Can conventional phase-change memory devices be scaled down to single-nanometre dimensions?

The scaling potential of 'mushroom-type' phase-change memory devices is evaluated, down to single-nanometre dimensions, using physically realistic simulations that combine electro-thermal modelling with a Gillespie Cellular Automata phase-transformation approach. We found that cells with heater contact sizes as small as 6 nm could be successfully amorphized and re-crystallized (RESET and SET) using moderate excitation voltages. However, to enable the efficient formation of amorphous domes during RESET in small cells (heater contact diameters of 10 nm or less), it was necessary to improve the thermal confinement of the cell to reduce heat loss via the electrodes. The resistance window between the SET and RESET states decreased as the cell size reduced, but it was still more than an order of magnitude even for the smallest cells. As expected, the RESET current reduced as the cells got smaller; indeed, RESET current scaled with the inverse of the heater contact diameter and ultra-small RESET currents of only 19 μA were achieved for the smallest cells. Our results show that the conventional mushroom-type phase-change cell architecture is scalable and operable in the sub-10nm region.

[1]  C. Peng,et al.  Experimental and theoretical investigations of laser-induced crystallization and amorphization in phase-change optical recording media , 1997 .

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

[3]  V. Weidenhof,et al.  Structural transformations of Ge2Sb2Te5 films studied by electrical resistance measurements , 2000 .

[4]  Rong Zhao,et al.  Ultrafast phase-change logic device driven by melting processes , 2014, Proceedings of the National Academy of Sciences.

[5]  H. Wong,et al.  Generalized Phase Change Memory Scaling Rule Analysis , 2006, 2006 21st IEEE Non-Volatile Semiconductor Memory Workshop.

[6]  D. Ielmini,et al.  Self-aligned nanotube-nanowire phase change memory. , 2013, Nano letters.

[7]  F. Rao,et al.  Superlattice-like electrode for low-power phase-change random access memory , 2012 .

[8]  C. David Wright,et al.  An optoelectronic framework enabled by low-dimensional phase-change films , 2014, Nature.

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

[10]  P. Ashwin,et al.  Threshold switching via electric field induced crystallization in phase-change memory devices , 2012 .

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

[12]  Harish Bhaskaran,et al.  Integrated all-photonic non-volatile multi-level memory , 2015, Nature Photonics.

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

[14]  S.Y. Lee,et al.  Process technologies for the integration of high density phase change RAM , 2005, 2005 International Conference on Integrated Circuit Design and Technology, 2005. ICICDT 2005..

[15]  Eric Pop,et al.  Energy-Efficient Phase-Change Memory with Graphene as a Thermal Barrier. , 2015, Nano letters.

[16]  D. Ielmini,et al.  Modeling of Programming and Read Performance in Phase-Change Memories—Part I: Cell Optimization and Scaling , 2008, IEEE Transactions on Electron Devices.

[17]  Jiale Liang,et al.  An Ultra-Low Reset Current Cross-Point Phase Change Memory With Carbon Nanotube Electrodes , 2012, IEEE Transactions on Electron Devices.

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

[19]  Nano-scaled chalcogenide-based memories. , 2011, Nanotechnology.

[20]  Haralampos Pozidis,et al.  Non-resistance-based cell-state metric for phase-change memory , 2011 .

[21]  P. Ashwin,et al.  Fast simulation of phase-change processes in chalcogenide alloys using a Gillespie-type cellular automata approach , 2008 .

[22]  B. Gleixner,et al.  A 90nm Phase Change Memory Technology for Stand-Alone Non-Volatile Memory Applications , 2006, 2006 Symposium on VLSI Technology, 2006. Digest of Technical Papers..

[23]  E. Eleftheriou,et al.  All-memristive neuromorphic computing with level-tuned neurons , 2016, Nanotechnology.

[24]  C. Wright,et al.  Models for phase-change of Ge2Sb2Te5 in optical and electrical memory devices , 2004 .

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

[26]  A. Pirovano,et al.  Scaling analysis of phase-change memory technology , 2003, IEEE International Electron Devices Meeting 2003.

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

[28]  W. J. Wang,et al.  Breaking the Speed Limits of Phase-Change Memory , 2012, Science.

[29]  Zhitang Song,et al.  Low-power phase change memory with multilayer TiN/W nanostructure electrode , 2014 .

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