High resolution thermal microscopy

Journal ArticleA new high resolution thermal microscope has been demonstrated capable of imaging thermal fields with sub 1000 angstom resolution. It is based upon a non-contacting near field thermal probe. The thermal probe consists of a thermocouple sensor on the end of a tip with sub 1000 angstrom dimensions. The probe tip is scanned in close proximity to a solid or liquid surface and the local temperature is mapped with a resolution determined by the size of the tip. Material independent surface profiling has also been demonstrated with the thermal probe, providing a lateral resolution of approximately 300 angstroms. Temperature mapping and surface profiling results are presented on both electronic and biological materials

Encoder-Decoder-Based Intra-Frame Block Partitioning Decision

The recursive intra-frame block partitioning decision process, a crucial component of the next-generation video coding standards, exerts significant influence over the encoding time. In this paper, we propose an encoder-decoder neural network (NN) to accelerate this process. Specifically, a CNN is utilized to compress the pixel data of the largest coding unit (LCU) into a fixed-length vector. Subsequently, a Transformer decoder is employed to transcribe the fixed-length vector into a variable-length vector, which represents the block partitioning outcomes of the encoding LCU. The vector transcription process adheres to the constraints imposed by the block partitioning algorithm. By fully parallelizing the NN prediction in the intra-mode decision, substantial time savings can be attained during the decision phase. The experimental results obtained from high-definition (HD) sequences coding demonstrate that this framework achieves a remarkable 87.84\% reduction in encoding time, with a relatively small loss (8.09\%) of coding performance compared to AVS3 HPM4.0

A Practical Simulation Flow for Singing Capacitor based Acoustic Noise Analysis

Multilayer ceramic capacitors (MLCCs) are widely used in modern electronics. Due to the piezoelectric effect of the ceramic material, however, MLCCs subjected to electrical noise may vibrate and generate acoustic noise, as \u27singing\u27. Acoustic noise can be annoying for users, especially within mobile devices, so it becomes important to perform acoustic noise analysis before a product is released. In this paper, a practical simulation flow for singing capacitor based acoustic noise is presented. The simulation flow and analysis method are developed on Ansys Sherlock and Mechanical. In Ansys Sherlock, local library and Approved Vendor List (AVL) files were used to build the model efficiently. After the PCB and all parts were set correctly, the model was imported to Ansys Mechanical for further modal analysis and harmonic analysis. Using the proposed simulation flow the simulation model could be easily created, and the inherent vibration properties and frequency response of the structure could be estimated

Fast Impedance Prediction for Power Distribution Network using Deep Learning

Modeling and simulating a power distribution network (PDN) for printed circuit boards with irregular board shapes and multi-layer stackup is computationally inefficient using full-wave simulations. This paper presents a new concept of using deep learning for PDN impedance prediction. A boundary element method (BEM) is applied to efficiently calculate the impedance for arbitrary board shape and stackup. Then over one million boards with different shapes, stackup, integrated circuits (IC) location, and decap placement are randomly generated to train a deep neural network (DNN). The trained DNN can predict the impedance accurately for new board configurations that have not been used for training. The consumed time using the trained DNN is only 0.1 s, which is over 100 times faster than the BEM method and 10 000 times faster than full-wave simulations

Influence of WeChat Use on the College Students Campus Life Satisfaction-A Perspective on Bridging Social Capital

The theories of social capital and Rosenberg's self-esteem scale were used to measure the campus life satisfaction of college students, this paper made an empirical analysis on the WeChat media use of 1000 college students from Hong Kong, Macao, Taiwan and mainland China (M=18.81 years old, SD=0.96) of a University in Guangdong province, and discussed the relationship among college students' social media use intensity, campus life satisfaction and social capital. The study found that there was a significant positive correlation between WeChat use intensity of college students and social capital, that the intensity of WeChat use had a direct effect on college students' satisfaction with campus life, and that self-esteem had a moderating effect between WeChat use intensity and social capital

A Methodology For Predicting Acoustic Noise From Singing Capacitors In Mobile Devices

Multilayer ceramic capacitors (MLCCs) connected to a power distribution network (PDN) can create acoustic noise through a combination of the power rail noise at the MLCCs and the piezoelectric effect of the capacitor\u27s ceramic material. The deformation of the MLCCs brought on by power supply noise creates vibrations which cause the printed circuit board (PCB) to vibrate and generate the audible acoustic noise. In the following paper, a simulation methodology is presented to analyze the acoustic noise created by MLCCs on a PCB. A simulation model for the PCB vibration modal response is built and the modal superposition method is used to analyze the harmonic response of the PCB excited by the capacitor. By multiplying the measured power noise spectrum on the MLCC with the simulated deformation of the PCB found from the harmonic response analysis, the total response is obtained. Simulated results show a good correlation with the measured acoustic noise. The proposed method shows promise for analyzing and predicting the acoustic noise from singing capacitors