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 | 2016-03-24 17:37:09.0
Today's Electronic Industry is changing at a high pace. The root causes are manifold. So world population is growing up to eight billions and gives new challenges in terms of urbanization, mobility and connectivity. Consequently, there will raise up a lot of new business models for the electronic industry. Connectivity will take a large influence on our lives. Concepts like Industry 4.0, internet of things, M2M communication, smart homes or communication in or to cars are growing up. All these applications are based on the same demanding requirement – a high amount of data and increased data transfer rate. These arguments bring up large challenges to the Printed Circuit Board (PCB) design and manufacturing.This paper investigates the impact of different PCB manufacturing technologies and their relation to their high frequency behavior. In the course of the paper a brief overview of PCB manufacturing capabilities is be presented. Moreover, signal losses in terms of frequency, design, manufacturing processes, and substrate materials are investigated. The aim of this paper is, to develop a concept to use materials in combination with optimized PCB manufacturing processes, which allows a significant reduction of losses and increased signal quality.
Technical Library | 2020-10-08 00:55:22.0
This article presents the development of a stretchable sensor network with high signal-to-noise ratio and measurement accuracy for real-time distributed sensing and remote monitoring. The described sensor network was designed as an island-and-serpentine type network comprising a grid of sensor "islands" connected by interconnecting "serpentines." A novel high-yield manufacturing process was developed to fabricate networks on recyclable 4-inch wafers at a low cost. The resulting stretched sensor network has 17 distributed and functionalized sensing nodes with low tolerance and high resolution. The sensor network includes Piezoelectric (PZT), Strain Gauge(SG), and Resistive Temperature Detector (RTD) sensors. The design and development of a flexible frame with signal conditioning, data acquisition, and wireless data transmission electronics for the stretchable sensor network are also presented. The primary purpose of the frame subsystem is to convert sensor signals into meaningful data, which are displayed in real-time for an end-user to view and analyze. The challenges and demonstrated successes in developing this new system are demonstrated, including (a) developing separate signal conditioning circuitry and components for all three sensor types (b) enabling simultaneous sampling for PZT sensors for impact detection and (c)configuration of firmware/software for correct system operation. The network was expanded with an in-house developed automated stretch machine to expand it to cover the desired area. The released and stretched network was laminated into an aerospace composite wing with edge-mount electronics for signal conditioning, processing, power, and wireless communication.
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 | 2019-09-04 21:35:53.0
Since the European Directives, RoHS (Restriction of Hazardous Substances) and REACH (Registration, Evaluation, Authorization and Restriction of Chemicals), entered into force in 2006-7, the number of regulated substances continues to grow. REACH adds new substances roughly twice a year, and more substances will be added to RoHS in 2019. While these open-ended regulations represent an ongoing burden for supply chain reporting, some ability to remain ahead of new substance restrictions can be achieved through full material declarations (FMD) specifically the IPC-1752A Class D Standard (the "Standard"), which was developed by the IPC - Association Connecting Electronic Industries. What is important to the supply chain is access to user-friendly, easily accessible or free, fully supported tools that allow suppliers to create and modify XML (Extensible Markup Language) files as specified in the Standard. Some tools will provide enhancements that validate required data entry and provide real-time interactive messages to facilitate the resolution of errors. In addition, validation and auto-population of substance CAS (Chemical Abstract Service) numbers, and Class D weight rollup validation ensure greater success in the acceptance of the declarations in customer systems that automate data gathering and reporting. A good tool should support importing existing IPC-1752A files for editing; this capability reduces the effort to update older declarations and greatly benefits suppliers of a family of products with similar composition. One of the problems with FMDs is the use of "wildcard" non-CAS numbers based on a declarable substance list (DSL). While the substances in different company's lists tend to have some overlap, no two DSL’s are the same. We provide an understanding of the commonality and differences between representative DSLs, and the ability to configure how much of a non-DSL substance percent is allowed. Case studies are discussed to show how supplier compliance data, can be automatically loaded into the customer's enterprise compliance system. Finally, we briefly discuss future enhancements and other developments like Once an Article, Always an Article (O5A) that will continue to require IPC standards and supporting tools to evolve.
