Technical Library: mapping (Page 1 of 1)

Solder Paste Inspection Technologies: 2D-3D Correlation

Technical Library | 2008-05-28 18:41:53.0

This paper describes correlation between a true 2D area measurement (e.g. printer) and a height map generated area from a SPI system. In addition, this paper will explore the correlation between area/volume measurements and bridge detection between 2D/3D techniques. The ultimate goal is to arm the process engineers with information that can be used to make decision that will impact defects, cost, throughput and Return On Investment.

Speedline Technologies, Inc.

PCB Sourcing Using PCQR 2

Technical Library | 2017-12-13 23:58:32.0

In a global market, it is often difficult to determine the best PCB suppliers for your technology needs, while also a chieving the lowest costs for your products. Considering each PCB supplier has their own niche in t erms of equipment, process, and performance, uniform test data from the IPC -9151D Process Capability, Quality, and Relative Reliability (PCQR 2 ) Benchmark Test Standard can help find the right source for the board based on its specific technology requirements. By using a data-based approach to vendor selection, this can remove the subjective nature of sourcing, reduce the need for PCB process experts to map suppliers into technologies, and eliminate irrational sourcing decisions.

National Instruments

How to Use the Right Flux for the Selective Soldering Application

Technical Library | 2017-05-17 22:33:43.0

The selective soldering application requires a combination of performance attributes that traditional liquid fluxes designed for wave soldering applications cannot fulfill. First, the flux deposition on the board needs to be carefully controlled. Proper fine tuning of the flux physicochemical characteristics combined with a process optimization are mandatory to strike the right balance between solderability and reliability. However, localization of the flux residue through the drop jet process is not enough to guarantee the expected performance level. The flux needs to be designed to minimize the impact of unavoidable spreading and splashing events.From this perspective a fundamental understanding of the relationships between formulation and reliability is critical. In this application, thermal history of the flux residues (from room temperature to solder liquidus) is a key performance driver. Finally, it is necessary to conduct statistically designed experiments on industrial selective soldering machines in order to map the relationships between flux characteristics and selective process friendliness.

Kester

Estimating Recycling Return of Integrated Circuits Using Computer Vision on Printed Circuit Boards

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.

University of Pernambuco

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