BackgroundFor complex two-dimensional (2D) patterns, optical proximity correction (OPC) model calibration flows cannot always satisfy accuracy requirements with the standard cutline-based input data. Utilizing after-development inspection e-beam metrology image contours, better model predictions of 2D shapes and wafer hotspots can be realized.AimWe compare model accuracy performance of conventional cutline-based and contour-based OPC models on the regular and hotspots patterns.ApproachBy utilizing image contours that are directly extracted from large field of view (LFoV) e-beam metrology, OPC models were calibrated and verified with both cutline-based and contour-based modeling flows. We also used a wafer sampling plan that contained bridging hotspots. Using that sampling plan, a hotspot-aware three-dimentional resist (R3D) compact model was created.ResultsFirst, a contour-based OPC model was generated with <1 nm root mean square error of contour sites. Compared with cutline-based models, it shows better predictions on 2D feature corners. Second, when combined with a hotspot sampling plan, a hotspot-aware compact model could be generated. The accuracy of hotspot predictions on false positives and false negatives was reduced to around 1% with this approach.ConclusionsOPC model calibration and verification with LFoV image contours provide improved predictions on corner rounding shapes and great potential to increase design space coverage. We also observed improved accuracy of hotspot predictions when using an update hotspot aware model when comparing with that of the OPC model. Furthermore, the combination of R3D and stochastic compact models also demonstrated great potential on predictions of rare wafer failure events.
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.
The method to perform Optical Proximity Correction (OPC) model calibration with contour-based input data from both small field of view (SFoV) and large field of view (LFoV) e-beam inspection is presented. For advanced OPC models - such as Neural Network Assisted Models (NNAM) [1], pattern sampling is a critical topic, where pattern feature vectors utilized in model training, such as image parameter space (IPS) is critical to ensure accurate model prediction [2-5]. In order to improve the design space coverage, thousands of gauges with unique feature vector combinations might be brought into OPC model calibration to improve pattern coverage. The time and cost in conventional Critical Dimension Scanning Electron Microscope (CD-SEM) metrology to measure this large amount of CD gauges is costly. Hence, an OPC modeling solution with contourbased input has been introduced [6]. Built on this methodology, a single inspection image and SEM contour can include a large amount of information along polygon edges in complex logic circuit layouts. Namely, a better feature vector coverage could be expected [7]. Furthermore, much less metrology time is needed to collect the OPC modeling data comparing to conventional CD measurements. It is also shown that by utilizing large field 2D contours, which are difficult to characterize by CD measurements, in model calibration the model prediction of 2D features is improved. Finally, the model error rms of conventional SFoV modeling and LFoV contour modeling between SEM contours and simulation results are compared.
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