| DOI | Trouver le DOI : https://doi.org/10.1109/IFETC46817.2019.9073711 |
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| Auteur | Rechercher : Griffin, Ryan H.1; Rechercher : Root, D. E.; Rechercher : Xu, J.; Rechercher : Dadvand, A.1; Rechercher : Chu, T.-Y.a1; Rechercher : Tao, Y1 |
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| Affiliation | - Conseil national de recherches du Canada. Électronique et photonique avancées
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| Format | Texte, Article |
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| Conférence | 2019 IEEE International Flexible Electronics Technology Conference, IFETC 2019, August 11-14, 2019, Vancouver, Canada |
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| Sujet | artificial neural network; OTFT; OFET; modelling; simulation; circuit simulation |
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| Résumé | An ever expanding landscape of material systems and deposition processes makes accurate and unified modelling of printed electronics systems a daunting challenge. Here we have used an artificial neural network to model inkjet printed organic thin film transistors in an approach which could readily be applied to other material systems and deposition processes. Measured data were used to train the artificial neural network to produce models that could be easily imported into an integrated circuit design environment. Without relying on the underlying physics involved, the artificial neural network was able to model and simulate both DC and AC device characteristics. A monolithically integrated organic complementary logic inverter was used to describe and validate the models. The ability to accurately simulate organic circuits and logic systems, in the absence of robust understanding of the devices, provides the potential for more rapid design and application of printed circuits and systems. |
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| Date de publication | 2019-08 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | d09670ac-480b-41aa-be88-6ba92d395f30 |
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| Enregistrement créé | 2021-01-25 |
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| Enregistrement modifié | 2021-01-25 |
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