Particle swarm optimization over non-polynomial metamodels for fast process variation resilient design of Nano-CMOS PLL

Authors:
Oleg Garitselov;Saraju Mohanty;Elias Kougianos;Geng Zheng
Affiliations:
University of North Texas, Denton, TX, USA;University of North Texas, Denton, TX, USA;University of North Texas, Denton, TX, USA;University of North Texas, Denton, TX, USA
Venue:
Proceedings of the great lakes symposium on VLSI
Year:
2012

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Abstract

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 numbers of design parameters, are used to predict the statistical process variation effects instead of exhaustive Monte Carlo simulations. The PSO algorithm is used for optimization of the AMS-SoC components using their metamodels instead of the actual circuit. The PSO algorithm followed a two step approach: local and global. The physical design of a Phase Locked Loop (PLL) is considered as a case study circuit. The proposed design flow is approximately 5 times faster while the error is under 2% compared to the Monte Carlo analysis.