Memristive Ag2S Synapses: towards Arti cial Neural Networks

101 Pages·2013·16.59 MB·English

Memristive Ag S Synapses: towards
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Arti(cid:28)cial Neural Networks
Catarina Dias
Thesis submitted to the
Faculty of Sciences of the University of Porto
in partial ful(cid:28)llment of the requirements
for the degree of
Master in Physics Engineering
Supervisor: Prof. Joªo Oliveira Ventura
co-Supervisor: Prof. Joªo Pedro Araœjo
Department of Physics and Astronomy
Faculty of Sciences of the University of Porto
September 2013
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To my mother, grandparents and uncle.
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Acknowledgments
First and above all, I would like to thank my supervisor, Professor Joªo Oliveira Ventura for his dedication,
motivationandpatience. Iamtrulythankfultohim,forbelievinginmyworkmorethanmeandforproviding
me this opportunity. Without him none of this would be possible. I have learned enormously and I look
forward to continue to improve with him.
Thanks to my co-supervisor, Professor Joªo Pedro Araœjo, for accepting me as his student and for his early
morning sympathy.
To Gon(cid:231)alo Pimentel for all the help with software that made my work easier and to Lu(cid:237)s Guerra for his
detailed overview of my thesis. But mainly, for their patience having me as a partner. I could not see better
persons to start my investigations with.
AspecialacknowledgmentforPauloAguiar,ofCMUP,forhisshareofknowledgeandinterestdemonstrated
in the work performed.
To members of IFIMUP-IN for the sympathy, interest and great work environment. A special thanks (in
order of appearance) to Mariana Proen(cid:231)a for the time spent with electrodeposition, to Arlete ApolinÆrio for
herlunchhourspassedinSEM,toFranciscoCarpinteiroforhiswideknowledgeandsupportandtoProfessor
Joaquim Agostinho Moreira for his interest in my work.
From CEMUP, also to Professor Carlos SÆ for his critical thinking and, sincerely, to Rui Rocha for his great
professionalism and patience during so many EDS hours.
To the INESC-MN for the collaboration and specially to Dr. Susana Freitas for her availability, suggestions
and helpful share of knowledge, and to Joªo Pereira for the guidance.
I would also like to thank for the opportunity to bene(cid:28)t from an ANICT masters grant for the development
of my masters thesis in (cid:16)Memristive Ag S Synapses: towards Arti(cid:28)cial Neural Networks(cid:17).
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In this (cid:28)ve years of journey, thanks to my colleagues for the admiration and con(cid:28)dence. To my friends an
apology for my crankiness and absence this last year. A special word for my other half for all the serenity,
comprehension and support, and for su(cid:27)ering stay up late, nights, weekends and holidays in the faculty.
To my family, they are the reason I am here.
Thank you all.
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Resumo
As capacidades de processamento dos computadores actuais estªo a tornar-se numa limita(cid:231)ªo na resposta (cid:224)s
necessidades tecnol(cid:243)gicas modernas. Assim, abordagens em alternativa (cid:224) arquitectura computacional de von
Neumannsªoimperativas,sendoofuncionamentoeestruturadocØrebromodelosverdadeiramenteapelativos.
Os memristors sªo caracterizados por uma rela(cid:231)ªo nªo linear entre o hist(cid:243)rico de corrente e a tensªo e foi
demonstrado que apresentam propriedades semelhantes (cid:224)s das sinapses biol(cid:243)gicas. Aqui discutimos o uso de
dispositivosmemresistivosbaseadosemestruturasmetal-isolador-metalemredesneuronaiscapazesdesimular
as capacidades de aprendizagem e de adapta(cid:231)ªo existentes nos cØrebros humanos.
A primeira parte deste trabalho (Cap(cid:237)tulo 3) passou pela fabrica(cid:231)ªo das amostras atravØs de diversos
mØtodos (deposi(cid:231)ao por feixe i(cid:243)nico, annealing tØrmico, eletrodeposi(cid:231)ªo e submersªo em solu(cid:231)ıes) no sentido
de optimizar os processos e escolher o mais apropriado. Fomos capazes de controlar a estequiometria do
Ag S e de caracterizar as amostras usando tØcnicas como difrac(cid:231)ªo por raio-X (XRD), espectroscopia de
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Raman,espectroscopiadeinfravermelhos(FTIR),espectroscopiadeenergiadispersiva(EDS)eespectroscopia
por varrimento electrıes (SEM), todas descritas no Cap(cid:237)tulo 2, bem como efectuar medidas de resistividade
elØctrica.
Medidas de transporte elØctrico foram realizadas nas amostras obtidas (Cap(cid:237)tulo 4). O comportameno
esperado de mudan(cid:231)a de resistŒncia associado ao sistema modelo de Ag S foi obtido e diferentes comporta-
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mentos de mudan(cid:231)a de resistŒncia foram observados nos dispositivos: curvas memresistivas do tipo I e II,
comportamentos bipolar e unipolar, para os quais tanto o processo de escrever e como o de apagar foram
observados para tensıes negativas ou positivas em diferentes ciclos, e mudan(cid:231)a resistiva complementar. AlØm
disso, a dependŒncia da resistŒncia elØctrica no tempo foi estudada, estudos estat(cid:237)sticos foram realizados e
aindaforamobservadasmodi(cid:28)ca(cid:231)ıesdependentesdaactividade. Estesefeitosforamatribu(cid:237)dos(cid:224)forma(cid:231)ªode
um (cid:28)lamento metÆlico no interior da camada dielØctrica devido a electromigra(cid:231)ªo.
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Abstract
Present computer processing capabilities are becoming a restriction to meet modern technological needs.
Therefore, approaches beyond the von Neumann computational architecture are imperative and the brain’s
operation and structure are truly attractive models. Memristors are characterized by a nonlinear relationship
between current history and voltage and were shown to present properties resembling those of biological
synapses. Here we discuss the use of metal-insulator-metal-based memristive devices in neural networks
capable of simulating the learning and adaptation features present in human brains.
The (cid:28)rst part of this work (Chapter 3) consisted in the fabrication of the samples using diversi(cid:28)ed methods
(ion beam deposition, thermal annealing, electrodeposition and dipping in solutions) in order to optimize the
processes and select the appropriate one. We were able to control the Ag S stoichiometry and to characterize
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the samples using techniques such as X-ray Di(cid:27)raction (XRD), Raman Spectroscopy, Fourier Transform
Infrared Spectroscopy (FTIR), Energy Dispersive Spectroscopy (EDS) and Scanning Electron Microscopy
(SEM), all described in Chapter 2, as well as electrical resistivity measurements.
Transport measurements were then performed in the obtained samples (Chapter 4). The expected resistive
switchingbehaviorassociatedwiththeAg Smodelsystemwasobtainedanddi(cid:27)erentswitchingbehaviorswere
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observed in the devices: memristive types I and II curves, bipolar and unipolar behavior, in which both set
andresetwereseenatnegativeorpositivevoltagesfordi(cid:27)erentcycles,andcomplementaryresistiveswitching.
Furthermore, dependence of the electrical resistance with time was studied, statistical studies were performed
and activity-dependent modi(cid:28)cations were also observed. These e(cid:27)ects were attributed to the formation of a
metallic (cid:28)lament due to electromigration inside the dielectric layer.
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