Abstract

With the rapid development of semiconductor technology, chip integration is getting beyond imagination. Aging has become one of the main threats to circuit reliability. In order to develop aging degradation prediction, it is critical to evaluate aging to avoid circuit failures. At present, the research on aging prediction is mainly focused at the transistor and gate levels: at transistor level, the precision is high, but the speed is low; whereas, the gate-level accuracy is not high, but the speed is very fast. In this paper, a path-level aging prediction framework based on the novel critical gate is proposed. The 10-year Negative Bias Temperature Instability (NBTI) aging delay of the critical subcircuit extracted by the novel critical gate is obtained, and the aging delay trend is learnt by using a linear regression model. Then the critical path aging delay can be obtained quickly based on the framework developed by machine learning using the linear regression model. The experimental results of ISCAS’85 and ISCAS’89 benchmark circuits based on 45-nm PTM show that the proposed framework is superior to the existing methods.

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