In semiconductor inspection and metrology on scanning electron microscopy (SEM) images, image noise affects the results of inspection and metrology. Image accumulation is effective for denoising but slows image grabbing duration. To get low noise images in high throughput inspection and metrology, we developed a novel denoising algorithm that converts a lowaccumulated image into a clean image like a high-accumulated image. Noise2Noise is one of the image denoising technologies by deep learning for natural images. In this method, clean images are not required for training because the Noise2Noise model is generated with pairs of original images and noisy images created by artificially adding Gaussian noise to the original images. It is more practical than other deep learning methods because collecting clean images is usually difficult. On the other hand, Noise2Noise doesn’t perform enough in SEM images because the noise on the SEM image is not Gaussian noise. To solve this problem, in this study, we analyzed SEM noise characteristics by changing SEM conditions to create the artificial SEM noise. Furthermore, we developed the novel denoising algorithm which is based on Noise2Noise but is specialized to train the artificial SEM noise. We confirmed the improvement of the roughness precision of the proposed method compared to the deep denoise model trained using simple artificial noise. We discuss the impact on throughput advantage of inspection and metrology by applying the proposed method in NGR3500.
Up until now, the main driving force for the semiconductor industry is the continual shrinkage of device feature sizes, thereby incorporating more devices per unit area, reducing manufacturing cost and enhancing their performance have been achieved. However, the shrinkage of feature size leads to a reduction of process window imposing an extremely tight requirement for parameters such as critical dimension (CD), edge and width roughness of spaces/trenches, contacts, lines, and tip to tip (T2T) values. At sub 14 nm technology nodes these parameters have a significant influence on the overall device performance. With EUV based pattering becoming the sole option at these advanced nodes, a thorough characterization of the patterning process is of utmost importance before it can be a high-volume manufacturing solution.
In this work, we show how e-beam inspection has been used to characterize a single exposure EUV M2 (Metal 2 layer, BEoL) to have an understanding of the different hotspots and intra-field signatures present. Design Based Metrology (DBM) with wide SEM image was employed to measure CD distribution and Edge Placement Error (EPE) distribution of metal layer pattern on the 10nm logic wafer.
We present a new technique for accurate and fast evaluation of lithographic imaging performance at critical dimensions (CDs) of 100 nm and below. Its advantages over traditional methods that use either SEM or electrical CD metrologies are based on two key factors. First, it exploits a specially designed mark corresponding to a particular CD. Second, instead of mark dimensions the mark image irradiance is measured with a CCD TV camera. In combination, these provide an easy-to-implement and inexpensive technique for controlling exposure tool imaging performance. In actual application, best focus determination shows a repeatability of less than 5 nm.
Conference Committee Involvement (1)
Photomask and Next-Generation Lithography Mask Technology XIII
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