Technical Library | 2013-07-25 14:02:15.0
Bottom-termination components (BTC), such as QFNs, are becoming more common in PCB assemblies. These components are characterized by hidden solder joints. How are defects on hidden DFN joints detected? Certainly, insufficient solder joints on BTCs cannot be detected by manual visual inspection. Nor can this type of defect be detected by automated optical inspection; the joint is hidden by the component body. Defects such as insufficients are often referred to as "marginal" defects because there is likely enough solder present to make contact between the termination on the bottom-side of the component and the board pad for the component to pass in-circuit and functional test. Should the board be subjected to shock or vibration, however, there is a good chance this solder connection will fracture, leading to an open connection.
Technical Library | 2021-05-06 13:41:55.0
Quality control is of vital importance during electronics production. As the methods of producing electronic circuits improve, there is an increasing chance of solder defects during assembling the printed circuit board (PCB). Many technologies have been incorporated for inspecting failed soldering, such as X-ray imaging, optical imaging, and thermal imaging. With some advanced algorithms, the new technologies are expected to control the production quality based on the digital images. However, current algorithms sometimes are not accurate enough to meet the quality control. Specialists are needed to do a follow-up checking. For automated X-ray inspection, joint of interest on the X-ray image is located by region of interest (ROI) and inspected by some algorithms. Some incorrect ROIs deteriorate the inspection algorithm.
Technical Library | 2021-11-22 20:44:44.0
Many automated optical inspection (AOI) companies use supervised object detection networks to inspect items, a technique which expends tremendous time and energy to mark defectives. Therefore, we propose an AOI system which uses an unsupervised learning network as the base algorithm to simultaneously generate anomaly alerts and reduce labeling costs. This AOI system works by deploying the GANomaly neural network and the supervised network to the manufacturing system. To improve the ability to distinguish anomaly items from normal items in industry and enhance the overall performance of the manufacturing process, the system uses the structural similarity index (SSIM) as part of the loss function as well as the scoring parameters. Thus, the proposed system will achieve the requirements of smart factories in the future (Industry 4.0).
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 | 2021-08-04 18:46:25.0
The process of printed circuit board assembly (PCBA) involves several machines, such as a stencil printer, placement machine and reflow oven, to solder and assemble electronic components onto printed circuit boards (PCBs). In the production flow, some failure prevention mechanisms are deployed to ensure the designated quality of PCBA, including solder paste inspection (SPI), automated optical inspection (AOI) and in-circuit testing (ICT). However, such methods to locate the failures are reactive in nature, which may create waste and require additional effort to be spent re-manufacturing and inspecting the PCBs. Worse still, the process performance of the assembly process cannot be guaranteed at a high level. Therefore, there is a need to improve the performance of the PCBA process. To address the aforementioned challenges in the PCBA process, an intelligent assembly process improvement system (IAPIS) is proposed, which integrates the k-means clustering method and multi-response Taguchi method to formulate a pro-active approach to investigate and manage the process performance.
Technical Library | 2013-08-15 13:12:11.0
An automated visual PCB inspection is an approach used to counter difficulties occurred in human’s manual inspection that can eliminates subjective aspects and then provides fast, quantitative, and dimensional assessments. In this study, referential approach has been implemented on template and defective PCB images to detect numerous defects on bare PCBs before etching process, since etching usually contributes most destructive defects found on PCBs. The PCB inspection system is then improved by incorporating a geometrical image registration, minimum thresholding technique and median filtering in order to solve alignment and uneven illumination problem. Finally, defect classification operation is employed in order to identify the source for six types of defects namely, missing hole, pin hole, underetch, short-circuit, mousebite, and open-circuit.
Technical Library | 2022-06-27 16:50:26.0
Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.
Technical Library | 2023-05-22 17:46:29.0
Over the past several years, much research has been performed and published on the benefits of stencil nano-coatings and solvent under wipes. The process improvements are evident and well-documented in terms of higher print and end-of-line yields, in improved print volume repeatability, in extended under wipe intervals, and in photographs of the stencil's PCB-seating surface under both white and UV light. But quantifying the benefits using automated Solder Paste Inspection (SPI) methods has been elusive at best. SPI results using these process enhancements typically reveal slightly lower paste transfer efficiencies and less variation in print volumes to indicate crisper print definition. However, the improvements in volume data do not fully account for the overall improvements noted elsewhere in both research and in production.
Technical Library | 2016-08-18 15:38:09.0
The Selective Reflow Rework Process is an approach to improving the high volume rework process, increasing process capabilities and process repeatability by using a standard reflow oven of 12 zones, pick and place machinery, semi-automated printing gear and Solder Paste Inspection (SPI) implementations. This approach was able to reduce the amount of rework equipment by more than half. Our human resource requirements (indirect and direct labor) were cut by more than 50% and our rolled throughput yield increased from 68.9% to 84.14%. The Selective Reflow Rework Process is less reliant upon operators and has become a repeatable, stable rework process.
Technical Library | 2023-08-04 15:38:36.0
The MicroLeadFrame® (MLF®)/Quad Flat No-Lead (QFN) packaging solution is extremely popular in the semiconductor industry. It is used in applications ranging from consumer electronics and communications to those requiring high reliability performance, such as the automotive industry. The wide acceptance of this packaging design is primarily due to its flexible form factors, size, scalability and thermal dissipation capabilities. The adaptation and acceptance of MLF/QFN packages in automotive high reliability applications has led to the development of materials and processes that have extended its capabilities to meet the performance and quality requirements. One of process developments that is enabling the success of the MLF/QFN within the automotive industry has been the innovation of side wettable flanks that provide the capability to inspect the package lead to printed circuit board (PCB) interfaces for reliable solder joints. Traditionally, through-board X-ray was the accepted method for detecting reliable solder joints for leadless packages. However, as PBC layer counts and routing complexities have increased, this method to detect well-formed solder fillets has proven ineffective and incapable of meeting the inspection requirements. To support increased reliability and more accurate inspection of the leadless package solder joints, processes to form side-wettable flanks have been developed. These processes enable the formation of solder fillets that are detectable using state-of-the-art automated optical inspection (AOI) equipment, providing increased throughput for the surface mount technology (SMT) processes and improved quality as well.
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