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.
In this paper, we propose an unique metrology technique for the measurement of three-dimensional (3D) nanoscale structures of semiconductor devices, employing imaging-based massive Mueller-matrix spectroscopic ellipsometry (MMSE) with ultra-wide field of view (FOV) of 20×20 mm2. The proposed system enables rapid measurement of 10 million critical dimension (CD) values from all pixels in the image, while the conventional point-based metrology technique only measures a single CD value. We obtain Mueller matrix (MM) spectrum by manipulating wavelength and polarization states using a custom designed optical setup, and show that the proposed method characterizes complex 3D structures of the semiconductor device. We experimentally demonstrate CD measurement performance and consistency in the extremely large FOV, and suggest that the combination of MMSE and massive measurement capability can provide valuable insights: fingerprints originated from the manufacturing process, which are not easily obtained with conventional techniques.
An innovative metrology technique has been devised to address current limitations of optical critical dimension (OCD) in advanced semiconductor metrology. This technique is based on multiple self-interferometric pupil imaging, called Mueller matrix self-interferometric pupil ellipsometry (M-SIPE). The system integrates an innovatively designed interference generator in both illuminating and imaging optics, allowing for the massive acquisition of full polarization information across entire angles around the device. The vast amount of information can offer fully comprehensive structural analysis, accomplishing enhanced sensitivity and the ability to break the well-known parameter correlation issues. The system employs a single-shot holographic measurement technique on the pupil plane, enabling rapid acquisition of three-dimensional spectral information, such as wavelengths, incidence angles, and azimuth angles. Thus, unlike conventional OCD tools, M-SIPE can obtain multi-angular and full polarization information without any mechanical movements. We verified the performance of M-SIPE by the experiment of non-patterned wafers of various conditions using an optical testbed. Our results confirmed good agreement between the experiment and theoretical simulations across all angular ranges. Furthermore, the actual device simulation was conducted to show sensitivity enhancement and ability for breaking the parameter correlation issues. The results confirmed that the large amount of angular information from M-SIPE technique could overcome current metrological challenges.
In recent years, the overlay specifications of advanced semiconductor devices have become extremely stringent. This challenging situation becomes severe for every new generation of the device development. However, conventional overlay metrology systems have limited throughput due to their point-based nature. Here, we first demonstrate the novel imaging Mueller-matrix spectroscopic ellipsometry (MMSE) technique, which can measure the overlay error of all cell blocks on a device wafer with extremely high throughput, much faster than conventional point-based spectroscopic ellipsometry (SE) technologies. It provides the super large field of view (FOV) ~ 20 × 20 mm2 together with high sensitivity based on Mueller information, which will be truly innovated solution not only for the overlay metrology, but also for critical dimension (CD) measurement, eventually maximizing process control and productivity of advanced node.
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.
The inspection of thin-film thickness on a wafer is one of the key steps for the semiconductor manufacturing processes. This paper proposes estimating the film thickness profile of the wafer, where the 3-band RGB color imaging camera and the hyperspectral imaging module are utilized to achieve the robust metrology performance. The simulation results are designed for investigating the characteristics of estimated film thickness profiles based on the Gaussian process regression. We demonstrate this cost-effective solution is beneficial for monitoring the CMP process with small computational power. The proposed measurement method has a great potential to solve bottlenecks from the physical metrology processes.
KEYWORDS: Data modeling, Semiconducting wafers, Metrology, Image processing, General packet radio service, Visual process modeling, Sensors, Chemical mechanical planarization, RGB color model
Measuring the thickness of thin films on a wafer is one of the most important steps for the semiconductor manufacturing process. This paper proposes a vision-based methodology for estimating a film thickness profile of the wafer. The scalability and industrial applicability of obtaining film thickness for the wafer with a small computational cost are demonstrated. Experimental results and numerical simulations are designed for investigating the characteristics of estimated solutions based on multiple representative nonlinear regression methods. The regression models are trained with the training data which consists of image value and thickness value pairs where the thickness value is obtained from the physical metrology system. There is an inevitable trade-off between the accuracy and the computational time in the spectrum-based film thickness measurement system in general, but the performance of the proposed methodology satisfied both the accuracy and the estimation time to a moderate extent.
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