Efficient Feature Selection-Based on Random Forward Search for Virtual Metrology Modeling

Cited 19 time in Web of Science Cited 22 time in Scopus

Kang, Seokho; Kim, Dongil; Cho, Sungzoon

Issue Date
IEEE-Institute of Electrical and Electronics Engineers
IEEE Transactions on Semiconductor Manufacturing, Vol.29 No.4, pp. 391-398
Efficient Feature Selection-Based on Random Forward Search for Virtual Metrology Modeling자연과학Virtual metrologyfeature selectionprediction modelwafer-to-wafer quality control
Virtual metrology (VM) has been successfully applied to semiconductor manufacturing as an efficient way of achieving wafer-to-wafer quality control. VM involves the estimation of metrology variables of wafer inspection using a prediction model trained with process parameters and measurements prior to the actual implementation of metrology. VM modeling should incorporate a number of process parameters and measurements collected from each piece of process equipment, which results in a greater number of input variables. Therefore, it is necessary to resolve the problem of high dimensionality through feature selection. A suitable feature selection method for VM modeling should effectively address the high dimensionality by lowering the computational cost, while also achieving high prediction accuracy as an essential requirement for the practical deployment of VM. In this paper, a feature selection method based on random forward search is proposed for efficient VM modeling. This method selects relevant variables sequentially from disjoint random subsets of candidate variables by incorporating randomness. Our preliminary experimental results obtained with real-world semiconductor manufacturing data demonstrate that the proposed feature selection method achieves comparable prediction accuracy yet has the advantages of being computationally more efficient, thus merits further investigation.
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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