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Journal ArticleDOI

Voltage and Temperature Scalable Logic Cell Leakage Models Considering Local Variations Based on Transistor Stacks

Janakiraman Viraraghavan, +2 more
- 01 Dec 2008 - 
- Vol. 4, Iss: 3, pp 301-319
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TLDR
Results from the ISCAS'85 benchmark circuits show that neural network based stack models can predict the PDF of leakage current of large circuits across supply voltage and temperature accurately with the average error in mean being less than 2% and that in standard deviationbeing less than 7% when compared to SPICE.
Abstract
We propose a logic gate leakage model based on transistor stacks, which includes local transistor level process variation parameters along with global process variation parameters and supply and temperature The stack models include both subthreshold as well as gate leakage and consider the input vector state We examine cells from an industrial standard cell library and find that most cells can be modeled with simple stacks, which have a linear chain of transistors However some gates like XOR, Majority or Muxes need complex stacks and we show how these can be modeled Our experiments show that only 18 different stack models are needed to predict the leakage of all gates in this industrial library Re-use of the same models for pass transistor logic circuits and multi-finger transistors is also demonstrated We explicitly include voltage and temperature into the models to support joint estimation of power supply IR drops and leakage currents, as well as enable analysis for dynamic voltage scaling applications We use artificial neural networks to create unified models which include global and local process variations, supply voltage in the range of V DD /2- V DD and temperature in the range 0-100 °C These models are very useful for performing statistical leakage analysis of large circuits Results from the ISCAS'85 benchmark circuits show that neural network based stack models can predict the PDF of leakage current of large circuits across supply voltage and temperature accurately with the average error in mean being less than 2% and that in standard deviation being less than 7% when compared to SPICE Further gate level validation has been done for both an industrial 130 nm and 45 nm PTM model files

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Citations
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Journal ArticleDOI

Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

TL;DR: Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.
References
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Journal ArticleDOI

Statistical analysis of subthreshold leakage current for VLSI circuits

TL;DR: An analytical expression is derived to estimate the probability density function of the leakage current for stacked devices found in CMOS gates and an approach is presented to account for both the inter- and intra-die gate length variations to ensure that the circuit leakage PDF correctly models both types of variation.
Journal ArticleDOI

Is gate line edge roughness a first-order issue in affecting the performance of deep sub-micro bulk MOSFET devices?

TL;DR: In this paper, the effects of gate line edge roughness on the electrical characteristics of bulk MOSFET devices was performed. But the effect of gate LER was not considered.
Journal ArticleDOI

Application of Artificial Neural Networks to Statistical Analysis and Nonlinear Modeling of High-Speed Interconnect Systems

TL;DR: The application of artificial neural networks to accurately capture the nonlinear mappings between parameters and performance to speed up the analysis of high-speed interconnect systems is described.
Proceedings ArticleDOI

Full-chip leakage analysis in nano-scale technologies: mechanisms, variation sources, and verification

TL;DR: An effective algorithm to address the growing issue of full-chip leakage verification for actual-fabrication circuits by applying a quadratic model of the logarithm for the full- chip leakage current and is able to include both Gaussian and non-Gaussian parameter distributions.
Proceedings ArticleDOI

Width-dependent statistical leakage modeling for random dopant induced threshold voltage shift

TL;DR: A width-dependent statistical leakage model with an estimation error less than 5% is presented and design examples on SRAMs and domino circuits demonstrate the significance of the proposed model.
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