Predicting the curve with advanced modeling solutions
Learn how to predict the curve for curvilinear mask shapes and the impact on OPC modeling.
Curvilinear mask data has a new set of challenges in the OPC modeling flow and requires new modeling solutions. In this video, Germain Fenger discussed the how to predict the curve for curvilinear mask shapes and the impact on OPC modeling.
Curvilinear mask shapes for IC manufacturing
There have been many developments in IC manufacturing on how to fit curved geometries into computational lithography flow. There are also impacts of the curvilinear data on OPC modeling. The mask 3D EMF model needs to account for edge-to-edge interactions for all angled edges to meet new curvilinear requirements of rotational invariant. In this video, Germain discussed how to overcome this challenge related to mask 3D EMF simulation.
In the different area, the use of contour data modeling is becoming critical to describe real observed behavior on wafer. There are wafer patterns that cannot be described well with gauges ( 1D CD measurement data) only information, so there is rapid growth of the use of the contour data ( 2D information) in OPC modeling. In this video, Germain discussed the benefits of using contours in OPC modeling. He also discussed the new solutions and approaches of using Calibre SEMSuite, a package of tools offering full coverage of steps for modeling calibration.
Machine learning modeling solution is an area of interest for improving OPC modeling performance. In this video, Germain introduced the Neural Network Assisted Model Dual Stage Mask (N2M) solution for correcting mask 3D images with a CNN. The model prediction results are shared in this video showing substantial improvement in model accuracy using the new solution. Etch modeling is also found to become challenging and the specification is becoming tighter. In this video, Germain discussed a new RIE VEB Etch model, which can be used to accurately describe the physical effects that happen during reactive ion etch (RIE) process, together with the Neural Network Assisted Model Dual Stage Etch (N2E) solution.