Session 5—Defect Inspection and Reduction
Co-Chairs: Abhishek Vikram, Anchor Semiconductor | Israel Ne'eman, Applied Materials | Alex Joseph Varghese, IBM Research | Ralf Buengener, Intel | Oliver Patterson, Hermes Microvision
In-line inspection is critical for modern semiconductor processing. The session talks about defect inspection/review for yield and reliability improvement for a wide range of use cases. The session also discusses new applications/methodologies for process characterization and defect classification.
5.1 A Novel Deep Learning Architecture for Global Defect Classification: Self-Proliferating Neural Network (SPNet) | Yang YanFu, National Tsing Hua University; Sun Min, National Tsing Hua University
5.2 Multimodal Machine Learning for Display Panel Defect Layer Identification | Janghwan Lee, Samsung
5.3 Activation and Detection of Buried Defects by Negative Mode E-Beam Inspection | Ralf Buengener, Rongwei Fan, Jianze Zhao, Intel; Datong Zhang, Chih-Hung Wang, Junheng Wang, Hermes Microvision
5.4 An Active Deep Learning Method for the Detection of Defects in Power Semiconductors | Marco Bellini, Peter Kaspar, Luca De-Michielis, Lars Knoll, Hitachi ABB Power Grids; Georges Pantalos, ETH Zurich
5.5 Missing Via Defect Capture Enhancement Using a Novel, High-Precision Array Segmentation Inspection Technique | Graham Jensen, Vidyasagar Anantha, Alexa Greer, Raghav Babulnath, Satya Kurada, KLA; Brad Austin, Shravan Matham, Alex Joseph Varghese, IBM
5.6 A Holistic Characterization Methodology for Stochastic Printing Failures in EUV Contact Holes | Jennifer Church, Brad Austin, Luciana Meli, Alex Varghese, IBM; Teresa Esposito, DukKyun Moon, Nathaniel Mowell, Paz Yabbo, Uri Smolyan, Omri Baum, Aner Avakrat, Felix Levitov Applied Materials
5.7 Process Enabler and Design Opportunities for Fully Safeguarding Massive Presence of Reliability Defects. Lieyi Sheng, On Semiconductor
5.8 Non-contact C-V and Photoluminescence Measurements for More-Than-Moore SOI devices. Jeff Gambino, On Semiconductor; John Byrnes, Semilab