Guest‐Cage Atomic Interactions in a Clathrate‐Based Phase‐Change Material

Loke, Desmond K.; Skelton, Jonathan M.; Law, L. T.; Wang, Wei Jie; Li, Ming Hua; Wei, Song; Lee, Tae Hoon; Elliott, S. R.

doi:10.1002/adma.201304199

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…A previous study of the melting kinetics in GST 17 proposed a melting mechanism in which a crystalline cluster first fragments into disconnected medium-range-ordered structural units, namely planes and cubes of atoms, which subsequently break up into discrete four-fold rings, which themselves finally dissolve as the system melts. On the basis of the present results, this can be extended to the progressive melting modeled in the depression sequence.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…14 180-atom models were created in cubic supercells, with periodic boundary conditions, at a fixed density of 6.11 g cm -3 . These constant-volume conditions were chosen to mimic the effectively constant-volume setup of typical PCM "sandwich" device structures; the density is the typical value used in AIMD simulations on GST, [15][16][17][18][19] and is intermediate between the experimentally-measured amorphous and crystalline densities. We employed PAW pseudopotentials, 20 treating the outer s and p electrons as valence electrons, in conjunction with the Perdew-Burke-Enzerhof (PBE) exchange-correlation functional 21 and a plane-wave kineticenergy cut-off of 175 eV.…”

Section: Methodsmentioning

confidence: 99%

“…We prepared an initial starting configuration for the neuromorphic-computing simulations as follows. An amorphous structure was generated by a melt-quench procedure similar to that typically used in other PCM simulations. − An initial starting configuration was randomized at 3000 K for 20 ps, then equilibrated as a liquid at 1200 K for 40 ps. An amorphous model was then obtained by quenching to 300 K at a cooling rate of d T /d t = −15 K ps –1 .…”

Section: Methodsmentioning

confidence: 99%

“…Four-fold rings were defined, as in refs and , by four atoms forming a closed path with a maximum bonding distance (here 3.5 Å) and with all four three-atom bond angles, plus the angle between the planes defined by two triplets of atoms, being a maximum of 20° from the ideal angles of 90° and 180°, respectively. As in, for example, refs and , we quantified the chemical order in our models in terms of the atomic bonding. An ideal rocksalt structure contains only Ge/Sb–Te heteropolar bonds and thus could be viewed as being composed solely of [Ge,Sb] 2 Te 2 four-fold rings.…”

Section: Methodsmentioning

confidence: 99%

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Ab Initio Molecular-Dynamics Simulation of Neuromorphic Computing in Phase-Change Memory Materials

Skelton

Loke

Lee

et al. 2015

ACS Appl. Mater. Interfaces

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We present an in silico study of the neuromorphic-computing behavior of the prototypical phase-change material, Ge2Sb2Te5, using ab initio molecular-dynamics simulations. Stepwise changes in structural order in response to temperature pulses of varying length and duration are observed, and a good reproduction of the spike-timing-dependent plasticity observed in nanoelectronic synapses is demonstrated. Short above-melting pulses lead to instantaneous loss of structural and chemical order, followed by delayed partial recovery upon structural relaxation. We also investigate the link between structural order and electrical and optical properties. These results pave the way toward a first-principles understanding of phase-change physics beyond binary switching.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Ab Initio Molecular-Dynamics Simulation of Neuromorphic Computing in Phase-Change Memory Materials

Skelton

Loke

Lee

et al. 2015

ACS Appl. Mater. Interfaces

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“…Phase-change materials (PCM) are among the most promising candidates for SCM for data storage applications, and more recently, photonic devices and neuromorphic computing, which undergo rapid and reversible crystalline-to-amorphous structural transformation [7,8]. Phase change memory utilizes a sulfur-based compound material to carry out a transition between crystalline state and amorphous state after the stimulation of an electrical pulse.…”

Section: Introductionmentioning

confidence: 99%

Defect Engineering in Antimony Telluride Phase-Change Materials

Wang

Ren

Song

et al. 2019

MSF

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In the past 20 years, the phase-change memory technology has achieved rapid development, of which alloys along the GeTe-Sb2Te3 pseudobinary line are the most extensively researched materials. In recent years, Sb2Te3-based materials start to attract the attention of researchers. A recent study has shown that the Sb2Te3 (ST) material has a face-centered cubic (Fcc) phase which contains a high concentration of vacancies at low temperature. Due to the poor amorphous thermal stability of ST, the as-deposited film obtained by physical vapor deposition is crystalline (Fcc phase). Therefore, we proposed a vacancy control mechanism, using inert gas Ar to ion implantation of as-deposited ST films, redistributing vacancies in the as-deposited ST films. Through different doses of Ar ion implantation, we obtain amorphous ST materials with different resistivity. We find that after the injection dose reached 1 × 1016 cm-2, the effect of continued increase in the implantation dose on the resistivity of the thin film is negligible. After ion implantation, the transition temperature of the metastable Fcc phase to the hexagonal phase (Hex) is increased, which is beneficial to improve the power consumption and endurance of the device. The ST which is injected with a dose of 1 × 1016 cm−2 Ar ion based phase-change memory cell can perform erasing operation in 100 ns, showing low power consumption potential. Our work provides a new idea and method for the application of future defect control in phase-change memory research.

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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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Guest‐Cage Atomic Interactions in a Clathrate‐Based Phase‐Change Material

Cited by 5 publications

References 33 publications

Ab Initio Molecular-Dynamics Simulation of Neuromorphic Computing in Phase-Change Memory Materials

Ab Initio Molecular-Dynamics Simulation of Neuromorphic Computing in Phase-Change Memory Materials

Defect Engineering in Antimony Telluride Phase-Change Materials

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

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