# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 2 Literature Review
### Prediction Problems in Semiconductor Scheduling  
For the past few years, in both industry and academia, related industries have paid more and more attention to the application of various data mining methods in performance index prediction and expected to seek statistical rules from the manufacturing data of daily production activities to accurately predict the key performance indicators of semiconductor production line under the current scheduling scheme. As performance indicators control accurately and precisely, they help companies to develop scientific and effective daily production plans, optimize scheduling strategies, and grasp the real-time status of wafers throughout the life cycle of the production line. [PERSON] et al. [21] designed a multi-principal component analysis model to detect the cause of defects and establish corresponding defect classification rules. The framework simplifies the traditional defect monitoring and classification system with fewer key indicators. Based on the particle swarm optimization (PSO) algorithm and k-means clustering method, [PERSON] et al. [22] proposed a default detection and prediction model to mine the underlying knowledge behind the data while analyzing the data collected on the production line. [PERSON] et al. [23] put forward a model of key production factor identification for wafer defects; the PCA methodology was initially used to pick features, and the AdaBoost classifier and SVM classifier were then used to categorize the data. The difficulty in selecting a kernel function acceptable for a given situation and the lengthy process of solving model parameter equations are two drawbacks of this approach. Based on the logistic regression method, [PERSON] et al. [24] classified the input performances. Finally, they selected 108 indicators from 774 that could affect the cycle time, and the prediction accuracy outperformed other back propagation neural networks (BPNN) and traditional linear regression methods. [PERSON] et al. [25] used clustering methods to screen historical production data, making the accuracy of traditional decision-making methods based on decision trees and neural networks up to 76.7% and 88.2%. Their research first selected features in the dataset and then established the processing cycle prediction model. This may lead to incompatibility between the prediction method and the previous clustering method. Based on real-time data from complex production lines, [PERSON] et al. [26] proposed a real-time evaluation method for complex production line health to quantitatively analyze the behavior patterns and correlations among attributes within a system through big data analysis methods. Soft computing achieves low-cost solutions and robustness by tolerating uncertainties, inaccuracies, and incomplete truth values [27]. To efficiently complete daily duties, it imitates the molecular mechanisms of intelligent systems seen in nature (human perception, brain structure, evolution, immunity, etc.) [28]. Fuzzy logic, artificial neural networks, genetic algorithms, support vector machines, and chaos theory are only a few examples of the complimentary and interdependent computational patterns that make up soft computing [29]. Due to its capacity for nonlinear regression and prediction based on limited-sample statistical learning and structural risk reduction, support vector machine (SVM) has been used in machine learning for pattern identification, portrait recognition, and text categorization [30]. SVMs have made great progress in nonlinear regression problems and prediction problems, especially in landslide displacement prediction [31]. However, because parameter selection is so sensitive, SVMs must use efficient and shrewd approaches to find the best parameters [32]. [PERSON] et al. [33] built a novel SVM prediction model with parameter optimization based on obtained characteristic spectra and provided the basis for the establishment of a photosynthesis prediction model.