Counteractive Coupling IGZO/CNT Hybrid 2T0C DRAM Accelerating RRAM-based Computing-In-Memory via Monolithic 3D Integration for Edge AI

In this work, we demonstrate a novel backend-of-the-line (BEOL) compatible IGZO/CNT hybrid-polarity 2T0C DRAM cell, which is further integrated on our analog RRAM-based monolithic 3D (M3D) integration platform for edge artificial intelligence (AI) applications. Incorporating n-type ultra-low-leakage InGaZnOx (IGZO) for write transistor and p-type high-current carbon nanotubes (CNTs) for read transistor, this design achieves a decent retention and desirably large read currents with a VLSI-compatible low data voltage (Vdata). In addition, the unique IGZO-NFET/CNT-PFET hybrid-polarity 2T0C design enhances the effective sensing window and, more importantly, addresses the charge injection issue through counteractive coupling. This BEOL hybrid 2T0C cell achieves a long retention of 170s, a write speed of sub-20 ns and a read current of 29.7 μA/μm at VDS=1V with |Vdata| = 0.5V. The performance evaluation enables its utilization as a buffer layer on top of the computing-in-memory (CIM) layer with HfO2-based analog RRAM, empowering a prototype monolithic 3D chip (namely M3D-BRIC) for high-resolution (Hi-Res) videos processing. A YOLOv3 network is further implemented for the objects detection task, and the benchmarks show that the M3D-BRIC architecture of CIM/2T0C-DRAM could achieve a 48.25× higher processing capability than its 2D counterpart.