Diagnosis Outcome Prediction on Limited Data via Transferred Random Forest

Logic diagnosis aims to identify defects in falling integrated circuits (ICs) and thus plays an essential role in yield learning. Previous research has demonstrated that diagnosis outcome (defect number, resolution, etc.) can be accurately predicted using features derived from the data collected from failing ICs. This capability allows practitioners to better allocate resources during yield learning. However, a significant number of diagnosis must be conducted to obtain sufficient training data for building an accurate prediction model. To reduce the data collection cost, we utilize some prior knowledge through transfer learning. Specifically, a prior model is constructed from a correlated dataset and then adapted to very limited training samples from the current design of interest. Experiments performed using real industrial examples demonstrate that transfer learning can significantly improve prediction performance and save training data when a suitable prior knowledge exists.