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A cell classifier for RRAM process development (Dataset)

A cell classifier for RRAM process development (Dataset)
A cell classifier for RRAM process development (Dataset)
Devices that exhibit resistive switching are promising components for future nanoelectronics with applications ranging from emerging memory to neuromorphic computing and biosensors. In this work, we present an algorithm for identifying switchable devices i.e. devices that can be programmed in distinct resistive states and which change their state predictably and repeatably in response to input stimuli. The method is based on extrapolating the statistical significance of difference in between two distinct resistive states as measured from devices subjected to standardised bias protocols. The test routine is applied on distinct elements of 32x32 RRAM crossbar arrays and yields a measure of device switchability in the form of a statistical significance pvalue. Ranking devices by p-value shows that switchable devices are typically found in the bottom 10% and are therefore easily distinguishable from non-functional devices. Implementation of this algorithm dramatically cuts RRAM testing time by granting fast access to the best devices in each array as well as yield metrics.
University of Southampton
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Berdan, Radu
e259cd5a-6e30-4439-94c0-9c44903e1e75
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Berdan, Radu
e259cd5a-6e30-4439-94c0-9c44903e1e75

Gupta, Isha, Serb, Alexantrou, Khiat, Ali, Prodromakis, Themistoklis and Berdan, Radu (2015) A cell classifier for RRAM process development (Dataset). University of Southampton doi:10.5258/SOTON/384034 [Dataset]

Record type: Dataset

Abstract

Devices that exhibit resistive switching are promising components for future nanoelectronics with applications ranging from emerging memory to neuromorphic computing and biosensors. In this work, we present an algorithm for identifying switchable devices i.e. devices that can be programmed in distinct resistive states and which change their state predictably and repeatably in response to input stimuli. The method is based on extrapolating the statistical significance of difference in between two distinct resistive states as measured from devices subjected to standardised bias protocols. The test routine is applied on distinct elements of 32x32 RRAM crossbar arrays and yields a measure of device switchability in the form of a statistical significance pvalue. Ranking devices by p-value shows that switchable devices are typically found in the bottom 10% and are therefore easily distinguishable from non-functional devices. Implementation of this algorithm dramatically cuts RRAM testing time by granting fast access to the best devices in each array as well as yield metrics.

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More information

Published date: 2015
Organisations: Electronics & Computer Science, Nanoelectronics and Nanotechnology
Projects:
Plasticity in NEUral Memristive Architectures
Funded by: UNSPECIFIED (EP/J00801X/1)
5 September 2011 to 14 April 2013
Reliably unreliable nanotechnologies
Funded by: UNSPECIFIED (EP/K017829/1)
2 September 2013 to 1 September 2018
Real neurons-nanoelectronics Architecture with Memristive Plasticity (RAMP)
Funded by: UNSPECIFIED (612058)
1 November 2013 to 31 October 2016

Identifiers

Local EPrints ID: 384034
URI: https://eprints.soton.ac.uk/id/eprint/384034
PURE UUID: 212dba29-3216-4af9-8849-b6e56cb01101
ORCID for Themistoklis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 08 Apr 2016 09:18
Last modified: 08 Aug 2018 00:30

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Contributors

Creator: Isha Gupta
Creator: Alexantrou Serb
Creator: Ali Khiat
Creator: Radu Berdan

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