Utilizing a unique high NA optical system, a new methodology to measure device overlay accurately has been developed with a key differentiation. Historically, optical techniques to measure features below the image resolution require supporting measurement techniques to be used as a reference to anchor the optical measurement. This novel selfreference methodology enables accurate and robust optical metrology for device features after etch eliminating the need for external reference measurements such as Decap, x-sections or high landing energy SEMs. In this paper, we discuss how a high NA Optical Metrology system enables measurements on small area device replica targets, which enables the ability to create a reference target for device measurements. The methodology utilizes this reference target to enable accurate direct on device overlay measurements without the need for an external reference. Furthermore, the technique is expanded to improve the robustness of the measurement and monitor live in production the health of the recipe, ensuring accuracy overtime. This ultimately leads to a method to extend the recipes in real-time based on the health KPIs. The improved accurate and robust device overlay measurements have proven to improve the overlay performance compared to other techniques. This, combined with the speed of optical systems, enables unconstrained dense measurements directly on device structures after etch, allowing for improved overlay control.
In this work a novel machine learning algorithm is used to calculate the after etch overlay of the memory holes in a 3DNAND device based on OCD metrology by YieldStar S1375. It is shown that the method can distinguish the overlay signals from the process induced signals in the acquired pupil image and therefore, enables for an overlay metrology approach which is highly robust to process variations. This metrology data is used to characterize and correct the process induced intra-die stress and the DUV scanner application fingerprint.
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