Inverse Reticle Optimization With Quantum Annealing and Hybrid Solvers
Reticle optimization is a computationally demanding task in optical microlithography for advanced semiconductor fabrication. In this study, we explore the effectiveness of D-Wave’s quantum annealing (QA) and hybrid steepest descent (SD) solvers in solving pixelated binary reticle optimiza...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10445252/ |
| _version_ | 1797269114744471552 |
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| author | Po-Hsun Fang Yan-Syun Chen Jhih-Sheng Wu Peichen Yu |
| author_facet | Po-Hsun Fang Yan-Syun Chen Jhih-Sheng Wu Peichen Yu |
| author_sort | Po-Hsun Fang |
| collection | DOAJ |
| description | Reticle optimization is a computationally demanding task in optical microlithography for advanced semiconductor fabrication. In this study, we explore the effectiveness of D-Wave’s quantum annealing (QA) and hybrid steepest descent (SD) solvers in solving pixelated binary reticle optimization problems. We show that the energy derived from the objective function depends on annealing time and inter-sample correlation. Specifically, longer annealing times and reduced inter-sample correlations result in lower energy. Moreover, introducing efficient pausing strategies in forward annealing could reduce the QA runtime by approximately 100-fold while achieving similar results to long annealing times. Finally, reticles with increased variables lead to widespread irregular values in default sorted QA energies due to quantum chain breakages, which could potentially limit the probability of attaining the optimal solution. A hybrid approach that applies the classical SD algorithm to the QA results increases the probability of locating the global minimum solution and reduces runtime to about one-third compared to the classical SD solver. These findings facilitate our comprehension of quantum computing for accelerating computational lithography in semiconductor manufacturing. |
| first_indexed | 2024-04-25T01:43:14Z |
| format | Article |
| id | doaj.art-7328248b3adc45b5b2b2890363cbcc78 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-25T01:43:14Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7328248b3adc45b5b2b2890363cbcc782024-03-08T00:00:35ZengIEEEIEEE Access2169-35362024-01-0112330693307810.1109/ACCESS.2024.337047510445252Inverse Reticle Optimization With Quantum Annealing and Hybrid SolversPo-Hsun Fang0Yan-Syun Chen1https://orcid.org/0009-0001-6331-5611Jhih-Sheng Wu2Peichen Yu3https://orcid.org/0000-0002-4332-8933Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanReticle optimization is a computationally demanding task in optical microlithography for advanced semiconductor fabrication. In this study, we explore the effectiveness of D-Wave’s quantum annealing (QA) and hybrid steepest descent (SD) solvers in solving pixelated binary reticle optimization problems. We show that the energy derived from the objective function depends on annealing time and inter-sample correlation. Specifically, longer annealing times and reduced inter-sample correlations result in lower energy. Moreover, introducing efficient pausing strategies in forward annealing could reduce the QA runtime by approximately 100-fold while achieving similar results to long annealing times. Finally, reticles with increased variables lead to widespread irregular values in default sorted QA energies due to quantum chain breakages, which could potentially limit the probability of attaining the optimal solution. A hybrid approach that applies the classical SD algorithm to the QA results increases the probability of locating the global minimum solution and reduces runtime to about one-third compared to the classical SD solver. These findings facilitate our comprehension of quantum computing for accelerating computational lithography in semiconductor manufacturing.https://ieeexplore.ieee.org/document/10445252/Inverse lithography technologyoptical proximity correctionquantum annealingquantum computingsemiconductor |
| spellingShingle | Po-Hsun Fang Yan-Syun Chen Jhih-Sheng Wu Peichen Yu Inverse Reticle Optimization With Quantum Annealing and Hybrid Solvers IEEE Access Inverse lithography technology optical proximity correction quantum annealing quantum computing semiconductor |
| title | Inverse Reticle Optimization With Quantum Annealing and Hybrid Solvers |
| title_full | Inverse Reticle Optimization With Quantum Annealing and Hybrid Solvers |
| title_fullStr | Inverse Reticle Optimization With Quantum Annealing and Hybrid Solvers |
| title_full_unstemmed | Inverse Reticle Optimization With Quantum Annealing and Hybrid Solvers |
| title_short | Inverse Reticle Optimization With Quantum Annealing and Hybrid Solvers |
| title_sort | inverse reticle optimization with quantum annealing and hybrid solvers |
| topic | Inverse lithography technology optical proximity correction quantum annealing quantum computing semiconductor |
| url | https://ieeexplore.ieee.org/document/10445252/ |
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