The adoption of tier stacking (dual deck) leads to increasingly high aspect ratios and poses challenges in controlling overlay, tilt, and misalignment in the manufacturing processes for next generation 3D NAND devices. In this work we address metrology challenges such as tilt and overlay separation, measurement robustness influenced by process variation, and nonlinearity of spectral response to asymmetries. We show that Mueller measurement can separate overlay and tilt signals through distinct spectral response analyzed by a machine learning method with reference data. To reduce asymmetry measurement errors caused by process variation such as critical dimension (CD) and thickness changes, we propose and demonstrate improvement of tilt measurements on blind test wafers by feeding forward CD measurement results to the analysis of tilt signal. We also investigate nonlinear regression and show its capability to extend overlay measurement limit from linear response range, ±0.25pitch, to ±0.43pitch. In addition, for small structural asymmetries introduced by channel hole tilt, test RMSE is reduced by 20–40% from nonlinear regression alone or combined with CD feed-forward. We demonstrate that spectroscopic Mueller matrix measurements, paired with advanced machine learning analysis, provide nondestructive and accurate measurement of tilt, overlay, and misalignment for 3D NAND devices with high throughput and fast recipe creation.
With the aggressive scaling of semiconductor devices, the increasing complexity of device structure coupled with tighter metrology error budget has driven up Optical Critical Dimension (OCD) time to solution to a critical point. Machine Learning (ML), thanks to its extremely fast turnaround, has been successfully applied in OCD metrology as an alternative solution to the conventional physical modeling. However, expensive and limited reference data or labeled data set necessary for ML to learn from often leads to under- or overlearning, limiting its wide adoption. In this paper, we explore techniques that utilize process information to supplement reference data and synergizing physical modeling with ML to prevent under- or overlearning. These techniques have been demonstrated to help overcome the constraint of limited reference data with use cases in challenging OCD metrology for advanced semiconductor nodes.
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