Miao Yuan, Yiyu Sun, Yanqiu Li. Advanced Computational Lithography[J]. Laser & Optoelectronics Progress, 2022, 59(9): 0922009
- Laser & Optoelectronics Progress
- Vol. 59, Issue 9, 0922009 (2022)
Fig. 1. Flow of computational lithography
![Imaging formation based on the vector imaging model[32]](/richHtml/lop/2022/59/9/0922009/img_02.jpg)
Fig. 2. Imaging formation based on the vector imaging model[32]
![Process window for the initial source and mask, OPC algorithm, and HSMO algorithm[16]. (a) Isolate line-space pattern; (b) semi-dense line-space pattern; (c) dense contact holes](/Images/icon/loading.gif){kind=icon}
Fig. 3. Process window for the initial source and mask, OPC algorithm, and HSMO algorithm[16]. (a) Isolate line-space pattern; (b) semi-dense line-space pattern; (c) dense contact holes
Fig. 4. Process window of conventional SMO and the ELASMO[60]
Fig. 5. The convergence curves of IHTs SMO and Newton-IHTs SMO[90]
Fig. 6. Pattern error comparison of full-chip SO method simulation[92]
Fig. 7. Comparison of pattern errors of SMO results between traditional imaging model and IPSF model[93]
Fig. 9. Principle of the FlexWave manipulator[113]
Fig. 10. Schematic diagram of the PWO method to compensate for TMIA[19]
Fig. 11. Pupil for different methods[124]. (a) Scalar pupil for PWO method; (b) vector pupil for VPO method
Fig. 12. Convergence curve comparison between PWO method and VPO method[124]
Fig. 13. Comparison of EL-DOF curves obtained by various SMO methods[125]
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Table 1. DOF values of different methods at the corresponding EL of 3%, 5%, and 8%[16]
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Table 2. Comparison of running time of SMO results between traditional imaging model and IPSF model[93]
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