Development, characterization, and modeling of a TaOx ReRAM for a neuromorphic accelerator

Matthew J. Marinella; Patrick R. Mickel; Andrew J. Lohn; David R. Hughart; Robert Bondi; Denis Mamaluy; Harry Hjalmarson; James E. Stevens; Seth Decker; Roger Apodaca; Brian Evans; J. Bradley Aimone; Fredrick Rothganger; Conrad James; Erik P. DeBenedictis

doi:10.1149/06414.0037ecst

Development, characterization, and modeling of a TaO_x ReRAM for a neuromorphic accelerator

Matthew J. Marinella, Patrick R. Mickel, Andrew J. Lohn, David R. Hughart, Robert Bondi, Denis Mamaluy, Harry Hjalmarson, James E. Stevens, Seth Decker, Roger Apodaca, Brian Evans, J. Bradley Aimone, Fredrick Rothganger, Conrad James, Erik P. DeBenedictis

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

Resistive random access memory (ReRAM), or memristors, may be capable of significantly improve the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAMbased neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer some insight into the factors responsible for this variability.

Original language	English (US)
Pages (from-to)	37-42
Number of pages	6
Journal	ECS Transactions
Volume	64
Issue number	14
DOIs	https://doi.org/10.1149/06414.0037ecst
State	Published - 2014
Externally published	Yes
Event	Symposium on Nonvolatile Memories 3 - 2014 ECS and SMEQ Joint International Meeting - Cancun, Mexico Duration: Oct 5 2014 → Oct 9 2014

ASJC Scopus subject areas

General Engineering

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Marinella, M. J., Mickel, P. R., Lohn, A. J., Hughart, D. R., Bondi, R., Mamaluy, D., Hjalmarson, H., Stevens, J. E., Decker, S., Apodaca, R., Evans, B., Aimone, J. B., Rothganger, F., James, C., & DeBenedictis, E. P. (2014). Development, characterization, and modeling of a TaO_x ReRAM for a neuromorphic accelerator. ECS Transactions, 64(14), 37-42. https://doi.org/10.1149/06414.0037ecst

Marinella, MJ, Mickel, PR, Lohn, AJ, Hughart, DR, Bondi, R, Mamaluy, D, Hjalmarson, H, Stevens, JE, Decker, S, Apodaca, R, Evans, B, Aimone, JB, Rothganger, F, James, C & DeBenedictis, EP 2014, 'Development, characterization, and modeling of a TaO_x ReRAM for a neuromorphic accelerator', ECS Transactions, vol. 64, no. 14, pp. 37-42. https://doi.org/10.1149/06414.0037ecst

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abstract = "Resistive random access memory (ReRAM), or memristors, may be capable of significantly improve the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAMbased neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer some insight into the factors responsible for this variability.",

author = "Marinella, {Matthew J.} and Mickel, {Patrick R.} and Lohn, {Andrew J.} and Hughart, {David R.} and Robert Bondi and Denis Mamaluy and Harry Hjalmarson and Stevens, {James E.} and Seth Decker and Roger Apodaca and Brian Evans and Aimone, {J. Bradley} and Fredrick Rothganger and Conrad James and DeBenedictis, {Erik P.}",

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AU - Bondi, Robert

AU - Mamaluy, Denis

AU - Hjalmarson, Harry

AU - Stevens, James E.

AU - Decker, Seth

AU - Apodaca, Roger

AU - Evans, Brian

AU - Aimone, J. Bradley

AU - Rothganger, Fredrick

AU - James, Conrad

AU - DeBenedictis, Erik P.

PY - 2014

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AB - Resistive random access memory (ReRAM), or memristors, may be capable of significantly improve the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAMbased neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer some insight into the factors responsible for this variability.

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Development, characterization, and modeling of a TaO_x ReRAM for a neuromorphic accelerator

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