TCAD-Enabled Machine Learning Defect Prediction to Accelerate Advanced Semiconductor Device Failure Analysis

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 measurable 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.