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

Getting more bits out of PCRAM Phase-change random access memory (PCRAM) has the ability to both store and process information. It also suffers from noise and electrical drift due to damage that accumulates during the cycling process. Ding et al. developed a phase-change heterostructure where a phase-change material is separated by a confinement material, creating an alternating stack (see the Perspective by Gholipour). This architecture results in ultralow noise, lower drift, and stable multilevel storage capacity, which are potentially useful for new forms of computing. Science, this issue p. 210; see also p. 186 A phase-change heterostructure with ultralow noise and electrical drift potentially allows for a multiple-bit memory cell. Artificial intelligence and other data-intensive applications have escalated the demand for data storage and processing. New computing devices, such as phase-change random access memory (PCRAM)–based neuro-inspired devices, are promising options for breaking the von Neumann barrier by unifying storage with computing in memory cells. However, current PCRAM devices have considerable noise and drift in electrical resistance that erodes the precision and consistency of these devices. We designed a phase-change heterostructure (PCH) that consists of alternately stacked phase-change and confinement nanolayers to suppress the noise and drift, allowing reliable iterative RESET and cumulative SET operations for high-performance neuro-inspired computing. Our PCH architecture is amenable to industrial production as an intrinsic materials solution, without complex manufacturing procedure or much increased fabrication cost.

[1]  C. Wright,et al.  Arithmetic and Biologically-Inspired Computing Using Phase-Change Materials , 2011, Advanced materials.

[2]  Jan Siegel,et al.  Femtosecond x-ray diffraction reveals a liquid–liquid phase transition in phase-change materials , 2019, Science.

[3]  Wei Zhang,et al.  Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing , 2017, Science.

[4]  Behrad Gholipour,et al.  Characterization of supercooled liquid Ge2Sb2Te5 and its crystallization by ultrafast-heating calorimetry. , 2012, Nature materials.

[5]  H.-S. Philip Wong,et al.  In-memory computing with resistive switching devices , 2018, Nature Electronics.

[6]  G. Kresse,et al.  From ultrasoft pseudopotentials to the projector augmented-wave method , 1999 .

[7]  Michele Parrinello,et al.  First-principles study of liquid and amorphous Sb 2 Te 3 , 2010 .

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

[9]  Xi Dai,et al.  Topological insulators in Bi2Se3, Bi2Te3 and Sb2Te3 with a single Dirac cone on the surface , 2009 .

[10]  K. Novoselov,et al.  2D materials and van der Waals heterostructures , 2016, Science.

[11]  Junji Tominaga,et al.  High‐Speed Bipolar Switching of Sputtered Ge–Te/Sb–Te Superlattice iPCM with Enhanced Cyclability , 2019, physica status solidi (RRL) – Rapid Research Letters.

[12]  S.Y. Lee,et al.  Full integration and cell characteristics for 64Mb nonvolatile PRAM , 2004, Digest of Technical Papers. 2004 Symposium on VLSI Technology, 2004..

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

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

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

[16]  Songlin Feng,et al.  Direct observation of titanium-centered octahedra in titanium–antimony–tellurium phase-change material , 2015, Nature Communications.

[17]  J. Yang,et al.  Memristive crossbar arrays for brain-inspired computing , 2019, Nature Materials.

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

[19]  Min Zhu,et al.  Thermal Barrier Phase Change Memory. , 2019, ACS applied materials & interfaces.

[20]  H.-S. Philip Wong,et al.  Phase Change Memory , 2010, Proceedings of the IEEE.

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

[22]  G. Kresse,et al.  Ab initio molecular dynamics for liquid metals. , 1993 .

[23]  Tow Chong Chong,et al.  Phase change random access memory cell with superlattice-like structure , 2006 .

[24]  D. Ielmini,et al.  Logic Computation in Phase Change Materials by Threshold and Memory Switching , 2013, Advanced materials.

[25]  Stephen R. Elliott,et al.  Computer‐simulation design of new phase‐change memory materials , 2010 .

[26]  Richard Dronskowski,et al.  Crystal orbital Hamilton populations (COHP): energy-resolved visualization of chemical bonding in solids based on density-functional calculations , 1993 .

[27]  C. David Wright,et al.  In-memory computing on a photonic platform , 2018, Science Advances.

[28]  Xianju Zhou,et al.  Electronic spectra and crystal field analysis of energy levels of Ho3+ in HoF6(3-). , 2011, The journal of physical chemistry. A.

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

[30]  Wei Zhang,et al.  Designing crystallization in phase-change materials for universal memory and neuro-inspired computing , 2019, Nature Reviews Materials.

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

[32]  Yan-Wei Li,et al.  Configuration correlation governs slow dynamics of supercooled metallic liquids , 2018, Proceedings of the National Academy of Sciences.

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

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

[35]  Yusuf Leblebici,et al.  Neuromorphic computing with multi-memristive synapses , 2017, Nature Communications.

[36]  Armantas Melianas,et al.  Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing , 2019, Science.

[37]  Abu Sebastian,et al.  Tutorial: Brain-inspired computing using phase-change memory devices , 2018, Journal of Applied Physics.

[38]  Richard Dronskowski,et al.  LOBSTER: A tool to extract chemical bonding from plane‐wave based DFT , 2016, J. Comput. Chem..

[39]  H.-S. Philip Wong,et al.  Phase-Change Memory—Towards a Storage-Class Memory , 2017, IEEE Transactions on Electron Devices.

[40]  J. Feldmann,et al.  All-optical spiking neurosynaptic networks with self-learning capabilities , 2019, Nature.

[41]  Zhitang Song,et al.  Ti-Sb-Te alloy: a candidate for fast and long-life phase-change memory. , 2015, ACS applied materials & interfaces.

[42]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

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

[44]  Paolo Fantini,et al.  Experimental investigation of transport properties in chalcogenide materials through 1∕f noise measurements , 2006 .

[45]  Andrea Redaelli,et al.  Evidence for Thermal‐Based Transition in Super‐Lattice Phase Change Memory , 2019, physica status solidi (RRL) – Rapid Research Letters.

[46]  Volker L. Deringer,et al.  Crystal orbital Hamilton population (COHP) analysis as projected from plane-wave basis sets. , 2011, The journal of physical chemistry. A.

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

[48]  C. Wright,et al.  Beyond von‐Neumann Computing with Nanoscale Phase‐Change Memory Devices , 2013 .

[49]  Manuel Le Gallo,et al.  Monatomic phase change memory , 2018, Nature Materials.

[50]  C. David Wright,et al.  Precise computing with imprecise devices , 2018 .

[51]  Rajeev Ahuja,et al.  Structure of phase change materials for data storage. , 2006, Physical review letters.

[52]  Richard Dronskowski,et al.  Analytic projection from plane‐wave and PAW wavefunctions and application to chemical‐bonding analysis in solids , 2013, J. Comput. Chem..

[53]  P Fons,et al.  Interfacial phase-change memory. , 2011, Nature nanotechnology.

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

[55]  Ke Deng,et al.  Experimental progress on layered topological semimetals , 2019, 2D Materials.

[56]  Pritish Narayanan,et al.  Equivalent-accuracy accelerated neural-network training using analogue memory , 2018, Nature.

[57]  Wei Zhang,et al.  Progressive amorphization of GeSbTe phase-change material under electron beam irradiation , 2019, APL Materials.

[58]  Joost VandeVondele,et al.  cp2k: atomistic simulations of condensed matter systems , 2014 .