Abstract:
Apparatus and methods are provided for predicting a plurality of unknown parameter values (e.g. overlay error or critical dimension) using a plurality of known parameter values. In one embodiment, the method involves training a neural network to predict the plurality of parameter values (114, 700, 800, 900). In other embodiments, the prediction process does not depend on an optical property of a photolithography tool. Such predictions may be used to determine wafer lot disposition (114).
Abstract:
A method for optimizing alignment performance in a fleet of exposure systems involves characterizing each exposure system in a fle of exposure systems to generate a set of distinctive distortion profiles (301) associated with each exposure system The set of distinct distortion profiles are stored in a database (303) A wafer having reference pattern formed thereon is provided for further pattern fabpcation (305) and an exposure system is selected from the fleet to fabricate a next layer on the wafer (307) Linear and higher ord parameters of the selected exposure system are adjusted using the distinctive distortion profiles to model the distortion of the referen pattern (309) Once the exposure system is adjusted, it is used to form a lithographic pattern on the wafer (311).
Abstract:
A method for optimizing alignment performance in a fleet of exposure systems involves characterizing each exposure system in a fleet of exposure systems to generate a set of distinctive distortion profiles associated with each exposure system. The set of distinctive distortion profiles are stored in a database. A wafer having reference pattern formed thereon is provided for further pattern fabrication and an exposure system is selected from the fleet to fabricate a next layer on the wafer. Linear and higher order parameters of the selected exposure system are adjusted using the distinctive distortion profiles to model the distortion of the reference pattern. Once the exposure system is adjusted, it is used to form a lithographic pattern on the wafer.
Abstract:
Disclosed are methods and apparatus for analyzing the quality of overlay targets. In one embodiment, a method of extracting data from an overlay target is disclosed. Initially, image information or one or more intensity signals of the overlay target are provided. An overlay error is obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. A systematic error metric is also obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. For example, the systematic error may indicate an asymmetry metric for one or more portions of the overlay target. A noise metric is further obtained from the overlay target by applying a statistical model to the image information or the intensity signal(s) of the overlay target. Noise metric characterizes noise, such as a grainy background, associated with the overlay target. In other embodiments, an overlay and/or stepper analysis procedure is then performed based on the systematic error metric and/or the noise metric, as well as the overlay data.