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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 th...

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Main Authors: Wu, Y., Cai, F., Thomas, L., Liu, T., Nourbakhsh, A., Hebding, J., Smith, E., Quon, R., Smith, R., Kumar, A., Pang, A., Holt, J., Someshwar, R., Nardi, F., Anthis, J., Yen, S-H., Chevallier, C., Uppala, A., Chen, X., Breil, N., Sherwood, T., Wong, K., Cho, W., Thompson, D., Hsu, J., Ayyagari, B., Krishnan, S., Lu, Wei. D., Chudzik, M.
Format: Conference Proceeding
Language:English
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Summary: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.
ISSN:2156-017X
DOI:10.1109/IEDM45625.2022.10019450