Nanoscale Manufacturing Research Articles

Three-dimensional visualization of large-area, nanoscale topography measurements

High-resolution metrology is a critical area of development for nanoscale manufacturing, especially as it affects production throughput and fabrication quality. Atomic force microscopy (AFM) is one of the most popular tools for nanometrology, and high-resolution AFM often requires a significant time commitment and produces datasets of several million points. It is therefore critical for the development of data processing techniques to keep pace with the requirements of analyzing this type of data, and for these techniques to be portable as miniaturization in AFM is becoming more common. This work presents a data fitting algorithm designed for reducing the parameters of large-area data sets which utilizes well-established spline fitting techniques. In this paper we show that basis-spline fitting can be used to accurately represent large AFM data sets, including data sets with noisy data and sharp features, while achieving at least 90% parameter reduction in all test cases.

Femtosecond laser-induced dewetting of sub-10-nm nanostructures on silicon in ambient air

To realize nanoscale manufacturing based on laser direct writing technology, objective lenses with high numerical apertures immersed in water or oil are necessary. The use of liquid medium restricts its application in semiconductors. Achieving nanoscale features on silicon by laser direct writing in a low refractive index medium has been a challenge. In this work, a microsphere assisted femtosecond laser far-field induced dewetting approach is proposed. A reduction in the full-width at half-maximum of the focused light spot is realized by modulating tightly focused light through microspheres and achieving a minimum feature size of 9 nm on silicon in ambient air with energy smaller than the ablation threshold. Theoretical analysis and numerical simulation of laser processing are performed based on a two-temperature model. Furthermore, we explored the potential of femtosecond laser-induced dewetting in nanolithography and demonstrated its ability to achieve an arbitrary structure on silicon. Our work enables laser-based far-field sub-10-nm feature etching on a large-scale, providing a novel avenue for nanoscale silicon manufacturing.

Beyond the Microscale: Advances in Surface Nanopatterning by Laser‐Driven Self‐Organization

AbstractDesigning complex local properties that seamlessly integrate efficient functions into processed materials presents a formidable challenge. A promising solution has emerged in the form of ultrafast laser‐surface structuring. Through time‐controlled polarization ultrafast irradiation at the picosecond timescale, spontaneous self‐organization of surfaces can be induced. The thermal gradient length scale unfolds on the micro‐ and nanoscale, instigating thermoconvection that leads to structured surfaces upon quenching. Convective instabilities dynamically shape intricate yet self‐regulated periodic relief structures. The ability to achieve laser‐induced self‐organization in both surface dimensions holds immense scientific importance, as it unlocks the potential to create uniform periodic 2D patterns by harnessing the inherent regulation of nonlinear dynamics processes in fluids. This comprehensive review explores recent advances in understanding and leveraging ultrafast laser‐induced self‐organization for precise patterning across versatile scales and applications. The insights herein hold the potential to drive significant advancements in nanoscale manufacturing through 2D laser‐induced periodic surface structures.

Modeling temporal dual variations for return air temperature prediction of mK-level temperature-controlled clean chamber

Clean chamber with mK-level temperature stability is required for precision manufacturing and optical equipment to achieve nanoscale manufacturing tolerances and measurement accuracy, however, the equipped precision air conditioning system cannot respond immediately to temperature fluctuation in the chamber due to large time delay. Predicting the future return air temperature gives the control module a prescient ability to act in advance, thus aiding in temperature regulation. To this end, this paper deals with the mK-level temperature prediction problem under a high-precision temperature control situation, and a novel prediction network model dubbed TDVNet is designed based on convolutional neural network (CNN) and gated recurrent unit (GRU), where Temporal Dual Variations of time series data, namely variations-between-phases and variations-across-periods, are modeled to achieve a higher accuracy of return air temperature prediction. Utilizing chamber temperature data collected on-site for model training and evaluation, the experimental results show that capturing data change patterns from dual perspectives has reduced the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the TDVNet model by 21.4% and 24.3%, respectively, compared to the model only capturing the variations-between-phases. Under a signal sampling interval of 10 s and a temperature fluctuation of (66.9 ± 25.8) mK@10min, the TDVNet model can accurately predict the return air temperature change in the next 5 min, and the proportion of prediction errors within 15 mK in the test set can reach over 90.1%.

Advanced Computer Vision and AI Techniques for Nano-Scale Quality Control in Aerospace Manufacturing

This paper examines the integration of advanced computer vision (CV) techniques and Artificial Intelligence (AI) algorithms to improve quality control (QC) for nano-scale manufacturing processes in the space industry. As nanotechnology is regularly used in the space industry for manufacturing electromechanical components such as NEMS (Nanoelectromechanical Systems), solar panels, and energy storage devices, it's becoming increasingly important to detect defects or imperfections in one of those systems to prevent the loss of life and a costly catastrophe. In order to help mediate this issue, this paper will discuss the methods and processes that can be implemented to capture and analyze nano-scale images and data in order to detect possible flaws in the components.

Slip system level strengthening in nano-indentation of single crystalline copper: Numerical study via a non-local CPFEM model

A three-dimensional non-local crystal plasticity finite element model (CPFEM) integrating the evolution of geometrically necessary dislocations (GNDs) and statistically stored dislocations (SSDs) was developed to simulate the nano-indentation of single crystal copper. The simulation accurately reproduced published experimental results when considering force-depth curves, indentation size effect (ISE), anisotropic plastic deformation and sink-in phenomenon. The accumulated slip, i.e., shear strain in individual slip systems, was quantitively investigated. It is anticipated that the developed non-local CPFEM model can support future sub-micro and nanoscale manufacturing research as it enables the rapid exploration of slip system level mechanical response of a diverse range of crystalline alloys.

Molecular Mechanism of Selective Al2O3 Atomic Layer Deposition on Self-Assembled Monolayers.

Area-selective atomic layer deposition (AS-ALD) of insulating metallic oxide layers could be a useful nanopatterning technique for making increasingly complex semiconductor circuits. Although the alkanethiol self-assembled monolayer (SAM) has been considered promising as an ALD inhibitor, the low inhibition efficiency of the SAM during ALD processes makes its wide application difficult. We investigated the deposition mechanism of Al2O3 on alkanethiol-SAMs using temperature-dependent vibrational sum-frequency-generation spectroscopy. We found that the thermally induced formation of gauche defects in the SAMs is the main causative factor deteriorating the inhibition efficiency. Here, we demonstrate that a discontinuously temperature-controlled ALD technique involving self-healing and dissipation of thermally induced stress on the structure of SAM substantially enhances the SAM's inhibition efficiency and enables us to achieve 60 ALD cycles (6.6 nm). We anticipate that the present experimental results on the ALD mechanism on the SAM surface and the proposed ALD method will provide clues to improve the efficiency of AS-ALD, a promising nanoscale patterning and manufacturing technique.

Predictive combining multiple variously hybridized low-dimensional nanocarbons in a single additive for nano-sized energetic materials performance enhancement

We propose to uncover new opportunities for predictive nano-sized energetic materials performance enhancement through manipulating by vibrational interactions, energy exchange as well as heat transfer enhancement within the reaction zones at nanoscale. The combination of multiple carbon nanostructured materials with various hybridizations within a single substance can uncover new unique properties. Due to a recent fundamental discovery the collective atomic vibrations, called phonon waves, manifested in transition domains of multilayer nanostructures, incorporation of self-organized arrays of metastable nanostructures are capable controlling vibrational interactions and energy exchange within the reaction zones at nanoscale. For using this phenomenon, we propose predictive incorporation into the nano-sized energetic material composition of various carbon-based allotropes, used as catalytic nano-additives, combined with assembling them by the self-organized arrays of differently hybridized low-dimensional nanocarbon promoters. For predictive combining of multiple differently hybridized nanocarbons within a single substance we propose to use the energy-driven initiation of the allotropic phase transformations in nanocarbon promoters by concurrent electron and ion irradiation. For fine tuning the collective atomic vibrations, nanoarchitecture and functionality of the mentioned arrays of differently hybridized nanocarbon promoters we propose using combination of a set of techniques: concurrent electron and ion irradiation, using the surface acoustic waves combined with heteroatom doping along with application external electromagnetic fields and using the data-driven nanoscale manufacturing approach.

Advances in TEER measurements of biological barriers in microphysiological systems

Biological barriers are multicellular structures that precisely regulate the transport of ions, biomolecules, drugs, cells, and other organisms. Transendothelial/epithelial electrical resistance (TEER) is a label-free method for predicting the properties of biological barriers. Understanding the mechanisms that control TEER significantly enhances our knowledge of the physiopathology of different diseases and aids in the development of new drugs. Measuring TEER values within microphysiological systems called organ-on-a-chip devices that simulate the microenvironment, architecture, and physiology of biological barriers in the body provides valuable insight into the behavior of barriers in response to different drugs and pathogens. These integrated systems should increase the accuracy, reproducibility, sensitivity, resolution, high throughput, speed, cost-effectiveness, and reliable predictability of TEER measurements. Implementing advanced micro and nanoscale manufacturing techniques, surface modification methods, biomaterials, biosensors, electronics, and stem cell biology is necessary for integrating TEER measuring systems with organ-on-chip technology. This review focuses on the applications, advantages, and future perspectives of integrating organ-on-a-chip technology with TEER measurement methods for studying biological barriers. After briefly reviewing the role of TEER in the physiology and pathology of barriers, standard techniques for measuring TEER, including Ohm's law and impedance spectroscopy, and commercially available devices are described. Furthermore, advances in TEER measurement are discussed in multiple barrier-on-a-chip system models representing different organs. Finally, we outline future trends in implementing advanced technologies to design and fabricate nanostructured electrodes, complicated microfluidic chips, and membranes for more advanced and accurate TEER measurements.

Laser polarization associated periodic oscillation of thermal response in silicon nanotip

The nanotip's thermal response and its regulation are essential to tip-assisted nanoscale manufacturing. Laser polarization provides a promising solution for the dynamic tuning of laser energy absorption. However, the thermal response of nanotip under laser irradiation has yet to be fully understood. Here, the temperature and stress response of silicon nanotip under laser heating at different polarization angles is studied by Raman thermometry technique. The temperature oscillates between ∼584 and ∼742 K, with the polarization angle changing from 90 to 0°, showing sound dependence with laser polarization. Finite element simulations indicate that such oscillation effect of high temperatures is caused by the electric enhancement induced by the lightning rod effect and the low thermal conductivity of ∼21 W/(m⋅K) due to the high temperature and the size effect of heat conduction. Meanwhile, polarization-dependent thermal stress occurs due to the large electric distribution gradient. The periodic oscillation of the temperature and stress with the polarization angle verifies the reversibility of the tuning process, which paves the way toward precise nano-manufacturing.

Curvature-Adjustable Polymeric Nanolens Fabrication Using UV-Controlled Nanoimprint Lithography.

Nanolenses are gaining importance in nanotechnology, but their challenging fabrication is thwarting their wider adoption. Of particular challenge is facile control of the lens' curvature. In this work, we demonstrate a new nanoimprinting technique capable of realizing polymeric nanolenses in which the nanolens' curvature is optically controlled by the ultraviolet (UV) dose at the pre-curing step. Our results reveal a regime in which the nanolens' height changes linearly with the UV dose. Computational modeling further uncovers that the polymer undergoes highly nonlinear dynamics during the UV-controlled nanoimprinting process. Both the technique and the process model will greatly advance nanoscale science and manufacturing technology.

Characterizing vibration-assisted atomic force microscopy (AFM)-based nanomachining via perception of acoustic emission phenomena using a sensor-based real-time monitoring approach

The emerging atomic force microscope (AFM)-based nanolithography offers promising opportunities for nanopatterning applications. However, critical issues reside in the nanomachining process because the heterogeneous material properties and machine tool (AFM tip) quality variations can create significant uncertainties at this nanoscale. Therefore, in-process monitoring is essential for timely anomaly detection and real-time process characterizations. This paper reports a sensor-based monitoring approach that allows classifications of different conditions of vibration-assisted nanopatterning in real-time by automatically selecting acoustic emission spectral responses that distinguish different amounts of material removal. It opens up an avenue toward process characterizations and mechanisms discovery in vibration-assisted nanoscale manufacturing.

Flexoelectricity and surface effects on coupled electromechanical responses of graphene reinforced functionally graded nanocomposites: A unified size-dependent semi-analytical framework

Owing to inhomogeneous strain and high surface-to-volume ratio in nanostructures, it is imperative to account for the flexoelectricity as well as surface effect while analyzing the size-dependent electromechanical responses of nano-scale piezoelectric materials. In this article, a semi-analytical ‘single-term extended Kantorovich method (EKM)’ and ‘Ritz method’ based powerful framework is developed for investigating the static and dynamic electromechanical responses of graphene reinforced piezoelectric functionally graded (FG) nanocomposite plates, respectively. The residual surface stresses, elastic and piezoelectric surface modulus, and direct flexoelectric effects are taken into account while developing the unified governing equations and boundary conditions. The modified Halpin Tsai model and rules of mixture are implemented to predict the effective bulk properties. Our results reveal that the static deflection and resonance frequency of the proposed FG nanoplates are significantly influenced due to the consideration of flexoelectricity and surface effects. While such outcomes emphasize the fact that such effects cannot be ignored, these also open up the notion of on-demand property modulation and active control. The effects are more apparent for nanoplates of lesser thickness, but they diminish as plate thickness increases, leading to the realization and quantification of a size-dependent behavior. Based on the developed unified formulation, a comprehensive numerical investigation is further carried out to characterize the electromechanical responses of nanoplates considering different critical parameters such as plate thicknesses, aspect ratios, flexoelectric coefficients, piezoelectric multiples, distribution, and weight fraction of graphene platelets along with different boundary conditions. With the recent advances in nano-scale manufacturing, the current work will provide the necessary physical insights in modeling size-dependent multifunctional systems for active control of mechanical properties and harvesting electromechanical energy.

Microscopic and data-driven modeling and operation of thermal atomic layer etching of aluminum oxide thin films

With increasing demands for microchips and increasing needs in the nano-scale semiconductor manufacturing industry, atomic layer etching (ALE) has been developing into a critical etching process. Unlike its counterpart in the film deposition domain, atomic layer deposition (ALD), which has been extensively studied, ALE has not been fully studied yet from a modeling and operation point of view. Therefore, this work develops microscopic models to characterize the thermal ALE process of aluminum oxide thin films with two precursors (hydrogen fluoride and trimethylaluminum). First, the reaction mechanisms for the two half-cycles for the thermal ALE process are established. Electronically predicted geometries of the Al2O3 structure with two precursors are optimized. Along with the optimized geometries, possible reaction pathways are proposed and calculated by density functional theory (DFT)-based electronic structure calculations. The proposed reaction paths and their kinetic parameters are used in a kinetic Monte Carlo (kMC) algorithm, which is capable of capturing the features of the thermal ALE of aluminum oxide. The kMC simulation provides an etch time for the given steady-state operating conditions, which are validated via comparison with available experimental results. Finally, data sets collected from the kMC simulation are used to train a feed-forward artificial neural network (FNN) model. The trained FNN model accurately predicts an etch time and dramatically reduces the computation time compared to the kMC simulation, thereby making it possible to carry out real-time, model-based operational parameter calculations. In addition, the trained FNN model can be used to establish a feasible range of operating conditions without demanding experimental work.

Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks

In the past 20 years, flat‐optics has emerged as a promising light manipulation technology, surpassing bulk optics in performance, versatility, and miniaturization capabilities. As of today, however, this technology is yet to find widespread commercial applications. One of the challenges is obtaining scalable and highly efficient designs that can withstand the fabrication errors associated with nanoscale manufacturing techniques. This problem becomes more severe in flexible structures, in which deformations appear naturally when flat‐optics structures are conformally applied to, for example, biocompatible substrates. Herein, an inverse design platform that enables the fast design of flexible flat‐optics that maintain high performance under deformations of their original geometry is presented. The platform leverages on suitably designed evolutionary large‐scale optimizers, equipped with fast‐trained neural network predictors based on encoder decoder architectures. This approach supports the implementation of flexible flat‐optics robust to both fabrication errors or user‐defined perturbation stress. This method is validated by a series of experiments in which broadband flexible light polarizers, which maintain an average polarization efficiency of 80% over 200 nm bandwidths when measured under large mechanical deformations, are realized. These results could be helpful for the realization of a robust class of flexible flat‐optics for biosensing, imaging, and biomedical devices.

Unraveling the Role of Grain Boundary Anisotropy in Sintering: Implications for Nanoscale Manufacturing

Unraveling the Role of Grain Boundary Anisotropy in Sintering: Implications for Nanoscale Manufacturing

Special Issue on Advanced Manufacturing Science and Technologies

In recent years, manufacturing technologies have been progressing with the high demands of industry. In the automobile and aircraft industries, for example, the manufacturing processes have been requiring for technologies that allow for high machining rates of lightweight and/or difficult-to-cut materials. Medical device production includes the machining of biocompatible materials that have high mechanical strength. Information devices require high quality in ultra-precision manufacturing processes. Measurement and characterization technologies in manufacturing have also been essential in the progress. Along with evolution of manufacturing technologies, scientific studies have been done on manufacturing phenomena and the control of processes, based on physical and/or mathematical aspects. This special issue is promoted by the International Conference on Leading Edge Manufacturing/Materials & Processing (LEM&P2020), which was canceled to protect the health and wellness of our community from COVID-19, and by the Research Committee of Cutting Technologies in the Japan Society for Precision Engineering. This special issue includes 17 papers that detail progress and innovations in the following areas: - Characterization of materials - Fundamental study and modeling of material removal process - Manufacturing control and optimization - Manufacturing processes for new/hard materials - Micro-/Nano-scale manufacturing - Tool manufacturing and performance - Metrology and evaluation - Surface characterization This special issue also includes not only technical but also scientific discussions, suggesting new key technologies for future manufacturing. We hope this will help the readers to understand the manufacturing processes and improve their operations. We thank the authors and the reviewers for their generous cooperation and the editing staffs for their many contributions.

Strategies for Constructing and Operating DNA Origami Linear Actuators.

Linear actuators are ubiquitous components at all scales of engineering. DNA nanotechnology offers a unique opportunity for bottom-up assembly at the molecular scale, providing nanoscale precision with multiple methods for constructing and operating devices. In this paper, DNA origami linear actuators with up to 200nm travel, based on a rail threading a topologically locked slider, are demonstrated. Two strategies, one- and two-pot assembly, are demonstrated whereby the two components are folded from one or two DNA scaffold strands, respectively. In order to control the position of the slider on the rail, the rail and the inside of the slider are decorated with single-stranded oligonucleotides with distinct sequences. Two positioning strategies, based on diffusion and capture of signaling strands, are used to link the slider reversibly to determined positions on the rail with high yield and precision. These machine components provide a basis for applications in molecular machinery and nanoscale manufacture including programmed chemical synthesis.

Analysis of Key Parameters in PBGA Manufacturing Process Using Heuristic Approach and General Regression Neural Network

In the printed circuit board (PCB) manufacturing industry, the yield is an important management factor as it significantly affects the product cost and quality. However, in real situations, it is difficult to ensure a high yield in a manufacturing process, because the products are manufactured through numerous nanoscale manufacturing operations. Thus, for improving the yield, it is necessary to analyze the key process parameters and equipment parameters that result in a low yield. In this study, critical equipment parameters that affect the yield were extracted through a mutual analysis of the equipment parameters (x) and process parameters (y) in the plastic ball grid array (PBGA) manufacturing process. To this end, the study uses the correlation coefficient to apply the heuristic algorithm that extracts critical parameters that keep the redundancy among the equipment parameters to a minimum and exert maximum impact on the critical process parameters. Additionally, by using the general regression neural network technique, the effects of the critical equipment parameters on the process parameters were confirmed. The test results were applied to the PBGA production line and an improvement in the yield was confirmed.

In-vacuum active colour sensor and wireless communication across a vacuum-air interface

In situ sensing with wireless digital-data transfer is a potential processing scheme that works very closely to the location of an event monitored by a sensor and converts the sensor’s raw output into digitized and informative small-volume bits, as suggested by recent proposals for edge computing and the Internet of Things (IoT). Colour perception may be a target of in situ sensor data acquisition; however, in contrast to from other sensing devices, colour sensors that detect visible light signals are usually located away from light-emitting sources, collecting light transmitting through the space and attenuating it in some manner. For example, in a vacuum chamber whose gas pressure is much less than the ambient atmosphere in which the sensors usually work, there are many veiled light sources, such as discharge plasma, for various industrial purposes including nanoscale manufacturing. In this study, we designed an in-vacuum colour sensor that can work with analogue-to-digital conversion and transfer data by wireless communication; this sensor is active in a low-pressure plasma chamber, detecting light signals and transferring them to a personal computer located outside the vacuum chamber. In addition to detecting lights with controlled spectra from outside successfully, we achieved complete operation of our in-vacuum active sensor for plasma emissions generated at 100 Pa. Comparing the signals with data from simultaneous monitoring by a monochromator, we established that the recorded signals arose from the plasma, confirming successful direct detection of low-pressure plasma emissions without any filtering effects between the sensor and the target object.