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

Yunyeong Choi, Wookyung Sun, Jisun Park, Hyungsoon Shin

Research output: Contribution to journalArticlepeer-review


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-state (DOS) parameters to text current-voltage (I-V) characteristics, and evaluating the model performance in terms of curve fitting accuracy. We investigate in detail the effect of data preprocessing methods and model structure on the performance of the model. The primary finding is that the input data type and the last hidden layer significantly affect the performance of the regression model. Using double-type input data composed of several voltages and linear current values is more advantageous than using log-scale current. Moreover, the number of nodes in the last hidden layer of a regression model with multiple output nodes should be large enough to avoid interference between the output values. The proposed model outputs five DOS parameters, and the resulting parameters are decoded to an text I-V curve through interpolation based on the nearest 32 data from the given dataset. We evaluate the model performance using the threshold voltage and on-current difference between a target curve and the decoded curve. The proposed model calibrates 97.1% of the 14,400 curves within the threshold voltage difference of 0.2V and on-current error of 5%. Hence, the proposed model is verified to effectively extract DOS parameters with high accuracy based on the current characteristics of oxide thin-film transistors. We expect to improve the efficiency of defect analysis by replacing the iterative manual technology computer aided design (TCAD) curve fitting with an automatic DNN model.

Original languageEnglish
Pages (from-to)15909-15920
Number of pages12
JournalIEEE Access
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Deep neural network (DNN)
  • defect
  • density of states (DOS)
  • hidden layer
  • input data type
  • model structure
  • preprocessing
  • regression model
  • subgap state
  • supervised learning
  • TCAD
  • thin-film transistor (TFT)


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