In:
ECS Journal of Solid State Science and Technology, The Electrochemical Society, Vol. 11, No. 5 ( 2022-05-01), p. 055004-
Kurzfassung:
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.
Materialart:
Online-Ressource
ISSN:
2162-8769
,
2162-8777
DOI:
10.1149/2162-8777/ac6894
Sprache:
Unbekannt
Verlag:
The Electrochemical Society
Publikationsdatum:
2022