This paper presents a predictive model for the Negative Bias Temperature Instability (NBTI) of PMOS under both short term and long term operation. Based on the reactiondiffusion (R-D) mechanism, this model accurately captures the dependence of NBTI on the oxide thickness (tox), the diffusing species (H or H2) and other key transistor and design parameters. In addition, we derive a closed form expression for the threshold voltage change (∆V th ) under multiple cycle dynamic operation. Model accuracy and efficiency were verified with 90nm experimental and simulation data. We further investigated the impact of NBTI on representative digital circuits.

No abstract

No abstract

No abstract

Portable embedded computing systems require energy autonomy. This is achieved by batteries serving as a dedicated energy source. The requirement of portability places severe restrictions on size and weight, which in turn limits the amount of energy that is continuously available to maintain system operability. For these reasons, efficient energy utilization has become one of the key challenges to the designer of battery-powered embedded computing systems.In this paper, we first present a novel analytical battery model, which can be used for the battery lifetime estimation. The high quality of the proposed model is demonstrated with measurements and simulations. Using this battery model, we introduce a new "battery-aware" cost function, which will be used for optimizing the lifetime of the battery. This cost function generalizes the traditional minimization metric, namely the energy consumption of the system. We formulate the problem of battery-aware task scheduling on a single processor with multiple voltages. Then, we prove several important mathematical properties of the cost function. Based on these properties, we propose several algorithms for task ordering and voltage assignment, including optimal idle period insertion to exercise charge recovery.This paper presents the first effort toward a formal treatment of battery-aware task scheduling and voltage scaling, based on an accurate analytical model of the battery behavior.