Insights into the physical aging in chalcogenide glasses: A case study of a first-generation As2Se3 binary glass

Pal, Shweta; Mehta, N.; Mikla, Victor I.; Horvat, Andrew; Minkovich, V.V.; Dahshan, A.

doi:10.1016/j.ccr.2021.213992

Cited by 8 publications

(1 citation statement)

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“…Among phase-change materials, Ge 2 Sb 2 Te 5 has relatively balanced device-performance characteristics, and, therefore, has been the subject of the most attention in the field of neuromorphic computing. 10–12 However, the melt-quenched amorphous phase of Ge 2 Sb 2 Te 5 is a non-equilibrium state and it can therefore tend to suffer spontaneous structural relaxation towards a lower-energy glassy state (termed aging), 13 causing a steady temporal increase in the electrical resistance ( i.e., ‘resistance drift’), which significantly reduces the (multilevel) computing accuracy and is detrimental to neuromorphic-computing applications. 14,15 Although tentative efforts have been made to explore the aging process of Ge 2 Sb 2 Te 5 , very different and sometimes conflicting physical mechanisms have been proposed to account for the resistance drift, 16 such as an increase 17 or decrease 18,19 of disorder, an increase 20 or decrease 21 of defect density, an impact 22 or no impact 23 of mechanical stress.…”

Section: Introductionmentioning

confidence: 99%

Ultrahigh overall-performance phase-change memory by yttrium dragging

Liu

Zhou

et al. 2023

J. Mater. Chem. C

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Section: Introductionmentioning

confidence: 99%

Ultrahigh overall-performance phase-change memory by yttrium dragging

Liu

Zhou

et al. 2023

J. Mater. Chem. C

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Screening Switching Materials with Low Leakage Current and High Thermal Stability for Neuromorphic Computing

Jia

Gotoh

et al. 2022

Adv Elect Materials

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Neuromorphic computing implemented with spiking neural networks is an energy efficient computing paradigm to break through the Von Neumann bottleneck in the future. Ovonic threshold switching (OTS) selector is considered to be a promising spiking neuron candidate. As ≈1011 artificial neurons are needed for brain‐inspired computing, leakage current of OTS devices would waste enormous power. OTS devices with ultralow leakage current are deeply desired. Since the leakage current is closely related to the bandgap of OTS materials and only the amorphous one shows the volatile switching property, here binary gallium sulfide (GaS), characterized by 2.53 eV large band‐gap and ≈550 °C high crystallization temperature is singled out, as the most appealing OTS material. High‐density trap states, an essential prerequisite of the OTS material, are detected in the amorphous GaS. Indeed, the OTS behaviors of the GaS‐based device are directly observed. The simple OTS device could provide 21.23 MA cm−2 large ON current density, and ≈10−8 A low OFF current and ≤10 ns switching speed. Furthermore, it is also used to build GaS‐based leakage integrate and fire neurons for future neuromorphic computing. This study provides a new idea for material design before device preparation and takes a neuron as an example.

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