Simulation Studies of Two-Layer Hopfield Neural Networks for Automatic Wafer Defect Inspection

Chang, Chuan‐Yu; Wang, Hung‐Jen; Lin, Si-Yan

doi:10.1007/11779568_119

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Meanwhile, it has no damaged advantages of image pixels characteristics and pixel dots surrounding structure information. Chang [3] and others suggested applying self-organizing neural network (SONN) and Hopfield neural network (HNN) two kinds of algorithms to detect the defected location in wafer images, which have a very good application effect of detection. We also proved that the image processing is not affected by the amplification.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition for PCB Soldering Platform Controlled by Embedded Microchip Based on Hopfield Neural Network

Wu¹,

Hu²

2010

JCP

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In the study, embedded BASIC Stamp 2 (BS2) microchip controller is used to design with Hopfield neural network (HNN) as the foundation of sample training, which applies for a soldering platform of mechanical vision and accomplishes PCB soldering positioning technology. The proposed system design method in this paper can be divided into two parts: 1) the control rules of RC servo motor is designed by BASIC, and 2) human-machine interface is established to acquire images for pre-processing via C++ Builder. For the method of system image recognition, HNN is employed to do PCB soldering recognition positioning. The system is verified by MATLAB and Simulink to set up the simulation of PCB image soldering positioning. The experiment proves that the proposed method improves the traditional low efficiency of PCB soldering technology, and to achieve the feasibility of PCB image positioning and promote the soldering quality.

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Section: Introductionmentioning

confidence: 99%