Zheng Zhang 교수

In this work, an acoustic emission (AE) technique was applied to online condition monitoring (CM) of solder fatigue in a clip-bonding SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> power assembly during short and long power cycling tests (PCTs). Solder fatigue caused by PCTs was identified in the clip-bonding assembly via scanning acoustic tomography and scanning electron microscopy and successfully diagnosed via the AE-based onli

Detecting abnormal surface features is an important method for identifying abnormal fish. However, existing methods face challenges in excessive subjectivity, limited accuracy, and poor real-time performance. To solve these challenges, a real-time and accurate detection model of abnormal surface features of in-water fish is proposed, based on improved YOLOv5s. The specific enhancements include: 1) We optimize the complete intersection over union and non-maximum suppression through the normalized

The recognition of fish feeding behavior based on machine vision is essential for optimizing fish feeding strategies and enhancing the efficiency of aquaculture. Building an efficient, multi-feature extraction model for fish feeding recognition, especially on mobile and edge devices, remains a significant challenge. In the paper, we propose a novel multi-feature extraction (MFE)-MobileViTv3 model, which improve MobileViTv3 with the MFE blocks. It can extract spatio-temporal features while obtain

• Deposition rate of Electroless Cu was enhanced with the addition of Ni and increase of reaction temperature. • High content of Ni addition can prohibit the self-grain and epitaxial grain growth of Cu. • Nanovoids resulting from hydrogen inclusion during the electroless Cu reaction were identified in the electroless layer. • Dimension and quantity of the nanovoid are closely related to the electroless Cu deposition rate. In this work, we investigated the microstructure evolution of electroless

Fine-pitch copper (Cu) pillar interconnects have been widely adopted in flip-chip packaging to enable high-density integration. However, achieving reliable bonding with non-planarized Cu pillars is challenging which typically requires harsh bonding conditions to ensure adequate bonding quality. In this study, we successfully achieved non-planarized Cu pillar bonding under mild bonding conditions by utilizing a silver (Ag) cap as the bonding intermediate layer. Non-planarized Cu pillar chips with

Microvias play a key role in the high density interconnect (HDI) printed circuit board (PCB) substrate, which provides electrical interconnect among build-up layers. To achieve a high quality microvia structure, a flawless electroless Cu seed layer is necessary. In this work, we investigated the nanovoids in the electroless layer caused by hydrogen bubbles and explore the effect of Ni additive on the nanovoid generation. It is found that a high Ni concentration can increase Cu reaction speed and

ZnO nanostructures possess unique advantages for the biosensor applications, such as giant surface areas, high sensitivity, biological compatibility, and integratability with Si-based electronics.This dissertation addresses the development of ZnO nanotip-based acoustic wave sensors, and their biological applications. ZnO nanostructures are grown on the surfaces of various sensors, including surface acoustic wave (SAW) sensors and quartz crystal microbalance (QCM) sensors, by metalorganic chemica