# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 1 Introduction  
A semiconductor manufacturing system is a typical complex manufacturing system containing hundreds or thousands of processing steps [1; 2]. Unlike conventional manufacturing and assembly systems, semiconductor manufacturing involves a re-entrant phenomenon, in which a workpiece may queue for its turn to be processed by one piece of equipment and visit this equipment repeatedly [3]. In addition, complex products and processes, as well as expensive equipment, make managing semiconductor manufacturing systems a challenge [4].  
Semiconductor manufacturing is a capital-intensive industry [5], which has high standards for overall equipment effectiveness, a high on-time delivery rate, quick cycle time, etc. Semiconductor manufacturing scheduling is a complicated and difficult task with multiple crucial characteristics, such as changing demands, varying task priorities, imbalanced capacity, moving bottlenecks, etc. [6]. More unwanted outcomes, task rework or discard, and other unfavorable uncertainties will arise from the increase in process complexity and the decrease in feature size [7]. To choose acceptable principles for semiconductor scheduling, [PERSON] et al. [8] suggested an evolutionary optimization technique basedon simulation. To solve material handling problems, [PERSON] et al. [9] put forward a dynamic simulation-based dispatching rule. Simultaneously considering the dispatching urgency degree and load level of downstream equipment, [PERSON] et al. [10] proposed an original dynamic dispatching rule and verified its effectiveness. Therefore, scheduling policies must be able to respond to rework phenomena and interruption conditions. In addition, due to the complex coupling relationship between upstream and downstream equipment, researchers prefer fab-widely dynamic scheduling methods.  
Unlike the above, data-based methods pay more attention to the knowledge hidden in manufacturing systems. Applying data-driving methods to solve scheduling problems is a burgeoning study direction [11]. By analyzing production data, researchers can excavate hidden information about relevant problems and then turn it into available knowledge through modeling. If available knowledge is used to identify and solve problems, they rely on data mining rather than human experience. In this way, manufacturing knowledge can be utilized more effectively [12]. Although some significant successes in dynamic scheduling, goal-driving, and closed-loop mechanisms have been achieved, they are often considered separate decisions in scheduling problems. Thus, scheduling methods are unable to adapt well to real-time changes in production environments. Therefore, this paper proposes a PSO-SVM-based scheduling method with a performance-driving and feedback mechanism.  
The arrangement of this paper is as follows. Section 2 describes the proposed scheduling system. The proposed scheduling system is introduced in Section 3, which includes composite dispatching rules, feature selection, states-performance prediction, and the (states and performance)-parameters optimization. Section 4 describes the simulation platform, experiment design and arrangement, and discussion of the simulation results. Section 5 concludes this research and future works.