Fab Simulation Research Articles

A matheuristic for making order acceptance decisions in multi-product, multi-stage manufacturing systems

We discuss a planning model with load-dependent lead times for making order acceptance decisions in multi-product, multi-stage manufacturing systems. Semiconductor wafer fabrication facilities (wafer fabs) belong to this class of manufacturing systems. A profit-based objective function is considered. Clearing functions are used in the planning formulation to correctly represent the lead time behavior in the case of a congested system. Order acceptance decisions are made with respect to flexible due dates, i.e., it is possible to reject certain orders if there is not enough capacity. Such acceptance decisions are important, for instance, in short-term demand supply matching algorithms that are crucial for demand fulfillment and available to promise decisions in semiconductor manufacturing. The resulting planning problem is formulated as a mixed integer linear program. We first show that the resulting planning problem is NP-hard. Hence, computationally tractable approaches must be designed. Therefore, variable neighborhood search is hybridized with linear programming to solve large-sized problem instances in reasonable amount of computing time in the present paper. Results of computational experiments for problem instances that are derived from a scaled-down wafer fab simulation model are provided and analyzed. The computational results demonstrate that the proposed matheuristic outperforms time-based decomposition approaches from the literature.

Qualification Management in Wafer Fabs: Optimization Approach and Simulation-Based Performance Assessment

Machines need to be individually qualified to run lots of certain families in semiconductor wafer fabrication facilities (wafer fabs). A qualification time window that lasts a couple of days is associated with each family and each tool. The time window can be reinitialized with additional qualification effort as needed and can be extended by on-time processing of qualified families. A mixed-integer linear programming formulation is proposed. The objective function takes into account the backlog costs and inventory holding costs. Moreover, it penalizes qualifications. The optimization approach is applied in a rolling horizon setting using a wafer fab simulation model. The demand for a planning window of several periods is derived using feedback from the shop floor. The results of the simulation experiments demonstrate that an appropriate tradeoff between production objectives and performed qualifications can be achieved by an appropriate configuration of the model. Note to Practitioners —In this article, we propose a mathematical programming-based approach to decide when the tools in wafer fabs have to be qualified for a certain family. These decisions have high practical relevance because qualifications are expensive and time-consuming. A dispatching scheme to implement the qualification decisions is designed. The overall approach is assessed in a rolling horizon setting using a simulation model of a large-scale wafer fab. An appropriate tradeoff between production and qualification goals can be achieved using the proposed approach.

An efficient adaptive dispatching method for semiconductor wafer fabrication facility

To cope with uncertainty in semiconductor wafer fabrication facilities (fabs), scheduling methods are required to produce quick real-time responses. They should be well tuned to track the changes of a production environment to obtain good operational performance. This paper presents an efficient adaptive dispatching method (ADM) with parameters determined dynamically by real-time state information of fabs. ADM is composed of a dispatching rule considering both batch and non-batch processing machines to obtain improved fab-wide performance, several feature selection methods to determine key scheduling-related real-time state information, and a linear regression model to find the relations between the weighting parameters of the dispatching rule and the determined real-time state information. A real fab simulation model is used to demonstrate the proposed method. The simulation results show that ADM is adaptive to changing environment with better performance than a number of commonly used rules (such as FIFO, EDD, CR, LPT, LS, SRPT, and SPT) and an adaptive dispatching rule that considers only real-time ratio of hot jobs to the number of all jobs in a fab and the ratio of jobs with one third of photo steps left to the number of all jobs.

Strengthening the Competitiveness and Sustainability of a Semiconductor Manufacturer with Cloud Manufacturing

Cloud manufacturing (CMfg) is a new-generation service-oriented networked manufacturing model that provides distributed users centralized managed manufacturing resources, ability, and services. CMfg is applied here to a semiconductor manufacturing factory. Benefits are classified into five aspects: cost savings, efficiency, additional data analysis capabilities, flexibility, and closer partner relationships. A strength, weakness, opportunity, and threat (SWOT) analysis is done which guides a semiconductor manufacturer in planning CMfg implementation projects. Simulation of a wafer fabrication factory (wafer fab) is used as an example. Several CMfg services are proposed for assisting the fab simulation activities through the collaboration of cloud service providers, software vendors, equipment suppliers, and the wafer fab. The connection with the competitiveness and sustainability of a wafer fab is also stressed.

Adaptive Dispatching Rule for Semiconductor Wafer Fabrication Facility

Uncertainty in semiconductor fabrication facilities (fabs) requires scheduling methods to attain quick real-time responses. They should be well tuned to track the changes of a production environment to obtain better operational performance. This paper presents an adaptive dispatching rule (ADR) whose parameters are determined dynamically by real-time information relevant to scheduling. First, we introduce the workflow of ADR that considers both batch and non-batch processing machines to obtain improved fab-wide performance. It makes use of such information as due date of a job, workload of a machine, and occupation time of a job on a machine. Then, we use a backward propagation neural network (BPNN) and a particle swarm optimization (PSO) algorithm to find the relations between weighting parameters and real-time state information to adapt these parameters dynamically to the environment. Finally, a real fab simulation model is used to demonstrate the proposed method. The simulation results show that ADR with constant weighting parameters outperforms the conventional dispatching rule on average; ADR with changing parameters tracking real-time production information over time is more robust than ADR with constant ones; and further improvements can be obtained by optimizing the weights and threshold values of BPNN with a PSO algorithm.

Decomposition-based classified ant colony optimization algorithm for scheduling semiconductor wafer fabrication system

Due to its typical features, such as large-scale, multiple re-entrant flows, and hybrid machine types, the semiconductor wafer fabrication system (SWFS) is extremely difficult to schedule. In order to cope with this difficulty, the decomposition-based classified ant colony optimization (D-CACO) method is proposed and analyzed in this paper. The D-CACO method comprises decomposition procedure and classified ant colony optimization algorithm. In the decomposition procedure, a large and complicate scheduling problem is decomposed into several subproblems and these subproblems are scheduled in sequence. The classified ACO algorithm then groups all of the operations of the subproblems and schedules them according to machine type. To test the effect of the method, a set of simulations are conducted on a virtual fab simulation platform. The test results show that the proposed D-CACO algorithm works efficiently in scheduling SWFS.