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This paper proposes a novel method for Virtual Metrology (VM) in plasma etch processes based on analysis of all time and wavelength samples of Optical Emission Spectroscopy (OES) signals. The new method flattens each OES signal into a single vector, after which Singular Value Decomposition (SVD) is performed on the matrix formed by vectors of flattened OES signals in the training dataset. Low rank SVD projections of flattened and standardized OES recordings served as inputs for Ridge Regression, Artificial Neural Network, and Random Forest based VM models. A VM study is then conducted on a dataset gathered from a major 300 mm wafer fabrication facility, showing that the use of newly proposed SVD-based OES features consistently outperformed benchmark VM model features. Additional analysis of feature importance performed based on the analytically tractable Ridge Regression VM model form demonstrated distinct time-frequency patterns of OES signal portions that were highly informative for prediction of relevant Critical Dimensions, clearly justifying the need to use the entire OES signals for VM. [ABSTRACT FROM AUTHOR]