Symbiotic Simulation Control in Semiconductor Manufacturing

1. Symbiotic Simulation Control in Semiconductor Manufacturing Authors: Heiko Aydt, Stephen J. Turner, Wentong Cai, Malcolm Yoke Hean Low, Peter Lendermann, and Boon Ping Gan Presented by Heiko Aydt Parallel and Distributed Computing Centre Nanyang Technological University Singapore

2. Outline • Background • Symbiotic Simulation and DDDAS • Symbiotic Simulation Control System (SSCS) • Wet Bench Tool Set (WBTS) • Conclusions and Future Work

3. Background: Semiconductor Manufacturing • Semiconductor Manufacturing is • Highly complex: several hundred processing steps and up to three month for production [1] • Asset intensive: a single tool can cost up to US$ 2 million [2] • Ongoing improvement of the manufacturing process is crucial to stay competitive • Integrated automation solutions [3] • Investment range between US$ 130 million and US$ 180 million • Include real-time control of equipment

4. Background: Challenges and Need for Symbiotic Simulation • Decisions regarding tool configuration is made by engineers and carried out by workers • Problems: • Mostly based on experience • Workers don’t always strictly follow the instructions • Demand for fully automated control solutions • Symbiotic simulation control is such a solution

5. Physical System Implement Measure “What if” experiments simulate Output Analysis Optimizer simulate simulate Symbiotic Simulation and DDDAS

6. Symbiotic Simulation and DDDAS • Symbiotic simulation and DDDAS are very similar paradigms which are overlapping to some extend • Symbiotic simulation • Close relationship between a physical system and a simulation system • Concept of what-if analysis is essential in symbiotic simulation • Multiple simulations (of what-if scenarios) are dynamically data-driven • Control feedback aims to modify the physical system • DDDAS • Not necessarily limited to dyamically data-driven simulations • Control feedback aims to steer the measurement process

7. Symbiotic Simulation Control System (SSCS) • Symbiotic Simulation Control System (SSCS) • An SSCS evaluates decision alternatives (e.g, alternative configurations) by means of simulation • The ‘best’ decision is directly implemented in the physical system using corresponding actuators • An agent-based generic framework for symbiotic simulation systems has been developed • Reference implementation using Jadex/JADE • Applicable in application scenarios of DDDAS/Symbiotic Simulation • Framework provides various functional components • Implemented as agent capabilities [4,5] • Standard implementations are provided • Allows flexible design of application-specific symbiotic simulation systems

8. Symbiotic Simulation Control System (SSCS) • S-C: Sensor • WORC-C: Workflow Control • SCEM-C: Scenario Management • SIMM-C: Simulation Management Various Capabilities (x-C): • SIMA-C: Simulation Analysis • DECM-C: Decision Management • A-C: Actuator

9. WetBench Bath 1 Bath 2 Bath 3 Bath 4 Batch 2 Batch 1 Batch 3 Lot Lot Lot Lot Lot Lot Wet Bench Tool Set (WBTS) • A wet bench is used to clean wafers after certain fabrication steps • A wet bench consists of a number of baths with chemical liquids • Wafers lots are processed strictly according to recipes • Several wet benches with different setups are typically operated

10. Wet Bench Tool Set (WBTS) • Particles of contaminants are introduced into the baths during the cleaning process [6] • Some recipes introduce particles faster than others • We therefore distinguish between ‘clean’ and ‘dirty’ recipes • Wafers are processed in different wet benches depending on whether they are ‘clean’ or ‘dirty’ • Thus, a wet bench operates either in ‘clean’ mode or ‘dirty’ mode • Switching the operation • From ‘clean’ to ‘dirty’: does not require any activity • From ‘dirty’ to ‘clean’: requires a complete change of liquids

11. WBTS: Control Approaches • Performance depends on product mix and operation modes of wet benches • Reconfiguration required if product mix is changing • Common practise approach: • Engineers make decisions based on experience: difficult to model • Following heuristic used: • If the number of pending lots is exceeds limit: determine the critical recipe • Identify all wet benches which are capable of processing the critical recipe but which are not configured yet • Reconfigure one of the wet benches and wait some time (settling-in period) • If, after the settling-in time, the situation did not improve: repeat • SSCS approach: • Observes queues and trigger what-if analysis if number of pending lots exceed limit • Create and evaluate a number of alternative configurations • Determine best configuration implement it in the physical system

12. WBTS: Experiments • Emulator was used instead of a real physical system • Paced simulation • Runs 3600 times faster than real-time • Two kinds of experiments performed • Alternating product mix • High workload

13. WBTS: Experimental Results for Alternating Product Mixes • Two different product mixes were used with constant load • The product mix is changed every 2 weeks Figure 1: Cycle Time of the WBTS over a period of 20 weeks using the SSCS (left) and the common practise control approach (right). • SSCS produces a more homogeneous performance with • Less variability • Lower mean cycle time • Disadvantage of the common practise approach: • Needs several attempts to find a stable configuration

14. WBTS: Experimental Results for High Workload • At high load, it is necessary to oscillate between configurations • Only two wet benches have a bath setup which provides many recipes • At high loads, these two wet benches become a bottleneck Figure 2: Cycle Time of the WBTS over a period of one month using a load of 1000 (left) and 1200 (right) lots per day. • Common practise approach becomes unstable when using 1200 lots per day • SSCS can handle both loads without problems

15. WBTS: Experimental Results for High Workload Figure 3: Cycle Time of the WBTS over a period of one month using a load of 1500 (left) and 1700 (right) lots per day. • The SSCS can handle 1500 lots per day but becomes unstable for 1700 lots per day • Critical loads • For common practise approach: 1000-1200 lots per day • For SSCS approach: 1500-1700 lots per day • Performance improvement of 25-70% when using the SSCS approach

16. Conclusions and Future Work • A dynamic data-driven application, more particularly a symbiotic simulation system, has been used to for real-time control of semiconductor manufacturing equipment • Our results show that using the SSCS yields a notable performance improvement over common practise • Symbiotic simulation is therefore a promising candidate for integrated automation solutions • Future work will be concerned with the application of an SSCS to an entire semiconductor factory

17. References • Potordi, J., Boon, O., Fowler, J., Pfund, M., Mason, S.: Using simulation-based scheduling to maximize demand fufillment in a semiconductor assembly facility. In: Proceedings of the Winter Simulation Conference. (2002) 1857-1861 • Scholl, W., Domaschke, J.: Implementation of modeling and simulation in semiconductor wafer fabrication with time constraints between wet etch and furnace operations. In: IEEE Transactions on Semiconductor Manufacturing. Volume 13. (August 2000) 273-277 • Gan, B.P., Chan, L.P., Turner, S.J.: Interoperating simulations of automatic material handling systems and manufacturing processes. In: Proceedings of the Winter Simulation Conference. (2006) 1129-1135 • Busetta, P., Howden, N., Rönnquist, R., Hodgson, A.:Structuring BDI agents in functional clusters. In ATAL. Volume 1757 of Lecture Notes in Computer Science, Springer (1999) 277-289 • Braubach, L., Pokahr, A., Lamersdorf, W.: Extending the capability concept for flexible BDI agent modularization. In PROMAS. Volume 3862 of Lecture Notes in Computer Science, Springer (2005) 139-155 • Gan, B.P., Lendermann, P., Quek, K.P.T., van der Heijden, B., Chin, C.C., Koh, C.Y.: Simulation analysis on the impact of furnace batch size increase in a deposition loop. In: Proceedings of the Winter Simulation Conference. (2006) 1821-1828