Variation and failure characterization through pattern classification of test data from multiple test stages

We describe a framework for characterizing systematic variations and failures through exploring the hidden patterns of test data from multiple test stages. The framework provides prediction of process variations with a fine resolution based on a limited number of probed process parameters. An unsupervised biclustering technique is then utilized to extract grayscale and binary spatial patterns from process parameters and production test results, respectively, through analyzing both item-to-item and die-to-die correlations in subsets of the test data. A template matching technique exploits these spatial patterns to discover connections between process variations and failures detected by production tests. The proposed framework has been verified by an industrial test dataset of a non-volatile memory product. The discovery of comprehensible correlations between process parameters and some production test items was confirmed by the engineers who have insights to the test dataset.