Analysis and evolution of dynamics in 1R memristive crossbars: from sneak currents to analog computing

Memristors have shown strong potential for constructing compact and efficient computing systems. The 1R memristor crossbar offers a regular structure capable of representing multi-level memory states through analog resistance variations. While sneak currents in nanoscale memristor crossbars are often considered a challenge for reliable memory operation, this work demonstrates that they can instead be harnessed to build nonlinear dynamical systems for computation. We first explore and analyze the behavior of sneak currents in 1R memristor crossbars under different input–ground configurations, showing how these currents can be exploited to form useful dynamic responses. We then propose an evolutionary algorithm that automatically determines the assignment of input and ground signals, optimizing both their positions and timing. This strategy allows circuit synthesis without auxiliary components, relying solely on the memristor crossbar. Finally, the evolved crossbar configurations are applied to multi-task time-series prediction, showing improved accuracy and efficiency over conventional approaches. The findings confirm the promise of 1R memristor crossbars as a compact and effective platform for nonlinear dynamical computation.