Behavior Modeling of Semiconductor Fab with Service Differentiation

A semiconductor supply chain is a system of nodes that provide manufacturing services—in fact, a variety of services. The new paradigm of manufacturing services requires new methods of operation management. Service differentiation, namely, prioritization, is common in operations of semiconductor supply chain (SSC). The grand challenges of this research will be to overcome the scalability and predictability problems with respect to service differentiation, and variability that are exacerbated by rapidly increasing product varieties and process variations in the chains. The service differentiation ensures that the quality of service (QoS) is differentiable, and quality of service affects how to charge prices and allocate resources. And the variability affects the performance of both network nodes and chains of nodes. In order to model the behavior of the SSC, our research is needed in follow aspects: performance metrics that are predictable, scalable with respect to chain structure, and fundamental understanding of the behavior of nodes and chains under variability.
This study is to develop behavior models and modeling methods that enhance the scalability and predictability of the semiconductor supply chain management with respect to varieties, and service differentiation. We aim at the behavior modeling of fabs that provides a cornerstone for supply chain management. The fab behavior models describe how priority, resource allocation and sources of variations affect fab performance metrics such as mean and variability of cycle time, wafer-in-process, throughputs, and machine utilizations. Considering a fab with multiple part types, failure prone machines, and re-entrant process flows as a failure-free, batch-free and re-entrant OQN. Then we design and develop a hybrid decomposition approximation-based approach for network modeling with a focus on capturing operation priority and variations in fabs. The hybrid decomposition approximation-based approach (hybrid SDA+QNA) is to decompose the fab network model into many independent service nodes by QNA and model single service node behavior by sequential decomposition approximation (SDA) among priorities in one node, then study networking relationship among service nodes to approximate network performances. The hybrid decomposition approximation can handle scalability that allows chain metrics to be decomposed into node metrics. And the priority network models constructed by the hybrid decomposition approximation can handle predictability that allows very quick evaluation of mean and variability of both node and system level output performance metrics with various input options. Besides, we consider model tuning to fit the empirical data.
Comparisons with simulation results over two priorities fab models demonstrate the accuracy and computing efficiency of hybrid decomposition approximation-based approach and its potential for applications of real fab with service differentiation. The relative errors of cycle time performances are mostly within 10%; only for the cycle time standard deviation of hot lots, the relative error is high to 50% but the absolute error is very small (about 1.2 ~1.6). Applications of hybrid SDA+QNA to fab models with 60 processing steps only require less than 4 seconds of CPU time on a 2.8 GHz personal computer, which is about 2 to 3 orders faster than simulation. Quick evaluation of various input options in terms of capacity allocation, priority mix, etc. may be combined with supply chain optimization to determine the best fab input option that leads to a desirable performance. The fab behavior model therefore provides a tool for what-if analysis to fab or supply chain planners/ managers.