Guided GA-Based Multiobjective Optimization of Placement and Assignment of TSVs in 3-D ICs

TLDR: This paper proposes an evolutionary computation approach to handle the problem of placement followed by the assignment of through-silicon vias of 3-D ICs in a novel way so that, on exploration of a variety of nondominated solutions, the search process converges to a near-optimum solution in reasonable time.

Abstract: The advent of 3-D IC technology facilitates the fabrication of large electronic circuits on small-area chips ensuring high performance. For a 3-D IC, the problem of placement followed by the assignment of through-silicon vias (TSVs) involves optimizing various design objectives such as intertier wirelength, power density, congestion, and separation between the TSVs. Each of the existing techniques for the placement of TSVs deals only with a subset of these objectives. In this paper, we propose an evolutionary computation approach $MO\_{}TSV$ to handle this multiobjective optimization problem. The operators, parameters, and constituents of the framework of genetic algorithm (GA)-based multiobjective optimization have been designed in a novel way so that, on exploration of a variety of nondominated solutions, the search process converges to a near-optimum solution in reasonable time. Experimental results on ISCAS’85, ISCAS’89, ITC’99, and IBM (ISPD’98) benchmarks yield quality solutions in terms of all the parameters as well as convergence times, which are encouraging.