International audienceProduction planning in the Semiconductor Industry (SI) has emerged as the most complex process due to its process complexity, technological constraints and high-mix low-volume production characteristics. In this paper, we present two different production planning approaches, developed by STMicroelectronics and G-SCOP research laboratory, to better control the production in 300mm production line at Crolles. At first, a mixed integer program (MIP) is proposed that projects the production lot trajectories (start and end dates) for the remaining subsequent steps, taking into account finite production capacities. A heuristic is then proposed to simplify the problem of finite capacity by neglecting equipment capacity. This approach results in the development of an infinite capacity WIP projection engine that complies with lots due dates and takes into account cycle time variability

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In semiconductor high-mix fabs, several technology nodes are run on the same line, using tool types from different generations. In this context, processability is a function defining which products can be processed on a given machine considering the current status of both the product and the machine. So in a high-mix context, having an information system that provides reliable information on processability and that can support the evolution of processability rules is fundamental. In this paper, we analyze the key elements for such a system. Based on the example of the implementation of fab constraints at STMicroelectronics' Crolles300 production unit, we illustrate the consequences of the integration of new processability rules and propose flexible and agile UML class diagrams enabling information system to meet evolution requirements. The approach is validated on real fab data, and its impact is discussed.

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International audienceA finite capacity planning heuristic is developed for semiconductor manufacturing with high-mix low-volume production, complex processes, variable cycle times and reentrant flows characteristics. The proposed algorithm projects production lots trajectories (start and end dates) for the remaining process steps, estimates the expected load for all machines and balances the workload against bottleneck tools capacities. It takes into account lots' priorities, cycle time variability and equipment saturation. This algorithm helps plant management to define feasible target production plans. It is programmed in java, and tested on real data instances from STMicroelectronics Crolles300 production plant which allowed its assessment on the effectiveness and efficiency. The evaluation demonstrates that the proposed heuristic outperforms current practices for capacity planning and opens new perspectives for the production line management