Physics-Based Modeling Strategies of Phase-Change Random Access Memory

Cai, Zhikuang; Wan, Xiang; Liu, Xiaoyan; Ren, Qingying; Lian, Xiaojuan; Wang, Lei

doi:10.1109/ted.2022.3215550

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…The nucleation of small crystallites and their resultant growth are two key unique aspects of crystallization mechanisms, based on classical nucleation theory. [31][32][33][34][35][36] Fast growth at an elevated temperature can result after a rapid nucleation rate at modest temperatures. [37][38][39] Herein, to alleviate the difficulties in memristive-conductance modulation, we harness proposed tan-dem (T) material states in phase-change memory (PCM) elements/ devices, viz., the primed-amorphous state and the partially-crystallized state, by rejuvenating and modifying the crystallization mechanism, and demonstrate a design of a hybrid system for in-memory computing and NNs (Figure 1a).…”

Section: Introductionmentioning

confidence: 99%

Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning

Lim,

Go,

Tan

et al. 2024

Advanced Physics Research

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Memristive hardware with reconfigurable conductance levels are leading candidates for achieving artificial neural networks (ANNs). However, owing to difficulties in device character design and circuit combination, the ability to perform complicated online‐learning tasks on a memristive network is not well understood. Here, tandem (T) material states are harnessed in a phase‐change memory (PCM) element, i.e., the primed‐amorphous state and the partial‐crystallized state, by utilizing an impetus‐and‐consequent pair pulse through a large degree of configurational ordering, and illustrate the development of an integrated system for achieving in‐memory computing and neural networks (NNs). A correct classification of 96.1% of 10,000 separate test images from the conventional Modified‐National‐Institute‐of‐Standards‐and‐Technology (MNIST) database in the tandem neural‐network (T‐NN) model is achieved, as well as image recognition for 28×28‐pixel pictures. The T‐NN configuration exhibits an in situ learning, with 50% of the elements stuck in the low‐conductance state, and at the same time, maintains an identification accuracy of ≈90%. The structural origin of the large degree of configurational‐ordering‐enhanced improvement in the extent of the conductance uniformity in the T‐based memristive element is revealed by theoretical studies. This work opens the door for attaining a widely relevant hardware system capable of performing artificial intelligence tasks with a large power‐time efficacy.

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Section: Introductionmentioning

confidence: 99%

Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning

Lim,

Go,

Tan

et al. 2024

Advanced Physics Research

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show abstract

“…1) To store and use effectively such a large amount of electronic data, higher density and higher capacity nonvolatile memories are desperately needed. Various novel nonvolatile memories are currently being developed, such as resistance change memory 2) including resistive random access memories, phase-change memories 3) and ferroelectric random access memories. 4) On the other hand, molecular-based memories are attracting attention and further development is expected in the future.…”

Section: Introductionmentioning

confidence: 99%

Stacked structure dependence on resistive switching characteristics in sumanene molecular memory

Kawai,

Ashihara,

Ishikawa

et al. 2024

Jpn. J. Appl. Phys.

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Nonvolatile memories using molecular (molecular memories) are attracting attention. This is because these materials are suitable for miniaturization and higher capacity of memories in terms of the properties and dimensions. We have already demonstrated that the MIM devices with sumanene-inserted bilayer graphene show1) huge resistive switching characteristics. However, the reason why the resistive switching occurs in graphene / sumanene / graphene structure has yet to be clarified. In this work, to investigate the mechanisms for resistive switching phenomenon in sumanene-inserted bilayer graphene, the plural kinds of stacked MIM structures are fabricated and evaluated. As a results, the measurement results clearly show that the graphene / sumanene / graphene structure is indispensable for resistive switching phenomenon. Furthermore, based on the temperature dependence of the resistive switching, it is confirmed that a significant ION/IOFF ratio can be obtained at higher operation temperatures.

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Networking and Security Architectures for IoE Networks

Ullah,

Ullah

2023

Internet of Things

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Physics-Based Modeling Strategies of Phase-Change Random Access Memory

Cited by 4 publications

References 43 publications

Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning

Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning

Stacked structure dependence on resistive switching characteristics in sumanene molecular memory

Networking and Security Architectures for IoE Networks

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