Takaki Hashimoto

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.

Fast source optimization by clustering algorithm based on lithography properties

Lithography is a technology to make circuit patterns on a wafer. UV light diffracted by a photomask forms optical images on a photoresist. Then, a photoresist is melt by an amount of exposed UV light exceeding the threshold. The UV light diffracted by a photomask through lens exposes the photoresist on the wafer. Its lightness and darkness generate patterns on the photoresist. As the technology node advances, the feature sizes on photoresist becomes much smaller. Diffracted UV light is dispersed on the wafer, and then exposing photoresists has become more difficult. Exposure source optimization, SO in short, techniques for optimizing illumination shape have been studied. Although exposure source has hundreds of grid-points, all of previous works deal with them one by one. Then they consume too much running time and that increases design time extremely. How to reduce the parameters to be optimized in SO is the key to decrease source optimization time. In this paper, we propose a variation-resilient and high-speed cluster-based exposure source optimization algorithm. We focus on image log slope (ILS) and use it for generating clusters. When an optical image formed by a source shape has a small ILS value at an EPE (Edge placement error) evaluation point, dose/focus variation much affects the EPE values. When an optical image formed by a source shape has a large ILS value at an evaluation point, dose/focus variation less affects the EPE value. In our algorithm, we cluster several grid-points with similar ILS values and reduce the number of parameters to be simultaneously optimized in SO. Our clustering algorithm is composed of two STEPs: In STEP 1, we cluster grid-points into four groups based on ILS values of grid-points at each evaluation point. In STEP 2, we generate super clusters from the clusters generated in STEP 1. We consider a set of grid-points in each cluster to be a single light source element. As a result, we can optimize the SO problem very fast. Experimental results demonstrate that our algorithm runs speed-up compared to a conventional algorithm with keeping the EPE values.

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.