Statistical Leakage Estimation Based on Sequential Addition of Cell Leakage Currents

This paper presents a novel method for full-chip statistical leakage estimation that considers the impact of process variation. The proposed method considers the correlations among leakage currents in a chip and the state dependence of the leakage current of a cell for an accurate analysis. For an efficient addition of the cell leakage currents, we propose the virtual-cell approximation (VCA), which sums cell leakage currents sequentially by approximating their sum as the leakage current of a single virtual cell while preserving the correlations among leakage currents. By the use of the VCA, the proposed method efficiently calculates a full-chip leakage current. Experimental results using ISCAS benchmarks at various process variation levels showed that the proposed method provides an accurate result by demonstrating average leakage mean and standard deviation errors of 3.12% and 2.22%, respectively, when compared with the results of a Monte Carlo (MC) simulation-based leakage estimation. In efficiency, the proposed method also demonstrated to be 5000 times faster than MC simulation-based leakage estimations and 9000 times faster than the Wilkinson's method-based leakage estimation.

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