A two-stage stochastic programming approach for new tape-out allocation decisions for demand fulfillment planning in semiconductor manufacturing

Demand fulfillment and capacity utilization directly affects customer satisfaction, market growth, and the profitability of the company in the semiconductor industry. These characteristics boost the significance of allocating various customer demands to a number of wafer fabrication facilities (fabs) with different capacity configurations. Before volume production, the introduction of new semiconductor product, namely new tape-out (NTO), requires extremely sophisticated and lengthy qualification with high-cost masks and pilot runs in the qualified fabs. Thus, the NTO allocation will affect future product mix of the qualified fabs, and the flexibility to fulfill the volume demands of the allocated NTOs in the corresponding fabs. This research aims to construct a two-stage stochastic programming (2-SSP) demand fulfillment model. The first stage considers NTO allocation decisions to a number of qualified fabs before the corresponding demand volume is realized. The second stage allocates the capacity to fulfill demand requirements based on the results of four options of capacity reconfiguration: (1) qualifying a product to more than one fab (share); (2) physically transferring a set of masks for a product from one fab to another, where a requalification is required (transfer); (3) moving tools from under-loaded fabs to over-utilized fabs (backup); and (4) utilizing different technologies to capacity inside a fab to support the technology with insufficient capacities (exchange). Both the share and transfer options require long lead time for qualification, whereas the backup and exchange options can be accomplished within a planned timeframe. A numerical study based on real settings is conducted to estimate the validity of the proposed 2-SSP model via values of stochastic solution (VSS) and expected values of perfect information (EVPI). The results showed the benefits of adopting 2-SSP models, especially in an environment with high-demand fluctuation. Furthermore, the proposed 2-SSP can provide near-optimal solutions similar to those of deterministic models with perfect information.

[1]  Bernhard Fleischmann,et al.  Strategic Planning of BMW's Global Production Network , 2006, Interfaces.

[2]  Jonathan F. Bard,et al.  AN OPTIMIZATION APPROACH TO CAPACITY EXPANSION IN SEMICONDUCTOR MANUFACTURING FACILITIES , 1999 .

[3]  Robert R. Inman,et al.  A mass production product-to-plant allocation problem , 2001 .

[4]  Chen-Fu Chien,et al.  Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle , 2010 .

[5]  William C. Jordan,et al.  Principles on the benefits of manufacturing process flexibility , 1995 .

[6]  Oktay Günlük,et al.  Robust capacity planning in semiconductor manufacturing , 2005 .

[7]  Zhibin Jiang,et al.  A review on strategic capacity planning for the semiconductor manufacturing industry , 2009 .

[8]  Kai Huang,et al.  The Value of Multistage Stochastic Programming in Capacity Planning Under Uncertainty , 2009, Oper. Res..

[9]  Chen-Fu Chien,et al.  Mini–max regret strategy for robust capacity expansion decisions in semiconductor manufacturing , 2012, J. Intell. Manuf..

[10]  David L. Woodruff,et al.  Production planning with load dependent lead times: an update of research , 2007, Ann. Oper. Res..

[11]  Shengwei Ding,et al.  Economic Efficiency Analysis of Wafer Fabrication , 2007, IEEE Transactions on Automation Science and Engineering.

[12]  Zhibin Jiang,et al.  Stochastic programming based capacity planning for semiconductor wafer fab with uncertain demand and capacity , 2009, Eur. J. Oper. Res..

[13]  Zhi-Long Chen,et al.  A scenario-based stochastic programming approach for technology and capacity planning , 2002, Comput. Oper. Res..

[14]  Chen-Fu Chien,et al.  Modeling strategic semiconductor assembly outsourcing decisions based on empirical settings , 2008, OR Spectr..

[15]  S. D. Wu,et al.  Managing Capacity in the High-Tech Industry: A Review of Literature , 2005 .

[16]  Nikolaos V. Sahinidis,et al.  An Approximation Scheme for Stochastic Integer Programs Arising in Capacity Expansion , 2003, Oper. Res..

[17]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[18]  Reha Uzsoy,et al.  Exact and heuristic procedures for capacity expansion problems with congestion , 2008 .

[19]  Meral Azizoglu,et al.  Operation assignment and capacity allocation problem in automated manufacturing systems , 2009, Comput. Ind. Eng..

[20]  Herbert Meyr,et al.  Strategic network planning for an international automotive manufacturer , 2009, OR Spectr..

[21]  C. Mouli,et al.  Tapeout Execution System (TES), a key enabler of DFM/Co-optimization , 2007, 2007 International Symposium on Semiconductor Manufacturing.

[22]  D. Tirupati,et al.  Capacity planning in manufacturing networks with discrete options , 1989 .

[23]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[24]  S.-M. Wang,et al.  A resource portfolio model for equipment investment and allocation of semiconductor testing industry , 2007, Eur. J. Oper. Res..

[25]  K. R. Mackie,et al.  Landfill gas emission prediction using Voronoi diagrams and importance sampling , 2009, Environ. Model. Softw..

[26]  Amy H. I. Lee,et al.  Capacity allocation model for photolithography workstation with the constraints of process window and machine dedication , 2006 .

[27]  ROBERT M.E. CHRISTIE,et al.  Semiconductor capacity planning: stochastic modeling and computational studies , 2002 .