Mortise-tenon–shaped memristors for scientific computing

Abstract

In-memory computing hardware based on memristors has emerged as a promising option for scientific computing due to its large-scale parallel data processing capability. However, the nonuniformity issue of the memristors renders the practical deployment of in-memory computing hardware complex, requiring peripheral circuits to ensure the accuracy of scientific computing, thereby resulting in increased power consumption. Here, we present a mortise-tenon-shaped (MTS) memristor with ultrahigh uniformity by introducing a mortise-shaped h-BN flake on the HfO₂ switching layer. The MTS memristor exhibits ultrasmall cycle-to-cycle (~2.5%) and device-to-device (~6.9%) variations compared to the HfO₂ memristor without the MTS structure. Furthermore, we use the MTS memristors to build a partial differential equation solver and demonstrate a convergence speed of solving the Poisson equation five times faster than the solver based on the traditional HfO₂ memristors. This work provides a promising approach for notably reducing the hardware resources required for fast and high-accuracy scientific computing.