Pixelated mask optimization on quantum computers

Okudaira, Yosuke; Yashiki, Satoshi

doi:10.1117/12.2550780

Cited by 2 publications

(3 citation statements)

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“…The existing mask optimization method 12 applied QA provided by D-Wave. 11 However, the existing method optimizes binary masks so that the intensity distribution approaches the target without considering fidelity to target patterns and tolerance to process variation.…”

Section: Selection Of Ising Machinesmentioning

confidence: 99%

“…A mask optimization method that applies QA provided by D-Wave 11 has been proposed. 12 However, the method optimizes binary masks so that the intensity distribution approaches the target without considering fidelity to target patterns and tolerance to process variation. Moreover, it is not available for large problems in the execution of QA due to the specification of D-Wave.…”

Section: Introductionmentioning

confidence: 99%

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A formulation of mask optimization into QUBO model for Ising machines

Kohira,

Nakayama,

Nonaka

et al. 2023

Photomask Technology 2023

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Section: Selection Of Ising Machinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A formulation of mask optimization into QUBO model for Ising machines

Kohira,

Nakayama,

Nonaka

et al. 2023

Photomask Technology 2023

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“…It is therefore of practical interest to investigate the applicability and performance of D-Wave's QA solver in computational lithography. In this study, we formulate a classic ILT problem, the pixelated binary mask optimization, into a QUBO model [27]. The associated Hamiltonian coefficients are embedded into the physical qubit grids on the D-Wave Advanced QA Hardware Advantage 6 system [28,29].…”

Section: Introductionmentioning

confidence: 99%

Inverse Reticle Optimization With Quantum Annealing and Hybrid Solvers

Fang,

Chen,

et al. 2024

IEEE Access

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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 intersample 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.

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Pixelated mask optimization on quantum computers

Cited by 2 publications

References 0 publications

A formulation of mask optimization into QUBO model for Ising machines

A formulation of mask optimization into QUBO model for Ising machines

Inverse Reticle Optimization With Quantum Annealing and Hybrid Solvers

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