Proceedings Article | 26 May 2022
Senmao Zeng, Xue Huang, Tianyu Zhang, Nick Lu, Dean Wu, Yaobin Feng, Jun Wang, Pandeng Xuan, Ningqi Zhu, Cynthia Li, Eric Xiao, Mi Zhang, Jin Zhu, Jason Pei, Kevin Huang
KEYWORDS: Optical alignment, Optical parametric oscillators, Overlay metrology, Manufacturing, Feedback control, Calibration, Scanners, Process modeling, Performance modeling, Model-based design
As 3D NAND advanced technology moves to increasingly denser storage capacity, the film stacks required to create these devices correspondingly increase in quantity and height, creating additional manufacturing and yield challenges. This is especially true for photolithography and specifically for on-product overlay (OPO) control.[1] Multiple sources of process variation impacting overlay need to be considered, controlled, and optimized to yield functional products: complex alignment and overlay schemes, etch tilt effect for deep, high aspect ratio contact etch processes, high order in-die stress overlay, and more. Traditional lithography feedback control is insufficient to cover all these sources of process variation and requires a potentially high Pi-run rate and rework rate, which in turn results in higher costs and lower revenues.
Generally, data feedforward is the right solution to handle large lot-to-lot (LxL) overlay process variation. However, as more variation factors interact in the 3D NAND manufacturing processes, a simple feed-forward methodology is insufficient to keep OPO in spec. One critical challenge is that when the alignment and overlay reference layer are not the same, feed-forward will over-correct based on alignment [2]. Further, as other sources of variation may come from different layers and components, feed-forward from only one single layer becomes inadequate for accurate, robust, and optimal OPO control.
In this work, we deploy a methodology and corresponding system to provide a feed-forward solution for critical layers. With a workflow from the latest generation 5D Analyzer® data analytics solution, inline OPO is simulated with this new feed-forward strategy. A run-to-run (R2R) system is deployed and validated in a real manufacturing production line. Finally, we observed inline OPO results have improved 50%, and the rework rate has been reduced to < 10%.
