A Centralized approach to Factory Simulation
Steven Brown, Infineon Technologies
Frank Chance, Ph.D., FabTime Inc.
John Fowler, Ph.D., Arizona State University
Jennifer Robinson, Ph.D., FabTime Inc.
Abstract
This paper describes the goals, practices, and methodologies of the Siemens Semiconductor Division’s Factory Modeling and Simulation team. The team’s charter is to implement performance modeling capability (simulation, capacity analysis, and cost analysis) at factories throughout the division, both wafer fabrication and back-end operations. Findings of recent modeling activities are discussed, along with applications of simulation and a hierarchical modeling approach. The authors wish to use articles such as this to initiate discussions with other organizations using modeling techniques to analyze factory performance.
Integrating Cost, Capacity, and Simulation Analysis
Frank Chance, Ph.D.Jennifer Robinson, Ph.D.
FabTime Inc.
Abstract
In this article, we discuss the integration of cost, capacity, and simulation analysis. Cost analysis is often treated as a separate task from capacity and simulation modeling activities. Several important factory-level cost performance measures, however, rely on detailed capacity analysis calculations. In fact, much of the difficult groundwork for making these calculations has already been completed in existing capacity and simulation analysis tools. We argue that these activities fit quite naturally together, and give a more complete picture of factory performance than isolated analyses. Also, capacity and simulation analysis tools are increasingly used to support strategic and tactical business decisions. These decisions are most often framed in terms of their impact on the bottom line. Therefore, effective decision-support tools must speak not only in terms of capacity and cycle time, but also in terms of dollars.
We focus on three factory-level cost and revenue performance measures, namely dollar-valued throughput, dollar-valued inventory, and operating expenses. We describe the input parameters added to Factory Explorer®, an existing factory analysis tool, to support estimation of these performance measures. With one additional parameter, it is also possible to perform detailed product cost analysis. We present some sample uses for this integrated analysis framework. On a cautionary note, we also present an example where the use of product cost to the exclusion of factory-level measures can lead to sub-optimal results. We close by discussing the advantages present in an integrated cost, capacity, and simulation analysis framework.
Validating Simulation Model Cycle Times at Seagate Technology
Navdeep Grewal, Timbur Wulf, Alvin Bruska
Seagate Technology
Jennifer Robinson, Ph.D., FabTime Inc.
Abstract
This paper describes the validation of cycle times in a factory simulation model of a new Recording Head Wafer manufacturing facility at Seagate Technology, Minneapolis, MN. The project goals were to determine which factors were causing cycle time deltas between the model and the actual factory, and to add detail to the simulation model to bring cycle times closer to reality. The study found that the most significant contributors to the cycle time delta were number of tools, number of operators, level of operator cross-training, and assumptions about rework, downtime, and equipment dedication.
Getting to Good Answers: Effective Methods for Validating Complex Models
Frank Chance, Ph.D., Jennifer Robinson, Ph.D.
FabTime Inc.
Nancy Winter, Industrial Design & Construction
Abstract
Model validation is like a trip to the dentist. You know it’s necessary, but other tasks always seem to take precedence. The actual event can be painful, and there’s always an element of uncertainty about problems that may come to light. But after it’s over, you’ve either got a clean bill of health, or a good idea where the true problems lie. And in your heart, you know these problems are best addressed sooner rather than later. Validation for semiconductor and electronics manufacturing models is particularly challenging (some would say painful) due to the amount of input and output data involved. Good answers, however, require valid models. In this paper, we present several case studies in model validation, and describe validation methods we have found to be particularly effective.
Supporting Manufacturing With Simulation: Model Design, Development, and Deployment
Frank Chance, Ph.D., Jennifer Robinson, Ph.D.
FabTime Inc.
John Fowler, Ph.D., Arizona State University
Abstract
In this paper, we identify and discuss the features we believe are key to the successful use of simulation as a manufacturing support tool. The discussion begins with three sample projects drawn from the authors’ industrial and consulting experiences. Using these projects as motivation, we discuss the ideal project lifecycle model design, development, and deployment. For model design, we emphasize the importance of a clear and consistent specification, articulated in a written document. This specification should identify project customers, goals, and deliverables. We next review a range of model development options, stressing the existence of many non-simulation alternatives. We also discuss methods for model verification and validation. Finally, we consider the difficulties of model deployment, including simulation output analysis, data maintenance, and model integration. We close with several suggestions on how best to present simulation results to a management audience.
A Rapid Modeling Technique for Measurable Improvements in Factory Performance
Andreas Peikert, Steven Brown, Josef Thoma
Infineon
Abstract
This paper discusses a methodology for quickly investigating problem areas in semiconductor wafer fabrication factories by creating a model for the production area of interest only (as opposed to a model of the complete factory operation). All other factory operations are treated as “black boxes”. Specific assumptions are made to capture the effect of reentrant flow. This approach allows a rapid response to production questions when beginning a new simulation project. The methodology was applied to a cycle-time and capacity analysis of the photolithography operation for Infineon’s Dresden wafer fab. The results of this simulation study are presented.
Measurable Improvements in Cycle-Time-Constrained Capacity
John Fowler, Ph.D., Arizona State University
Steven Brown, Hermann Gold, Alexander Schoemig
Infineon
Abstract
This study uses simulation-based analysis to evaluate the operating practices of a high-volume, multiple-product semiconductor fab, with the goal of finding potential areas for productivity improvement that will yield a quantifiable increase in fab capacity. The parameters setup, batching, tool/operator dedication, lot release, and dispatch rule were studied. The analysis revealed that some of the current operating practices of the factory were beneficial while changing some other practices would increase “cycle-time-constrained capacity” by up to 12%. A significant opportunity for potential improvement for this factory lies in implementing a strict setup avoidance policy. The first implementation in the actual fab is a relaxation of the photolithography equipment dedication that has helped the factory achieve a 25% reduction in cycle time and inventory.
Integrating Targeted Cycle-Time Reduction Into the Capital Planning Process
Navdeep S. Grewal, Timbur M. Wulf, Alvin C. Bruska
Seagate Technology
Jennifer Robinson, Ph. D., FabTime Inc.
Abstract
This paper describes the development and application of an integrated static capacity and dynamic simulation analysis methodology for purchasing equipment capacity. The goal of the study is to address targeted cycle time objectives in a start up Recording Head Wafer manufacturing facility at Seagate Technology, Minneapolis, MN. The short product cycle time, coupled with the competitive nature of the disc drive industry, has made cycle time reduction one of the most important objectives of production capacity planning. This paper describes an equipment procurement strategy in which static capacity analysis is used to identify an initial equipment set with a low slack capacity variable on each tool group. Simulation analysis is then used to identify the critical tool groups that contribute to cycle time delays. The Seagate Industrial Engineering team used the simulation analysis tool Factory Explorer® from Wright Williams & Kelly, Inc. to perform the cycle time reduction analysis. This targeted approach is compared to the traditional static capacity planning approach of globally applying reserve capacity buffers of 20% or more to achieve the same cycle time reduction goal. Overall, the targeted approach has proven to be efficient in terms of minimizing capital equipment expenditures and also effective on the factory floor.
Effective Implementation of Cycle Time Reduction Strategies for Semiconductor Back-End Manufacturing
Steven Brown, Joerg Domaschke, Franz Leibl
Infineon
Jennifer Robinson, Ph. D., FabTime Inc.
Abstract
Using discrete-event simulation models, a study was conducted to evaluate the current production practices of a high-volume semiconductor back-end operation. The overall goal was to find potential areas for productivity improvement that would collectively yield a 60% reduction in manufacturing cycle time. This paper presents the simulation methodology and findings pertaining to analysis of the Assembly, Burn-In, and Test operations. Many of the recommendations identified can be implemented at no additional cost to the factory. The most significant opportunities for improvement are in the Test area, the system constraint. Additionally, the model is extremely sensitive to changes in operator staffing levels, an accurate reflection of many back-end operations. The model shows that the cumulative impact of these recommendations is a 41% reduction in average cycle time, a significant contribution to the overall goal.
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