:EUV

Fang-Ling Yang1, Zong-Biao Ye1, Yu-Qi Chen1

  • 1Key Laboratory of Radiation Physics and Technology, Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, No. 24 South Section 1, Yihuan Road, 610065 Chengdu, People's Republic of China.

概括

机器学习通过预测电离潜力,加速发现有效的极紫外线光刻 (EUVL) 材料. 这种方法识别了新的EUVL化合物,并为商业应用提供了结构-属性关系的见解.