Visual Inspection Algorithms for Printed Circuit Board Patterns A SURVEY

The importance of the inspection process has been magnified by the requirements of the modern manufacturing environment. In electronics mass-production manufacturing facilities, an attempt is often made to achieve 100 % quality assurance of all parts, subassemblies, and finished goods. A variety of approaches for automated visual inspection of printed circuits have been reported over the last two decades. In this survey, algorithms and techniques for the automated inspection of printed circuit boards are examined. A classification tree for these algorithms is presented and the algorithms are grouped according to this classification. This survey concentrates mainly on image analysis and fault detection strategies, these also include the state-of-the-art techniques. Finally, limitations of current inspection systems are summarized

AUTOMATIC OPTICAL INSPECTION-BASED PCB FAULT DETECTION USING IMAGE PROCESSING

Increased Printed Circuit Board (PCB) route complexity and density combined with the growing demand for low-scale rapid prototyping has increased the desire for Automated Optical Inspection (AOI) that reduces prototyping time and production costs by detecting defects early in the production process. Traditional defect detection method of human visual inspection is not only error prone but is also time-consuming given the growing complex and dense circuitry of modern-day electronics. Electric contact-based testing, either in the form of a bed of nails testing fixture or a flying probe system, is costly for low-rate rapid prototyping. An AOI is a non-contact test method using an image processing algorithm that quickly detects and reports failures within the PCB layer based on the captured image. A low-cost AOI system has been created using commercial off-the-shelf components specifically for low-rate production prototyping testing allowing testing of varying layers or various electronic designs without additional setup cost. Once the AOI system is physically configured, the image processing defect detection algorithm compares the test image with a defect-free reference image or by a set of pre-defined rules generated through Electronic Design and Analysis software. Detected defects are then classified into two main categories: fatal and potential. Fatal defects lead to the board\u27s rejection, while potential defects alert the operator to determine if the board should be rejected or will still satisfy pre-defined prototyping criteria. The specifications of an imaging system, camera sensor, imaging lens, and illumination set-up used in the creation of the AOI were designed considering a test PCB article already in production. The algorithm utilized is based on a non-reference defect detection method utilizing mathematical morphology-based image processing techniques to detect defects in the PCB under test

A golden template self-generating method for patterned wafer inspection

This paper presents a novel golden template self-generating technique for detecting possible defects in periodic two-dimensional wafer images. A golden template of the patterned wafer image under inspection can be obtained from the wafer image itself and no other prior knowledge is needed. It is a bridge between the existing self-reference methods and image-to-image reference methods. Spectral estimation is used in the first step to derive the periods of repeating patterns in both directions. Then a building block representing the structure of the patterns is extracted using interpolation to obtain sub-pixel resolution. After that, a new defect-free golden template is built based on the extracted building block. Finally, a pixel-to-pixel comparison is all we need to find out possible defects. A comparison between the results of the proposed method and those of the previously published methods is presented

A golden block based self-refining scheme for repetitive patterned wafer inspections

This paper presents a novel technique for detecting possible defects in two-dimensional wafer images with repetitive patterns using prior knowledge. It has a learning ability that is able to create a golden block database from the wafer image itself, modify and refine its content when used in further inspections. The extracted building block is stored as a golden block for the detected pattern. When new wafer images with the same periodical pattern arrives, we do not have to re-calculate its periods and building block. A new building block can be derived directly from the existing golden block after eliminating alignment differences. If the newly derived building block has better quality than the stored golden block, then the golden block is replaced with the new building block. With the proposed algorithm, our implementation shows that a significant amount of processing time is saved. And the storage overhead of golden templates is also reduced significantly by storing golden blocks only

Automatic surface mount solder joints inspection

This thesis reports the research results on automatic inspection of solder joints on printed circuit boards. The previous work on this subject has been advanced significantly in the following three aspects. With the support of AT&T Bell Lab, the most updated surface mount solder joints are inspected in this work instead of larger simulation solder joints or traditional through hole solder joints in the previous work. A small set of features is extracted for surface mount solder joints in both infrared and visual light inspection. A new image processing software named Khoros has been applied to improve the quality of images. It has been demonstrated that infrared imaging technique can identify solder joints of surface mount printed circuit boards according to their solder volumn. The correct classification rate was found to be in the range of 89% to 100%. For the sample joints provided by AT&T Bell Laboratory, reasonably good inspection results have been obtained. The experimental results demonstrate that infrared imaging technique can be utilized to discriminate solder joints on surface mount printed circuit boards with different solder volumes quite reliably

Image-Based Detection of Modifications in Gas Pump PCBs with Deep Convolutional Autoencoders

In this paper, we introduce an approach for detecting modifications in assembled printed circuit boards based on photographs taken without tight control over perspective and illumination conditions. One instance of this problem is the visual inspection of gas pumps PCBs, which can be modified by fraudsters wishing to deceive costumers or evade taxes. Given the uncontrolled environment and the huge number of possible modifications, we address the problem as a case of anomaly detection, proposing an approach that is directed towards the characteristics of that scenario, while being well-suited for other similar applications. The proposed approach employs a deep convolutional autoencoder trained to reconstruct images of an unmodified board, but which remains unable to do the same for images showing modifications. By comparing the input image with its reconstruction, it is possible to segment anomalies and modifications in a pixel-wise manner. Experiments performed on a dataset built to represent real-world situations (and which we will make publicly available) show that our approach outperforms other state-of-the-art approaches for anomaly segmentation in the considered scenario, while producing comparable results on the popular MVTec-AD dataset for a more general object anomaly detection task