A review of Mott insulator in memristors: The materials, characteristics, applications for future computing systems and neuromorphic computing

Ran, Yunfeng; Pei, Yifei; Zhou, Zhenyu; Wang, Hong; Sun, Yong; Wang, Zhongrong; Hao, Mengmeng; Zhao, Jianhui; Chen, Jingsheng; Yan, Xiaobing

doi:10.1007/s12274-022-4773-9

Cited by 13 publications

(7 citation statements)

References 195 publications

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“…The leading contenders for achieving in-memory computing in intelligent workloads and hardware realization of ANNs are next-generation neuromorphic computing material platforms, including the MM system with high stackability, low programming energy, short switching time, varied analog conductance states, high linearity and symmetry, adaptable plasticity, and minimized footprint. [498][499][500] Furthermore, by combining sensing operations in MM systems, the computing sensor architecture can be harnessed for in-memory computing.…”

Section: Outlooks Challenges and Opportunitiesmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

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Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

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Section: Outlooks Challenges and Opportunitiesmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

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show abstract

“…Metal-insulator transition has been extensively studied in many novel materials, like VOx [ 73 ], NbOx [ 74 ], etc. Mott insulators [ 75 ] are of special interest among materials exhibiting MIT transition due to their sharp ON/OFF transition, which can be electrically modulated. Recently, X. Zhang et al [ 76 ] demonstrated an artificial afferent spiking neuron device using a NbOx mott memristor.…”

Section: Resistive Switchingmentioning

confidence: 99%

Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations

Udaya Mohanan

2024

Nanomaterials

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Neuromorphic computing has emerged as an alternative computing paradigm to address the increasing computing needs for data-intensive applications. In this context, resistive random access memory (RRAM) devices have garnered immense interest among the neuromorphic research community due to their capability to emulate intricate neuronal behaviors. RRAM devices excel in terms of their compact size, fast switching capabilities, high ON/OFF ratio, and low energy consumption, among other advantages. This review focuses on the multifaceted aspects of RRAM devices and their application to brain-inspired computing. The review begins with a brief overview of the essential biological concepts that inspire the development of bio-mimetic computing architectures. It then discusses the various types of resistive switching behaviors observed in RRAM devices and the detailed physical mechanisms underlying their operation. Next, a comprehensive discussion on the diverse material choices adapted in recent literature has been carried out, with special emphasis on the benchmark results from recent research literature. Further, the review provides a holistic analysis of the emerging trends in neuromorphic applications, highlighting the state-of-the-art results utilizing RRAM devices. Commercial chip-level applications are given special emphasis in identifying some of the salient research results. Finally, the current challenges and future outlook of RRAM-based devices for neuromorphic research have been summarized. Thus, this review provides valuable understanding along with critical insights and up-to-date information on the latest findings from the field of resistive switching devices towards brain-inspired computing.

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“…As for the candidate materials for memristors, major targets have been oxides for around 50 years. , In recent years, a special material, the Mott insulator, has received attention, which could complete a phase change from the insulator phase to the metal phase by changing their electronic band structures according to the classical energy band theory in single-electron approximation models. In the insulator–metal translation (IMT) process, the Mott insulator presents insulator characteristics with a band gap between the fully occupied valence band and the empty conduction band, and the Fermi level is in the band gap. − At some factors stimulation, the insulator phase could transform to a metal phase with a half-full band due to the overlap of energy bands. In fact, the phenomenon is explained by Mott, who used the theory of interaction between electrons and electrons, and this type of material is typically called a Mott insulator .…”

Section: Introductionmentioning

confidence: 99%

Resistive Switching Properties in Memristors for Optoelectronic Synaptic Memristors: Deposition Techniques, Key Performance Parameters, and Applications

Khan,

Rehman,

Iqbal

et al. 2023

ACS Appl. Electron. Mater.

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Due to the fast evolution of information technology, high-speed and scalable memory devices are being investigated for data storage and datadriven computation. Resistive switching random access memory (RRAM) is one of the very popular types of memristors, because of its quick program/erase speed and high density as a result of its simple two-terminal structure, low power consumption, and low cost of fabrication. In this review, the market for promising memristor-based memories for smart systems is examined. The switching memory devices are described and classified according to their I−V behavior to underline the physical switching mechanisms. The various filament mechanisms of the RRAM memristor, including the valence change mechanism (VCM), electrochemical metallization mechanism (ECM), and thermochemical mechanism (TCM), are especially highlighted in various structures. The performance of resistive switching devices with different reported electrode materials and resistive layers is summarized. This study provides a detailed review of materials of the different deposition techniques used for surface modification and coating. The two major areas of interest, physical and chemical vapor deposition techniques and the influence parameter on the resistive switching, are discussed in detail. The prospective applications of RRAM memristors to various fields such as security, neuromorphic computing, and non-volatile logic systems for artificial intelligence are addressed briefly as well as the future outlook.

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A review of Mott insulator in memristors: The materials, characteristics, applications for future computing systems and neuromorphic computing

Cited by 13 publications

References 195 publications

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations

Resistive Switching Properties in Memristors for Optoelectronic Synaptic Memristors: Deposition Techniques, Key Performance Parameters, and Applications

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