The development of the complete X-factor contribution measurement for improving cycle time and cycle time variability

Reducing variability in a manufacturing process lowers system cycle times. Semiconductor manufacturing is a variable process due in part to product mix, reentry lot flows, batching, and machine breakdowns. This paper examines the issue of identifying machines that introduce variability into the system and constrain the system capacity. We develop a new X-factor contribution measurement, the complete X factor, that considers processing time variability and lot arrival variability among the constraining qualities of the machine groups. This new measure uses machine level data to indicate the normalized system cycle time which has typically been estimated by the ratio of the entire process time and the raw processing time at the end of production. With this measure it becomes possible for factory floor managers to identify a capacity constraining machine and its impact on the overall cycle time directly. We first qualitatively present the justification of the complete X factor for representing the normalized cycle time using queuing theory. Then, the complete X-factor measure was tested on a full-scale simulation model to demonstrate its accuracy for detecting capacity constraining machine groups and for representing normalized cycle time. We also explore the propagation of variability and the effect a highly variable machine group has on product cycle time and cycle time variability in relation to process routing. In a full-scale model machines identified by the complete X-factor contribution measure (CXC) measure lowered cycle time as effectively as highly utilized machines by adding capacity or streamlining breakdowns but had a more prominent effect on lowering cycle time variability. After a brief study on the propagation of variability, the CXC measure identified a lower utilized backend process that reduced cycle time and cycle time variability of the system

[1]  D.R. Delp.  A new X-factor contribution measure for identifying machine level capacity constraints and variability , 2004, 2004 IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop (IEEE Cat. No.04CH37530).

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

[3]  A. Schömig,et al.  On the corrupting influence of variability in semiconductor manufacturing , 1999, WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038).

[4]  D. P. Martin.  The advantages of using short cycle time manufacturing (SCM) instead of continuous flow manufacturing (CFM) , 1998, IEEE/SEMI 1998 IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop (Cat. No.98CH36168).

[5]  Kan Wu,et al.  An examination of variability and its basic properties for a factory , 2005, IEEE Transactions on Semiconductor Manufacturing.

[6]  Jacobus E. Rooda,et al.  Characterization of operational time variability using effective process times , 2003 .

[7]  T. Yamaguchi,et al.  Optimum tool planning using the X-factor theory , 1999, 1999 IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings (Cat No.99CH36314).

[8]  Frank Chance,et al.  WAFER FAB CYCLE TIME MANAGEMENT USING MES DATA , 2000 .

[9]  J. Si,et al.  Availability-adjusted X-factor , 2005 .

[10]  K. Narasimhan,et al.  The Goal: A Process of Ongoing Improvement , 2005 .

[11]  G. D. Taylor,et al.  A comparison of mean reduction versus variance reduction in processing times in flow shops , 1999 .

[12]  J. Si,et al.  A dynamic system regulation measure for increasing effective capacity: the X-factor theory , 2003, Advanced Semiconductor Manufacturing Conference and Workshop, 2003 IEEEI/SEMI.

[13]  Shu Li,et al.  Minimum inventory variability schedule with applications in semiconductor fabrication , 1996 .

[14]  Yuhchang Hwang,et al.  A systematic study of the extended X-factor in relation to effective system capacity , 2005 .