Pairwise proximity-based features for test escape screening

Test escapes are chips that pass the chip-level test program but fail system-level test or in the field. It is known that statistical analysis based on chip production test data could identify abnormalities for screening test escapes. It has also been shown that from the chip test data, we can generate revealing features for statistical analysis by comparing the measurement data to different references such as the measurement mean of a wafer, the spatial pattern of a wafer, and the measurements of neighboring chips. Given these existing features as the base features, this paper proposes a new class of transformations which could generate additional informative features based on pairwise proximities between chips on the same wafer. Specifically, we apply multiple distance functions in a feature space composed of the base features and calculate the corresponding pairwise proximities between each pair of chips. Each of the resulting proximities could potentially embed some unique information that reveals the abnormalities of some test escapes. Then we convert the proximities into Euclidean vector spaces using constant shift embedding (CSE), which preserves the cluster structure through the conversion, so that traditional outlier analysis algorithms such as local outlier factor (LOF) can be applied. The LOF value and the first dimension in each embedded space are used as additional features for each sample. These new features, jointly analyzed with the base features, provide more revealing information about test escapes and thus further improve the test escape detection rate in our experiment based on production test data.