@inproceedings{cfb0126ba1194c328f4eb8545adcdffd,
title = "TCAD-Enabled machine learning defect prediction to accelerate advanced semiconductor device failure analysis",
abstract = "In this work, we present a unique approach of combining TCAD modelling and machine learning to detect the defect locations of a bridging defect in a single-fin FinFET. The prediction of the defect location is guided by the predictive model consisting of Random Forest algorithm which is trained with the measureable electrical attributes from the I-V. High accuracy in predicting the defect location is achieved by the proposed scheme which can further enhance the FA success rate, expediting the cycle of design to product.",
keywords = "Defect Location Prediction, FinFET, Machine Learning, TCAD",
author = "Teo, {Chea Wei} and Low, {Kain Lu} and Vinod Narang and Thean, {Aaron Voon Yew}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 24th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2019 ; Conference date: 04-09-2019 Through 06-09-2019",
year = "2019",
month = sep,
doi = "10.1109/SISPAD.2019.8870440",
language = "English",
series = "International Conference on Simulation of Semiconductor Processes and Devices, SISPAD",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Francesco Driussi",
booktitle = "Proceedings of 2019 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2019",
}