the factors in determining job sequence is a management decision that varies from factory to factory. But, we can start with a simple rule, “Schedule first if the lots are going to an idle furnace”, “Schedule last if the lots are going to a furnace which has enough inventory for the next 12 hrs”, “Do not schedule if the lots cannot meet the queue time restriction”, and so on.
Sort the batches by ρ-factor With sorted batch list by ρ-factor, the server selection procedure checks out the batching size first. If there are more than one furnace which meets the batching size, the sever selection procedure picks the earliest available furnace. Then, we update the available time of selected furnace. The sever selection procedure also checks if the batch can meet the delay time restriction.
GENERATING JOB SEQUENCE By using the above wet scheduling order, the wet scheduler generates job sequence.
Algorithm 2. Sever Selection Procedure (Furnace) Set V ={}, Vc ={1, …, n} While (until set Vc is empty) Create set V* with top n jobs (n is maximum batch size) with same recipe. If count of V* is less than minimum batch size Set V*.scheduled = ‘Need More’ Else Pick furnace with earliest available time with less recipe count. If the V* can meet queue time with the furnace V*.scheduled = FurnaceID Update earliest available time of the furnace Else V*.scheduled = “Cannot meet queue” End while loop
Lots going to furnace
Lots going to non-furnace
With given inventory, wet scheduler generates best sequences.
Figure 4. Job Sequence DISPATCHER The wet dispatcher picks up the output from the wet scheduler and dispatches the lots accordingly. The wet dispatcher mainly considers three factors: furnace batching, balanced inventory, and equipment preference.
DISPATCHING The furnace dispatcher simply picks up the output from furnace scheduler and dispatches the lot accordingly.
EXCEPTION HANDLER When unexpected situations occur, for example, equipment states change from down to up, or up to down, or engineering prevents a lot from running on a tool, we need to reschedule the lots to adjust for the changes in equipment status.
II. WET SCHEDULER Once the furnace scheduler generates the order saying I need lot X, Y and Z for specific furnace at certain time, the wet scheduler tries to meet the order by generating a loading sequence in the wet tool. Since wet serves multiple furnaces and other toolsets, there is competition. • What if multiple furnaces are asking wafers at the same time?
SUMMARY An intelligent way of scheduling the wet-furnace has been introduced to Samsung Austin Semiconductor. This approach is different from previous methods in that it con-
146
nects furnace scheduling and wet scheduling through the use of a “pull” methodology. The furnace scheduler generates an ideal “wish-list” and the wet scheduler tries to meet the furnace needs while it serves other areas. The scheduler has a goal to minimize cycle time, maximize throughput, minimize queue time violations, and optimize line balance
[3]
[4] RESULTS Through the use of a wet furnace scheduler we have experienced significant improvements in several areas. Cycle time at wet hoods has experienced a marked decrease along with corresponding furnace idle time. We have also seen a dramatic increase in total wet hood throughput while reducing queue time violations and reclean situations. The primary cause of these results has been an overall higher wet hood optimization level resulting from our improved scheduling. Also, we recently successfully developed and implemented the Continuous Batch Process (CBP) logic to utilize the furnace even more. The detail logic was not covered in this paper, but the goal of the logic is to keep the furnace in CBP mode.
[5]
[6]
[7]
[8]
ACKNOWLEDGMENTS The authors would like to appreciate manufacturing specialists for providing countless feedback.
esses, IEEE Transactions on Semiconductor Manufacturing, 1992. Robinson, J.K., Fowler, J.W. and Bard, J. F, The use of upstream and downstream information in scheduling semiconductor batch operations, International Journal of Production Research, 33, 1849-1869, 1995 Choung et al., Design of a Scheduling System for Diffusion Process, Semiconductor Manufacturing Operational Modeling and Simulation Symposium (part of 2001 Advanced Simulation Technologies Conference). Sun et al., Scheduling and Timing Control for TimeConstrained Processes in Semiconductor Manufacturing, International Symposium on Semiconductor Manufacturing, 2005. Zee, D., Look-Ahead Strategies for Controlling Batch Operations in Industry – an overview, Winter Simulation Conference, 2003. Ibrahim, K., Efficient Lot Batching System for Furnace Operation, IEEE/SEMI Advanced Semiconductor Manufacturing Conference, 2003. Ham, M. and Dillard, F., Dynamic Photo Stepper Dispatching/Scheduling in Wafer Fabrication, IEEE International Symposium on Semiconductor Manufacturing, 2005.
BIOGRAPHY Myoungsoo Ham received a master in OR/IE at University of Texas at Austin. He is working on PhD in IE at Arizona State University under Dr. Fowler and is also working as manufacturing scheduling manager at Samsung Austin Semiconductor. Prior to his current position, he worked for Texas Instruments as IE manager, worked for AMD as senior IE engineer, and worked for Samsung Korea Semiconductor as automation engineer. His research interests include dispatching & scheduling of semiconductor manufacturing.
REFERENCES [1] C. Roger Glassey and W. Willie Weng, Dynamic Batching Heuristic for Simultaneous processing, IEEE Transactions on Semiconductor Manufacturing, VOL. 4, NO. 2, MAY 1991 [2] Fowler, J. W. et al., Real-Time Control of Multiproduct Bulk-Service Semiconductor Manufacturing Proc-
147
