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A novel convolutional neural network for electronic component classification with diverse backgrounds

International Journal of Modeling, Simulation, and Scientific Computing 2022年第1期13卷 53-69页

作者： Longfei Zhou Lin Zhang Massachusetts Institute of technology Cambridge MA 02139USA Beihang University Beijing 100191P.R.China

The rapid development of computer vision techniques has brought new opportunities for manufacturing industries,accelerating the intelligence of manufacturing systems in terms of product quality assurance,automatic assembly,and industrial robot *** the electronics manufacturing industry,intensive variability in component shapes and colors,background brightness,and visual contrast between components and back-ground results in difficulties in printed circuit board image *** this paper,we apply computer vision techniques to detect diverse electronic components from their background images,which is a challenging problem in electronics manufacturing indus-tries because there are multiple types of components mounted on the same printed circuit ***,a 13-layer convolutional neural network(ECON)is proposed to detect electronic components either of a single category or of diverse *** proposed network consists of five Convolution-MaxPooling blocks,followed by a flat-tened layer and two fully connected *** electronic component image dataset from a real manufacturing company is applied to compare the performance between ECON,Xception,VGG16,and *** this dataset,there are 11 categories of components as well as their background *** show that ECON has higher accuracy in both single-category and diverse component classification than the other networks.

关键词： Printed circuit board surface-mount technology machine vision convolu-tional neural network classification

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Evaluation of Four Geomembrane-mounted PV Systems for Land Reclamation in Southern Arizona

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Journal of Energy and Power Engineering 2013年第5期7卷 834-840页

作者： Adria E. Brooks Nathan Allen Vincent P. Lonij Alexander D. Cronin Department of Physics University of Arizona Tucson AZ 85721 USA Biosphere 2 College of Science University of Arizona Tucson AZ 85739 USA

Four different PV （photovoltaic） systems deployed around Tucson Arizona on geomembranes are used to test the feasibility of converting mine tailings and landfills into solar energy generating sites. Differences between these deployed systems include： two types of geomembrane materials, two different module anatomies and two different locations. Module mounting techniques unique to mine tailing sites are described. Several system failures observed during the first two years of operation are explained here in detail. Validated predictions for the operating temperature of these systems and their associated electrical performance are presented. It was determined that PV modules mounted on light-colored thermoplastic with shielded wiring operate at lower temperatures, are structurally stable, and experience fewer wiring failures.

关键词： Photovoltaic systems system performance current-voltage characteristics surface-mount technology land reclamation mining applications polyethylene geomembrane.

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Process Induced Stress for CMOS Performance Improvement

Process Induced Stress for CMOS Performance Improvement

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2006 8th International Conference on Solid-State and Integrated Circuit technology

作者： S.Fang S.S.Tan T.Dyer Z.Luo J.Yan J.J.Kim N.Rovedo Z.Lun J.Yuan X.Chen V.Chan T.J.Tang R.Amos H.Ng M.Ieong S.Iyer S.Crowder IBM Semiconductor Research and Development Center(SRDC) Hopewell JunctionNY 12533 Chartered Semiconductor Manufacturing Infineon Technologies AG Samsung Electronics Co. Ltd

＜正＞The integration of 3 major techniques of process induced stress,stress memory technique（SMT）,dual stress liners （DSL）,and stress proximity technique（SPT）,has been demonstrate for advanced CMOS *** device p... 详细信息

ISBN: (纸本)1424401607

关键词： CMOS process surface-mount technology Tensile stress Compressive stress DSL CMOS technology Costs MOSFET circuits Electron mobility Capacitive sensors

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