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A cell classifier for RRAM process development

A cell classifier for RRAM process development
A cell classifier for RRAM process development
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.
1057-7130
1-5
Gupta, I.
30097cf6-f819-4e86-8d92-6a2d1d1754c8
Serb, A.
30f5ec26-f51d-42b3-85fd-0325a27a792c
Berdan, R.
082f1f5b-eaee-48a6-b728-414fc65f72bd
Khiat, A.
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Regoutz, A.
f10acbf6-a56e-4193-88d5-5167f3ecb385
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf
Gupta, I.
30097cf6-f819-4e86-8d92-6a2d1d1754c8
Serb, A.
30f5ec26-f51d-42b3-85fd-0325a27a792c
Berdan, R.
082f1f5b-eaee-48a6-b728-414fc65f72bd
Khiat, A.
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Regoutz, A.
f10acbf6-a56e-4193-88d5-5167f3ecb385
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf

Gupta, I., Serb, A., Berdan, R., Khiat, A., Regoutz, A. and Prodromakis, T. (2015) A cell classifier for RRAM process development IEEE Transactions on Circuits and Systems II Express Briefs, pp. 1-5. (doi:10.1109/TCSII.2015.2415276).

Record type: Article

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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Published date: 19 March 2015
Organisations: Nanoelectronics and Nanotechnology

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Local EPrints ID: 377540
URI: http://eprints.soton.ac.uk/id/eprint/377540
ISSN: 1057-7130
PURE UUID: 83600b33-c284-40d6-8c22-3e95d9f9d8e6

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Date deposited: 09 Jun 2015 10:33
Last modified: 17 Jul 2017 20:59

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Contributors

Author: I. Gupta
Author: A. Serb
Author: R. Berdan
Author: A. Khiat
Author: A. Regoutz
Author: T. Prodromakis

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