Due to material mismatch, silicon differential resonant accelerometers (SDRA) experience unpredictable thermal stress that is challenging to account for. An SDRA with a stress-isolated frame that is co-faced with the sensitive structural layer is shown in this study. Analytical construction is used to create the stress transmission model of SDRA, which incorporates the equivalent stiffness of a stress-isolated frame. It offers a technique to balance the scale factor of the device with thermal stress resistance. Then, from -40°C to 85°C, the proposed SDRA devices’ temperature behavior with and without a stress-isolated frame were examined theoretically, numerically, and experimentally. The measured temperature sensitivities of the device with the stress-isolated structure were around -1.15Hz /°C, 0.19mg/°C, and 37.23ppm /°C for the frequency, bias, and scale factors, respectively. The bias instability of the novel device is 0.72μg. Under the identical experimental conditions, each of these metrics outperformed the device without stress isolation by one to two orders of magnitude, demonstrating the efficiency of the novel stress-isolated structure.

In practical applications, the quick startup of micro-electro-mechanical systems (MEMS) sensors is particularly crucial, and the temperature drift during a power-on startup has become a major concern. In this paper, a universal analysis method for temperature variation during the power-on startup of MEMS resonant sensors is proposed. In this research, a one-dimensional simplified thermal resistance model was established by quantitatively calculating the equivalent thermal power consumption of the complete heat source and the equivalent thermal resistance of different heat transfer methods. The device startup temperature drift characteristics was verified via finite element analysis with the system based on the printed circuit board (PCB) as an example. The experimental results showed that the method correctly predicted the temperature drift and stabilization time after startup with an error of <10%. The goal of this research was to advance the theoretical study of MEMS sensors during the power-on startup and to provide an accurate temperature field model for future design optimization and error correction.

In differential MEMS resonant sensors, a pair of resonators are interconnected with other structural components while sharing a common substrate. This leads to mutual coupling of vibration energy between resonators, interfering with their frequency outputs and affecting the sensor’s static performance. This paper aims to model and analyze the vibration coupling phenomena in differential common-based MEMS resonators (DCMR). A mechanical model of the DCMR structure was established and refined through finite element simulation analysis. Theoretical calculations yielded vibration coupling curves for two typical silicon resonant accelerometer (SRA) structures containing DCMR: SRA-V1 and SRA-V2, with coupling stiffness values of 2.361 × 10⁻⁴ N/m and 1.370 × 10⁻² N/m, respectively. An experimental test system was constructed to characterize the vibration coupling behavior. The results provided coupling amplitude-frequency characteristic curves and coupling stiffness values (7.073 × 10⁻⁴ N/m and 1.068 × 10⁻² N/m for SRA-V1 and SRA-V2, respectively) that validated the theoretical analysis and computational model. This novel approach enables effective evaluation of coupling intensity between 5resonators and provides a theoretical foundation for optimizing device structural designs.

Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by earth orbiting satellites. Due to the extremely large image coverage area (e.g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices. Existing short imaging distance-based infrared datasets and target detection methods cannot be well adopted to the space-based surveillance task. To address these problems, we develop a space-based infrared tiny ship detection dataset (namely, NUDT-SIRST-Sea) with 48 space-based infrared images and 17598 pixel-level tiny ship annotations. Each image covers about 10000 square kilometers of area with 10000X10000 pixels. Considering the extreme characteristics (e.g., small, dim, changeable) of those tiny ships in such challenging scenes, we propose a multi-level TransUNet (MTU-Net) in this paper. Specifically, we design a Vision Transformer (ViT) Convolutional Neural Network (CNN) hybrid encoder to extract multi-level features. Local feature maps are first extracted by several convolution layers and then fed into the multi-level feature extraction module (MVTM) to capture long-distance dependency. We further propose a copy-rotate-resize-paste (CRRP) data augmentation approach to accelerate the training phase, which effectively alleviates the issue of sample imbalance between targets and background. Besides, we design a FocalIoU loss to achieve both target localization and shape description. Experimental results on the NUDT-SIRST-Sea dataset show that our MTU-Net outperforms traditional and existing deep learning based SIRST methods in terms of probability of detection, false alarm rate and intersection over union.

In high-power applications, multi-chip parallel power modules have been widely used. When analyzing the thermal behavior of multi-chip parallel modules, the specific internal parallel structure and power loss characteristics under working condition should be taken into account. Existing thermal coupling models require individual chip heating for extracting thermal coupling parameters, which is not feasible in multi-chip parallel modules. Moreover, the requirement for extracting numerous parameters adds complexity to practical applications. In this paper, a novel thermal coupling model that combines multiple chips for modelling is proposed. This method analyses the power loss characteristics of power modules under operating condition and simplifies the input of the model. The model is applicable to multi-chip parallel power modules and has the advantage of requiring fewer parameters to be identified. This model has been validated by finite element simulation.