ASMC 2019 - Session 6

Wednesday, May 8

Session 6 – Yield Enhancement/Yield Methodologies I

Chairs:  Ishtiaq Ahsan, IBM Research; Armando Anaya, Northrop Grumman; Kazunori Nemoto, Hitachi Hi-Technologies
Process characterization techniques for driving yield are critical for successful semiconductor manufacturing. This session covers innovative machine learning methods for yield learning, case studies for bump metal processing and etch processing, and a case study for leakage measurements.

6.1   Application of Bayesian Machine Learning to Create A Low-Cost Silicon Failure Mechanism Pareto
Chris Schuermyer, Steve Palosh, Synopsys; Pietro Babighian, Yan Pan, GLOBALFOUNDRIES

6.2   Identification of Suspicious Semiconductor Devices Using Independent Component Analysis with Dimensionality Reduction (student paper)
Jenny Bartholomäus, Swen Wunderlich, Infineon Technologies Dresden; Zoltán Sasvári, Technische Universität Dresden

6.3 Resolving Integration Issues from Bump Metal Processing
David Tucker, Ryan Edmonds and Chris Chamberlain, Texas Instruments

6.4 Study of Plasma Arcing Mechanism in High Aspect Ratio Slit Trench Etch
Yao-An Chung, Cheng-Yi Lung, Yuan-Chieh Chiu, Hong-Ji Lee, Nan-Tzu Lian, Tahone Yang, Kuang-Chao Chen and Chih-Yuan Lu, Macronix International

6.5   Methods for RFSOI Damascene Tungsten Contact Etching
Daisy Vaughn, Felix Anderson, Ron Meunier, Thai Doan, Anthony Stamper, GLOBALFOUNDRIES

11:30 Boxed Lunch