Enhancing efficiency and accuracy in virtual metrology for semiconductors with innovative sparse dimensionality reduction and advanced ensemble learning for multi-source data analysis
Creators: name: Xie, Yifan
Publisher: name: No Publisher Supplied
Year: 2024

description: In the semiconductor industry, virtual metrology (VM) serves as a cost-effective and efficient strategy for monitoring processes across different wafers. This approach involves creating a predictive model that leverages real-time data from equipment sensors alongside measured wafer quality characteristics. Before crafting this prediction model, it's crucial to carefully select relevant input variables to ensure the subsequent analysis remains efficient, despite the high dimensionality of sensor data. However, the extensive use of multiple sensors in wafer production can lead to increased costs. Principal component analysis (PCA) is crucial for extracting meaningful information from these large datasets, enabling the identification of key variables that reveal underlying patterns. As the number of variables increases, the sparse PCA (SPCA) becomes essential for dimensionality reduction and improving interpretability. However, existing SPCA methods often fail to identify a small but highly influential subset of input variables, reducing the effectiveness of the analysis and potential cost savings. Firstly, this dissertation introduces new technique that address various aspects of sparse principal components (PCs), exploring a spectrum of SPCA variants. We unveil a variant named true sparse PCA (TSPCA), which aims to use a small subset of input variables in the initial PCs, thereby reducing the need for numerous sensors. Our experiments demonstrate that TSPCA not only lessens sensor dependency but also provides a comparable level of variance explanation to existing SPCA methods. Moreover, to counter the inefficiencies of traditional enumeration algorithms with large-scale data, we propose the dynamic TSPCA (D-TSPCA), a high-efficiency algorithm designed for TSPCA to solve high-dimensional datasets. D-TSPCA innovatively secures true sparse loading vectors through a floating selection process, overcoming the constraints of previous algorithms by allowing for the dynamic selection and deselection of variables. This method effectively minimizes essential sensor numbers in the first several numbers of PCs while maintaining a similar level of explained variance. Our results demonstrate D-TSPCA's capability in handling complex, high-dimensional problems. Furthermore, we present the least angle SPCA (LA-SPCA), designed to tackle computational hurdles inherent in SPCA. Utilizing the least angle method, LA-SPCA identifies sparse PCs with minimal deviation from their ordinary PCA counterparts, significantly streamlining computational demands and boosting efficiency in processing ultra-high dimensional datasets. Our comprehensive experiments affirm the superior performance and efficiency of this novel SPCA technique. Finally, we have developed a data-fusion model to recognize the variability in sensor data characteristics and the potential degradation in model performance from aggregating diverse data sources in VM process. This model integrates multiple data sources into a cohesive multi-source framework, enhancing the AdaBoost regression algorithm for VM applications. By merging residuals from individual and collective data sources to adjust thresholds adaptively, we ensure the accuracy of predictions for each weak learner. Real-world processing data from semiconductor manufacturers have demonstrated that our approach surpasses single learning algorithms in robustness and performance, marking a significant advancement in the field of VM.