We present a novel method that can automatically correct astigmatism and focus error with great accuracy in the scanning electron microscopy (SEM). Here, an iterative deconvolution method and the feature-based compensation algorithm were applied to the beam control sequence, enabling us to obtain the clear SEM image without any distortion. A proof of concept was fully verified by both mathematical analysis and experimental results. By utilizing the proposed method, accurate beam profile optimization is possible without malfunction even when imaging a sample with anisotropic pattern.
We present advanced application of novel ellipsometry technique, referred to as self-interference pupil ellipsometry (SIPE), integrating self-interference and pupil microscopy to overcome the sensitivity limitations raised from the conventional spectroscopic ellipsometry. We investigated various samples including a SiO2 monolayer, grating patterned wafers, and DRAM wafers to demonstrate outstanding capability of SIPE for metrology. The angular range corresponds to approximately 5,000 acquisition of conventional ellipsometry tools with 2º angular step scanning. From the experimental results and simulation, we expect the sensitivity of SIPE for structure metrology is at least 0.15 nm at a single wavelength and even better for multispectral measurements.
Background: High-throughput three-dimensional metrology techniques for monitoring in-wafer uniformity (IWU) and in-cell uniformity (ICU) are critical for enhancing the yield of modern semiconductor manufacturing processes. However, owing to physical limitations, current metrology methods are not capable of enabling such measurements. For example, the optical critical dimension technique is not suitable for ICU measurement, because of its large spot size. In addition, it is excessively slow for IWU measurement.
Aim: To overcome the aforementioned limitation, we demonstrate a line-scan hyperspectral imaging (LHSI) system, which combines spectroscopy and imaging techniques to provide sufficient information for spectral and spatial resolution, as well as high throughput.
Approach: The proposed LHSI system has a 5-μm spatial resolution together with 0.25-nm spectral resolution in the broad-wavelength region covering 350 to 1100 nm.
Results: The system enables the simultaneous collection of massive amounts of spectral and spatial information with an extremely large field of view of 13 × 0.6 mm2. Additionally, throughput improvement by a factor of 103 to 104 can be achieved when compared with standard ellipsometry and reflectometry tools.
Conclusions: Owing to its high throughput and high spatial and spectral resolutions, the proposed LHSI system has considerable potential to be adopted for high-throughput ICU and IWU measurements of various semiconductor devices used in high-volume manufacturing.
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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