Performance evaluation of photolithography cluster tools

The photolithography cluster tool is typically the most expensive tool set utilized in the production of semiconductor wafers and is often selected as a fabricator bottleneck. Modeling such a tool as a serial processing cluster tool, we deduce measures of tool performance. Queueing models reveal that the mean cycle time in the presence of a Poisson arrival process is related to the parallelism inherent in the system configuration. As a consequence, the normalized mean cycle time behavior has a different form than that of the standard single server queue. The process time of a lot and the throughput are evaluated in the presence of disruptions common in practical manufacturing environments. For multiple products with different process rates, it is shown that the throughput is not influenced by the order in which the lots are sequenced.

[1]  Tae-Eog Lee,et al.  Virtual control - a virtual cluster tool for testing and verifying a cluster tool controller and a scheduler , 2004, IEEE Robotics Autom. Mag..

[2]  Tae-Eog Lee,et al.  Scheduling analysis of time-constrained dual-armed cluster tools , 2003 .

[3]  Raghavan Srinivasan,et al.  Modeling and performance analysis of cluster tools using Petri nets , 1998 .

[4]  K. Connerney,et al.  Determining the capacity components of different classes of multi chamber tools , 2001, 2001 IEEE/SEMI Advanced Semiconductor Manufacturing Conference (IEEE Cat. No.01CH37160).

[5]  J. Matthews,et al.  How differentiating between utilization of effective availability and utilization of effective capacity leads to a better understanding of performance metrics , 2001, 2001 IEEE/SEMI Advanced Semiconductor Manufacturing Conference (IEEE Cat. No.01CH37160).

[6]  J.R. Morrison,et al.  Performance Evaluation of Serial Photolithography Clusters: Queueing Models, Throughput and Workload Sequencing , 2006, The 17th Annual SEMI/IEEE ASMC 2006 Conference.

[7]  W.M. Zuberek,et al.  Cluster tools with chamber revisiting-modeling and analysis using timed Petri nets , 2004, IEEE Transactions on Semiconductor Manufacturing.

[8]  Niranjan Chandrasekaran,et al.  Operational Models for Evaluating the Impact of Process Changes on Cluster Tool Performance , 2000 .

[9]  S. Venkatesh,et al.  An evaluation of deadlock-handling strategies in semiconductor cluster tools , 2005, IEEE Transactions on Semiconductor Manufacturing.

[10]  S. C. Wood,et al.  Simple performance models for integrated processing tools , 1996 .

[11]  S. C. Wood,et al.  Systems of multiple cluster tools: configuration, reliability, and performance , 2003 .

[12]  R. S. Gyurcsik,et al.  Single-wafer cluster tool performance: an analysis of throughput , 1994 .

[13]  S. Rostami,et al.  An optimal residency-aware scheduling technique for cluster tools with buffer module , 2004, IEEE Transactions on Semiconductor Manufacturing.

[14]  M. Kishimoto,et al.  Optimized operations by extended X-factor theory including unit hours concept , 2001 .

[15]  E. A. MacNair,et al.  Application of cluster tool modeling to a 300 mm fab simulation , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[16]  S. Venkatesh,et al.  A steady-state throughput analysis of cluster tools: dual-blade versus single-blade robots , 1997 .

[17]  S. C. Wood,et al.  Systems of multiple cluster tools: configuration and performance under perfect reliability , 1998 .

[18]  R. S. Gyurcsik,et al.  Single-wafer cluster tool performance: an analysis of the effects of redundant chambers and revisitation sequences on throughput , 1996 .

[19]  Todd LeBaron,et al.  Using emulation to validate a cluster tool simulation model , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[20]  H. L. Oh.  Reducing complexity of wafer flow to improve quality and throughput in a single-wafer cluster tool , 1999, Twenty Fourth IEEE/CPMT International Electronics Manufacturing Technology Symposium (Cat. No.99CH36330).