Genetic Algorithm Based 3D IC Partitioning Approach for TSV Minimization and Efficient Layer Assignment

Abstract

The rise of three-dimensional (3D) IC layouts necessitates the development of unique partitioning methods that will be suitable for the 3D component designs. Net lists partitioning is a key aspect of the physical design of a 3D IC. Reducing wire lengths between gates is possible with 3D-ICs since they have a small footprint as well as vertical connectivity among dies. As a result, they consume less power and have shorter connection delays. This paper presents an efficient genetic algorithm (GA) based layer partitioning technique for 3D ICs that focuses on proper layer assignment to reduce area deviation and minimization of Through Silicon Vias (TSVs) to reduce IC footprint. The method starts with random initial solutions and refines the solutions with efficient crossover and mutation operator till the optimized solution is achieved. Our methodology produces high-quality solutions on well-known benchmarks. The experimental findings are compared with state-of-the-art methodologies and the suggested strategy is found to be very effective in the case of decreasing the TSV count and space need of the 3-dimensional IC design. In addition, the performance of our strategy is also compared with two recent publications that use GA and Particle Swarm Optimization (PSO) and it is found that the proposed method achieves 20.18% and 38.66% average reduction in terms of TSV count respectively. Thus, the suggested technique is beneficial for multilayered 3D IC partitioning, as it can reduce TSV count while also satisfying balancing criteria and latency.