An Analytical Placement Framework for Advanced Hybrid-Row-Height Circuit Designs

Recently, hybrid-row-height designs have been introduced to achieve performance and area co-optimization in advanced nodes. Hybrid-row-height designs pose significant layout/placement challenges due to the heterogeneous cell and row structures. In this paper, we present an effective framework to address the hybrid-row-height placement problem in three major stages: (1) global placement, (2) legalization, and (3) detailed placement. Inspired by the multi-channel processing method in convolutional neural networks (CNNs), we use the feature extraction technique to equivalently transform the hybrid-row-height global placement problem into two sub-problems that can be solved effectively. We propose a multi-layer nonlinear framework with alignment guidance and a self-adaptive parameter adjustment scheme, which can obtain a high-quality solution to the hybrid-row-height global placement problem. In the legalization stage, we formulate the hybrid-row-height legalization problem as a convex quadratic programming (QP) problem, then apply the robust modulus-based matrix splitting iteration method (RMMSIM) to solve the QP efficiently. After RMMSIM-based global legalization, Tetris-like allocation is used to resolve remaining physical violations. In the detailed placement stage, we develop a whitespace-clustered cell reordering method and feature-aware cell matching method for wirelength optimization, then congestion-aware cell sliding is used to alleviate placement congestion. Compared with the state-of-the-art work, experiments on the 2015 ISPD Contest benchmarks show that our framework can achieve a 7% reduction in final total wirelength and 2.23 × speedup.