Production Scheduling And System Configuration For Capacitated Flow Lines With Application In The Semiconductor Backend Process

A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.

This book systematically discusses the intelligent scheduling problem of complex semiconductor manufacturing systems from theory to method and then to application. The main contents include data-driven scheduling framework of semiconductor manufacturing system, data preprocessing of semiconductor manufacturing system, correlation analysis of performance index of semiconductor production line, intelligent release control strategy, dynamic dispatching rules simulating pheromone mechanism, and load balancing dynamic scheduling of semiconductor production line, performance index-driven dynamic scheduling method of semiconductor production line, scheduling trend of semi-conductor manufacturing system in big data environment. This book aims to provide readers with valuable reference and assistance in the theoretical methods, techniques, and application cases of semiconductor manufacturing systems and their intelligent scheduling.

The focus of these books is upon the new achievements in mathematical programming, modeling and their implications for optimization technology. The emphasis is on the practical value of optimization methods. Examples in the following areas are presented and discussed: production, distribution and transportation scheduling; product and process design; financial investment and resource allocation. Through the demonstration of recent developments, the reader is provided with an overview of authentic mathematical programming applications.

Analyzes the fabrication capacity versus workload from current work-in-process (WIP) in order to create a short-term scheduling system which balances WIP flows in observance of WIP targets and the output schedule. Research focuses only on the front end of the production of semiconductor wafers in order to develop an effective and optimal material/fabrication input schedule, a precise capacity utilization analysis, and a feasible and optimal material/fabrication output schedule.

Develops a two-stage continuous-flow control distribution production scheduling method. The first stage consists of off-line optimization setup reconfiguration frequencies and production target levels for individual operations over time. The solution of a global continuous-flow model uses an approximation of material flow in the production system with the objective of meeting product demands and minimizing costs due to setup, inventory, and backlog. The second stage uses a simple on-line feedback policy to make the setup and production decisions in order to track the optimized production targets and setup reconfiguration frequencies of the first stage. Analyzes the stability of a class of guided create-a-fraction (GCAF) on-line setup and production tracking policies.