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The behavior of permeable, elastic particles sliding along a repulsive wall is examined computationally. It is found that particles will stick or slip depending on the interplay of elastohydrodynamic and repulsive forces, and the flow in the porous particle. Particles slip when either the elastohydrodynamic lift or repulsive forces are large and create a supporting lubricating film of fluid. However, for lower values of elastohydrodynamic lift or repulsive forces, the flow within the porous particle reduces the pressure in the thin film, resulting in the particles making contact and sticking to the surface. The criteria for the slip-stick transition is presented, which can be used to design systems to promote or suppress slip for such suspensions. 

We explore the rheology during a startup flow of well-characterized polyelectrolyte microgel suspensions, which form soft glasses above the jamming concentration. We present and discuss results measured using different mechanical histories focusing on the variations of the static yield stress and yield strain. The behavior of the shear stress growth function is affected by long-lived residual stresses and strains that imprint a slowly decaying mechanical memory inside the materials. The startup flow response is not reversible upon flow reversal and the amplitude of the static yield stress increases with the time elapsed after rejuvenation. We propose an experimental protocol that minimizes the directional memory and we analyze the effect of aging. The static yield strain γ p and the reduced static yield stress σ p / σ y , where σ y is the dynamic yield stress measured from steady flow measurements, are in good agreement with our previous simulations [Khabaz et al., “Transient dynamics of soft particle glasses in startup shear flow. Part I: Microstructure and time scales,” J. Rheol. 65, 241 (2021)]. Our results demonstrate the need to consider memory and aging effects in transient measurements on soft particle glasses. 

Permeable particles are closer to the wall than impermeable particles and stick below a critical particle velocity.

 

The thermodynamics of the shear-induced phase transition of soft particle glasses is presented. Jammed suspensions of soft particles transform into a layered phase in a strong shear flow from a stable glassy phase at lower shear rates. The thermodynamics of the two phases can be computed based on the elastic energy and excess entropy of the system. At a critical shear rate, the elastic energy, the excess entropy, the free energy, the temperature, and the shear stress undergo discontinuous jumps at the phase transitions from the glassy to the layered phase. An effective temperature is defined from the derivative of the elastic energy and the excess entropy. The Helmholtz free energy is constructed using the elastic energy, excess entropy, and derived temperature. At a fixed shear rate, there is no equilibrium between the states. However, at a fixed temperature, the glassy and layered states may coexist, as indicated by the equality of their Helmholtz free energies. While this first-order phase transition is possible, it cannot be observed in simple shear because the stress is the same in both phases at the same temperature. Thus, shear banding cannot be observed in this system. Finally, an equation of state, which relates the shear stress to the excess entropy, is presented. This equation of state shows that all dynamical properties (e.g., shear-induced diffusivity and first and second normal stresses) of these jammed non-Brownian suspensions can be determined solely by measuring the shear stress.

 

A method for automated creation and optimization of multistep etch recipes is presented. Here we demonstrate how an automated model-based process optimization approach can cut the cost and time of recipe creation by 75% or more as compared with traditional experimental design approaches. Underlying the success of the method are reduced-order physics-based models for simulating the process and performing subsequent analysis of the multi dimensional parameter space. SandBox Studio™ AI is used to automate the model selection, model calibration and subsequent process optimization. The process engineer is only required to provide the incoming stack and experimental measurements for model calibration and updates. The method is applied to the optimization of a channel etch for 3D NAND devices. A reduced-order model that captures the physics and chemistry of the multistep reaction is automatically selected and calibrated. A mirror AI model is simultaneously and automatically created to enable nearly instantaneous predictions across the large process space. The AI model is much faster to evaluate and is used to make a Quilt™, a 2D projection of etch performance in the multidimensional process parameter space. A Quilt™ process map is then used to automatically determine the optimal process window to achieve the target CDs.