Simulated annealing has been one of the most popular stochastic optimization methods used in the VLSI CAD eld in the past two decades. Recently, a new Monte Carlo and optimization method, named dynamic weighting Monte Carlo WL97 , has been introduced and successfully applied to the traveling salesman problem, neural network training WL97 , and spin-glasses simulation LW99 . In this paper, we h a v e successfully applied dynamic weighting Monte Carlo algorithm to the constrained oorplan design with consideration of both area and wirelength minimization. Our application scenario is the constrained oorplan design for mixed signal MCMs, where we need to place all the analog modules together in groups so that they can share common power and ground planes, which are separate from those used by the digital modules. Our experiments indicate that the dynamic weighting Monte Carlo algorithm is very effective for constrained oorplan optimization. It outperforms the simulated annealing for a real mixed signal MCM design by 19:5 in wirelength, with slight area improvement. This is the rst work adopting the dynamic weighting Monte Carlo optimization method for solving VLSI CAD problems. We believe that this method has applications to many other VLSI CAD optimization problems.
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