MACHINE LEARNING BASED EXAMINATION FOR PROCESS MONITORING

• Main Authors: YOGEV, Shay, CHOONA, Lilach, SINITSA, Sergey, STURLESI, Boaz, ARIEL, Assaf, PRES, Shaul, TAL, Noam, LEVANT, Boris
• Format: Patent
• Language: English
• Published: 22.08.2024
• Subjects: CALCULATING; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES; MEASURING; PHYSICS; TESTING

There is provided a system and method of examination of semiconductor specimens. The method includes generating a sequence of anomaly scores corresponding to a sequence of specimens sequentially fabricated and examined during a fabrication process, comprising, for each given specimen: obtaining an image of the given specimen acquired by an examination tool; using a machine learning (ML) model to process the image and obtaining an anomaly map indicative of pattern variation in the image; and deriving, based on the anomaly map, an anomaly score indicative of level of pattern variation presented in the given specimen, wherein the anomaly score is correlated with a defectivity score related to defect detection in a correlation relationship, and has higher detection sensitivity than the defectivity score; and analyzing the sequence of anomaly scores to monitor on-going process stability, thereby providing defect related prediction along the fabrication process based on the correlation relationship.