| Patent application number | Description | Published |
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| 20090116010 | Apparatus for Deriving an Iso-Dense Bias - Embodiments of an apparatus for deriving an iso-dense bias are generally described herein. Other embodiments may be described and claimed. | 05-07-2009 |
| 20090116040 | Method of Deriving an Iso-Dense Bias Using a Hybrid Grating Layer - Embodiments of methods for deriving an iso-dense bias using a hybrid grating profile are generally described herein. Other embodiments may be described and claimed. | 05-07-2009 |
| 20090118857 | Method of Controlling a Fabrication Process Using an Iso-Dense Bias - Embodiments of controlling a fabrication process using an iso-dense bias are generally described herein. Other embodiments may be described and claimed. | 05-07-2009 |
| 20100042388 | COMPUTATION EFFICIENCY BY DIFFRACTION ORDER TRUNCATION - A method for improving computation efficiency for diffraction signals in optical metrology is described. The method includes simulating a set of diffraction orders for a three-dimensional structure. The diffraction orders within the set of diffraction orders are then prioritized. The set of diffraction orders is truncated to provide a truncated set of diffraction orders based on the prioritizing. Finally, a simulated spectrum is provided based on the truncated set of diffraction orders. | 02-18-2010 |
| 20100157315 | HYBRID DIFFRACTION MODELING OF DIFFRACTING STRUCTURES - Diffraction modeling of a diffracting structure employing at least two distinct differential equation solution methods. In an embodiment, a rigorous coupled wave (RCW) method and a coordinate transform (C) method are coupled with a same S-matrix algorithm to provide a model profile for a scatterometry measurement of a diffracting structure having unknown parameters. In an embodiment, a rigorous coupled wave (RCW) method and a coordinate transform (C) method generate a modeled angular spectrum of diffracted orders as a prediction for how a diffracting photolithographic mask images onto a substrate. | 06-24-2010 |
| Patent application number | Description | Published |
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| 20080241974 | Determining photoresist parameters using optical metrology - To generate a simulated diffraction signal, one or more values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, are obtained. One or more values of one or more profile parameters are derived using the one or more values, of the one or more photoresist parameters. The one or more profile parameters characterize one or more geometric features of the structure. A simulated diffraction signal is generated using the one or more values of the one or more profile parameters. The simulated diffraction signal characterizes behavior of light diffracted from the structure. The generated simulated diffraction signal is associated with the one or more values of the one or more photoresist parameters. The generated simulated diffraction signal, the one or more values of the one or more photoresist parameters, and the association between the generated simulated diffraction signal and the one or more values of the one or more photoresist parameters are stored. | 10-02-2008 |
| 20080241975 | Automated process control using optical metrology and photoresist parameters - To control a photolithography cluster using optical metrology, a structure is fabricated on a wafer using the photolithography cluster. A measured diffraction signal off the structure is obtained. The measured diffraction signal is compared to a simulated diffraction signal. The simulated diffraction signal is associated with one or more values of one or more photoresist parameters. The one or more photoresist parameters characterize behavior of photoresist when the photoresist undergoes processing steps in the photolithography cluster. The simulated diffraction signal was generated using one or more values of one or more profile parameters. The one or more values of the one or more profile parameters used to generate the simulated diffraction signal were derived from the one or more values of the one or more photoresist parameters associated with the simulated diffraction signal. If the measured diffraction signal and the simulated diffraction signal match, then one or more values of one or more photoresist parameters used in the photolithography cluster are determined to be the one or more values of the one or more photoresist parameters associated with the matching simulated diffraction signal. One or more process parameters or equipment settings of the photolithography cluster are adjusted based on the one or more values of the one or more photoresist parameters. | 10-02-2008 |
| 20080243730 | Training a machine learning system to determine photoresist parameters - To train a machine learning system, a set of different values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, is obtained. A set of diffraction signals is obtained using the set of different values of the one or more photoresist parameters. The machine learning system is trained using the set of measured diffraction signals as inputs to the machine learning system and the set of different values of the one or more photoresist parameters as expected outputs of the machine learning system. | 10-02-2008 |
| 20080259357 | OPTICAL METROLOGY OF SINGLE FEATURES - The profile of a single feature formed on a wafer can be determined by obtaining an optical signature of the single feature using a beam of light focused on the single feature. The obtained optical signature can then be compared to a set of simulated optical signatures, where each simulated optical signature corresponds to a hypothetical profile of the single feature and is modeled based on the hypothetical profile. | 10-23-2008 |
| 20080285054 | OPTICAL METROLOGY OPTIMIZATION FOR REPETITIVE STRUCTURES - An optical metrology model for a structure to be formed on a wafer is developed by characterizing a top-view profile and a cross-sectional view profile of the structure using profile parameters. The profile parameters of the top-view profile and the cross-sectional view profile are integrated together into the optical metrology model. The profile parameters of the optical metrology model are saved. | 11-20-2008 |
