Robust algorithms for area and power optimization of digital integrated circuits under variability

As device geometries shrink, variability of process parameters becomes pronounced, resulting in a significant impact on the power and timing performance of integrated circuits. Deterministic optimization algorithms for power and area lack capabilities for handling uncertainty, and may lead to over-conservative solutions. As a result, there is an increasing need for statistical algorithms that can take into account the probabilistic nature of process parameters. Statistical optimization techniques however suffer from the limitation of high computational complexity. The objective of this work is to develop efficient algorithms for optimization of area and power under process variability while guaranteeing high yield. The first half of the dissertation focuses on two design-time techniques: (i) a gate sizing approach for area minimization under timing variability; (ii) an algorithm for total power minimization considering variability in timing and power. Design-time methods impose an overhead on each instance of the fabricated chip since they lack the ability to react to the actual conditions on the chip. In the second half of the dissertation we develop joint design-time and post-silicon co-optimization techniques which are superior to design-time only optimization methods. Specifically, we develop (i) a methodology for optimization of leakage power using design-time sizing and post silicon tuning using adaptive body bias; (ii) an optimization technique to minimize the total power of a buffer chain while considering the finite nature of adaptability afforded. The developed algorithms effectively improve the overconservatism of the corner-based deterministic algorithms and permit us to target a specified yield level accurately. As the magnitude of variability increases, it is expected that statistical algorithms will become increasingly important in future technology generations.