Spatio-temporal thickness variation and transient Marangoni number in striations during spin coating

Shiratori, Suguru; Kato, Daiki; Sugasawa, Kyosuke; Nagano, Hideaki; Shimano, Kenjiro

doi:10.1016/j.ijheatmasstransfer.2020.119678

Cited by 21 publications

(18 citation statements)

References 25 publications

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“…This phenomenon has been observed in other processes and it is known as the Marangoni effect. [39][40][41][42][43][44] The Marangoni effect can either be caused by a difference in surface tension causing a flow from areas of low surface tension to areas of high surface tension [45] and/or with temperature gradients. [41][42][43][44] The Marangoni effect has been observed in other crystal growth processes before, [46,47] but to this point has not been reported in perovskite processing.…”

Section: The Marangoni Effectmentioning

confidence: 99%

Ambient Processing Conditions and Their Effects on Perovskite Device Performance

Manspeaker

Merrion

Krouse

et al. 2022

Crystal Research and Technology

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Perovskite-based solar devices have gained a worldwide development effort as they hold the potential of affordable solar power. This assumption is based on the inexpensive deposition and growth methods which are centered on ambient processing conditions. However, the data presented here indicate that the ambient conditions have a profound effect on the resulting device performance. Presented here are the effects of ambient conditions of temperature, pressure and solvent concentrations on device performance for devices grown using solvent engineering deposition methods. The results show that ambient conditions effect solvent evaporation rates which have a profound effect on device performance. Additionally, the Marangoni effect, described and observed here for the first time in perovskite solar devices, is also observed via its influence on the film parameters. Finally, the case is made that reliable and consistent perovskite devices will need a processing chamber to control the effects of temperature, pressure, and solvent conditions for perovskite devices to be truly commercializable.

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Section: The Marangoni Effectmentioning

confidence: 99%

Ambient Processing Conditions and Their Effects on Perovskite Device Performance

Manspeaker

Merrion

Krouse

et al. 2022

Crystal Research and Technology

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“…The derivation of Eq. ( 2) is described, for instance, in Oron et al [1] or Shiratori et al [13]. The equations are nondimensionalized using the scale shown in Table 1.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The mechanism of striation has been investigated and, Marangoni-Bénard instability is considered to play a fundamental role [4][5][6][7][8][9][10][11][12]. Shiratori et al investigated the formation process of striations by measuring spatio-temporal thickness variations during a spin coating for mixtures of epoxy resin and xylene solvent [13]. They found that short-wavelength thickness variation may suddenly vanish in the middle of the process.…”

Section: Introductionmentioning

confidence: 99%

Physics-informed neural network applied to surface-tension-driven liquid film flows

Nakamura,

Shiratori,

Takagi

et al. 2021

Preprint

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A physics-informed neural network (PINN), which has been recently proposed by Raissi et al. [J. Comp. Phys. 378, pp. 686-707 (2019)], is applied to the partial differential equation (PDE) of liquid film flows. The PDE considered is the time evolution of the thickness distribution h(x, t) owing to the Laplace pressure, which involves 4th-order spatial derivative and 4th-order nonlinear term. Even for such a PDE, it is confirmed that the PINN can predict the solutions with sufficient accuracy. Nevertheless, some improvements are needed in training convergence and accuracy of the solutions. The precision of floating-point numbers is a critical issue for the present PDE. When the calculation is executed with a single precision floating-point number, the optimization is terminated due to the loss of significant digits. Calculation of the automatic differentiation (AD) dominates the computational time required for training, and becomes exponentially longer with increasing order of derivatives. By splitting the original 4th-order one-variable PDE into 2nd-order two-variable PDEs, the computational time for each training iteration is greatly reduced. The sampling density of training data also significantly affects training convergence. For the problem considered in this study, improved convergence was obtained by allowing the sampling density of training data to be greater in earlier time ranges, where the rapid diffusion of the thickness occurs.

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“…Bornside et al combined the numerical simulation and experimental methods to study the influence of gas flow in the spin-coating process on the radial thickness of films [15]. Kozuka and Shiratori et al studied the fringe characteristics during the spin-coating process [16,17]. In their model, the effects of centrifugal force and effects on the film thickness were considered.…”

Section: Introductionmentioning

confidence: 99%

Evaporation Effect on Thickness Distribution for Spin-Coated Films on Rectangular and Circular Substrates

Yan

Liu

et al. 2021

Coatings

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Spin-coating is widely applied in the field of thin-film fabrication due to its simplicity and high film uniformity. To prepare thin films on rectangular substrates by spin-coating, the simulation and experimental methods were used to study the characteristics of the film thickness in this work. The two-phase flow simulations of spin-coating on a rectangular substrate and circular substrate were carried out with the volume of fluid (VOF) method. The simulation results showed that the airflow field and the substrate geometry had little effect on the evolution of spin-coated film thickness. However, in the experimental results, there was a significant difference in the thickness of the spin-coated film on the rectangular substrate and the circular substrate. According to further study, the solvent evaporation that was neglected in the simulation was the dominant factor of the differences. In addition, it was concluded that the non-uniform evaporation caused by the surface tension and edge accumulation in the later spin-coating stage was the main reason for the film accumulation of the windward area on the rectangular substrate. This work is useful to obtain a deeper understanding of the thin-film formation mechanism of spin-coating.

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Spatio-temporal thickness variation and transient Marangoni number in striations during spin coating

Cited by 21 publications

References 25 publications

Ambient Processing Conditions and Their Effects on Perovskite Device Performance

Ambient Processing Conditions and Their Effects on Perovskite Device Performance

Physics-informed neural network applied to surface-tension-driven liquid film flows

Evaporation Effect on Thickness Distribution for Spin-Coated Films on Rectangular and Circular Substrates

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