2019 Korea Mask Synthesis Seminar

May 30, 2019
9:00 a.m. to 4:30 p.m.

JW Marriott Hotel Seoul
Salon 4,5 Conference Rooms, 3rd Floor
176 Shinbanpo-ro Seocho-gu, Seoul

Please join our Mask Synthesis Seminar to review the latest industry challenges and Synopsys solutions, with focused topics on EUV technology, OPC, etch-aware hotspot analysis, and ML-augmented lithography models presented by Synopsys’ industry leaders.


9:00      Registration and Refreshments

Advanced Applications Session

9:30      Keynote: Systemic Solutions for Advanced Technology Development

10:00   Stochastic Effects in EUV Lithography:  Exploring Sensitivities Through Simulation

10:30   OPC in Hi NA EUV Lithography

11:15   Avoiding Complex Post-Etch Failures With 3D Etch-Aware Hotspot Analysis

11:45   Lunch and Lucky Drawing #1

Manufacturing Flows and Machine Learning Workshop

1:10    Rigorous Mask Synthesis With Novel Simulation Speedup For Large Area Applications

1:40    Advanced All Angle Correction Support For Curvilinear Masks And Maximum Process Window

2:10    Break

2:30    Workshop: Assessing Generalization Of ML-Augmented Lithography Models

4:00    Reception and Lucky Drawing #2


Keynote:  Systemic Solutions for Advanced Technology Deployment - Srini Raghvendra

This presentation takes a high level look at solutions that are being developed and delivered to improve the efficiency of technology development down to the 2nm node and beyond as well as the software applications needed to drive continued QoR, TAT, and hardware enablement into production.

OPC in Hi NA EUV Lithography – Wolfgang Demmerle

This presentation describes Hi NA EUV and the reasons for anamorphic imaging.  It will also cover the implications of the Hi NA system on compact models and model forms such as the inclusion of the obscuration in the optical system.  Correction and MRC implications of the anamorphic imaging system will also be discussed.

Stochastic effects in EUV lithography:  Exploring Sensitivities Through Simulation – Larry Melvin

This presentation provides a general introduction into stochastic effects, their definition and occurrence in photo lithography.  We will outline the importance of stochastic effects in the computational characterization of line resist pattern edge roughness as well as the formation of nano-defects.  Moreover, we demonstrate how simulation can be used to assess the severity of stochastic effects, depending on mask or imaging parameters, exposure settings, and resist material properties.  For large layout clips, stochastic compact models can be used to analyze a layout with respect to defect formation probability.

Avoiding complex post-etch failures with 3D etch-aware hotspot analysis – Mariya Braylovska

Common post etch failures encountered at advanced nodes will be presented while exploring the detection challenges associated with post-etch failures.  Learn how rigorous resist 3D EUV stochastic profiles can be used by TCAD emulation to provide 3D post-etch profiles for etch-aware hotspot analysis.

Rigorous mask synthesis with novel simulation speedup for large area applications – Travis Brist

A novel approach to the rigorous treatment of mask topography (M3D) effects in DUV and EUV lithography simulation will be presented achieving significant runtime improvements without sacrificing accuracy.  An analysis of the turnaround time impact associated with an EUV lithography-based correction and verification flow will be demonstrated on a several µm² large layout clip and include 3D resist profile aware analysis.

Advanced all angle correction support for curvilinear masks and maximum process window – Kosta Selinidis

The need for all angle processing is increasing as we push the limits of low k1 processing.  The drivers for all angle processing will be presented along with their challenges.  Holistic solutions to address all angle processing including layer operations, correction, and mask rule constraints will be reviewed and examples of the results presented.  The presentation will conclude with a look ahead to future needs / requirements.

Assessing Generalization of ML-Augmented Lithography Models – Larry Melvin

Machine Learning augments software to improve accuracy and speed, conditioned on the availability of sufficient data. In lithography, ML-augmented software can produce more accurate photoresist models, faster OPC, faster assist feature placement and more accurate thick mask simulation. One of the primary limits on the rate of introduction of ML-augmented systems into production is the ability of these systems to generalize.  This talk will take a closer look at ML-augmented photoresist modeling to illustrate what makes ML work, and how to gauge confidence in a model’s capability to generalize for a given layout.