Science and Technology Production
An improvement-based MILP optimization approach to complex AWS scheduling

Article

Authorship
Aguirre, Adrian Marcelo ; MENDEZ, CARLOS ALBERTO ; Gutierrez, Gloria Maribel ; de Prada, Cesar
Date
2012
Publishing House and Editing Place
Elsevier
Magazine
COMPUTERS AND CHEMICAL ENGINEERING, vol. 47 (pp. 217-226) Elsevier
Summary Information provided by the agent in SIGEVA
The automated wet-etch station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. Due to its inherent complexity, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported i... The automated wet-etch station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. Due to its inherent complexity, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported in literature to provide feasible solutions with reasonable CPU times. This work introduces an improvement MILP-based decomposition strategy that combines the benefits of a rigorous continuous-time MILP (mixed integer linear programming) formulation with the flexibility of heuristic procedures. The schedule generated provides enhanced solutions over time to challenging real-world automated wet etch station scheduling problems with moderate computational cost. This methodology was able to provide more than a 7% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed.
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Key Words
Large-scale scheduling problemsMILP-based strategiesModeling and OptimizationHybrid decomposition approachWafer fabricationSemiconductor Manufacturing System (SMS)
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