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.

Highlights

  • At imaging based overlay (OVL) metrology, a propriety quality metric, called “quality metric results (Qmerit),” can be used to quantify target process imperfections

  • The accuracy of the results of each target design and metrology setting is verified by critical dimension–scanning electron microscopy (CDSEM) data collected on a device correlated metrology (DCM) hybrid target

  • 3.1 Qmerit Results As discussed in the process-induced asymmetry part, we have found that different WLs show different sensitivities to asymmetry in the measured pattern

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Summary

Introduction

At imaging based overlay (OVL) metrology, a propriety quality metric, called “Qmerit,” can be used to quantify target process imperfections. 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. Both the quality metric and calibration methodology are designed to be on-the-fly applications that do not affect measurement time, making it optimal for the production environment. By measuring a DCM hybrid OVL target with both Archer and CDSEM tools and comparing the results, we were able to estimate which condition is the most accurate

Process-Induced Asymmetry and Influence on Identify Overlay Results
Using Qmerit to Estimate the Process Asymmetry
Archer Self-Calibration
Device Correlated Metrology Hybrid Target
Results and Discussion
Archer Self-Calibration Calibrated Data
Imaging Simulation Results
Summary
Full Text
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