Nonlinear quantized conductance dynamics in vertical SiN RRAM for scalable memory-learning integration
  • Park, Jihee
  • Kim, Nawoon
  • Na, Hyesung
  • Kim, Hyungjin
  • Kim, Sungjun
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초록

We report a vertical resistive random-access memory device based on a Pt/SiN/Ti stack, designed for multi-bit storage and neuromorphic computing. The device exhibits stable bipolar switching and achieves up to 7-bit (128-level) conductance states through precise control of compliance current and reset voltage. Quantized conductance plateaus, corresponding to integer and half-integer multiples of the quantum conductance G<inf>0</inf> = 2e2/h, reveal atomic-scale filament dynamics governed by nonlinear conduction processes. Diverse synaptic plasticity functions, including spike-number-, spike-rate-, spike-duration-, and spike-amplitude-dependent plasticity, were experimentally emulated. Neuromorphic simulations for the Modified National Institute of Standards and Technology dataset achieved classification accuracies exceeding 94 %, confirming the device's suitability for high-precision weight modulation. The vertical architecture ensures scalability toward three-dimensional integration, while robust retention and compatibility with current-based multi-bit modulation highlight its potential for complex-system-inspired edge AI and in-memory computing hardware. © 2025

키워드

Conductance quantizationMulti-bit memoryNeuromorphic computingSynaptic plasticityVertical rramRANDOM-ACCESS MEMORYRESISTIVE SWITCHING CHARACTERISTICSLOW-POWERARCHITECTUREDEVICESEVOLUTIONMECHANISMSTRAPHFOX
제목
Nonlinear quantized conductance dynamics in vertical SiN RRAM for scalable memory-learning integration
저자
Park, JiheeKim, NawoonNa, HyesungKim, HyungjinKim, Sungjun
DOI
10.1016/j.jmst.2025.11.034
발행일
2026-09
유형
Article
저널명
Journal of Materials Science and Technology
266
페이지
76 ~ 91