Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network

Shiran Nabha Barnea; Grazia Lo Sciuto; Nathaniel Hai; Rafi Shikler; Giacomo Capizzi; Marcin Wozniak; Dawid Polap

doi:10.1007/978-3-319-59060-8_34

Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network

Shiran Nabha Barnea, Grazia Lo Sciuto, Nathaniel Hai, Rafi Shikler, Giacomo Capizzi, Marcin Wozniak, Dawid Polap

Department of Electrical & Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

In this paper we present a new RBFNNs neural networks based model to relate the overall OLEDs electroluminescent density as a function of the voltage and current at different wavelengths. The polymer-based OLEDs considered in this paper are realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev. The simulation results show a good agreement between the experimental data and those obtained with the proposed model. This results prove that the model is capable of repeating and interpreting the experimental data.

Original language	English
Title of host publication	Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings
Editors	Jacek M. Zurada, Lotfi A. Zadeh, Ryszard Tadeusiewicz, Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer
Publisher	Springer Verlag
Pages	378-389
Number of pages	12
ISBN (Print)	9783319590592
DOIs	https://doi.org/10.1007/978-3-319-59060-8_34
State	Published - 1 Jan 2017
Event	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 - Zakopane, Poland Duration: 11 Jun 2017 → 15 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10246 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017
Country/Territory	Poland
City	Zakopane
Period	11/06/17 → 15/06/17

Keywords

Electroluminescent spectrum
OLED
RBFNNs neural networks

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science (all)

Access to Document

10.1007/978-3-319-59060-8_34

Cite this

Barnea, S. N., Lo Sciuto, G., Hai, N., Shikler, R., Capizzi, G., Wozniak, M., & Polap, D. (2017). Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network. In J. M. Zurada, L. A. Zadeh, R. Tadeusiewicz, L. Rutkowski, M. Korytkowski, & R. Scherer (Eds.), Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings (pp. 378-389). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10246 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-59060-8_34

Barnea, Shiran Nabha ; Lo Sciuto, Grazia ; Hai, Nathaniel et al. / Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network. Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. editor / Jacek M. Zurada ; Lotfi A. Zadeh ; Ryszard Tadeusiewicz ; Leszek Rutkowski ; Marcin Korytkowski ; Rafal Scherer. Springer Verlag, 2017. pp. 378-389 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ebb64256442b48ddabc7848523d92fd4,

title = "Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network",

abstract = "In this paper we present a new RBFNNs neural networks based model to relate the overall OLEDs electroluminescent density as a function of the voltage and current at different wavelengths. The polymer-based OLEDs considered in this paper are realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev. The simulation results show a good agreement between the experimental data and those obtained with the proposed model. This results prove that the model is capable of repeating and interpreting the experimental data.",

keywords = "Electroluminescent spectrum, OLED, RBFNNs neural networks",

author = "Barnea, {Shiran Nabha} and {Lo Sciuto}, Grazia and Nathaniel Hai and Rafi Shikler and Giacomo Capizzi and Marcin Wozniak and Dawid Polap",

note = "Funding Information: Authors acknowledge contribution to this project from the “Diamond Grant 2016” No. 0080/DIA/2016/45 funded by the Polish Ministry of Science and Higher Education. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 ; Conference date: 11-06-2017 Through 15-06-2017",

year = "2017",

month = jan,

day = "1",

doi = "10.1007/978-3-319-59060-8_34",

language = "English",

isbn = "9783319590592",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "378--389",

editor = "Zurada, {Jacek M.} and Zadeh, {Lotfi A.} and Ryszard Tadeusiewicz and Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer",

booktitle = "Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings",

address = "Germany",

}

Barnea, SN, Lo Sciuto, G, Hai, N, Shikler, R, Capizzi, G, Wozniak, M & Polap, D 2017, Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network. in JM Zurada, LA Zadeh, R Tadeusiewicz, L Rutkowski, M Korytkowski & R Scherer (eds), Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10246 LNAI, Springer Verlag, pp. 378-389, 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, Zakopane, Poland, 11/06/17. https://doi.org/10.1007/978-3-319-59060-8_34

Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network. / Barnea, Shiran Nabha; Lo Sciuto, Grazia; Hai, Nathaniel et al.

Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. ed. / Jacek M. Zurada; Lotfi A. Zadeh; Ryszard Tadeusiewicz; Leszek Rutkowski; Marcin Korytkowski; Rafal Scherer. Springer Verlag, 2017. p. 378-389 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10246 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network

AU - Barnea, Shiran Nabha

AU - Lo Sciuto, Grazia

AU - Hai, Nathaniel

AU - Shikler, Rafi

AU - Capizzi, Giacomo

AU - Wozniak, Marcin

AU - Polap, Dawid

N1 - Funding Information: Authors acknowledge contribution to this project from the “Diamond Grant 2016” No. 0080/DIA/2016/45 funded by the Polish Ministry of Science and Higher Education. Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In this paper we present a new RBFNNs neural networks based model to relate the overall OLEDs electroluminescent density as a function of the voltage and current at different wavelengths. The polymer-based OLEDs considered in this paper are realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev. The simulation results show a good agreement between the experimental data and those obtained with the proposed model. This results prove that the model is capable of repeating and interpreting the experimental data.

AB - In this paper we present a new RBFNNs neural networks based model to relate the overall OLEDs electroluminescent density as a function of the voltage and current at different wavelengths. The polymer-based OLEDs considered in this paper are realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev. The simulation results show a good agreement between the experimental data and those obtained with the proposed model. This results prove that the model is capable of repeating and interpreting the experimental data.

KW - Electroluminescent spectrum

KW - OLED

KW - RBFNNs neural networks

UR - http://www.scopus.com/inward/record.url?scp=85020921721&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-59060-8_34

DO - 10.1007/978-3-319-59060-8_34

M3 - Conference contribution

AN - SCOPUS:85020921721

SN - 9783319590592

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 378

EP - 389

BT - Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings

A2 - Zurada, Jacek M.

A2 - Zadeh, Lotfi A.

A2 - Tadeusiewicz, Ryszard

A2 - Rutkowski, Leszek

A2 - Korytkowski, Marcin

A2 - Scherer, Rafal

PB - Springer Verlag

T2 - 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017

Y2 - 11 June 2017 through 15 June 2017

ER -

Barnea SN, Lo Sciuto G, Hai N, Shikler R, Capizzi G, Wozniak M et al. Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network. In Zurada JM, Zadeh LA, Tadeusiewicz R, Rutkowski L, Korytkowski M, Scherer R, editors, Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. Springer Verlag. 2017. p. 378-389. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59060-8_34

Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this