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.
Authors
- 俞庆森
- Su‐Ting Han (ORCID: https://orcid.org/0000-0003-3392-7569)
- Yongbiao Zhai
- Sunyingyue Geng
- Guanglong Ding
- Ziyu Lv (ORCID: https://orcid.org/0000-0002-7062-3647)
- Ye Zhou (ORCID: https://orcid.org/0000-0002-0273-007X)
- Haitao Zhou (ORCID: https://orcid.org/0000-0002-4885-8888)
- Ying Luo (ORCID: https://orcid.org/0000-0002-6368-1937)
- Weixiao Sun
Institutions
- Hong Kong Polytechnic University (HK)
- Shenzhen University (CN)
- Shenzhen Polytechnic University (CN)
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
Funders
- National Natural Science Foundation of China
- Guangdong Science and Technology Department
- Science, Technology and Innovation Commission of Shenzhen Municipality