J. Mater. Sci. Technol. ›› 2026, Vol. 266: 76-91.DOI: 10.1016/j.jmst.2025.11.034

• Research article • Previous Articles     Next Articles

Nonlinear quantized conductance dynamics in vertical SiN RRAM for scalable memory-learning integration

Park Jiheea,1, Kim Nawoona,1, Na Hyesunga, Kim Hyungjinb,*, Kim Sungjuna,*   

  1. aDivision of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea;
    bDivision of Materials Science and Engineering and Department of Semiconductor Engineering, Hanyang University, Seoul 04763, Republic of Korea
  • Received:2025-08-12 Revised:2025-11-20 Accepted:2025-11-22 Published:2026-09-20 Online:2025-11-28
  • Contact: *E-mail addresses: hkim12@hanyang.ac.kr (H. Kim), sungjun@dongguk.edu (S. Kim).
  • About author:1 These authors contributed equally to this work.

Abstract: 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 G0 = 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.

Key words: Vertical rram, Conductance quantization, Multi-bit memory, Neuromorphic computing, Synaptic plasticity