In the implementation of EUV lithography, the stochastic effects in photoresist patterning are significant. The stochastic characteristic of Chemically Amplified Resist (CAR) for EUV demands novel modeling methods instead of the continuous model used in DUV. The previous model is directly derived from Gillespie algorithm, which sorts all cells into logarithmic classes based on the magnitude of the propensity functions. It takes a lot of time to update the classes in each iteration. Moreover, it splits the whole system only once, and for large systems a logarithmic class can include still larger number of cells, which can also take up a large amount of computing time. In this study, a new stochastic EUV resist model is proposed to improve the speed of PEB simulation by using a modified minimal process algorithm, which employs a splitting method called cascade classification. It divides evenly all cells into small groups in some arbitrary manner and each group is further divided evenly into smaller ones, and so on, until the smallest groups include a sufficiently small number of cells. The research shows that the new algorithm is more efficient than the previous one while maintaining accuracy.
Although existing methods have achieved impressive accomplishments in the field of low-light image enhancement, the phenomenon of overenhancement remains a challenge. To address this issue, a multiport output enhancement structure combined with multiloss functions supervision is designed to obtain multiple images with different enhancement intensity. Subsequently, an enhancement estimation module is proposed to flexibly select the most suitable enhancement image from these series of enhanced images, thereby reducing overexposure. To this end, the enhancement estimation network (EENet) for enhancing low-light images is introduced. The proposed EENet enhances low-illumination images with different brightness levels more perfectly because the phenomenon of overenhancement is reduced. For qualitative comparison, the experimental results demonstrate that the proposed method enhances low-illumination images more naturally, especially for enhancing low-illumination images that have brighter illumination. For quantitative evaluation, the proposed method obtains the highest peak signal-to-noise ratio and structural similarity on the public single image contrast enhancer dataset and reconstructed low-light* dataset compared with those listed methods. In addition, the EENet was proven to outperform state-of-the-art methods in dark image face detection, indicating that the EENet has great practical potential.
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