Technical Library | 2013-03-07 18:25:36.0
The market for high-layer-count printed circuit boards (PCB) containing blind and buried vias was once relatively small, and focused on specialized applications in the military and high end computing. The demand for these types of PCBs today is being driven by an increasing number of commercial applications in the telecommunications and semiconductor test market segments. These applications typically require high-aspect-ratio plated-through-holes (PTHs) and blind and buried vias in order to meet the applications interconnect density requirements. Blind and buried vias and high aspect ratio PTHs continue to present manufacturing challenges and frequently are the limiting features to achieving high fabrication yield... First published in the 2012 IPC APEX EXPO technical conference proceedings
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 | 2019-03-15 16:26:50.0
While there have been quite dramatic and evident improvements in almost every facet of manufacturing over the last several decades owing to the advent and mass adoption of computer automation and networking, there is one aspect of production that remains stubbornly unaffected. Massive databases track everything from orders, to inventory, to personnel. CAD systems allow for interactive and dynamic 3D rendering and testing, digital troubleshooting, and simulation and analysis prior to mass production. Yet, with all of this computational power and all of this networking capability, one element of production has remained thoroughly and firmly planted in the past. Nearly all manufacturing or assembly procedures are created, deployed, and stored using methodologies derived from a set of assumptions that ceased to be relevant fifty years ago. This set of assumptions, referred to below as the “Paper Paradigm” has been, and continues as the dominant paradigm for manufacturing procedures to this day. It is time for a new paradigm, one that accounts for the vastly different technological landscape of this era, one that provides a simple, efficient interface, deep traceability, and dynamic response to rapidly changing economic forces.This paper seeks to present an alternative. Instead of enhancing and improving on systems that became irrelevant with the invention of a database, instead of propping up an outdated, outmoded and inefficient system with incremental improvements; rewrite the paradigm. Change the underlying assertions to more accurately reflect our current technological capability. Instead of relying on evolutionary improvements, it is time for a revolution in manufacturing instructions.
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