I implemented the data from a significant semiconductor fabrication company. Through supervised machine learning, I build a Random Forest classifier with up to 96% accuracy to detect defective wafers/lots after they have been produced, and I study which particular signals indicate the most to faults in fabricating. This research can provide information and support to prevent future failures in semiconductor fabrication.
➤ Version 1 (2019-11-04)
Zhenhan Huang (2019). Diagnostic analysis for failures in semiconductor fabrication based on Random Forest. Researchers.One, https://researchers.one/articles/diagnostic-analysis-for-failures-in-semiconductor-fabrication-based-on-random-forest/5f52699c36a3e45f17ae7e04/v1.