Low Area Adaptive Fail-Data Compression Methodology for Defect Classification and Production Phase Prognosis

With the shrinking technology and increasing statistical defects, multiple design respins are required based on yield learning. Hence, a solution is required to efficiently diagnose the failure types of memory during production in the shortest time frame possible. This paper introduces a novel method of fault classification through image based prognosis of predefined fail signature dictionary. In contrary to the existing Bitmap Diagnosis methodologies, this method predicts the compressed failure map without generating and transferring complete Bitmap to the tester. The proposed methodology supports testing through a very low cost ATE. This architecture is partitioned to achieve sharing among various memories and at-speed testing.