A meta-heuristic search-based input vector control approach to co-optimize NBTI effect, PBTI effect, and leakage power simultaneously

Abstract

As technology advances, Bias temperature instability (BTI) has become a severe aging challenge that affects the reliability of a device. Previously, NBTI was the most significant aging issue, but with the development of the high-k metal-gate technology, the impact of PBTI can no longer be overlooked. Similarly, in deep submicron technology, leakage power also remains a serious concern. So, minimizing the consequence of the BTI effect and leakage power is the key design goal in today’s technology. As the leakage power and the BTI effects have a strong dependency on the input patterns of the circuit, the input vector control (IVC) technique can be employed to mitigate both of these effects. Previously, IVC techniques have been used to co-optimize the NBTI degradation and the leakage power. In this work, the NBTI effect, PBTI effect, and leakage power are considered simultaneously. Here, a Genetic Algorithm-based (GA) multi-objective meta-heuristic approach is proposed to select the optimum input vector for simultaneous co-optimization of NBTI effect, PBTI effect, and standby leakage power. The proposed approach shows a maximum improvement of NBTI, PBTI, and leakage power by 39.89%, 40.62%, and 37.32%, respectively, with respect to the worst-case solutions of each effect.