Microsystem Technologies最新文献

Crystal oscillators are fundamental to an extensive range of electronic systems, spanning computers, mobile phones, and automotive electronics. Their significance is accentuated in high-precision applications such as global positioning systems (GPS) and aerospace systems where the frequency-temperature characteristics and thermal hysteresis phenomena are of paramount importance. This study introduces a groundbreaking approach for predicting frequency deviations arising from thermal hysteresis using Long Short-Term Memory (LSTM) networks. Contrary to prior research which predominantly utilized cubic functions to model frequency-temperature characteristics and frequently overlooked thermal hysteresis, this investigation distinguishes itself by leveraging LSTM. The proposed methodology is aptly designed to model both time-dependent and temperature-dependent variations, consequently offering a heightened precision in predicting frequency deviations. By integrating transfer learning techniques, the model's adaptability to diverse databases is augmented, broadening its utility. Experimental evaluations with real-world data underscore the preeminence of the introduced method, registering a root mean square error (RMSE) of less than 0.05 ppm, more favorable than that by the traditional cubic functions and all the prior arts.

This paper focuses on the design of a micropump specifically tailored for drug delivery applications. The micropump is an essential component in microfluidics systems that require precise handling of small volumes of fluids. Its main objective is to achieve a high flow rate while operating at a low voltage of 90 V_P-P. To meet this goal, the micropump utilizes two stacked ring-type piezoelectric actuators (SPZT). The adoption of the ring-type actuators offers several advantages. Firstly, it reduces the contact area between the actuator and the membrane, minimizing the need for gluing. This enhances the overall reliability and robustness of the micropump. Additionally, the stacked configuration of the actuators allows for greater strain generation at lower applied voltages. This leads to improved performance and efficiency of the micropump. The paper includes a comprehensive study of membrane displacement by varying the inner radius of the ring-type SPZT actuator. This parametric analysis is conducted using finite element method (FEM) numerical analysis, providing insights into the optimal design parameters for achieving the desired flow rate. Through the proposed design and analysis, the micropump demonstrates a flow rate of 800 μl/min, making it suitable for drug delivery applications. The findings of this study contribute to the advancement of micropump technology and its potential use in various fields, including healthcare systems, microelectronic cooling devices, and more. Overall, this paper presents a detailed investigation into the design, performance, and optimization of a micropump specifically tailored for drug delivery applications. The utilization of stacked ring-type SPZT actuators and the achieved high flow rate highlight the potential of this micropump design in enhancing the efficiency and effectiveness of drug delivery systems.

In the dynamic field of agriculture, the prompt and accurate identification of plant diseases plays a crucial role in ensuring strong crop yields. To address this need, we present an innovative framework that combines Model Agnostic Meta-Learning (MAML) with DeepLabV3 to identify plant diseases precisely and assess their severity. Deep Plant Guard utilizes the outstanding segmentation capabilities of DeepLabV3 to pinpoint areas of diseased plants while simultaneously determining the specific type of disease and its severity level. The framework is enhanced by Bayesian task augmentation-MAML with multi-scale spatial attention, allowing for swift fine-tuning even with limited data. During training, a composite loss function harmonizes segmentation and classification efforts. Following meta-training excels in adapting to new tasks, providing detailed segmentation masks, and offering valuable insights into disease type and severity. Evaluation results, based on datasets such as Plant Village, Plant Doc, and a newly introduced plant disease dataset, showcase impressive results, including a 99.1% accuracy rate, 99.5% sensitivity, and 98.7% specificity. These results highlight the framework's effectiveness in addressing disease types and severity assessments.

This paper explores the impact of ambient temperature on the RF performance parameters of CombFET device. The CombFET has been considered one of the most realistic alternatives for investigating high frequency applications at sub-10-nm technology nodes. CombFET offers a higher drive current than existing gate-all-around (GAA) nanosheet FET (NSFET) under the same footprint. By taking into account the temperature range of military applications (280 to 400 K), the effect of ambient temperature on the device’s electrical performance is addressed. Further, the RF performance of the device is demonstrated at various ambient temperatures as well as various crucial metrics like transconductance (g_m), TGF, h₂₁, f_T, and TFP are analyzed. Moreover, the zero temperature coefficient (ZTC) operating points are identified from the well calibrated simulation results. The ZTC drain drive current is observed at a gate biasing of 0.77 V, for an applied drain biasing (V_DS) of 1 V. The observed drain drive current for different ambient temperatures is observed to be 28μA (V_DS = 1 V). Also, at the ZTC point, the observed g_m value is 0.094mS (V_DS = 1 V). These findings will therefore provide performance insights into the CombFET device's response to thermal changes.