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.
del Valle, Javier, et al. "Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing." Journal of Applied Physics, vol. 124, no. 21, Dec. 2018. https://doi.org/10.1063/1.5047800
del Valle, Javier, Ramirez, Juan Gabriel, Rozenberg, Marcelo J., & Schuller, Ivan K. (2018). Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing. Journal of Applied Physics, 124(21). https://doi.org/10.1063/1.5047800
del Valle, Javier, Ramirez, Juan Gabriel, Rozenberg, Marcelo J., et al., "Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing," Journal of Applied Physics 124, no. 21 (2018), https://doi.org/10.1063/1.5047800
@article{osti_1481895,
author = {del Valle, Javier and Ramirez, Juan Gabriel and Rozenberg, Marcelo J. and Schuller, Ivan K.},
title = {Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing},
annote = {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},
url = {https://www.osti.gov/biblio/1481895},
journal = {Journal of Applied Physics},
issn = {ISSN 0021-8979},
number = {21},
volume = {124},
place = {United States},
publisher = {American Institute of Physics (AIP)},
year = {2018},
month = {12}}
Energy Frontier Research Centers (EFRC) (United States). Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C); Univ. Paris-Sud, Orsay (France); Univ. of California, San Diego, CA (United States); Univ. of Los Andes, Bogotá (Colombia)
Sponsoring Organization:
Office of Naval Research (ONR) (United States); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); Univ. of Los Andes (Colombia)
Grant/Contract Number:
SC0019273
OSTI ID:
1481895
Journal Information:
Journal of Applied Physics, Journal Name: Journal of Applied Physics Journal Issue: 21 Vol. 124; ISSN 0021-8979