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Title: Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Abstract

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. Here, 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.

Authors:
ORCiD logo [1];  [1];  [1];  [2];  [1];  [3];  [1];  [1];  [3];  [2];  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Electrical Engineering and Computer Science
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1667424
Alternate Identifier(s):
OSTI ID: 1804234
Report Number(s):
SAND-2020-9164J
Journal ID: ISSN 0935-9648; 690303
Grant/Contract Number:  
AC04-94AL85000; NA0003525; DE‐NA‐0003525
Resource Type:
Accepted Manuscript
Journal Name:
Advanced Materials
Additional Journal Information:
Journal Volume: 32; Journal Issue: 45; Journal ID: ISSN 0935-9648
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; resistive switching; deterministic; point defects; neuromorphic computing

Citation Formats

Li, Yiyang, Fuller, Elliot J., Sugar, Joshua D., Yoo, Sangmin, Ashby, David S., Bennett, Christopher H., Horton, Robert D., Bartsch, Michael S., Marinella, Matthew J., Lu, Wei D., and Talin, A. Alec. Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching. United States: N. p., 2020. Web. https://doi.org/10.1002/adma.202003984.
Li, Yiyang, Fuller, Elliot J., Sugar, Joshua D., Yoo, Sangmin, Ashby, David S., Bennett, Christopher H., Horton, Robert D., Bartsch, Michael S., Marinella, Matthew J., Lu, Wei D., & Talin, A. Alec. Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching. United States. https://doi.org/10.1002/adma.202003984
Li, Yiyang, Fuller, Elliot J., Sugar, Joshua D., Yoo, Sangmin, Ashby, David S., Bennett, Christopher H., Horton, Robert D., Bartsch, Michael S., Marinella, Matthew J., Lu, Wei D., and Talin, A. Alec. Tue . "Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching". United States. https://doi.org/10.1002/adma.202003984.
@article{osti_1667424,
title = {Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching},
author = {Li, Yiyang and Fuller, Elliot J. and Sugar, Joshua D. and Yoo, Sangmin and Ashby, David S. and Bennett, Christopher H. and Horton, Robert D. and Bartsch, Michael S. and Marinella, Matthew J. and Lu, Wei D. and Talin, A. Alec},
abstractNote = {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. Here, 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.},
doi = {10.1002/adma.202003984},
journal = {Advanced Materials},
number = 45,
volume = 32,
place = {United States},
year = {2020},
month = {9}
}

Journal Article:
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