Microsystem Technologies最新文献

The transport properties of graphene nanochannels have been studied for symmetric and asymmetric junction configurations using an open-source Python-based tool “Kwant”. In the design process, the arrangement of a narrow channel connected between the two wide graphene nanoribbons appeals to shapes like U and H. Both zigzag (ZNR) and armchair graphene nanoribbons (AGNR) are considered as case studies, and the effect of side junctions on the conductance and density of states are analysed as a function of nanochannel width (W_C). It is observed that, in all the shapes as W_C increases the conductance enhances around the zero Fermi energy. Unity conductance is achieved with W_C = 8, 12, and 16 atoms for unmodulated ZNR channels of length 60 Å. However, for U- and H-shapes with narrow channels (W_C = 8 or 12 atoms), the scattering effect is prominent at the junction leading to not only in reduction but also fluctuation of the conductance. A wider channel (W_C = 16 atoms), reduces the scattering effect and leads to unity conductance. On the other hand, for the AGNR-based U-shaped structure although the channels with W_C = 23, 29, and 35 atoms satisfy metallic conditions (W_C = 3p + 2), the conductance is still zero. However, for the H-shaped structure, the channel with W_C = 35 atoms possess unity conductance. Moreover, studying the effect of asymmetry in the junction alignment of the channel in the H-shape, the conductance fluctuates for the AGNR case but remains unchanged for the ZNR case.

This work introduces a novel AlN/(beta)-Ga₂O₃ MOSHEMT design incorporating a field plate for enhanced power switching applications. The study investigates the impact of varying field plate length (L_FP) on key device parameters through extensive analysis paving the way for optimized device design. The AlN/(beta)-Ga₂O₃ combination, facilitated by the high bandgap of (beta)-Ga₂O₃ and the formation of a significant two-dimensional electron gas, n_s = 10¹³ cm^-2 at the AlN interface, leads to exceptional DC and RF performance. Key findings reveal a peak breakdown voltage of 175 V for a 400 nm field plate, highlighting its suitability for high-voltage applications. The output power exhibits a clear L_FP dependence, ranging from 10.5 kW at 100 nm to 18.1 kW at 400 nm, showcasing the device’s potential for high-power operation. Additionally, the on-state drain current (I_ON) remains stable across varying L_FP. Technology Computer Aided Design (TCAD) simulations demonstrate effective electric field management with the 400 nm field plate reaching a peak of 8.58 MV/cm and decreasing significantly with shorter L_FP. Furthermore, detailed analysis explores the device’s linearity performance. This includes transconductance, its higher-order derivatives, and crucial linearity figures-of-merit (FOMs) like VIP2, VIP3, IIP3, and IMD3. Distortion parameters (HD2 and HD3) also reveal an improved dynamic range and reduced intermodulation interference. These promising results establish the proposed AlN/(beta)-Ga₂O₃ MOSHEMT with a field plate as a compelling candidate for power switching applications demanding high breakdown voltage, significant output power, and exceptional linearity.

Artificial neural networks (ANN) are becoming highly prominent in the optimization of micro-RF devices, which are very significant in wireless communication applications. In this manuscript, we present the optimization of RF MEMS switches using ANN and the design of an antenna with frequency reconfigurability. A unique procedure is proposed to design reconfigurable antennas with RF MEMS switches using ANN. The novelty of this work lies in the creation of a dedicated dataset for the considered RF MEMS switch with FEM tool simulation and the utilization of cascade feed-forward neural networks for optimization. The design of the dataset and the optimization of RF MEMS switches in different aspects using ANN are the key contributions of this work. Comprehensive analysis was performed using a neural network with the designed dataset. Cascade feed-forward neural networks are highly efficient when compared with other neural networks. The weights and biases of the network were selected using the Xavier approach. The cascade feed-forward neural network is optimized using the LM training algorithm. The optimized cascade feed-forward neural network is further used to predict the optimized RF MEMS switch dimensions for the desired application. The network produces an accuracy of 94.9%. An RF MEMS switch was designed from the dimensions predicted by the cascade feed-forward neural network. The designed switch offers – 55 dB Isolation and – 0.2 dB Insertion. Eventually, an antenna was designed by incorporating identical switches which offer frequency reconfigurability.

In today’s digital era, the threat of problematic smartphone usage is very prevalent. To mitigate this threat, a deeper understanding of user behavior is essential. This study focuses on the prediction of problematic smartphone usage patterns among users, considering various demographic variables (gender, marital status, employment, and education). To achieve the study aims, the WhatsApp status seen time primary data is collected from 189 participants for 128 days from Indian students representing different demographic backgrounds. To analyze the collected data, we employed descriptive statistics with three prominent time series models, namely ARIMA, Prophet, and LSTM. The results posit that females, bachelor’s degree students, unmarried, and unemployed participants were found to have a relatively higher risk of problematic smartphone usage. Lastly, the results confirmed that the ARIMA forecasting algorithm is more efficient in forecasting behavior than Prophet and LSTM. While the prophecy algorithm gives better results than LSTM. To the best of our knowledge, none of the previous studies considered marital status and employment status as analysis parameters, and no study used time-series data to provide insight into problematic smartphone usage. The study findings can prove to be a better guide for parents, psychologists, educators, social workers, and policymakers in understanding problematic smartphone usage among students, who are the youth and future of the country.

A precise motion control for compliant mechanisms hinges on an accurate kinematics model, particularly when dealing with intricate nonlinear coupled mechanisms. The motivation driving this research lies in leveraging existing knowledge to direct traditional neural networks (NN) in acquiring a nonlinear kinematics model (grey box), even with a limited dataset. Within this study, the 3-RRR (Revolute-Revolute-Revolute) flexure mechanism was selected due to its inherent nonlinear multi-input multi-output (MIMO) configuration. In relation to this type of flexure mechanism, the convolutional modeling approach based on compliance matrix theory aptly captures the relationship between inputs and outputs. Nonetheless, its linearity poses challenges in achieving utmost precision. In contrast, the NN modeling technique (black box) excels in accurately fitting kinematics models, yet its reliance on extensive data samples hinders practical engineering applications. To achieve a finely-tuned nonlinear kinematic model with a minimal dataset, theoretical prior knowledge serves as a guiding force for the NN to discern the intricate kinematic correlations within the 3-RRR nanopositioner. In-depth, the grey-box network’s training process is steered by a refined learning rate, tailored through convolutional modeling theory (adaptive learning rate). Ultimately, the validation outcomes underscore a substantial enhancement in modeling accuracy.

This study investigates the buckling analysis of a bi-directional functionally graded nanobeam (BD-FGNB) on a Winkler foundation through machine learning (ML) methodologies and semi-analytical solution based on Fourier series and Stokes’ transform. Buckling is investigated via nonlocal strain gradient theory that incorporates the effects of both nonlocal theory and strain gradient theory into the problem. The nonlocal strain gradient theory is employed to model the nanobeam and generate the dataset for training ten distinct ML models. The predictive capabilities of models are evaluated and the ML model with best predictive accuracy is identified by comparing their outcomes against analytical results. Results indicate the exceptional performance of the XGBoost (XGB) model in precisely predicting buckling loads while maintaining high computational efficiency. The R², MAE, and RMSE evaluation metrics demonstrate remarkable values of 0.999, 2.05, and 3.58, respectively, affirming the model's accuracy. Utilizing the SHAP approach, it is found that the foundation parameter has the highest impact on the initial buckling mode, while its impact reduces in subsequent modes. The results from SHAP are validated using the analytical solution where both approaches show that higher values of foundation and material length scale parameters increases the buckling load, however higher values of nonlocal parameter and material grading coefficient in y and z directions decreases the buckling load.

This paper has conducted a thorough investigation of InAlN HEMT using Silvaco TCAD Single Event Upset (SEU) module, which captures the degradation brought on by the heavy ion (H-ion) strike. A range of energies varying from very low, i.e. 0.001pC/µm to 5pC/µm has been used using Linear Energy Transfer (LET) function for the investigation. The effect caused by the barrier thickness has also been captured by combining the influence of the indium mole fraction. Additionally, a comparative analysis has been performed between InAlN and AlGaN HEMT against H-ion strike at different temperatures, barrier thicknesses and multiple H-ion strike. The presented results prove the applicability of InAlN HEMT for space applications, exhibiting a radiation hardened behaviour with high current density. To further expand the device viability for space applications, a GaN cap layer has been introduced, which further adds additional current carrying capacity along with lower leakage current and more radiation hardened characteristics.

In this study, we explore the realm of cloud computing with a particular emphasis on optimizing Virtual Machine (VM) migration, focusing primarily on the effective utilization of CPU resources. The primary objective of our research is to enhance VM migration processes by introducing a novel CPU-centric approach, thereby improving resource management, and reducing operational costs within cloud environments. We conducted extensive experimentation to develop and validate our methods. The core of our methodology revolves around advanced load balancing techniques that prioritize CPU usage. This strategic focus on CPU allocation is designed to address the common challenges in VM migration, such as resource inefficiency and high operational expenses. Our results indicate a marked improvement in VM migration efficiency compared to traditional methods. Specifically, we observed a 78% reduction in the costs associated with VM migrations, underscoring the economic viability of our approach. Additionally, our method exhibited a notable increase in the accuracy and efficiency of resource allocation during the migration process. We achieved a 100% accuracy rate in maintaining optimal load levels, a significant advancement over existing techniques. This enhancement is crucial in ensuring seamless VM operations and minimizing disruptions during migration. Our research contributes to the field of cloud computing by proposing a CPU-focused strategy for VM migration. This approach not only advances the efficiency of VM migrations but also offers substantial economic benefits. By addressing both the technical and cost-related aspects of VM migration, our study provides a comprehensive solution that could be instrumental in shaping future developments in cloud-based resource management and VM operations.

In this manuscript detection of biomolecules has been performed using both the dielectric modulation method as well as gate work function engineering technique for the proposed device ferroelectric GaN HEMT-based biosensor. Many previous literature reports have focused on the underlap technique in most of the biosensor devices but for the first time since we have implemented this innovative concept which has never been implemented before for ferroelectric GaN HEMT biosensor devices. This work has been carried out using Silvaco Atlas TCAD software. From the results it noticed that in comparison to devices without the introduction of biomolecules and with immobilization of biomolecules there is an increase in current value three times, also a positive shift in threshold voltage, and higher sensitivity value as it depends upon factors such as drain current and threshold voltage, etc., and also a reduction in leakage current. The high-concentration 2-DEG results in higher sensitivity to the surface state and gate voltage, and the merits of the device, such as high-voltage and high-frequency. Therefore we conclude that a significant increase in electrostatic properties has been noticed for the case of triple materials gate-based devices with the increase in biomolecule concentration for both side cavity devices.. Therefore it can be concluded that there is increase in performance for DC and Analog performance.

This paper reports the design and analysis of Si-Ge hetero structure planner TFET employed for the detection of various neutral biomolecules having dielectric constants ranging from 2.1 to 46.7 in both dry and wet environments. The proposed TFET sensor consists of the p ⁺Ge source attached with an n ⁺SiGe pocket extending towards the p ⁺Si channel which is attached to the n ⁺Si drain. SiO₂ acts as the receptor layer and the region of gate oxide is sculpted into a shape of rectangular cavity in which biomolecules may be included. A well-calibrated SILVACO ATLAS device simulator is employed to obtain the device transfer characteristics which are exploited to extract the sensitivity of biomolecules. The impact of molar concentration in SiGe, and also the gate source overlap length on sensitivity of biomolecules in both dry and wet environments are investigated. The variation of sensitivity is obtained with the dielectric constant of biomolecules and a comparative analysis is conducted for both dry and wet environments. The design of the sensing device is then optimised and the maximum sensitivity of 2.38 V is obtained in the wet environment condition which is higher or comparable to earlier reported data.