| dc.description.abstract | When the PCB factory receives the original design from the upstream customer, it must be edited with CAM (Computer-Aided Manufacturing) software to meet the production specifications of the factory, without affecting the original function of the PCB. This step is to avoid defects in the manufacturing process.
One of the common specifications is the limit of minimum spacing between circuits. On a PCB, there are many circular pads and line objects. These objects represent the configuration of electronic circuits, conducting various electronic components soldered on the PCB. Engineers often have to cut the shape of the pads, or move the lines to make space between the circuits, to prevent short circuit. This graphical editing process is often long, trivial, monotonous and highly repetitive. Engineers spend an average of 3 to 5 hours on editing a piece of PCB. However, these processes heavily rely on manual judgment and operation by engineers. Therefore, it’s not easy to replaced human by machine automation.
Based on the CAM software Genesis2000, this paper proposes an automatic solution for two common spacing problems.
For the case of insufficient spacing between pads, we use the deep learning model VGG16 for image recognition to judge the configuration of pad objects in the software screen, and predict the length and direction of the cutting operation.
For the case of insufficient spacing between pad and line, we collect all the related object information, describe the distance relationship of these objects with linear inequalities, and use a linear objective function as the target we want to optimize. Namely, we convert the entire problem to a linear programming problem. | en_US |