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

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.

Performance Analysis of Rare-earth Doped Oxide Thin-Film Transistors Using Neural Network Method

“…In addition to fitting device I-V curves, previous studies have explored methods of utilizing deep neural networks (DNNs) to extract essential characteristics of TFT devices such as carrier mobility, sub-threshold swing (SS), threshold voltage (Vth), and current switching ratio (Ion/Ioff). The I-V curve was used as input, and the network was trained to output each of above four characteristics through a DNN network, enabling the network to extract the four characteristics from the curve [16]. However, this approach focused on extracting characteristics rather than predicting device characteristics with changing element type, doping concentration, and other key factors.…”