Te and Ge solid-state reaction: comparison between the 2D and 3D growth of α-GeTe

Roland, Guillaume; Portavoce, A.; Bertoglio, M.; Descoins, Marion; Remondina, Jacopo; Lorut, F.; Putero, Magali

doi:10.1039/d2tc05062e

Cited by 1 publication

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cluster population of the melt-quenched amorphous phase n am increases progressively to the steady-state distribution n ss at an increased temperature in a specified period, following a temperature change, as elucidated in theoretical calculations using the general kinetic theory of nucleation. [58][59][60] A thermally-induced event in the growth of clusters results in a time dependence, which involves the atomic attachment or detachment through thermal fluctuations. The layer temperature increases after administering electrical pulses through Joule heating in the PCM layer.…”

Section: Resultsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

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

View full text Add to dashboard Cite

show abstract

Te and Ge solid-state reaction: comparison between the 2D and 3D growth of α-GeTe

Abstract: In this work, solid-state α-GeTe growth is studied during the reactive diffusion of a polycrystalline thin film of hexagonal Te deposited on an amorphous Ge thin film (Te-on-Ge) using in...

Cited by 1 publication

References 55 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

Contact Info

Product

Resources

About