Transition density, a stochastic measure of activity in digital circuits
DAC '91 Proceedings of the 28th ACM/IEEE Design Automation Conference
Fundamentals of modern VLSI devices
Fundamentals of modern VLSI devices
Stand-by power minimization through simultaneous threshold voltage selection and circuit sizing
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Independent component analysis: algorithms and applications
Neural Networks
Intrinsic leakage in deep submicron CMOS ICs—measurement-based test solutions
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on system-level interconnect prediction
Average Leakage Current Estimation of CMOS Logic Circuits
VTS '01 Proceedings of the 19th IEEE VLSI Test Symposium
Statistical analysis of subthreshold leakage current for VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Gate oxide leakage current analysis and reduction for VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Full-chip analysis of leakage power under process variations, including spatial correlations
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the 42nd annual Design Automation Conference
Asymptotic probability extraction for non-normal distributions of circuit performance
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Projection-based performance modeling for inter/intra-die variations
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Projection-based statistical analysis of full-chip leakage power with non-log-normal distributions
Proceedings of the 43rd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
Width-dependent statistical leakage modeling for random dopant induced threshold voltage shift
Proceedings of the 44th annual Design Automation Conference
Modeling and estimation of full-chip leakage current considering within-die correlation
Proceedings of the 44th annual Design Automation Conference
Statistical analysis of full-chip leakage power considering junction tunneling leakage
Proceedings of the 44th annual Design Automation Conference
Characterizing process variation in nanometer CMOS
Proceedings of the 44th annual Design Automation Conference
Modeling and analysis of non-rectangular gate for post-lithography circuit simulation
Proceedings of the 44th annual Design Automation Conference
Proceedings of the 45th annual Design Automation Conference
IEEE Spectrum
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Statistical timing analysis under spatial correlations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Energy efficient computation: A silicon perspective
Integration, the VLSI Journal
Impact of technology scaling on leakage power in nano-scale bulk CMOS digital standard cells
Microelectronics Journal
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This work proposes a full-chip leakage analysis framework for 65nm technology and beyond. Analytical models are first constructed to capture the impact of process parameters on leakage current. Then a methodology is introduced to characterize leakage-related process variations in a systematic manner. On such a basis, an efficient procedure is developed to analyze the state-dependent power dissipation due to leakage of a large circuit block by taking into account different leakage mechanisms. Unlike many traditional approaches that rely on log-normal approximations, the proposed algorithm applies a quadratic model of the logarithm for the full-chip leakage current. It is able to handle both Gaussian and non-Gaussian parameter distributions. The model is validated with test chips manufactured with a commercial 65nm CMOS process. Validation results prove that the proposed modeling methodology could achieve a higher accuracy than that from existing methods. Moreover, a full-chip leakage analysis using the developed model can be orders of magnitude faster than a Monte Carlo based approach.