Compact modeling to predict and correct stochastic hotspots in EUVL

Levinson, Zachary A.; Kandel, Yudhishthir; Zhang, Yunqiang; Yan, Qiliang; Miyagi, Makoto; Li, Xiaohai; Lucas, Kevin

doi:10.1117/12.2554099

Cited by 1 publication

(1 citation statement)

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“…The second method is fast enough to be applied at a full-chip scale but does not have the same accuracy as the Monte Carlo method. 10) Moreover, Monte Carlo techniques allow one to investigate the contributions of individual process components. For this study, we will use a model which utilizes Monte Carlo techniques.…”

Section: Stochastic Defect Modelsmentioning

confidence: 99%

Applying stochastic simulation to study defect formation in EUV photoresists

Melvin

Welling

Kandel

et al. 2022

Jpn. J. Appl. Phys.

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EUV lithography resolves features below 11 nm. However, photonic and atomic variations at these photon energies and dimensions lead to less than 1:109 potential stochastic defects causing device failures in stable manufacturing processes. This study investigates a methodology intended to identify root causes of stochastic defects with potential mitigation paths. Simulation techniques using pseudo random numbers are used to identify failing photonic and chemical event or distribution combinations. Failing combinations occurring in many photon-chemical configurations are thought to have potential mitigation methodologies. Photonic effects demonstrated significant impacts on stochastic defect formation with approximately 73% of the photon seeds resulting in a failure in at least 60% of the trials. The material results were mixed with large failure quantities that demonstrated low impacts. The photonic shot noise based failures were dominating in this study and these failures will not be mitigated by material enhancement alone.

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Section: Stochastic Defect Modelsmentioning

confidence: 99%