Semiconductor miniaturization demands precise nano-pattern fidelity and deep understanding of EUV-PR materials. This research integrates synchrotron analysis and advanced deep learning across the EU EUV lithography process. Synchrotron methods optimize EUV-PR development and elucidate EUV/E-beam PR photoreaction mechanisms, providing crucial performance insights. Non-destructive synchrotron CD-SAXS precisely evaluates nano-pattern morphology, including Critical Dimension and Sidewall Angle. To address limited synchrotron accessibility, we propose a novel deep learning denoising model. Trained on high-quality synchrotron data, this model restores lab CD-SAXS data to synchrotron-comparable SNR, enabling accurate data interpretation even from noisy raw data. This integrated approach accelerates CD-SAXS system commercialization, enhances next-gen EUV lithography control, and fosters new directions in EUV PR material development, significantly contributing to semiconductor self-reliance.