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- W2087546841 abstract "AbstractIn this article, an explicit formula is derived for determining appropriate number of simulationruns to estimate the parametric yield or violation probability of VLSI circuits. The formula involvesno approximation and thus oers a rigorous control of the statistical error of estimation. Moreover,the formula is substantially less conservative than existing methods and hence can be used to avoidunnecessary computation. The application of the formula is illustrated by the timing analysis of ann -input NAND gate with a capacitive load. Keywords : Computer aided design, integrated circuits, parametric yield, Monte Carlo analysis. 1 Introduction As the VLSI circuit features continue to decrease, the statistical variation of the device characteristicsbecome more prominent, since the variations of circuit elements are not decreasing as quickly as the featuresizes [8]. Especially, the increasing process parameter variations have become one of the major shortcomingsthat the CMOS devices face at nanometer scale. Due to limitations of the fabrication processes (e.g., sub-micrometer wavelength lithography and etching) and variations in the number of dopants in the channelof short channel devices, device parameters such as channel length, width, oxide thickness, thresholdvoltage etc. suer large variations. Variations in the device parameters translate into variations in circuitparameters like delay and leakage power, leading to degradation of the reliability and performance of VLSICMOS circuits [3].The analysis of process variations on VLSI circuits has been an active area of research for many years.A central theme of this line of research is the estimation and maximization of parametric yield of VLSIcircuits based on statistical methods, linear and non-linear programming techniques (see, e.g., researchmonographs [5], [11], [13], [14] and the references therein). In particular, the Monte Carlo analysis hasbeen established as a powerful tool for evaluating the performance of VLSI circuits under parametricuncertainty. A crucial step in the Monte Carlo analysis is the determination of simulation runs. Clearly, ifthe number of runs is too small, the evaluation result may be misleading and useless. On the other hand,an overly conservative choice of simulation runs is costly in terms of computational eort. In this paper,we are aimed at developing a simple method for determining the appropriate number of simulation runsthat permits rigorous control of statistical error while avoiding unnecessary computational eort." @default.
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- W2087546841 date "2010-04-23" @default.
- W2087546841 modified "2023-10-16" @default.
- W2087546841 title "An analytic formula for determination of simulation runs for analysis of VLSI circuits" @default.
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- W2087546841 doi "https://doi.org/10.1117/12.852500" @default.
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