Predicting integrated circuit (IC) functionality based on process control monitoring (PCM) parameters without individual die testing is a major challenge for manufacturers due to the high cost of electrical die measurement on the wafer. Complex dependencies between individual PCM parameters can be used to explain certain patterns of dice failure using Deep learning (DL) algorithms. However, random failure patterns due to process defects cannot be detected by this method. Combining PCM and in-process defect inspection data can be an ultimate prediction technique. In some cases, however, the availability of defect inspection data is much lower than the availability of PCM data, so direct ensemble training is rather ambiguous. This paper shows how to efficiently utilize both defect and PCM data to train a model to predict IC functionality. Such a hybrid model outperforms PCM-only solutions, and in contrast to a defect-only model predicts also failure areas across the wafer.


Published at Materials Research Society (MRS) Spring Meeting & Exhibit in Honolulu (USA) in 2022 and available in MRS Advances.

  • Integrated Circuit
  • Yield
  • Prediction
  • Deep Learning
  • XGBoost