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Calibre Manufacturing at SPIE Advanced Lithography 2024
Join us at the SPIE Advanced Lithography conference 2024 at the San Jose Convention Center.
Exhibition dates/times
- Feb. 27: 10 a.m. – 5 p.m.
- Feb. 28: 10 a.m. – 4 p.m.
Visit us at Booth #727
If you need assistance outside of booth hours, email us with questions.
"We are pleased to again be collaborating with SPIE to award the sixth annual Nick Cobb Memorial Scholarship to Mr. Nicholas Jenkins. The scholarship honors Nick Cobb's fundamental work in the field of lithography and his many contributions as the chief architect of the Siemens EDA Calibre OPC solutions."
Steffen Schulze, VP, Product Management Calibre Semiconductor Solutions, Siemens EDA
Calibre IC Manufacturing papers at SPIE 2024
Join Calibre IC Manufacturing at SPIE Advanced Lithography 2024, Feb. 25 - 29, at the San Jose Convention Center. Siemens will be presenting 18 papers. (All presentations are listed in Pacific Time.)
Feb. 28, 2024 • 10:30 a.m. - 10:55 a.m.
An artificial intelligence machine leaning (AI/ML) approach with cross-technology node learning for multi-layer process defect predictions (Invited Paper)
Paper 12954-14
Feb. 28, 2024 • 11:10 a.m. - 11:35 a.m.
Machine learning (ML) based SEM contour extraction accelerated by GPU for etch modeling application (Invited Paper)
Paper 12958-25
Feb. 28, 2024 • 11:10 a.m. - 11:35 a.m.
OPC and modeling solution towards 0.55na EUV stitching
Paper 12953-29
Feb. 28, 2024 • 2 p.m. - 2:20 p.m.
Low landing energy as an enabler for optimal contour based OPC modeling in the EUV era
Paper 12955-39
Feb. 28, 2024 • 2:25 p.m. - 2:45 p.m.
Improving OPC model accuracy of dry resist for low k1 EUV patterning
Paper 12954-21
Feb. 28, 2024 • 3:25 p.m. - 3:45 p.m.
Cloud flight plan for post-tapeout flow jobs
Paper 12954-24
Feb. 28, 2024 • 5:30 p.m. - 7 p.m.
Advanced process control by machine learning based virtual metrology for high product mix manufacturing
Paper 12955-74
Feb. 28, 2024 • 5:30 p.m. - 7 p.m.
Using machine learning method to improve design sampling efficiency for fab applications
Paper 12954-46
Feb. 28, 2024 • 5:30 p.m. - 7 p.m.
Towards efficient and accurate cost functions for EUVL stochastic-aware OPC correction and verification: via failure probability versus image and process variation band metrics
Paper 12953-72
Feb. 28, 2024 • 5:30 p.m. - 7 p.m.
The effect of edge placement error on deformity and roughness calculation
Paper 12955-117
Feb. 28, 2024 • 5:30 p.m. - 7 p.m.
Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis
Paper 12955-133
Feb. 28, 2024 • 5:30 p.m. - 7 p.m.
Contour metrology for process matching and OPC qualification with machine learning-based site selection
Paper 12955-134
Feb. 29, 2024 • 10:30 a.m. - 10:55 a.m.
Layout simulation for directed self-assembly with chemo-epitaxy methodology
Paper 12954-30
Feb. 29, 2024 • 11:30 a.m.- 11:50 a.m.
Principal components and optimal feature vectors of EUVL stochastic variability: applications of Karhunen-Loève Expansion to efficient estimation of stochastic failure probabilities and stochastic metrics
Paper 12957-47
Feb. 29, 2024 • 1:45 p.m. - 2:05 p.m.
A new era DFM solution for yield enhancement using machine learning
Paper 12954-35
Feb. 29, 2024 • 3:45 p.m. - 4:05 p.m.
An integrated verification flow for curvilinear mask
Paper 12954-39
Feb. 29, 2024 • 4:25 p.m. - 4:45 p.m.
Fast full chip curvilinear MRC for advanced manufacturing nodes
Paper 12954-41
Feb. 29, 2024 • 4:45 p.m. - 5:05 p.m.
Model-based OPC using the MEEF matrix III
Paper 12954-42