Technical Library | 1999-05-06 13:41:18.0
Invented in 1971, the microprocessor evolved from the inventions of the transistor (1947) and the integrated circuit (1958). Essentially a computer on a chip, it is the most advanced application of the transistor. The influence of the microprocessor today is well known, but in 1971 the effect the microprocessor would have on everyday life was a vision beyond even those who created it. This paper presents the history of the microprocessor in the context of the technology and applications that drove its continued advancements.
Technical Library | 2024-04-29 21:39:52.0
In this paper, we develop and put into practice an Automatic Optical Inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device and CCD cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5µm could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole and the incorrect location of the holes. However, previous work only focusses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users analyze the causes of errors and immediately correct the problems. Additionally, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1mm image resolution which is better than others used in industry. We set a detecting standard based on 2mm diameter of circles to diagnose the quality of the holes within 10 seconds.
Technical Library | 2013-08-08 15:23:11.0
In this project Machine Vision PCB Inspection System is applied at the first step of manufacturing, i.e., the making of bare PCB. We first compare a PCB standard image with a PCB image, using a simple subtraction algorithm that can highlight the main problem-regions. We have also seen the effect of noise in a PCB image that at what level this method is suitable to detect the faulty image. Our focus is to detect defects on printed circuit boards & to see the effect of noise. Typical defects that can be detected are over etchings (opens), under-etchings (shorts), holes etc...
Technical Library | 2023-11-06 17:08:44.0
A new process has been developed for RF shielding on compact electronic communications devices using automated solder paste dispensing. The process is known as Shield Edge Interconnect (SEI). SEI designs enable parts to be processed though underfill before placing of the RF shield and allows more complete use of valuable PCB real estate to achieve reduced form factor requirements and/or for added components on products such as smartphones and tablets. The reduced form factor creates challenges for the assembly of those devices. This process, enabled by Speedline dispensing technology, relies on extremely accurate dispensing of solder paste on copper traces located along the outer edge of the PCB. The result is a robust process solution for SEI in which proprietary closed loop dispenser, pump, vision, and software technologies enable a high volume manufacturing (HVM) process.
Technical Library | 2024-04-29 21:19:42.0
Over the years, computer vision and machine learning disciplines have considerably advanced the field of automated visual inspection for Printed Circuit Board (PCB-AVI) assurance. However, in practice, the capabilities and limitations of these advancements remain unknown because there are few publicly accessible datasets for PCB visual inspection and even fewer that contain images that simulate realistic application scenarios. To address this need, we propose a publicly available dataset, "FICS-PCB"1, to facilitate the development of robust methods for PCB-AVI. The proposed dataset includes challenging cases from three variable aspects: illumination, image scale, and image sensor. This dataset consists of 9,912 images of 31 PCB samples and contains 77,347 annotated components. This paper reviews the existing datasets and methodologies used for PCBAVI, discusses challenges, describes the proposed dataset, and presents baseline performances using feature engineering and deep learning methods for PCB component classification.
Technical Library | 2020-08-27 01:15:10.0
Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers' first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.
Technical Library | 2023-11-20 17:42:33.0
Zero-defect strategies and increased demands on the production of assemblies are making quality assurance in electronics production increasingly important. Continous miniaturization of components, ever higher packing densities and the associated hard-to-view assembly areas, as well as the increased use of components such as BGAs, QFNs and QFPs, pose a considerable challenge when it comes to high-precision quality control.
Technical Library | 2020-03-26 14:55:29.0
This paper introduces line confocal technology that was recently developed to characterize 3D features of various surface and material types at sub-micron resolution. It enables automatic microtopographic 3D imaging of challenging objects that are difficult or impossible to scan with traditional methods, such as machine vision or laser triangulation.Examples of well-suited applications for line confocal technology include glossy, mirror-like, transparent and multi-layered surfaces made of metals (connector pins, conductor traces, solder bumps etc.), polymers (adhesives, enclosures, coatings, etc.), ceramics (components, substrates, etc.) and glass (display panels, etc.). Line confocal sensors operate at high speed and can be used to scan fast-moving surfaces in real-time as well as stationary product samples in the laboratory. The operational principle of the line confocal method and its strengths and limitations are discussed.Three metrology applications for the technology in electronics product manufacturing are examined: 1. 3D imaging of etched PCBs for micro-etched copper surface roughness and cross-sectional profile and width of etched traces/pads. 2. Thickness, width and surface roughness measurement of conductive ink features and substrates in printed electronics applications. 3. 3D imaging of adhesive dots and lines for shape, dimensions and volume in PCB and product assembly applications.
Technical Library | 2023-11-20 18:10:20.0
The electronics production is prone to a multitude of possible failures along the production process. Therefore, the manufacturing process of surface-mounted electronics devices (SMD) includes visual quality inspection processes for defect detection. The detection of certain error patterns like solder voids and head in pillow defects require radioscopic inspection. These high-end inspection machines, like the X-ray inspection, rely on static checking routines, programmed manually by the expert user of the machine, to verify the quality. The utilization of the implicit knowledge of domain expert(s), based on soldering guidelines, allows the evaluation of the quality. The distinctive dependence on the individual qualification significantly influences false call rates of the inbuilt computer vision routines. In this contribution, we present a novel framework for the automatic solder joint classification based on Convolutional Neural Networks (CNN), flexibly reclassifying insufficient X-ray inspection results. We utilize existing deep learning network architectures for a region of interest detection on 2D grayscale images. The comparison with product-related meta-data ensures the presence of relevant areas and results in a subsequent classification based on a CNN. Subsequent data augmentation ensures sufficient input features. The results indicate a significant reduction of the false call rate compared to commercial X-ray machines, combined with reduced product-related optimization iterations.
Technical Library | 2021-06-07 19:06:32.0
The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and electronic equipment waste is the fastest-growing rate in the industrialized world. One of the elements of electronic equipment is the printed circuit board (PCB) and almost every electronic equipment has a PCB inside it. While waste PCB (WPCB) recycling may result in the recovery of potentially precious materials and the reuse of some components, it is a challenging task because its composition diversity requires a cautious pre-processing stage to achieve optimal recycling outcomes. Our research focused on proposing a method to evaluate the economic feasibility of recycling integrated circuits (ICs) from WPCB. The proposed method can help decide whether to dismantle a separate WPCB before the physical or mechanical recycling process and consists of estimating the IC area from a WPCB, calculating the IC's weight using surface density, and estimating how much metal can be recovered by recycling those ICs. To estimate the IC area in a WPCB, we used a state-of-the-art object detection deep learning model (YOLO) and the PCB DSLR image dataset to detect the WPCB's ICs. Regarding IC detection, the best result was obtained with the partitioned analysis of each image through a sliding window, thus creating new images of smaller dimensions, reaching 86.77% mAP. As a final result, we estimate that the Deep PCB Dataset has a total of 1079.18 g of ICs, from which it would be possible to recover at least 909.94 g of metals and silicon elements from all WPCBs' ICs. Since there is a high variability in the compositions of WPCBs, it is possible to calculate the gross income for each WPCB and use it as a decision criterion for the type of pre-processing.
Industrial Sensor Vision International specializes in advanced camera technology of high resolution fast speed cameras for automation, AOI, 2-D/3-D, SPI inspection and wafer inspection.
3 Morse Road 2A
Oxford, CT USA
Phone: +1 203 592 8723
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