We have developed a massive on-cell overlay metrology system based on Mueller matrix measurements. By integrating microscopic techniques into ellipsometry, we achieved high-throughput and extensive sampling coverage, with 1-shot/field per 1-field of view (FOV) measurement capability within a 34 x 34 mm2 FOV. Analyzing the off-diagonal components of the Mueller matrix allowed for on-cell overlay measurement across the wafer. This system provides measurement sensitivity comparable to e-beam-based technologies while offering high coverage, enabling precise reticle correction or high-order overlay correction in photolithography processes. This advancement represents a significant improvement in overlay metrology, offering both sensitivity and resolution for enhanced semiconductor manufacturing processes.
This study introduces Microsphere-Assisted Hyperspectral Imaging (MAHSI), the world’s first metrology system combining microsphere super-resolution and hyperspectral imaging. The system achieves an ultra-small measurement spot size of 14.4 nm and an optical resolution of 66 nm, overcoming the diffraction limit and enabling precise non-destructive measurements of complex 3D semiconductor structures. A high-speed novel autofocus method has been developed specifically for the ultra-close working distances required by microsphere super-resolution optics for the first time. This innovative technique only requires two spectra acquisition, as a result, it can achieve fast and precise approach of the objective lens, ensuring accurate measurements without damaging the sample. The system has successfully monitored the uniformity of cell blocks in DRAM, and demonstrates its feasibility for semiconductor metrology. As semiconductor processes become increasingly refined, the proposed MAHSI system can be innovative and effective solution for encountered metrology and inspection challenges in semiconductor device analysis.
To achieve high accuracy and precision in optical metrology for advanced semiconductors, it is crucial to identify and compensate for errors from optical components and environmental perturbations. In this study, we investigated the sources of the errors in the interferometric ellipsometer developed for next-generation OCD. The objective lens and beam splitters, the critical optical components of the system, are intensively investigated. The system errors induced by temperature fluctuation, wavelength inaccuracy, and defocus were quantitatively examined. We also proposed methods for compensating individual errors and analyzed the effect of the compensation. As a result of error compensation, the accuracy and precision of the system is improved by 6.9 times and 2.3 times, respectively. Although the investigation was conducted based on our interferometric ellipsometry system, the finding is not limited to this system, as these errors are commonly found in most optical metrology systems. The proposed method for error compensation will be essential strategies for various ellipsometry systems suffering from a low level of accuracy and precision.
Optimal color weighting (OCW) is a promising technique to improve accuracy and robustness of alignment mark measurement in the lithography process. Measurement based OCW shows remarkable improvement of overlay error under laboratory conditions. In those conditions, one of the wafer processes is split and a simple form of mark deformation is present. However, the OCW effect has not been confirmed in the case of on-product overlay since various of deformations are mixed in the real FAB. We perform simple simulation showing that mixed deformations can deteriorate the performance of OCW and show that utilizing spatial characteristics of the wafer with OCW further improves the overlay error. As a result, we have improved on-product-overlay form 3.78 nm to 3.51 nm or 7.1% using data of 86 lots, 268 wafers in DUV layer of 3 nm logic device.
The random error has been increased relative to the systematic error in overlay misalignment, as the Critical Dimension(CD) of semiconductor-design shrinks to under the 20 nm on DRAM and single-digit nanometer on Logic. The random error comprises diverse factors including non-lithography context, which caused by intricate process other than the scanner itself, hence it’s hard to control through conventional control methods using control knobs of scanner . In this study, we show that how effectively control and reduce on product overlay(OPO) error through making the most use of the conventional control knobs aided by machine learning. In addition to showing improved results, we address that conventional overlay feedback control with weighted moving average(WMA) can give rise to fluctuation of OPO error over entire wafer area, especially on the edge of wafer, due to the lack of control capability or flexibility. As a result, we show that 15.7% of OPO error can be trained and predicted for in-fab data and OPO has been improved from 2.29 nm to 2.08 nm or 9.2% on average over 5-steps of 1,201 lots with simulator.
KEYWORDS: Nanostructures, Data modeling, Machine learning, Scanning electron microscopy, 3D modeling, Signal processing, Semiconductors, Performance modeling, Nondestructive evaluation, Image processing
The trend to produce semiconductor devices having more complex nanostructures results in the increasing importance of exquisite systems measuring multiple Critical Dimensions (CDs) of nanostructures. However, from a practical point of view, it is difficult to apply conventional methodologies to mass production because of cost and complexity issues. In this study, we propose an application of machine learning techniques utilizing optical information to measure nanoscale profiles of channel holes in High-Aspect-Ratio (HAR) structure of vertical NAND flash, which is applicable to mass production. By combining the conventional methodologies, the proposed method yields data pairs for supervised learning which include optical spectra obtained with Rotating Polarizer Ellipsometer (RPE) and images obtained with Scanning Electron Microscopy (SEM). Several preprocessing steps and machine learning techniques are introduced to train a model with sufficient performance to be applicable to mass production. In experiments, we obtained a model with coefficient of determination (R2) of 0.8 and Root Mean Square Error (RMSE) of 1.3 nm when predicting hundreds of nanoscale profiles of the channel holes which are measured with SEM. Furthermore, we confirmed that only 500 samples of data are sufficient to achieve the model performance with R2 greater than 0.7 and RMSE less than 1.5 nm. The proposed method is capable of replacing the conventional methods of profile measurement in the mass production stage by reducing the cost of destructive methods and accurately measuring the profiles of complex nanostructures without theoretical modeling.
Conventional semiconductor etching process control has been performed by separated steps: process, metrology, and feedback control. Uniformity of structures such as Critical Dimension (CD) is an important factor in determining completeness of etching process. To achieve better uniformity, several feedback control has been performed. However, it is difficult to give feedback to the process after metrology due to the lack of process knowledge. In this study, we propose a machine learning technique that can create process control commands from the measured structure using a miniaturized Integrated Metrology (IM) of Spectroscopic Ellipsometery (SE) form. And it is possible to learn the physical analysis through machine learning without introducing a physical analysis method. The proposed analysis consists of two machine learning part: the first neural network for CD metrology, and second network for command generation. The first neural network takes a spectrum sampled at 2048 wavelengths obtained from IM as an input, and outputs CDs of structures. Finally, the second artificial neural network takes a changes of temperatures in a wafer and outputs the control commands of powers. As a result, we have improved the CD range of poly mask in a wafer from 1.69 nm to 1.36 nm.
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