Infineon Villach wafer fab facility has a unique characteristic of producing products of different wafer sizes with shared resources, both operators and tools. Daily adaptation of resource allocation to manage changing workloads is done dynamically and imposes challenges in providing high accuracy simulation forecast. In this paper, we discuss the modelling approach that we have taken to represent this dynamic feature, where resources are adjusted in simulation based on WIP situation. The concept is similar to acquiring KANBAN prior to running a production lot. KANBAN quantity is kept at a minimum number in low WIP situation, while the quantity is kept at maximum number at high WIP situation. These values were adjusted by studying the historical moves observed for a period of 3 months. Using this approach, we managed to improve the forecast quality by 7 percentage points in moves and 64 percentage points in WIP.

The Infineon Dresden 300mm fab is the first wafer fab in the world to run high volume production of power technology products. This requires the coordination of multiple production facilities in the supply chain. Output from one facility is fed as an input to the subsequent facilities in a push or pull operation mode, depending on technology lines. In a push operation mode, lots transition from one facility to another in a continuous manner, while in the pull operation mode, lots are stored and continuation of the lots is triggered by customer order to the supply chain. The latter case triggers the need of synchronizing material flows from one facility to another, and thus requiring an accurate material forecast for better planning and execution. In this paper, we discuss the modelling issues and solutions associated with this challenge, and the use cases of the simulation forecast.

Operations meeting in a wafer fab involves daily alignment of action items among key stakeholders: operations, maintenance, engineering and planning department. They have conflicting job functions. The maintenance department is required to conduct preventive maintenance (PM) to improve tools' reliability The engineering department is required to qualify new products and processes. Both cases interfere with the flow of production lots. The planning department must ensure production ramp up. This can have a short term impact on overall fab delivery and capacity. The primary challenge is to reach aligned decisions e.g. the best timing for PM or optimizing dispatch prioritization of production and development lots to ensure on-time delivery while maximizing capacity and tool utilization. In this paper we discuss the associated modelling issues of a 7-day simulation-based forecast, providing forecast of incoming WIP, moves and utilization at work center level. The simulation forecast consistently achieved an accuracy above 90%.

Technology and product development have high priority in an advanced semiconductor manufacturing facility such as the Infineon Dresden fab. From the perspective of line performance this means that short cycle times for development lots have to be guaranteed to enable the required learning cycles. Long-term simulation is used in dynamic capacity planning to find a compromise between short cycle times for the development corridor and high utilisation of the installed tool capacity. All products in the fab run with customer-specific due dates. As such, negative side-effects caused by the accelerated development lot corridor through increased dispatch priorities have to be minimised. In turn, for day-to-day operations short-term simulation is used for early detection of bottleneck situations and other sudden resource availability problems. With focus on the development corridor, a Lot Cycle Time Forecaster was realised. The aforementioned manifold applications of discrete-event simulation are described in this paper in more detail.

Material flow forecast based on Short-Term Simulation has been established as a decision support solution for fine-tuning of Preventive Maintenance (PM) timing at Infineon Dresden. To ensure stable forecast quality for effective PM decision making, the typical tool uptime behavior needs to be portrayed accurately. In this paper, we present a hybrid tool down modeling approach that selectively combines deterministic and random down time modeling based on historical tool uptime behavior. The method allowed to approximate the daily uptime of reality in simulation. A generic framework to model historical down behavior of any distribution type, described by the two parameters Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) is also discussed.