High throughput CD-SEM metrology using image denoising based on deep learning

Accompanying the microfabrication and the complexity of the semiconductor manufacturing process, measurement and inspection using a scanning electron microscope (SEM) have become increasingly important for semiconductor manufacturing. Therefore, we have introduced an image denoising algorithm based on supervised deep learning with measurements for model training that transform a low signal-to-noise ratio (S/N) SEM image into a high S/N one, thereby improving the measurement success rate and maintaining measurement precision. Our experimental results demonstrated its effectiveness by an algorithm for enhancing throughput. However, performance may degrade when dealing with images containing features not included in the training dataset because deep learning models generally rely on trained features. Therefore, we propose high throughput CD-SEM metrology using image denoising based on deep learning that include a technique to statistically monitor deviations from the training images during model operation. In this study, we mainly discuss about monitoring module. To verify effectiveness of our proposed monitoring module, we first acquired sets of normal images used for training a deep learning model and sets of deviated images in which the SEM imaging recipe was partially changed. Then, the distribution of statistical values for noise and brightness features in the normal image set was used as a reference to compare the deviated image sets by the proposed method. As a result, the detection rate of the deviated images achieves 100%, and the false detection rate achieve 0% by combining of multiple statistical value distributions. By detecting deviated images that may degrade measurement performance, it is possible to maintain measurement precision and operate high-throughput measurement by using a denoising model based on deep learning.