This paper presents a new timing driven force directed placement algorithm that meets physical net length constraints as well as constraints on specific pin sets. It is the first force directed placement algorithm that meets precise half perimeter bounding box constraints on critical nets. It builds on the work of Eisenmann et al. [12], adding a new net model that changes the contribution of constrained nets in the quadratic programming problem, during solving for each force generation step. We propose several methods for selecting and constraining critical nets to achieve improved timing. Our work suggests that the force directed method with net constraints is a powerful tool for placement and timing convergence, achieving an average worst negative slack optimization exploitation of 64% and average total negative slack optimization exploitation of 48% results on 16 industry circuits from a 1.5GHz microprocessor.

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This paper presents a novel placement algorithm for timing optimization based on a new and powerful concept, which we term differential timing analysis. Recognizing that accurate optimization requires timing information from a signoff static timing analyzer, we propose an incremental placement algorithm that uses timing information from a signoff static timing engine. We propose a set of differential timing analysis equations that accurately capture the effect of placement perturbations on changes in timing from the signoff timer. We have formulated an incremental placement optimization problem based on differential timing analysis as a single linear programming (LP) problem which is solved to generate the new timing-optimized placement.Our experiments show that the worst negative slack (WNS) improves by an average of 30% and the total negative slack (TNS) improves by 33% on average for a set of circuits from a 3.0 GHz microprocessor that were already synthesized and placed by a leading industrial physical synthesis tool. We also show that multiple iterations of our engine give further TNS improvements -an average improvement of 51%, which implies that our placer will significantly speed up timing convergence.