Deep Neural Networks for Determining Subgap States of Oxide Thin-film Transistors

In this study, we propose a deep neural network (DNN) model that extracts the subgap states in the channel layer of oxide thin-film transistors. We have developed a framework that includes creating a model training set, preprocessing the data, optimizing the model structure, decoding from density-of...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access 2023-01, Vol.11, p.1-1
Hauptverfasser:	Choi, Yunyeong, Sun, Wookyung, Park, Jisun, Shin, Hyungsoon
Format:	Artikel
Sprache:	eng
Schlagworte:	Analytical models Artificial neural networks CAD Computational modeling Computer aided design Current voltage characteristics Curve fitting Data models Deep Neural Network (DNN) defect Density of states density of states (DOS) Design defects hidden layer input data type Interpolation Iterative methods model structure Neural networks Nodes Parameters Performance evaluation Predictive models Preprocessing regression model Regression models Semiconductor device modeling Semiconductor devices subgap state supervised learning Tail TCAD Thin film transistors Thin-film Transistor (TFT) Threshold voltage Transistors
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!