  To extend the antenna design on printed circuit boards (PCBs) for more
engineers of interest, we propose a simple method that models PCB antennas with
a few basic components. By taking two separate steps to decide their geometric
dimensions and positions, antenna prototypes can be facilitated with no
experience required. Random sampling statistics relate to the quality of
dimensions are used in selecting among dimension candidates. A novel
image-based classifier using a convolutional neural network (CNN) is introduced
to further determine the positions of these fixed-dimension components. Two
examples from wearable products have been chosen to examine the entire
workflow. Their final designs are realistic and their performance metrics are
not inferior to the ones designed by experienced engineers.
