Machine Learning–Assisted Thin-Film Transistor Characterization: A Case Study of Amorphous Indium Gallium Zinc Oxide (IGZO) Thin-Film Transistors
Machine learning was applied to classify the device characteristics of indium gallium zinc oxide (IGZO) thin-film transistors (TFTs). A K-means approach was employed for initial clustering of IGZO transfer curves into three of four grades (high, medium-high, medium, and low) of TFT performance accor...
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Published in | ECS journal of solid state science and technology Vol. 11; no. 5; pp. 55004 - 55013 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
IOP Publishing
01.05.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Machine learning was applied to classify the device characteristics of indium gallium zinc oxide (IGZO) thin-film transistors (TFTs). A K-means approach was employed for initial clustering of IGZO transfer curves into three of four grades (high, medium-high, medium, and low) of TFT performance according to qualitative features. A 2-layered artificial neural network (ANN) and 4-layered deep neural network (DNN) were used to extract mobility, threshold voltage, on/off current ratio, and sub-threshold slope device parameters from high-grade and medium-high-grade oxide TFTs. Ground-truth device parameters were calculated using in-house codes based on a rules-based approach consistent with the definitions employed to train the ANN and DNN. The DNN-predicted parameters were in closer agreement with manual and macro-based calculations than were those obtained from the ANN. Synergistic integration of K-means clustering and DNN effectively extracted TFT device parameters encountered in processing high volumes of data in industrial and academic domains of the microelectronics field. |
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Bibliography: | JSS-102169.R1 |
ISSN: | 2162-8769 2162-8777 |
DOI: | 10.1149/2162-8777/ac6894 |