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A Stochastic Coolability Analysis for a Microprocessor Considering Chip Power Fluctuation
Keywords: stochastic, coolability analysis, chip power fluctuation
As process technology continues to evolve, gate sizes in today's microprocessors continue to shrink with each lithography generation. Accompanying these shrinks is a notable increase in the power dissipated by the various blocks and sub-blocks on the die. While the increased power is a challenge to the cooling design in its own right, another significant aspect that the thermal design engineer must keep in mind is that the distribution of power on the die (silicon) can vary dynamically depending on the software application that is running. As multi-core, multi-threaded chips become more prominent, these problems are compounded even further. Each core of the chip is capable of running independent threads supporting the software applications, and the resulting die power distribution can be significantly 'non-uniform' in real time. As such, it becomes all the more imperative to study the effects of such power non-uniformity on the microprocessor cooling capability. There is another aspect of package cooling analysis which is traditionally ignored, and that is the part-to-part variability in terms of materials and stack up geometry. These can significantly influence the cooling design, and it is important to be aware of their impact. In this study we focus on a stochastic analysis of a hypothetical multi core multi-threaded microprocessor. The power in each block is assumed to a random variable with fluctuations as high as +/-20% of the block nominal power. Additionally the thickness and conductivities of the die, TIM1, lid (heatspreader), TIM2 and the sink thermal performance are also considered as independent random variables. A detailed Monte Carlo analysis of the package coolability is conducted, considering one scenario where power fluctuation is enabled and the other where power is treated as being static. The findings highlight the significance of considering the realistic power fluctuations in the cooling solution design. Furthermore, the simulation data permit pareto analysis to identify the geometry/material characteristics that most significantly influence package coolability, so thermal designers can focus attention on those areas.
Sai Ankireddi,
Sun Microsystems Inc.
Santa Clara, CA

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