Distance-to-Failure-Maximization Optimization Algorithm for SFQ Logic Cells

SFQ logic (such as RSFQ and ERSFQ) is one of the contenders for future low power, high-speed electronics. SFQ logic can also function as the interface to superconducting quantum qubits. It is therefore essential to have robust SFQ cell libraries that can be used in these designs. One significant challenge in SFQ circuit design is to find the best optimized design, starting from a nominal working circuit, that circuit designers should layout. Various optimization algorithms and tools are available (such as largest inscribed hypersphere, critical margin optimization, particle swarm, and various other algorithms), but all of them have different shortcomings that make them difficult to use with real-world cells. We present a new optimization algorithm, the distance-to-failure-maximization method, that uses some of the insight of the previous algorithms as well as a few new characteristics that make the optimization algorithm practical to use in real-world cases. We also provide an open-source implementation that was used to optimize real-world RSFQ cells for the ColdFlux project. We compare the optimization algorithm with other approaches and show the improvements above the current state of the art.