Multi-source ensemble method with random source selection for virtual metrology

In the era of Industry 4.0, the complexity of semiconductor production is growing very fast, raising the possibility of unnoticed defective wafers and subsequent wasteful use of resources. One of the key advantages of Industry 4.0 is the accessibility to big data, which can be obtained from a number of sensors, including multiple sensor data and extensive data repositories. Recently, engineers have developed data fusion strategies for virtual metrology (VM) prediction models to effectively handle data from multiple sources. This research explores a novel approach for data-driven VM prediction model for multi-source data, namely multi-source ensemble method with random source selection. By utilizing the bagging principle for multi-source data and tree-based prediction paradigms, the proposed approach randomly selects subsets of data sources to construct each tree learner, thus reducing interdependence among the trees and minimizing the risk of overfitting, which can be a challenge faced by existing tree-based prediction models. To validate and illustrate the practical applicability of our proposed method, we use real-world data from the plasma etching process, aiming to provide potential benefits and effectiveness of our methodology.