Accurate models for EUV lithography

Abstract

Accurate modeling of EUV Lithography is a mandatory step in driving the technology towards its foreseen insertion point for 22-16nm node patterning. The models are needed to correct EUV designs for imaging effects, and to understand and improve the CD fingerprint of the exposure tools. With a full-field EUV ADT from ASML now available in the IMEC cleanroom, wafer data can be collected to calibrate accurate models and check if the existing modeling infrastructure can be extended to EUV lithography. As a first topic, we have measured the CD on wafer of a typical OPC dataset at different flare levels and modeled the evolution of wafer CD through flare, reticle CD, and pitch using Brion's Tachyon OPC engine. The modeling first requires the generation of a flare map using long-range kernels to model the EUV specific long-range flare. The accuracy of the flare map can be established independently from the CD measurements, by using the traditional disappearing pad test for flare determination (Kirk test). The flare map is then used as background intensity in the calibration of the traditional optical models with short-range kernels. For a structure set of 600 features and over a flare range of 4-6%, an rms fit value of 0.9nm was obtained. As a second aspect of the modeling, we have calibrated a full resist model to process window data. The full resist model is then used in a combination with experimental measurements of reticle CD, slit intensity uniformity, focal plane behavior, and EUV thick mask effects to model the evolution of wafer CD across the exposure field. The modeled evolution of CD across the exposure field was found to be a good match to the experimentally seen evolution of CD across the field, and confirms that the 4 factors mentioned above are main contributions to the CD uniformity across the field. As such the modeling work enables a better understanding of the errors contributing to CD variation across the field for EUV technology.

Improving Hypernymy Detection with an Integrated Path-based and Distributional Method

“…Relation classification is a related task whose goal is to classify the relation that is expressed between two target terms in a given sentence to one of predefined relation classes. To illustrate, consider the following sentence, from the SemEval-2010 relation classification task dataset (Hendrickx et al, 2009): "The [apples] e 1 are in the [basket] e 2 ". Here, the relation expressed between the target entities is Content − Container(e 1 , e 2 ).…”