Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning
Yijun Li, Jianshi Tang, Bin Gao, Jian Yao, Anjunyi Fan, Bonan Yan, Yuchao Yang, Yue Xi, Yuankun Li, Jiaming Li, Wen Sun, Yiwei Du, Zhengwu Liu, Qingtian Zhang, Song Qiu, Qingwen Li, He Qian, Huaqiang Wu
Abstract
Abstract In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlO x -based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta 2 O 5 -based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.