Coupled mesoscale—continuum simulations of copper electrodeposition in a trench

Drews, Timothy O.; Webb, Eric G.; David, L.; Alameda, Jay; Braatz, Richard D.; Alkire, Richard C.

doi:10.1002/aic.10021

Cited by 52 publications

(44 citation statements)

References 32 publications

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“…10,11 Different domains can be described through a multiscale algorithm that utilizes a lattice-based kinetic Monte Carlo (KMC) model to capture the surface behavior coupled with a continuum model to describe the behavior of the fluid phase. This framework has been used extensively to model a wide range of multiscale process systems such as thin film growth, 12,13 crystallization, 14,15 copper electrodeposition, 16,17 and spatially homogeneous catalytic reactors. [18][19][20] This approach accounts for lateral interactions and other surface nonuniformities in catalytic reactors through the lattice-based KMC model.…”

Section: Introductionmentioning

confidence: 99%

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

2016

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in Wiley Online Library (wileyonlinelibrary.com) Multiscale models have been developed to simulate the behavior of spatially-heterogeneous porous catalytic flow reactors, i.e., multiscale reactors whose concentrations are spatially-dependent. While such a model provides an adequate representation of the catalytic reactor, model-plant mismatch can significantly affect the reactor's performance in control and optimization applications. In this work, power series expansion (PSE) is applied to efficiently propagate parametric uncertainty throughout the spatial domain of a heterogeneous multiscale catalytic reactor model. The PSE-based uncertainty analysis is used to evaluate and compare the effects of uncertainty in kinetic parameters on the chemical species concentrations throughout the length of the reactor. These analyses reveal that uncertainty in the kinetic parameters and in the catalyst pore radius have a substantial effect on the reactor performance. The application of the uncertainty quantification methodology is illustrated through a robust optimization formulation that aims to maximize productivity in the presence of uncertainty in the parameters.

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Section: Introductionmentioning

confidence: 99%

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

2016

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“…From our experiments, we found that a conformal deposition is not the regular fill profile for the electrochemical copper deposition, which is sometimes assumed for modeling a superfill behavior. [26][27][28] Conformal deposition is only achieved when a sufficient suppressor is present, which verifies that the suppressor adsorbs conformally across the structure.…”

Section: -10mentioning

confidence: 94%

Influence of Additive Coadsorption on Copper Superfill Behavior

Liske

Wehner

Preuße

et al. 2009

J. Electrochem. Soc.

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The interaction between the additive components chloride, accelerator, and suppressor is the focus of the present paper. Based on Frumkin and Damaskin's adsorption theory, an advanced concept for the superfilling mechanism is introduced. Cyclovoltammetry measurements show the additive impact on the current-potential behavior and their synergetic effect on the charge transfer across the electrode-electrolyte interface. The measurements are supported by partial fill experiments. Cross-section scanning electron microscopy micrographs reveal that the synergetic impact of an accelerator and a suppressor is very different from their individual contributions. Chloride ions support the adsorption of suppressor molecules, while the accelerator enhances the desorption of the suppressor molecules from the copper surface. The higher the local accelerator concentration in an electrolyte, the more suppressor molecules desorb from the surface. The synergetic behavior between the chloride, suppressor, and accelerator can be explained by coadsorption, which is an important key in the process of copper superfilling. For the application of interconnect structures, manifold investigations have been carried out regarding the role of plating bath additives in the electrochemical copper deposition process.To achieve void-free copper interconnects, superfilling is crucial. Superfilling with an increased copper growth rate at the bottom structure results from the impact of plating bath additives. At least two additives, called suppressor and accelerator, are required for the superfilling process. The active component of the suppressor often consists of poly͑ethylene glycol͒, a long chained organic molecule with a molar mass of up to 10,000 g/mol. Due to its functional end groups, it is soluble in water and acids, while the ether groups possess free electron pairs that interact with copper ions.1,2 Small amounts of chloride ions are necessary for the suppressor to function as a surfactant. 3,4The active component of the accelerator ͓bis͑3-sulfopropyl͒-disulfide or mercaptopropylsulfonic acid͔ adsorbs on Au and Ag surfaces by forming thiolate bonds. Controversies exist about the surface activity of the accelerator on copper especially in acidic solutions.5 Spectroscopic measurements did not identify a specific coordination of the accelerator to a copper surface.6 Mathematical superfilling models usually implement the accelerator adsorption. Those models are based on the competitive adsorption mechanism, which states that the adsorption of suppressor molecules is weaker than the adsorption of accelerator molecules.7-10 Therefore, the suppressor molecules become displaced by catalytic accelerator molecules. The accelerator concentration on the copper surface is assumed to increase inside the trench features due to growth-related surface shrinkage. 11,12 In the present paper, Frumkin's adsorption theory is briefly introduced, focusing on the principles of the coadsorption of two additives. Based on that theory, the effect of chloride on the ...

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“…That is instead of developing complex feedback control algorithms that may not be effective to maintain the nanoscopic or microscopic properties of the systems at a target value, a more efficient strategy consists in designing materials at the fine scale with characteristics that enhance the performance or the product's quality at the coarse scale (Drews et al, 2004;Vlachos, 2005;Braatz et al, 2006a). A key success with this design-oriented approach consists in using the underlying multiscale model as a tool to develop an optimal design of experiments that allow the optimal process and product design.…”

Section: Model-based Controllers For Multiscale Systemsmentioning

confidence: 99%

Current challenges in the design and control of multiscale systems

Ricardez‐Sandoval

2011

Can J Chem Eng

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Multiscale modelling is a new emerging field in process systems engineering. Although the idea of linking events occurring across time and length scales is not new, the numerical solution of these models is challenging because of computational limitations and the difficulty in coupling modelling methods with different characteristics. Although an extensive set of tools are currently available to improve the performance of processes described using continuum models, most of these tools are not suitable to design and control a multiscale process. This work presents the approaches that are currently available to perform multiscale modelling and identifies the key challenges that need to be addressed to improve the performance of macroscopic processes by controlling events occurring at the atomistic, molecular and nanoscopic levels.

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Coupled mesoscale—continuum simulations of copper electrodeposition in a trench

Abstract: Copper electrodeposition in submicron trenches

Cited by 52 publications

References 32 publications

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Influence of Additive Coadsorption on Copper Superfill Behavior

Current challenges in the design and control of multiscale systems

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