Highly Deterministic One-Shot Set-Reset Programming Scheme in PCMO Resistive Random-Access Memory

doi:10.1021/acsaelm.2c00918

2022-09-28

PRE

Omkar Phadke

Vivek Saraswat *

Udayan Ganguly

DOI：10.1021/acsaelm.2c00918

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摘要

En 中文

Resistive random-access memory (RRAM) devices are very versatile with applications ranging from digital nonvolatile memories to analog synapses and integratefire neurons. Recently, RRAM based stochastic neurons have also been proposed for optimization networks that solve NP-hard problems. These applications rely on reliably writing a given conductance state repeatedly. A Pr1-xCaxMnO3 (PCMO) based RRAM device is a nonfilamentary, bulk switching RRAM which demonstrates low device-todevice variations compared to filamentary RRAMs. However, a low complexity programming scheme to demonstrate low cycle-to-cycle variations (C2CV) as well is needed. In this work, we propose a one-shot Set (initialize) and Reset (program) scheme to experimentally demonstrate low C2CV (<15%) for multiple devices. Compared to oneshot programming on a filamentary device, there is a 2-4x increment in the number of levels that can be stored per device using PCMO RRAM. Further, one-shot programming in PCMO RRAM leads to a 35x (10x) reduction in the number of pulses for 3 (2) bits per device, respectively. This greatly simplifies the memory controller design for these devices compared to any iterative write-verify schemes. This scheme gives a further insight into the self-heating limited Set operation controlled by a series resistor while reinforcing the analog and precise nature of the Reset operation. We studied the impact of the C2CV in programming RRAM-based stochastic neurons on the Max-Cut optimization problem using Boltzmann machines. PCMO RRAM combined with the one-shot programming scheme emerges as the clear choice for a well-controlled RRAM device with minimal peripheral complexity.

Keyword:

RRAM

stochastic neuron

PCMO

programming scheme

variability

optimization

Boltzmann machine