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R&D: Monolithic 3D Integration of RRAM-Based Hybrid Memory Architecture

For one-shot learning

Nature Communications has published an article written by Yijun Li, School of Integrated Circuits, Tsinghua University, Beijing, China, Jianshi Tang, Bin Gao, School of Integrated Circuits, Tsinghua University, Beijing, China, and Beijing Advanced Innovation Center for Integrated Circuits, Tsinghua University, Beijing, China, Jian Yao, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Science, Suzhou, China, Anjunyi Fan, Bonan Yan, Institute for Artificial Intelligence, Peking University, Beijing, China, and Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China, Yuchao Yang, Institute for Artificial Intelligence, Peking University, Beijing, China, Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China, School of Electronic and Computer Engineering, Peking University, Shenzhen, China, and Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, China, Yue Xi, Yuankun Li, Jiaming Li, Wen Sun, Yiwei Du, School of Integrated Circuits, Tsinghua University, Beijing, China, Zhengwu Liu, School of Integrated Circuits, Tsinghua University, Beijing, China, Qingtian Zhang, School of Integrated Circuits, Tsinghua University, Beijing, China, and Beijing Advanced Innovation Center for Integrated Circuits, Tsinghua University, Beijing, China, Song Qiu, Qingwen Li, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Science, Suzhou, China, He Qian, and Huaqiang Wu, School of Integrated Circuits, Tsinghua University, Beijing, China, and Beijing Advanced Innovation Center for Integrated Circuits, Tsinghua University, Beijing, China.

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 HfAlOx-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 Ta2O5-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.

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