Technical Library | 2020-10-30 10:27:39.0
Preview of the Complete Aneryn's Orbotech Trion 2340 Manual. It explains how to programme the AOI machine, how to train new components (shapes), how to connect it to your network, among other processes. Following the Guide, you will minimize false alarms and escapes rates easily. Full Manual available at https://aneryn.com/creation/trion-2340-manual/ Digital development by Sinfonía Digital
Technical Library | 2012-12-14 14:25:37.0
The popularity of low voltage technologies has grown significantly over the last decade as semiconductor device manufacturers have moved to satisfy market demands for more powerful products, smaller packaging, and longer battery life. By shrinking the size of the features they etch into semiconductor dice, IC manufacturers achieve lower costs, while improving speed and building in more functionality. However, this move toward smaller features has lead to lower breakdown voltages and increased opportunities for component overstress and false failures during in-circuit test.
Technical Library | 2016-05-30 22:24:00.0
As a part of series of studies on X-Ray inspection technology to quantify solder defects in BGA balls, we have conducted inspection of 3 level POP package by using a new AXI that capable of 3D-CT imaging. The new results are compared with the results of earlier AXI measurements. It is found that 3D measurements offer better defect inspection quality, lower false call and escapes.
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 | 2012-11-12 14:06:48.0
With consumers constantly looking for lower prices on their technology products and manufacturers trying to squeak out higher margins from their production lines, the need for process control and lower overhead costs have become even more important. One sector that is often overlooked is the hand soldering area of the factory. Many factories have been struggling with antiquated soldering systems for years. In some cases they are trying to make their investment in stations last much longer than they were designed for, or they are falsely trying to recoup their original investment ‐ all at the cost of higher operating expenses or even worse, reduced operator thru‐put.
Technical Library | 2020-07-02 13:29:37.0
Industry standards such as J-STD-005 and JIS Z 3284-1994 call for the use of viscosity measurement(s) as a quality assurance test method for solder paste. Almost all solder paste produced and sold use a viscosity range at a single shear rate as part of the pass-fail criteria for shipment and customer acceptance respectively. As had been reported many times, an estimated 80% of the defects associated with the surface mount technology process involve defects created during the printing process. Viscosity at a single shear rate could predict a fatal flaw in the printability of a solder paste sample. However, false positive single shear rate viscosity readings are not unknown.
Technical Library | 2021-01-21 02:04:27.0
Traditional single level microvia structures are generally considered the most robust type of interconnection within a printed wire board (PWB) substrate. The rapid implementation of HDI technology now commonly requires between 2, 3 or 4 levels of microvias sequentially processed into the product. Recent OEM funded reliability testing has confirmed that by increasing the levels (stack height) these structures are proving less reliable, when compared to their single or double level counterparts. Recently false positive results have been recorded on products tested with traditional thermal shock testing methodology (cycling between -40°C and 125°C, or 145°C). A number of companies are incurring product failures resulting in increased costs associated with replacing the circuit boards, components and added labour.
Technical Library | 2016-04-14 13:49:44.0
A system level test, usually built-in test (BIT), determines that one or more subsystems are faulty. These subsystems sent to the depot or factory repair facility, called units under test (UUTs) often pass that test, an event we call No-Fault-Found (NFF). With more-and more electronics monitored by BIT, it is more likely that an intermittent glitch will trigger a call for a maintenance action resulting in NFF. NFFs are often confused with false alarm (FA), cannot duplicate (CNDs)or retest OK (RTOK) events. NFFs at the depot are caused by FAs, CNDs, RTOKs as well as a number of other complications. Attempting to repair NFF scan waste precious resources, compromise confidence in the product, create customer dissatisfaction, and the repair quality remains a mystery. The problem is compounded by previous work showing that most failure indications calling for repair action at the system level are invalid. NFFs can be caused by real failures or may be a result of system level false alarms. Understanding the cause of the problem may help us distinguish between units under test (UUTs) that we can repair and those that we cannot. In calculating the true cost of repair we must account for wasted effort in attempting to repair unrepairable UUTs.This paper will shed some light on this trade-off. Finally, we will explore approaches for dealing with the NFF issue in a cost effective manner.
Technical Library | 2020-08-27 01:15:10.0
Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers' first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.
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