A Hyper-Parameter Based Margin Calculation Algorithm for Single Flux Quantum Logic Cells

This paper presents a novel method for evaluating the robustness of single flux quantum (SFQ) logic cells in a superconducting electronic circuit. The proposed method improves the state-of-the-art by accounting for the global sources of variation, clustering cell parameters into hyper-parameters, and considering the co-dependency of these hyper-parameters when calculating a feasible parameter region in which cell functions correctly, given any combination of the parameter values. The average parametric yield inside the reported feasible parameter region is more than 98%. Additionally, a machine learning based method is presented to estimate the parametric yield both inside and outside the feasible parameter region. The average accuracy of the developed yield model is 96% for five SFQ cells.