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

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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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DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Negative Differential Resistance Electronics: From Nonmonotonic Transport Physics to Functionally Compressed Computing
Date Crossref
05/09/2026
Éditeur
Wiley
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

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Sujets associés

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeural Networks and Reservoir Computing

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