Heterogeneous integration of 2D memristor arrays and silicon selectors for compute-in-memory hardware in convolutional neural networks.

ABSTRACT: Memristor crossbar arrays (CBAs) based on two-dimensional (2D) materials have emerged as a potential solution to overcome the limitations of energy consumption and latency associated with conventional von Neumann architectures. However, current 2D memristor CBAs encounter specific challenges such as limited array size, high sneak path current, and lack of integration with peripheral circuits for hardware compute-in-memory (CIM) systems. In this work, we demonstrate a hardware CIM system leveraging heterogeneous integration of scalable 2D hafnium diselenide (HfSe₂) memristors and silicon (Si) selectors, as well as their integration with peripheral control-sensing circuits. The 32 × 32 one-selector-one-memristor (1S1R) array mitigates sneak current, achieving 89% yield. The integr

SUBMITTER: Jain S 

PROVIDER: S-EPMC11923061 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature