3-D Vertical Resistive Switching Random Access Memory (3D-VRRAM) With Multilevel Programming for High-Density, Energy-Efficient In-Memory Computing

Abstract

Resistive random access memory (RRAM) devices offer a broad range of attractive properties for in-memory computing (IMC) applications, such as nonvolatile storage, low read current, and high scalability. IMC allows to overcome the memory bottleneck of data-intensive workloads, such as deep learning on the edge. In this context, 3-D vertical RRAM (3D-VRRAM) is a promising option to achieve high memory cell capacity with low fabrication cost. In this work, we present an HfOx-based 3D-VRRAM crossbar array (CBA) capable of IMC with precise multilevel programming. We show an extensive experimental demonstration of both matrix-vector multiplication (MVM) and inverse/pseudoinverse matrix calculation via IMC on 3D-VRRAM. To further support the parallel IMC application in real-life scenarios, the work also reports a demonstration of relatively large-size problems adopting 2D-RRAM and SRAM-based memory arrays. These results support 3D-VRRAM for high-density, energy-efficient IMC for edge computing applications.