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

Indian Institute of Technology Bombay; Indian Institute of Technology Bombay; Indian Institute of Technology Bombay
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Publisher: ACS
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Abstract AITranslate

Resistive random-access memory (RRAM) devices are very versatile with applications ranging from digital nonvolatile memories to analog synapses and integrate-fire 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-to-device 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 one-shot programming on a filamentary device, there is a 2–4× 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 35× (10×) 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.

KeyWords AITranslate

RRAM stochastic neuron PCMO programming scheme variability optimization Boltzmann machine
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Basic Information:

DOI:https://doi.org/10.1021/acsaelm.2c00918

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Citation Information:

Resistive random-access memory (RRAM) devices are very versatile with applications ranging from digital nonvolatile memories to analog synapses and integrate-fire 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-to-device 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 one-shot programming on a filamentary device, there is a 2–4× 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 35× (10×) 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.

quote

GB/T 7714-2015 [1] Omkar Phadke, Vivek Saraswat, Udayan Ganguly. ACS Applied Electronic Materials, 2022(4). DOI:10.1021/acsaelm.2c00918.
MLA [1] Omkar Phadke, et al., ACS Applied Electronic Materials, no. 4, 2022, https://doi.org/10.1021/acsaelm.2c00918.
APA [1] Omkar Phadke, Vivek Saraswat, & Udayan Ganguly. (2022). ACS Applied Electronic Materials(4). https://doi.org/10.1021/acsaelm.2c00918
IEEE [1] Omkar Phadke, Vivek Saraswat, and Udayan Ganguly, ACS Applied Electronic Materials, no. 4, 2022, doi: 10.1021/acsaelm.2c00918. keywords: {RRAM stochastic neuron PCMO programming scheme variability optimization Boltzmann machine}