Visual Inspection Algorithms for Printed Circuit Board Patterns A SURVEY

The importance of the inspection process has been magnified by the requirements of the modern manufacturing environment. In electronics mass-production manufacturing facilities, an attempt is often made to achieve 100 % quality assurance of all parts, subassemblies, and finished goods. A variety of approaches for automated visual inspection of printed circuits have been reported over the last two decades. In this survey, algorithms and techniques for the automated inspection of printed circuit boards are examined. A classification tree for these algorithms is presented and the algorithms are grouped according to this classification. This survey concentrates mainly on image analysis and fault detection strategies, these also include the state-of-the-art techniques. Finally, limitations of current inspection systems are summarized

Inspection System And Method For Bond Detection And Validation Of Surface Mount Devices Using Sensor Fusion And Active Perception

A hybrid surface mount component inspection system which includes both vision and infrared inspection techniques to determine the presence of surface mount components on a printed wiring board, and the quality of solder joints of surface mount components on printed wiring boards by using data level sensor fusion to combine data from two infrared sensors to obtain emissivity independent thermal signatures of solder joints, and using feature level sensor fusion with active perception to assemble and process inspection information from any number of sensors to determine characteristic feature sets of different defect classes to classify solder defects.Georgia Tech Research Corporatio

Printing of wirelessly rechargeable solid-state supercapacitors for soft, smart contact lenses with continuous operations

Recent advances in smart contact lenses are essential to the realization of medical applications and vision imaging for augmented reality through wireless communication systems. However, previous research on smart contact lenses has been driven by a wired system or wireless power transfer with temporal and spatial restrictions, which can limit their continuous use and require energy storage devices. Also, the rigidity, heat, and large sizes of conventional batteries are not suitable for the soft, smart contact lens. Here, we describe a human pilot trial of a soft, smart contact lens with a wirelessly rechargeable, solid-state supercapacitor for continuous operation. After printing the supercapacitor, all device components (antenna, rectifier, and light-emitting diode) are fully integrated with stretchable structures for this soft lens without obstructing vision. The good reliability against thermal and electromagnetic radiations and the results of the in vivo tests provide the substantial promise of future smart contact lenses

AUTOMATIC OPTICAL INSPECTION-BASED PCB FAULT DETECTION USING IMAGE PROCESSING

Increased Printed Circuit Board (PCB) route complexity and density combined with the growing demand for low-scale rapid prototyping has increased the desire for Automated Optical Inspection (AOI) that reduces prototyping time and production costs by detecting defects early in the production process. Traditional defect detection method of human visual inspection is not only error prone but is also time-consuming given the growing complex and dense circuitry of modern-day electronics. Electric contact-based testing, either in the form of a bed of nails testing fixture or a flying probe system, is costly for low-rate rapid prototyping. An AOI is a non-contact test method using an image processing algorithm that quickly detects and reports failures within the PCB layer based on the captured image. A low-cost AOI system has been created using commercial off-the-shelf components specifically for low-rate production prototyping testing allowing testing of varying layers or various electronic designs without additional setup cost. Once the AOI system is physically configured, the image processing defect detection algorithm compares the test image with a defect-free reference image or by a set of pre-defined rules generated through Electronic Design and Analysis software. Detected defects are then classified into two main categories: fatal and potential. Fatal defects lead to the board\u27s rejection, while potential defects alert the operator to determine if the board should be rejected or will still satisfy pre-defined prototyping criteria. The specifications of an imaging system, camera sensor, imaging lens, and illumination set-up used in the creation of the AOI were designed considering a test PCB article already in production. The algorithm utilized is based on a non-reference defect detection method utilizing mathematical morphology-based image processing techniques to detect defects in the PCB under test

The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly

Automatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of preprocessing steps or a complex illumination system to improve the accuracy. This paper studies the effectiveness of using CNN to detect soldering bridge faults in a PCB assembly. The paper presents a method for designing an optimized CNN architecture to detect soldering faults in a PCBA. The proposed CNN architecture is compared with the state-of-the-art object detection architecture, namely YOLO, with respect to detection accuracy, processing time, and memory requirement. The results of our experiments show that the proposed CNN architecture has a 3.0% better average precision, has 50% less number of parameters and infers in half the time as YOLO. The experimental results prove the effectiveness of using CNN in AOI by using images of a PCB assembly without any reference image, any complex preprocessing stage, or a complex illumination system. Doi: 10.28991/HIJ-2022-03-01-01 Full Text: PD

On flexibly integrating machine vision inspection systems in PCB manufacture

The objective of this research is to advance computer vision techniques and their applications in the electronics manufacturing industry. The research has been carried out with specific reference to the design of automatic optical inspection (AOI) systems and their role in the manufacture of printed circuit boards (PCBs). To achieve this objective, application areas of AOI systems in PCB manufacture have been examined. As a result, a requirement for enhanced performance characteristics has been identified and novel approaches and image processing algorithms have been evolved which can be used within next generation of AOI systems. The approaches are based on gaining an understanding of ways in which manufacturing information can be used to support AOI operations. Through providing information support, an AOI system has access to product models and associated information which can be used to enhance the execution of visual inspection tasks. Manufacturing systems integration, or more accurately controlled access to electronic information, is the key to the approaches. Also in the thesis methods are proposed to achieve the flexible integration of AOI systems (and computer vision systems in general) within their host PCB manufacturing environment. Furthermore, potential applications of information supported AOI systems at various stages of PCB manufacturing have been studied. It is envisaged that more efficient and cost-effective applications of AOI can be attained through adopting the flexible integration methods proposed, since AOI-generated information can now be accessed and utilized by other processes