Demonstration of a Multi-Level μA-Range Bulk Switching ReRAM and its Application for Keyword Spotting

Despite the great promises of resistive random-access memory (ReRAM) for fast, low-power in memory computing, the models deployed on ReRAM crossbars suffer from accuracy loss, due to poor yield, inaccurate switching and high noise. In this paper, we report a forming-free bulk ReRAM (b-ReRAM) cell that can be programmed up to 128 levels between 400nA $(4\mu \mathrm{S})$ and $4\mu A (40\mu S)$. The device operates by continuous modulation of bulk oxygen vacancies, therefore exhibiting favorable characteristics including forming-free operation, analog switching, low noise and low operating currents [1], [2]. The multilayer ReRAM stack is deposited using a specially built 300mm deposition system that features a clustered sequence of Physical Vapor Deposition (PVD) and Atomic Layer Deposition (ALD), leading to high wafer-level yield and uniformity. High programming accuracy can be achieved over 25k b-ReRAM devices across 15 dies. A fully integrated system on chip (SoC) with BEOL-integrated b-ReRAM arrays is built with 65nm CMOS technology, and keyword spotting (KWS) is demonstrated with accuracy equivalent to the software quantized model and high energy efficiency at 98.5 TOPS/W. Moreover, we evaluate the performance of the bitcell for large neural network (NN) applications in a custom hardware-aware simulation platform and show that software comparable accuracy can be achieved. This work for the first-time reports that high yield and high programming accuracy can be achieved with b-ReRAM at the wafer-level scale and demonstrates that superior analog behavior enables the mapping of NN models onto the ReRAM-based SoC prototype with no accuracy loss and high energy efficiency.