Synaptic plasticity in a memristive device below 500 mV

S. R. Nandakumar, Bipin Rajendran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

While machine learning algorithms are being successfully employed for a wide variety of high level cognitive tasks, their wide-spread adoption on mobile platforms faces challenges because of the large network sizes and extremely long training times involved in learning underlying features of user-dependent data. In this paper, we first discuss the requirements and target specifications of nanoscale devices that could enable hardware platforms supporting on-chip, real-time learning based on third generation spiking neural networks. We then survey some recent developments from the field of emerging non-volatile memory technologies such as chalcogenide based phase change memory and other memristive devices that could be used to build cognitive learning systems. We will then discuss how we have implemented synaptic plasticity in a Cu/SiO2/W memristive device at voltages below 500 mV using bio-mimetic waveforms. These devices hold promise to herald the next generation of information processing systems that are inspired by the brain.

Original languageEnglish (US)
Title of host publicationEmerging Materials for Post CMOS Devices/Sensing and Applications 8
EditorsD. Misra, P. Hesketh, Z. Karim, S. De Gendt, Y. Obeng, P. Srinivasan
PublisherElectrochemical Society Inc.
Pages31-40
Number of pages10
Edition2
ISBN (Electronic)9781607688051
DOIs
StatePublished - 2017
EventSymposium on Emerging Materials for Post CMOS Devices/Sensing and Applications 8 - 231st ECS Meeting 2017 - New Orleans, United States
Duration: May 28 2017Jun 1 2017

Publication series

NameECS Transactions
Number2
Volume77
ISSN (Print)1938-6737
ISSN (Electronic)1938-5862

Other

OtherSymposium on Emerging Materials for Post CMOS Devices/Sensing and Applications 8 - 231st ECS Meeting 2017
CountryUnited States
CityNew Orleans
Period5/28/176/1/17

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Synaptic plasticity in a memristive device below 500 mV'. Together they form a unique fingerprint.

Cite this