Efficient PCB Fault Detection System using Deep Learning and Image Processing

Abstract

Printed Circuit Boards (PCBs) are essential parts that support the operation of electronic equipment in a variety of sectors. Nevertheless, difficulties that manufacturers regularly face during the PCB manufacturing process lead to defective devices. However, current inspection processes occur after etching, wasting a lot of material and rendering defective PCBs unusable. This study discusses relevant practical difficulties and offers a method for identifying errors in real PCB images using Matlab tools. Common single-layer PCB flaws such as
pinholes, nicks, cut patterns, and short patterns are examined. By redesigning the PCB, defects discovered prior to etching can be fixed, increasing output and decreasing waste. A printed circuit board is a platform with precisely specified and engineered circuitry that joins electronic components on a composite sandwich of conductive and non-conductive layers both mechanically and electrically. Opens, shorts, copper exposure, nodules, missing components, and soldering problems are typical PCB flaws. By using realistic photos, we
address real-world problems such as illumination variance and image quality while simultaneously detecting errors. Furthermore, we incorporate computer vision algorithms to tackle problems like perspective distortion
and image tilt, guaranteeing precise defect identification. Our goal is to establish a new benchmark for PCB quality assurance in the contemporary manufacturing period by means of constant innovation and improvement.