TCAD-Enabled machine learning defect prediction to accelerate advanced semiconductor device failure analysis

Chea Wei Teo, Kain Lu Low, Vinod Narang, Aaron Voon Yew Thean*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

40 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2019
EditorsFrancesco Driussi
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728109404
DOIs
Publication statusPublished - Sept 2019
Externally publishedYes
Event24th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2019 - Udine, Italy
Duration: 4 Sept 20196 Sept 2019

Publication series

NameInternational Conference on Simulation of Semiconductor Processes and Devices, SISPAD
Volume2019-September

Conference

Conference24th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2019
Country/TerritoryItaly
CityUdine
Period4/09/196/09/19

Keywords

  • Defect Location Prediction
  • FinFET
  • Machine Learning
  • TCAD

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