Search State Compatibility Based Incremental Learning Framework and Output Deviation Based X-filling for Diagnostic Test Generation

  • Authors:
  • Maheshwar Chandrasekar;Nikhil P. Rahagude;Michael S. Hsiao

  • Affiliations:
  • , Blacksburg, USA 24060;, Blacksburg, USA 24060;, Blacksburg, USA 24060

  • Venue:
  • Journal of Electronic Testing: Theory and Applications
  • Year:
  • 2010

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Abstract

Silicon Diagnosis is the process of locating potential defect sites (candidates) in a defective chip. These candidates are then used as an aid during physical failure analysis. It is desired that the cardinality of the candidate set returned by silicon diagnosis be as small as possible. To this end, effective test patterns that can distinguish as many fault-pairs in the candidate set are critical. Generation of such diagnostic patterns is referred to as Automatic Diagnostic Test Generation (ADTG). In this paper, we propose an aggressive and efficient learning framework for such a diagnostic test generation engine. It allows us to identify and prune non-trivial redundant search states thereby allowing to easily solve hard to distinguish or hard to prove equivalent fault-pairs. Further, we propose an incremental flow for ADTG, where the information learned during detection-oriented test generation is passed to and incrementally used by ADTG. Finally, we propose an interesting output deviation based X-filling of detection test patterns with the objective of enhancing test set's diagnostic ability. Experimental results on full-scan versions of ISCAS89/ITC99 circuits indicate that our incremental learning framework achieves up to 2脳 speed-up and/or resolves more initially unresolved fault-pairs for most circuits. Also, results indicate that the proposed X-filling method has the potential to distinguish more fault-pairs than the random X-filling method.