In-memory computing with resistive switching devices

Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which data can be transferred between the processing unit and the memory unit represents a fundamental limitation of modern computers, known as the memory wall. In-memory computing is an approach that attempts to address this issue by designing systems that compute within the memory, thus eliminating the energy-intensive and time-consuming data movement that plagues current designs. Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital, analogue, and stochastic computing schemes that have been proposed, and explore the microscopic physical mechanisms involved. Finally, we discuss the challenges in-memory computing faces, including the required scaling characteristics, in delivering next-generation computing.This Review Article examines the development of in-memory computing using resistive switching devices.

[1]  D. Ielmini,et al.  Reliability Impact of Chalcogenide-Structure Relaxation in Phase-Change Memory (PCM) Cells—Part I: Experimental Study , 2009, IEEE Transactions on Electron Devices.

[2]  Yoon-Ha Jeong,et al.  Optimization of Conductance Change in Pr1–xCaxMnO3-Based Synaptic Devices for Neuromorphic Systems , 2015, IEEE Electron Device Letters.

[3]  S. Ambrogio,et al.  Normally-off Logic Based on Resistive Switches—Part I: Logic Gates , 2015, IEEE Transactions on Electron Devices.

[4]  Giacomo Indiveri,et al.  Scaling mixed-signal neuromorphic processors to 28 nm FD-SOI technologies , 2016, 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[5]  Denis C. Daly,et al.  Through the Looking Glass - The 2018 Edition: Trends in Solid-State Circuits from the 65th ISSCC , 2018, IEEE Solid-State Circuits Magazine.

[6]  U. Böttger,et al.  Beyond von Neumann—logic operations in passive crossbar arrays alongside memory operations , 2012, Nanotechnology.

[7]  H-S Philip Wong,et al.  Memory leads the way to better computing. , 2015, Nature nanotechnology.

[8]  D. Ielmini,et al.  Phase change materials and their application to nonvolatile memories. , 2010, Chemical reviews.

[9]  An Chen,et al.  Utilizing the Variability of Resistive Random Access Memory to Implement Reconfigurable Physical Unclonable Functions , 2015, IEEE Electron Device Letters.

[10]  Peng Lin,et al.  Fully memristive neural networks for pattern classification with unsupervised learning , 2018 .

[11]  J. Grollier,et al.  A ferroelectric memristor. , 2012, Nature materials.

[12]  D. Jeong,et al.  Memristors for Energy‐Efficient New Computing Paradigms , 2016 .

[13]  D. Jeong,et al.  Nanofilamentary resistive switching in binary oxide system; a review on the present status and outlook , 2011, Nanotechnology.

[14]  Kyeong-Sik Min,et al.  New Memristor-Based Crossbar Array Architecture with 50-% Area Reduction and 48-% Power Saving for Matrix-Vector Multiplication of Analog Neuromorphic Computing , 2014 .

[15]  Giovanni De Micheli,et al.  The Programmable Logic-in-Memory (PLiM) computer , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[16]  Shimeng Yu,et al.  An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation , 2011, IEEE Transactions on Electron Devices.

[17]  Abu Sebastian,et al.  Accumulation-Based Computing Using Phase-Change Memories With FET Access Devices , 2015, IEEE Electron Device Letters.

[18]  Alessandro Calderoni,et al.  Voltage-Controlled Cycling Endurance of HfOx-Based Resistive-Switching Memory , 2015, IEEE Transactions on Electron Devices.

[19]  Lifeng Liu,et al.  Reconfigurable Nonvolatile Logic Operations in Resistance Switching Crossbar Array for Large‐Scale Circuits , 2016, Advanced materials.

[20]  P. Kapur,et al.  Technology and reliability constrained future copper interconnects. I. Resistance modeling , 2002 .

[21]  D. Ielmini,et al.  Recovery and Drift Dynamics of Resistance and Threshold Voltages in Phase-Change Memories , 2007, IEEE Transactions on Electron Devices.

[22]  Daniele Ielmini,et al.  Analytical model for subthreshold conduction and threshold switching in chalcogenide-based memory devices , 2007 .

[23]  Chris H. Kim,et al.  A Magnetic Tunnel Junction based True Random Number Generator with conditional perturb and real-time output probability tracking , 2014, 2014 IEEE International Electron Devices Meeting.

[24]  H.-S. Philip Wong,et al.  Resistive RAM-Centric Computing: Design and Modeling Methodology , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[25]  Alex Pappachen James,et al.  Resistive Threshold Logic , 2013, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[26]  S. Yuasa,et al.  Giant room-temperature magnetoresistance in single-crystal Fe/MgO/Fe magnetic tunnel junctions , 2004, Nature materials.

[27]  D. Ielmini,et al.  Modeling the Universal Set/Reset Characteristics of Bipolar RRAM by Field- and Temperature-Driven Filament Growth , 2011, IEEE Transactions on Electron Devices.

[28]  Qi Liu,et al.  Real‐Time Observation on Dynamic Growth/Dissolution of Conductive Filaments in Oxide‐Electrolyte‐Based ReRAM , 2012, Advanced materials.

[29]  Andrea L. Lacaita,et al.  Cell-to-Cell and Cycle-to-Cycle Retention Statistics in Phase-Change Memory Arrays , 2015, IEEE Transactions on Electron Devices.

[30]  Denis C. Daly,et al.  Through the Looking Glass -- The 2017 Edition: Trends in Solid-State Circuits from ISSCC , 2017, IEEE Solid-State Circuits Magazine.

[31]  Mark Horowitz,et al.  1.1 Computing's energy problem (and what we can do about it) , 2014, 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC).

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

[33]  Jaejin Lee,et al.  25.2 A 1.2V 8Gb 8-channel 128GB/s high-bandwidth memory (HBM) stacked DRAM with effective microbump I/O test methods using 29nm process and TSV , 2014, 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC).

[34]  Saurabh Sinha,et al.  ASAP7: A 7-nm finFET predictive process design kit , 2016, Microelectron. J..

[35]  Qing Wu,et al.  A novel true random number generator based on a stochastic diffusive memristor , 2017, Nature Communications.

[36]  Alessandro Calderoni,et al.  Physical Unbiased Generation of Random Numbers With Coupled Resistive Switching Devices , 2016, IEEE Transactions on Electron Devices.

[37]  Massimiliano Di Ventra,et al.  The parallel approach , 2013 .

[38]  Pierre-Emmanuel Gaillardon,et al.  Memristive logic: A framework for evaluation and comparison , 2017, 2017 27th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS).

[39]  U. Böttger,et al.  Ferroelectricity in hafnium oxide thin films , 2011 .

[40]  M. Rozenberg,et al.  A Leaky‐Integrate‐and‐Fire Neuron Analog Realized with a Mott Insulator , 2017 .

[41]  Carver A. Mead,et al.  A single-transistor silicon synapse , 1996 .

[42]  Walter Hartner,et al.  FeRAM technology for high density applications , 2001, Microelectron. Reliab..

[43]  D. B. Strukov,et al.  Programmable CMOS/Memristor Threshold Logic , 2013, IEEE Transactions on Nanotechnology.

[44]  A. Sawa Resistive switching in transition metal oxides , 2008 .

[45]  W. Lu,et al.  High-density Crossbar Arrays Based on a Si Memristive System , 2008 .

[46]  Kinam Kim,et al.  A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O(5-x)/TaO(2-x) bilayer structures. , 2011, Nature materials.

[47]  Andre K. Geim,et al.  The rise of graphene. , 2007, Nature materials.

[48]  A. Fert,et al.  The emergence of spin electronics in data storage. , 2007, Nature materials.

[49]  Giacomo Indiveri,et al.  Memory and Information Processing in Neuromorphic Systems , 2015, Proceedings of the IEEE.

[50]  F. Pellizzer,et al.  Optimization metrics for Phase Change Memory (PCM) cell architectures , 2014, 2014 IEEE International Electron Devices Meeting.

[51]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[52]  J Feldmann,et al.  Calculating with light using a chip-scale all-optical abacus , 2017, Nature Communications.

[53]  Hiroshi Imamura,et al.  Spin dice: A scalable truly random number generator based on spintronics , 2014 .

[54]  R. Waser,et al.  Nanoionics-based resistive switching memories. , 2007, Nature materials.

[55]  Gregory S. Snider,et al.  ‘Memristive’ switches enable ‘stateful’ logic operations via material implication , 2010, Nature.

[56]  Georgios Ch. Sirakoulis,et al.  Boolean Logic Operations and Computing Circuits Based on Memristors , 2014, IEEE Transactions on Circuits and Systems II: Express Briefs.

[57]  G H Bernstein,et al.  Nanomagnet logic: progress toward system-level integration , 2011, Journal of physics. Condensed matter : an Institute of Physics journal.

[58]  S. Yuasa,et al.  A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy , 2016, Scientific Reports.

[59]  S. Balatti,et al.  Resistive Switching by Voltage-Driven Ion Migration in Bipolar RRAM—Part II: Modeling , 2012, IEEE Transactions on Electron Devices.

[60]  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.

[61]  David A. Patterson,et al.  In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).

[62]  C. Gerber,et al.  Reproducible switching effect in thin oxide films for memory applications , 2000 .

[63]  J Joshua Yang,et al.  Memristive devices for computing. , 2013, Nature nanotechnology.

[64]  Byung Joon Choi,et al.  High‐Speed and Low‐Energy Nitride Memristors , 2016 .

[65]  Jan Reineke,et al.  Ascertaining Uncertainty for Efficient Exact Cache Analysis , 2017, CAV.

[66]  Stefano Ambrogio,et al.  True Random Number Generation by Variability of Resistive Switching in Oxide-Based Devices , 2015, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[67]  Farnood Merrikh-Bayat,et al.  Training and operation of an integrated neuromorphic network based on metal-oxide memristors , 2014, Nature.

[68]  D. Ielmini,et al.  Understanding cycling endurance in perpendicular spin-transfer torque (p-STT) magnetic memory , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).

[69]  Joel Emer,et al.  Eyeriss: an Energy-efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks Accessed Terms of Use , 2022 .

[70]  Jiantao Zhou,et al.  Stochastic Memristive Devices for Computing and Neuromorphic Applications , 2013, Nanoscale.

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

[72]  Srinivas Devadas,et al.  Physical Unclonable Functions and Applications: A Tutorial , 2014, Proceedings of the IEEE.

[73]  Sally A. McKee,et al.  Hitting the memory wall: implications of the obvious , 1995, CARN.

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

[75]  Siegfried Selberherr,et al.  Implication logic gates using spin-transfer-torque-operated magnetic tunnel junctions for intrinsic logic-in-memory , 2013 .

[76]  Alessandro Calderoni,et al.  Statistical Fluctuations in HfOx Resistive-Switching Memory: Part I - Set/Reset Variability , 2014, IEEE Transactions on Electron Devices.

[77]  Snider,et al.  Digital logic gate using quantum-Dot cellular automata , 1999, Science.

[78]  Ya-Chin King,et al.  A Contact-Resistive Random-Access-Memory-Based True Random Number Generator , 2012, IEEE Electron Device Letters.

[79]  Shimeng Yu,et al.  A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation , 2013, Advanced materials.

[80]  J. Thomas Pawlowski,et al.  Hybrid memory cube (HMC) , 2011, 2011 IEEE Hot Chips 23 Symposium (HCS).

[81]  N. Righos,et al.  A stackable cross point Phase Change Memory , 2009, 2009 IEEE International Electron Devices Meeting (IEDM).

[82]  M. Trentzsch,et al.  A 28nm HKMG super low power embedded NVM technology based on ferroelectric FETs , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).

[83]  황철성,et al.  Nanofilamentary resistive switching in binary oxide system , 2011 .

[84]  M. Mitchell Waldrop,et al.  The chips are down for Moore’s law , 2016, Nature.

[85]  V. Cros,et al.  Spin-torque building blocks. , 2014, Nature Materials.

[86]  R. Wiesendanger,et al.  Realizing All-Spin–Based Logic Operations Atom by Atom , 2011, Science.

[87]  D. Querlioz,et al.  Visual Pattern Extraction Using Energy-Efficient “2-PCM Synapse” Neuromorphic Architecture , 2012, IEEE Transactions on Electron Devices.

[88]  N. Yamada,et al.  Rapid‐phase transitions of GeTe‐Sb2Te3 pseudobinary amorphous thin films for an optical disk memory , 1991 .

[89]  J. Slonczewski Current-driven excitation of magnetic multilayers , 1996 .

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

[91]  Wolfgang Maass,et al.  Noise as a Resource for Computation and Learning in Networks of Spiking Neurons , 2014, Proceedings of the IEEE.

[92]  Andrew D Kent,et al.  A new spin on magnetic memories. , 2015, Nature nanotechnology.

[93]  O. Richard,et al.  10×10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation , 2011, 2011 International Electron Devices Meeting.

[94]  H. Hwang,et al.  HfZrOx-Based Ferroelectric Synapse Device With 32 Levels of Conductance States for Neuromorphic Applications , 2017, IEEE Electron Device Letters.

[95]  Jiaming Zhang,et al.  Analogue signal and image processing with large memristor crossbars , 2017, Nature Electronics.

[96]  Bing Chen,et al.  Efficient in-memory computing architecture based on crossbar arrays , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[97]  C. Lam,et al.  A phase change memory cell with metallic surfactant layer as a resistance drift stabilizer , 2013, 2013 IEEE International Electron Devices Meeting.

[98]  Dmitri E. Nikonov,et al.  Overview of Beyond-CMOS Devices and a Uniform Methodology for Their Benchmarking , 2013, Proceedings of the IEEE.

[99]  Shimeng Yu,et al.  HfOx-based vertical resistive switching random access memory suitable for bit-cost-effective three-dimensional cross-point architecture. , 2013, ACS nano.

[100]  Ligang Gao,et al.  Physical Unclonable Function Exploiting Sneak Paths in Resistive Cross-point Array , 2016, IEEE Transactions on Electron Devices.

[101]  S. Ambrogio,et al.  Statistical Fluctuations in HfOx Resistive-Switching Memory: Part II—Random Telegraph Noise , 2014, IEEE Transactions on Electron Devices.