Ultra-high density perovskite nanowire array memristor-based multi-layer perceptron

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Authors: Swapnadeep Poddar, Biswadeep Khan, Shivam Kumar, Jinghao Li, Yucheng Ding, Zhesi Chen, Kaichen Wang, Chenxi Jin, Jinchen Wei, Zhirong Peng, On-mongkol Jaesiri, Mansun Chan
+2 more Julie L. Semmelhack, Zhiyong Fan
Cited by: 1
References: 51

Abstract

Passive memristor crossbars present a high-density and low-power platform for neuromorphic computing. Although perovskite memristors present advantages such as re-configurability between electrochemical metallization and valence change mechanisms compared to traditional oxides, they face issues with uniformity, variability, and filamentary instability, which impede their large-scale integration. This work demonstrates that these limitations are overcome by a three-dimensional perovskite nanowire array architecture, where a precisely engineered ITO barrier is instrumental in maintaining controlled, analog conductance modulation. The resulting memristors exhibit 139 non-overlapping Ohmic conduction states within an optimal analog range (10–100 µS), long retention ( > 10⁶ s), endurance exceeding 4 × 10⁵ cycles, an asymmetric non-linearity factor of 0.3, and minimal variability (device-to-device

Fields of Study & Subjects

Advanced Memory and Neural Computing Perovskite Materials and Applications Transition Metal Oxide Nanomaterials

Publication details

Journal / source
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
17
Issue
1
Publication date
27 May 2026
Language
EN