An IDDQ Statistical Post-Processing™ (SPP) outlier screen is presented based on the computation of statistically independent sources of variation in the IDDQ measurements. IDDQ measurements from die passing all other tests are modeled using sources of variation extracted by Independent Component Analysis (ICA). Outliers are separated from the sample population based on residuals computed using these sources and a nearest neighbor spatial signature. An algorithm is presented for applying the proposed technique in production. The screen is demonstrated with 0.18?m and 0.11?m volume data and shown to effectively identify the outliers at the 0.11?m technology node.