Neighbor selection for variance reduction in IDDQ and other parametric data

The subject of this paper is variance reduction and nearest neighbor residual estimates for I_DDQ and other continuous-valued test measurements. The key, new concept introduced is data-driven neighborhood identification about a die to reduce the variance of good and faulty I_DDQ distributions. Using LSI Logic production data, neighborhood selection techniques are demonstrated. The main contribution of the paper is variance reduction by the systematic use of the die location and wafer- or lot-level patterns and improved identification of die outliers of continuous-valued test data such as I_DDQ.