Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching.

Autor: Li Y; Sandia National Laboratories, Livermore, CA, 94550, USA., Fuller EJ; Sandia National Laboratories, Livermore, CA, 94550, USA., Sugar JD; Sandia National Laboratories, Livermore, CA, 94550, USA., Yoo S; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA., Ashby DS; Sandia National Laboratories, Livermore, CA, 94550, USA., Bennett CH; Sandia National Laboratories, Albuquerque, NM, 87185, USA., Horton RD; Sandia National Laboratories, Livermore, CA, 94550, USA., Bartsch MS; Sandia National Laboratories, Livermore, CA, 94550, USA., Marinella MJ; Sandia National Laboratories, Albuquerque, NM, 87185, USA., Lu WD; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA., Talin AA; Sandia National Laboratories, Livermore, CA, 94550, USA.
Jazyk: angličtina
Zdroj: Advanced materials (Deerfield Beach, Fla.) [Adv Mater] 2020 Nov; Vol. 32 (45), pp. e2003984. Date of Electronic Publication: 2020 Sep 22.
DOI: 10.1002/adma.202003984
Abstrakt: 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. In this work, 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 TiO 2- 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.
(© 2020 Wiley-VCH GmbH.)
Databáze: MEDLINE
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