A Fully Si-Compatible Ni/Si <sub>3</sub> N <sub>4</sub> /Al <sub>2</sub> O <sub>3</sub> / <i>p</i> <sup>+</sup> Poly-Si RRAM Device for Analog Synapse and Its System-Level Assessment toward Processing-in-Memory Applications

We propose a fully Si-compatible resistive-switching random-access memory (RRAM) device consisting of a Ni/Si<sub>3</sub>N<sub>4</sub>/Al<sub>2</sub>O<sub>3</sub>/<i>p</i> <sup>+</sup> poly-Si structure for processing-in-memory (PIM) applications fabricated through a process integration with Si CMOS compatibility. By introduction of an ultrathin Al<sub>2</sub>O<sub>3</sub> tunneling layer between the Si<sub>3</sub>N<sub>4</sub> switching layer and <i>p</i> <sup>+</sup> poly-Si bottom electrode, the leakage current in the high-resistance state (HRS) is suppressed, resulting in stable bipolar switching characteristics with a high on/off current ratio of approximately 10<sup>4</sup>, maintained for more than 10<sup>7</sup> switching operations, as evidenced through durability measurements. The retention characteristics were also maintained without degradation for a considerable time (>10<sup>5</sup> s). When potentiation (6.5 V, 3 μs) and depression (-3.7 V, 3 μs) pulses with the same voltage and time conditions were applied, the device exhibited stable conductance change characteristics with high linearity and symmetry, demonstrating its potential for use as an analog synaptic device through repeatable signal modulation. The average conductance changes were 1.72 and 1.60 μS for potentiation and depression, respectively, confirming balanced weighting characteristics. Furthermore, based on experimentally obtained device characteristics, we implemented a VGG-8 convolutional neural network using the NeuroSim 1.4 framework and achieved high inference accuracy (90.25%), along with low latency and high energy efficiency on the CIFAR-10 data set. Thus, the proposed device holds promise for next-generation nonvolatile memory and analog neural network computing, particularly for energy-efficient processing-in-memory architectures.

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Journal
ACS Omega
Published
2025-11-19
DOI
https://doi.org/10.1021/acsomega.5c08019
Primary Topic
Advanced Memory and Neural Computing
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article
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article

A Fully Si-Compatible Ni/Si 3 N 4 /Al 2 O 3 / p + Poly-Si RRAM Device for Analog Synapse and Its System-Level Assessment toward Processing-in-Memory Applications

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Advanced Memory and Neural Computing
article

A Fully Si-Compatible Ni/Si 3 N 4 /Al 2 O 3 / p + Poly-Si RRAM Device for Analog Synapse and Its System-Level Assessment toward Processing-in-Memory Applications

Seongjae Cho, Wonbo Shim, Seong‐Min Kim, Sungjun Kim, Yeji Lee, Soo-Min Kim
article en

Abstract

We propose a fully Si-compatible resistive-switching random-access memory (RRAM) device consisting of a Ni/Si<sub>3</sub>N<sub>4</sub>/Al<sub>2</sub>O<sub>3</sub>/<i>p</i> <sup>+</sup> poly-Si structure for processing-in-memory (PIM) applications fabricated through a process integration with Si CMOS compatibility. By introduction of an ultrathin Al<sub>2</sub>O<sub>3</sub> tunneling layer between the Si<sub>3</sub>N<sub>4</sub> switching layer and <i>p</i> <sup>+</sup> poly-Si bottom electrode, the leakage current in the high-resistance state (HRS) is suppressed, resulting in stable bipolar switching characteristics with a high on/off current ratio of approximately 10<sup>4</sup>, maintained for more than 10<sup>7</sup> switching operations, as evidenced through durability measurements. The retention characteristics were also maintained without degradation for a considerable time (>10<sup>5</sup> s). When potentiation (6.5 V, 3 μs) and depression (-3.7 V, 3 μs) pulses with the same voltage and time conditions were applied, the device exhibited stable conductance change characteristics with high linearity and symmetry, demonstrating its potential for use as an analog synaptic device through repeatable signal modulation. The average conductance changes were 1.72 and 1.60 μS for potentiation and depression, respectively, confirming balanced weighting characteristics. Furthermore, based on experimentally obtained device characteristics, we implemented a VGG-8 convolutional neural network using the NeuroSim 1.4 framework and achieved high inference accuracy (90.25%), along with low latency and high energy efficiency on the CIFAR-10 data set. Thus, the proposed device holds promise for next-generation nonvolatile memory and analog neural network computing, particularly for energy-efficient processing-in-memory architectures.

ACS OmegaVol. 10(47)
Seoul National University of Science and Technology (KR), Ewha Womans University (KR), Dongguk University (KR), Ewha Womans University Medical Center (KR)
Openalex Percentile: Top 65%
Advanced Memory and Neural Computing
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