Yasunobu Kai

Source and mask optimization to mitigate hotspots in etch process

A new optical metric, termed resist deformation factor (RDF), to represent deformation of three-dimensional (3D) resist profile has been introduced into a source and mask optimization (SMO) flow to mitigate defects caused by a reactive ion etching (RIE) process at the lithography stage. Under the low-k1 lithography conditions with both a highly-coherent source and a complicated mask, the 3D resist profile is subject to top-loss or bottom footing, resulting in hotspots and/or defects after the RIE process. In order to represent the 3D resist profile on a fast lithography simulation, a sliced latent image along resist depth direction is used to define RDF as the ratio of integrated optical intensities within the resist pattern to those around its surrounding area. Then the SMO flow incorporating the RDF into its cost function is implemented to determine both a source and a mask as the 3D resist profile is less likely to deform. The result of new SMO flow with RDF shows 30% improvement of resist top-loss.

Robust SMO methodology for exposure tool and mask variations in high volume production

A robust source mask optimization (RSMO) methodology has been developed for the first time to decrease variations of critical dimension (CD) and overlay displacement on wafer caused by extremely complex exposure tools and mask patterns. The RSMO methodology takes into account exposure tool variations of source shape, aberrations and mask as well as dose and focus to get source shapes and mask patterns robust to the exposure tool variations. A comparison between the conventional SMO and the new RSMO found that the RSMO improved the edge placement error (EPE) and displacement sensitivity to coma and astigmatism aberrations by 14% and 40%, respectively. Interestingly, even a greatly-simplified source from the RSMO provides totally smaller EPE than uselessly complex source shape from the conventional SMO. Thus, the RSMO methodology is much more effective for semiconductor products with high volume production.