21 April 2016 Improving reticle defect disposition via fully automated lithography simulation
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Abstract
Most advanced wafer fabs have embraced complex pattern decoration, which creates numerous challenges during in-fab reticle qualification. These optical proximity correction (OPC) techniques create assist features that tend to be very close in size and shape to the main patterns as seen in Figure 1. A small defect on an assist feature will most likely have little or no impact on the fidelity of the wafer image, whereas the same defect on a main feature could significantly decrease device functionality. In order to properly disposition these defects, reticle inspection technicians need an efficient method that automatically separates main from assist features and predicts the resulting defect impact on the wafer image. Analysis System (ADAS) defect simulation system[1]. Up until now, using ADAS simulation was limited to engineers due to the complexity of the settings that need to be manually entered in order to create an accurate result. A single error in entering one of these values can cause erroneous results, therefore full automation is necessary. In this study, we propose a new method where all needed simulation parameters are automatically loaded into ADAS. This is accomplished in two parts. First we have created a scanner parameter database that is automatically identified from mask product and level names. Second, we automatically determine the appropriate simulation printability threshold by using a new reference image (provided by the inspection tool) that contains a known measured value of the reticle critical dimension (CD). This new method automatically loads the correct scanner conditions, sets the appropriate simulation threshold, and automatically measures the percentage of CD change caused by the defect. This streamlines qualification and reduces the number of reticles being put on hold, waiting for engineer review. We also present data showing the consistency and reliability of the new method, along with the impact on the efficiency of in-fab reticle qualification.
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Raunak Mann, Eliot Goodman, Keith Lao, Steven Ha, Anthony Vacca, Peter Fiekowsky, Dan Fiekowsky, "Improving reticle defect disposition via fully automated lithography simulation", Proc. SPIE 9778, Metrology, Inspection, and Process Control for Microlithography XXX, 97783U (21 April 2016); doi: 10.1117/12.2230847; https://doi.org/10.1117/12.2230847
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