Phase-change heterostructure enables ultralow noise and drift for memory operation

Ding, Keyuan; Wang, Jiangjing; Zhou, Yuxing; Tian, He; Lu, Lu L.; Mazzarello, Riccardo; Jia, Chun‐Lin; Zhang, Wei; Rao, Feng; Ma, Evan

doi:10.1126/science.aay0291

Cited by 312 publications

(254 citation statements)

References 62 publications

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“…[ 4 ] Neuromorphic computing architectures for fully connected [ 5–7 ] and convolutional [ 8,9 ] neural networks have been developed. Despite significant research into memory technologies such as conductive‐bridge random access memory, [ 10–12 ] ferroelectric memory, [ 13 ] phase‐change memory, [ 14–16 ] among others, the search for a CMOS compatible analogue non‐volatile memory element, or artificial synapse, with accurate and efficient switching has been elusive.…”

Section: Figurementioning

confidence: 99%

Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching

et al. 2020

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Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue‐memory‐based neuromorphic computing can be orders of magnitude more energy efficient at data‐intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer‐sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria‐stabilized zirconia (YSZ), toward eliminating filaments. Filament‐free, bulk‐RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk‐RRAM devices using TiO2‐X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk‐RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy‐efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.

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Section: Figurementioning

confidence: 99%

Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching

et al. 2020

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“…The encoded data, in the power-off state, can be reliably stored for tens of years at ambient temperatures 5 . Such swift and nonvolatile features, as well as the merits 4,6 of high scalability, low power consumption, and long cycling endurance, make PCRAM the best candidate for realizing an ideal "universal memory" to renovate the current computing system based on the classic von Neumann architecture 7 . Since 2015 until very recently, Intel's Optane DC 8 and Micron's X100 NVMe (https://www.micron.com/ products/advanced-solutions/3d-xpoint-technology/ x100) chips, both employing 3D Xpoint PCRAM technology, served as storage-class memory to mitigate the widening performance gap (memory wall) between volatile dynamic random-access memory (DRAM) and nonvolatile solid-state drive flash memory.…”

Section: Introductionmentioning

confidence: 99%

Recipe for ultrafast and persistent phase-change memory materials

Ding

Chen

et al. 2020

NPG Asia Mater

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The contradictory nature of increasing the crystallization speed while extending the amorphous stability for phase-change materials (PCMs) has long been the bottleneck in pursuing ultrafast yet persistent phase-change random-access memory. Scandium antimony telluride alloy (ScxSb2Te3) represents a feasible route to resolve this issue, as it allows a subnanosecond SET speed but years of reliable retention of the RESET state. To achieve the best device performances, the optimal composition and its underlying working mechanism need to be unraveled. Here, by tuning the doping dose of Sc, we demonstrate that Sc0.3Sb2Te3 has the fastest crystallization speed and fairly improved data nonvolatility. The simultaneous improvement in such ‘conflicting’ features stems from reconciling two dynamics factors. First, promoting heterogeneous nucleation at elevated temperatures requires a higher Sc dose to stabilize more precursors, which also helps suppress atomic diffusion near ambient temperatures to ensure a rather stable amorphous phase. Second, however, enlarging the kinetic contrast through a fragile-to-strong crossover in the supercooled liquid regime should require a moderate Sc content; otherwise, the atomic mobility for crystal growth at elevated temperatures will be considerably suppressed. Our work thus reveals the recipe by tailoring the crystallization kinetics to design superior PCMs for the development of high-performance phase-change working memory technology.

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“…However, the poor amorphous thermal stability of GST results in resistance drift of the phase. This factor has a negative effect on data storage in multiple intermediate memory levels, leading to memory failure 8 . Therefore, although multilevel storage is an effective approach to increase the data storage capacity, it is still challenging to prepare phase-change films with desired properties.…”

Section: Introductionmentioning

confidence: 99%

Conversion of p–n conduction type by spinodal decomposition in Zn-Sb-Bi phase-change alloys

et al. 2020

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Phase-change films with multiple resistance levels are promising for increasing the storage density in phase-change memory technology. Diffusion-dominated Zn 2 Sb 3 films undergo transitions across three states, from high through intermediate to low resistance, upon annealing. The properties of the Zn 2 Sb 3 material can be further optimized by doping with Bi. Based on scanning transmission electron microscopy combined with electrical transport measurements, at a particular Bi concentration, the conduction of Zn-Sb-Bi compounds changes from p-to n-type, originating from spinodal decomposition. Simultaneously, the change in the temperature coefficient of resistivity shows a metal-toinsulator transition. Further analysis of microstructure characteristics reveals that the distribution of the Bi-Sb phase may be the origin of the driving force for the p-n conduction and metal-to-insulator transitions and therefore may provide us with another way to improve multilevel data storage. Moreover, the Bi doping promotes the thermoelectric properties of the studied alloys, leading to higher values of the power factor compared to known reported structures. The present study sheds valuable light on the spinodal decomposition process caused by Bi doping, which can also occur in a wide variety of chalcogenide-based phase-change materials. In addition, the study provides a new strategy for realizing novel p-n heterostructures for multilevel data storage and thermoelectric applications.

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Phase-change heterostructure enables ultralow noise and drift for memory operation

Cited by 312 publications

References 62 publications

Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Recipe for ultrafast and persistent phase-change memory materials

Conversion of p–n conduction type by spinodal decomposition in Zn-Sb-Bi phase-change alloys

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