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Particle swarm optimization over non-polynomial metamodels for fast process variation resilient design of Nano-CMOS PLL
2012
Proceedings of the great lakes symposium on VLSI - GLSVLSI '12
An automated top-down design flow to achieve physical design of Analog/Mixed-Signal Systems-on-Chip (AMS-SoCs) is difficult, especially for nano-CMOS. Process variation effects have profound impact on the performance of silicon versus layout design. In this paper metamodels, (surrogate models) and Particle Swarm Optimization (PSO) have been combined in an automated physical design flow for fast design exploration of AMS-SoCs. Neural network based non-polynomial metamodels that handle large
doi:10.1145/2206781.2206843
dblp:conf/glvlsi/GaritselovMKZ12
fatcat:ioggazglxzfuba6azgfzllcgyq