High Area Efficiency (6 TOPS/mm2) Multimodal Neuromorphic Computing System Implemented by 3D Multifunctional RRAM Array

For the first time, we demonstrated a multifunction three-dimensional (3D) vertical random-access memory (RRAM) array (MF-ЗDRRAM) where different layers exhibit nonvolatile properties and volatile characteristics respectively, to implement multimodal neuromorphic computing. The RRAM cells in the 1
^st
layer (WL: TiN) and the 2
^nd
layer (WL: Ru) have different dynamic characteristics, which are used to construct multi-scale reservoirs (M-RC). The RRAM in 3
^rd
layer (WL: W) exhibits analog switching behavior, applying for convolutional neural network (CNN) and full connection (FC) layer. A multimodal neuromorphic computing system with the network of M-RC+CNN is implemented by the MF-3DRRAM. The multifunction of the fabricated MF-3DRRAM chip is validated through the multimodal video recognition task, exhibiting high accuracy (98%), high area efficiency (6 TOPS/mm2) and low energy consumption (1.4pJ/operation). This proposed MF-3DRRAM is of great significance for miniaturized, low-power hardware implementations for edge computing.