This paper presents a new analog ATPG (AATPG) framework that generates near-optimal test stimulus for the digitally-assisted adaptive equalizers in high-speed serial links. Based on the dynamic-signature-based testing scheme developed recently, our AATPG utilizes a Genetic Algorithm (GA) which attempts to maximize the difference between the fault-free and faulty dynamic signatures of the target fault. Our test generation framework takes into account process variations and signal noise in selecting the test stimulus, which minimizes the number of misclassified devices. The experimental results on a 5-tap feed-forward adaptive equalizer demonstrate that the GA-tests generated by our framework can effectively detect faults that are hard to detect by the hand-crafted tests.
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