Machine Learning-Assisted Thin-Film Transistor Characterization: A Case Study of Amorphous Indium Gallium Zinc Oxide (IGZO) Thin-Film Transistorsopen access
- Authors
- Oh, Jiwon; Song, Hyewon; Shin, Euncheol; Yang, Heesun; Lim, Jongtae; Hwang, Jin-Ha
- Issue Date
- 1-May-2022
- Publisher
- ELECTROCHEMICAL SOC INC
- Keywords
- Machine Learning; IGZO TFT; TFT Parameters; K-Means Clustering; Neural Networks
- Citation
- ECS JOURNAL OF SOLID STATE SCIENCE AND TECHNOLOGY, v.11, no.5
- Journal Title
- ECS JOURNAL OF SOLID STATE SCIENCE AND TECHNOLOGY
- Volume
- 11
- Number
- 5
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/29480
- DOI
- 10.1149/2162-8777/ac6894
- ISSN
- 2162-8769
- Abstract
- 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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- Appears in
Collections - College of Engineering > Materials Science and Engineering Major > 1. Journal Articles
- College of Engineering > School of Electronic & Electrical Engineering > 1. Journal Articles
- Graduate School > Materials Science and Engineering > 1. Journal Articles
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