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Title: Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing

Abstract

This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled) implementation using transition metal oxides materials, which exhibit unique properties that emulate key functionalities needed for this application. In the Introduction, we compare the main differences between a conventional computational machine, based on the Turing-von Neumann paradigm, to a Neuromorphic machine, which tries to emulate important functionalities of a biological brain. We also describe the main electrical properties of biological systems, which would be useful to implement in a charge-based system. In Chapter II, we describe the main components of a possible solid-state implementation. In Chapter III, we describe a variety of Resistive Switching phenomena, which may serve as the functional basis for the implementation of key devices for Neuromorphic computing. In Chapter IV we describe why transition metal oxides, are promising materials for future Neuromorphic machines. Theoretical models describing different resistive switching mechanisms are discussed in Chapter V while existing implementations are described in Chapter VI. Chapter VII presents applications to practical problems. We list in Chapter VIII important basic research challenges and open issues. Wemore » discuss issues related to specific implementations, novel materials, devices and phenomena. The development of reliable, fault tolerant, energy efficient devices, their scaling and integration into a Neuromorphic computer may bring us closer to the development of a machine that rivals the brain.« less

Authors:
 [1];  [2];  [3];  [1]
  1. Univ. of California, San Diego, CA (United States)
  2. Univ. of Los Andes, Bogotá (Colombia)
  3. Univ. Paris-Sud, Orsay (France)
Publication Date:
Research Org.:
Univ. of California, San Diego, CA (United States); Univ. Paris-Sud, Orsay (France); Univ. of Los Andes, Bogotá (Colombia)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); Office of Naval Research (ONR) (United States); Univ. of Los Andes (Colombia)
OSTI Identifier:
1481895
Grant/Contract Number:  
SC0019273; N00014-15-1-2848; 120471250659; 120424054303
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Applied Physics
Additional Journal Information:
Journal Volume: 124; Journal Issue: 21; Journal ID: ISSN 0021--8979
Country of Publication:
United States
Language:
English
Subject:
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; 97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE

Citation Formats

del Valle, Javier, Ramirez, Juan Gabriel, Rozenberg, Marcelo J., and Schuller, Ivan K.. Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing. United States: N. p., 2018. Web. doi:10.1063/1.5047800.
del Valle, Javier, Ramirez, Juan Gabriel, Rozenberg, Marcelo J., & Schuller, Ivan K.. Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing. United States. doi:10.1063/1.5047800.
del Valle, Javier, Ramirez, Juan Gabriel, Rozenberg, Marcelo J., and Schuller, Ivan K.. Fri . "Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing". United States. doi:10.1063/1.5047800.
@article{osti_1481895,
title = {Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing},
author = {del Valle, Javier and Ramirez, Juan Gabriel and Rozenberg, Marcelo J. and Schuller, Ivan K.},
abstractNote = {This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled) implementation using transition metal oxides materials, which exhibit unique properties that emulate key functionalities needed for this application. In the Introduction, we compare the main differences between a conventional computational machine, based on the Turing-von Neumann paradigm, to a Neuromorphic machine, which tries to emulate important functionalities of a biological brain. We also describe the main electrical properties of biological systems, which would be useful to implement in a charge-based system. In Chapter II, we describe the main components of a possible solid-state implementation. In Chapter III, we describe a variety of Resistive Switching phenomena, which may serve as the functional basis for the implementation of key devices for Neuromorphic computing. In Chapter IV we describe why transition metal oxides, are promising materials for future Neuromorphic machines. Theoretical models describing different resistive switching mechanisms are discussed in Chapter V while existing implementations are described in Chapter VI. Chapter VII presents applications to practical problems. We list in Chapter VIII important basic research challenges and open issues. We discuss issues related to specific implementations, novel materials, devices and phenomena. The development of reliable, fault tolerant, energy efficient devices, their scaling and integration into a Neuromorphic computer may bring us closer to the development of a machine that rivals the brain.},
doi = {10.1063/1.5047800},
journal = {Journal of Applied Physics},
number = 21,
volume = 124,
place = {United States},
year = {Fri Nov 16 00:00:00 EST 2018},
month = {Fri Nov 16 00:00:00 EST 2018}
}

Journal Article:
Free Publicly Available Full Text
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