Characterization of Electrodeposited Copper Foil Surface Roughness for Accurate Conductor Power Loss Modeling

As computer data rates have increased, designers observed that the standard models for estimating the impact of copper foil surface roughness on conductor loss are limited to a few GHz. The more recent snowball model (a “snowball” estimation of the Huray Model) has demonstrated improved conductor loss predictions up to 50 GHz by estimating the necessary parameters using reasonable assumptions about the geometric surface features of electrodeposited copper foil. Since then, the Huray Model has been incorporated into commercial electromagnetic field simulators. However, a standard method of characterizing the electrodeposited copper foil used in high-speed circuits to directly implement the snowball model has not yet been fully established. Therefore, the primary objective of this thesis was to develop a method of more accurately characterizing the geometric parameters of electrodeposited copper foil surfaces for accurate conductor loss modeling as defined by the snowball model. This thesis demonstrates the first methods of directly characterizing electrodeposited copper foil surface roughness to obtain snowball model parameters. Additionally, this research further legitimizes the analytic form of the snowball model as it pertains to scattered power, demonstrates the practical impact of its parameters, and reveals a source of existing irregularities between the estimated model parameters and actual performance measurements

Automatic Particle Detection and Counting by One-Class SVM from Microscope Image

Abstract. Asbestos-related illnesses become a nationwide problem in Japan. Now human inspectors check whether asbestos is contained in building material or not. To judge whether the specimen contains as-bestos or not, 3,000 particles must be counted from microscope images. This is a major labor-intensive bottleneck. In this paper, we propose an automatic particle counting method for automatic judgement system whether the specimen is hazardous or not. However, the size, shape and color of particles are not constant. Therefore, it is difficult to model the particle class. On the other hand, the non-particle class is not var-ied much. In addition, the area of non-particles is wider than that of particles. Thus, we use One-Class Support Vector Machine (OCSVM). OCSVM identifies “outlier ” from input samples. Namely, we model the non-particle class to detect the particle class as outlier. In experiments, the proposed method gives higher accuracy and smaller number of false positives than a preliminary method of our project.