Wafer Map Failure Pattern Recognition Using Artificial Intelligence

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Future Work • More training sets with new patterns to refine pattern detection • Possible upstream and downstream integration : Etest data input, Final Test data input • Explore other KE techniques, models. 32
Thank You • We would like thank the following teams for the successful completion of the project : ISS-NUS KE22 FYP Team : Supervisors: Miss Fan Zhen Zhen, Dr Famming, Charles Pang Team members: Tran The Anh, Tai JiaJun, Chew Poh Kwee http://www.iss.nus.edu.sg/iss/browse.jsp?cid=7&pid=45&rid=45&type=9&name=Our_Staff GLOBALFOUNDRIES Team : Sponsor: Yeo Chak Huat Team members: Lee Eu Jin, Zhong Jie 33
Page 1 and 2: Wafer Map Failure Pattern Recogniti
Page 3 and 4: Background • Disposition challeng
Page 5 and 6: Objectives • Create a tool to hel
Page 7 and 8: Approach • Extract useable parame
Page 9 and 10: Phase 1 : Data preparation (1) •
Page 11 and 12: Phase 1 : Data preparation (3) •
Page 13 and 14: Phase 3 : System structure (1) •
Page 21 and 22: Phase 4 : Pattern Recognition • A
Page 23 and 24: Phase 4 : Pattern Recognition • S
Page 25 and 26: Phase 5 : Testing and Results (2)
Page 27 and 28: • Features Other Considerations -
Page 29 and 30: List of tools used (2) Tools Eclips
Page 31: Conclusion • It is concluded that

Wafer Map Failure Pattern Recognition Using Artificial Intelligence

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