Margin and Yield Optimization of Single Flux Quantum Logic Cells Using Swarm Optimization Techniques

Abstract

Single flux quantum (SFQ) logic family is an attractive alternative to CMOS technology with the promise of more than two–three orders of magnitude improvement in energy-delay product. However, component-level parameter variations that arise during the fabrication process of SFQ logic circuits tend to be very high, which means that the fabricated circuits may run into major functional and/or performance issues. Therefore, optimizing SFQ logic cells to maximize their operating parameter margins under different variability sources is required. In this article, a swarm optimization technique combining the best of automatic niching particle swarm optimization and fireworks algorithm is presented where the objective is to maximize the summation of the upper and lower bound margins over all design parameters of an SFQ logic cell. The proposed method eliminates all functional errors and improves the critical margin range and parametric yield values for six different logic cells by 22.83 and 15.22% on average, when compared to a previously optimized open-source cell library.