Micromachines最新文献

Microfluidic technology plays a crucial role in organ-on-a-chip (OoC) systems by replicating human physiological processes and disease states, significantly advancing biomedical research and drug discovery. This article reviews the design and fabrication processes of microfluidic devices. It also explores how these technologies are integrated into OoC platforms to simulate human physiological environments, highlighting key principles, technological advances, and diverse applications. Through case studies involving the simulation of multiple organs such as the heart, liver, and lungs, the article evaluates the impact of OoC systems' integrated microfluidic technology on drug screening, toxicity assessment, and personalized medicine. In addition, this article considers technical challenges, ethical issues, and future directions, and looks ahead to further optimizing the functionality and biomimetic precision of OoCs through innovation, emphasizing its critical role in promoting personalized medicine and precision treatment strategies.

This paper presents a new small-signal model for double-channel (DC)-high-electron-mobility transistors, developed through an analysis of the unique coupling effects between channels in devices. Unlike conventional single-channel HEMTs, where electrons only transport laterally in the channel, DC-HEMTs exhibit additional vertical transport between the two channels along the material direction. This double-channel coupling effect significantly limits the applicability of traditional small-signal models to DC-HEMTs. Firstly, the coupling effect between the two channels is characterized by introducing the double-channel coupling sub-model, which consists of R_GaN, R_AlN, and C_AlN. At the same time, by introducing parameters gm_{_upper} and gm_{_lower}, the new model can accurately characterize the properties of double channels. Secondly, initial values for R_GaN, R_AlN, and C_AlN are calculated based on the device's physical structure and material properties. Similarly, initial values for gm_{_upper} and gm_{_lower} are derived from the device's DC measurement and TCAD simulation results. Furthermore, a comprehensive parameter extraction method enables the optimized extraction of intrinsic parameters, completing the model's construction. Finally, validation of the model's fitting reveals a significantly reduced error compared to traditional small-signal models. This enhanced accuracy not only verifies the precise representation of the device's physical characteristics but also demonstrates the model's effectiveness.

Colored 3D printing, as one of the crucial directions in 3D printing technology, has been widely applied in various fields in recent years. Compared to traditional 3D printing, colored 3D printing introduces color information to achieve multi-material identification of different regions in the model structure, enabling the fabrication of heterogeneous and complex components. This presents unique advantages in both visual effects and functionality, making it of significant value in fields such as metal manufacturing, bioengineering, and artistic design. However, during the construction of colored models, technical challenges such as low-slicing contour accuracy and poor color reproduction persist. Existing slicing methods for colored models are often accompanied by contour offset, deformation, color distortion, and low rendering efficiency, severely limiting the application scope of colored 3D printing technology. To address these challenges, this paper proposes a "Fast Slicing Method for Colored Models Based on Colored Triangular Prisms and OpenGL". This method first constructs colored triangular prisms to effectively solve the problems of color contour offset and deformation, achieving uniform thickness offset of the colors. Then, by utilizing OpenGL rendering technology, the method overcomes color abruptness, simplifies bitmap rendering processes, and ensures smooth color transitions while significantly improving rendering efficiency. In summary, the proposed slicing method can effectively enhance the accuracy of slicing contours and color reproduction, significantly expanding the application range of colored 3D printing.

This paper describes a novel micro-electro-mechanical system (MEMS) tuning fork gyroscope (TFG) design that employs a chevron-shaped displacement mechanism to amplify the displacement generated by the Coriolis force, thereby increasing the TFG's mechanical sensitivity. This approach was evaluated using both theoretical modeling and finite element analysis (FEA), and the results showed a high degree of agreement between the two methods. A conventional TFG having a comparable area was also designed and analyzed for comparison purposes. By introducing the displacement amplification mechanism, the proposed MEMS TFG design provides an output displacement about 2.5 times higher than the conventional design, according to the computation, without increasing the device footprint. Theoretical analysis and FEA on the TFG with amplification and a conventional TFG confirmed that the amplified displacement significantly improves the mechanical sensitivity of the gyroscope compared to conventional TFG designs.

The chemical-mechanical polishing (CMP) of silicon wafers involves high-precision surface machining after double-sided lapping. Silicon wafers are subjected to chemical corrosion and mechanical removal under pressurized conditions. The multichip CMP process for 4~6-inch silicon wafers, such as those in MOSFETs (Metal Oxide Semiconductor Field Effect Transistors), IGBTs (Insulated-Gate Bipolar Transistors), and MEMS (Micro-Electromechanical System) field materials, is conducted to maintain multiple chips to improve efficiency and improve polish removal uniformity; that is, the detected TTV (total thickness variation) gradually increases from 10 μm to less than 3 μm. In this work, first, a mathematical model for calculating the small deflection of silicon wafers under pressure is established, and the limit values under two boundary conditions of fixed support and simple support are calculated. Moreover, the removal uniformity of the silicon wafers is improved by improving the uniformity of the wax-coated adhesion state and adjusting the boundary conditions to reflect a fixed support state. Then, the stress distribution of the silicon wafers under pressure is simulated, and the calculation methods for measuring the TTV of the silicon wafers and the uniformity measurement index are described. Stress distribution is changed by changing the size of the pressure ring to achieve the purpose of removing uniformity. This study provides a reference for improving the removal uniformity of multichip silicon wafer chemical-mechanical polishing.

With the rapid advancement of social productivity and technological innovation, the insulated gate bipolar transistor (IGBT) has emerged as a cornerstone in modern power electronic devices [...].

Demodulation of fiber optic Fabry-Pérot (F-P) acoustic sensors with high sensitivity and a large dynamic range continues to pose significant challenges. In this paper, we propose an advanced phase-generated carrier (PGC) demodulation algorithm, applied innovatively to membrane-free F-P acoustic sensors operating under high sound pressure. The algorithm optimizes acoustic demodulation results by adjusting the mixing phase delay, achieving the best signal to noise and distortion ratio (SINAD) and total harmonic distortion (THD) (<1%). Additionally, by introducing the cosine component of the acoustic signal obtained directly after filtering the interference signal, into the demodulation algorithm process, the sensitivity of the sensor at high sound pressure is significantly improved. The experimental results show that the ameliorated algorithm obtains a demodulation sensitivity of 34.95 μrad/Pa and a THD of 0.87%, both of which are superior to traditional PGC demodulation algorithms under the same experimental conditions. At the same time, the minimum detectable sound pressure of 129.73 mPa/Hz1/2 was obtained, and the sound pressure tested in the experiment at a frequency of 1 kHz was as high as 3169.78 Pa (164 dB). With the proposed algorithm, the flatness of the frequency response is ±0.82 dB from 100 Hz to 33 kHz, and a dynamic range of up to 102.6 dB was obtained, making it relevant in the field of aerospace acoustic measurements.

Lithium-ion batteries (LIBs) have become integral to modern technology, powering portable electronics, electric vehicles, and renewable energy storage systems. This document explores the complexities and advancements in LIB technology, highlighting the fundamental components such as anodes, cathodes, electrolytes, and separators. It delves into the critical interplay of these components in determining battery performance, including energy density, cycling stability, and safety. Moreover, the document addresses the significant sustainability challenges posed by the widespread adoption of LIBs, focusing on resource depletion and environmental impact. Various recycling practices, including hydrometallurgy, pyrometallurgy, and direct recycling, are evaluated for their efficiency in metal recovery and ecological footprint. The advancements in recycling technologies aim to mitigate the adverse effects of LIB waste, emphasizing the need for sustainable and scalable solutions. The research underscores the importance of ongoing innovation in electrode materials and recycling methodologies, reminding us of our responsibility and commitment to finding and implementing these solutions, as this continuous improvement is crucial to enhance the performance, safety, and sustainability of LIBs, ensuring their continued relevance in the evolving energy storage landscape.

Organic Light Emitting Diode (OLED) technology is preferred in modern display applications due to its superior efficiency, color quality, and flexibility. It also carries a high potential of applicability in military displays where emission color tuning is required for MIL-STD-3009 Night Vision Imaging Systems (NVISs), as compatibility is critical. Herein, we report the effects of different OLED device layer materials and thicknesses such as the hole injection layer (HIL), hole transport layer (HTL), and electron transport layer (ETL) on the color coordinates, luminance, and efficiency of OLED devices designed for night vision (NVIS) compatibility. In this study, simulation tools like SETFOS^® (Semi-conducting Emissive Thin Film Optics Simulator), MATLAB^®, and LightTools^® (Illumination Design Software) were used to verify and validate the luminance, luminance efficiency, and chromaticity coordinates of the proposed NVIS-OLED devices. We modeled the OLED device using SETFOS^®, then the selection of materials for each layer for an optimal electron-hole balance was performed in the same tool. The effective reflectivity of multiple OLED layers was determined in MATLAB^® in addition to an optimal device efficiency calculation in SETFOS^®. The optical validation of output luminance and luminous efficiency was performed in LightTools^®. Through a series of simulations for a green-emitting OLED device, we observed significant shifts in color coordinates, particularly towards the yellow spectrum, when the ETL materials and their thicknesses varied between 1 nm and 200 nm, whereas a change in the thickness of the HIL and HTL materials had a negligible impact on the color coordinates. While the critical role of ETL in color tuning and the emission characteristics of OLEDs is highlighted, our results also suggested a degree of flexibility in material selection for the HIL and HTL, as they minimally affected the color coordinates of emission. We validated via a combination of SETFOS^®, MATLAB^®, and LightTools^® that when the ETL (3TPYMB) material thickness is optimized to 51 nm, the cathode reflectivity via the ETL-EIL stack became the minimum enabling output luminance of 3470 cd/m2 through our emissive layer within the Glass/ITO/MoO₃/TAPC/(CBP:Ir(ppy)₃)/3TPYMB/LiF/Aluminum OLED stack architecture, also yielding 34.73 cd/A of current efficiency under 10 mA/cm² of current density. We infer that when stack layer thicknesses are optimized with respect to their reflectivity properties, better performances are achieved.

The deep learning object detection algorithm has been widely applied in the field of synthetic aperture radar (SAR). By utilizing deep convolutional neural networks (CNNs) and other techniques, these algorithms can effectively identify and locate targets in SAR images, thereby improving the accuracy and efficiency of detection. In recent years, achieving real-time monitoring of regions has become a pressing need, leading to the direct completion of real-time SAR image target detection on airborne or satellite-borne real-time processing platforms. However, current GPU-based real-time processing platforms struggle to meet the power consumption requirements of airborne or satellite applications. To address this issue, a low-power, low-latency deep learning SAR object detection algorithm accelerator was designed in this study to enable real-time target detection on airborne and satellite SAR platforms. This accelerator proposes a Process Engine (PE) suitable for multidimensional convolution parallel computing, making full use of Field-Programmable Gate Array (FPGA) computing resources to reduce convolution computing time. Furthermore, a unique memory arrangement design based on this PE aims to enhance memory read/write efficiency while applying dataflow patterns suitable for FPGA computing to the accelerator to reduce computation latency. Our experimental results demonstrate that deploying the SAR object detection algorithm based on Yolov5s on this accelerator design, mounted on a Virtex 7 690t chip, consumes only 7 watts of dynamic power, achieving the capability to detect 52.19 512 × 512-sized SAR images per second.