Negative Differential Resistance Electronics: From Nonmonotonic Transport Physics to Functionally Compressed Computing

Negative differential resistance (NDR), a counterintuitive transport phenomenon in which current decreases with increasing voltage, challenges conventional transistor-centric computing paradigms based on monotonic electronic transport and opens new opportunities for beyond-Boolean computing. Here, we present a comprehensive and concept-driven review of NDR devices spanning memristor-based, diode-type, and transistor-based platforms. We establish a unified framework that links diverse NDR mechanisms, including resonant tunneling, electrothermal feedback, defect dynamics, and ferroelectric polarization, through their shared nonmonotonic transport characteristics. Beyond device-level classification, we further propose NDR as a physical foundation for functionally compressed computing, in which circuit functionalities traditionally implemented using multiple transistors and feedback networks can be partially embedded into the intrinsic nonlinear response of a single NDR device or compact device unit. We further compare representative NDR technologies using common performance metrics and analyze the key challenges that currently limit large-scale deployment, including variability, CMOS compatibility, compact modeling, and the distinction between intrinsic NDR behavior and measurement-induced artifacts. Finally, we discuss future opportunities in materials-by-design, heterogeneous and 3D integration, physics-informed modeling, and closed-loop intelligent systems. By connecting nonmonotonic transport physics with circuit and system-level functionality, NDR electronics offers a promising route toward compact and energy-efficient computing architectures in the post-Moore era.

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Publication Details

Journal
Advanced Materials
Published
2026-09-05
DOI
https://doi.org/10.1002/adma.74921
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Negative Differential Resistance Electronics: From Nonmonotonic Transport Physics to Functionally Compressed Computing

俞庆森, Su‐Ting Han, Yongbiao Zhai, Sunyingyue Geng et al.
Advanced Materials
Advanced Memory and Neural Computing
article

Negative Differential Resistance Electronics: From Nonmonotonic Transport Physics to Functionally Compressed Computing

俞庆森, Su‐Ting Han, Yongbiao Zhai, Sunyingyue Geng, Guanglong Ding, Ziyu Lv, Ye Zhou, Haitao Zhou, Ying Luo, Weixiao Sun
article en

Abstract

Negative differential resistance (NDR), a counterintuitive transport phenomenon in which current decreases with increasing voltage, challenges conventional transistor-centric computing paradigms based on monotonic electronic transport and opens new opportunities for beyond-Boolean computing. Here, we present a comprehensive and concept-driven review of NDR devices spanning memristor-based, diode-type, and transistor-based platforms. We establish a unified framework that links diverse NDR mechanisms, including resonant tunneling, electrothermal feedback, defect dynamics, and ferroelectric polarization, through their shared nonmonotonic transport characteristics. Beyond device-level classification, we further propose NDR as a physical foundation for functionally compressed computing, in which circuit functionalities traditionally implemented using multiple transistors and feedback networks can be partially embedded into the intrinsic nonlinear response of a single NDR device or compact device unit. We further compare representative NDR technologies using common performance metrics and analyze the key challenges that currently limit large-scale deployment, including variability, CMOS compatibility, compact modeling, and the distinction between intrinsic NDR behavior and measurement-induced artifacts. Finally, we discuss future opportunities in materials-by-design, heterogeneous and 3D integration, physics-informed modeling, and closed-loop intelligent systems. By connecting nonmonotonic transport physics with circuit and system-level functionality, NDR electronics offers a promising route toward compact and energy-efficient computing architectures in the post-Moore era.

Advanced Materials
Hong Kong Polytechnic University (HK), Shenzhen University (CN), Shenzhen Polytechnic University (CN)
National Natural Science Foundation of China, Guangdong Science and Technology Department, Science, Technology and Innovation Commission of Shenzhen Municipality
Affordable and clean energy
Openalex Percentile: Top 19%
Advanced Memory and Neural Computing
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