Dynamic learning based scan chain diagnosis

ABSTRACT

Scan chain defect diagnosis is important to silicon debug and yield enhancement. Traditional simulation-based chain diagnosis algorithms may take long run time if a large number of simulations are required. In this paper, a novel dynamic learning based scan chain diagnosis is proposed to speedup the diagnosis run time. Experimental results illustrate that by using the proposed dynamic learning techniques, the diagnosis run time can be reduced about 10Xon average.

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