pcb layout guidelines



1. for prototype Whenever possible avoid using bread-board or vari-board with RF modules The long tracks inside these types of prototyping board introduce large capacitances /inductances to the circuit which can badly distort / detune rf signals. Ideally prototype or evaluation pcbs should be used.

2. Tracks connected to the antenna (RF input / output) pin of transmitter and receiver modules should be as short as possible. Any conductor connected to this track will act as an antenna, so it will lengthen and detune the actual antenna.

3. Design PCBs with an adequate ground plane. A ground plane is an area of conductive PCB connected to the circuits ground. It helps with RF propogation and is generally better when placed perpendicular to the antenna. It should be placed on the pcb in a large area, however it should not be placed directly underneath the RF module.

4. Use decoupling capacitors on the power supply circuitry to prevents rf interference passing through the power lines. Low power modules work on high frequencies eg. 433MHz so the capacitors should be of small value eg 10nF to cut out high frequency interference.

5. Use smoothing capacitors and a regulator to ensure a stable constant supply.

PCB Layout lecture notes

what is the difference between high speed pcb layout and normal pcb layout.

In high speed board design, the emphasis would be on placing components near enough together to minimize timing delays while still avoiding signal crosstalk.

PCB design means physical realization of the board, laying components, routing tracks, preparing manufacturing files. This part of design process should be done according some rules (i.e. high speed signals requirements – PCI-E, SATA, LVDS etc, length matching requirements – PCI, PATA)

High Speed Board design it is whole design process, starting from component selection, appropriate schematic and high speed PCB design (track impedance matching, controlled track length

PCB Defect Detection and Classification



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as camera, frame grabber and personal computer is also essential for actual performance verification of defect detection and classification of PCBs. , 'Segmentation of PCB Images into Simple Generic

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techniques of defect analysis are common for all of them.gives a good summary of some defects and their causes that occur during the fabrication of PCBs.The conventional bare PCB inspection image is compared against the image of an ideal part. The

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190 number of defects for each classified group. Thealgorithm. This project proposes a PCBdefectdetection and classification system using a morphological image segmentation algorithm and image processing theories. This

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KEY WORDS: PCB- Printed circuit board, PCBDefectdetection, Classification.defect detection algorithm is effective as the computational complexity is concern and for classification the neural to classify all the 14 known defects and each defect should get classified separately.

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An automatic PCBdefectdetection is an approach that can be used to counter difficultiesreference based, image processing approach for defect detection and classification and simple Discrepancies between the images are considered defects and are classified based on

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PCBdefectdetection based on pattern matchingDisadvantages:Defects are not realized properly Classification accuracy is lessThick line and thin line segn is not properProduction cost in long run isThis project minimizes the number of defects for each classified groups

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Moganti. Divides the methods for PCBdefectdetection in to three groups:1) Referential Approach 2) Non- Referential Approach and 3) Hybrid Approach.From literature all the 14 defects were not classified. Hence, most of the works tend to search in the classification methods.

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Index TermsPCBDefectdetection, Thresholding, Image Subtraction, Feature extractionFor this network implementation, only defects classified in two groups will be used for training (iethe time of writing this paper, this was the only algorithm designed for defect classification.

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Pre-processing technique is basic and important step towards the success of PCB defectdetection. Inimages. Stage 5: The proposed defect classification algorithms are used to classify all defects occurred in both images. The

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proposed a PCBdefectdetection and classification system using a morphological image segmentation algorithm and simple the image processing theories. However, besides the need to detect the defects, it is also essential to classify and locate

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All methods have successfully classified the machine condition without any false positives.The new methods were able to classify a machine condition from a PCBDefectDetection, Classification and Localization using Mathematical Morphology and Image Processing Tools.

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difficult. This research use d the discriminate function proposed by Fisher (1936) to set up a model. Since the training images for the model were al most all from past researches, the problem involved supervi sed classification. The

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Image recognition is to process, dispose and abstract character of the acquired image so that the image can be estimated or classified.[2] Z.Ibrahim, Performance evaluation of wavelet-based PCBdefectdetection and localization algorithm[C].Industrial Technology, 2002.IEEE
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