Journal Article10.1080/14686996.2023.2252725
Microstructure characterization, phase transition, and device application of phase-change memory materials
Kaili Jiang,Shubing Li,Fangfang Chen,Liping Zhu,Wenwu Li +4 more
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TL;DR: Phase-change memory materials review summarizes the latest research on the microstructure, phase transition, and device application of phase-change memory materials. The review covers the primary device mechanics, modeling, and characterization of nanoscale PCM devices.
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Abstract: ABSTRACT Phase-change memory (PCM), recently developed as the storage-class memory in a computer system, is a new non-volatile memory technology. In addition, the applications of PCM in a non-von Neumann computing, such as neuromorphic computing and in-memory computing, are being investigated. Although PCM-based devices have been extensively studied, several concerns regarding the electrical, thermal, and structural dynamics of phase-change devices remain. In this article, aiming at PCM devices, a comprehensive review of PCM materials is provided, including the primary PCM device mechanics that underpin read and write operations, physics-based modeling initiatives and experimental characterization of the many features examined in nanoscale PCM devices. Finally, this review will propose a prognosis on a few unsolved challenges and highlight research areas of further investigation.
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Resistance behavior of Sb7Se3 thin films based on flexible mica substrate
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Feng Rao,Feng Rao,Keyuan Ding,Keyuan Ding,Yuxing Zhou,Yonghui Zheng,Mengjiao Xia,Shilong Lv,Zhitang Song,Songlin Feng,Ider Ronneberger,Riccardo Mazzarello,Wei Zhang,Evan Ma,Evan Ma +14 more
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A new family of ultralow loss reversible phase‐change materials for photonic integrated circuits: Sb 2 S 3 and Sb 2 Se 3
TL;DR: S3 and Sb2Se3 as mentioned in this paper, which are reversible alternatives to the standard commercially available chalcogenide PCMs, have been demonstrated as a class of low loss, reversible alternatives.
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Accurate deep neural network inference using computational phase-change memory.
Vinay Joshi,Vinay Joshi,Manuel Le Gallo,Simon Haefeli,Simon Haefeli,Irem Boybat,Irem Boybat,S. R. Nandakumar,Christophe Piveteau,Christophe Piveteau,Martino Dazzi,Martino Dazzi,Bipin Rajendran,Abu Sebastian,Evangelos Eleftheriou +14 more
TL;DR: In this article, the authors propose a methodology to train ResNet-type convolutional neural networks that results in no appreciable accuracy loss when transferring weights to phase-change memory (PCM) devices.