A Deep Neural Network Modeling Methodology for Efficient EMC Assessment
of Shielding Enclosures using MECA-Generated RCS Training Data
Abstract
We develop a deep neural network (DNN) modeling methodology to predict
the radiated emissions of a shielding enclosure in terms of its aperture
attributes including aperture shape, size, pitch, and quantity. The
target structure is the inside of a three-dimensional (3D) enclosure
comprised of perfect electric conductor (PEC) boundaries with dimensions
of a desktop personal computer (PC) containing thermal dissipation
apertures on the surface of its back panel. The DNN model is developed
to compute the radar cross section (RCS) as a function of aperture
attributes and to enable the efficient assessment of the PC’s
electromagnetic compatibility (EMC).
To generate training data for machine learning (ML), we implement the
modified equivalent current approximation (MECA) method and validate it
against analytical methods and a commercial field-solver. We use MECA to
compute RCS data for approximately 55,000 experiments across a wide
range of aperture attributes. We examine numerous DNN models across
parameters such as number of layers and nodes per layer, activation
function, optimization algorithm, loss function, batch size, and epoch,
to identify the optimal DNN model based on (a) accuracy, (b) computation
time, and (c) memory usage. Results show excellent agreement between
MECA and DNN predictions for previously unseen cases.