Bio-Inspired Methods for Defect-Tolerant Function-Mapping in Nano-Crossbar Arrays

Nanoscale chips inherently suffer from higher defect densities than traditional lithography-based VLSI designs. So defect tolerant designs are required at this scale. Mapping different functions in a very large nanoscale crossbar containing defective cross-points is a hard searching problem. In this work, we studied traditional sorting-based methods followed by two other proposed approaches based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to find a proper assignment of different functions in the nanoscale crossbar circuit having defective cross-points. The objective of the proposed algorithm is to map a very large number of functions within the very large size defective-crossbar if there exists any solution. The number of generations required to get a solution may vary depending on the position of defect points and on-inputs of the functions inspite of having the same defect percentage. Our proposed methods based on GA and PSO work more efficiently with the increase of number of functions, crossbar area and defect percentage in comparison with the traditional sorting-based methods. Experimental results show that our meta-heuristic-based approaches outperform the earlier sorting-based approaches in terms of mapping success percentage.