Highly‐Uniform Passive Crossbar Arrays of Resistive Switching Random Access Memory (RRAM) for In‐Memory Computing Applications

ABSTRACT

Resistive switching random‐access memory (RRAM) is gaining attention for its ability to support various data‐intensive computing tasks via in‐memory computing (IMC). The use of passive RRAM arrays is hindered by sneak‐path issues, programmability challenges, and concerns regarding reliability and robustness. In this study, we address the initialization problem and propose a strategy to efficiently program passive arrays, thereby overcoming these limitations. We validate our approach using 32 × 32 crossbar arrays of Pt/HfO ₂ /Ti RRAM, achieving 99.5% functionality and encoding various conductance weights with an error below 3%. To demonstrate the potential of RRAMs, we experimentally perform 24 × 24 discrete cosine transform (DCT) image reconstruction, achieving an accuracy higher than 95%. Also, we implement a single‐layer classifier for feature recognition on the MNIST digit dataset, achieving 84.6% accuracy compared to the theoretical 86.1%. We finally develop an in‐memory solver for combinatorial optimization problems, such as the quadratic assignment problem, leveraging the full parallelism of the crossbar configuration and implementing a quantum‐inspired parallel annealing method, and demonstrate non‐linear regression up to the fourth order.