Technical Library: wafer inspection (Page 1 of 1)

Next-Generation Test Equipment For High-Volume Wafer Production

Technical Library | 2010-06-23 21:59:03.0

Quality control is one of the main bottlenecks in the production of micro-opto-electromechanical systems/microelectromechanical systems (MOEMS/MEMS) because each structure on a wafer is serially inspected and scanned stepwise over the entire wafer area.

SPIE - International Society for Optical Engineering

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

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.

Institute of Electrical and Electronics Engineers (IEEE)

Head-on-Pillow Defect Detection – X-ray Inspection Limitations

Technical Library | 2020-05-26 22:28:56.0

Both the number and the variants of Ball Grid Array packages (BGAs) are tending to increase on network Printed Board Assemblies (PBAs)with sizes ranging from a few mm die size Wafer Level Packages (WLPs) with low ball count up to large multi-die System-in-Package (SiP) BGAs with 60-70 mm side lengths and thousands of I/Os.

Ericsson AB

Alternative Methods For Cross-Sectioning Of SMT And PCB Related Architectures

Technical Library | 2021-09-21 20:20:22.0

The electronics industry has been using the epoxy puck for the processing of the vast majority of electronics microsections since the 1970s. Minimal advancements have been seen in the methods used for precision micro-sections of PCBs, PCBAs, and device packages. This paper will discuss different techniques and approaches in performing precision and analytical micro-sections, which fuse the techniques and materials common in preparation of silicon wafers and bulk materials. These techniques have not only been found to produce excellent optical results, but transfer effectively to SEM for high magnification inspection and further analysis with minimal post-lapping preparation needed. Additionally, processing time is reduced primarily due to a significant reduction of bulk material removal earlier in the preparation, therefore needing less removal at later lapping steps compared to traditional sectioning methods. Additional techniques are introduced that mitigate some classic challenges experienced by technicians over the decades.

Foresite Inc.

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