Design time body bias selection for parametric yield improvement

Circuits designed in aggressively scaled technologies face both stringent power constraints and increased process variability. Achieving high parametric yield is a key design objective, but is complicated by the correlation between power and performance. This paper proposes a novel design time body bias selection framework for parametric yield optimization while reducing testing costs. The framework considers both inter- and intra-die variations as well as power-performance correlations. This approach uses a feature extraction technique to explore the underlying similarity between the gates for effective clustering. Once the gates are clustered, a Gaussian quadrature based model is applied for fast yield analysis and optimization. This work also introduces an incremental method for statistical power computation to further reduce the optimization complexity. The proposed framework improves parametric yield from 39% to 80% on average for 11 benchmark circuits while runtime is linear with circuit size and on the order of minutes for designs with up to 15K gates.