Micro and Nano Engineering最新文献

Mesoporous germanium (MP-Ge) emerges as a very appealing material for many applications such as anode material for Lithium-Ion batteries due to it high specific area and large void spaces or, in optoelectronics as sacrificial layer for III-V materials growth and detachment, allowing notably several uses of a single Ge substrate. These porous nanostructures are distinguished by a large specific surface area and are prone to degradation with time due to exposure to the environment. To understand and be able to reduce this effect, we studied the chemical and morphological evolution of porous germanium layers under various ambient storage conditions for 3 months to identify the main parameters responsible for material degradation. This study demonstrates that the ambient air environment leads to the growth of native oxide, leading to major morphology changes. Scanning electrons microscope (SEM) showed the formation of clusters and the enlargement of the pores after 90 days. These structural modifications are caused by the oxidation of Ge, and more specifically by the creation of GeO₂ matrices due to the synergy of dioxygen (O₂) and humidity (H₂O_(g)). The energy brought by light can exacerbate these phenomena and thus accelerate the degradation rate of the pore morphology. Based on these experimental results, we propose efficient solutions to limit the GeO₂ proportions and the clusters' appearance, by storing them under a dry neutral atmosphere (Ar) or by adding a hydrogen halide pre-treatment (10s 1% HBr solution).

This work presents a Python-based architecture packaged as a standalone tool, to enable the parameterization of lithography structures without the need for scripting. By feeding a lithography template file obtained from an existing layout editor into the tool, a ‘scaffold’ shape is created and recognised. The tool allows for the parameterization of created geometries and the establishment of parameterized rules between geometric features, which can be conveniently modified in tabular format. This work facilitates no-code procedural generation of geometrically distinct instances, significantly reducing the time required for complex lithography template development compared to traditional scripting methods.

Manufacturing of microstructures using a microfluidic device is a largely empirical effort due to the multi-physical nature of the fabrication process. As such, in moving toward autonomous manufacturing, models are desired that will predict microstructure attributes (e.g., size, porosity, and stiffness) based on known inputs, such as sheath and core fluid flow rates. Potentially more useful is the prospect of inputting desired microfiber features into a design model to extract appropriate manufacturing parameters. In this study, we demonstrate that deep neural networks (DNNs) trained with sparse datasets augmented by synthetic data can produce accurate predictive and design models to accelerate materials development. For our predictive model with known sheath and core flow rates and bath solution percentage, calculated solid microfiber dimensions are shown to be greater than 95% accurate, with porosity and Young's modulus exhibiting greater than 90% accuracy for a majority of conditions. Likewise, the design model is able to recover sheath and core flow rates with 95% accuracy when provided values for microfiber dimensions, porosity, and Young's modulus. As a result, DNN-based modeling of the microfiber fabrication process demonstrates high potential for reducing time to manufacture of microstructures with desired characteristics.

Membranes play a critical role in diverse applications, including filtration and tissue engineering. The importance of membrane performance optimization highlights the necessity of accurately characterizing the pore structure. Traditional Pore Size Distribution methodologies are widely used to quantify size uniformity. Uniformity though, integrates both size and spatial pore structure aspects, thus necessitating the synergy of complementary techniques to analyze pore structure. This work empowers classic pore metrology with stochastic geometry, specifically the Nearest Neighbour Index (NNI) to assess the spatial uniformity of pores in membrane Scanning Electron Microscopy (SEM) images. Through a comprehensive analysis of Polytetrafluoroethylene (PTFE) before and after plasma etching, along with nanofilament coated Polyethersulfone (PES) membranes, this analysis enhances our understanding of membrane morphology through pore structure and pore spatial arrangement. The findings indicate that increasing magnification leads to a decrease in apparent spatial uniformity, indicative of effects regarding the inclusion in analysis of families of finer pores. In almost all cases, NNI values show higher uniformity compared to a fully random scenario. Additionally, it is found that plasma etching does not have significant effects on spatial uniformity introducing only a slight uniformity in pore centroid arrangement, reflected in a small NNI increase. Furthermore, a pore area shuffling technique reveals the effects of pore density and size on spatial uniformity, highlighting patterns inherent to the materials under study.

Nanomechanical resonators can detect various small physical quantities with high sensitivity using changes in resonant properties. However, viscous damping in liquids significantly reduces the measurement sensitivity. This study proposes convolutional neural network (CNN) vibration spectrum analysis to evaluate the highly sensitive vibration states of nanomechanical resonators, which are useful for in-liquid measurements. This research was carried out through the measurement of acetone concentration. First, we compared the concentration classification ability between the proposed and conventional methods and determined that the proposed method of analyzing vibration spectral changes using the CNN model can provide higher measurement sensitivity than the conventional measurement method of observing resonance properties changes and comparing the values for each measurement condition. This result shows that CNN-based spectral analysis is effective for the vibration spectra of in-liquid measurements. Next, gradient-weighted class activation mapping (Grad-CAM) was applied to verify which frequency bands are important for concentration classification in CNN model decision-making. The vibration states in these frequency bands were analyzed in terms of oscillation modes. This analysis revealed significant oscillation modes of the nanomechanical resonator in the liquid environment. Notably, in addition to the resonance states utilized in the conventional method, several other oscillation modes were found to be significant for measurements. This finding suggests that these oscillation modes may be highly sensitive for measurements in liquid environments. Among these oscillation modes, the mode with very small amplitude is highly promising for achieving unprecedented levels of sensitivity in sensing technologies.

As the world moves towards integrating new functionalities into everyday objects, the demand for diverse substrates grows, making additive manufacturing an invaluable tool. Organic electronic materials have played a major role in this transition thanks to their excellent electronic and mechanical properties, adaptability and solution processability.

The aim of this study is to compare spin coating, inkjet printing (IJP), and aerosol jet printing (AJP) for applying poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) as the channel material in organic electrochemical transistors (OECTs). This work investigates the often-overlooked impact of deposition techniques on the electrical performance of OECTs. Spin coating has been analysed as a reference technique, while AJP and IJP are addressed as promising pathways towards fully printed OECTs.

The normalized transconductance and I_on/I_off ratio have been analysed as figures of merit for this study. AJP devices have shown the best performance, displaying a normalized transconductance of 885 S∙nm and an I_on/I_off ratio around 10³. The spin coated OECTs showed a slightly lower normalized transconductance (740 S∙nm) and much lower I_on/I_off ratio in the order of 10¹. Last, IJP exhibited a transconductance of 433 S∙nm and a I_on/I_off ratio in the order of 10².

This work could be beneficial for a wide range of applications, adding an additional degree of freedom to the tunability of the OECT channel properties. It also opens the discussion for more comprehensive studies on the films from a materials perspective.

Although this opinion paper tracks the career of Mike Hatzakis (as he liked to be called), and explains the impact he made on the IT industry, it is not intended to be comprehensive insofar as the work that was underway during his career is concerned. Thus, the intent is not to cite all relevant work in the field of Semiconductor lithography, where Mike made such an impact, but to provide a historic and human perspective of this remarkable man from the land of the Minotaur (Crete) and his career, the work he championed in his labs in the US and later Greece and his very human approach to science, technology, and to people.

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A gold micro-electro-mechanical-systems (Au-MEMS) capacitive accelerometer having Ti/Au multi-layered structures is a promising device to detect very weak accelerations, such as muscle sounds, because of the high mass density of Au. However, Au is a soft metal, which raises concerns about the structural stability of the Au-MEMS capacitive accelerometers for practical use. In this work, we clarify the key geometric parameters to enhance their long-term structural stability by conducting a long-term vibration test for a total of 240 Ti/Au multi-layered micro-cantilevers with different geometric parameters, such as the length, width, and thickness of the micro-cantilevers, and the number of Ti/Au multi-layered structures. The long-term structural stability is evaluated from the change in the tip height of the micro-cantilevers before and after the vibration tests. These tests demonstrate that the micro-cantilevers with a shorter length, larger thickness, and more Ti/Au multi-layered structures are found to show better long-term structural stability.

Zinc oxide (ZnO) has emerged as one of the most promising candidates for mass-producing cost-efficient optoelectronic devices. This is primarily because it can be synthesized in high-quality nanostructures on a wide range of substrates through relatively simple chemical methods. However, producing p-type ZnO, regardless of the chosen method, remains an open and controversial issue. In this work, Li-doped ZnO nanostructures of varying Li-cocnentration were produced via a two-step hydrothermal growth synthesis and an in-depth analysis based on with Field Emission Scanning Electron Microscopy (FE-SEM), X-ray diffraction (XRD), Raman Spectroscopy, Extended X-Ray Absorption Fine Structure (EXAFS) Spectroscopy, and temperature-dependent Photoluminescence (PL) was carried out in an effort to gain insights into the Li-incorporation mechanisms. The findings indicated a strong interplay between the native defects responsible for the inherent n-type character of the material and Li incorporation. It is suggested that this interplay hinders the successful conversion of the Li-doped nanorods into p-type nanostructures and that when employing the hydrothermal approach it is essential to identify the precise conditions necessary for genuine Li incorporation as a Zn substitutional.

In this study we present a novel device for the direct transduction of optical radiation in the near-infrared region into mechanical actuation, which is based on a plasmonic optical nanoantenna integrated in a microcantilever. We propose and demonstrate the feasibility of a simple fabrication process consisting in the nano-tailoring of a commercially available Atomic Force Microscope (AFM) cantilever by means of the Focused Ion Beam (FIB) milling technique. Furthermore, the comprehensive analysis of the device performance characteristics included in this work reveals the different sensitivity values of these characteristics to the fabrication process tolerances of the most relevant geometric design parameters.