Machine Learning–Assisted Thin-Film Transistor Characterization: A Case Study of Amorphous Indium Gallium Zinc Oxide (IGZO) Thin-Film Transistors

• Published in: ECS journal of solid state science and technology Vol. 11; no. 5; pp. 55004 - 55013
• Main Authors: Oh, Jiwon, Song, Hyewon, Shin, Euncheol, Yang, Heesun, Lim, Jongtae, Hwang, Jin-Ha
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.05.2022
• Subjects: IGZO TFT; K-Means Clustering; Machine Learning; Neural Networks; TFT Parameters
• ISSN: 2162-8769; 2162-8777
• DOI: 10.1149/2162-8777/ac6894

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.