Cycle time estimation for semiconductor final testing processes with Weibull-distributed waiting time

Accurate cycle time is an essential planning basis required for many production applications, especially on due date commitments, performance metrics analysing, capacity planning, and scheduling. The re-entrant final testing process is the final stage of the complicated semiconductor manufacturing process. To enhance the ability of quick responses and to achieve better on-time delivery in final testing factories, it is essential to develop an accurate cycle time estimation method. In this paper, we provide a statistical approach to calculate the cycle time for multi-layer semiconductor final testing involving the sum of multiple Weibull-distributed waiting times. In addition, percentiles of the cycle time are obtained which are useful to industrial practitioners for due date commitments satisfying the targeted on-time delivery rate. To demonstrate the applicability of the proposed cycle time estimation model, a real example in a semiconductor final testing factory which is located on the Science-based Industrial Park in Hsinchu, Taiwan, is presented.

[1]  Barry L. Nelson,et al.  Estimating Cycle Time Percentile Curves for Manufacturing Systems via Simulation , 2008, INFORMS J. Comput..

[2]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[3]  Ying-Chyi Chou,et al.  Analytic approximations for multiserver batch-service workstations with multiple process recipes in semiconductor wafer fabrication , 2001 .

[4]  W.L. Pearn,et al.  Due-Date Assignment for Wafer Fabrication Under Demand Variate Environment , 2007, IEEE Transactions on Semiconductor Manufacturing.

[5]  P. Backus,et al.  Factory cycle-time prediction with a data-mining approach , 2006, IEEE Transactions on Semiconductor Manufacturing.

[6]  Chin Soon Chong,et al.  A simulation based analysis of cycle time distribution, and throughput in semiconductor backend manufacturing , 2001, Comput. Ind..

[7]  T. S. Raghu,et al.  Due-date setting methodologies based on simulated annealing—an experimental study in a real-life job shop , 1995 .

[8]  Fu-Kwun Wang,et al.  Capacity-constrained scheduling for a logic IC final test facility , 2004 .

[9]  David L. Woodruff,et al.  CONWIP: a pull alternative to kanban , 1990 .

[10]  Shu-Hsing Chung,et al.  Cycle time estimation for wafer fab with engineering lots , 2002 .

[11]  A.J. de Ron,et al.  A Lumped Parameter Model for Product Flow Times in Manufacturing Lines , 2006, IEEE Transactions on Semiconductor Manufacturing.

[12]  Barry L. Nelson,et al.  Efficient generation of cycle time‐throughput curves through simulation and metamodeling , 2005, WSC '05.

[13]  R. Serfling Approximation Theorems of Mathematical Statistics , 1980 .

[14]  Ali Tamer Unal,et al.  A probabilistic cost-based due date assignment model for job shops , 1993 .

[15]  Toly Chen,et al.  Predicting Wafer-Lot Output Time With a Hybrid FCM–FBPN Approach , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Shengwei Ding,et al.  Queueing Theory for Semiconductor Manufacturing Systems: A Survey and Open Problems , 2007, IEEE Transactions on Automation Science and Engineering.

[17]  Samuel Kotz,et al.  The cycle time distribution , 2008 .

[18]  Gerald T. Mackulak,et al.  D-Optimal Sequential Experiments for Generating a Simulation-Based Cycle Time-Throughput Curve , 2002, Oper. Res..

[19]  James R. Morrison,et al.  Practical Extensions to Cycle Time Approximations for the $G/G/m$-Queue With Applications , 2007, IEEE Transactions on Automation Science and Engineering.

[20]  Gerald T. Mackulak,et al.  Indirect cycle time quantile estimation using the Cornish–Fisher expansion , 2009 .

[21]  W. L. Pearna,et al.  A case study on the multistage IC final testing scheduling problem with reentry , 2004 .

[22]  Pei-Chann Chang,et al.  Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory , 2006, Appl. Soft Comput..

[23]  Horst Rinne,et al.  The Weibull Distribution: A Handbook , 2008 .

[24]  Chia-Nan Wang,et al.  Neural-network-based delivery time estimates for prioritized 300-mm automatic material handling operations , 2004 .

[25]  Kevin J. Dooley,et al.  Dynamic rules for due-date assignment , 1991 .

[26]  Toly Chen,et al.  A fuzzy back propagation network ensemble with example classification for lot output time prediction in a wafer fab , 2009, Appl. Soft Comput..

[27]  Robert J. Graves,et al.  Cycle time estimation for printed circuit board assemblies , 1998, Twenty Third IEEE/CPMT International Electronics Manufacturing Technology Symposium (Cat. No.98CH36205).

[28]  Toly Chen An intelligent mechanism for lot output time prediction and achievability evaluation in a wafer fab , 2008, Comput. Ind. Eng..

[29]  Chen-Fu Chien,et al.  Analyzing repair decisions in the site imbalance problem of semiconductor test machines , 2003 .

[30]  Tali Freed,et al.  In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations , 2007 .

[31]  Pei-Chann Chang,et al.  A fuzzy neural network for the flow time estimation in a semiconductor manufacturing factory , 2008 .

[32]  Enrico Scalas,et al.  The waiting-time distribution of LIFFE bond futures , 2000 .

[33]  Shu-Hsing Chung,et al.  A case study on the multistage IC final testing scheduling problem with reentry , 2004 .