Assessment of stochastic fail rate using E-beam massive metrology

He, Wallace; Xu, Camille; Bae, Daniels; Chen, Kunyuan; Lan, Andy; Yang, Richer; Elmalk, Abdalmohsen; Jiang, Aiqin; Wang, Fuming; Chung, Double; Su, Shane; Pao, K.; Patterson, Oliver D.; Raghunathan, Sudharshanan; Kea, Marc; Liao, Jason J. Z.

doi:10.1117/12.2584739

Cited by 1 publication

(4 citation statements)

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“…The large field of view (LFOV, within 8k x 8k scan range) nonuniformity are smaller than 0.25 nm for three CAR conditions (not show here). The FocusIndex trend chart (one of the Key Performance Index (KPIs) to qualify each image via image sharpness [5]) as an indicator of image quality stability also matches with monitor chart. Stochastic hard defects (missing & bridging) also play an important role to failure rate calculation.…”

Section: Metrology and Defect Inspection Data Analysismentioning

confidence: 83%

“…In the PWM loop [5], modeled CD distributions with matched CD and defect results for each focus/dose condition are generated and linked to failure rate, where the failures include hard defects and soft defects contributions, where soft defects are referring to out of process control specification on CD related metrics (ex. smaller contact hole CDs are suspected as partial open or missing, and larger contact hole CDs are suspected as bridging or electrical short in the downstream process or testing), which has high risk to failure and causing line yield loss.…”

Section: Metrology and Pwm Data Analysismentioning

confidence: 99%

“…From statistical point of view, limited sampling of CDSEM or considering only normal distribution may result in inaccurate quantification of distribution tails, this will under-estimate the stochastic failure rate (non-normal nature of the fail rate distribution [5]) up to few orders. Therefore, massive metrology is highly suggested to capture the CD-defect distribution tails (non-Gaussian distribution) as stochastics contribution.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we need a failure rate model to predict at all F/D conditions based on average 10M measurements/hour. To help customer accelerating process optimization [7], together with eP6, ASML-HMI provides Process Window Metrology (PWM) application [5] to bring accurate control for High NA EUV process through stochastic aware process windows (SAPW), as shown in Fig. 2.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of high NA thin resist metrology and stochastic performance

Wu,

Freeman,

Elmalk

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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The next milestone in the resolution of high-volume manufacturing will be achieved by High-NA EUV Lithography (HiNA EUVL) systems. In addition to the development of high-NA EUV scanners, the implementation of next generation resist materials remains a critical challenge. Thinner resists are required to accommodate DoF decrease on higher NA physics, which may lead to e-beam metrology and e-beam inspection sensitivity loss due to less electron signal and more severe stochastic failures and variabilities. Due to worse resist imaging contrast and larger pattern roughness in HiNA EUVL, accurate and high resolution CD and defect metrology as well as precise process window (PW) evaluation which considers stochastic failure rate (FR) are needed for resist screening to support the gradual reduction of resist thickness.In this paper we present a thin resist evaluation flow based on next generation e-beam metrology tool (eP6), including metrology setup and qualification and stochastic-aware process window (SAPW) analysis. We will show ADI (after development imaging) evaluation results for a contact hole array with pitch of 32nm for three CAR resist deposited thicknesses from 60nm to 30nm (note: ~40% resist thickness loss post-development). First, we will illustrate some of the metrology responses associated with thin resist and highlight the benefits of improved precision (0.7x), resolution (1.5nm) and sensitivity of next generation metrology tool. For each resist thickness, we will show CD, LCDU and defect measurements across a focus-exposure matrix. LCDU decomposition will be used to quantify SEM, mask, and stochastic contributions to the total measured LCDU. Finally, we will exhibit the SAPW result (Dose-to-Size, Depth of Focus, Exposure Latitude … etc) for the range of resist thicknesses, which can be used to optimize process centering and qualify thin resist performance for HiNA with high correlation coefficient (> 0.9) between measured FR and modeled FR.

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Section: Metrology and Defect Inspection Data Analysismentioning

confidence: 83%

Section: Metrology and Pwm Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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