Explore the vast range of titles available.

Size, surface charge, and material compositions are known to influence cell uptake of nanoparticles. However, the effect of particle geometry, i.e., the interplay between nanoscale shape and size, is less understood. Here we show that when shape is decoupled from volume, charge, and material composition, under typical in vitro conditions, mammalian epithelial and immune cells preferentially internalize disc-shaped, negatively charged hydrophilic nanoparticles of high aspect ratios compared with nanorods and lower aspect-ratio nanodiscs. Endothelial cells also prefer nanodiscs, however those of intermediate aspect ratio. Interestingly, unlike nanospheres, larger-sized hydrogel nanodiscs and nanorods are internalized more efficiently than their smallest counterparts. Kinetics, efficiency, and mechanisms of uptake are all shape-dependent and cell type-specific. Although macropinocytosis is used by both epithelial and endothelial cells, epithelial cells uniquely internalize these nanoparticles using the caveolae-mediated pathway. Human umbilical vein endothelial cells, on the other hand, use clathrin-mediated uptake for all shapes and show significantly higher uptake efficiency compared with epithelial cells. Using results from both upright and inverted cultures, we propose that nanoparticle internalization is a complex manifestation of three shape- and size-dependent parameters: particle surface-to-cell membrane contact area, i.e., particle–cell adhesion, strain energy for membrane deformation, and sedimentation or local particle concentration at the cell membrane. These studies provide a fundamental understanding on how nanoparticle uptake in different mammalian cells is influenced by the nanoscale geometry and is critical for designing improved nanocarriers and predicting nanomaterial toxicity.

This article discusses the transition of a form of nanoimprint lithography technology, known as Jet and Flash Imprint Lithography (J-FIL), from research to a commercial fabrication infrastructure for leading-edge semiconductor integrated circuits (ICs). Leading-edge semiconductor lithography has some of the most aggressive technology requirements, and has been a key driver in the 50-year history of semiconductor scaling. Introducing a new, disruptive capability into this arena is therefore a case study in a “high-risk-high-reward” opportunity. This article first discusses relevant literature in nanopatterning including advanced lithography options that have been explored by the IC fabrication industry, novel research ideas being explored, and literature in nanoimprint lithography. The article then focuses on the J-FIL process, and the interdisciplinary nature of risk, involving nanoscale precision systems, mechanics, materials, material delivery systems, contamination control, and process engineering. Next, the article discusses the strategic decisions that were made in the early phases of the project including: (i) choosing a step and repeat process approach; (ii) identifying the first target IC market for J-FIL; (iii) defining the product scope and the appropriate collaborations to share the risk-reward landscape; and (iv) properly leveraging existing infrastructure, including minimizing disruption to the widely accepted practices in photolithography. Finally, the paper discusses the commercial J-FIL stepper system and associated infrastructure, and the resulting advances in the key lithographic process metrics such as critical dimension control, overlay, throughput, process defects, and electrical yield over the past 5 years. This article concludes with the current state of the art in J-FIL technology for IC fabrication, including description of the high volume manufacturing stepper tools created for advanced memory manufacturing. Nanomanufacturing: A step closer to nanoimprint based integrated–circuit fabrication An appraisal of sub-40nm half-pitch lithography technologies for high-volume manufacture of semiconductor integrated circuits is provided. Although cutting-edge semiconductor lithography has been an important driver for the electronics industry, only a small subset of researched nanolithography techniques have been explored for mass production in semiconductor integrated circuits fabrication facilities. By assessing the evolution of a nanoimprint technique known as jet and flash imprint lithography (J-FIL) stepper technology, S. V. Sreenivasan at the University of Texas at Austin, United States, has identified the main characteristics for insertion of J-FIL for fabrication of semiconductor memory with sub-20nm half-pitch structures. The demonstrated ability of nanoimprint to pattern resist structures of less than 5 nanometers makes it an attractive choice for potentially extending the scaling roadmap for high volume semiconductor manufacturing.

Emerging nanoscale applications in energy, electronics, optics, and medicine can exhibit enhanced performance by incorporating nanoshaped structures (nanoshape structures here are defined as shapes enabled by sharp corners with radius of curvature < 5 nm). Nanoshaped fabrication at high-throughput is well beyond the capabilities of advanced optical lithography. Although the highest-resolution e-beams and large-area e-beams have a resolution limit of 5 and 18 nm half-pitch lines or 20 nm half-pitch holes, respectively, their low throughput necessitates finding other fabrication techniques. By using nanoimprint lithography followed by metal-assisted chemical etching, diamond-like nanoshapes with ~3 nm radius corners and 100 nm half-pitch over large areas have been previously demonstrated to improve the nanowire capacitor performance (by ~90%). In future dynamic random-access memory (DRAM) nodes (with DRAM being an exemplar CMOS application), the implementation of nanowire capacitors scaled to <15 nm half-pitch is required. To scale nanoshape imprint lithography down to these half-pitch values, the previously established atomistic simulation framework indicates that the current imprint resist materials are unable to retain the nanoshape structures needed for DRAM capacitors. In this study, the previous simulation framework is extended to study improved shape retention by varying the resist formulations and by introducing novel bridge structures in nanoshape imprinting. This simulation study has demonstrated viable approaches to sub-10 nm nanoshaped imprinting with good shape retention, which are matched by experimental data.

We present a neural-network-based approach to solve the image-to-image translation problem in microscale droplets squeeze flow. A residual convolutional neural network is proposed to address the inverse problem: reconstructing a low-resolution (LR) droplet pattern image from a high-resolution (HR) liquid film thickness imprint. This enables the prediction of initial droplet configurations that evolve into target HR imprints after a specified spreading time. The developed neural network architecture aims at learning to tune the refinement level of its residual convolutional blocks by using function approximators that are trained to map a given film thickness to an appropriate refinement level indicator. We use multiple stacks of convolutional layers, the output of which is translated according to the refinement level indicators provided by the directly connected function approximators. Together with a non-linear activation function, the translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. We believe that this work holds value for the semiconductor manufacturing and packaging industry. Specifically, it enables desired layouts to be imprinted on a surface by squeezing strategically placed droplets with a blank surface, eliminating the need for customized templates and reducing manufacturing costs. Additionally, this approach has potential applications in data compression and encryption.

This paper describes a method to increase the aspect ratio of polymeric pillar arrays formed by electrohydrodynamic instabilities. Pillar arrays form spontaneously across a narrow capacitor gap when an electric field is applied normal to a thin, fluidic film. This simple technique is appealing because of its ability to rapidly form arrays of small structures in an inexpensive manner. The columnar structures formed using this technique have low-aspect ratios, which are non-ideal for patterning applications. Theory suggests that stretching the structures post-formation is one of the only ways to increase the aspect ratio of the pillars. We developed a tool to physically stretch these structures to increase their aspect ratio from ∼0.1 to ∼0.5. The capabilities and limits of this stretching technique have been discussed.

Complex nanoshaped structures (nanoshape structures here are defined as shapes enabled by sharp corners with radius of curvature <5 nm) have been shown to enable emerging nanoscale applications in energy, electronics, optics, and medicine. This nanoshaped fabrication at high throughput is well beyond the capabilities of advanced optical lithography. While the highest-resolution e-beam processes (Gaussian beam tools with non-chemically amplified resists) can achieve <5 nm resolution, this is only available at very low throughputs. Large-area e-beam processes, needed for photomasks and imprint templates, are limited to ~18 nm half-pitch lines and spaces and ~20 nm half-pitch hole patterns. Using nanoimprint lithography, we have previously demonstrated the ability to fabricate precise diamond-like nanoshapes with ~3 nm radius corners over large areas. An exemplary shaped silicon nanowire ultracapacitor device was fabricated with these nanoshaped structures, wherein the half-pitch was 100 nm. The device significantly exceeded standard nanowire capacitor performance (by 90%) due to relative increase in surface area per unit projected area, enabled by the nanoshape. Going beyond the previous work, in this paper we explore the scaling of these nanoshaped structures to 10 nm half-pitch and below. At these scales a new “shape retention” resolution limit is observed due to polymer relaxation in imprint resists, which cannot be predicted with a linear elastic continuum model. An all-atom molecular dynamics model of the nanoshape structure was developed here to study this shape retention phenomenon and accurately predict the polymer relaxation. The atomistic framework is an essential modeling and design tool to extend the capability of imprint lithography to sub-10 nm nanoshapes. This framework has been used here to propose process refinements that maximize shape retention, and design template assist features (design for nanoshape retention) to achieve targeted nanoshapes.

The effect of nano boric acid and nano copper based engine and transmission oil additives in different volume ratios (1:10, 2:10, and 3:10) on friction and wear performance of cast iron and case carburized gear steel has been investigated. The results show that coefficient of friction increases with increase in volume ratio of engine oil additives and decreases with increasing in volume ratio of transmission oil additives. Cast iron substrate shows higher wear damage than case carburized gear steel. Nano copper additive with crystalline atomic structure shows more severe three body wear compared to boric acid with layered lattice structure.

This paper introduces a methodology for designing multistable equilibrium (MSE) systems. The methodology derives design criteria that relate system equilibrium characteristics to a potential energy curve. These design criteria are then used in a performance index that guides a candidate’s potential energy to approach the desired potential energy curve. As an example, a four-bar linkage with linear translational springs attached demonstrates the design methodology and the difficulties inherent in synthesizing MSE systems. To solve for the unknown design variables of the example problem a genetic algorithm is used.

We propose a platform based on neural networks to solve the image-to-image translation problem in the context of squeeze flow of micro-droplets. In the first part of this paper, we present the governing partial differential equations to lay out the underlying physics of the problem. We also discuss our developed Python package, sqflow, which can potentially serve as free, flexible, and scalable standardized benchmarks in the fields of machine learning and computer vision. In the second part of this paper, we introduce a residual convolutional neural network to solve the corresponding inverse problem: to translate a high-resolution (HR) imprint image with a specific liquid film thickness to a low-resolution (LR) droplet pattern image capable of producing the given imprint image for an appropriate spread time of droplets. We propose a neural network architecture that learns to systematically tune the refinement level of its residual convolutional blocks by using the function approximators that are trained to map a given input parameter (film thickness) to an appropriate refinement level indicator. We use multiple stacks of convolutional layers the output of which is translated according to the refinement level indicators provided by the directly-connected function approximators. Together with a non-linear activation function, such a translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. The proposed platform can be potentially applied to data compression and data encryption. The developed package and datasets are publicly available on GitHub at https://github.com/sqflow/sqflow.

As new nanolithography technologies continue to be developed, their potential as cost-effective alternatives to optical lithography methods continues to grow. Low surface energy release layers comprised of amorphous fluoropolymers have already been proven to be effective for improving defect levels in contact print lithography. It is unclear at this early stage of development whether nano imprint lithographies will supplant more established optical methodologies. However, it is clear that a successful release layer technology is required to control defects and enable the potential of imprint lithography to be achieved.