The rigorous coupled-wave analysis (RCWA) is a semi-analytic solver to Maxwell's equation, which is one of the most successful methods for modeling periodic optical structure. The repetitive nature of semiconductors has made RCWA widely applied in the semiconductor metrology industry. However, devices with high aspect ratio units, such as vertical NANDs(V-NANDs), require lengthy computation times, making them difficult to model in practice even with fully parallelized RCWA applications. This is because RCWA involves a time-consuming process of eigendecomposition and matrix inversion for each layer sliced along the vertical axis. In order to circumvent such computations, we propose a neural network based approach: channel-hole approximating network in the electromagnetic aspect (CHANEL). Based on the characteristic that the horizontal cutting plane is topologically consistent along the vertical axis of the channel-hole, CHANEL directly predicts the scattering matrix of each layer from its structural and optical parameters. In the scattering matrix of each layer, we found salient regions for Jones matrix calculation, which enhanced the accuracy of Jones matrix prediction with intensive learning on that area. In this paper, we demonstrate that CHANEL outperforms the traditional CPU-based RCWA implementations in terms of time, performing diffraction simulation more than 10 times faster.
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.
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