This paper proposes to develop a data-driven via’s depth estimator of the deep reactive ion etching process based on statistical identification of key variables.Several feature extraction algorithms are presented to reduce the high-dimensional data and effectively undertake the subsequent virtual metrology(VM) model building process.With the available on-line VM model,the model-based controller is hence readily applicable to improve the quality of a via’s depth.Real operational data taken from a industrial manufacturing process are used to verify the effectiveness of the proposed method.The results demonstrate that the proposed method can decrease the MSE from 2.2×10~(-2) to 9×10~(-4) and has great potential in improving the existing DRIE process. This paper proposes to develop a data-driven via’s depth estimator of the deep reactive ion etching process based on statistical identification of key variables. Abstract feature extraction algorithms are presented to reduce the high-dimensional data and effectively undertake subsequent virtual metrology (VM) model building process .With the available on-line VM model, the model-based controller is hereby readily available to improve the quality of a via’s depth. Operating operational data taken from a industrial manufacturing process are used to verify the effectiveness of the proposed method . The results demonstrate that the proposed method can decrease the MSE from 2.2 × 10 -2 to 9 × 10 -4 and has great potential in improving the existing DRIE process.