Quality and accuracy are key aspects of any product to eliminate errors, and so are printed circuit boards, as they are a critical component in all digital and electromechanical devices. Therefore, PCB inspection is a critical aspect of PCB manufacturing and assembly. During the manufacturing process, blank boards are made, and assembly includes mounting circuits and components. Ideally, PCB inspection is performed at every step of the manufacturing and assembly process. While basic visual inspection may reveal some defects and are still in use, many types of automated test tools and techniques are used by PCB manufacturers and assembly service providers. This article provides types of PCB inspections and what they mean.
Reference and non-reference PCB inspection techniques
Three types of boards are widely used - rigid, flexible and rigid-flex. Flexible and rigid flex boards have seen the greatest applications across industries because they are flexible and durable, have large bend radii, and are able to accommodate multiple components on thin substrates. These boards have different PCB inspection algorithms based on their type and properties. As mentioned before, a blank PCB is thoroughly inspected before component mounting and soldering. There are algorithms that distinguish possible defect types such as shorts, protruding copper spurs, dust on the board, open circuits and conductors, etc. Below are two common inspection algorithms used to identify PCB problems.
These algorithms are reference-based, which means they have a ready-made model or design that can be used to compare the characteristics of new boards. This check includes matching of images and templates. The circuit is also compared in this way with the 3D model used as a reference. Even now, these reference methods are widely used. However, they have their own set of shortcomings. For example, when you have a reference board or model and compare it, erroneous readings can occur due to noise, image distortion, and misinterpretation. Therefore, non-reference algorithms are also developed and used extensively.
There are no reference models or designs here. Therefore, the readings and observations of the performed PCB inspection are absolute. They are pristine, which means that images of the board can be observed and their patterns, properties and possible defects are recorded in the form of data. This is verified against the Design Rule Verification Criteria. Defects are detected after applying these specific algorithms. After this, form and analyze the data sheet left and right to take further steps. Non-reference algorithms are used to examine external or morphological features, plate boundaries and run-length encodings. When using these algorithms, it is absolutely necessary to validate them against a set standard. In addition, while there is no design preview or comparison reference, it must be ensured that the design requirements are met and that there are no defects associated with them. This is one of the challenges of non-reference algorithms. Therefore, a hybrid algorithm with reference and non-reference features is used most of the time.
This is an ideal combination to overcome the shortcomings of the above two methods. This approach achieves two things - it prevents any defect violations based on the customer's specific design requirements. Additionally, it meets the required validation criteria.
Among all types of circuit boards, flexible circuit boards can present certain challenges in accurately detecting defects. This is mainly because they are thin and bendable, and may deform reflecting their ability to stretch. These boards may exhibit dimensional distortion and overall pattern non-uniformity, making it difficult for algorithms to detect defects. Fortunately, there are ways to overcome these challenges.
Overcoming Defect Detection of Flexible PCBs
First, capture an image of a blank board and divide it into small frames or sets. Although the frames of these images may overlap, the overlap width is considered and set according to the maximum deviation between the test image and the reference image. In this way, one can overcome problems such as distorted physics and uneven contrast levels. This is achieved by processing each image frame separately. Defects in fine pitch patterns can be found by sub-pixel analysis. This is also used for defects related to edge detection and alignment of boards. There are algorithms that detect foreign objects like dust on the board by color value, irregular size and geometry.