Monatomic phase change memory

Phase change memory has been developed into a mature technology capable of storing information in a fast and non-volatile way1–3, with potential for neuromorphic computing applications4–6. However, its future impact in electronics depends crucially on how the materials at the core of this technology adapt to the requirements arising from continued scaling towards higher device densities. A common strategy to fine-tune the properties of phase change memory materials, reaching reasonable thermal stability in optical data storage, relies on mixing precise amounts of different dopants, resulting often in quaternary or even more complicated compounds6–8. Here we show how the simplest material imaginable, a single element (in this case, antimony), can become a valid alternative when confined in extremely small volumes. This compositional simplification eliminates problems related to unwanted deviations from the optimized stoichiometry in the switching volume, which become increasingly pressing when devices are aggressively miniaturized9,10. Removing compositional optimization issues may allow one to capitalize on nanosize effects in information storage.Monatomic glasses formed by rapidly quenching Sb films from a molten state are shown to work as phase change materials for memory applications at room temperature.

[1]  R. O. Jones,et al.  Nucleus-driven crystallization of amorphous Ge2Sb2Te5: A density functional study , 2012 .

[2]  Byoungil Lee,et al.  Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.

[3]  Bart J. Kooi,et al.  Size-dependent and tunable crystallization of GeSbTe phase-change nanoparticles , 2016, Scientific Reports.

[4]  Noboru Yamada,et al.  From local structure to nanosecond recrystallization dynamics in AgInSbTe phase-change materials. , 2011, Nature materials.

[5]  Jan Schroers,et al.  Nanoscale size effects in crystallization of metallic glass nanorods , 2015, Nature Communications.

[6]  Simone Raoux,et al.  Crystallization properties of ultrathin phase change films , 2008 .

[7]  Kurt Binder,et al.  The relaxation dynamics of a supercooled liquid confined by rough walls , 2003 .

[8]  Matthias Wuttig,et al.  How fragility makes phase-change data storage robust: insights from ab initio simulations , 2014, Scientific Reports.

[9]  Matthias Wuttig,et al.  Computational Study of Crystallization Kinetics of Phase Change Materials , 2017 .

[10]  Bart J. Kooi,et al.  Stress-Induced Crystallization of Ge-Doped Sb Phase-Change Thin Films , 2013 .

[11]  Paolo Cappelletti,et al.  Non volatile memory evolution and revolution , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[12]  Christopher J. Ellison,et al.  The distribution of glass-transition temperatures in nanoscopically confined glass formers , 2003, Nature materials.

[13]  T Uruga,et al.  Toward the ultimate limit of phase change in Ge(2)Sb(2)Te(5). , 2010, Nano letters.

[14]  M. Wuttig,et al.  Phase-change materials for rewriteable data storage. , 2007, Nature materials.

[15]  Ider Ronneberger,et al.  Crystallization Properties of the Ge2Sb2Te5 Phase‐Change Compound from Advanced Simulations , 2015 .

[16]  Teter,et al.  Separable dual-space Gaussian pseudopotentials. , 1996, Physical review. B, Condensed matter.

[17]  Manuel Le Gallo,et al.  Stochastic phase-change neurons. , 2016, Nature nanotechnology.

[18]  Robert O. Jones,et al.  Crystallization of supercooled liquid antimony: A density functional study , 2017 .

[19]  Haralampos Pozidis,et al.  Recent Progress in Phase-Change Memory Technology , 2016, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[20]  Pritish Narayanan,et al.  Neuromorphic computing using non-volatile memory , 2017 .

[21]  Matthias Wuttig,et al.  Aging mechanisms in amorphous phase-change materials , 2015, Nature Communications.

[22]  Simone Raoux,et al.  Influence of interfaces and doping on the crystallization temperature of Ge–Sb , 2009 .

[23]  Matthias Krack,et al.  Efficient and accurate Car-Parrinello-like approach to Born-Oppenheimer molecular dynamics. , 2007, Physical review letters.

[24]  A. F. Crawley,et al.  The density and viscosity of liquid antimony , 1972 .

[25]  Matthias Krack,et al.  Static and Dynamical Properties of Liquid Water from First Principles by a Novel Car-Parrinello-like Approach. , 2009, Journal of chemical theory and computation.

[26]  Michele Parrinello,et al.  Quickstep: Fast and accurate density functional calculations using a mixed Gaussian and plane waves approach , 2005, Comput. Phys. Commun..

[27]  A. L. Greer,et al.  New horizons for glass formation and stability. , 2015, Nature materials.

[28]  G. W. Burr,et al.  Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element , 2015, 2014 IEEE International Electron Devices Meeting.

[29]  Heiner Giefers,et al.  Mixed-precision in-memory computing , 2017, Nature Electronics.

[30]  Matthias Wuttig,et al.  Threshold field of phase change memory materials measured using phase change bridge devices , 2009 .

[31]  R. O. Jones,et al.  Structural phase transitions on the nanoscale: The crucial pattern in the phase-change materials Ge2Sb2Te5 and GeTe , 2007 .

[32]  Thomas P. Parnell,et al.  Temporal correlation detection using computational phase-change memory , 2017, Nature Communications.

[33]  Jií Kolafa,et al.  Time‐reversible always stable predictor–corrector method for molecular dynamics of polarizable molecules , 2004, J. Comput. Chem..

[34]  Michele Parrinello,et al.  Signature of tetrahedral Ge in the Raman spectrum of amorphous phase-change materials. , 2010, Physical review letters.

[35]  M. Salinga,et al.  Phase-Change Memories on a Diet , 2011, Science.

[36]  S. Elliott,et al.  Microscopic origin of the fast crystallization ability of Ge-Sb-Te phase-change memory materials. , 2008, Nature materials.

[37]  Chung Lam,et al.  Self‐Healing of a Confined Phase Change Memory Device with a Metallic Surfactant Layer , 2018, Advanced materials.

[38]  Jan Schroers,et al.  Condensed-matter physics: Glasses made from pure metals , 2014, Nature.

[39]  Keiji Watanabe,et al.  Structural origin of enhanced slow dynamics near a wall in glass-forming systems. , 2011, Nature materials.

[40]  Daniele Ielmini,et al.  Statistics of Resistance Drift Due to Structural Relaxation in Phase-Change Memory Arrays , 2010, IEEE Transactions on Electron Devices.

[41]  Jiangwei Wang,et al.  Formation of monatomic metallic glasses through ultrafast liquid quenching , 2014, Nature.

[42]  Evangelos Eleftheriou,et al.  Projected phase-change memory devices , 2015, Nature Communications.

[43]  Gökmen Tayfun,et al.  Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations , 2016, Front. Neurosci..

[44]  Linda J. Broadbelt,et al.  Structural Relaxation of Polymer Glasses at Surfaces, Interfaces, and In Between , 2005, Science.

[45]  Burke,et al.  Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.

[46]  Alessandro Curioni,et al.  Structural origin of resistance drift in amorphous GeTe , 2016 .

[47]  Eric Pop,et al.  Low-Power Switching of Phase-Change Materials with Carbon Nanotube Electrodes , 2011, Science.

[48]  Kumar Virwani,et al.  Evidence of Crystallization–Induced Segregation in the Phase Change Material Te-Rich GST , 2011 .

[49]  J. J. Hauser Hopping conductivity in amorphous antimony , 1974 .