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Enregistrement W4245000976 · doi:10.1149/ma2019-01/25/1243

(Invited) Evolutionary Parameter Extraction for Organic TFT Compact Models Including Contact Effects

2019· article· en· W4245000976 sur OpenAlexaboutno aff
A. Romero, Jesús González, Rodrigo Picos, M. Jamal Deen, J. A. Jiménez-Tejada

Notice bibliographique

RevueECS Meeting Abstracts · 2019
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Sensor and Energy Harvesting Materials
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTransistorVoltageSubthreshold conductionComputer scienceExtraction (chemistry)Contact resistanceElectronic engineeringBiological systemMaterials scienceElectrical engineeringNanotechnologyEngineering

Résumé

récupéré en direct d'OpenAlex

The development of accurate and computationally efficient models is critical to reduce the cycle time between design, manufacturing and characterization of electronic devices, circuits and systems. The models must include degrading effects in order to better describe the performance of manufactured device. This is the case of contact effects in the modelling of organic thin film transistors (OTFTs). In fact, much effort was made to include contact effects into compact models. In this work, we consider a generic analytical model for the current-voltage (ID-VD) characteristics of OTFTs, valid for all the operation regions of the transistor, including the subthreshold region [1]. This model was later redefined with the inclusion of a model for the current-voltage (ID-VC) curves of the contact region, and a parameter extraction procedure, in which a sequence of iterative steps is carried out until a good agreement between experimental and simulated current voltage curves is obtained [2]. The parameter extraction procedure was accelerated in [3] with the proposal of an evolutionary procedure to extract the parameters that best fit experimental current-voltage characteristics. Later in [4], improvements over this evolutionary procedure were presented. Due to the explosion of different applications of OTFTs such as phototransistors and a wide variety of physical and chemical sensors, novel materials, contacts, and designs for transistor were proposed and used. For these applications, more accurate models and better extraction parameter procedures are needed. These new structures impose more requirements to the extraction methods and models, and therefore to the extracted parameters. From this point of view, the evolutionary extraction procedures proposed in [3, 4] are a good option to be used in combination with the above mentioned generic analytical model for the current-voltage (ID-VD) characteristics of OTFTs [1, 2]. In order to adapt both the compact model and the extraction procedure to new applications of the OTFTs, rules are added in form of optimization objectives and constrains for the different parameters. In this work, we present such rules applied to different sets of thin film phototransistors and sensors. In the first place, we consider the multi-objective evolutionary algorithm (MOEA), NSGA-II [5]. It was used in [3, 4], and seeks values of the parameter in the compact model that best meet some user defined objectives. In [3], two objectives were optimized, while in [4], four objectives were optimized, along with some defined constraints. It is known that classic MOEAs, such as the NSGA-II, have serious limitations when coping with more than three objectives. These problems are referred as many-objective optimization problems (MaOPs). Among the limitations of MOEAs to treat MaOPs are the selection operators, computational cost, visualization of the Pareto optimal front (POF), and more importantly, the convergence to an optimal solution. Recently, a many-objective implementation of the NSGA-II, the NSGA-III, was released [5]. NSGA-III was designed to specially deal with MaOPs, incorporating different operators to the ones used by its predecessor. Thus, in a second part of this work, we substitute the NSGA-II algorithm with the NSGA-III one for the characterization of the same sets of OTFTs. Finally, the results of using both algorithms are compared and will be discussed. Acknowledgments This work was supported by projects MAT2016-76892-C3-3-R and TIN2015-67020-P funded by the Spanish Government, European Regional Development Funds (ERDF) and the Canada Research Chair Program. References [1] O. Marinov, M. J. Deen, U. Zschieschang, H. Klauk, Organic thin-film transistors: Part I-compact dc modeling, IEEE Trans. Electron Devices 56 (2009) 2952–2961. [2] J. A. Jiménez Tejada, J. A. López Villanueva, P. López Varo, K. M. Awawdeh, M. J. Deen, Compact modeling and contact effects in organic transistors, IEEE Trans. Electron Devices 61 (2) (2014) 266–277. [3] A. Romero, J. González, R. Picos, M. J. Deen, J. A. Jiménez-Tejada, Evolutionary parameter extraction for an organic TFT compact model including contact effects, Organic Electronics 61 (2018) 242-253. [4] A. Romero, J. González, J.A. Jiménez-Tejada, Constrained Many-Objective Evolutionary Extraction Procedure for an OTFT Compact Model including Contact Effects, in: Spanish Conference on Electron Devices, 2018. [5] K. Deb, H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evolutionary Computation 18(4) (2014) 577-601.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,010

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,253
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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