GenFin: Genetic Algorithm-Based Multiobjective Statistical Logic Circuit Optimization Using Incremental Statistical Analysis

GenFin: Genetic Algorithm-Based Multiobjective Statistical Logic Circuit Optimization Using Incremental Statistical Analysis As the semiconductor technology node scales into the deep submicrometer regime, it has become very difficult to obtain high IC yields because the process-voltage-temperature variations induce large spreads in delay and power. In this paper, we propose a new framework, called GenFin, which is, as far as we know, the first to target the multiobjective yield optimization of logic circuits. Since FinFETs are a promising substitute for CMOS at 22-nm technology node and beyond, we evaluate the framework with a 22-nm FinFET logic library. By combining the power of genetic algorithm (GA) and adaptive multiobjective optimization, GenFin produces a set of nondominated logic circuits whose timing, leakage power, and dynamic power yields are simultaneously optimized. This can help designers make tradeoff decisions wisely and avoid suboptimal solutions. We also propose an incremental statistical circuit analyzer, called incremental FinPrin, that speeds up the statistical static timing analysis by up to 9.6x and the statistical power analysis by up to 2235.7x, while incurring errors of only up to 0.031% in mean and 0.74% in standard deviation relative to nonincremental analysis. We use heuristics based on the deterministic timing analysis and gate criticality to reduce the GA search space and also improve the quality of its solutions. We present extensive experimental results to demonstrate the efficacy of GenFin.