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Abstract. The performance of overlay metrology as total measurement uncertainty, design rule compatibility, device correlation, and measurement accuracy has been challenged at the 2× nm node and below. The process impact on overlay metrology is becoming critical, and techniques to improve measurement accuracy become increasingly important. We present a methodology for improving the overlay accuracy. A propriety quality metric, Qmerit, is used to identify overlay metrology measurement settings with the least process impacts and reliable accuracies. Using the quality metric, a calibration method, Archer self-calibration, is then used to remove the inaccuracies. Accuracy validation can be achieved by correlation to reference overlay data from another independent metrology source such as critical dimension-scanning electron microscopy data collected on a device correlated metrology hybrid target or by electrical testing. Additionally, reference metrology can also be used to verify which measurement conditions are the most accurate. We provide an example of such a case. © The Authors. IntroductionAt imaging based overlay (OVL) metrology, a propriety quality metric, called "Qmerit," can be used to quantify target process imperfections. The metric employed in order to identify the optimal measurement conditions which are less sensitive to process impacts and therefore report the most accurate OVL values. This quality metric can be used in comparative analysis for a range of overlay target designs and metrology settings, thereby identifying good candidate combinations of target designs and metrology settings. The accuracy of the results of each target design and metrology setting is then verified by critical dimension-scanning electron microscopy (CDSEM) data collected on a device correlated metrology (DCM) hybrid target. Furthermore, simulation of the light spectrum behavior per target geometry and film stack information also supports the target designs and metrology settings selection based on the anticipated precision.Using the quality metric results (Qmerit), an innovative calibration method, the Archer self-calibration (ASC), is used to remove inaccuracies. Using the measurement information from various target or metrology settings, the calibration methodology estimates the inaccuracies and calibrates the overlay data for the most accurate behavior. This in turn results in significant improvement in correlation to reference CDSEM data measured on a DCM target for all available targets and metrology settings combinations.

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As fabs transition from 200 to 300mm wafers with shrinking design rules, the risk and cost associated with overlay excursions become more severe. This significantly impacts the overall litho-cell efficiency. Effective detection, identification, and reduction of overlay excursions are essential for realizing the productivity and cost benefits of the technology shifts. We have developed a comprehensive overlay excursion management method that encompasses baseline variation analysis, statistical separation and characterization of excursion signatures and their frequencies, as well as selection of sampling plans and control methods that minimize material at risk due to excursion. A novel baseline variance estimation method is developed that takes into account the spatial signature and temporal behavior of the litho-cell overlay correction mechanisms. Spatial and temporal excursion signatures are identified and incorporated in a cost model that estimates the material at risk in an excursion cycle. The material at risk associated with various sampling plans, control charts, and cycle times is assessed considering various lot disposition and routing decisions. These results are then used in determining an optimal sampling and control strategy for effective excursion management. In this paper, we describe and demonstrate the effectiveness of the methods using actual 300mm fab overlay data from several critical layers. With a thorough assessment of the actual baseline and excursion distributions, we quantify the amount of wafer-to-wafer and within-wafer sampling necessary for detecting excursions with minimal material at risk. We also evaluate the impact of shorter cycle time and faster response to excursion, which is made possible through automation and alternative metrology configurations.