Signature Driven Hierarchical Post-Manufacture Tuning of RF Systems for Performance and Power

Banerjee, A.; Chatterjee, Abhijit

doi:10.1109/tvlsi.2014.2309114

Cited by 8 publications

(1 citation statement)

References 41 publications

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“…Continuous process scaling has led to a large increase in process, voltage, and temperature (PVT) variability and a wide spread fluctuation in integrated circuit (IC) performance. This increasing variability brings significant impact on the parametric yield of today's chip design (Mani et al, 2005;Radfar and Singh, 2014;Banerjee and Chatterjee, 2015). To be specific, 30% variation in effective channel length could cause over 20× fluctuation in leakage power (Rao et al, 2004a;Kanj et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

Sun

Xiao

et al. 2016

Frontiers Inf Technol Electronic Eng

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With shrinking technology, the increase in variability of process, voltage, and temperature (PVT) parameters significantly impacts the yield analysis and optimization for chip designs. Previous yield estimation algorithms have been limited to predicting either timing or power yield. However, neglecting the correlation between power and delay will result in significant yield loss. Most of these approaches also suffer from high computational complexity and long runtime. We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic (CAA) and the adaptive weighted sum (AWS) method. Both power and timing yield are set as objective functions in this framework. The two objectives are optimized simultaneously to maintain the correlation between them. The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations. Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function (CDF) bounds. Finally, the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions. Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.

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